IIT Bombay - Optiver AI Innovation Lab

A world-class hub for advanced AI-led research, innovation, and talent development.

Lab Objective

"To establish a world-class Finance Lab at IIT Bombay that will drive advanced AI-led research, innovation, and talent development in financial services, build cutting-edge data infrastructure, and position the institute as a global leader in shaping the AI enabled finance ecosystem"

The Facility

The Lab will feature a dedicated 2,000 sq. ft facility equipped with high-performance computing servers, research terminals, and collaborative spaces to enable cutting-edge finance research.

15 Workstations

High Power GPU's & Servers

Infrastructure & Assets

Terminals & Data

• Bloomberg Terminals, Refinitiv Eikon,
• Real time data from NSE Data & Analytics Limited, Mutual Fund Ratings and Ranks, ESG Ratings, etc.

Software Subscriptions

Subscriptions for SPSS, SAS, R, Mathematica, MATLAB, Stata, etc.

Hardware

15 workstations with sufficient processing power. Hardware GPU's and servers.

Current Research Themes

Optimization and scalable training of foundation models

Causal and Counterfactual Learning for Decision Systems

Robust Sequential Decision Making in Adaptive Environments

Trustworthy and Dependable AI Systems

AI for Education and Upskilling

Inviting Research Proposals

Average Budget

₹7 Lakhs - ₹14 Lakhs

Submission Deadline

30/09/2026

Submit via Google Form

1 Optimization and scalable training of foundation models

Optimization lies at the core of how large models are trained, adapted and scaled. We are interested in optimizers that make use of the geometry of parameter space, in how training and hyperparameters behave as model size grows, and in optimization for parameter-efficient training settings. This theme also covers federated training of foundation models in heterogeneous settings, where the data is non-IID and the participating nodes differ in compute, memory and availability.

2 Causal and Counterfactual Learning for Decision Systems

Many AI systems make predictions that influence actions, which in turn affect future data and outcomes. Methods based only on observational correlations may therefore fail when deployed as part of an intervention or decision-making process. We are interested in causal learning, causal representation and counterfactual reasoning for decision systems, particularly under unobserved or partially observed confounding, feedback, selection effects and distribution shift.

3 Robust Sequential Decision Making in Adaptive Environments

We seek methods for sequential decision-making and resource allocation under uncertainty, risk, non-stationarity and interactions among multiple cooperating or competing agents. Reinforcement learning with verifiable rewards and test-time planning has moved this area forward recently, but the methods still need to work under distribution shift and over long horizons. Example settings include cooperative perception and navigation, autonomous and swarm systems, and dynamic resource allocation under uncertain demand.

4 Trustworthy and Dependable AI Systems

Robustness, privacy and verifiability for AI systems that are deployed in high-stakes or regulated settings. As systems move towards multi-step, tool-using agents and shared infrastructure, being dependable matters as much as being accurate. This theme covers models that stay reliable under adversarial and shifting conditions, efficient ways to serve models and route between them and their agents, and methods that ground the output of a model in verifiable sources.

5 AI for Education and Upskilling

We invite research on AI-driven methods and systems designed to enhance learning, skill acquisition, and personalized education. Areas of interest include intelligent tutoring systems and adaptive learning pathways; AI-assisted curriculum design, assessment, and automated feedback; and scalable upskilling frameworks for lifelong learning. Submissions should emphasize pedagogical effectiveness, learner engagement, fairness, and human-centred design to ensure equitable and impactful outcomes for diverse learning populations.

Ready to submit?

Submit your research ideas, objectives, and scope of work using the form below.

Go to Google Form

Upcoming Event

National Symposium
on AI Innovations in BFSI Services

National Symposium on AI Innovations in BFSI

Explore the full symposium experience featuring industry titans, academic leaders, and cutting-edge research presentations.

View Symposium

Organized by AI Innovation Lab, IIT Bombay - Optiver

Academic Programs

Educational initiatives at the intersection of AI and Finance

Advanced research opportunities for Ph.D. students in AI and Innovation

The Lab supports highly motivated Ph.D. students in the mentioned areas, assistantship is as per rule of IIT Bombay for Ph.D. scholars.

Apply

The Benefits

Funding

The fellowship covers tuition, research related expenses, and living costs

Annual Research Grants

To cover research related expenses and conference travel.

Professional Development

Access to workshops, seminars, and support from industry leaders.

Mentorship

Guidance from experienced faculty members and industry professionals

Eligibility Requirement (At least one)

Indian Candidates

• Four year Bachelor in any discipline with 60% marks/6.5 CPI (55%/6.0 CPI for SC/ST) and at least two years work experience.
• Master in any discipline (MBA/M.Tech./M.S./M.Sc./M.A./M.Com./MCA/LLM/M.Pharm./M.Phil., etc.) or 2 year PG Diploma in Management from institutions/universities
• Executive MBA of at least one year duration from IITs/IIMs
• CA/CMA/CS with 60%/6.5 CPI (55%/6.0 CPI for SC/ST) in the preceding degree/Bachelor's (B.Sc./B.Com./BA)

Foreign Candidates

• Four year Bachelor degree with 60% marks or 6.5 CPI and two years work experience OR Masters in any discipline with 60% marks or 6.5 CPI
• The Candidate should have valid TOEFL score

Research Proposal

A detailed research proposal covering introduction, literature review, research gaps, research objectives/ questions / hypotheses, research design (variables, data, period, methodology, etc.) should be submitted along with the application.

Applications without research proposal will not be considered.

Research Interest

A demonstrated interest or experience in quantitative finance, or related areas is desirable

Post-Doctoral Research

Post-Doctoral fellowships are available in IITB-Optiver Innovation Lab. The tenure of Post-Doctoral Fellows (PDF) would be generally for two years (initially for one year and extendable for another year, subject to a satisfactory academic performance in the 1st year). There is a possibility for extension for the third year in exceptional cases.

Apply

Program Benefits

Non-lapsable contingency grant of Rs. 1,00,000/- per annum.
Consolidated salary of Rs. 80,000/- per month with HRA as per Government of India norms (currently 27% of consolidated salary).

Eligibility & Evaluation

→Candidates who wish to be considered for such an appointment may be fresh PhDs or PhDs with less than two years of experience or research scholars who have submitted their thesis and are awaiting examination.
→Candidates should be preferably below 32 years of age.
→Academic units will evaluate these applications through a process similar to that of faculty selections.
→The Search Committees meet regularly to consider the applications and arrange for invited seminars and/or interviews as the need arises.
There is no last date for applications

Duties and Facilities

→A PDF will have a mentor whose area is closest to that of the PDF.
→In addition to research, the Head of the Academic units may assign reasonable academic/administrative tasks depending on the need.
→Financial support is provided for one international conference during the tenure of the fellowship for the presentation of their research work done at IIT Bombay.
→Support for one national conference per year is also provided.

Our People

Leadership and Expertise

Advisory Board

Prof. S.V.D. Nageswara Rao

Professor and Head, Shailesh J. Mehta School of Management, IIT Bombay

As Head of Shailesh J. Mehta School of Management, he played a pivotal role in setting up the lab and establishing a strong foundation for cutting-edge research and innovation in finance. He helped foster collaborations between academia and industry, and enabled the lab to become a hub for advanced quantitative finance and data-driven problem solving.

He received the doctoral title of Fellow of IIM Ahmedabad and served a Senior Manager of ICRA Ltd., New Delhi. He is former Chairman of Metropolitan Stock Exchange of India, and also served as Independent Director on the boards of other companies.

He was a consultant to leading companies/organizations like ICRA, L&T, Ashok Leyland, CBI, Traco Cables Company Ltd., MERC, and Power Exchange India Ltd. (PXIL). His areas of research interest include Corporate Finance, Investment Banking, Capital Markets, Mutual Funds, Corporate Governance, Financial Engineering, ESG Investing, and Corporate Social Responsibility.

He has guided 21 Ph.D. scholars, 46 Masters dissertations, and currently guiding 6 Ph.D. students. He has Presented papers at national and international conferences, and published in reputed journals. He has conducted more than 40 short and long duration Management Development Programmes (MDPs) for executives from different companies, and senior government officials.

Prof. D. Manjunath

Head, Centre for Machine Intelligence and Data Science (CMInDS), IIT Bombay

Prof. D. Manjunath is the Head of the Centre for Machine Intelligence and Data Science (CMInDS) at IIT Bombay. A founding member of the CMInDS Steering Committee, he has played a key role in shaping the Centre’s vision since its inception. He is a Professor in the Department of Electrical Engineering and has been a faculty member at IIT Bombay since 1998. He served as an Institute Chair Professor from 2014 to 2021.

His research interests include communication networks, queueing systems, stochastic modeling, network economics, and distributed systems. He has published over 100 papers in leading international journals and conferences and is the co-author of a widely used graduate-level textbook—Communication Networking: An Analytical Approach and Wireless Networking.

Prof. Manjunath has held visiting positions at several prominent academic and research institutions, including the University of Toronto, Microsoft Research Cambridge, University of Waterloo, University of Bristol, and Ohio State University.

Beyond academia, he is actively engaged in national technology and policy initiatives. He currently serves on the boards of the National Payments Corporation of India (NPCI), the Clearing Corporation of India Ltd. (CCIL), and the Bombay Stock Exchange (BSE). He has also been a consultant to the Telecom Regulatory Authority of India (TRAI) and serves on multiple national advisory committees focused on digital infrastructure and data governance.

Prof. Pratik Jawanpuria

Associate Professor, Centre for Machine Intelligence and Data Science (C-MInDS), IIT Bombay

Prof. Pratik Jawanpuria's research focuses on the intersection of machine learning and non-linear optimization. He received his B.Tech in Computer Science and Engineering and his Ph.D. degrees from the IIT Bombay in 2009 and 2014, respectively.

After completing his Ph.D., Pratik undertook postdoctoral research at Saarland University before transitioning to industry. At Amazon, he was a part of the India Machine Learning team in Bengaluru, where he worked on problems in the retail domain, including competitive price prediction and style/product recommendation.

Prior to joining C-MInDS, he served as a Principal Applied Scientist at Microsoft (Office India Intelligence team), where he developed AI/ML solutions for a range of products, including Office Lens, Kaizala, Microsoft 365, and Copilot.

Prof. Sawan Rathi

Assistant Professor, Shailesh J. Mehta School of Management, IIT Bombay

Prof. Sawan Rathi is a faculty member in Strategy & Technology Management at the Shailesh J. Mehta School of Management, IIT Bombay. He teaches Technology Management, Innovation Management, and Strategic Management for the MBA and Executive MBA programmes at the B-School.

Before joining IIT Bombay, he was a Post-Doctoral Research Fellow at the Science Policy Research Unit (SPRU) at the University of Sussex Business School, UK. He holds a Ph.D. from the Indian Institute of Management Ahmedabad (IIMA) and an MBA from IIM Lucknow.

His research interests span Technology Diffusion, Innovation Management, and Health Economics.

He brings fifteen years of global corporate experience across training, education management, IT services, and project management (PRINCE2). He delivers executive education programmes for senior managers and public sector leaders on themes such as digital transformation, technology strategy, and innovation governance.

The AI Innovation Lab is a collaborative effort between IIT Bombay's top faculty and Optiver's leading researchers. We are constantly expanding our team of visionary thinkers.