Scholar
Somak Aditya
Google Scholar ID: 2shiHpwAAAAJ
Assistant Professor, IIT Kharagpur
Knowledge Representation
Commonsense Reasoning
Natural Language Processing
Natural Language Understanding
Visual Reasoning
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Citations & Impact
All-time
Citations
733
H-index
13
i10-index
15
Publications
20
Co-authors
26
list available
Contact
Email
saditya@cse.iitkgp.ac.in
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Publications
11 items
AFGNN: API Misuse Detection using Graph Neural Networks and Clustering
2026
Cited
0
AD$^2$: Analysis and Detection of Adversarial Threats in Visual Perception for End-to-End Autonomous Driving Systems
2026
Cited
0
PragWorld: A Benchmark Evaluating LLMs' Local World Model under Minimal Linguistic Alterations and Conversational Dynamics
2025
Cited
0
Harnessing Synthetic Preference Data for Enhancing Temporal Understanding of Video-LLMs
2025
Cited
0
EduVidQA: Generating and Evaluating Long-form Answers to Student Questions based on Lecture Videos
2025
Cited
0
NLKI: A lightweight Natural Language Knowledge Integration Framework for Improving Small VLMs in Commonsense VQA Tasks
2025
Cited
0
AURA: Affordance-Understanding and Risk-aware Alignment Technique for Large Language Models
2025
Cited
0
LOGICPO: Efficient Translation of NL-based Logical Problems to FOL using LLMs and Preference Optimization
2025
Cited
0
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Resume (English only)
Academic Achievements
Two papers accepted at EMNLP 2025: main conference paper 'EduVidQA' and Findings paper 'NLKI', a lightweight framework for commonsense VQA.
Paper accepted at ACL 2025 Findings on evaluating LLMs’ math and coding skills via ontology-guided interventions.
Long paper accepted at NAACL 2025 main track on word sensitivity estimation for adversarial text generation.
Awarded USD 8000 Azure credits from Microsoft AFMR for the 'Education Video Copilot' project.
Served as Demo Track Co-Chair for CODS 2025.
Invited speaker at IIIT-H CVIT Summer School, Microsoft Research India Academic Summit, IISc, and National Research Council Canada.
Background
Assistant Professor (Grade I) in the Department of Computer Science and Engineering at IIT Kharagpur.
Research focuses on enhancing, evaluating, and explaining complex reasoning capabilities of AI systems.
Works at the intersection of classical and statistical AI, commonly known as neuro-symbolic methods.
Aims to enable deployable AI systems to reason with human-like commonsense knowledge.
Research spans reasoning in Transformer-based language models, multi-hop reasoning, visual question answering, and image understanding.
Co-authors
26 total
Chitta Baral
Professor of Computer Science, Arizona State University
Yezhou (YZ) Yang
School of Computing and AI, Arizona State University
Monojit Choudhury
Professor of Natural Language Processing, MBZUAI
Aloimonos
Professor of Computer Science, University of Maryland, Director Computer Vision Laboratory
Cornelia Fermuller
Research Scientist, Computer Vision and Human Vision, University of Maryland
Atharva Naik
PhD Student, Carnegie Mellon University
Deepanway Ghosal
Google DeepMind
Co-author 8
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