Why It’s Difficult for me to Drop Classes

At this time, many Berkeley students are selecting their tentative courses for the Fall 2015 semester. I’m doing the same as well. I’m thinking of taking EE 227BT, Convex Optimization, which is a math class describing the wonders and treasures of convexity, and maybe CS 287, Advanced Robotics, which pertains to the math behind robot motion and decision making. In a few weeks, I’ll need to let Berkeley’s Disabled Students Program (DSP) know about my courses so that they can make arrangements to secure semester-long services.

I have to make such course decisions early and I have to be sure about what I am taking. The reason is that it is difficult for me to add or drop a class once a semester starts.

Most students do not have this problem. Schools usually have an add/drop period during the beginning of the semester. In that time, students can show up to a variety of classes and decide to drop a few that turned out not to be what they expected. (The overwhelming reason why students drop a class is because it demands more work than they can handle.) Depending on the class policies, students can also enroll in new classes within this period even if they didn’t show up to the first few lectures.

For me, I don’t have that luxury because class accommodations require weeks of advance preparation. To start, I must inform Berkeley’s Disabled Student Program about the classes I am taking so that they can make the necessary preparations. Securing a semester-long CART provider or sign language interpreter is not automatic because availability varies; I have experienced cases where I got accommodations with a day’s notice, and others where I couldn’t get any despite a week’s notice or more.

Those were for one-time events, though. It takes a longer time to secure CART providers or interpreters for semester-long jobs, and if I were to show up to a class for a few weeks and decide to drop it when there were still eight weeks to go, then those people would effectively lose up to eight weeks’ worth of money. (Replacing the funding with other interpreting jobs is not always easy, because demand occurs at varying times and locations.) In fact, when I was in my second semester at Williams, I enrolled in a class’s lab section that met on Thursday afternoons. I quickly secured accommodations for that lab session … and then just before the semester began, I decided to switch to having that session meet on Wednesday afternoons, because it greatly simplified my schedule.

It was a routine switch, but doing so cost that Thursday interpreter about $600 dollars’ worth of payment in a month. While I did secure a different interpreter for that lab session, the original one did not work for me again in my remaining time at Williams, and I constantly regret my choice to switch sessions. He obviously had the opportunity to work for me in later semesters, but because I dropped that lab session on short notice, he (understandably) did not want to take the risk of losing more money. Furthermore, Williams is isolated and does not have an interpreting workforce, so the interpreters I did have (from Albany, New York) had to drive long distances to get to work. Thus, a one-hour commitment at the school could have easily taken up four hours in a day, which reduces the chances of finding other interpreting work in the same day. This is one reason why I often tried to schedule consecutive classes to maximizes the monetary benefit for my interpreters.

As a result of that experience, I did not drop any Williams classes other than that lab session, which barely counts since it was part of the same general course. It does mean I have to “tough it out” in classes that turn out to be really difficult or boring, but I was a good student so this usually was not an issue. This line of thinking is carrying over to Berkeley, where I aim to complete all classes I enroll in and to minimize sudden schedule chances. I really want to maintain a good relationship between Berkeley’s DSP and the San Francisco agency that provides interpreting services.

Nevertheless, it’s important to retain perspective and realize the best case, most probable case, and worst case scenarios. Having hassles relating to adding and dropping classes is better than not getting any accommodations.

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The Missing Deaf American Politician

Hillary Rodham ClintonIt’s time to gear up for the 2016 United States Presidential election race! Ted Cruz, Rand Paul, Hillary Clinton, and Marco Rubio — in that order — have all announced or will announce that they will be running for president.

Now marks the beginning of the inevitable wait until Rand Paul becomes the next president. But in the meantime, I wonder about whether the United States has ever had a prominent deaf politician. Anyone in the Senate? How about the House of Representatives? Or even a member in a state legislature, or a mayor for a large city? Due to their average age, I’m sure we have had some slightly hearing-impaired politicians, but those don’t count to me. I’m talking about someone who was born deaf or became deaf at a young age, and who knows sign language, and who has strong connections to the Deaf community? Here, I’m using the capital “D” to indicate association with the community.

Unfortunately, to the best of my knowledge, America has never had one. On Wikipedia, there’s currently two relevant pages: “Deaf politicians” and “List of Deaf People“. (I know there are politicians who don’t have Wikipedia pages, but the simple existence of such a page indicates that there is some prestige to the position to which the politician is elected.)

The “Deaf politicians” page currently lists 14 relevant names. What’s interesting to me is that none of these people are or were American politicians. There are four British, two Hungarian, one French, one Austrian, one Greek, one Belgian, one Icelander, one Canadian, one South African, and one New Zelander.

It’s also intriguing that the list of deaf politicians is dominated by Europeans. It seems like a future topic of investigation would be to see if there exist additional biases against deaf people in non-European countries as compared to European countries. I’m particularly curious about the treatment of deaf people in Asian countries.

That second page, “List of Deaf People” does not provide any new deaf politicians outside of what the first page did.

Thus, it looks like America has lacked prominent deaf politicians in its entire existence. From my investigation, the closest thing we have had as a deaf politician is that Canadian guy, Gary Malkowski, because he spoke in American Sign Language while on the job. (Here is a biography of him, and another one on lifeprint.com, which is also an excellent resource for getting up-to-speed on ASL.) Mr. Malkowski was probably the first truly elected deaf politician in the world, serving on the Legislative Assembly of Ontario from 1990 to 1995 and becoming one of the world’s foremost advocates of rights for people with disabilities. Not being Canadian, I don’t have a good idea of how prestigious his position was, but I imagine it’s comparable to being a member of an American state legislature? His background includes a Bachelor’s degree in Social Work and Psychology from Gallaudet University.

While it is disappointing that the Deaf American Politician has never come into play, I am sure that within the next thirty years, we will be seeing at least one such person, given how numerous barriers have eroded over the years to allow a more educated deaf population, though I’m guessing there will be some debate over the “level of deafness” of such a candidate who shows up. I would bet that if this future politician has a background in American Sign Language and has even a weak connection to the Deaf Community, he or she will be able to win the vote of most of the severely hearing-impaired population (which includes the Deaf Community). The main question, of course, would be if the general population can provide the necessary votes.

To be clear, a deaf person should not automatically vote for a deaf politician, akin to how a black person should not automatically vote for Barack Obama or a woman should not automatically vote for Hillary Clinton. But such demographic information is a factor, and people can relate to those who share similar experiences. For instance, being deaf is key for positions such as the presidency of Gallaudet University.

To wrap up this post, here’s my real prediction for the two ultimate candidates for the 2016 U.S. Presidential Election: Hillary Clinton and Scott Walker. Jeb Bush is not winning his party’s nomination since voters will (possibly unfairly) associate him with his brother.

I’ll come back to this post in a little over a year to see if I predicted correctly. By then, hopefully there will be a deaf person who is making a serious run for a political position, but I doubt it.

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Do I Inconvenience You?

Like many deaf people, I often have to request for assistance or accommodations for events ranging from meetings and social events in order to benefit from whatever they offer. These accommodations may be in the traditional realm of sign language interpreters, note-taking services, and captioned media, but they can also be more informal, such as asking a person to talk in a certain manner, or if I can secure a person who will stay with me at all times throughout a social event. (Incidentally, I’ve decided that the only way I’ll join a social event nowadays is if I know for sure that someone else there is willing to stay with me the entire time, since this is the best way to prevent the event from turning into a “watch this person talk about a mysterious subject for thirty seconds and then switch to watching another person” situation.)

On the other hand, when I request for assistance, I worry that I inconvenience others. This is not new for me (I wrote about this a year and a half ago), but with the prospect of having to attend more group meetings and events in the future, I worry about if others will view me as a burden, if they do not think so already.

Unfortunately, I preoccupy myself about whether I inconvenience others way too often than is healthy or necessary. For instance, I often wonder if sign language interpreters distract other students. I remember my very first class at Williams (wow, that was a long time ago…) where the professor remarked that a lot of the students were exchanging glances at the sign language interpreters (though to be clear, she was not saying this in a derogatory manner, and I have never had another professor say this in any other class). So other students do notice them, but for how long? For the sake of their own education, I hope the novelty factor wears off in the first few minutes and then it will be as if they were in a “normal” lecture without sign language interpreters. Now that I think about this, I really should have asked the people who shared many classes with me about if the interpreters affected their focus. I also wonder about how this affects whoever is lecturing. My professors have varied wildly in how much they interact with the interpreters, both during and outside of class.

Sign language interpreting services are the prominent reason why I worry I inconvenience others because they are very visible. Another, possibly less intrusive accommodation would be captioned media. I use captions as much as possible, but hearing people don’t need them. If they are there, is it an inconvenience for them? Captions that have white text and black background can obscure a lot of the screen. This is why even though I’ve only used them twice, I am already a huge fan of closed captioning glasses. They provide the best case scenario: high-quality accommodations with minimal hassle to others.

The vast majority of people do not express overtly negative reactions when my accommodations are present, but likewise, I have had few direct reassurances from others that I do not inconvenience them. I remember exactly one time where a non-family member told me I was not inconveniencing her: a few years ago, a Williams professor relieved me of a few concerns when she told me that having extra accommodations in lectures was not distracting her at all.

While this blog post might convey a bleak message, there is, oddly enough a very simple yet hard to follow method to ensure that you don’t feel like you are inconveniencing others, especially in workforce-related situations.

That method is to do outstanding work. If you can do that, and others are impressed, then you know that you’ve been able to overcome the various minor hassles related to accommodations and that you’re an accepted member of the community. If not, then either the situation doesn’t fit in this kind of framework, or it might be necessary to re-evaluate your objectives.

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Another Hearing Aid Fails to Live Up to Its Water Resistant Label

aquaris-waveToday, I played basketball for the first time since I arrived in Berkeley. It was a lot of fun, and I was at Berkeley’s Rec Sports Facility for 1.5 hours. Unfortunately, I also received a sobering reminder that my water resistant hearing aids are not actually water resistant.

My Oticon Sensei hearing aids worked great for about half an hour … then I heard that all-too-familiar beeping sequence in both ears, and then a few minutes later, the hearing aids stopped working. So I didn’t have any hearing and had to rely on various body language cues and last-resort tactics (honed over the years) to understand what others were saying. Fortunately, in basketball, communication among players in game situations tends to be blunt and simple and from experience, I’ve learned what players typically say to each other.

It is not uncommon for my hearing aids to stop working while I’m engaging in some physical activity. In fact, I get surprised if my hearing aids last through a session of pickup basketball. Thus, I already knew that I would have to reduce the amount of sweat near my hearing aids. I tried using my shirt and the gym’s towel cloth to absorb some of it, but they can only help out so much.

I understand that water resistant does not mean water proof, but I just cannot fathom how a water resistant hearing aid stops functioning after a half hour of physical activity. Out of curiosity, I re-checked my manual and it states that the Oticon Sensei has an IP57 classification. This means that it was still able to function properly after being immersed in water for 30 minutes at a depth of 1 meter.

I am somewhat surprised, because 30 minutes is about the time it took for the hearing aids to stop working after playing basketball. Oh well. At least I have a functional hearing aid dryer. Within a few hours after arriving home, I had them working. But it’s still incredibly annoying. Honestly, the biggest problem with hearing aid breakdowns is not the lack of communication on the court, but what happens off the court. Between pickup games, players are constantly talking to each other about who should be playing the next game or what they want to do after basketball’s over. A more important issue is that I drive to the gym, and driving without working hearing aids is something I would rather avoid.

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Make the Best Peer Reviews Public

NIPS-2014-Poster-ThumbnailThe annual Neural Information Processing Systems (NIPS) conference is arguably the premier machine learning conference along with the International Conference on Machine Learning (ICML). I read a lot of NIPS papers, and one thing I’ve only recently found out was that NIPS actually makes the paper reviews (somewhat) public.

As I understand it, the way NIPS works is:

  1. Authors submit papers, which are eight pages of text, and a ninth one for references. Unlimited supplementary material is allowed with the caveat that reviewers do not need to read it.
  2. The NIPS committee assigns reviewers to peer-review the submissions. These people are machine learning faculty, graduate students, and researchers. (It has to be like that because there’s no other qualified group of people to review papers.) One key point is that NIPS is double-blind, so reviewers do not know the identity of the papers they read while reviewing, and authors who submit papers do not know the identity of the people reviewing their papers.
  3. After a few months, reviewers make their preliminary comments and assign relative scores to papers. Then the original authors can see the reviews and respond to them during the “author rebuttal” phase. Naturally, during all this time, the identity of the authors and reviewers is a secret, though I’ve seen cases when people post submitted NIPS papers to Arxiv before acceptance/rejection, and Arxiv requires full author identity, so I guess it is the reviewer’s responsibility to avoid searching for the identity of the authors.
  4. After a few more months, the reviewers make their final decision on which papers get accepted. Then the authors are notified and have to modify their submitted papers to include their actual names (papers in submissions don’t list the authors, of course!), any acknowledgments, and possibly some minor fixes suggested by the reviewers.
  5. A few months after that (yeah, we’re getting a lot of months here), authors of accepted papers travel to the conference where they discuss their research.

This is a fairly typical model of a computer science conference, though possibly an aytpical model when compared to other academic disciplines. But I won’t get into that discussion; what I wanted to point out here is that NIPS, as I said earlier, makes their reviews public, though the identity of the reviewers is not shown. Judging by the list of NIPS proceedings, this policy of making reviews public began in 2013, and happened again in 2014. I assume NIPS will continue with this policy. (You can click on that link, then click on the 2013/2014 papers lists, click on any paper, and then there’s a “Reviews” tab.) Note that the author rebuttals are also visible.

I was pleasantly surprised when I learned about this policy. This seems like a logical step towards transparency of reviews. Why don’t all computer science conferences do this?

On the other hand, I also see some room for improvement. To me, the obvious next step is to include the name of the reviewers who made those reviews (only for accepted papers). NIPS already gives awards for people who make the best reviews. Why not make it clear who wrote the reviews? It seems like this would incentivize a reviewer to do a good job since their reviews might be made public. Incidentally, those awards should be made more prestigious, perhaps by announcing them in the “grand banquet” or wherever the entire crowd gathers?

You might ask, why not make the identity of reviewers known for all reviews (of accepted papers)? I think there are several problems with this, but none seem to be too imposing, so this might not be a bad idea. One is that the current model for computer science seems to assign people too many papers to review, which necessarily lowers the quality of each individual review. I am not sure if it is necessary or fair to penalize an overworked researcher for making his/her token reviews public. Another is that it is a potential source of conflict between future researchers. I could image someone obsessively remembering a poor public review and using that against the reviewer in the future.

These are just my ideas, but I am not the only one thinking about the academic publishing model. There’s been a lot of discussion on how to change the computer science conference model (see, for instance, “Time For Computer Science to Grow Up“), but at least for the current model, NIPS got it mostly right by making reviews somewhat public. I argue that one additional step towards greater clarity would be helpful to the machine learning field.

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Review of Natural Language Processing (CS 288) at Berkeley

siriThis is the much-delayed review of the other class I took last semester. I wrote a little bit about Statistical Learning Theory a few weeks months ago, and now, I’ll discuss Natural Language Processing (NLP). Part of my delay is due to the fact that the semester’s well underway now, and I have real work to do. But another reason could be because this class was so incredibly stressful, more so than any other class I have ever taken, and I needed some amount of time to pass before writing this.

Before I get to that, let’s discuss what the class is about. Natural Language Processing (CS 288) is about the study of natural languages as it pertains to computers. It applies knowledge from linguistics and machine learning to develop algorithms that computers can run to perform a variety of language-related applications, such as automatic speech recognition, parsing, and machine translation. My class, being in the computer science department, was focused on the statistical portion of NLP, where we focus on the efficiencies of algorithms and justify them probabilistically.

At Berkeley, NLP seems to be offered every other year to train future NLP researchers. Currently we only have one major NLP researcher, Dan Klein, who teaches it (Berkeley’s hiring this year so maybe that number will turn into two). There are a few other faculty that have done work in NLP, most notably Michael Jordan and his groundbreaking Latent Dirichlet Allocation algorithm (over 10,000 Google Scholar citations!), but none are “pure” NLP like Dan.

CS 288 was a typical lecture class, and the grading was based exclusively on five programming projects. They were not exactly easy. Look at the following slide that Dan put up on the first day of class:

CS288_SlideI come into every upper-level computer science expecting to be worked to oblivion, so this slide didn’t intimidate me, but seeing that text there gave me an initial extra “edge” to make sure I was focused, doing work early, and engaging in other good habits.

Let’s talk about the fun part: the projects! There were five of them:

  1. Language Modeling. This was heavy on data structures and efficiency. We had to implement Kneser-Ney Smoothing, a fairly challenging algorithm that introduced me to the world of “where the theory breaks down.” Part of the difficulty in the project comes from how we had to meet strict performance criteria, so naive implementations would not suffice.
  2. Automatic Speech Recognition. This was my favorite project of the class. We implemented automatic speech recognition based on Hidden Markov Models (HMMs), which provided the first major breakthrough in performance. The second major breakthrough came from convolutional neural networks, but HMMs are surprisingly a good architecture on their own.
  3. Parsing. This was probably the most difficult project, where we had to implement the CYK parsing algorithm. I remember doing a lot of debugging and checking indices of matrices to make sure they were aligned. There’s also the problem of dealing with unary expressions, since that’s a special case that’s not commonly described in most textbook descriptions of the CKY parsing algorithm (actually, the concept of “special cases not described by textbook descriptions” could be applied to most projects we did…).
  4. Discriminative Re-ranking. This was a fairly relaxing project because a lot of the code structure was built for us and the objective is intuitively obvious. Given a candidate set of parses, the goal was to find the highest ranking one. The CYK parsing algorithm can do this, but it’s better if that algorithm gives us a set of (say) 100 parses, and we run more extensive algorithms on those top parses to pick the best of those, hence the name “re-ranking.”
  5. Word Alignment. This was one that I had some high-level experience with before the class. Given two sentences of different languages, but which mean the same thing, the goal is to train a computer to determine the word alignment. So for an English-French sentence pair, the first English word might be aligned to the third French word, the second English word might be aligned to no French word, etc.

I enjoyed most of my time thinking about and programming these projects. They trained me to stretch my mind and to understand when the theory would break down for an algorithm in practice. They also forced me to brush up my non-existent debugging skills.

Now, that having been said, while the programming projects were somewhat stressful (though nothing unexpected given the standards of a graduate level class), and the grading was surprisingly lax (we got As just for completing project requirements) there was another part of the class that really stressed me out, far beyond what I thought was even possible. Yes, it was attending the lectures themselves.

A few months ago, in the middle of the semester, I wrote a little bit about the frustration I was having with remote CART, a new academic accommodation for me. Unfortunately, things didn’t get any better after I had written that post, and I think they actually worsened. My CART continued to be plagued by technical issues, slow typing, and the rapid pace of lecture. There was also construction going on near the lecture room. I remember at least one lecture that was filled with drilling sound while the professor was lecturing. (Background noise is a killer for me.)

I talked to Dan a few weeks into the course about the communication issues I was having in the class. He understood and thanked me for informing him, though we both agreed that slowing down the lecture rate might reduce the amount of material we could cover (for the rest of the students, of course, not for me).

Nonetheless, the remaining classes were still insanely difficult for me to learn from, and during most lectures, I found myself completely lost within ten minutes! What was also distressing was knowing that I would never be able to follow the question/answer discussions that students had with the professor in class. When a student asks a question, remote CART typically puts in an “inaudible” text due to lack of reception and the relatively quiet voice of the students. By my own estimate, this happened 75 percent of the time, and that doesn’t mean the remaining 25 percent produced perfect captions! CS 288 had about 40-50 students, but we were in a small room so everyone except me could understand what students were asking. By the way, I should add that while I do have hearing from hearing aids and can sometimes understand the professor unaided, that hearing ability virtually vanishes when other students are asking questions or engaging in a discussion.

This meant that I didn’t have much confidence in asking questions, since I probably would have embarrassed myself by repeating an earlier question. I like to participate in class, but I probably spoke up in lecture perhaps twice the entire semester. It also didn’t help that I was usually in a state of confusion, and asking questions isn’t always the ticket towards enlightenment. In retrospect, I was definitely suffering from a severe form of imposter syndrome. I would often wonder why I was showing up to lecture when I understood almost nothing while other students were able to extract great benefits from them.

Overall verdict: I was fascinated with the material itself, and reasonably liked the programming projects, and the course staff was great. But the fact that the class made it so hard for me to sit comfortably in lecture caused way more stress than I needed. (I considered it a victory if I learned anything non-trivial from a lecture.) At the start of the semester, I was hoping to leave a solid impression on Dan and the other students, but I think I failed massively at that goal, and I probably asked way too many questions on the class Piazza forum than I should have. It also adversely affected my CS 281a performance, since that lecture was right after CS 288, which meant I entered CS 281a lectures in a bad mood as a result of CS 288.

Wow, I’m happy the class is done. Oh, and I am also officially done with all forms of CART.

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Day in the Life of a Graduate Student

I was recently thinking about my daily routine at Berkeley, because I always feel like I am never getting enough work done. I wonder how much of my schedule is common among other graduate students (or among people in other, vastly unrelated careers). Let’s compare! Here’s my typical weekday:

5:45am: Wake up, shower, make and eat breakfast, which is usually three scrambled pastured eggs, two cups of berries, and a head of raw broccoli. Pack up a big-ass salad to bring with me to work.

6:45am: Leave for work. I usually drive — it takes ten minutes at this time — though at least one day of the week I’ll take the bus.

7:00am: Arrive at Soda Hall. Angrily turn off the lights in the open areas outside of my office after finding out that the people there last night left them on after leaving. Put my salad in the refrigerator. Unlock the door to my shared office, turn on laptop, pull out research and classwork notes. Check calendar and review my plan for the day.

7:15am to 9:15am: Try to make some headway on research. Check latest commits on github for John Canny‘s BID Data Project. Pull out my math notes and double-check related code segment from last night’s work to make sure it’s working the way it should be. Make some modifications and run some tests. Find out that only one of my approaches gets even a reasonable result, but it still pales in comparison to the benchmark I’ve set. Pound my fist on the table in frustration, but fortunately no one else notices because I’m still the only one on this floor.

9:30am: Realize that a lecture for my Computer Vision class is about to start. Fortunately, this is Berkeley, where lectures strangely start ten minutes after their listed time, but I need to get there early to secure a front row seat so I can see the sign language interpreters easily. (I can always ask people to move if I have to, and they probably will, but it’s best if I avoid the hassle.)

9:40am to 11:00am: Jitendra Malik lectures about computer vision and edge detectors. I concentrate as hard as I can while rapidly switching my attention between Jitendra, his slides, and my interpreters. Make mental notes of which concepts will be useful for my homework due the following week.

11:00am: Class is finished. Attempt to walk around in the huge crowd of entering/leaving students. Decide that since I don’t have anyone to eat lunch with, I’ll grab something from nearby Euclid street to take to my office.

11:15am to 11:45am: Eat lunch by myself in my office, wishing that there was someone else there. Browse Wikipedia-related pages for Computer Vision concepts from lecture today. Get tripped up by some of the math and vow that I will allocate time this weekend to re-review the concepts.

noon to 2:00pm: Try to get back to research regarding the BID Data Project. Write some more code and run some tests. Get some good but not great results, and wish that I could be better, knowing that John Canny would have been able to do the same work I do in a third of the time. Skim and re-read various research papers that might be useful for my work.

2:00pm to 3:00pm: Take a break from research to have a meeting with another Berkeley professor who I hope to work with. Discuss some research topics and what would be good but not impossible problems to focus on. Tell him that I will do this and that before our next meeting, and conclude on a good note.

3:15pm to 4:30pm: Arrive back in my office. Get my big-ass salad from the refrigerator and drizzle it with some Extra Virgin Olive Oil (I keep a bottle of it on my desk). My office-mate is here, so I strike up a quick chat. We talk for a while and then get back to work. My mood has improved, but I suddenly feel tired so end up napping by mistake for about fifteen minutes. Snap out of it later and try to get a research result done. End up falling short by only concluding that a certain approach will simply not work out.

4:30pm to 5:00pm: Decide to take a break from research frustration to make some progress on my Computer Vision homework. Get stuck on one of the easier physics-related questions and panic. Check the class Piazza website, and breathe a sigh of relief upon realizing that another classmate already asked the question (and got a detailed response from the professor). Click the “thanks” button on Piazza, update my LaTeX file for the homework, and read some more of the class notes.

5:00pm to 5:30pm: Take a break to check the news. Check Google Calendar just in case I didn’t forget to go somewhere today. Check email for the first time today. Most are from random mailing lists. In particular, there are 17 emails regarding current or forthcoming academic talks by visiting or current researchers, but they would have been a waste of time for me to attend anyway due to lack of related background information, and the short notice means it can be hard to get interpreting services. Some of those talks also provide lunches, but I hate going to lunches without having someone already with me, since it’s too hard to break into the social situation. Delete most of the email, respond to a few messages, and soon my inbox is quite clean. (The advantage of being at the bottom of the academic and social totem poles is that I don’t get much email, so I don’t suffer from the Email Event Horizon.)

5:45pm to 6:30pm: Try to break out of “email mood” to get some more progress done on homework. Rack my brain for a while and think about what these questions are really asking me to do. Check Piazza and Wikipedia again. Make some brief solution sketches for the remaining problems.

6:40pm to 7:00pm: Hit a good stopping point, so drive back home. (Still not in the greatest mood, but it’s better than it was before my 2:00pm meeting.) At this point most cars have disappeared from Hearst parking lot, which makes it easier for me to exit. Cringe as my car exits the poorly-paved roadway to the garage, but enjoy the rest of the ride back home as the roads aren’t as congested as I anticipated.

7:15pm: Think about whether I want to go to Berkeley’s Recreational Sports Facility to do some barbell lifting. It’s either going to be a “day A” session (5 sets of 5 for the squat, 5 sets of 5 for the bench) or a “day B” session (3 sets of 5 for the squat, 5 sets of 5 for the overhead press, and 1 set of 5 for the deadlift). I didn’t go yesterday, which means I have to go either now or tomorrow night. After a brief mental war, conclude that I’m too exhausted to do some lifting and mark down “RSF Session” on my calendar for tomorrow night.

7:30pm to 8:00pm: Cook and eat dinner, usually some salad (spring mix, spinach, arugula, carrots, peppers, etc.), more berries (strawberries or blueberries) a half-pound of meat (usually wild Alaskan salmon), and a protein shake. Browse random Internet sites while I eat in my room or out on my apartment’s table.

8:30pm to bedtime: Attempt to get some more work done, but end up getting making no progress, so pretend to be productive by refreshing email every five minutes and furiously responding to messages. Vow that I will be more productive tomorrow, and set my alarm clock an hour before I really should be waking up.

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Harvard and MIT’s Lack of Closed Captions

In the future, I will try not to discuss random news articles here, because often the subject might be a fad and fade in obscurity. Today, I’ll make an exception with this recent New York Times article about how Harvard and MIT are being sued over lack of closed captions. The actual suing/lawsuit action itself will probably be forgotten by most soon, but the overall theme of lack of captions and accessibility is a recurring news topic. Online education is real, and accommodations for those materials will also be necessary to ensure a maximal range of potential beneficiaries.

I don’t take part in online courses or video resources that much since there’s already plenty that I can learn from standard in-person lectures, and the material that I need to know (advanced math, for instance) is not something that I can learn from MOOCs, which by their very definition are for popular and broadly accessible subjects. For better or worse, the concepts I do need to know inside-out are embedded in dense, technical research papers.

Fortunately, the few online education resources I have experience with provide closed captions. The two that I’m most familiar with are MIT OpenCourseWare and Cousera, and both are terrific with captions. Coursera is slightly better, being more “modern” and also allows the video to be paused and sped up, while for MIT OCW one needs to use external tools, but both are great.

Apparently, using MIT OCW and Coursera (and sparingly at that) has probably led me to forget about how most online sources do not contain closed captions. It’s especially frustrating to me since in the few cases when I want to look at videos, I have to rely on extensive rewinding and judicious pauses to make sense of the material. I think in the next few years, I may need to employ those cumbersome tactics when I watch research talks.

It’s nice to see that captions are getting more attention, and I believe this issue will continue to reappear in news in the near future. Perhaps the brand names of “Harvard” and “MIT” are playing a role here, but I don’t view that as a bad sign: if they can take the initiative and be leaders in accessibility, then other universities should try and emulate them. After all, those universities want Harvard and MIT’s ranking…

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Deaf-Friendly Tactic: Provide an Email Address

Update 1/31/2015: I realized just after writing this that video relay is possible with the same phone number … whoops, that shows how long it’s been since I’ve made a single phone call! But in any case, I think the ideas in this article are still valid, and not every deaf person knows sign language.

Original article: In my search for deaf-friendly tactics that are straightforward to implement, I initially observed that it’s so much easier for me to understand someone when he or she speaks clearly (not necessarily loudly). I also pointed out that in a group situation, two people (me and one other person) is optimal (not three, not four…). Two recent events led me to think of another super simple deaf-friendly tactic. In retrospect, I’m surprised it took me a few years to write about it.

I recently had to schedule an appointment with Toyota of Berkeley to get my car serviced. I also received a jury duty summons for late February, and I figured that it would be best if I requested a sign language interpreter to be with me for my summons. Unfortunately, for both of these cases, calling Toyota and the California courts, respectively, seemed to be the only way that I could achieve my goals.

In fact, my jury summons form said the following:

Persons with disabilities and those requiring hearing assistance may request accommodations by contacting the court at [phone number redacted].

There was nothing else. I checked the summons form multiple times. There was no email address, no TTY number, no video relay service number, nothing. Yes, I am not joking. Someone who is hearing impaired — and logically will have difficulty communicating over the phone — will have to obtain jury duty accommodations by … calling the court! I actually tried to call with my iPhone 6. After multiple attempts, I realized that there was a pre-recorded message which said something like: “for doing X, press 1, for doing X, press 2…”, so I had to press a number to talk to a human. Actually, I think it’s probably best that there was no human on the other end, because otherwise I probably would have frustrated him or her by my constant requests for clarification.

I will fully admit that the iPhone 6 is not perfect for hearing aid users because its Hearing Aid Compatible rating is M3, T4 rather than the optimal M4, T4 rating, but still, even after about five or six attempts at calling, I did not understand what numbers corresponded to what activities. Sure, I’m rusty since I make around two phone calls a year to people outside of my immediate family, but I don’t see experience being much of a factor here.

This motives the following simple deaf-friendly tactic:

Provide an email address (perhaps in addition to a telephone number) that people can use to contact for support, scheduling services, and other activities.

I am aware that deaf people can easily use alternative services, such as TTY or video relay. Such services, however, are far inferior to email in many ways. Email nowadays is so prevalent in our lives and is incredibly easy to use. It’s rare when I don’t have some form of Internet access, so I can effectively check email whenever I want. The fact that I’m also writing instead of talking means that I can do things like revise my ideas more clearly and paste relevant web links. The process of forming an email can sometimes result in me resolving my own situation! I’ve often been in the process of writing an email, but then I realized I needed to add more information to show the person on the other end that I had done my research, but then that extra research I do can lead to an answer.

Furthermore, the set of people who regularly use email form effectively a proper superset over those people who use TTY and video relay services. In other words, the vast majority of TTY and video relay users also use email, but the converse is not true. In my case, I have not used TTY and video relay in years; email forms the foundation of almost all my communication nowadays. As long as it doesn’t become an obsession (as in checking it 50 times a day), I don’t see how it interferes that much in my daily life, and I would argue that a telephone call can drag on and on.

Conclusion: if you’re going to provide a phone number for contact, I would strongly urge you to also provide an email address.

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Gallaudet University is Searching for a President

The news is out: Gallaudet University is searching for its eleventh president. Here’s the Presidential Search Advisory Committee web portal and here’s the specific job description, including desired candidate qualifications. I’ll be anxiously following the news. While I have never been on the campus before, I am obviously aware of its history as a college for the deaf (even though it was never on my college radar) and I know several current and former students.

Choosing a president of a college that caters at a specific group of people is a sensitive issue, because often the president is expected to share the same characteristic. For instance, students, faculty, and staff at an all-women’s college or a historically black college might be more favorable towards a female and a black president, respectively. Wellesley College has only had female presidents in its history, and Mount Holyoke College has had mostly female presidents.

Gallaudet is unique in that, as the world’s only university that caters to deaf and hard of hearing students across the board, the president is now expected to be deaf. The first seven presidents of Gallaudet were hearing, and it was not until the now famous 1988 Deaf President Now (DPN) saga that they had a deaf president.

It’s also not enough to just be deaf; the Gallaudet culture prides itself on American Sign Language (ASL), so the president is now expected to be fluent in that language (and immersed in deaf culture). I’m reminded of the 2006 fiasco when Gallaudet appointed Dr. Jane Fernandes as president. Students protested for a variety of reasons, but their argument can be succinctly stated as: “she wasn’t deaf enough.” The board of trustees eventually revoked her appointment. Strangely enough, I don’t remember personally knowing anything about it back in 2006. When I first learned about the incident a few years later, I thought the students mostly embarrassed themselves, but now I’ve become more understanding of their perspective. Incidentally, Dr. Fernandes still ended up with a strong career, as she’s now the president of Guilford College.

Thus, if the next president does not meet the de facto profile requirements, expect the students (and maybe faculty) to protest. The current job description asks that the candidate “has a deep understanding of bilingualism and biculturalism in the deaf community,” though it does not explicitly state that he or she be deaf or be fluent in ASL.

So, as I said, I’ll be anxiously following the news.

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New Year’s Resolutions: 2015 Edition

It’s that time of the year when many people are creating New Year’s resolutions.

Wait, scratch that. We’re a week into 2015, so I think it’s more accurate for me to say: it’s that time of the year when many people have forgotten or given up on their New Year’s resolutions. After all, this guy from Forbes claims that only eight percent of people achieve their resolutions.

Why am I discussing this subject? Last semester, I was in a continuous “graduate student” state where I would read, read, take a few notes, attend classes, do homework, read more research papers, do odd hobbies on weekends, and repeat the cycle. I rarely got the chance to step back and look at the big picture, so perhaps some New Year’s resolutions would be good for me. And before you claim that few people stick with them, I also had New Year’s resolutions for 2014, and I kept my text document about it on my desktop. Thus, I was able to keep them in mind throughout the full year, even if I ended up falling short on many goals (I set the bar quite high).

For a variety of reasons, I had a disappointing first semester, so most of my resolutions are about making myself a better researcher. I think one obstacle for me is the pace in which I read research papers. I’ve always thought of myself as someone who relies less on lectures and more on outside reading in classes than most (Berkeley computer science graduate) students, so I was hoping that my comparative advantage would be in reading research papers. Unfortunately, to really understand even an 8-page conference paper that I need for research, I may end up spending days just to completely get the concepts and to fill in the technical details omitted from the paper due to page limits.

When reading research papers, it’s not uncommon for me to lose my focus, which means I spend considerable time backtracking. Perhaps this could be rectified with better reading habits? I’m going to try and follow the advice in this blog post about reading real books, rather than getting all my news from condensed newspaper or blog articles. (Ironically, I just broke my own rule, but I will cut back on reading blogs and arbitrary websites … and also, I came up with this idea about two weeks ago, so it’s nice to see that there’s someone who agrees with me.) Last week, I read two high-octane thrillers — Battle Royale and The Maze Runner — to get me back into “reading mode” and am moving on to reading non-fiction, scholar-like books. Maybe books will help me quit Minecraft for good (so far, it’s working: I’ve played zero seconds of Minecraft in 2015).

I’ve also recorded some concrete goals for weight lifting (specifically, barbell training), which is one of my primary non-academic hobbies. For the past four years, my motivation to attend the gym has been through the roof. I’ve never missed substantial gym time unless I was traveling. In retrospect, I think programs like Stronglifts and Starting Strength (which I loosely follow) are so popular because they generate motivation. Both use the same set of basic, compound lifts, but as you proceed throughout the programs, you add more weight if it is safe to do so. Obviously, the more weight you can lift, the stronger you are! I often juxtapose weight lifting and addictive role-playing games (RPGs), where my personal statistics in real life barbell lifts correspond to a hypothetical “strength” attribute in an RPG game that I continually want to improve.

Here’s a video of me a few days ago doing the bench press, which is one of the four major lifts I do, the others being the squat, deadlift, and overhead press. I know there’s at least one reader of this blog who also benches, and we’re neck-to-neck on it so maybe this will provide some motivation (yeah, there’s that word again…).

This is one set of five reps for 180 pounds; I did five sets that day. (The bar is 45 pounds, the two large plates on both sides are 45 pounds, and each side has two 10-pound plates and one 2.5-pound plate.) I remember when I was a senior in high school and couldn’t do a single rep at 135 pounds, so seeing these new results shows how far I’ve come from my earlier days. I’m definitely hoping the same feeling will transition to my research and motivation in general.

Motivation. It’s an incredibly powerful concept, and a must for graduate students to possess with respect to research.

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Independent Component Analysis — A Gentle Introduction

In this post, I give a brief introduction to independent component analysis (ICA), a machine learning algorithm useful for a certain niche of problems. It is not as general as, say, regression, which means many introductory machine learning courses won’t have time to teach ICA. I first describe the rationale and problem formulation. Then I discuss a common algorithm to solve ICA, courtesy of Bell and Sejnowski.

Motivation and Problem Formulation

Here’s a quick technical overview: the purpose of ICA is to explain some desired non-Gaussian data by figuring out a linear combination of statistically independent components. So what does that really mean?

This means we have some random variable — or more commonly, a random vector — that we observe, and which comes from a combination of different data sources. Consider the canonical cocktail party problem. Here, we have a group of people conversing at some party. There are two microphones stationed in different locations of the party room, and at time indices i = \{1, 2, \ldots \}, the microphones provide us with voice measurements x_1^{(i)} and x_2^{(i)}, such as amplitudes.

For simplicity, suppose that throughout the entire party, only two people are actually speaking, and that their speech signals are independent of each other (this is crucial). At time index i, they speak with signals s_1^{(i)} and s_2^{(i)}, respectively. But since the two people are in different locations of the room, the microphones each record signals from a different combination of the two people’s voices. The goal of ICA is, given the time series data from the microphones, to figure out the original speakers’ speech signals. The combination is assumed to be linear in that

  • x_1^{(i)} = a_{11}s_1^{(i)} + a_{12}s_2^{(i)}
  • x_2^{(i)} = a_{21}s_1^{(i)} + a_{22}s_2^{(i)}

for unknown coefficients a_{11},a_{12},a_{21},a_{22}.

Here’s a graphical version, from a well-known ICA paper. The following image shows two (unrealistic) wavelength diagrams of two people’s voices:

ica1The data that is observed from the two microphones is in the following image:

ica2The goal is to recover the original people’s wavelengths (i.e., the two graphs in the first of the two images I posted) when we are only given the observed data (i.e., the two graphs from the second image). Intuitively, it seems like the first observed wavelength must have come from a microphone closer to the first person, because its shape more closely matches person 1’s wavelength. The opposite is true for the second microphone.

More generally, consider having n microphones and n independent speakers; the numerical equality of microphones and speakers is for simplicity. In matrix form, we can express the ICA problem as x^{(i)} = As^{(i)} where A is an unknown, square, invertible mixing matrix that does not depend on the time interval. Like the assumptions regarding n, the invertibility of A is to make our problem simple to start. We also know that all x^{(i)} and s^{(i)} are n-dimensional random vectors. The goal is to recover the unseen sources s^{(i)}. To simplify the subsequent notation, I omit the i notation, but keep in mind that it’s there.

How does the linear combination part I mentioned earlier relate to this problem formulation? When we express problems in x = As form, that can be viewed as taking linear combinations of components of s along with the appropriate row of A. For instance, the first component (remember, there are n of them) of the vector x is the dot product of the first row of A and the full vector s. This is a linear combination of independent source signals s_1,s_2,\ldots,s_n with coefficients a_{11},a_{12}, \ldots, a_{1n} based on the first row of A.

Before moving on to an algorithm that can recover the sources, consider the following insights:

  1. What happens if we know A? Then multiply both sides of x = As by A^{-1} and we are done. Of course, the point is that we don’t know A. It is what computer scientists call a set of latent variables. In fact, one perspective of our problem is that we need to get the optimal A^{-1} based on our data.
  2. The following ambiguities regarding A will always hold: we cannot determine the variance of the components of s (due to scalars canceling out in A) and we also cannot determine ordering of s (due to permutation matrices). Fortunately, these two ambiguities are not problematic in practice.
  3. One additional assumption that ICA needs is that the independent source components s_1, s_2, \ldots, s_n are not Gaussian random variables. If they are, then the rotational symmetry of Gaussians means we cannot distinguish among the distributions when analyzing their combinations. This requirement is the same as ensuring that the s vector is not multivariate Gaussian.

Surprisingly, as long as the source components are non-Gaussian, ICA will typically work well for a range of practical problems! Next, I’d like to discuss how we can “solve” ICA.

The Bell and Sejnowski ICA Algorithm

We describe a simple stochastic gradient descent algorithm to learn the parameter A^{-1} of the model. To simplify notation, let W = A^{-1} so that its rows can be denoted by w_i^\top. Broadly, the goal is to figure out some way of determining the log-likelihood of the training data that depends on the parameter W, and then perform updates to iteratively improve our estimated W. This is how stochastic gradient descent typically works, and the normal case is to take logarithms to make numerical calculations easier to perform. Also, we will assume the data x_i are zero-mean, which is fine because we can normally “shift” a distribution to center it at 0.

For ICA, suppose we have m time stamps i = \{1, 2, \ldots, m\}. The log-likelihood of the data is

\ell(W) = \sum_{i=1}^{m} \left( \log |W| + \sum_{j=1}^{n} \log g'(w_j^\top x^{(i)}) \right),

where we note the following:

  1. |W| is the determinant of W
  2. g' is the derivative of the sigmoid function g (not the sigmoid function itself!)

Let’s explain why this formula makes sense. It comes from taking logarithms of the density of x^{(i)} at each time stamp. Note that x = As so if we let p denote the density function of x^{(i)}, then p(x^{(i)}) = |W| \prod_{j=1}^{n} p_j(w_j^\top x^{(i)}), where p_j is the density of the individual source j. We can split the product this way due to the independence among the sources, and the w_j terms are just (vector) constants so they can be separated as well. For a more detailed overview, see Andrew Ng’s lecture notes; in particular, we need the |W| term due to the effect of linear transformations.

Unfortunately, we don’t know the density of the individual sources, so we approximate them with some “good” density and make them equal to each other. We can do this by taking the derivative of the sigmoid function:

Logistic-curveThe reason why this works is that the sigmoid function satisfies the properties of a cumulative distribution function, and by differentiating such a function, we get a probability density function. And since it works well in practice (according to Andrew Ng), we might as well use it.

Great, so now that we have the log-likelihood equation, what is the stochastic gradient descent update rule? It is (remember that g is the sigmoid function):

W_{t} = W_{t-1} + \alpha \left( \begin{bmatrix} 1-2g(w_1^\top x^{(i)}) \\ 1-2g(w_2^\top x^{(i)}) \\ \vdots \\ 1-2g(w_n^\top x^{(i)}) \end{bmatrix} (x^{(i)})^\top + ((W_{t-1})^\top)^{-1} \right),

where \alpha is the standard learning rate parameter, and the i that we pick for each iteration update varies (ideally sampling from the m training data pieces uniformly). Notice that the term in the parentheses is a matrix: we’re taking an outer product and then adding another matrix. To get that update rule from the log-likelihood equation, we take the gradient \nabla_W \ell(W), though I think we omit the first summation over m terms. Matrix calculus can be tricky and one of the best sources I found for learning about this is (surprise) another one of Andrew Ng’s lecture notes (look near the end). It took a while for me to verify but it should work as long as the m summation is omitted, i.e., we do this for a fixed x^{(i)}. To find the correct outer product vectors to use, it may help to use the sigmoid’s nice property that g'(x) = g(x) (1-g(x)). Lastly, don’t forget to take the logarithm into account when taking the derivatives. I can post my full gradient calculation later if there’s enough demand.

There are whole books written on how to decide when to stop iterating, so I won’t get into that. Once it converges, perform s^{(i)} = Wx^{(i)} and we are done, assuming we just wanted the s vectors at all times.

Well, that’s independent component analysis. Remember that this is just one way to solve related problems, and it’s probably on the easier side.

References and Further Reading

  1. Independent Component Analysis: Algorithms and Applications
  2. Andrew Ng’s lecture notes

Let me know if you found this article useful.

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Why I am Against Affirmative Action

Update 1/1/2015: Happy 2015 everyone! I read Ta-Nehisi Coates’ article The Case for Reparations, which talks about affirmative action a little bit but mostly is about the unfortunate news that, even after slavery ended and after the civil rights era, African Americans are really not equal to whites, a claim that I agree with wholeheartedly. I was surprised and devastated when he described how Congress failed to even consider the possibility of providing any form of reparations. Sadly, as the years go by, the likelihood of making substantial or meaningful reparations declines. Consider the payments to Japanese-Americans who were deported to camps in World War II. (Had I lived in California as a child at that time, I might have been among those people.) But at least some of those victims were able to get reparations during their lifetime. The question of how to do the same for African Americans is much trickier.

Coates’ article brings up affirmative action in several contexts. One is when President Barack Obama said that his children should not benefit from that policy, so I have another supporter there that I didn’t list in my original article. The second is that affirmative action is a tricky policy because there is no clear definition of it (I agree), and Coates appears to be doubtful about affirmative action’s effectiveness in reaching equality among blacks and whites (and Asians?).

When I was reading Coates’ article, I kept thinking about ways to boost minority enrollment in STEM. One effective way, I think, would be to implement affirmative action-like policies not for career positions, but for temporary programs that are sort of “breeding grounds” for such careers or college positions. For instance, many affluent families send their high school children to math and science summer programs. I believe if those programs made conscious efforts to reach out to minorities, or if there were even programs that only accepted minorities, that could serve to be a better place to practice such affirmative action-like policies. I was able to benefit from a program like that, and I think I know a few others like this, but it’s going to take years to see a difference in the racial and gender diversity among academics because of the scarcity of positions and the tenure aspect (which means the previous generation sticks around for decades).

That’s all I have to say for this (first) update. Here is my original post:

Affirmative action is the process of giving favoritism in some way (usually for employment) to groups of disadvantaged people who have historically been victims of discrimination. Affirmative action policies vary according to country. In the United States, having a pure quota on making sure that X percent of a workforce belongs to a certain group is illegal, but affirmative action does exist. I would like to present an argument against this policy based on one simple idea, though I should first add the disclaimer that I do not think I have benefited from it or will benefit from in the future. The only way I would is if some employer wants to hire a deaf person, but I rarely see this discussed in the two cases that I’m familiar with: college admissions, and faculty recruitment in STEM fields. Discussions about the lack of diversity in STEM are dominated around women, African Americans and Hispanics. It annoys me that people with disabilities are often ignored, but maybe I should talk about that later.

I am against affirmative action mostly because it often makes the people who benefit from affirmative action feel like the reason why they were hired is because of affirmative action, and not due to merit. (There’s also the real problem of resentment over those who think they aren’t getting their jobs, but I think that is less important.) Over the past few years, I have become far more sensitive to issues regarding race and gender, and I have learned about countless stories from people who have lamented how others view them as an “affirmative action hire.” Of these stories, the one that stuck to me the most was of current Supreme Court Justice Clarence Thomas, who would constantly remark that others stigmatized his Yale law degree as the product of affirmative action rather than his own merit:

Affirmative action (though it wasn’t yet called that) had become a fact of like at American college and universities, and before long I realized that those blacks who benefited from it were being judged by a double standard. As much as it stung to be told that I’d done well in the seminary DESPITE my race, it was far worse to feel that I was now at Yale BECAUSE of it. I sought to vanquish the perception that I was somehow inferior to my white classmates by obtaining special permission to carry more than the maximum number of credit hours and by taking a rigorous curriculum of courses in such traditional areas as corporate law, bankruptcy, and commercial transactions. How could anyone dare to doubt my abilities if I excelled in such demanding classes?

One more recent story was from Professor Carlotta Berry’s recent New York Times editorial, where she said that she wants to be viewed professionally, but understands that some may view her faculty hiring as a product of affirmative action, even as she points out, she does not believe she benefited from it.

Having worked with thousands of students, I know for a fact that for many — though by no means all, or even most — there is already a presumption that I, as a female and African-American, am less qualified than my white male colleagues, or at the very least that I was hired in order to meet a double minority quota. And I get it — anti-affirmative-action ideologues have managed to not only demolish the legitimacy of that policy, but tar the reputation of anyone who might have benefited from it (even if, like me, they did not).

Here’s another perspective from a non-beneficiary (I think) by Professor David Aldous of Berkeley, who defended the merit of the statistics faculty members when asked about affirmative action at Berkeley:

But putting aside the cynical view, here’s the bottom line. There are the various cultural pressures we all recognize that traditionally have reduced the number of women and minorities in math and science. Almost nobody objects to the principle of trying to counteract these pressures, but it’s the bureaucratic hassles involved in conforming to rules that create the cynicism. In my Stat department, we have maybe 5 out of 20-odd faculty being women, and they’re all perceived as having been hired on merit, not because of affirmative action. As for minorities, at the faculty level there are so few that it’s not on the radar.

I absolutely agree with him on all counts here. The statistics faculty here at Berkeley are amazing (all of them), and I would love to help reduce the cultural pressures and barriers to STEM for women and minorities.

At Berkeley, undergraduate admissions is race-blind, and one African American student is happy about this because she got in on merit rather than affirmative action:

“I got into Berkeley on my own,” said Nile Taylor, who entered Cal three years after the ban on affirmative action began. “I didn’t get in because they had to meet a quota. I got in because my application was good enough to get into Berkeley. Part of the stigma of people who get in under affirmative action is one, they only got in because of affirmative action — that they’re not considered to be good enough otherwise — and two, I think affirmative action is a Band-Aid. I don’t think it’s a solution.”

I believe these stories reinforce my argument. If there was no affirmative action, I don’t think there would be a need to constantly defend a woman or minority admission or hire on merit. (Note: “minorities” here do not include Asians; Berkeley has plenty of Asian professors.)

Whenever I think about a contentious issue, I often try to understand the opposite perspective: how would I feel if I were someone who clearly is a possible beneficiary of affirmative action? And I believe that I would still agree with my thoughts here. If I were to eventually become a professor or work at some of the many industry alternatives (Google, Microsoft, etc.) I would never want to feel like I was hired on the basis of race or gender. It would make me feel inadequate, and also make me feel guilty about taking away spots from potentially qualified people. Academic jobs are especially scarce nowadays, and there’s no need to increase tension among applicants by forcing affirmative action as a policy.

An argument in favor of affirmative action might be that it helps minorities by increasing the pool of “similar” people (never mind the danger in lumping people together in a group), thus resulting in increased productivity and expertise in the classroom or workforce. For instance, someone who is the only minority in a class might have to work by himself/herself all the time due to social exclusion, but with more minorities, then this increases the pool of people who are easier to work with, which therefore results in better grades, better job performance, etc. I’m a little skeptical of this perspective, because it still carries some stigma. Realize that I say this as someone who has felt excluded from other students in all levels of my education.

Justice Sonia Sotomayor provided other arguments in support of affirmative action when the Supreme Court backed Michigan’s ban on the policy in its public university admissions. One of them was about other aspects of college admissions:

Athletes, children of alumni and students from underrepresented parts of the state, she said, remained free to try to persuade university officials to give their applications special weight.

I agree that this is a problem. I am also against athletes, alumni, and those from underrepresented geographical locations getting special weight from the holistic college admissions process, but I do not think these aspects carry as much stigma as affirmative action does, and the point of my argument is that I’m trying to reduce the stigma associated with the policy as much as possible. Again, here’s a disclaimer: I was neither an athlete, nor an alumni, nor from an underrepresented geographical location when I applied to college. I lived in New York, which is probably the most over-represented state in many northeastern schools, but I somehow still got in Williams, and I am not sure if there was anyone who thought I got in for reasons other than merit. I don’t want the opposite perspective — that admission was a product of affirmative action — to hold true for me or anyone else.

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Review of Statistical Learning Theory (CS 281a) at Berkeley

statistical_learning_theoryNow that I’ve finished my first semester at Berkeley, I think it’s time for me to review how I felt about the two classes I took: Statistical Learning Theory (CS 281a) and Natural Language Processing (CS 288). In this post, I’ll discuss CS 281a, a class that I’m extremely happy I took even if it was a bit stressful to be in lecture (more on that later).

First of all, what is statistical learning theory? I view the subject as one that principally deals with the problem of finding a predictive function of data that minimizes a loss function (e.g., squared loss) on training data, and analyzes this problem in a framework that conflates machine learning and probability methods. Whereas a standard machine learning course might primarily describe various learning algorithms, statistical learning theory focuses on the subset of these that are most well-suited to statistical analysis. For instance, regression is a common learning algorithm, and regularization is a common (statistical?) technique we use to improve our predictors.

At Berkeley, statistical learning theory is a popular course that attracts an unusually diverse audience of students (by graduate-course standards), not just machine learning theorists. It attracts students from all computer science and statistics research areas, as well as students from mathematics, psychology, and various engineering disciplines. For some reason, this year it was even more popular than usual — we had over 100 at the start (overflowing the largest room in the electrical engineering building). I would have thought that since the popular Professor Michael I. Jordan taught it last spring, that would have pulled away some of the students in this year’s cycle, but I guess not.

In past years, I think CS 281a focused almost exclusively on graphical models. My class seemed different: I had Professor Ben Recht, who was teaching it for the first time, and he changed the material so that we only discussed graphical models for about four lectures, giving us time to go over detection theory, hypothesis testing, and other fields. He told me personally that he dislikes graphical models (and also the EM-algorithm!) so I’m assuming that’s the reason why. We didn’t even get to talk about the junction tree algorithm.

We had five problem sets, which were each challenging but not impossible, and the workload got easier as the class went on. I probably spent an average of 15-20 hours on each problem set, including the “LaTeX-ing” process, but not including general mathematical review, of which I had to do a lot because of some shocking gaps in my linear algebra and probability intuition.

Digression: this semester gave me my first experience with Piazza, a private online forum where students can ask and answer questions related to the class material. (Students can be anonymous to other classmates if desired.) Even though it has some obvious shortcomings, I enjoyed it because it gave me a chance to discuss some of the homework problems “virtually.” Combined with a few in-person collaborations, CS 281a gave me a collaboration experience that I never had at Williams in my math courses. Having Piazza would have made some of those classes much easier!

Back to CS 281a: somewhat unexpectedly, we had a midterm! It was a 25.5-hour take-home midterm, open note, and open internet (!). At first, I was disappointed about having to take a midterm because I think I have proven my ability to understand concepts and describe them under timed exam constraints, but I ended up enjoying the test and benefited from it. I didn’t check, but I’m pretty sure none of these questions could be found online. 24-hour take home exams are the norm at Williams so I had tons of experience with this exam format. In lieu of a final exam, we had a final project, which I described in a previous post.

In terms of the lectures themselves, Professor Recht would alternate between discussing a concept at a high level and then writing some math on the blackboard. Unfortunately, the technical terms in this class made the captioning difficult, as I discussed earlier this semester. (Here’s a sample: Gaussians, Kullback-Liebler Divergence, Baum-Welch, Neyman-Pearson, and Lagrangians. Pretend you don’t know any math beyond calculus and try to spell these correctly.) And also, I didn’t mention this earlier, but for a large lecture course, we had a surprisingly high number of question-answer exchanges, which made it tougher on the captioner, I think, because of the need to listen to multiple people talking. The result was that the screen I looked at, which was supposed to contain the captions, had a lot of gibberish instead, and I bet the students sitting behind me were wondering what was going on. (I sat in the front row.)

I was still able to learn a lot of the material in part because I did a lot of reading — both from the assigned list and from random online sources — to really understand some of this material. I probably need to rely on out-of-class reading more than most (Berkeley computer science graduate) students, so I don’t mind that, and it’s something that graduate students are supposed to do: if you don’t understand something, then learn about it yourself (at first).

Overall verdict: I’m happy I took it. It’s a good introduction to what graduate courses are like, and I will probably take the sequel, CS 281B, the next time it’s offered.

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The Advantages of Recitations in Large Lecture Courses

williams_auditorium

At Williams, the largest mathematics or computer science course I took had probably 55 students. The normal size was 20 to 35 students. This meant I had many opportunities to talk to the professors without too much competition among the students.

The upper limit of 55 students pales in comparison to one of my courses at Berkeley. Last semester, the Statistical Learning Theory course (CS 281a or STAT 241a) initially had over 100 students after the first few lectures. Eventually, the number of students dropped to roughly 80 by the time the course concluded, but that’s still a considerable amount, especially considering that this was a graduate-level course, so shouldn’t there only be a handful of people who are interested in the subject? Of course, even that enrollment pales in comparison to the enrollment in Berkeley’s introductory computer science courses. Here, computer science majors (technically “EECS” majors) are “supposed” to start with the CS 61A, 61B, and 61C series, which are basically “intro to CS,” data structures, and computer organization/architecture. I was told that the enrollment in those three courses last semester after add/drop were (brace yourselves) 1243, 752, and 411 students, respectively! I’m not sure if there’s a single room on the Williams campus that can hold 411 people!

It’s no surprise, therefore, that at CS-intensive universities like Berkeley, MIT, and Washington, large lecture courses like 61{A,B,C} split up students into smaller recitation sessions. These can be thought of an extra lecture session, but led by a graduate student or an advanced undergraduate. The reduced number of students in these sessions makes it easier to ask questions and to go over confusing concepts.

One understandable reaction to this kind of situation is … why would anyone prefer Berkeley-style lectures compared to Williams-style lectures, where no recitations with (presumably less-talented) instructors are needed? Certainly this would have been my thought as a high school student, because it seems like I would much rather prefer the advantage of having a more personal relationship with the brilliant professors and being able to ask real questions during lectures. But on the flip side, there are several advantages to the recitation style.

  1. The recitation instructors are also incredibly brilliant! By making the most out of recitations, students can obtain valuable advice, insights, and strategies, from the recitation instructor that might be tailored more towards the student’s needs. HUGE disclaimer: I go to Berkeley, where people in CS are expected to be geniuses or crazy hard-workers. I’m aware that many universities have bad TAs.
  2. Sometimes, when it directly concerns specific class assignments, the recitation instructors can be better than the professors at answering questions! This is not the same thing, of course, as saying that the recitation instructors know more about the subject than the professor (or the class lecturer). Professors have clear views on how their field is moving and a broad knowledge base, but a graduate student might have had more time to look specifically at the code for one of the projects and can answer very specific questions, such as whether a line of code is functioning correctly. I often find that when I get past the initial hurdle of understanding an assignment at a high-level, it’s the tiny details that remain before I can confidently turn in my work.
  3. I also believe that due to the reduction of question-answering in large lectures, and because of an “extra” lecture/class session due to recitation, computer science courses in a school like Berkeley are able to cover more material than the corresponding Williams courses. At Williams, I remember some lectures that ended up in a back-and-forth question-answering session among the students and the professor. While this does mean we get to answer students’ questions, it also means we don’t make that much ground on material as compared to a standard lecture style course. For instance, after reviewing lectures and syllabi, I think that Berkeley’s data structures course (CS 61B) covers more material than Williams’ data structures course (CS 136), even when ignoring the last two weeks of Berkeley’s class. (Berkeley semesters are two weeks longer than Williams semesters due to the lack of a Winter Study.)

I wish I had better understood the tradeoffs between lectures in large universities and small liberal arts colleges back in high school when I was considering which college to attend. Then again, since I got rejected from almost everywhere I applied despite high GPA and SAT scores, perhaps it wouldn’t have mattered in the end, but I might have considered expanding the pool of my college applications to include more universities.

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