University of Virginia · Data Science & CS
ऐक्यम् — Oneness

Aikyam
Lab

Building the science of Trustworthy AI from explainability and safety to alignment and generalization in frontier models with a focus on multilingual reasoning and multimodal models and agents. Aikyam is Sanskrit for oneness and is a nod to our belief that breakthroughs happen when diverse people, ideas, and data come together.

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The Lab

Aikyam Lab is a research group at the University of Virginia working on the foundations of Trustworthy Artificial Intelligence — making AI systems that are accurate, explainable, safe, and aligned.

We are affiliated with the School of Data Science, the Department of Computer Science, and School of Medicine. Our work spans Computer vision, NLP, graphs, and multimodal AI.

We are always looking for motivated Ph.D. students and collaborators. See open positions.

An unexplainable system is an unauditable system
Chirag Agarwal
Chirag Agarwal Principal Investigator · About →
Hewlett Foundation Grant NSF AI & Cybersecurity Award MaceAI Fellowship NeurIPS'26 Workshop Spotlight Google Gemini Academic Award ICML'26 · ECCV'26 · ACL'26
Latest Research

Current Work

All Publications
CURE-Med research
LLMs · Multilingual Reasoning

CURE-Med: Curriculum-Informed Reinforcement Learning for Multilingual Medical Reasoning

Improving multilingual medical reasoning in LLMs via curriculum-informed reinforcement learning.

ACL'26 Oral 🏆
VLM research
Multimodal · Vision-Language Models

Do Vision Language Models Need to Process Image Tokens?

Investigating whether VLMs actually need to process image tokens for visual reasoning tasks.

TRUE-V Workshop CVPR'26 Oral 🏆
SynopticBench research
Benchmark · Vision-Language Models · Climate

SynopticBench: Evaluating Vision-Language Models on Generating Weather Forecast Discussions

A benchmark for evaluating VLMs on generating synoptic-scale weather forecast discussions.

Climate Informatics'26 Oral 🏆
Polarity probing research
Alignment · Probing

Polarity-Aware Probing for Quantifying Latent Alignment in Language Models

Quantifying latent alignment in language models through polarity-aware probing.

AAAI'26 Oral 🏆
Updates

What's Happening

2026
We received a William and Flora Hewlett Foundation Grant with Stephen Turner and Tom Hartvigsen! 🎉
2026
Awarded an NSF AI and Cybersecurity Innovation grant as Interpretability PI, with Wajih Hassan (SEAS)!
2026
Selected for the MaceAI Fellowship!
2026
Honored to receive the Outstanding Mentoring Award from the School of Data Science!
More recent updates
2026Mechanistic Interpretability of Atmospheric Rivers in GraphCast selected as a Spotlight at the NeurIPS'26 Tackling Climate Change with ML Workshop! Congratulations, Madelyn and Tim!!
2026Won the SPARC Grant with Sriparna Saha (IIT Patna)
2026Won two Environmental Institute Colab Awards for 2026!
2026Rethinking Explainability in the Era of Multimodal AI published in AAAI Magazine
2026Founded the Interpretability for AI Safety and Science research group at SDS and the Explainable and Safe AI Research Hub at UVA
2026Selected as Area Chair for NeurIPS'26
2026Talks at the Agyeya AI Safety and Interpretability Workshop @ IISc Bangalore, IIT Patna, the WHO South-East Asia Office Webinar Series, and as Research Spotlight Speaker at Capital One's IFX Science Speaker Series
2026Akash Ghosh is joining Harvard as a Postdoc! Welcome to new lab members Khai-Nguyen Nguyen and Lavanya Joshi!
06/26
2026We won the Google Gemini Academic Research Award! 🎉
06/26
2026CLINIC accepted to ICML'26 and GridVQA-X accepted to ECCV'26!
04/15
2026We won the Baugh Family Fund award!
04/06
2026Four papers accepted to ACL'26! Congrats, Eric and Ahson!
03/12
2026Gave a talk in the AIxHealth seminar at GeorgiaTech on "The Illusion of Transparency: Explainability and Safety in Healthcare".
03/11
2026SynopticBench got an Oral Presentation in Climate Informatics! Congratulations, Tim!!
02/27
2026Gave a talk at the Charles L. and Ann Brown Distinguished Colloquium series in the ECE Department!
01/25
2026Polarity-Aware Probing for got Oral Accept at AAAI'26 🎉 and I got selected to AAAI New Faculty Highlight program!
12/12
2025Verifying LLM Reasoning through Interactive Explanation Interfaces accepted to IUI'2026! Congratulations, Runtao!!
12/08
2025We won the CapitalOne Fellowship!
12/01
2025Talk at FAR.AI Alignment Workshop @ San Diego
11/08
2025Invited talk at the Multimodal Reasoning Workshop, ACM SIGMM
11/06
2025We won the Tinker Research Grant!
10/31
2025First podcast 🎉 at AI Exchange @ UVA
10/17
2025Invited talk at Computer Science Colloquia, UMass Lowell
08/30
2025Selected as Area Chair for AAAI'26 and ICLR'26
08/20
2025Two papers accepted to EMNLP on Multilingual Reasoning and Hallucinations in Egocentric Video Understanding
07/31
2025Talk on TrustworthyML in the Era of Frontier Models at IIIT-H
06/30
2025Awarded OpenAI Researcher Access Program
06/19
2025Won the Environmental Institute's Spring 2025 Colab Award!
06/16
2025New preprint: Rethinking Explainability in the Era of Multimodal AI
05/01
2025Three papers at NAACL'25: Memorization via Model Attribution · Fine-tuning on CoT Reasoning · Right to Data Protection
03/19
2025Won the Cohere For AI Research Grant
02/14
2025The Multilingual Mind — survey on Multilingual Reasoning in LLMs is out
01/23
2025Awarded the 2025 Fellowships in AI Research from the LaCross AI Institute
12/06
2024Invited talk at Value Chain of Ethical AI conference at LaCross AI Institute, Darden Business School
11/22
2024Invited talk at Privacy and Interpretability in Generative AI workshop, IDEAL Chicago
11/16
2024Preprint: Towards Operationalizing Right to Data Protection
10/24
2024Lightning talk at Bay Area Alignment Workshop, FAR.AI
09/26
2024Medical Safety Benchmark accepted to NeurIPS'24
08/10
2024Started Assistant Professor role at UVA
07/16
2024Adversarial Robustness and Actionable Explanations accepted to AIES 2024
07/10
2024Certifying Robustness for LLMs accepted to COLM 2024
07/01
2024Preprint: On the Hardness of Faithful Chain-of-Thought Reasoning in LLMs
05/01
2024Iterative Prompting for Truthfulness accepted at ICML 2024
03/14
2024Preprint: Faithfulness vs. Plausibility: On the (Un)Reliability of Explanations from LLMs
01/23
2024Tutorial on Explainability in Graph Deep Learning for Biomedicine at ISMB 2024
01/19
2024Quantifying Uncertainty in NL Explanations of LLMs accepted to AISTATS'24
11/21
2023Selected as Top Reviewer for NeurIPS'2023
11/20
2023Won the Harvard Data Science Initiative (HDSI) Azure Credits Award
10/27
2023Papers at NeurIPS XAIA and Ro-FoMo (Spotlight!) workshops
09/13
2023Won the Adobe Data Science Research Award
08/15
2023Workshop on "Regulatable ML" at NeurIPS'23 is accepting submissions
04/05
2023Explain Like I am BM25 accepted to SIGIR'2023
03/18
2023Evaluating Explainability for Graph Neural Networks accepted in Nature Scientific Data
02/27
2023DeAR: Debiasing Vision-Language Models accepted to CVPR'23
01/21
2023Graph Unlearning and Explaining RL Decisions with Trajectories accepted at ICLR 2023
09/16
2022OpenXAI accepted at NeurIPS'22 · live on GitHub
03/03
2022VoG accepted to CVPR 2022
09/18
2020Paper at ACCV'20: Explaining image classifiers by removing input features
02/26
2020Oral at CVPR'20: SAM: Sensitivity of Attribution Methods to Hyperparameters