About Me

Director of AI & Optimization Research at Franklin Templeton, specializing in the intersection of advanced AI and quantitative finance.

Sukrit leverages Generative AI and Reinforcement Learning to conceptualize and develop innovative financial products and solutions across global markets. He also serves as Guest Faculty at IIIT Hyderabad.

Key Expertise
  • Generative AI
  • Reinforcement Learning
  • Quantitative Finance
  • Optimization Algorithms
Get in Touch Download Resume

Work Experience

International Institute of Information Technology (IIIT) Hyderabad
Jan. 2025 - Present

Guest Faculty

Homepage Course: Mathematics for Finance
Franklin Templeton
Jun. 2022 - Present
  • Director, AI & Optimization Research (Sep. 2025 - Present)
  • Sr. Research Scientist (Jan. 2024 - Aug. 2025)
  • Quant Development, Specialist (Jun. 2022 - Dec. 2023)
Michigan State University
2020

Visiting Researcher

Mahindra & Mahindra Ltd.
Aug. 2016 - Jun. 2018
  • Design Engineer (Aug. 2017 - Jun. 2018)
  • Graduate Engineer Trainee (Aug. 2016 - Jul. 2017)

Education

  • 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)

Activities

  • 2026 Generative AI Models & Their Applications (Feb 2026, IIT Jodhpur, India, Link)
    [Keynote] Generative AI in Finance?
  • 2025 ACM Winter School - AI & Finance (Dec 2025, IIIT Hyderabad, India, Link)
    [Speaker] Multiple sessions (Link)
  • 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
  • 2025 IIIT Hyderabad (Feb 2025, Hyderabad, India, Link)
    [Talk] Harnessing Reinforcement Learning for Goals-based Wealth Management
  • 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

Book(s)

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

Refereed Publications

  1. A Meta Reinforcement Learning Approach to Goals-Based Wealth Management (with Sanjiv Das, Harshad Khadilkar, Daniel Ostrov, Deep Srivastav and Hungjen Wang), Journal of Finance and Data Science, 2026. (Link)
  2. Interpreting Omega Ratio for Goals Based Wealth Management (with Harshad Khadilkar, Sirisha Gorjala, Hungjen Wang, Anand Radhakrishnan and Deep Srivastav), Journal of Wealth Management, 2026. (Link)
  3. 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)
  4. 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)
  5. 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)
  6. 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)
  7. 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)
  8. 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)
  9. A Unified Automated Innovization Framework Using Threshold-based Clustering (with Dhish Kumar Saxena and Kalyanmoy Deb), IEEE Congress on Evolutionary Computation (CEC), 2020. (Link)
  10. 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)
  11. 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)
  12. 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)
  13. 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)