Sukrit Mittal, Ph.D.

Sukrit Mittal is a Senior Research Scientist with the AI & Digital Transformation Team at Franklin Templeton. He is an active contributor to the award-winning Goals Optimization Engine (or GOE). He conceptualizes fitment against various industry use cases, develop new product variants, and design innovative features and enhancements across multiple distribution lines and geographies. His role involves leveraging core skills encompassing Fintech including quantitative finance, data science capabilities and product development to bring personalized investment solutions to the market.

He has close to six years of research experience in the domain of optimization, where he has actively collaborated with renowned optimization and machine learning researchers. Prior to his current engagement, he had been invited as a visiting researcher at Michigan State University. Currently, he is actively pursuing research in applying reinforcement learning methods for solving the goals-based wealth management problem.

He holds a Bachelor of Technology in Mechanical Engineering and a Doctorate in Optimization Algorithms from the Indian Institute of Technology Roorkee. He also holds a Data Science for Investment Professionals Certificate from the CFA Institute, US. He has co-authored a book titled “Machine Learning Assisted Evolutionary Multi and Many-objective Optimization”, and several research papers in high-impact journals. He also holds a granted patent in his name.

• Franklin Templeton - Sr. Research Scientist (2022 - Present)
• Michigan State University - Visiting Researcher (2020 - 2020)
• Mahindra & Mahindra Ltd. - Design Engineer (2016 - 2018)

• CFA Institute - Data Science for Investment Professionals Certificate (2023 - 2024, Credential)
• Indian Institute of Technology Roorkee - Ph.D. (2018 - 2022, Thesis)
• Indian Institute of Technology Roorkee - B.Tech. (2012 - 2018)

• 2025 Genetic and Evolutionary Computation Conference (Jul 2025, Malaga, Spain, Link)
  [Tutorial] Machine Learning Assisted Evolutionary Multi- and Many-objective Optimization
• 2025 IEEE Congress on Evolutionary Computation (Jun 2025, Hangzhou, China, Link)
  [Tutorial] Machine Learning Assisted Evolutionary Multi- and Many-objective Optimization
• 2024 Global Analytics Summit - AI in Finance (Nov 2024, UT Austin, USA, Link)
  [Keynote] A Meta Reinforcement Learning Approach to Goals-Based Wealth Management
• 2024 JOIM Conference - AI in Finance (Oct 2024, MIT, Boston, USA)
  [Talk] A Meta Reinforcement Learning Approach to Goals-Based Wealth Management

• Machine Learning Assisted Evolutionary Multi- and Many-Objective Optimization (with Dhish Kumar Saxena, Kalyanmoy Deb and Erik Goodman), 2024 (Link)

For more details about these papers, click here.

1. Reinforcement learning for Multiple Goals in Goals-Based Wealth Management (with Sanjiv Das, Daniel Ostrov, Anand Radhakrishnan, Deep Srivastav and Hungjen Wang), Artificial Intelligence for Business (AIxB), 2024. (Link)
2. A Unified Innovized Progress Operator for Performance Enhancement in Evolutionary Multi- and Many-Objective Optimization (with Dhish Kumar Saxena, Kalyanmoy Deb and Erik Goodman), IEEE Transactions on Evolutionary Computation, 2024. (Link, Code)
3. A Localized High-Fidelity-Dominance-Based Many-Objective Evolutionary Algorithm (with Dhish Kumar Saxena, Sarang Kapoor and Kalyanmoy Deb), IEEE Transactions on Evolutionary Computation, 2023. (Link, Code)
4. Enhanced Innovized Progress Operator for Evolutionary Multi- and Many-Objective Optimization (with Dhish Kumar Saxena, Kalyanmoy Deb and Erik Goodman), IEEE Transactions on Evolutionary Computation, 2022. (Link)
5. A Learning-based Innovized Progress Operator for Faster Convergence in Evolutionary Multi-objective Optimization (with Dhish Kumar Saxena, Kalyanmoy Deb and Erik Goodman), ACM Transactions on Evolutionary Learning & Optimization, 2022. (Link)
6. Embedding a Repair Operator in Evolutionary Single and Multi-objective Algorithms - An Exploitation-Exploration Perspective (with Kalyanmoy Deb, Dhish Kumar Saxena and Erik Goodman), Evolutionary Multi-Criterion Optimization (EMO), 2021. (Link)
7. A Unified Automated Innovization Framework Using Threshold-based Clustering (with Dhish Kumar Saxena and Kalyanmoy Deb), IEEE Congress on Evolutionary Computation (CEC), 2020. (Link)
8. A Generic and Computationally Efficient Automated Innovization Method for Power-Law Design Rules (with Kanish Garg, Anish Mukherjee, Dhish Kumar Saxena and Kalyanmoy Deb), Genetic and Evolutionary Computation Conference (GECCO '20), 2020. (Link)
9. Learning-based multi-objective optimization through ANN-assisted online Innovization (with Dhish Kumar Saxena and Kalyanmoy Deb), Genetic and Evolutionary Computation Conference (GECCO '20), 2020. (Link)
10. Innovative Design of Hydraulic Actuation System for Operator Fatigue Reduction and Its Optimization (with Divyam Aggarwal and Dhish Kumar Saxena), Advances in Multidisciplinary Analysis and Optimization, 2019. (Link)
11. Social entrepreneurship through forest bioresidue briquetting: An approach to mitigate forest fires in Pine areas of Western Himalaya, India (with Kapil Joshi and Vinay Sharma), Renewable and Sustainable Energy Reviews, 2015. (Link)