Maruan Al-Shedivat

8223 Gates & Hillman

5000 Forbes Avenue

Pittsburgh, PA 15213

I’m a Ph.D. student in Machine Learning Department at CMU, advised by Eric Xing.

Research interests:
Sequential decision making, multi-task learning, deep learning, graphical models, interpretable ML. Applications in healthcare, natural language, multi-agent systems, and general AI.

I’ve recently co-founded Carnegie Mellon AI+. If you are interested in sponsoring events, please contact me by email.

I hold M.Sc. in Computer Science from KAUST where I worked with Khaled Salama and collaborated with 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.

Besides my professional activities, I am a member of Carnegie Marathon Club, love doing sports and hiking in beautiful places. Also, I grew up in Moscow. Feel free to talk to me in Russian.

news

May 5, 2018

Some highlights from ICLR 2018:

Our paper on Continuous Adaptation via Meta-Learning (joint work with Trapit, Yura, Ilya, Igor, Pieter at OpenAI) received a Best Paper Award! Check out: poster, talk (starts around 1:20:50).
We presented DiCE: The Infinitely Differentiable Monte-Carlo Estimator at the workshop. Here is the poster. Reach out if you have questions!

Apr 15, 2018

Very grateful to be awarded the 2018 CMLH Fellowship in Digital Healthcare by the Center of Machine Learning and Health. Looking forward to doing a lot of new exciting work in #ML4Health!

Apr 3, 2018

I gave a talk at ML Lunch seminar on meta-learning for continuous adaptation. Here are the slides.

Jan 29, 2018

Two papers accepted for oral presentation:

Dec 20, 2017

Contextual Explanations Networks were presented at two NIPS workshops.
Materials are now online:

Interpretable ML Symposium: abstract, poster, spotlight.
Machine Learning for Healthcare Workshop: abstract, poster, spotlight.

May 5, 2018	Some highlights from ICLR 2018: Our paper on Continuous Adaptation via Meta-Learning (joint work with Trapit, Yura, Ilya, Igor, Pieter at OpenAI) received a Best Paper Award! Check out: poster, talk (starts around 1:20:50). We presented DiCE: The Infinitely Differentiable Monte-Carlo Estimator at the workshop. Here is the poster. Reach out if you have questions!
Apr 15, 2018	Very grateful to be awarded the 2018 CMLH Fellowship in Digital Healthcare by the Center of Machine Learning and Health. Looking forward to doing a lot of new exciting work in #ML4Health!
Apr 3, 2018	I gave a talk at ML Lunch seminar on meta-learning for continuous adaptation. Here are the slides.
Jan 29, 2018	Two papers accepted for oral presentation: Continuous Adaptation via Meta-Learning at ICLR 2018. Learning with Opponent-Learning Awareness at AAMAS 2018.
Dec 20, 2017	Contextual Explanations Networks were presented at two NIPS workshops. Materials are now online: Interpretable ML Symposium: abstract, poster, spotlight. Machine Learning for Healthcare Workshop: abstract, poster, spotlight.