Secured six-figure sole-source SBIR Phase III contract award by Chief of Wargaming, Office of the Under Secretary of Defense for Acquisition & Sustainment, and US Air Force Autonomy Prime to scale large-scale multi-agent decision-support AI.
With my team at Multi AI, I have developed a Multi-Agent Coordination Platform for large-scale transportation and inventory problems in the military. This high-fidelity platform assists wargamers and logisticians in testing and optimizing their …
Awarded a six-figure SBIR Phase III contract by Air Force Autonomy Prime to advance decision-support AI for large-scale multi-agent vehicle coordination. Spearheaded cutting-edge AI innovations to enhance autonomous system capabilities in defense applications.
Invited as a panelist to the Applied Intelligence Summit 2023, sharing insights on Generative AI with industry leaders and decision-makers. Contributed to discussions on emerging trends, ethical considerations, and real-world applications of AI in business and technology.
Chosen for the prestigious Capital Factory Accelerator, the largest startup accelerator in Texas, providing exclusive mentorship, funding opportunities, and access to a vast network of investors and industry leaders.
During the second half of my PhD research, I became increasingly fascinated with multi-agent drone applications, especially for public safety. Coincidentally, I had also long aspired to start my own company. Thus, while still in the midst of my PhD …
Awarded a seven-figure SBIR Phase II contract by Air Force Agility Prime to advance decision-support AI for large-scale multi-agent vehicle coordination. Spearheaded cutting-edge AI innovations to enhance autonomous system capabilities in defense applications.
This paper presents a self-supervised Learning from Learned Hallucination (LfLH) method to learn fast and reactive motion planners for ground and aerial robots to navigate through highly constrained environments. The recent Learning from …
We present a novel unsupervised deep learning approach that utilizes an encoder-decoder architecture for detecting anomalies in sequential sensor data collected during industrial manufacturing. Our approach is designed to not only detect whether …
Inferring spatial-temporal properties from data is important for many complex systems, such as additive manufacturing systems, swarm robotic systems and biological networks. Such systems can often be modeled as a labeled graph where labels on the …