Aditya Mohan
Researcher And Ph.D. Candidate
Hanover, Lower Saxony, Germany
Summary
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Senior🎓
Top SchoolAditya Mohan is a reinforcement learning researcher and visiting researcher with nine years of experience bridging academic research and industry practice, currently affiliated with UT Austin and Leibniz Universität Hannover. His work focuses on leveraging contextual structure, temporal dependencies, and modular decompositions to make RL agents more sample-efficient and robust to changing environments, spanning Contextual RL, Meta-RL, AutoRL and representation learning. He is actively developing self-supervised RL methods that let agents learn behaviors without explicit labels, and has applied these ideas in industry settings such as AWS (offline zero-shot RL for autoscaling). With a background that ranges from multi-agent meta-learning projects at TU Berlin to co-founding an ML-driven battery-optimization startup, he combines strong theoretical grounding with practical system-building. Based in Hanover, Germany, he brings a track record of interdisciplinary collaboration and hands-on prototypes that translate research insights into deployable solutions.
8 years of coding experience
3 years of employment as a software developer
Bachelor of Technology - BTech Electronics and Communication Engineering, Bachelor of Technology - BTech Electronics and Communication Engineering at Manipal Institute of TechnologyIoT for Industry 4.0 Summer School, IoT for Industry 4.0 Summer School at Technical University of MunichM.Sc Autonomous Systems, M.Sc Autonomous Systems at EURECOMM.Sc ICT-Innovation Autonomous Systems Track, M.Sc ICT-Innovation Autonomous Systems Track at Technische Universität BerlinDoctor of Philosophy - PhD, Doctor of Philosophy - PhD at Leibniz Universität HannoverMaster of Science - MS Autonomous Systems Track, Master of Science - MS Autonomous Systems Track at EIT Digital Alumni
English, Hindi, German
Github Skills (17)
reinforcement-learning10
implicit10
generalization10
multi-objective-optimization9
robust9
ros8
robotics7
autonomous-driving6
autonomous-vehicles5
benchmark4
pytorch4
benchmarking4
machine-learning4
deep-learning4
automl2
Programming languages (4)
Github contributions (5)
automl/IMFAS
Apr 2022 - Nov 2022
Implicit Multi-Fidelity Algorithm Selection
Contributions:1 review, 130 commits, 7 PRs in 7 months
implicitmulti-fidelitymultiobjective-optimizationmulti-objective-optimizationselection
automl/DAC4RL
Feb 2022 - Jul 2022
DAC4RL track of DAC4AutoML competition at AutoML Conf
Contributions:42 commits, 4 pushes, 1 branch in 4 months
pytorchdeep-learningmachine-learningconfautoml
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