30167 Hannover
I am interested in developing generalizable and deployable Reinforcement Learning pipelines that can abstract useful structures from multiple kinds of environments, and then use these structures for prediction, planning, and learning. To this end, I believe techniques from the field of AutoML can help scale sequential decision-making techniques like Reinforcement Learning, and thus allow them to move beyond games and simulators to solving real-world problems like targeted medicine, resource optimization, etc. My long-term goal is to develop autonomous agents that can operate in sparse data regimes and integrate seamlessly into existing value chains fairly and equitably.
Research Interests
- Generalization adn Deployability in Reinforcement Learning
- Automated Reinforcement Learning
- Meta Reinforcement Learning
- Multi-fidelity Information Fusion for Reinforcement Learning
Curriculum Vitae
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Work Experience
October, 2021 - Present: Doctoral Researcher in the Automated Machine Learning group at Leibniz University of Hannover
September, 2020 - December, 2020: Research Intern at Learning and Intelligent Systems Group at the Technical University of Berlin
July, 2018 - September, 2019: Analyst in the Risk Consulting team in KPMG India
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Education
October, 2021 - Present: Ph.D. candidate at the Leibniz University Hannover
2019 - 2021: M.Sc. in Autonomous Systems at the Teschnische Universität Berlin and EURECOM
- Thesis: AI agents that quickly adapt to a partner for Ad.hoc cooperation in the game of Hanabi
- Supervisor: Prof. Dr. Klaus Obermayer
2014 - 2018: B.Tech in Electronics and Communication Engineering at Manipal Institute of Technology
Publications
Benjamins C, Eimer T, Schubert FG, Mohan A, Döhler S, Biedenkapp A et al. Contextualize Me – The Case for Context in Reinforcement Learning. Transactions on Machine Learning Research. 2023.
Loni M, Mohan A, Asadi M, Lindauer M. Learning Activation Functions for Sparse Neural Networks. In Second International Conference on Automated Machine Learning. PMLR. 2023
Mohan A, Benjamins C, Wienecke K, Dockhorn A, Lindauer M. AutoRL Hyperparameter Landscapes. In Second International Conference on Automated Machine Learning. PMLR. 2023 doi: 10.48550/arXiv.2304.02396
Ruhkopf T, Mohan A, Deng D, Tornede A, Hutter F, Lindauer M. MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information. Transactions on Machine Learning Research. 2023 Apr 18. Epub 2023 Apr 18.
Mohan A, Ruhkopf T, Lindauer M. Towards Meta-learned Algorithm Selection using Implicit Fidelity Information. In ICML Workshop on Adaptive Experimental Design and Active Learning in the Real World (ReALML). 2022
