Siddhartha Mukherjee

I work at the intersection of AI/ML, robotics, control, space systems, quantum sensing, and embedded autonomy, with a research focus on resilient intelligent systems for complex and degraded environments. My current doctoral research direction focuses on GNSS-denied navigation, multimodal sensing, state estimation, embodied AI, mechatronics, quantum-class perception, QML, astrodynamics, and space-relevant autonomous systems. My current research initiative, Q-RAVEN, unifies these themes as an avian-inspired autonomous platform concept for GNSS-denied and extreme environments, integrating: • Quantum-class sensing: NV-center magnetometry, radical-pair biophysics, and spin-based signal interpretation • Multimodal perception: computer vision fused with IMU, LiDAR, RF, radar/SAR, and other sensing modalities for resilient environmental understanding • Embedded intelligence: SLM/LLM-assisted reasoning, physics-informed neural networks, operator learning, quantization, pruning, and accelerated edge inference • Morphology and actuation: adaptive structures, reconfigurable mechanisms, mechatronic integration, and MEMS/NEMS-scale propulsion concepts • Autonomy: SLAM, FDIR, multisensor fusion, reinforcement learning, behavior trees, and Transformer-assisted planning frameworks Across industry and research, my experience spans autonomous driving, unmanned systems, embedded AI, perception, sensor fusion, signal processing, mapping, control, edge deployment, and simulation-to-field validation. I have worked across full-stack autonomy pipelines—from sensing and localization to planning, actuation, middleware, and real-world deployment. My broader research interests include resilient robotic autonomy, embodied AI, GNSS-denied navigation, quantum sensing, QML-assisted perception, astrodynamics-aware autonomy, UAV/UGV systems, space robotics, planetary explorat