Karan Dua

Builds large-scale multimodal data pipelines, synthetic generation workflows, and evaluation benchmarks for generative AI systems.

Hansa Meghwani

Holds an MSc from LJMU, UK. Specializes in the architecture of enterprise-grade RAG and agentic workflows with a focus on attribution. Expertise spans the rigorous evaluation of multilingual LLMs and VLMs.

Meizhu Liu

Holds a PhD from the University of Florida. Specializes in the development of multimodal retrieval architectures and generative tools for image editing. Over 70+ publications in top-tier conferences.

Michael Avendi

Holds a PhD from UC Irvine. Focuses on generative AI, vision-language-action models, and robotics learning to bridge perception and physical autonomy.

Matthew Rowe

Researches multimodal and hybrid search with a focus on enterprise-scale RAG. Specializes in architecting retrieval systems for high-throughput production deployments.

Yassi Abbasi

Holds a PhD from USC. Specializes in multimodal AI with an emphasis on image-text representation learning, generative modeling, and the design of robust evaluation metrics.

Peerat Limkonchotiwat

Research Fellow at AI Singapore, NUS. Leads SEA-LION, SEA-HELM, and SEA-Guard; collaborates on LLM, multimodal, safety, and dataset research. Contributor to SEACrowd and SEA-VL; invited researcher at Chulalongkorn University and SIGSEA advisory board member.

Taki Hasan

PhD candidate at Hanyang University, South Korea. Research focuses on trustworthy AI, including efficient test-time adaptation of vision-language and multimodal models for handling distribution shifts.

Praneet Pabolu

Holds a Master’s from Cornell and leads foundation model development at Splunk, focusing on representation learning and generative reasoning for complex environments.

Dr. Pao-Ann Hsiung

Professor of Computer Science and Dean of the College of Engineering. His work spans AI, IoT, and smart city applications, and he previously led smart city initiatives in Chiayi City that earned international recognition.

Bronson Bakunga

Ugandan ML engineer, NLP researcher, and co-founder of Crane AI Labs. He builds compact offline-first language models for low-power devices to improve education access across Sub-Saharan Africa.

Sai Ashish Somayajula

Holds a PhD from UC San Diego and works on NL2SQL, enterprise analytics, and robust reasoning across structured and unstructured data. Served as SRW Chair for EACL 2026, where the workshop expanded submissions, mentorship, and reviewer participation.

Bhargava Kumar

TD Securities

Director at TD Securities, where he leads the AI Practice and supports AI strategy to production delivery for financial markets. He is currently focused on building agentic solutions to support business workflows across the firm. He has co-authored papers at ICLR, ICML, and ACL, among others, and reviews for major ML and NLP conferences. He holds an MS in Operations Research from Columbia University.

Tejaswini Kumar

Apple

Senior Engineering Program Manager at Apple with a strong interest in applied AI, particularly generative AI and agentic systems. Her published research appears at ACL, NAACL, and AACL, among other venues. She holds an MS from the Industrial Engineering and Operations Research (IEOR) department at Columbia University.

Eun Woo Im

Arizona State University

Ph.D. student at ASU focused on multimodal reasoning, retrieval, and its application to video understanding.

Tampu Ravi Kumar

Arizona State University

Ph.D. student at ASU focused on multimodal reasoning, QA over structured and unstructured data, and speech disfluency and audio reasoning.

Manan Roy Choudhury

Arizona State University

Ph.D. student at ASU focused on LLM reasoning and planning, anomaly and discrepancy detection, multi-modal robustness and perturbation analysis, and adversarial attacks in agentic frameworks.

Tejas Anvekar

Arizona State University

Ph.D. student at ASU specializing in the personalized and trustworthy evaluation of MLLMs. Develops agent-driven, explainable frameworks to identify risks and bias across diverse tasks.

Abhijit Chakraborty

MongoDB / Arizona State University

Technical Sales Director for North America Solution Consulting at MongoDB and a Computer Science Ph.D. candidate at Arizona State University's CoRAL Lab, with nearly two decades of experience building data platforms and AI systems across Fortune 500 enterprises. His research spans knowledge graph embeddings, federated retrieval-augmented generation, and trustworthy multi-agent AI, with recent work submitted to venues like NeurIPS and EMNLP. He serves the community as a NeurIPS and EMNLP 2025 reviewer, a Harvard Business Review Board of Advisors member, and an open-source contributor for Apache.

Yash Shah

JPGlobal / Arizona State University

Applied AI Intern at JPGlobal and an M.S. Computer Science thesis candidate at Arizona State University, working on scalable AI systems. His research spans diffusion language models, federated RAG, and reliable agentic AI systems, with work accepted at ACL and EACL.

Ashish Raj Shekhar

Arizona State University

M.S. Computer Science student at Arizona State University's CoRAL Lab, advised by Dr. Vivek Gupta. His research spans AI in education, AI safety, and multi-agent systems, with work accepted at EACL and ACL. He previously worked as a Data Engineer at Amazon and LendingKart.

Shiven Agarwal

Arizona State University

Master's student in Computer Science at Arizona State University conducting research in the CoRAL Lab under Dr. Vivek Gupta. His work spans multi-agent systems, LLM evaluation and robustness, AI safety, and privacy-preserving machine learning, with a focus on taking research systems from paper to deployed product. He is the first author of GamED.AI, a multi-agent framework for automated educational game generation accepted to the ACL 2026 System Demonstrations track, and has additional work published at EACL 2026. Prior to graduate studies, he spent three years as a software engineer building healthcare technology platforms.

Adarsh Singh

Arizona State University

Specializing in Information Retrieval over structured data and retrieval-augmented question answering. Focuses on retrieval, reranking, and representation learning methods that enable robust and scalable reasoning over structured knowledge.