Maruan Al-Shedivat

Machine Learning DepartmentSchool Of Computer ScienceCarnegie Mellon University

8019 GHC,

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).

Research Interests:
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.

Professional:
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.

Besides my professional activities, I am a member of Carnegie Marathon Club, love doing sports and hiking in beautiful places. Also, I happened to grow up in Moscow and do speak Russian.

highlights [archive]

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! :tada:
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! :fire:

(recent) selected papers [full list]

  1. arXiv
    Federated Learning via Posterior Averaging:
    A New Perspective and Practical Algorithms
    arXiv preprint (in submission), 2020
  2. Contextual Explanation Networks
    Al-Shedivat, M., Dubey, A., Xing, E.P.
    Journal of Machine Learning Research (JMLR), 2020
  3. NAACL Full Oral
    Consistency by Agreement in Zero-shot Neural Machine Translation
    Al-Shedivat, M., Parikh, A.P.
    In Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2019
  4. ICLR Best Paper Award
    Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments
    In International Conference on Learning Representations (ICLR), 2018