Three CS Students Recognized By The Computing Research Association

For this year’s Outstanding Undergraduate Researcher Award, Payal Chandak, Sophia Kolak, and Yanda Chen were among students recognized by the Computing Research Association (CRA) for their work in an area of computing research.


Payal Chandak
Finalist

Using Machine Learning to Identify Adverse Drug Effects Posing Increased Risk to Women
Payal Chandak Columbia University, Nicholas Tatonetti Columbia University

The researchers developed AwareDX – Analysing Women At Risk for Experiencing Drug toXicity – a machine learning algorithm that identifies and predicts differences in adverse drug effects between men and women by analyzing 50 years’ worth of reports in an FDA database. The algorithm automatically corrects for biases in these data that stem from an overrepresentation of male subjects in clinical research trials.

Though men and women can have different responses to medications – the sleep aid Ambien, for example, metabolizes more slowly in women, causing next-day grogginess – doctors may not know about these differences because most clinical trial data itself is biased toward men. This trickles down to impact prescribing guidelines, drug marketing, and ultimately, patients’ health. Unfortunately, pharmaceutical companies have a history of ignoring complex problems and clinical trials have singularly studied men, not even including women. As a result, there is a lot less information about how women respond to drugs compared to men. The research tries to bridge this information gap. 

 


Sophia Kolak
Finalist

It Takes a Village to Build a Robot: An Empirical Study of The ROS Ecosystem
Sophia Kolak Columbia University, Afsoon Afzal Carnegie Mellon University, Claire Le Goues Carnegie Mellon University, Michael Hilton Carnegie Mellon University, Christopher Steven Timperley Carnegie Mellon University

The Robot Operating System (ROS) is the most popular framework for robotics development. In this paper, the researchers conducted the first major empirical study of ROS, with the goal of understanding how developers collaborate across the many technical disciplines that coalesce in robotics.

Building a complete robot is a difficult task that involves bridging many technical disciplines. ROS aims to simplify development by providing reusable libraries, tools, and conventions for building a robot. Still, as building a robot requires domain expertise in software, mechanical, and electrical engineering, as well as artificial intelligence and robotics, ROS faces knowledge-based barriers to collaboration. The researchers wanted to understand how the necessity of domain-specific knowledge impacts the open-source collaboration model in ROS.

Virtually no one is an expert in every subdomain of robotics: experts who create computer vision packages likely need to rely on software designed by mechanical engineers to implement motor control. As a result, the researchers found that development in ROS is centered around a few unique subgroups each devoted to a different specialty in robotics (i.e. perception, motion). This is unlike other ecosystems, where competing implementations are the norm.

Detecting Performance Patterns with Deep Learning
Sophia Kolak Columbia University

Performance has a major impact on the overall quality of a software project. Performance bugs—bugs that substantially decrease run-time—have long been studied in software engineering, and yet they remain incredibly difficult for developers to handle. In this project, the researchers leveraged contemporary methods in machine learning to create graph embeddings of Python code that can be used to automatically predict performance.

Using un-optimized programming language concepts can lead to performance bugs and the researchers hypothesized that statistical language embeddings could help reveal these patterns. By transforming code samples into graphs that captured the control and data flow of a program, the researchers studied how various unsupervised embeddings of these graphs could be used to predict performance.  

Implementing “sort” by hand as opposed to using the built-in Python sort function is an example of a choice that typically slows down a program’s run-time. When the researchers embedded the AST and data flow of a code snippet in Euclidean space (using DeepWalk), patterns like this were captured in the embedding and allowed classifiers to learn which structures are correlated with various levels of performance.   

I was surprised by how often research changes directions,” said Sophia Kolak. In both projects, they started out with one set of questions but answered completely different ones by the end. “It showed me that, in addition to persistence, research requires open-mindedness.”

 


Yanda Chen
Honorable Mention

Cross-language Sentence Selection Via Data Augmentation and Rationale Training
Yanda Chen Columbia University, Chris Kedzie Columbia University, Suraj Nair University of Maryland, Petra Galuscakova University of Maryland, Rui Zhang Yale University, Douglas Oard University of Maryland, and Kathleen McKeown Columbia University

In this project, the researchers proposed a new approach to cross-language sentence selection, where they used models to predict sentence-level query relevance with English queries over sentences within document collections in low-resource languages such as Somali, Swahili, and Tagalog. 

The system is used as part of cross-lingual information retrieval and query-focused summarization system. For example, if a user puts in a query word “business activity” and specifies Swahili as the language of source documents, then the system will automatically retrieve the Swahili documents that are related to “business activity” and produce short summaries that are then translated from Swahili to English. 

A major challenge of the project was the lack of training data for low-resource languages. To tackle this problem, the researchers proposed to generate a relevance dataset of query-sentence pairs through data augmentation based on parallel corpora collected from the web. To mitigate the spurious correlations learned by the model, they proposed the idea of rationale training where they first trained a phrase-based statistical machine translation system and used the alignment information to provide additional supervision for the models. 

The approach achieved state-of-the-art results on both text and speech across three languages – Somali, Swahili, and Tagalog. 

 

Research by CS Undergrad Published in Cell

Payal Chandak (CC ’21) developed a machine learning model, AwareDX, that helps detect adverse drug effects specific to women patients. AwareDX mitigates sex biases in a drug safety dataset maintained by the FDA.

Below, Chandak talks about how her internship under the guidance of Nicholas Tatonetti, associate professor of biomedical informatics and a member of the Data Science Institute, inspired her to develop a machine learning tool to improve healthcare for women. 


Payal Chandak

How did the project come about? 
I initiated this project during my internship at the Tatonetti Lab (T-lab) the summer after my first year. T-lab uses data science to study the side effects of drugs. I did some background research and learned that women face a two-fold greater risk of adverse events compared to men. While knowledge of sex differences in drug response is critical to drug prescription, there currently isn’t a comprehensive understanding of these differences. Dr. Tatonetti and I felt that we could use machine learning to tackle this problem and that’s how the project was born. 

How many hours did you work on the project? How long did it last? 
The project lasted about two years. We refined our machine learning (ML) model, AwareDX, over many iterations to make it less susceptible to biases in the data. I probably spent a ridiculous number of hours developing it but the journey has been well worth it. 

Were you prepared to work on it or did you learn as the project progressed? 
As a first-year student, I definitely didn’t know much when I started. Learning on the go became the norm. I understood some things by taking relevant CS classes and through reading Medium blogs and GitHub repositories –– this ability to learn independently might be one of the most valuable skills I have gained. I am very fortunate that Dr. Tatonetti guided me through this process and invested his time in developing my knowledge. 

What were the things you already knew and what were the things you had to learn while working on the project? 
While I was familiar with biology and mathematics, computer science was totally new! In fact, T-Lab launched my journey to exploring computer science. This project exposed me to the great potential of artificial intelligence (AI) for revolutionizing healthcare, which in turn inspired me to explore the discipline academically. I went back and forth between taking classes relevant to my research and applying what I learned in class to my research. As I took increasingly technical classes like ML and probabilistic modelling, I was able to advance my abilities. 

Looking back, what were the skills that you wished you had before the project? 
Having some experience with implementing real-world machine learning projects on giant datasets with millions of observations would have been very valuable. 

Was this your first project to collaborate on? How was it? 
This was my first project and I worked under the guidance of Dr. Tatonetti. I thought it was a wonderful experience – not only has it been extremely rewarding to see my work come to fruition, but the journey itself has been so valuable. And Dr. Tatonetti has been the best mentor that I could have asked for! 

Did working on this project make you change your research interests? 
I actually started off as pre-med. I was fascinated by the idea that “intelligent machines” could be used to improve medicine, and so I joined T-Lab. Over time, I’ve realized that recent advances in machine learning could redefine how doctors interact with their patients. These technologies have an incredible potential to assist with diagnosis, identify medical errors, and even recommend treatments. My perspective on how I could contribute to healthcare shifted completely, and I decided that bioinformatics has more potential to change the practice of medicine than a single doctor will ever have. This is why I’m now hoping to pursue a PhD in Biomedical Informatics. 

Do you think your skills were enhanced by working on the project? 
Both my knowledge of ML and statistics and my ability to implement my ideas have grown immensely as a result of working on this project. Also, I failed about seven times over two years. We were designing the algorithm and it was an iterative process – the initial versions of the algorithm had many flaws and we started from scratch multiple times. The entire process required a lot of patience and persistence since it took over 2 years! So, I guess it has taught me immense patience and persistence. 

Why did you decide to intern at the T-Lab? 
I was curious to learn more about the intersection of artificial intelligence and healthcare. I’m endlessly fascinated by the idea of improving the standards of healthcare by using machine learning models to assist doctors. 

Would you recommend volunteering or seeking projects out to other students? 
Absolutely. I think everyone should explore research. We have incredible labs here at Columbia with the world’s best minds leading them. Research opens the doors to work closely with them. It creates an environment for students to learn about a niche discipline and to apply the knowledge they gain in class.