Advancing Knowledge
Exploring the frontiers of AI and design to create technology that benefits humanity. Our research focuses on responsible AI development and human-centered design.
Can AI Invent Calculus, or Just Mimic Math?
Gauri Kale, Holly Diamond, Ava Hedayatipour*, Rahul Vishwakarma, Amin Rezaei
Presented at the 2026 International Conference on Advances in Artificial Intelligence and Machine Learning in Tokyo, Japan.
Sparse Selective Hyper-Connections: A Unified Framework for Stable and Efficient Deep Residual Learning
Shrey Modi, Rahul Vishwakarma, Renju Rajeev, Tanay Parikh, Krishna Patel and Holly Diamond
Presented at SoutheastCon 2026 in Huntsville, AL, USA.
Poster: Where Do Billions in Research Funding Really Go? When Self-Citations Inflate Impact Scores by 20%
Rahul Vishwakarma (WorkOnward) & Sinchan Banerjee (UST GlobalInc)
Poster presentation at PyTorch Conference 2025 in San Francisco.
Design Specification and AI-Driven Digital Twin Architecture for Storage Devices
Rahul Vishwakarma (WorkOnward) & Hemant Gaikwad (Dell Technologies)
Presented at SNIA Storage Developer Conference 2025.
AAA-IDE: Autonomous Agentic AI for Data Engineering
Rahul Vishwakarma
Presented at the IEEE Enterprise Generative AI Summit 2025 in San Jose, CA.
SafeRoute: Adaptive Model Selection for Efficient and Accurate Safety Guardrails in Large Language Models
Seanie Lee*, Dong Bok Lee*, Dominik Wagner, Minki Kang, Haebin Seong, Tobias Bocklet, Juho Lee, Sung Ju Hwang
An adaptive framework for selecting safety guardrails to balance efficiency and accuracy in LLM deployment.
Endocrine-to-Synaptic: Learnable Signaling Primitives for Robust Multi-Agent AI
Rahul Vishwakarma + collaborators from USC & Emory
Presented at the AI Transformation in Industries and Infrastructure Track.
A Novel Bio-Causal Agent-to-Agent Protocol (BCA2P) Framework
Rahul Vishwakarma (WorkOnward) & Dr. Eric Wasiolek
Presented at the Frontline AI Track.
Design Specification and AI-Driven Digital Twin Architecture for Storage Devices
Rahul Vishwakarma (WorkOnward)
Presented as a solo online talk at the Systems & Robotics Joint Symposium.
HarmAug: Effective Data Augmentation for Knowledge Distillation of Safety Guard Models
Seanie Lee*, Haebin Seong*, Dong Bok Lee, Minki Kang, Xiaoyin Chen, Dominik Wagner, Yoshua Bengio, Juho Lee, Sung Ju Hwang
Proposed effective data augmentation strategies for training robust safety guard models in LLMs.
Collaborate With Us
Interested in contributing to our research? We welcome collaborations from researchers, practitioners, and organizations who share our vision for responsible technology.