Category: Academics

Welcome to another installment of Ask an Adult, where Rebecca Hsu, CC ’89, tells you what to do about life’s biggest problems. Have a question? Send it to thecolumbialion@gmail.com – God knows we aren’t qualified to answer, but we’ll pass it along! 

Q: It’s finals week — save us! What do we do about stress?

A: What is stress? I’m a doctor, so let’s start with the medical definition: stress is defined as an organism’s total response to environmental demands or pressures.

As you approach finals week, I’m sure you understand. There have been studies to show that the stress felt by students taking finals is like that of soldiers entering a battle. Well, I definitely took a few exams where I would have preferred bullets flying by my head than writing the answer to the question.

First and most importantly, you need to realize what you are stressed about. You think you are freaking out because you need to pull an all-nighter to finish your Lit Hum paper, but what you may be more worried about is what to wear on your next hot date, since you can’t decide between the red pumps and the black sandals.

Let’s make it simple by starting with the obvious and easy. Find a way to RELAX both your body and your mind. This usually means taking a study break.

Here are a few suggestions for one:

 

Engage in some intimate time with another person. For those of you with your mind in the gutter, yes, sex is on the list. However, aside from the postcoital high that may follow by a good nap, it shouldn’t be the only option that comes to mind. A long walk, an interesting conversation, a nice meal, a massage, or any time spent in the company of someone you like can be very good for you. Even if all you do is vent about how much you hate whatever, at least you get it out of your system.

Do some intense physical activity. A healthy body leads to a healthy mind. Go for a run (not in Central Park, alone, after midnight. This is a stress relief thing, not a suicide mission). Get some friends together and play a game that involves a lot of running around. No, shopping doesn’t count in this category, but do read on.

Do some shopping. When the going gets tough, the tough go shopping! At least that’s how they handled stress in Great Neck, Long Island where I grew up. This can be great, but expensive. Window shopping, without buying, counts. Then you have a great excuse to buy it AFTER you pass your exams…

Do something quiet that lets your mind wander. Read a novel (one that isn’t required reading, of course). If you enjoy meditating, painting, writing, or any type of craft or hobby, now is a great time to break it out and dust it off. You liked it before school, so why not indulge a little now?

Make some noise. Sing, dance, blast that stereo for just a little while. Blow something up-that’s what chemistry lab is for, isn’t it? As an archer, I’d feel better when I just plain shot something. It could work for you too!
Just make certain you do SOMETHING. Cook, clean, create, or destroy something. Do whatever it takes to take your mind off the current problem. Remember that taking too long can be a problem, so whatever you choose to do, make it a quickie!

What SHOULDN’T you do?

Don’t beat yourself up for feeling stressed. You will only become more stressed. Pain is a relative thing. Just because you think it can’t get worse doesn’t mean it won’t!

Drowning yourself in ANYTHING is not good. This includes, but is not limited to ethanol, drugs, sex, work, and swimming pools. Follow Aristotle-there is a balance to be struck that works.

The key to stress relief is to find something to keep you happy while your mind works out the details of how to handle whatever is making you stressed. You will be surprised how quickly that works or fails to work if you are too intoxicated to think about anything. Get yourself calm and you will start to ask: “What stress? I feel great!”

Ok, maybe not great, but as long as you do SOMETHING other than what you were stressing out about, you’ll find that you didn’t need to stress after all. To assist you in this, let me make a suggestion:

Wear the pumps. They work with everything.

Two Columbia University students, Shreyas Vissapragada (CC) and Ankeeta Shah (BC), were named as winners of the Barry Goldwater Scholarship. Three other Columbia students were named as honorable mentions. They were Irene Zhang (CC), Kristy Choi (CC), and Sarah Yang (SEAS). The full listings for each student are listed below. You can check out the rest of the winners and honorable mentions on the Goldwater Scholars website.

Winners:

Ankeeta B Shah
Institution: Barnard College
Major(s): Biology, Computer Science
Career Goal: Ph.D. in Systems Biology. Conduct biomedical research and teach at the university level.

Shreyas Vissapragada
Institution: Columbia University
Major(s): Astrophysics, Computer science
Career Goal: Ph.D. in astronomy with a specialization in astrochemistry. Conduct interdisciplinary research on the chemistry of exoplanet formation and teach at the university level.

Honorable Mentions:

Irene P Zhang
Institution: Columbia University
Major(s): Physics
Career Goal: Ph.D. in Condensed Matter Physics. Conduct research in materials science and teach at the university level.

Kristy Choi
Institution: Columbia University
Major(s): Computer Science-Statistics
Career Goal: Ph.D. in Computational Biology. Develop new statistical tools to conduct data-driven research in biology and teach at the university level.

Sarah J Yang
Institution: Columbia University
Major(s): Chemical Engineering
Career Goal: Ph.D. in Bioengineering or Chemical Engineering. Conduct research in protein or metabolic engineering and teach at the university level.

 

This week, ColorCode was pleased to learn that Professor Kale revoked the Robocop competition and issued a full apology for the original assignment, which, as he writes, “failed to provide adequate context” for a data set laden with historical and political racial trauma. We appreciate Professor Kale’s explanation of the assignment’s intended impact––to lead students to interrogate the policy implications of ML classifiers trained on racist data––and hope that future assignments can convey this lesson with the clarity that this assignment lacked. We sincerely applaud Professor Kale’s timely and appropriate correction, and hope that all professors at Columbia can follow his example in responding to student concerns with empathy and accountability.

 

Since our last statement, some of our peers have questioned whether the assignment’s revocation has deprived the class of an ethics lesson in handling politically challenging data sets. Lessons should not come at the cost of direct harm to the most marginalized groups involved. While we agree with Professor Kale’s professed intentions in assigning the Robocop competition, we stand by our original assessment (with which Professor Kale himself has agreed): that the assignment in its original form could not have produced the intended pedagogical outcome and discussion on data responsibility in Machine Learning. And while this particular incident has been sufficiently redressed by Professor Kale himself, we think it’s important to locate the Robocop assignment in the context of a larger department and school that excludes and silences Black students and students of color. We are studying computer science in a department with few Black students and no Black faculty, in an engineering school that builds on a legacy of close collaboration with the U.S. military and NYPD, at a university that is gentrifying Harlem to build its newest science center. From casual remarks about our intelligence by classmates, TAs, and professors, to academic policies not intended to help the most marginalized of us succeed– these experiences contribute to an academic atmosphere that repeatedly dismisses and delegitimizes our pain by “intellectualizing” academic work with horrific, racist implications and impacts. Computer Science at Columbia is steeped in a history of racism that still persists today. Within this context, an assignment “welcoming” students to a “future” of “cyborg law enforcers” trained on racist, violently-collected data is inexcusable.

 

We therefore point to the Robocop incident as evidence that massive reform is needed within the department to support Black students and other students of color, low income students, and other marginalized people in STEM. Professor Kale’s swift response gives us a lot of hope that change can happen here at Columbia. We will continue to hold professors, departments, and the university accountable to the impact of their academic work. We join Mobilized African Diaspora in demanding greater academic support for marginalized students of color, especially the hiring of Black faculty in Computer Science and SEAS. We also ask that SEAS as a whole reaffirm its commitment to its most marginalized students by expanding course offerings on research ethics and incorporating requirements in African American Studies and Ethnic Studies. We ask this with the recognition that technical knowledge is dangerous without an analysis of race and power. Finally, we urge current professors to build on pedagogy and research that is explicitly anti-racist and anti-oppressive, that gives students the opportunity to work on projects that uplift and liberate communities of color and other marginalized people.

 

We thank the following groups for their explicit support (running list). Please reach out to colorcodeboard@gmail.com if your organization would like to co-sign:

National Society of Black Engineers– Columbia

The Lion

No Red Tape

Students for Justice in Palestine

Divest Barnard

Photo Courtesy Bradley Davison (CC ’17)

NOTE:

A few days ago, the ColorCode team posted a response in regard to a “RoboCop” assignment assigned to students in Professor Satyen Kale’s Machine Learning (COMS 4771) course. In response Professor Kale wrote a response on his website, which can be found here. In order to make sure that both ColorCode and the Professor’s views are visible to interested parties, we have shared his piece below:

The original task description (“Robocop”) was regrettably written in a highly offensive manner. It was not our intention to suggest that imitating the “SQF” practices (or any racially-prejudiced practices) in the future is desirable in any way. In fact, the made-up setting for the task in a fictitious, dystopian future was meant to be an ironic indicator of precisely the opposite sentiment. We are strongly against practices such as SQF. While the primary intention for the task was purely pedagogical—to give students exposure to using machine learning techniques in practice—we acknowledge that not providing proper context for the task was poor judgement on our part, and we sincerely apologize for that.

Two original motivations for using this data set were (i) to illustrate the difficulties in developing any kind of “predictive policing” tool (which already exist today), and (ii) to assess how predictive modeling could help shed light on this past decision-making. For instance, at the coarsest level, it is evident that very few of the cases where a subject is stopped actually lead to an arrest; this raises the question why the stops should have been made in the first place. Moreover, if it is difficult to predict the arrest decision from the features describing the circumstance, then it may suggest that there is some unrecorded aspect of the circumstance that drives the decision; such a finding could have policy implications.

There are critical aspects of the data set that make it highly inappropriate for use in developing any kind of predictive policing tool. First, the data only reflects the arrest decisions of past police officers, which are decidedly not what one would want to imitate. Second, even if the arrest decisions (i.e., labels) in the data set were appropriately modified (thereby altering the conditional distribution of the label given the features), the set of the cases there may only be representative of suspects that past police officers chose to stop, necessarily introducing biases into the distribution.

We originally thought that these challenging aspects of the data set would be of interest to the class. However, our formulation of the task was in poor taste and failed to provide adequate context. Because we can only objectively evaluate the predictive modeling aspects of the project that are independent of the context of the data set, we have decided to change the data set to one that is completely unrelated to the SQF data set.

A link to the Professor Kale’s original posting on his website can be found here. To respond to this piece or submit an op-ed of your own, email submissions@columbialion.com

Photo Courtesy of Color Code

On Thursday, the ColorCode committee learned that Columbia University Computer Science professor Satyen Kale assigned to his Machine Language (COMS 4117) class a competition “to produce the eponymous cyborg law enforcer.” Drawing on data from the NYPD’s “Stop, Question and Frisk” records, students have been asked to create a machine learning algorithm to “decide when a suspect it has stopped should be arrested” based on characteristics ranging from “sex” and “race” to “suspect was wearing unseasonable attire”, “suspicious bulge”, and “change direction at sight of officer”. Stop­ and ­Frisk is a violently racist program that allows police to stop, question, and frisk any pedestrian who arouses “reasonable suspicion.” Numerous studies and investigations of the NYPD’s own data have shown that Stop­ and ­Frisk disproportionately targets Black people. It has torn apart Black communities in the city and contributes to a system of mass incarceration and policing that brutalizes, incarcerates, and kills Black people across the nation. The program has even been deemed unconstitutional by federal courts.

That a Columbia professor would ask students to implement a program that reproduces and aids Stop­ and Frisk policing with zero acknowledgement of the violence and harm inflicted by the actual program­­–and in fact suggest that machine learning algorithms like this constitute “the future” of machine learning applications— is an egregious example of racist, ahistorical, and irresponsible pedagogy. Data are not apolitical. Algorithms are not objective. To teach technical skills without also teaching anti­racist, anti­oppression developing principles is unforgivable, despicable, and dangerous. For us, as students of color who also are coders, entrepreneurs, and engineers, assignments like this confirm feelings of exclusion and isolation accumulated over many semesters here–­­­being one in a only handful of Black students in a lecture hall, for example, or graduating from SEAS not having had even a single Black professor. It confirms the department and university’s disregard for our wellbeing as students of color, which always is intertwined with the wellbeing of our communities.

Moving forward, ColorCode demands that this Machine Learning assignment be revoked, and that the professor issue an apology addressing the concerns above. We demand that students in the class be provided with alternate ways to receive credit. We demand that the professor and the department acknowledge these concerns, apologize, and make significant, structural changes to ensure this does not happen again. Finally, we support the demands of Mobilized African Diaspora/BCSN and in particular add our voices to demand that the School of Engineering commit to hiring more Black professors and underrepresented professors of color.

ColorCode is a group focused on getting people of color into the technology sector. To respond to this op-ed or submit one of your own, email submissions@columbialion.com