Shruti Mishra

I work as a Chapman–Schmidt AI in Science Fellow in the Department of Mathematics at Imperial College London. My research spans reinforcement learning, continuum mechanics, and mathematical modelling. Previously, I worked as a Research Scientist in reinforcement learning at Sony AI, developing methods for PlayStation games and open-source domains, and an Encode Fellow at the University of Cambridge. I earned my PhD in Applied Mathematics from Harvard University and have conducted research at DeepMind and Mila.

I am open to research opportunities for the academic year 2026 and beyond. I welcome enquiries from institutions where my experience and interests might be a good match.

Education

PhD, Applied Mathematics, Harvard University (2021)
SM, Applied Mathematics, Harvard University (2017)
MEng, Mechanical Engineering, Imperial College London (2014)

Publications

A reinforcement learning approach for mixing in stratified shear flows
S. Mishra, K.J. Burns, M. Cranmer, C-c.P. Caulfield
NeurIPS Conference, Sim2Science workshop (2026) [article]

Coachable agents for interactive gameplay
R. Capobianco, H. van Seijen, (+32 authors) S. Mishra, (+4 authors)
Transactions on Machine Learning Research (2026) [article] [code] [video]
[Featured Certification, Expert Certification]

Physical reinforcement learning
S. Dillavou, S. Mishra
Reinforcement Learning Conference, Reinforcement Learning in Big Worlds workshop (2026) [article] [code]

A perspective on fluid mechanical environments for challenges in reinforcement learning
S. Mishra, M. Chang, V. Spandan, S.M. Rubinstein
Reinforcement Learning Conference, Finding the Frame workshop (2025) [article] [code] [poster]

Policy composition via multi-objective reinforcement learning
S. Mishra, A. Anand, J. Hoffmann, N. Heess, M. Riedmiller, A. Abdolmaleki, D. Precup
Conference on Robot Learning, LocoLearn workshop (2024) [article] [poster]

Computing the viscous effect in early-time drop impact dynamics
S. Mishra, S.M. Rubinstein, C.H. Rycroft
Journal of Fluid Mechanics (2022) [article] [code]

On Multi-objective Policy Optimization as a Tool for Reinforcement Learning
A. Abdolmaleki, S.H. Huang, G. Vezzani, B. Shahriari, J.T. Springenberg, S. Mishra, D. Tirumala, A. Byravan, K. Bousmalis, A. György, C. Szepesvari, R. Hadsell, N. Heess, M. Riedmiller (2021) [article]

Coordinated crawling via reinforcement learning
S. Mishra, W.M. van Rees, L. Mahadevan
Journal of the Royal Society Interface (2020) [article]

Augmenting learning using symmetry in a biologically-inspired domain
S. Mishra, A. Abdolmaleki, A. Guez, P. Trochim, D. Precup
NeurIPS Conference, NewInML workshop (2019) [article]
[Best Oral Presentation Award]

Machine learning in a data-limited regime: Augmenting experiments with synthetic data uncovers order in crumpled sheets
J. Hoffmann, Y. Bar-Sinai, L.M. Lee, J. Andrejevic, S. Mishra, S.M. Rubinstein, C.H. Rycroft
Science Advances (2019) [article] [code]

Thermal design of the FETS chopper beam dump
S. Mishra, M. Aslaninejad, P. Savage, P.A. Posocco, J.K. Pozimski, A.P. Letchford
Proceedings of the Particle Accelerator Conference (2013) [article]

Professional Activities

Reviewing

Conference on Neural Information Processing Systems
Physical Review Letters
Reinforcement Learning Conference
AAAI Conference on Artificial Intelligence, workshops
International Conference on Machine Learning, workshops

Organising

Workshop at RLDM 2025, Saving the Phenomena of Minds
RL Social at ICML 2021
Workshop at NeurIPS 2020, NewInML

Teaching and Mentoring

Lecture on Introduction to reinforcement learning at Trinity Hall, Cambridge
Masterclass on reinforcement learning, MPhil in Data Intensive Science University of Cambridge
Inspirational lecture, Department of Mechanical Engineering, Imperial College
Teaching Fellow, Harvard University: CS249r Autonomous Systems, AM205 Advanced Scientific Computing, AM115 Mathematical Modeling, ES220 Fluid Mechanics
Teaching Assistant, Imperial College: ME1 Mathematics
Alumni Mentor, Imperial College

Panels

Opportunities and Challenges of AI for Science at the Isaac Newton Institute, 2026
Harvard alumnae panel, 2024
Imperial offer holders panel, 2022

Awards

Chapman–Schmidt AI in Science Fellowship (2026–)  
Encode: AI for Science Fellowship (2025–2026)  
G-Research Grant for students and postdocs in quantitative fields (2021)  
Summer Research Grant, Harvard University (2021)
Best Oral Presentation Award, NewInML workshop, NeurIPS conference (2019)
Young Scientist Travel Award, Harvard Brain Initiative (2019)  
Certificate of Distinction in Teaching, Harvard University (2019, 2017)  
Bramwell Medal, Imperial College (2014)  
Henry Ford II Scholar Award in Mechanical Engineering, Imperial College (2014)  
Santander Final Year Scholarship, Imperial College (2013)
Frank Turner Wilson Prize, Imperial College (2013, 2011)  
Sir Bruce White Laboratory Prize in Mechanical Engineering, Imperial College (2012)  
Faculty of Engineering Dean's List, Imperial College (2014, 2013, 2012, 2011)  
Gold Prize and Best in school, UK Maths Challenge (2010)  

Miscellaneous

Photography   ·   Gardening

News

Nov 2026: Tea talk at Mila
Sep 2026: Talk at NWTF AI-Enabled Wind-Tunnel Experiments Forum
Jun 2026: Invited talk at Durham HPC Days
Mar 2026: Panel on AI for Discovery at the Centre for Human-Inspired AI
Mar 2026: Panel on Opportunities and Challenges of AI for Science at the Isaac Newton Institute
Mar 2026: Invited talk at the Reinforcement Learning for Science workshop at the Isaac Newton Institute
Mar 2026: Lecture on Introduction to reinforcement learning at Trinity Hall, University of Cambridge
Mar 2026: Masterclass on reinforcement learning, MPhil in Data Intensive Science, University of Cambridge
Nov 2025: Inspirational lecture at Imperial College
Oct 2025: Presented "A perspective on fluid mechanical environments for challenges in reinforcement learning" at the Institute of Physics.
Sep 2025: Started an Encode Fellowship at DAMTP, Cambridge, working on AI for multiscale flows.
Aug 2025: Presented "A perspective on fluid mechanical environments for challenges in reinforcement learning" at the Finding the Frame workshop at RLC.
July 2025: Volunteered at EEML.
June 2025: Co-organised a workshop at RLDM.
May 2025: Invited talk at the AI4Good Lab on Applications of reinforcement learning in locomotion.