Sanjay Chandlekar

Email
sanjaychandlekar
@gmail.com
Phone
+91 9106390833
Location

Hyderabad, India

About me

Hi, I’m Sanjay Chandlekar, a Machine Learning Engineer and researcher focused on Reinforcement Learning (RL), Game Theory, and Applied AI Systems. I am working with the engineering team at Google, where I apply machine learning to large-scale production systems. Prior to Google, I worked as a Data Scientist at JP Morgan Chase, where I focused on applied machine learning solutions in the financial domain, including fraud detection and scalable ML system design.

I completed my Ph.D. in Computer Science in the Machine Learning Lab (MLL). My research centered on deep reinforcement learning, auction theory, multi-agent systems, and strategic decision-making in complex real-world environments.

A significant portion of my work involved PowerTAC (Power Trading Agent Competition), a realistic smart-grid simulation platform where autonomous broker agents compete in wholesale and retail electricity markets. I developed RL-driven bidding and pricing strategies, forecasting systems, and market decision models for autonomous energy brokers. These efforts contributed to championship-winning performances in PowerTAC 2021 and 2022.

During my research career, I published papers in leading AI conferences like IJCAI and AAMAS, contributed to workshop publications, and authored a Springer book chapter. I have also received travel grants from organizations including Microsoft Research India, ACM India, IJCAI, and CODS-COMAD.

Prior to my Ph.D., I graduated with a Gold Medal in Information Technology and worked as a researcher at Innovation Labs, where I began applying reinforcement learning and auction-based strategies to energy market challenges.

Timeline

8 June, 2026:

Joined Google as a Software Engineer III.
25 August, 2025:

Joined JP Morgan Chase as a Data Scientist.
August, 2025:

Got nominated for ACM India Doctoral Dissertation Award 2025 & IKDD Doctoral Dissertation Award 2025.
12 July, 2025:

Received my PhD degree at the 24th Convocation of IIIT Hyderabad.
5 May, 2025:

Successfully defended my final PhD thesis.
27-28 January, 2024:

Attended "Google DeepMind Research Symposium 2025" at Google India, Bangalore.
21 December, 2024:

Successfully submitted the final PhD thesis.
16 September, 2024:

Our paper "A Novel Bidding Strategy for PDAs using MCTS in Continuous Action Spaces" got published in "Lecture Notes in Computer Science (volume 15152)" book.
4 September, 2024:

Successfully completed Proposal Thesis Defense for PhD.
3-9 August, 2024:

Attended IJCAI'24 conference held in Jeju Island, South Korea and presented our both the papers.
18th May, 2024:

Our paper "Towards Revolutionized Smart Grids: An AI-Driven Broker for Improved Operational Efficiency" got accepted for publication at IJCAI'24 Doctoral Consortium (DC).
6 May-10 May, 2024:

Attended the AAMAS'24 conference and presented our paper at EMAS'24 workshop held in Auckland, New Zealand.
17th April, 2024:

Our paper "Optimizing Prosumer Policies in Periodic Double Auctions Inspired by Equilibrium Analysis" got accepted for publication at IJCAI'24.
10th April, 2024:

Our paper summarising our work on smart grids from business point of view got accepted for TCS Research's global competition TACTiCS2.0.
29th March, 2024:

Our paper "A Novel Bidding Strategy for PDAs using MCTS in Continuous Action Spaces" got accepted for publication at EMAS'24.
7 March, 2024:

Successfully completed Comprehensive Viva for PhD.
1-3 February, 2024:

Attended "Google Research Week 2024" at Google India, Bangalore.
4-7 January, 2024:

Got selected for "ACM India PhD Clinic" that happened during CODS-COMAD 2024 in IIIT Bangalore. In the PhD clinic, I had insightful discussions with Prof. Vineeth Balasubramanian and Prof. Janardhan Rao Doppa.
4 January, 2024:

Official converted from MS by Research to PhD in IIIT-H.
25 November, 2023:

Our paper "Multi-armed Bandit Based Tariff Generation Strategy for Multi-agent Smart Grid Systems" got published in "Lecture Notes in Computer Science (volume 14378)" book.

17 November, 2023:

Our book chapter "VidyutVanika: AI-Based Autonomous Broker for Smart Grids: From Theory to Practice" got published in "Energy Sustainability through Retail Electricity Markets" book.
16 November, 2023:

Presented our work at the Policy, Awareness, Sustainability and Systems (PASS) Workshop 2023.
19-25 August, 2023:

Presented our paper in-person at the IJCAI'23 conference held in Macau, S.A.R.
29 May-2 June, 2023:

Presented our papers in-person at the AAMAS'23 conference and EMAS'23 workshop held in London, UK.
20th April, 2023:

Our paper "A Novel Demand Response Model and Method for Peak Reduction in Smart Grids -- PowerTAC" got accepted for publication at IJCAI'23.
27th March, 2023:

Our paper "Multi-armed Bandit Based Tariff Generation Strategy for Multi-Agent Smart Grid Systems" got accepted for publication at EMAS'23.
3rd January, 2023:

Our paper "A Novel Demand Response Model and Method for Peak Reduction in Smart Grids -- PowerTAC" got accepted for publication at AAMAS'23.
15th December, 2022:

Our autonomous broker agent "VidyutVanika22" became the winner of the PowerTAC tournament second consecutive time.
September, 2022:

Submitted book chapter titled "VidyutVanika: AI-Based Autonomous Broker for Smart grids -- From Theory to Practice" for publication in Springer.
23-29 July, 2022:

Presented our paper in-person at the IJCAI'22 conference held in Vienna, Austria.
9-13 May, 2022:

Presented our paper virtually at the AAMAS'22 conference.
21st April, 2022:

Our paper "VidyutVanika21: Autonomous Intelligent Broker for Smart-grids" got accepted for publication at IJCAI'22.
12th March, 2022:

Our poster "PowerGame: Role of Artificial Intelligence and Game Theory in Smart-grids" won the best lab poster award in IIIT-H R&D Showcase 2022.
19th December, 2021:

Our paper "Multi-unit Double Auctions: Equilibrium Analysis and Bidding Strategy using DDPG in Smart-grids" got accepted for publication at AAMAS'22.
8-9 December, 2021:

Presented our autonomous broker agent "VidyutVanika21" in PASS2021 workshop.
27th October, 2021:

Our autonomous broker agent "VidyutVanika21" became the winner of the PowerTAC 2021 tournament.
10th August, 2020:

Started MS by Research in Computer Science at IIIT Hyderabad.
25th July, 2019:

Presented our paper "Vidyutvanika18: An autonomous broker agent for smart grid environment" at PASS2019 workshop held in Cologne, Germany.
23rd July, 2018:

Joined TCS Innovation Labs as a Researcher.

Resume

View the detailed Resume here

Work Experience

Software Engineer III, Google
Jun 2026 — Ongoing ▼
Data Scientist, JP Morgan Chase
Aug 2025 — Jun 2026 ▼
Worked on applying ML and DL techniques to prevent financial fraud. Built an in-house ML check fraud detection system to replace a third-party solution, achieving > 99% recall with minimal false positives.
Data Scientist, Aragen Life Sciences
Mar 2025 — Aug 2025 ▼
Focused on applying data science for drug discovery and chemical retrosynthesis in the life sciences domain. Implemented production-ready models for the same using transformers, MCTS and Graph Neural Networks.
Mentor, TalentSprint (Freelancing)
Jan 2024 — Dec 2025 ▼
Provided mentorship and hands-on training to working professionals for courses like ML, DL, Prob & Stat. Extensive hands-on experience in developing and teaching projects in natural language processing (NLP), computer vision (CV), speech processing, representation learning (GenAI), deep learning (DL) and RL.
Researcher, TCS Innovation Labs, Hyderabad
Jul 2018 — Feb 2025 ▼
Designed autonomous agents for a real-world smart grid simulation (PowerTAC). In PowerTAC, multiple broker agents compete for a shared electricity pool and customer base. Our goal is to design a broker agent with suitable strategies (mainly RL) in the wholesale, tariff and balancing market to achieve a healthy cash position.
Research Intern, TCS Innovation Labs, Hyderabad
Jan 2018 — May 2018 ▼
Implemented strategies for autonomous agents that place bids in an electricity market on behalf of an electricity generating company to sell power in open markets using techniques derived from RL.
Teaching Assistant, IIIT Hyderabad
2020 — 2022 ▼
Served as a Teaching Assistant for multiple courses at IIIT Hyderabad. Responsibilities included conducting lab sessions, grading assignments and exams, and providing mentorship to students. Courses taught include:
- -- Fairness, Privacy and Ethics in AI (Aug 2022 — Dec 2022)
- -- Introduction to Game Theory (Jan 2022 — May 2022)
- -- Topics in Machine Learning (RL) (Aug 2021 — Dec 2021)

Education

Ph.D., International Institute of Information Technology (IIIT) Hyderabad
2020 — 2024 ▼
Degree: Ph.D. in Computer Science and Engineering
CGPA: 8.67
Achievements:
- -- The Winner of the Power Trading Agent Competition (PowerTAC) in 2021 and 2022
- -- The Winner of the best lab poster award during IIIT H R&D showcase 2022
- -- Recipient of the Microsoft Research Travel Grant (2022 and 2023), ACM-India Travel Grant (2022 and 2023), IJCAI-AIJ Travel and Accessibility Grant (2022 and 2024), CODS-COMAD Travel Grant(2023 and 2024)
- -- Nominated for:
  - -- ACM India Doctoral Dissertation Award 2025
  - -- IKDD Doctoral Dissertation Award 2025
B.Tech., Institute of Technology, Nirma University
2014 — 2018 ▼
Degree: Bachelor of Technology (B.Tech.) in Information Technology
CGPA: 8.63
Achievements:
- -- Gold Medal for being the topper in the Information Technology Programme
- -- Recipient of Tuition Fee Wavier (TFW) and Nirma Institute of Technology Alumni Association (NITAA) Scholarship
Higher Secondary School Certificate (H.S.C.)
2012 — 2014 ▼
Board: Gujarat Higher Secondary Education Board (G.H.S.E.B.)
Percentage: 88.8
Achievements:
- -- Amul Vidhya-Bhooshan award for being the school-topper
Secondary School Certificate (S.S.C.)
2011 — 2012 ▼
Board: Gujarat Secondary Education Board (G.S.E.B.)
Percentage: 93.4
Achievements:
- -- The Winner of the School-level Drawing Competition

My skills

Research Interests: Reinforcement Learning, Game Theory, Deep Learning, Multi-agent Systems, System Design, Data Science
Programming Languange: Java, Python, C++, C, Matlab, Shell Script
Database System: SQL, MongoDB
Tools and Technology: Git, Latex, Flask, Django