Four from MIT named Meta Fellows for 2022
Meta the parent structure of Facebook Instagram WhatsApp and other subsidiaries recently announced the winners of its greatly competitive 2022 Meta Fellows program. The incoming cluster of fellows includes four MIT graduate students from three programs about the Institute.
Jaume Vives i Bastida is a PhD aspirant in the Department of Economics advised by Alberto Abadie Anna Mikusheva and Tobias Salz. His goal as a investigationer is to design econometric and machine-learning tools that better policymaking while being pellucid and powerful to different modeling assumptions. In particular his investigation has centreed on the properties of shrinkage estimators and on extensions to the synthetic control order a common tool used by applied investigationers and policycreators. On the useful side Bastida applies these orders to apprehend intricate interactions in the digital administration such as in online platforms in which data-driven decision-making plays a key role. He has had the chance to put these orders to work in real-life situations at Google and Quantco.
Prior to joining MIT Bastida obtained a BS in econometrics and mathematical economics from the London School of Economics and Political Science was an exchange student at the University of California at Berkeley and worked as a investigation professional at the University of Chicago below Eric Budish. In his spare time Bastida enjoys playing squash and chess and going back to his hometown of Barcelona where he says the best food in the globe can be establish.
Lucy Chai is a graduate student in electrical engineering and computer science (EECS) at the Computer Science and Artificial Intelligence Laboratory (CSAIL) advised by Phillip Isola. Her running investigation centrees upon computer vision and image synthesis. In particular she is interested in learning from data collections to engender augmented forms of images. The results of this can empower interactive image editing that combines user input with conversant 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 at Cambridge University where she earned her MPhil in machine learning studying uncertainty and interpretability in Bayesian neural networks. Chai earned her belowgraduate grade at the University of Pennsylvania in computer science and bioengineering where she worked with Danielle S. Bassett in computational neuroscience centreing on modeling neural processes as dynamic networked methods. Among other honors Chai has been awarded a NSF Graduate Research Fellowship and Adobe Research Fellowship.
Dishita Turakhia is a fourth-year PhD aspirant in the Human-Computer Interaction Engineering Group at CSAIL where she is advised by Professor Stefanie Mueller. Her running investigation lies at the intersection of method design and learning sciences with a particular centre on augmented and potential verity applications for autodidactic learning of expertnesss. Her projects empower autodidactic expertness learning of motor expertnesss fabrication expertnesss and creator expertnesss. Turakhias project on the adaptive learning of motor expertnesss was covered featured on MIT News.
Before starting her PhD Turakhia earned a dual masters from MIT in EECS and computational design (SMArchS) in accession to her masters in design technology (EmTech AA). Prior to returning to academia she worked as an constructor and computational designer for over five years in Mumbai London Singapore and Bern. Turakhia earned her bachelors in constructorure from KRVIA (Mumbai University).
Praneeth Vepakomma is a PhD student in the MIT Program in Media Arts and Sciences (Media Lab) advised by Ramesh Raskar. Vepakommas investigation centrees on distributed computation in statistics and machine learning below constraints of retirement communication and efficiency. Foundationally inspired by nonasymptotic statistics randomized algorithms combinatorics and methods design his investigation has applications in special independence testing and special k-sample testing in statistics; bridging retirement with collective choice speculation; special mechanisms for training and deduction in machine learning; specially estimating nonlinear measures of statistical dependence between multiple parties; and divide learning.
Among other honors Vepakomma has been selected as a Social and Ethical Responsibilities of Computing Scholar by MITs Schwarzman College of Computing. His paper "FedML: A investigation 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 deduction behind on-premises 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 divide learning has been featured in MIT Technology Review. Vepakomma was previously a scientist at Amazon Motorola Solutions and at different startups; accessionally he has interned at Apple Corning and TripleBlind. He holds an MS in mathematical and applied statistics from Rutgers University at New Brunswick.