Curriculum Vitae

Mudit Sharma

PhD Student, Computer Science and Engineering, Indian Institute of Technology Ropar
mudit.25csz0001@iitrpr.ac.in | mudit.sharma.ldh@gmail.com | Punjab, India | Google Scholar | ORCID | GitHub | LinkedIn

Profile

PhD researcher in Computer Science and Engineering at IIT Ropar, supervised by Dr. Shweta Jain. My research focuses on optimization and sequential decision-making, particularly multi-armed bandits, dueling bandits, online learning, and nonconvex optimization.

Education

Ph.D. in Computer Science and Engineering Indian Institute of Technology Ropar, Punjab, India | 2025-present | CGPA: 8.47/10

Advisor: Dr. Shweta Jain.

Bachelor of Technology in Computer Science and Engineering Chandigarh University, Punjab, India | 2020-2024 | CGPA: 7.81/10

Area of Research

Online learning, multi-armed bandits, dueling bandits, nonconvex optimization, sequential decision-making, exploration-exploitation, regret analysis, and algorithm design.

Research Experience

Junior Research Fellow IIT Ropar | Supervisor: Dr. Shweta Jain | August 2024-June 2025

Developed and analyzed algorithmic approaches for dueling multi-armed bandits using preference-based, pairwise-comparison feedback.

Investigated exploration-exploitation strategies and regret-minimization methods for sequential decision-making under uncertainty.

Formulated research questions and evaluated candidate algorithms through theoretical analysis and reproducible computational experiments.

Publications and Working Papers

Lipschitz Dueling Bandits over Continuous Action Spaces Mudit Sharma, Shweta Jain, Vaneet Aggarwal, and Ganesh Ghalme. Accepted at NeurIPS 2026; to appear. CORE Rank: A*.
Fairness-Aware Dueling BanditSatvik Shrivastava et al. Submitted to AAAI 2027.
Comparator-Path Dynamic Regret for Adversarial Lipschitz Bandits One-Point Learning and Two-Query Oracle AdaptationMudit Sharma, Shweta Jain, Vaneet Aggarwal, and Ganesh Ghalme. Under review at AAMAS 2027.
Adversarial Dueling Bandit under Preference FeedbackWorking paper.
Improving Dueling BanditsWorking paper.
NV3K Cutting Only Using Preference FeedbackWorking paper.

Teaching

GE 103: Introduction to Programming Teaching Assistant, IIT Ropar, Semester 1 - Summer 2025. Instructors: Dr. Shweta Jain and Dr. Shashi Shekhar Jha.

Mentored students in core C programming concepts and guided them through problem-solving and debugging exercises.

AI111: Mathematical Foundations of AI Teaching Assistant, IIT Ropar, Semester 2 - Winter 2025. Instructors: Dr. Puneet Kumar and Dr. Shweta Jain.

Guided students through mathematical concepts underpinning artificial intelligence and supported their application to foundational problems.

CS203: Data Structures Teaching Assistant, IIT Ropar, Semester 3 - Summer 2026. Instructors: Dr. Shweta Jain and Dr. Swapnil Dhalmal.

Facilitated learning of data structures and algorithms through implementation, debugging, and complexity-aware problem solving.

Technical Skills

Programming: Python, C++, Java. Tools: Linux, LaTeX, Docker, AWS. Research expertise: bandit algorithms, stochastic and nonconvex optimization, sequential decision-making, regret analysis, and algorithm design and analysis.

Languages

English (professional), Hindi (native), Punjabi (professional).

Achievements

Graduate Aptitude Test in Engineering (GATE) Qualified in 2024 and 2025.