Ashwin Kumar
Scholar

Ashwin Kumar

Google Scholar ID: HZ2INsEAAAAJ
Washington University in St Louis
Reinforcement LearningResource AllocationFairnessRide-sharingExplainable AI Planning
Citations & Impact
All-time
Citations
91
 
H-index
4
 
i10-index
3
 
Publications
18
 
Co-authors
17
list available
Resume (English only)
Academic Achievements
  • Paper 'Detecting Prefix Bias in LLM-based Reward Models' accepted at ACM FAccT 2025
  • Paper 'Dialectical reconciliation via structured argumentative dialogues' published at KR 2024
  • Workshop paper 'DECAF: Learning to be Fair in Multi-Agent Resource Allocation' accepted at RL Safety Workshop @ RLC 2024
  • Paper 'Using Simple Incentives to Improve Two-Sided Fairness in Ridesharing Systems' published at ICAPS 2023
  • Extended abstract 'Remember, but also, Forget: Bridging Myopic and Perfect Recall Fairness with Past-Discounting' accepted at AAMAS 2025 Workshop on Autonomous Agents for Social Good (AASG)
Research Experience
  • Works on temporal resource allocation problems involving interactions between resources and agents over time
  • Early work used simple incentives to improve two-sided fairness in ridesharing systems
  • Recently developed the DECAF framework to learn fair allocation policies in multi-agent settings using reinforcement learning
  • Investigates methods to bridge myopic and long-term fairness considerations
  • Collaborates with Stylianos Vasileiou on Human-Aware AI to improve human understanding of and interaction with AI systems
  • Developed a dialectical reconciliation framework using structured argumentative dialogues to resolve knowledge discrepancies between users and AI agents
Miscellany
  • Enjoys reading books and biking in Forest Park when not working
  • Likes playing the piano whenever possible
  • Passionate about traveling and has visited many places around the world
  • Always open to recommendations for new activities, books, and music
  • Designed this personal website using an agentic LLM; believes the next major advances will come from agentic systems and reinforcement learning