I am a Senior Research Scientist at DeepMind working on the intersection of Reinforcement Learning, Natural Language Understanding, and Representation Learning.
I am interested in building agents that can learn from a feedback signal while able to utilize unlabeled data available in the environment. I am interested in improving our understanding of the existing algorithms, as well as to develop new ones to enable real-world applications with positive social impact. I am in particular fascinated by the scientific applications of machine learning algorithms.
I defended my thesis "Learning and time: on using memory and curricula for language understanding" in 2018 with Christopher Manning as my external examiner. Currently, the research topics that I am working on include but not limited to reinforcement learning, offline RL, foundational models and representation learning (including self-supervised learning, new architectures, causal representations, and etc.) I have served as an area chair and reviewer to major machine learning conferences such as ICML, NeurIPS, ICLR, and journals like Nature and JMLR. I have published at numerous influential conferences and journals such as Nature, JMLR, NeurIPS, ICML, ICLR, ACL, EMNLP, etc... My work has received the best paper award at Nonconvex Optimization workshop at NeurIPS and an honourable mention for best paper at ICML 2019.
Feel free to get in touch with me via an email if you have any inquiries or questions.
Mathematics and Computer Science
University of Bahcesehir
Middle East Technical University
Computer Science & AI
University of Montreal-MILA
Our paper Active Offline Policy Selection is accepted to NeurIPS 2021.
Our paper On Instrumental Variable Regression for Deep Offline Policy Evaluation is on arXiv.
Our paper Regularized behavior value estimation on a single step policy improvement method is on arXiv.
Our paper Addressing Extrapolation Error in Deep Offline Reinforcement Learning got Oral at Offline RL Workshop at NeurIPS 2020.