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Ryan Shi
I am an Assistant Professor in the Department of Computer Science at the University of Pittsburgh. Through Nara, I work with public sector organizations to address societal challenges in food security, environmental conservation, and poverty alleviation using AI. Some of my research has been deployed at these organizations worldwide. I was the recipient of a 2024 Google Academic Research Award, the 2024 Allen Newell Award for Research Excellence, and a 2022 Siebel Scholar Award. I was named to the 2025 AAAI New Faculty Highlights program and the 2022 Rising Star in Data Science and ML & AI lists by UChicago and USC. Previously, I consulted for DataKind and interned at Microsoft and Facebook. I got my Ph.D. in Societal Computing from Carnegie Mellon University advised by Fei Fang and a B.A. in Mathematics and Computer Science from Swarthmore College.
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Recent News
August 2025
In Fall 2025, I will be teaching CS2750: Machine Learning at Pitt.
December 2024
Won a Pitt Cyber Accelerator Grant.
October 2024
September 2024
One paper accepted to NeurIPS-24.
May 2024
Won the 2024 Allen Newell Award for Research Excellence from CMU School of Computer Science.
May 2024
Won the 2024 John C. Mascaro Faculty Fellowship from Pitt Mascaro Center for Sustainable Innovation.
Volunteer Engagement for Food Security
Working with Food Rescue Hero, we developed a series of AI-based tools to make sure that as many food donations reach the underprivileged communities as possible. Our work has gone through user studies and randomized trials, and has been deployed in the real world.
Predicting and Presenting Task Difficulty for Crowdsourcing Food Rescue Platforms
Zheyuan Ryan Shi, Jiayin Zhi, Siqi Zeng, Zhicheng Zhang, Ameesh Kapoor, Sean Hudson, Hong Shen, Fei Fang
WWW-24: The 2024 ACM Web Conference
[Short video]
A Recommender System for Crowdsourcing Food Rescue Platforms
Zheyuan Ryan Shi, Leah Lizarondo, Fei Fang
WWW-21: The 2021 ACM Web Conference
Part of book chapter in AI for Social Impact
Improving Efficiency of Volunteer-Based Food Rescue Operations
Zheyuan Ryan Shi*, Yiwen Yuan*, Kimberly Lo, Leah Lizarondo, Fei Fang
IAAI-20: 32nd Annual Conference on Innovative Applications of Artificial Intelligence
Media Monitoring for Environmental Conservation
Working with World Wildlife Fund, we developed a set of tools powered by cutting edge language models to detect events that could pose a threat to conservation goals. Our tools have been adopted by WWF teams in multiple countries, monitoring 60K+ protected areas worldwide.
Where It Really Matters: Few-Shot Environmental Conservation Media Monitoring for Low-Resource Languages
Sameer Jain, Sedrick Scott Keh, Shova Chhetri, Karun Dewan, Pablo Izquierdo, Johanna Prussmann, Pooja Shrestha, César Suárez, Zheyuan Ryan Shi, Lei Li, Fei Fang
AAAI-24: The 38th Annual AAAI Conference on Artificial Intelligence
NewsPanda: Media Monitoring for Timely Conservation Action
Sedrick Scott Keh*, Zheyuan Ryan Shi*, David J. Patterson, Nirmal Bhagabati, Karun Dewan, Areendran Gopala, Pablo Izquierdo, Debojyoti Mallick, Ambika Sharma, Pooja Shrestha, Fei Fang
IAAI-23: 35th Annual Conference on Innovative Applications of Artificial Intelligence
Winner of IAAI Deployed Application Award
Use-inspired Technical AI Research
Specializing in multi-agent systems and sequential decision making, we develop generalizable models and methods to tackle the shared pain points we observed in diverse application areas.
Global Rewards in Restless Multi-Armed Bandits
Naveen Raman, Zheyuan Ryan Shi, Fei Fang
NeurIPS-24: the 38th Annual Conference on Neural Information Processing Systems
Bandit Data-Driven Optimization
Zheyuan Ryan Shi, Zhiwei Steven Wu, Rayid Ghani, Fei Fang
AAAI-22: the 36th AAAI Conference on Artificial Intelligence
[Full version] [Source code]
Designing the Game to Play: Optimizing Payoff Structure in Security Games
Zheyuan Ryan Shi*, Ziye Tang*, Long Tran-Thanh, Rohit Singh, Fei Fang
IJCAI-18: the 27th International Joint Conference on Artificial Intelligence
[Full version] [Source code]
Other Publications
MAVIPER: Learning Decision Tree Policies for Interpretable Multi-Agent Reinforcement Learning
Stephanie Milani, Zhicheng Zhang, Nicholay Topin, Zheyuan Ryan Shi, Charles Kamhoua, Evangelos E Papalexakis, Fei Fang
ECMLPKDD-22: the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
Pallet Estimation for Food Bank Logistics Management
Alison Hau, Fei Fang, Zheyuan Ryan Shi
COMPASS-21: the 4th ACM SIGCAS Conference on Computing and Sustainable Societies
Draining the Water Hole: Mitigating Social Engineering Attacks with CyberTWEAK
Zheyuan Ryan Shi, Aaron Schlenker, Brian Hay, Daniel Bittleston, Siyu Gao, Emily Peterson, John Trezza, Fei Fang
IAAI-20: 32nd Annual Conference on Innovative Applications of Artificial Intelligence
[Software @Chrome Web Store] [Full version]
Learning and Planning in the Feature Deception Problem
Zheyuan Ryan Shi, Ariel D. Procaccia, Kevin S. Chan, Sridhar Venkatesan, Noam Ben-Asher, Nandi O. Leslie, Charles Kamhoua, Fei Fang
GameSec-20: the 11th Conference on Decision and Game Theory for Security
Deep Reinforcement Learning for Green Security Games with Real-Time Information
Yufei Wang, Zheyuan Ryan Shi, Lantao Yu, Yi Wu, Rohit Singh, Lucas Joppa, Fei Fang
AAAI-19: the 33rd AAAI Conference on Artificial Intelligence
[Full version] [Source code]
Optimizing Peer Teaching to Enhance Team Performance
Zheyuan Ryan Shi, Fei Fang
TEAMAS-17: First International Workshop on Teams in Multiagent Systems, at AAMAS-17
In Autonomous Agents and Multiagent Systems: AAMAS 2017 Workshops, Best Papers, Springer.
Winner of Best Paper
Strategic Reporting in Exponential Family Prediction Markets
Zheyuan Ryan Shi, Sindhu Kutty
URTC-16: 2016 MIT IEEE Undergraduate Research Technology Conference
Team Nara
What is Nara?
Nara is our AI for social good research group at Pitt. We research cutting-edge AI methodologies to address the pain points of public sector organizations as they strive to make our world a better place. Leveraging this core research foundation, Nara scopes, deploys, and replicates solutions, fosters partnerships, engages stakeholders, and develops educational resources.
Why Nara?
Over the years, we've witnessed the immense potential of AI for social good but also the significant challenges that come with it. Nara is our response to this call. We go deep into the solution, stay long with our partner, and venture out of normal paradigms. Structural obstacles require creative pathways. We are not afraid to establish new frontiers and arenas, to deliver the promise of AI for good.
Nara's mission:
To make AI for good scalable, sustainable, generalizable, and responsible.
PhD students
Yuchen Dou
Other student collaborators
Bhiman Kumar Baghel
Jacob Emmerson
Ariana Tang
Teaching
CS2750 Machine Learning [Fall 2025] [Spring 2025]
CS1699/2099 Special Topics in CS: Machine Learning and Game Theory [Fall 2024]
CS3710 Advanced Topics in AI: AI for Social Good [Spring 2024]