Detection Theory Adventures (a.k.a. a Final Project)

Whew! I just spent the past week in a mad dash to finish up my Statistical Learning Theory final project (CS 281a at Berkeley). My write-up is online, in case you want to check it out. The overall goal of my project was to explore the area of detection theory, an important mathematical field that does have practical implications. I know every field likes to say that (and in a sense, maybe it’s true for all fields anyway) but seriously — detection theory is about trying to distinguish the signal from the noise. Suppose we see a bunch of data points that are generated from some underlying process. This goes on for a while, but then at some point, the chart we see spikes up. That could indicate that something’s wrong. There are tons of examples that match this kind of situation. The example I used in my report was monitoring a patient’s body temperature. If I’m taking my temperature every 12 hours, for instance, and I see the following numbers: 98.6, 98.6, … (a long time) …, 98.6, 99.1, 99.5, 99.7, 100.2, 100.0, 101.1, by the time I’m getting even past 99.5 I should be a little suspicious and think that the underlying process for my body temperature indicates that I have a fever.

I learned a lot from my final project, since I read about 15 academic papers (which are not easy to read) and skimmed over many others. Despite this, I am not that happy with how it ended up, because my experiments were not extensive or groundbreaking. On the other hand, perhaps this kind of work is what I should expect if I’ve got only four weeks for a class project. It wouldn’t be the first time that I’ve been excessively harsh on myself.

By the way, my report is written in the style and formatting of the Neural Information Processing Systems (NIPS) conference. NIPS is one of the top two academic machine learning research conferences, with the other one being the Internal Conference on Machine Learning (ICML). Their papers have a nine-page limit, with the ninth one reserved for references only, but I’ve noticed that in practice a lot of researchers end up putting a ton of proofs and other information in appendices or supplementary material after the first nine pages. I have seen supplementary material sections that were 30 pages long! This is allowed, because NIPS guidelines say that extra material after nine pages is fine with the understanding that reviewers are not obligated to read them. I found the eight page limit to be easy to reach with this simple project, which is funny because I’ve long viewed eight page papers/reports to be long for a high school or college class. Furthermore, many of my previous class papers had to be double-spaced in 12-point font, whereas in NIPS they cram everything down with single-spaced, 10-point font. I had to fiddle around with a lot of the text to get everything to squish into eight pages, and as my last step, I used the LaTeX command \vskip -10pt to condense the “Acknowledgments” subsection heading with its text. I guess that’s what academic writing is like?

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Brain Dump: Successfully Installing and Running the Moses Statistical Machine Translation System

I’m using Moses again. It’s an open-source statistical machine translation system. I first used it when I was at Bard in 2012, and I remember being clueless about the installation process and being overwhelmed by all the Linux/Unix commands I had to know. I think it took me more than a week before I had installed Moses and successfully completed the suggested baseline test. At that time, I was doing nothing but that … so I was pretty frustrated. Even at the end of my REU, the commands to run Moses felt like black magic.

But I’m back at it again. Armed with several more years of hacking and Linux/Unix experience, as well as a statistical natural language processing class for background material, I managed to install Moses and complete the baseline test on my laptop, which is a Macbook that I got last January. It still took me almost a week (Friday night, 9-5 Saturday, 9-5 Sunday, all day Monday and Tuesday…) to do that since I ran into some unexpected problems. To prevent me from getting this kind of headache again, I’ll be listing the steps I conducted to install and run the baseline, which hopefully will be applicable for anyone trying to use Moses right now. If you find this article useful, please let me know. (Keep in mind, however, that it will probably be obsolete in a few months.) You will need some Linux/Unix experience to follow this article.

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Steve Ballmer’s (Subtle) Jab at UC Berkeley

Well, the news is out. Former Microsoft CEO and current Los Angeles Clippers owner Steve Ballmer just donated enough money to the Harvard computer science department to fund twelve professorships. Twelve! To put that in perspective, that’s 50% more than the total number of computer science professors at Williams College, and about half of the current size of Harvard’s CS faculty.

While it’s no doubt thrilling to see the attention that computer science is getting nowadays, I couldn’t help but notice this little segment from The Crimson:

“Right now I think everybody would agree that MIT, Stanford, and Carnegie Mellon are the top places [for computer science],” Ballmer said, adding that some would also include the University of California at Berkeley. “I want Harvard on that list.”

Wait a second, did Ballmer just exclude Berkeley from the Stanford, CMU, and MIT group? Last I checked, they were all clustered together at rank one … perhaps the exclusion is related to how Berkeley’s a public university? I can’t really think of any other reason. And while he did mention the school, don’t you think that if he viewed the top schools as a group of four, he would have said “I think everyone would agree that MIT, Stanford, Carnegie Mellon, and Berkeley are the top places […]” instead?

Anyway, I hope Berkeley can maintain its reputation for the next few years. This is mainly so that people will be willing to take me seriously at a first glance/conversation when discussing research; beyond that, of course, they’ll care more about your actual record than the school you go to. But it helps to go to a highly-ranked school. And I’m sure that some of Harvard’s new faculty members will have gotten their Ph.D.s from Berkeley. Incidentally, the fourth and fifth year students at Berkeley who have strong publication records must be feeling ecstatic.

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The Berkeley Vision and Learning Center’s Fall 2014 Retreat


On November 5, I attended part of the Fall 2014 Retreat for the Berkeley Vision and Learning Center (BVLC). The BVLC is a new group of faculty, students, and industry partners in EECS that focuses on research in vision (from computer vision to visualization) and machine learning. The retreat was held in the Faculty Club, a nice, country-style building enclosed by trees near the center of the UC Berkeley campus. While there were events going on all morning (and the day before, actually), I only attended the poster session from 5:00pm to 7:00pm and the dinner after that.

The poster session wasn’t as enormous as I thought it would be, but there were still quite a few people crowded in such a small area. I think there were around 15 to 20 posters from various research groups. I brought one about the BID Data project, whose principal investigator is John Canny. I’m hoping to become a part of that project within the next few weeks.

As far as the people who actually attended, there were a good number of faculty, postdocs, senior graduate students, and even industry people (from Microsoft, NVIDIA, Sony, etc.).  For faculty, I saw Pieter Abbeel, Trevor Darrell, Alexei (Alyosha) Efros, Michael I. Jordan, and Jitendra Malik at various times throughout the evening. (Trevor is the head of the group so he was guaranteed to be there.) I had two interpreters for the poster session, which was probably overkill, but they were able to help me describe what a few people were saying when I went to see two specific posters that were interesting to me.

I didn’t have anyone there for dinner, though, which meant it was a struggle for me to communicate. Also, during dinner, we listened to guest speaker Andy Walshe of Red Bull Stratos. His talk was titled Leveraging Cross­modal Data from High ­Performance Athletes at Red Bull . Andy mostly talked about the limits of human performance, and as far as I can tell, his talk was not an advertisement for the actual drink known as Red Bull, which as everyone knows is dangerous to consume. Even so, I was often wondering why this kind of talk was being given, because I would have expected a “traditional” machine learning talk — but maybe I missed something at the start when Trevor Darrell was introducing Andy. (This is one of the things one should realize about me; dinners and talks are some of the most difficult situations for me to be in, while they may be quite easy to get involved in for other people.)

I could tell that the talk was not overly technical, which meant that there was a lot of discussion and questions once the talk was over. In particular, Michael Jordan and Alexei Efros asked consecutive questions that made everyone in the room (except me) roar with laughter. I’ll have to find someone who can explain what they said….

(Note: the image at the top — taken from the Faculty Club website — shows the location where we had dinner and where Andy gave his 30-minute multimedia presentation.)

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Richard Ladner’s Path from Theoretical Computer Science to Accessibility Technology

Richard Ladner showed me a link to the September 2014 SIGACCESS newsletter, which contains a personal essay on why he made a career transition from being a computer science theorist to an accessibility researcher. (Frequent readers of my blog will know that I met Richard Ladner as part of the Summer Academy.) As usual, I’m a bit late with posting news here on this blog — this one is a few months old — but here it is and hopefully you enjoy his essay. Some highlights:

  1. Richard: “Although I am not disabled, disability is in my fabric as one of four children of deaf parents. Both my parents were highly educated and were teachers at the California School for the Deaf, then in Berkeley, California. They both used American Sign Language (ASL) and speech for communication, although not simultaneously.”
  2. Richard: “When I started at the University of Washington in 1971 I had no intention of doing anything in the area of technology for people with disabilities. I worked exclusively in theoretical science where I had some modest success. Nonetheless, some where in the back of my mind the transformative nature of the TTY helped me realize the power of technology to improve people’s lives.”
  3. Richard: “A light bulb went off in my head when I realized that innovation in technology benefits greatly when people with disabilities are involved in the research, not just as testers, but as an integral part of the design and development team.”
  4. Richard: “In 2002, with the arrival of Sangyun Hahn, a new graduate student from Korea who happens to be blind, I began my transition from theoretical computer scientist to accessibility researcher. By 2008 the transition was complete.”
  5. Richard: “One activity that I am particularly proud of is the Summer Academy for Advancing Deaf and Hard of Hearing in Computing that I developed with the help of Robert Roth who is deaf. […] Eighty-three students completed the program over its 7-year run from 2007-13. About half of these students became computer science or information technology majors.”
  6. Richard: “For students who want to become accessibility researchers I also have one piece of advice. Get involved at a personal level with people with disabilities. With this direct knowledge you are more likely to create a solution to an accessibility problem that will be adopted, not one that will sit on the shelf in some journal or conference proceedings.”

On a related note, Richard isn’t the only scientist who has made a late-stage research transition. I personally know several scientists/professors (though none as well as Richard) who have substantially changed their research agenda. One interesting trend is that people who do make transitions tend to move towards more applied research. It’s almost never the other way around, and I suspect that it’s due to a combination of two factors. First, theory-oriented research requires a lot of mathematical background to make progress, which can be a deterring factor. And second, I think many theorists wish their work could have more of a real world impact.

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Any Social Advice for Mingling Sessions?


Lately, I’ve been disappointed at my lack of ability to effectively socialize in various mingling sessions. Examples of these include the Berkeley graduate student social events, the Williams College math and computer science “snack/social” gatherings, research-style poster sessions (see this Williams math post for some sample images), and basically any kind of party. Typically, if I attend these events, I end up saying hi to a few people, stand around awkwardly by myself for a while, and then leave long before the event concludes, feeling somewhat dejected. This has been an issue throughout my entire life.

I don’t normally have anyone with me (such as an ASL interpreter) to help me out with communication, so I know that I’m already disadvantaged to start with, but I would like to think that I can manage social events better. I’ve tried various tactics, such as coming early to events, or going with someone else. Even when I arrive early, though, when the event starts to gather some steam and more people arrive, they tend to immediately conglomerate into groups of two or more, and I am often left out of any conversation. Furthermore, there’s no easy way for me to convince a group of five laughing students to include me in their conversation, and to also ask them to move to a corner of the room to decrease background noise.

Also, in my past experience, when I’ve attended an event with at least one other person, I can briefly remain in a conversation with the group I went with, but we end up splitting at some point. This usually means they’ve found someone else to talk with, but I haven’t.

The worst case scenario, of course, is if I arrive alone and late to a loud social event. By that time, everyone’s found a group to stick with and I don’t know what else to do but watch a bunch of people chat about some mysterious topics … or leave.

So what should I do then? I’m not someone who can just walk in a room and command the attention of everyone else here, and as evident from past experience, I’m going to need to work to get involved in a non-trivial conversation.

Unfortunately, I can’t (and shouldn’t) avoid social events all together, and the reason has to do with academic conferences. Especially in a field like computer science, where top-tier conference publications are what “count” for a Ph.D. student’s job application, it’s crucial for Ph.D. students to attend conferences and network with other people in the field. Even though I’ve already started graduate school, I have still (!) not attended a single academic conference, though I hope to do so in the future, and I worry about how I will handle the various social events they offer. I wrote a little bit on the topic of academic conferences before, but I’m more concerned with the social aspect here, not about the process of obtaining accommodations, which hopefully won’t be too bad with the resources that Berkeley has at its disposal.

I don’t have any answers to this right now, so I would appreciate any advice if you have them. In the meantime, I’ll continue brainstorming different strategies to improve my social situation in events that involve mingling, because I’m attending a poster session in three days.

(Image from Australia China Alumni Association)

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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:


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


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?


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.

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On Data Wrangling


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


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?


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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