Hello, I'm
Postdoctoral Fellow
Monterey Bay Aquarium Research Institute (MBARI)
Multi-camera perception • 3D Reconstruction • SLAM • Underwater & Field Robotics
I am a Postdoctoral Fellow at the Monterey Bay Aquarium Research Institute (MBARI), where I work on multi-modal perception for underwater robots. My research spans multi-camera sensing, 3D reconstruction, and Simultaneous Localization and Mapping (SLAM) in dynamic and visually degraded environments — from the deep ocean to Arctic glaciers.
I earned my Ph.D. in Computer Science (Robotics) from Northeastern University, advised by Dr. Hanumant Singh, with thesis committee members from MIT and Toyota Research Institute. My work has taken me to some of the most extreme environments on Earth, including the Arctic, Antarctic, and deep ocean, where I design and deploy perception systems on autonomous and remotely operated vehicles.
Northeastern University
Multi-Camera Sensing for Robust Perception in Robotics
University of Florida
JNTU Anantapur, India
Selected updates across research, fieldwork, and appointments.
Conducted an ROV survey of whale bones in Utqiagvik, Alaska, building 3D models to study ecological change over time.
Joined MBARI as a Postdoctoral Fellow, focusing on multi-modal underwater perception, dense mapping, and self-supervised scene understanding.
Published A Certifiably Correct Algorithm for Generalized Robot-World and Hand-Eye Calibration in The International Journal of Robotics Research.
Presented Enhancing Situational Awareness in Underwater Robotics with Multi-modal Spatial Perception at the ICRA Workshop on Field Robotics.
Led a sea-ice survey aboard the USCGC Healy in the Chukchi Sea, collecting imagery and telemetry of newly forming pancake ice.
View full list on Google Scholar
The International Journal of Robotics Research, 2025
ICRA Workshop on Field Robotics, 2025
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024
IEEE International Conference on Robotics and Automation (ICRA), 2024
IEEE Robotics and Automation Letters (RA-L), vol. 8, no. 11, 2023
Building robust perception systems for robots operating in the world's most challenging environments
Multi-modal representation learning for underwater navigation and mapping via fusion of optical and acoustic sensing for AUVs and ROVs. Self-supervised learning to enhance object detection, scene understanding, and semantic mapping.
Multi-modal adaptations of 3D Gaussian splatting for real-time, large-scale dense mapping of underwater environments, enabling high-fidelity reconstruction of subsea scenes.
A generic visual SLAM framework supporting arbitrary multi-camera configurations using generalized camera models. Evaluated across indoor/outdoor datasets with challenging scenarios.
Information-theoretic approaches for optimal sensor arrangement on mobile robots, determining the best camera mount points for accurate and robust SLAM performance.
Enhancing situational awareness for autonomous and remotely operated underwater vehicles via multi-camera perception and sensor fusion for pipeline inspection and subsea infrastructure mapping.
A light field front-end for robust VSLAM that removes dynamic objects using synthetic aperture imaging combined with semantic segmentation, achieving near real-time performance.
3D mapping of dynamically calving glaciers using multi-sensor fusion of data collected from an Autonomous Surface Vehicle at Ny Alesund Arctic research station in Svalbard.
Human-in-the-loop learning for automated fish detection using CNNs with color-corrected underwater imagery and an automated ground-truth labeling pipeline.
Deploying robotic systems from the Arctic to the Antarctic
ROV survey studying the ecological significance of subsistence whale remains. Imagery used to construct and label 3D models and detect changes over time.
Led ROV survey of freshly forming pancake sea ice aboard the Icebreaker Healy, collecting imagery and telemetry to analyze structural properties of Arctic sea ice.
Integrated multi-camera rig with inertial sensing on ROV Minerva to inspect subsea infrastructure and the shipwreck Hercules in the Trondheim fjord.
ASV field operations mapping Kongsbreen and Blomstranda glaciers to detect changes at active calving fronts.
Designed camera units and software for the British Antarctic Survey's DEFiANT program, studying variability of Antarctic sea ice from aircraft.
Sensor integration and control software for Boston Dynamics' Atlas humanoid at IHMC. Team placed 2nd in the competition.
Moss Landing, CA
Multi-modal representation learning for underwater navigation. 3D Gaussian splatting for large-scale dense mapping. Self-supervised learning for scene understanding.
Trondheim, Norway
Multi-camera perception for underwater vehicle situational awareness. Real-time dense reconstruction of subsea infrastructure and shipwrecks.
Boston, MA
Toyota Research Institute collaboration on multi-modal SLAM. Optimal sensor arrangement research. Arctic glacier mapping expeditions.
Boston, MA
Ph.D. research on multi-camera systems for robust real-time robot perception. Hardware and software design for synchronized multi-camera arrays. AVATAR Xprize competition.
Cambridge, MA
Driver interruptibility prediction for ADAS speech interfaces. CAN bus data analysis for intelligent vehicle-driver communication timing.
Pensacola, FL
DARPA Robotics Challenge: Sensor integration, controller development, and point cloud processing for Boston Dynamics' Atlas humanoid robot.
A generic multi-camera SLAM system for arbitrary camera system configurations.
ROS driver for FLIR cameras integrating Spinnaker SDK with hardware synchronization.
Information-theoretic optimization for optimal camera arrangement on mobile robots.
Read/write, conversion, downsampling, filtering, and visualization of rosbags.
Multi-modal multi-floor SLAM dataset with cameras, IMU, and LiDAR sensors.
Lecturer • Fall 2023
Northeastern University. Complete teaching responsibility for this graduate-level course — designing syllabus, lectures, projects, and assignments.
Teaching Assistant • Spring 2020
Developed materials for the graduate-level course, mentored students on projects, and shared teaching and grading responsibilities.
Field Robotics Laboratory
Mentored multiple master's students on theses and projects in robotics and engineering, including students advised by Dr. Singh and Dr. Rosen.
I'm always interested in collaborations, speaking opportunities, and connecting with fellow researchers.