Biography
Atish Dixit is a Research Fellow at the APRIL AI hub, University of Edinburgh, working with Prof. Themis Prodromakis, Prof. Jacqui Cole and Prof. Subramanian Ramamoorthy. He is a seasoned researcher specializing in machine learning, mathematical modeling, and simulations. With a strong background in applied mathematics and statistics along with extensive experience in academia and industry, he brings expertise in leveraging AI and mathematical techniques to solve complex problems.
Interests
- Artificial Intelligence
- Materials Discovery
- Multi-agent Robotic Systems
- Deep Reinforcement Learning
- Computational Physics
Education
PhD in Deep Reinforcement Learning, 2023
Heriot Watt University
MTech in Mathematical Modelling, 2017
University of Pune
BEng in Mechanical Engineering, 2012
University of Pune
Skills
Technical
Programming
Machine Learning and Statistics
Mathematical Modelling and Computing
Hobbies
Study of Vedanta
Painting
Boxing
Experience
Research Fellow
AI-driven materials discovery for electronic materials
Research Associate
State-of-the-art research in multi-agent reinforcement learning
PhD Researcher
Research in deep reinforcemnt learning methods to solve opimal control problems in computational physics
Computational Engineer
- Mathematical modelling and back-end development of on-field applications
- Analysis and process recommendation for on-field drilling process
- Part of agile software development for various reservoir management tools
Research Fellow
- Engaged in research and development within the domain of Graph Theory
- Formulated a C++ object-oriented design to model graphs and implement the Christofides algorithm
Engineering Analyst
- Design recommendation and analysis of automobile system architectures for HVAC
- Building reports of design failures/improvements using computational modelling of vehicle aerodynamics
- Research and development of novel mathematical models to assess human comfort in automobile cabins
Recent Posts
Projects
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An open-source framework for connecting databases, machine learning models, and workflows for electronic materials discovery
A multi-grid approach in deep reinforcement learning to improve sample efficiency in computational physics based applications
An RL-based approach to learn the optimal well control policies used to improve the production efficiency in reservoir management
A statistical framework to reduce computational complexity of RL applications in computational physics based environments
A mathematical model of human bio-heat equation and assessment of human cumfort in various environment conditions
Artwork
Recent Publications
Quickly discover relevant content by filtering publications.
(2026).
A flexible language model-assisted electronic design automation framework.
arXiv preprint arXiv:2601.14098.
(2026).
Unifying Digital Discovery of Electronic Materials with EMOS.
Research Square preprint.
(2023).
Robust optimal well control using an adaptive multigrid reinforcement learning framework.
Mathematical Geosciences.
(2022).
A Multilevel Reinforcement Learning Framework for PDE-based Control.
arXiv preprint arXiv:2210.08400.
(2022).
Robust Well-Production Control Using Surrogate Assisted Reinforcement Learning.
ECMOR 2022.
Contact
For any inquiries or collaborations, feel free to reach out to me via the contact form below.
- adixit@ed.ac.uk
- 10 Crichton St, Newington, Edinburgh, Scotland EH8 9AB
- Enter Informatics Forum, register to your entry and take the stairs to Office 2.43 on Floor 2
- Monday to Friday 09:00 to 17:00
- Book an appointment