The Value of Standing Up

My office is in the fifth floor of Soda Hall, and is part of a larger laboratory that consists of several open cubicles in the center, surrounded by shared offices (for graduate students and postdocs) and personal offices (for professors). Thus, I can peek into other offices to see what people are doing. And the graduate students I observe are almost always ensconced in their chairs.

I know that even Berkeley students take breaks now and then, but I still think that many us end up sitting down for five to six hours daily. (That’s assuming graduate students work for only eight hours a day … definitely an underestimate!)

I don’t like sitting down all day. In fact, I think that’s dangerous, and lately, I’ve joined the crowd of people who alternate between sitting and standing while at work. My original plan when I arrived in Berkeley was to ask my temporary advisor to buy a computer station that has the capability to move up and down as needed. Fortunately, I haven’t had to do that, because I somehow lucked into an “office” that looks like this:

Office

Heck, I don’t even know what those metal-like objects are to the left. Fortunately, they’re set at the perfect height for a person like me, and they’re really heavy, so it’s provides a firm foundation for me to put my laptop there and stand while working. My current work flow is to default by standing up, and then sit down only when my feet start getting sore. Then I stand up once I start feeling stiff. Seriously, it doesn’t get any easier than that. You don’t need a fancy treadmill desk, though it’s an option — one faculty member at Cornell has this in her office. All you need is a nice stack of sturdy objects to put on top of something. And especially if you only plan to use your laptop, I can’t believe anyone (e.g., a boss) would complain if you built a simple station yourself. For more tips, you can also check out this excellent Mark’s Daily Apple article about standing at work.

There are other ways of avoiding the curse of a sitting-only job. For instance, some people might benefit from long walks during work, a thought that came to me due to a New York Times article that appears to have turned some heads. Personally, I find walking overrated. Every time I go for a walk, I can’t focus on my work — my mind always switches to whatever random thought happens to be flowing around. So I prefer to just sit and stand as needed during a pure work day, and I hope that other students (and faculty!) consider doing that.

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Rain, Berkeley Weather, and Hearing Aids

Berkeley_Weather

I’m sure that most long-time hearing aid users such as myself have gone through this scenario: you’re outside, wearing your hearing aids, and the weather (sunny, 75 degrees) is great. Perhaps you’re taking a walk around your neighborhood, or you and a friend are having lunch outside. But then all of a sudden, the weather takes a nasty turn and it’s pouring rain. Since you don’t have an umbrella or a rain jacket, you scramble to find shelter. While you are doing so, you also wonder if you should take off your hearing aids, as they are (sadly) not waterproof. You consider a few important questions. Is it raining hard enough? Can you reach shelter quickly? Is it safe to take off your hearing aids?

All this is due to one rather unfortunate feature of hearing aids: they are not (generally) waterproof. Even a waterproof label might be misleading because that means a hearing aid passed a specific test, not that you can throw it in your backyard pool and expect it to work when you pick it up a month later. I’m actually planning on writing a more extensive post on the issue of hearing aids and moisture, as I’ve only briefly mentioned that topic in this blog (e.g., in this article, where I talked about touch-screen hearing aids). But I can say from my own experience that I get disappointed every time I get what is advertised as “the latest water resistant hearing aid” only to see it break down midway through a game of Ultimate Frisbee. I don’t typically have problems with rain anymore, because I’m usually prepared with an umbrella — or I just stay indoors.

Anyway, I’m happy to report that hearing aid wearers in the San Francisco Bay Area need not worry about rain. I moved in Berkeley on August 13, so it’s been almost two months. And I only remember one day when it rained. That was a few weeks ago, and it was a light drizzle at that. I brought two umbrellas and a rain jacket when I moved in, and they’re just collecting dust in my room, waiting for the next rainy day to occur. As indicated by my screenshot of the current forecast, that may not come for a while. It’s not as if the weather is scorching hot either, which might induce unusual amounts of sweat (another threat to hearing aids). It’s usually around 60 to 85 degrees here.

There was a newspaper article a few weeks ago that touched on the topic of rain in the Bay Area, so from what I can tell, I should expect more rain once it’s winter, but probably not that much. (I’m also aware that California’s in a historic drought, so I do feel guilty for being happy about the lack of rain.) Needless to say, the weather here is vastly different from the weather in Williamstown, MA. I remember when it would rain for days in September, thus ruining the Ultimate Frisbee fields. So far, the weather in Berkeley has been terrific, which is probably one of many reasons why graduate students come here from all over the world.

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After a Few Weeks of CART, Why do I Feel Dissatisfied?

captioning

As I said in a recent post, I’ve been using a mixture of captioning (also known as CART) and interpreting services for various Berkeley-related events. For my two classes, I decided to forgo interpreting services in favor of captioning. Part of this was out of a desire to try something new, but I think most of it was because when I was at Williams, I experienced enormous frustration with my inability to sufficiently understand and follow technical lectures with interpreting services. (I had to rely on hours of independent reading before or after the talks for the material to make sense.)

This isn’t a knock on the interpreters, or a criticism of Williams. I’ve said before and will gladly continue to say that I was very happy with the accommodations Williams was able to provide me, and how my interpreters have put up with me for four years as I consistently enrolled in the classes that they hated the most.

The problem is the technical term dilemma that continues to plague my experience in the classroom.

In the best case scenario, using captioning services would let me focus primarily on the professor talking, and if there was something I missed, I could fall back on the captions to catch up on a few sentences. To make it clear, the way CART usually works is that the captioner will type on a laptop with the text small enough so that I can quickly look at the screen to see what was being said 10 seconds ago. With interpreting services, one can’t go “back in time.”

The other advantage I was hoping to gain from CART pertained to preserving the spelling of technical terms. An interpreter can’t really sign the word Gaussian,  but a captioner can at least type out that word correctly once the professor has said it often enough (or has written it on the board).

To top it all off, I was told during my first meeting with the Disabled Students’ Program (DSP) that CART would be able to capture content with 99 percent accuracy.

Unfortunately, theory hasn’t matched with reality and, if anything, my experience in Berkeley classes so far has been more frustrating than with my Williams classes.

Continue reading

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On Data Wrangling

data_science

Last month, the New York Times published an interesting article that connected with my experience working as a computer scientist. The idea is that there’s so much data out there — in case you’ve been living under a rock, it’s the age of Big Data — but it’s becoming increasingly harder for us to make sense of it so that we can actually use the data well. Here’s a relevant passage:

But if the value comes from combining different data sets, so does the headache. Data from sensors, documents, the web and conventional databases all come in different formats. Before a software algorithm can go looking for answers, the data must be cleaned up and converted into a unified form that the algorithm can understand.

So why does this article connect to me? Every major computer science project I’ve worked on has involved a nontrivial amount of data “wrangling” (for lack of a better word), such as the one I worked on at the Bard REU. I also had a brief internship last summer where my job was to implement Latent Dirichlet Allocation, and it took me a substantial amount of time to convert a variety of documents (plain text, .doc, .docx, .pdf, and others) into a format that the algorithm could easily use.

Fortunately, many researchers are trying to help us out, such as professors Jeff Heer at the University of Washington and Joe Hellerstein at the University of California, Berkeley. I met Jeff when I was visiting the school a few months ago, and he gave me an update on the amazing work he and his group have done.

Meanwhile, as I finished reading the article, I was also thinking about how our computer science classes should prepare us for the inevitable amount of data wrangling we’ll be doing in our jobs. The standard machine learning computer science project, for instance, will tell us to implement an algorithm and run it on some data. That data, though, is often formatted and “pre-packaged,” which makes it easier for students but typically doesn’t provide the experience of having to deal with a haphazard collection of data.

So I would suggest that in a data-heavy computer science class, at least one of the projects should involve some data wrangling. These might be open-ended projects, where the student is given little to no starter code and must implement an algorithm while at the same time figuring out how to deal with the data.

On a related note, I should also add that students should appreciate it when their data comes nicely formatted. Someone had to assemble the data, after all. In addition, for many computer science projects, such as the Berkeley Pacman assignments, much of the complicated, external code has already been written and tested, making our jobs much easier. So to anyone who is complaining about how hard their latest programming project is, just remember, someone probably had to work twice as hard as you did to prepare the project and its data in the first place.

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Good News: Accommodations for Berkeley Events are Quick and Easy to Obtain

berkeley_access

I’ve only been a Berkeley student for about three weeks, but I’m already appreciating how quick and easy it has been to get accommodations for various events. To do so, one just needs to go to the Disability Access Services website, fill out a two-page online form, and submit. I’ve filed about half a dozen requests already, an indication of how many meetings I’ll need to be attending to during my time in Berkeley. (Though I’m probably better off than the tenured professors here in that regard.)

The services one can request fall in two categories: communication and mobility. I’m only familiar with the communications aspect, which includes sign language interpreting and real-time captioning. Since this is the first time I’ve really been able to take advantage of captioning availability, I’m trying out a mix — some events with captioning, some with interpreting.

Not only is it easy to obtain these services, it’s also quite reliable. I’ve never had a request denied or forgotten. In fact, I even got a captioner for a new graduate student meeting despite giving only 36 hours of advance notice. (I had forgotten that it was happening … won’t do that again!) I’ve met a few of the people who work at the access services group, and they’re all really friendly. They are closely related to the Disabled Students’ Program at Berkeley, which is designed to help accommodate students for class-related purposes.

I think even people who aren’t affiliated with Berkeley in some way can request accommodations for events, though they might need to pay a small fee. Berkeley students can get them for free.

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Berkeley Orientation

soda_hallMy life has been busy in the past few weeks as I’ve gotten adjusted to life in Berkeley. Part of this process has been going through orientation. I sat through a new EECS graduate student orientation and a general graduate student orientation.

For the most part, what we discussed during the orientations wasn’t too surprising. Here are a few highlights from the EECS-specific one.

  1. There were 1,615 applicants to the computer science doctoral program. Berkeley accepted 83, for an acceptance rate of 5.1%. The yield was 43, not including five extra students coming in from last year’s cycle. Interestingly enough, this information doesn’t seem to be available anywhere and I’ve heard acceptance rates range from as high as 9% to as low as 2%, so it was nice to see these values come directly from the department chair. There were even more applicants for the electrical engineering program (at least 1,800). Coming from a school that has no engineering courses, I would have thought that computer science would have been more popular than electrical engineering. All together, we have 98 entering EECS Ph.D. students.
  2. The orientation made it clear that the department is passionate about supporting the well-being of its graduate students. The chair emphasized the need to be inclusive of people from all backgrounds. We also had a psychologist and a member from the Berkeley Disabled Students Program speak to us. Finally, there were representatives from the Computer Science Graduate Student Association (CSGSA), an organization designed by the students to support each other school (there’s also an EE version). I really did come out of this orientation feeling like Berkeley cares about their EECS graduate students.
  3. The end of the orientation was mostly about working and getting funding. There was too much information to absorb in one day, but fortunately the handouts we got contained the relevant information.

The general graduate student orientation, held the following day, was less useful than the department-specific one, and I could tell by the size of the crowd that most of the EECS students probably didn’t go. Some highlights:

  1. The most important one for me was learning about residency, residency, and residency. As a public school, Berkeley charges out-of-state students non-resident tuition, including graduate students. The EECS department pays for this during the first year, but from the second year onwards, we pay an extra $8,000 unless we’ve established California residency.
  2. I also attended workshops relating to student health services and “surviving and thriving” in Berkeley.
  3. And for any graduate student who expects to be hungry often, there was free breakfast and lunch.

In addition to orientation, I’ve had a few classes and research group meetings. I’ll talk about the research later — stay tuned.

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Reading the Oticon Sensei Hearing Aid Manual

oticon_senseiI’m 22 years old and have been wearing hearing aids for most of my life.  But for some reason, I’ve never read a hearing aid instructions manual. Now that I live in California, far away from my audiologist in New York, I’m going to need to be a bit more independent about managing my hearing aids. So I read the manual for my new Oticon Sensei hearing aids. Here are some of its important messages and the comments I have about them, which probably apply to many other types of hearing aids.

  1. “The Sensei BTE [Behind the Ear] 13 is a powerful hearing instrument. If you have been fitted with BTE 13, you should never allow others to wear your hearing instrument as incorrect usage could cause permanent damage to their hearing.” My comment: I already knew this, and I think it’s a point worth emphasizing again. Your hearing aids are for you and not for anyone else!
  2. “The hearing instrument hasn’t been tested for compliance with international standards concerning explosive atmospheres, so it is recommended not to us the hearing aids in areas where there is a danger of explosions.” My comment: again, this is straightforward, because generally anything with batteries can have a risk of explosion, but I think the better strategy is to not go near those places at all. (And if you’re a construction worker, I’d ask for a different work location.)
  3. “The otherwise non-allergenic materials used in hearing instruments may in rare cases cause a skin irritation or any other unusual condition.” My comment: I had the misfortune of experiencing skin irritation a few months ago. Some new earmolds I had were designed differently from what I was used to, causing skin in my inner ear to harden. I had to dig into an old reserve of earmolds and fit those to my hearing aids to comfortably wear them.
  4. “[When turning off hearing aids] Open the battery door fully to allow air to circulate whenever you are not using your hearing instrument, especially at night or for longer periods of time.” My comment: I sort of knew this, but now it’s concrete. From now on, I’ll keep the battery doors open when I put them in the dryer each night. Unfortunately, the manual didn’t specify whether the battery should stay in the compartment or not.
  5. “Hearing instruments are fitted to the uniqueness of each ear [...] it is important to distinguish between the left hearing instrument and the right.” My comment: For someone like me, who relies more on one ear for hearing than the other, keeping track of what goes left and what goes right is crucial. I’ve gotten confused several times about this when I replaced earmolds for various hearing aids.
  6. “Although your hearing instrument has achieved an IP57 classification, it is referred to as being water resistant, not waterproof. [...] Do not wear your hearing instrument while showering, swimming, snorkeling or diving.” My comment: as usual, one needs to be careful about the distinction between water resistant versus being waterproof. From my own experience, the Oticon Sensei does an excellent job resisting sweat, and I can only remember a handful of times when they stopped working normally during or after a gym session. (As I mentioned before, the same isn’t true for some types of hearing aids.)

I emphasize the importance of reading these manuals because if one is going to be using a hearing aid often, it’s important to know as much about them as possible, and I think this aspect gets glossed over in today’s busy lives. Similarly, don’t forget to learn more about your cars, houses, phones, laptops, and other expensive items — you might learn something useful.

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Is it Better to Work Without Hearing Aids?

no-hearing-3

Tomorrow, I will finish up a software engineering internship. I usually work at home, and lately I’ve been getting out of bed, wolfing down breakfast (berries, broccoli, and eggs), and conducting my morning coding session, all without putting on my hearing aids. Sometimes, I don’t touch them until the afternoon.

This raises the following question:

Is it better for someone like me to work without hearing aids?

Naturally, this would only apply during individual work sessions. If I’m working on a team project with a partner right by my side and we need constant communication, I’ll keep my hearing aids on. The one exception would be if that other person wants to speak using ASL, but that’s generally not a common occurrence.

I recall performing this “no hearing aid” tactic during my time working in the Williams College computer science lab. During peak hours, usually Sunday or Thursday evenings, the lab would get so packed that I couldn’t focus with all the screaming going on. (It sounds like screaming when 30 regular-volume conversations are happening in one small area.) If I wasn’t holding a TA session, then I would go to a corner of the back room of the lab, turn off my hearing aids, and work in peace.

The advantage of this is that I often reap the benefits of a short-term focus spike; it’s definitely nice to be able to mute all conversations under those circumstances. But should eschewing hearing aids be my default behavior when I work on something myself? Even if the only external noise is a fan?

Okay, I have to confess: part of the reason why I haven’t put on my hearing aids until so late during the past few days has partly been out of experimental interest. I want to see how effectively I work with and without hearing aids while having little to mild background noise. (It’s not a perfect experiment, because my surroundings are too quiet.) My impression is that I think turning off hearing aids can be useful under extremely noisy circumstances, but for most cases, I would not recommend it because there are too many downsides:

  • I’m more vulnerable to danger. If the roof of my house were about to collapse due to hail, but I couldn’t feel it (I know this example is crazy…) then you can imagine what would happen.
  • It creates some awkwardness if I need to turn on my hearing aids when someone wants to talk to me. My hearing aids — the Oticon Sensei — take roughly six seconds to start up from the moment I press the switch. So … I have to figure out how to stall for six seconds. And what if that person just wanted to say hi?
  • Related to that previous point, when I turn off my hearing aids, it’s not at all obvious to anyone else in the same room that I actually do have them off. My hearing aid’s on and off states are hard to distinguish unless a person has a clear side view of me. Perhaps if I physically took them out of my ears, but that creates a whole host of other complications. In this situation, if someone needs my attention, he or she is going to have to work a harder to reach me, and everyone else in the room will probably be watching us.
  • One thing I’ve also noticed in the past few days is that, when I turn off hearing aids, it blocks external noise but doesn’t silence my brain. It seems like if I don’t hear any natural sounds, sometimes my brain tries to “fill in” for me by repeating voices and sounds, which can be annoying. I think if I have my hearing aids on, some of the natural sounds can break that up (but not always).

Thus, while turning off hearing aids is useful when faced with prolonged noise exposure, it is not generally a long-term solution. With situations such as shared offices, which are a typical work environment for graduate students, I think the benefits decrease and the drawbacks (as stated earlier) become more striking. (At Berkeley, I’m pretty sure graduate students periodically interrupt each other to talk about research.) As a possible alternative, I could utilize noise-canceling headphones that cover my hearing aids (without causing any “ringing”) which would take care of some of the problems I mentioned. Interestingly enough, the last time I tried wearing noise-canceling headphones over my hearing aids, they didn’t cancel out any noise! So it seems to me that I just need to get used to working with background noise.

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What Happens the Summer Before Grad School?

In about a week, I’ll be heading over to Berkeley to begin my graduate career.[1] Consequently, I thought I’d take some time to reflect on what’s been going on this summer, particularly with regards to preparation for graduate school. Perhaps this will be useful to future generations of Berkeley EECS Ph.D. students.

Once students confirm that they are going to Berkeley, then they’ll be put on a mailing list (or more accurately, a “Google Group”) that includes all incoming EECS students, a few existing EECS students, and a few staff members. Important emails will be flying around by early May, so technically one’s preparation for Berkeley should start even before the summer begins.

Of the emails that are being sent, by far the most important ones to read are those pertaining to the quality of ice cream in the Berkeley area. The second most important emails to read are the ones about housing.

For people like me who don’t have any connections in the Bay Area, contacting other incoming students about housing opportunities is extremely important, unless you want to hedge your bets on living by yourself or with non-EECS students. Fortunately — at least during the summer of 2014 — there seemed to be enough people in my situation that finding a group to live with wasn’t too difficult. I did have to go through several failed attempts at forming a group, as well as one rejected housing application (that really hurt), but by the start of July, I had secured a place to live. One key tip is to keep in touch with the incoming students who are already around the Bay Area; they’ll be the ones conducting most of the house visits to make sure that the house you found on craigslist isn’t terrible. That reminds me: if you have no experience with craigslist, I suggest learning how to use it. And another tip about housing: I think it’s easier to get housing if you can find a nice place to rent and then advertise it to the group, rather than if you form a group first and then find a house.

Of course, there are other emails to read as well. Most of the non-housing emails fall into the category of incoming students asking current students questions. But worry about those after housing.

The Berkeley Graduate Division also sends out monthly emails. Those emails are short but have links to a bunch of detailed PDFs and websites. There’s too much information to absorb at once, but read as much as you can. You’ll also want to read a little more about the department’s Ph.D. requirements. Here’s a refresher.

At the start of July, you’ll also be assigned a temporary advisor. Send him or her a few emails (but not too many … see the Email Event Horizon for why). You may ask advice on what courses to enroll in, but the class schedule is online and most students have a good idea of what to take anyway. You can sign up for classes starting in August, but be careful not to take more than two a semester.

Finally, if you were to ask me advice on what to do during the summer before graduate school, I would recommend either a research or software engineering internship to keep your skills sharp, but it’s OK to use this time to travel or pursue other interests. While you can pursue them at Berkeley, the 167-hour work week makes things a little time-intensive.

1. Just in case you were wondering, I do plan on maintaining this blog during my time in Berkeley. I haven’t run out of things to say.

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Andrew Ng’s Machine Learning Class on Coursera

machine_learning_coursera

If you’re interested in taking a free online course, consider Coursera. It takes seconds to make an account and filter through the 700 or so classes currently in the database to find what interests you. Classes are generally affiliated with a university, and professors are often the ones lecturing in the videos online. In addition to video lectures, there are homework assignments and exams, which are submitted electronically, as well as user discussion forums where the students can discuss class concepts.

Coursera embodies the concept of the massive open online course (MOOC) which aims to have unlimited participation to allow (theoretically) anyone in the world to obtain an education for free. Founded in 2012 by Daphne Koller and Andrew Ng of Stanford University, Coursera now has over 7 million users and sports an impressive list of university partners. (Check out this paper for an interesting discussion about MOOCs.)

Coursera is similar to the well-known MIT OpenCourseWare, but it has several advantages. The biggest one is that courses on Coursera will have all class material eventually available to the students who sign up, whereas on MIT OpenCourseWare, you face the repeated problem of lack of video lectures, lack of exams and solutions, and other information, especially with the upper-level courses. Coursera’s website design is also vastly superior. On the other hand, Coursera classes requires the user to sign up in a certain date range, so if you go on Coursera right now, chances are high that some of the classes that you want to take aren’t offered in the near future (and you might have to add it to your “watch list” for the next session).

In the meantime, I’ve been checking out Andrew Ng’s machine learning class, which was what really started Coursera. It’s designed to be a ten-week course, with the following syllabus:

  • Week 1: Introduction, Linear Algebra Review, Linear Regression with One Variable
  • Week 2: Linear Regression with Multiple Variables
  • Week 3: Logistic Regression and Regularization
  • Week 4: Neural Networks (Representation)
  • Week 5: Neural Networks (Learning)
  • Week 6: Applying Machine Learning Algorithms
  • Week 7: Support Vector Machines
  • Week 8: Clustering, Dimensionality Reduction
  • Week 9: Anomaly Detection, Recommender Systems
  • Week 10: Large-Scale Machine Learning

A third of the grade is based on multiple-choice quizzes, and the rest is determined by programming assignments, to be done in MATLAB or Octave, the latter of which is an excellent free version of the former. Octave is one of the simplest programming languages out there, so it shouldn’t be too difficult for one to get used to it.

After going through the first few weeks of the course, here are some quick impressions:

  • Advantages: The class doesn’t have many prerequisites (no calculus, no probability, etc.) and is accessible to a broad audience. Professor Ng’s video lectures are excellent. In fact, it’s nice to see that someone who can write complicated papers can clearly explain the basics. There seems to be a lot of collaboration among the students. The class covers most of the concepts I’d expect in a machine learning class, but for some reason doesn’t seem to cover the naive bayes and decision tree learning algorithms.
  • Disadvantages: The simplicity of the class is also its major drawback — to someone like me who already knows machine learning, the class is too easy and I watch video lectures (for review purposes) at 1.5x or 1.75x the speed (a nice feature, by the way). Professor Ng often has to say “the discussion of this concept is beyond the scope of this course….” Consequently, a student at Stanford is better off taking Professor Ng’s “actual” machine learning course.

Again, if you’re interested in learning more about any subject, I encourage you to check out Coursera. There’s definitely a heavy focus on computer science — not surprising, given that the founders are computer science professors — but there are courses in subjects as diverse as health, law, engineering, and music.

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Cholesterol, Saturated Fat, Grains, Meat, and Other Diet Controversies: why are There so Many People Challenging Conventional Wisdom?

As I mentioned in my recent post introducing Mark’s Daily Apple, I have become more interested in understanding diet, nutrition, and health. Sadly, this doesn’t come without challenges, and in this post, I’d like to discuss some of the current controversies that make it difficult for me to decide what to eat in order to maintain a healthy life.

First, let me provide some background. During elementary and middle school, I learned about the United States Department of Agriculture’s infamous food pyramid. Of course, like most Americans, I didn’t adhere to it exactly, but I at least kept it in mind, so it did impact the way I ate for most of my life.

After reading Fast Food Nation, I also avoided most forms of fast food starting in high school. On the surface, this diet approach seems to be excellent — just follow the food pyramid and avoid McDonald’s. Unfortunately, up until now, I had been unaware of the vast amount of misinformation, politics, and shoddy science of food that plague the country and are likely correlated with the shocking prevalence of obesity, heart disease, diabetes, and other chronic illnesses.

Continue reading

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Introducing Mark’s Daily Apple

Summers are nice because they offer me a break from an intense academic environment. As a result, I’ve had the chance to explore other fields that interest me, and one of them concerns the human diet. Simply put, I’m trying to figure out what I should eat in order to maintain a healthy life.

The Original Food Pyramid

There’s a lot of information available that we can use for diet advice. For instance, the United States Department of Agriculture has their famous (or infamous, as I’ll get to shortly) 1992 food pyramid:

USDA_Food_PyramidLet’s suppose we use this as a guide to optimal health, which seems reasonable because it’s from a United States government organization. (It shouldn’t be, because that pyramid has already been scrapped in favor of new dietary guidelines, but it’s good to discuss it to see the historical perspective on food.)

Unfortunately, even without consulting outside sources, I can already see several problems:

  1. It makes no distinction between whole or minimally processed foods and heavily processed foods. (I’ll throw in whole grains in the “minimally processed foods” category.) The former group includes fruits, vegetables, and animal products obtained from their natural state. The latter group would include pizza, chemically-laden meats, and so on.
  2. It suggests consuming fats, oils, and sweets sparingly, but the dairy and protein groups already include substantial amounts of fat. And my understanding is that fat has long been essential for human health. Our early ancestors ate lots of plants, but they would also eat the complete carcass of animals, including fat-dense organs that we shun today.
  3. It suggests that the serving counts should not be exceeded, which might impose unnecessary restrictions. Consider my situation: I love eating huge salads, and I also have a habit of downing an entire bag of baby carrots as an afternoon snack. This means that I easily rack up 7-10 servings of vegetables daily (depending on how you define a serving), but according to this pyramid, I shouldn’t be eating so many vegetables.

I know that no pyramid can disseminate detailed information in such a small amount of space, but such simple modifications could go a long way.

This brings me to the next part of this post.

Mark’s Daily Apple

My quest for learning more about health, diet, nutrition, and food led me to Mark’s Daily Apple. It’s an extensive blog written by Mark Sisson, a well-known advocate of eating the Paleo diet (though he calls it “Primal”) and preventing chronic diseases of civilization (e.g., diabetes and heart disease) by lifestyle choices. I didn’t think much of this at first, but the more I thought about the food I ate, the more I kept coming back to his blog. It also didn’t hurt that he’s another Williams alum, which might have piqued my curiosity.

Mark Sisson advocates his own food pyramid, which emphasizes meats (including fish, eggs, and fowl), vegetables, fats, fruits, and some carbohydrates. Notice the distinct lack of bread, rice, cereal, and pasta! It’s a long story about why he excludes them, but Sisson explains this in his blog and has some decent (but in my opinion, not overwhelming) evidence to back up his claims. For the most part, I favor his food pyramid over the 1992 USDA food pyramid, but I think Sisson’s pyramid should have kept vegetables as the “base” group to reiterate how they should compose the bulk of the diet in terms of volume.

If you want more information about his philosophy towards food and life, I’ll refer you to his Start Here page. When reading Mark’s Daily Apple, realize that Mark Sisson’s focus is not just on nutrition, but indeed, on a lifestyle. His advice encompasses sleep, play, exercise, and many other factors that affect our health. There’s so much out there that Mark Sisson posts new entries daily and still has no shortage of topics to talk about. His blog has been on my About page for a while, but I thought it would be interesting to actually introduce it in one of my entries.

In a future blog post, I’ll delve more deeply into diet controversies. (Don’t worry — this digression doesn’t mean that I’m turning into a nutritionist…)

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More Deaf Computer Science Ph.D.s

About a year and a half ago, I wrote a blog entry about deaf computer science Ph.D.s. I recently revisited this topic and found out that I missed a few people from my earlier list, so this post is a continuation of my previous one. Here are the new Ph.D.s:

  1. Vinton Cerf (Ph.D., University of California, Los Angeles, 1972), though if we’re getting picky, he’s hard-of-hearing.
  2. Daniel Berry (Ph.D., Brown University, 1974)
  3. Dimitri Kanevsky (Ph.D., Moscow State University, in the 1970s), though again, if we’re picky with our criteria, he actually got his Ph.D. in math.

These three people are all established scientists with impressive resumes.

Vinton Cerf is known as one of the “fathers of the Internet,” which should say a lot about his contributions to computer science. For instance, he helped to form the Internet Corporation for Assigned Names and Numbers (ICANN), which manages the global domain naming system (its headquarters is in the U.S. … I’m not sure how other countries feel about that). Not surprisingly, Dr. Cerf has an array of awards and honors, including the Turing Award, the Presidential Medal of Freedom, and the National Medal of Technology. In 1997, he joined the board of trustees at Gallaudet University. Nowadays, he works at Google.

Daniel Berry has been deaf in both ears since birth, and can only use a hearing aid in one ear. He does not sign because his parents spoke English and he picked up lipreading, which may be one reason why I didn’t know of him until now (and he mentions on his website that he doesn’t have many hearing impaired acquaintances). In terms of academics, he got his computer science Ph.D. from Brown back in 1974. He then joined the faculty at UCLA from 1972 to 1987, The Israel Institute of Technology from 1987 to 1998, and then at the University of Waterloo from 1988 to now. Note that all three of these schools have outstanding computer science departments, which should give you an idea of his research ability. Professor Berry provides a brief paper on his website that describes his background in more detail, as well as his recommendations for making the web more accessible.

Dimitri Kanevsky got his Ph.D. in Russia in the 1970s (I can’t find the exact date) and held research positions at the Max Planck Institute in Germany and the Institute for Advanced Study in Princeton. Armed with mathematics background but a desire to make practical use of it, he joined IBM in 1986, where he still works today. Dr. Kanevsky specializes in speech recognition, so there’s a good chance that any recognition software today traces its origin to him. Dr. Kanevsky’s resume includes being named an IBM Master and more than 15o U.S. patents.

Unfortunately, I’ve never personally met any of these people, but it would be nice to do so someday.

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Padden Named Social Sciences Dean at UC San Diego

In deaf-related news, I just found that Carol Padden, professor of communication at the University of California, San Diego, has been named dean of the social sciences at her school. Her tenure will start in October.

I didn’t know her before this announcement, so it was interesting to read about her background. She was born as the second deaf child of two deaf faculty members at Gallaudet University. She started her education in a deaf school and then became a mainstreamed student at a public school system.

After graduating from Georgetown in 1978 with a Bachelor of Science in Linguistics, she went to UC San Diego to pursue a PhD in Linguistics, which she obtained in 1983. She has since been on the faculty (and, of course, will be a dean) at the same school, specializing in the study of American Sign Language. Professor Padden seems particularly interested about understanding the variety of sign languages that are developing throughout the world. For instance, what properties of sign languages develop after only one or two generations of use? Which ones require more time to evolve? While I can’t judge her research, it must be top-tier, since she was a MacArthur Fellow in 2010.

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Friendly Computer Science Textbooks

I’d been meaning to post this earlier, but I got sidetracked by Project Euler (more on that later). In any case, I want to list a few computer science textbooks that, in my opinion, are written in a friendly style and are easy for someone to read like a novel. These are my favorite kind of textbooks, because they often incorporate two important aspects: elaboration and examples. Notice that this does not mean that mastering the corresponding subject is easy! It just makes it easier for an experienced and educated reader to do so.

I’m inspired to think about this because, as much as I enjoyed my complex analysis class last fall, the textbook we used glosses over so many details that it made analyzing some of the proofs excruciatingly difficult. At least, for me … I can’t speak for everyone in the class, but my professor did have to explain that the goal of his lectures was to emphasize why the authors/book did something in their proofs.

In the past few weeks, I read parts of Methods of Mathematical Economics, a textbook about the mathematics behind linear programming and other popular applied mathematics techniques. It is written in a conversational style (the author uses “I” instead of “we”), and it was very helpful to me for a final project.

Here are four books on the computer science side that I’ve found to be very readable.

  1. Algorithm Design, by Jon Kleinberg, and Eva Tardos, presents an introduction to the common themes underlying an algorithms course. I enjoyed it because it emphasizes the decision-making behind many of the proofs. In addition, it contains several sample problems with detailed solutions. These solutions also explain why certain approaches might not work or are suboptimal.
  2. Artificial Intelligence: A Modern Approach, by Stuart Russell and Peter Norvig, is a surprisingly readable “encyclopedia-like” book about AI. I do not recommend reading the entire thing, especially in one sitting! But if you pick out a single chapter, the book should serve you well. I talked with Stuart when I was visiting Berkeley, and he told me I needed to know more learning theory. I asked him how I could learn more, and he said: “read the book.” Good — I’ll do that!
  3. Distributed Systems: Principles and Paradigms, by Andrew S. Tanenbaum and Maarten Van Steen, is about concepts of distributed systems (i.e., those relying on multiple computers/machines). This book is filled with examples. Almost every concept is explained with an immediate real-life example.
  4. Introduction to the Theory of Computation, by Michael Sipser, overlaps somewhat with Algorithm Design, but emphasizes automata, computability, and complexity, rather than pure algorithms. It is a concise book, but somehow provides the impression that it’s detailed and expansive. Fortunately, each chapter contains problems with full solutions.

I’d be interested in knowing if there are other popular, readable computer science textbooks.

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