Amazon awarded its second round of research fellowships to five graduate students with ties to the Carnegie Mellon University School of Computer Science. They are Emily Black, Saurabh Garg, Natalia Lombardi de Oliveira, Emre Yolcu and Minji Yoon.
The program supports graduate students researching automated reasoning, computer vision, robotics, language technology, machine learning, operations research and data science. The students will be invited to interview for a science internship at Amazon.
Black (right) is a Ph.D. candidate in the the Computer Science Department (CSD) whose research centers on understanding the impact of machine learning models in society. In particular, she focuses on showing novel ways that common machine learning models may act unfairly; finding ways to pinpoint when models are behaving in a harmful manner in practice; developing ways to mitigate harmful behavior when possible; and translating technical insight into technology policy recommendations. Her faculty advisor is Matt Fredrikson , an associate professor in CSD and the Institute for Software Research who specializes in algorithmic fairness.
Garg (left), a Ph.D. candidate in the Machine Learning Department (MLD), is interested in building robust and interpretable machine learning systems. He is researching the behavior of machine learning models in real-world scenarios and building provable methods to make progress toward relaxing simplifying assumptions to make robust and trustworthy models. Garg’s faculty advisors are Zachary Lipton , assistant professor of operations research and machine learning in MLD and the Heinz College of Information Systems and Public Policy , and a deputy dean in the Tepper School of Business ; and Sivaraman Balakrishnan , associate professor of statistics in MLD and the Dietrich College of Humanities and Social Sciences.
De Oliveira (right), a student in Dietrich’s Department of Statistics and Data Science , is pursuing her Ph.D. in data science and machine learning. She is focused on estimating generalization - the difference between the test and training performance of a predictive algorithm. Her faculty advisor is Ryan Tibshirani , a professor of statistics and machine learning in MLD and Dietrich and an Amazon Scholar.
Yolcu (left) is pursuing his Ph.D. in CSD. He is interested in logic, particularly proof complexity, satisfiability solving and related topics. His research so far has investigated the complexity of redundancy-based proof systems, a rewriting-based approach to the Collatz conjecture, and learning local search heuristics for satisfiability. Yolcu’s faculty advisor is Marijn Heule , an associate professor in CSD and an Amazon Scholar.
Yoon (right), a Ph.D. candidate in CSD, is working on deep graph learning with the goal to automate and democratize it by borrowing the strong generalization power of deep learning. She first identifies the essential building blocks of a graph learning pipeline, applies automation to each block using various deep learning approaches, then glues the automated blocks back into one pipeline. She hopes to provide a plug-and-play end-to-end graph-learning tool for users. Yoon’s faculty advisors are Christos Faloutsos , the Fredkin Professor of Computer Science and an Amazon Scholar, and MLD Professor Ruslan Salakhutdinov.
De Oliveira, Yolcu and Yoon were among the five CMU students to receive the first round of Amazon Graduate Research fellowships. Amazon started the program to expand its efforts to help amplify the work being done by master’s and Ph.D. students. Read more about the fellowship on the Amazon Science blog.