Scholar
Sahil Shah
Google Scholar ID: x49dOfMAAAAJ
Student, University of Texas at Austin
Citations & Impact
Citations
32
H-index
2
i10-index
1
Publications
7
Co-authors
0
Publications
Resume
Academic Achievements
- NeuS-QA: Grounding Long-Form Video Understanding in Temporal Logic and Neuro-Symbolic Reasoning, AAAI 2026
- A Challenge to Build Neuro-Symbolic Video Agents, NeuS 2025
- We'll Fix it in Post: Improving Text-to-Video Generation with Neuro-Symbolic Feedback, Submitted to ICLR 2026
- Neuro-Symbolic Evaluation of Text-to-Video Models using Formal Verification, CVPR 2025
- COFFEE: a High-Performance Approach to Convex Optimization for Thermodynamic Equilibrium Computations, SIEDS 2025 (Best Paper)
- Real-Time Privacy Preservation for Robot Visual Perception, TMLR 2025
- Towards Neuro-Symbolic Video Understanding, ECCV 2024 (Oral Presentation)
Research Experience
- Interned at NVIDIA, Tesla, and AWS, working on machine learning and computer architecture.
Education
- Master's student at The University of Texas at Austin studying Computer Engineering and Robotics, currently advised by Sandeep Chinchali at Swarm Lab.
Background
- Interested in developing systems that integrate vision-language models with formal logic to improve the reliability and interpretability of video-based reasoning. Focuses on constructing pipelines to reason about temporal event sequences for video understanding, generation, and agents by combining deep learning with automata-theoretic methods.