New recipients of Meta (Facebook) Fellowship for 2022

From left to right: Lucy Chai, Jaume Vives i Bastida, Dishita Turakhia , and Praneeth Vepakomma. Images courtesy of the subjects.

Meta (Facebook) recently announced the winners of its highly competitive 2022 fellowships. The incoming group of Fellowship recipients includes four MIT graduate students, two of whom study within the Department of EECS.

Jaume Vives i Bastida is a PhD candidate in the Economics Department, advised by Alberto Abadie, Anna Mikusheva and Tobias Salz. His goal as a researcher is to design econometric and machine learning tools that improve policy making, while being transparent and robust to different modeling assumptions. In particular, his research has focused on the properties of shrinkage estimators and on extensions to the synthetic control method, a popular tool used by applied researchers and policy makers. On the practical side, Jaume applies these methods to understand complex interactions in the digital economy, such as in online platforms, in which data driven decision making plays a key role. He has had the chance to put these methods to work in real life situations at Google and Quantco.

Prior to joining MIT, Jaume obtained a BSc. in Econometrics and Mathematical Economics from LSE, was an exchange student at UC Berkeley, and worked as a research professional at the University of Chicago under Eric Budish. In his spare time Jaume enjoys playing squash and chess, and going back to his hometown of Barcelona, where the best food in the world can be found.

Lucy Chai is a graduate student in EECS at MIT CSAIL, advised by Phillip Isola; her current research focuses upon computer vision and image synthesis. In particular, she is interested in learning from data collections to generate augmented forms of images. The results of this can enable interactive image editing that combines user input with learned image priors and be applied to investigate downstream visual analysis tasks. She has spent two summers at Adobe Research working with Richard Zhang, Jun-Yan Zhu, Michael Gharbi, and Eli Shechtman, and frequently collaborates with Ser-Nam Lim at Facebook.

Prior to joining MIT, Chai attended Churchill College, University of Cambridge, where she earned her MPhil in Machine Learning studying uncertainty and interpretability in Bayesian neural networks. Chai earned her undergraduate degree at the University of Pennsylvania in Computer Science and Bioengineering, where she worked with Dr. Danielle S. Bassett in computational neuroscience, focusing on modelling neural processes as dynamic networked systems Among other honors, Chai has been awarded a NSF Graduate Research Fellowship and Adobe Research Fellowship.

Dishita Turakhia is a fourth-year PhD candidate in the Human-Computer Interaction Engineering Group at MIT CSAIL, where she is advised by Professor Stefanie Mueller. Her current research lies at the intersection of system design and learning sciences, with a particular focus on AR/VR applications for autodidactic learning of skills. Her projects enable autodidactic skill learning of motor skills, fabrication skills, and maker skills. Turakhia’s project on the adaptive learning of motor skills was covered by MIT News.

Before starting her Ph.D., Turakhia earned a dual masters in EECS (MIT) and computational design (SMArchS, MIT) in addition to her masters in design technology (EmTech, AA).  Prior to returning to academia, she worked as an architect and computational designer for 5+ years in Mumbai, London, Singapore, and Bern. Turakhia earned her bachelor’s in architecture from KRVIA (Mumbai University).

Praneeth Vepakomma is a PhD Student within Media Arts & Sciences, advised by Ramesh Raskar. Vepakomma’s research focuses on distributed computation in statistics and machine learning under constraints of privacy, communication, and efficiency. Foundationally inspired by non-asymptotic statistics, randomized algorithms, combinatorics, and systems design, his research has applications in a) private independence testing and private k-sample testing in statistics, b) bridging privacy with social choice theory, c) private mechanisms for training and inference in ML, d) privately estimating non-linear measures of statistical dependence between multiple parties, and e) split learning.

Among other honors, Vepakomma has been selected as a SERC Scholar (Social and Ethical Responsibilities of Computing Scholar) by MIT’s Schwarzman College of Computing, while “FedML: A research library and benchmark for federated machine learning” won a Baidu Best Paper Award at NeurIPS 2020-SpicyFL, and “NoPeek-Infer: Preventing face reconstruction attacks in distributed inference after on-premise training” won the Mukh Best Paper Award at IEEE FG-2021. He was interviewed in the book “Data Scientist: The Definitive Guide to Becoming a Data Scientist” and his work on Split Learning has been featured in the MIT Technology Review. Vepakomma was previously a scientist at Amazon, Motorola Solutions, and at various startups; additionally, he has interned at Apple, Corning, and TripleBlind. He holds an MS in Mathematical and Applied Statistics from Rutgers University, New Brunswick.

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