5000 Forbes Avenue,
Pittsburgh, PA 15213
I’m a Ph.D. student in the Machine Learning Department at CMU, advised by Eric Xing. I’ve also spent time at OpenAI (2017) and Google Research (2018/20). My work is generously supported by the CMLH Fellowship (2018/19) and Google PhD Fellowship (2019/21).
Probabilistic modeling, deep learning, and massively multi-task learning, with a focus on computational frameworks for adaptation, interpretability, and personalization of statistical models learned from data.
Previously: [a more formal bio]
I hold M.Sc. in Computer Science from KAUST where I worked with Khaled Salama and Gert Cauwenberghs on neuromorphic approaches to machine learning. Before that I studied Physics at Lomonosov Moscow State University and Data Analysis at Yandex School of Data Analysis.
I’m a co-organizer of the Adaptive & Multitask Learning Workshop, a founding editor of the ML@CMU Blog, and a regular PC/reviewer for: ICLR, ICML, JMLR, NeurIPS, UAI, AAAI, IJCAI, AISTATS, and various ML/AI workshops.
|Sep 9, 2019||Honored to be part of the 2019 class of Google PhD Fellows in Machine Learning. Huge thank you to all my mentors, colleagues, and collaborators! And thank you, Google!|
|May 15, 2019||It was a lot of work (and fun!) to help teach PGM 2019 class this past Spring. Check out an excellent set of lecture notes written by students in distill-like style. Recordings of all lectures are now available on YouTube.|
|Apr 5, 2019||How do we make zero-shot NMT consistent? Our NAACL 2019 paper on Consistency by Agreement shows how to do that! Joint work with Ankur Parikh at Google NYC last year. Update (more resources): arXiv, NAACL19 slides, AI Science Seminar virtual talk.|
|Mar 29, 2019||Excited to be co-organizing a workshop on Adaptive & Multitask Learning this year at ICML. Please consider submitting your latest work!|
|Jan 25, 2019||
Grateful to be awarded $12,000 in Cloud Credits for Research from AWS.
Time to burn some compute!
(recent) selected papers [full list]
arXivFederated Learning via Posterior Averaging:
A New Perspective and Practical AlgorithmsarXiv preprint (in submission), 2020
Contextual Explanation NetworksJournal of Machine Learning Research (JMLR), 2020
NAACL Full OralConsistency by Agreement in Zero-shot Neural Machine TranslationIn Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2019
ICLR Best Paper AwardContinuous Adaptation via Meta-Learning in Nonstationary and Competitive EnvironmentsIn International Conference on Learning Representations (ICLR), 2018