Rohan Siva

I am pursuing a B.S. in Electrical and Computer Engineering Honors with a Minor in Stats & Data Science at the University of Texas at Austin, graduating in May 2027.

Currently, I am a Machine Learning Intern at Cisco Hypershield working on cybersecurity data ETL agents. I am also an AI Researcher at the VITA Lab and Center for Autonomy at UT Austin under Prof. Atlas Wang and Prof. Ufuk Topcu, focusing on formal methods, neurosymbolic AI, and VLM-based planning. At the Statistical Learning & AI Group under Prof. Qiang Liu, I work on GRPO reinforcement learning to understand RL's impact beyond distribution reshaping. I also collaborate with Prof. Hao Tang at Peking University on diffusion models for parallelizable chain-of-thought reasoning.

My research interests include embodied AI, natural language processing, autonomous systems, and vision-language models. I focus on developing AI systems that can perceive, reason, and act in complex environments, with particular emphasis on uncertainty quantification and neurosymbolic approaches for autonomous driving and robotics.

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Publications

Know Where You're Uncertain When Planning with Multimodal Foundation Models: A Formal Framework

MLSys, 2025 (Oral) 🏆
Bhatt, N. P., Yang, Y., Siva, R., Milan, D., Topcu, U., Wang, Z.

A novel framework for uncertainty-aware multimodal planning using conformal prediction for perception uncertainty and FMDP to quantify decision uncertainty, with formal verification guarantees. Building on this, we implement active sensing and automated refinement via SFT to meet task specifications, reducing variability by 40% and improving task success by 5%.

Project Page arXiv Code OpenReview

UNCAP: Uncertainty-Guided Planning Using Natural Language Communication for Cooperative Autonomous Vehicles

AAMAS, 2026 (Oral) 🏆
Bhatt, N. P., Li, P., Gupta, K., Siva, R., Milan, D., Hogue, A. T., Chinchali, S. P., Fridovich-Keil, D., Wang, Z., Topcu, U.

A framework for uncertainty-guided planning in cooperative autonomous vehicles using natural language communication. Leverages uncertainty quantification to improve coordination and decision-making in multi-agent autonomous driving scenarios.

Project Page arXiv Code

VLN-Zero: Rapid Exploration and Cache-Enabled Neurosymbolic Vision-Language Planning for Zero-Shot Transfer in Robot Navigation

Under Submission
Bhatt, N. P., Yang, Y., Siva, R., Samineni, P., Milan, D., Wang, Z., Topcu, U.

A neurosymbolic approach combining vision-language models with cache-enabled planning for zero-shot robot navigation. Enables rapid exploration and transfer learning in novel environments without task-specific training.

Project Page arXiv Code

RepV: Safety-Separable Latent Spaces for Scalable Neurosymbolic Plan Verification

Under Submission
Yang, Y., Bhatt, N. P., Samineni, P., Siva, R., Wang, Z., Topcu, U.

A novel framework for safety-verifiable reinforcement learning by learning safety-separable latent spaces that enable efficient neurosymbolic plan verification. Combines the representational power of deep learning with the formal guarantees of symbolic methods for scalable safety verification in complex environments.

Project Page arXiv Code