X23D®AG: our startup is developing the next generation of computer‐ and robot‐assisted surgery technologies that seamlessly integrate into existing clinical workflows, aiming to enhance both accuracy and simplicity in surgeries.

I oversaw and managed research projects focused on applying artificial intelligence to computer‐assisted surgery. My responsibilities included project management, student supervision, experimental study design, securing grants, and leading scientific writing efforts.

Defining, implementing and publishing a range of projects on: anatomical 3D reconstruction using sparse data, deep reinforcement learning for surgery planning, real‐time anatomy tracking using RGB‐D sensors, surgical model processing, brain‐computer interface for surgical use, augmented reality for intraoperative applications.

I have been involved in the instructional teams for these course: BMEG 500/501: Engineers in Scrubs Program (Biomedical Design) MECH 328: Mechanical Engineering Design ProjectMECH 305/306: Data Analysis and Mechanical Engineering LaboratoriesAPSC 366: The Art of the Possible: An Introduction to Engineering for Non-EngineersMECH 226: Technical Communications for Mechanical Engineers

I am developing a novel assessment framework for pedicle screw insertion surgeries. This is an intraopertaive system based on low dose modalities that is capable of measuring the correctness of inserted pedicle screws and visualizing their alignment. Such a system will benefit the patients by reducing the reoperation rates (currently on the order of 8%!) and postoperative complications.

Designed and implemented a new course in the Biomedical Engineering program (BMEG 591M: Technical Workshops for Biomedical Engineers). The course consisted of a variety of hands-on tutorial-style workshops on topics like: Python Programming, Machine Learning, Computer Vision, LaTeX, Introduction to Microprocessors, Printed Circuit Board Design, Laser Scanning and 3D printing.

During my fellowship at the Klinikum der Ludwig-Maximilians-Universität München I worked closely with orthopaedic and trauma surgeons to develop artificial intelligence systems for use in preoperative planning of spinal surgeries.

Throughout my MSc studies, I developed a photogrammetric framework to make advancements to C-arm usage in orthopaedic procedures. There were three major project that I worked on through this period:1. A fast, accurate and easy to use IM-nail pose estimation system: to intraoperatively reconstruct the position of the inserted intramedullary nail, based on C-arm X-rays.2. A C-arm base tracking system based on monocular odometry: to localize the position of the C-arm base, using commercially available camera assemblies that are attached to the C-arm structure.3. A C-arm base tracking system based on off-the-shelf computer mouse sensors: to localize the position of the C-arm base, using standard computer mouse sensors and by applying dead-reckoning algorithms.

C++ for Engineers (Fall 2012)

I was involved in a team working on making improvements on the widely use fluoroscopic c-arms. My project was particularly focused on design and validation of a C-arm base tracking system based on off-the-shelf computer mouse sensors. Optical flow and sensor calibration algorithms were applied to achieve this goal.

Point cloud registration with off-the-shelf stereo cameras

I was working as a technician at surveying division of urban design project