My research centers on spatial intelligence — 3D/4D geometry, multimodal grounding, and vision-language alignment — for real-world embodied and on-device systems. I build perception engines that are accurate, hardware-deployable, and safe in the real world.
I design transformer-based and Bird's-Eye-View (BEV) architectures for multi-camera 3D object detection, open-vocabulary perception, and monocular depth estimation. My work integrates multi-sensor fusion across camera, LiDAR, radar, and IMU modalities for robust autonomous driving. At Neubility, I deployed a MonoDETR-based 3D detection pipeline with Depth Anything V2 metric depth, achieving sub-30 ms end-to-end inference on a single NVIDIA Jetson Orin SoC.
I develop and evaluate VLM/VLA models for grounded autonomy, cross-modal spatial reasoning, and long-horizon task generalization in embodied AI systems. My work includes distilling large VLM teachers into compact student models (4× compression, 97% accuracy retention) deployable under a 10 W power budget on Jetson Orin. I validate VLA policies in closed-loop simulations using NVIDIA Isaac Sim and CARLA, bridging the sim-to-real gap via systematic domain randomization.
I engineer PTQ/QAT INT8–FP16 quantization pipelines with TensorRT and ONNX graph optimization that achieve 8× inference speedup with <1% mAP degradation versus FP32 baselines. My NanoMST architecture reduced compute by 4.7× over LSTM baselines via hardware-aware multiscale transformer design, targeting TinyML deployment with only 298K parameters and 8-bit quantization support. I apply LoRA/PEFT, knowledge distillation, and neural architecture search to enable real-time AI on resource-constrained hardware.
I develop closed-loop evaluation frameworks, digital twins, and domain-adaptive learning pipelines for scalable perception across diverse Operational Design Domains (ODDs). Using systematic domain randomization and sensor noise modeling in NVIDIA Isaac Sim and CARLA, I quantify and close the sim-to-real gap for autonomous robot deployment. I define and maintain perception KPI frameworks (mAP, recall at IoU thresholds, latency SLAs) across real and synthetic datasets to drive data-driven robustness improvements.
I design deep learning architectures for robust inertial navigation and localization across diverse environments. My DeepILS system achieves sub-meter accuracy in domain-invariant settings, while NanoMST achieves real-time performance with minimal compute. I also developed a particle-filter Visual-Inertial SLAM hardware accelerator on PYNQ-Z1 FPGA at 30 FPS, reducing SLAM latency by 60%. This work combines Extended Kalman Filters, particle filters, graph-based SLAM, and neural odometry.
I design differentially private and federated learning systems for distributed edge intelligence. ConvXformer provides formal ε-DP privacy guarantees for multimodal sensor fusion. ADP-QFed combines adaptive differential privacy with quantized federated learning for IoT edge sensing. My federated navigation framework (FedNav) enables privacy-preserving collaborative model training without centralizing sensitive sensor data.