Chinmay Chinara

About Me

I’m a software engineer and researcher building tools across open-source scientific software, electrophysiology, XR, and computer vision. I currently work as a Programmer Analyst at University of Southern California, where I collaboratively develop and maintain Brainstorm, a globally used MATLAB-based open-source platform for electrophysiology analysis. Previously, I spent several years at USC’s Institute for Creative Technologies building mixed reality, medical simulation, digital avatars, and computer vision applications.

My work sits at the intersection of engineering and applied research. I’ve contributed to open-source SDKs for realistic hand interaction in VR, AR systems for natural face-to-face communication, brain visualization tools, and semi-automated intracranial electrode localization workflows. I enjoy building software that is technically strong, practical to use, and meaningful in research or clinical settings.

Skills

MATLAB, Python, C++, C#, PyTorch, XR, Unity3D, Unreal Engine, Git, Docker, CI/CD, Adobe (Photoshop, Lightroom, Premiere Pro)

Work Experience

• Programmer Analyst II (September 2023 - Present)
  • USC Viterbi School of Engineering, Los Angeles, CA, USA
  • Develop, maintain and document Brainstorm, a globally used MATLAB-based cross-platform standalone open-source tool for electrophysiology analysis.
• Programmer Analyst I & II (Oct 2016 - August 2023)
  • USC Institute of Creative Technologies, Playa Vista, CA, USA
  • MedVR: Built an open-source Unity based SDK called Open Medical Gesture (OpenMG) that has a generic hand model supporting most sensors/VR devices that have hand-tracking for much more realistic interactions with virtual objects. Collaborated with vendors for medical simulation trials. (Hands-on demo at IMSH 2022)
  • CARD: Contributed to building various features into the RIDE framework (integrating Barracuda API, various NLP APIs, OAuth, authoring tool, gamepad-based controls)
  • VGL: Worked on generating multifarious face geometry based on identity and expression with high-fidelity using a non-linear morphable face model trained on a large dataset of high-resolution facial scans captured using USC ICT’s Light Stage.
  • VGL: Built a real-time system by training an unpaired image-to-image translation model using facial capture data from Pupil Labs’ cameras tethered to HoloLens to generate 3D avatars that remove occlusion and enable natural face-to-face AR interaction. (Oral presentation at HCII 2018)
  • MxR: Built multiple mixed reality applications using HoloLens and Oculus headsets.
• In-Vehicle Networking (IVN) R&D Engineer (August 2013 - July 2016)
  • Mahindra Research Valley - Automotive Division, Chennai, India
  • Model-Based Development (MBD) and ECU-level testing and simulation of IVN networking protocols and DSP-based noise detection and error analysis on vehicle-level Controller Area Network (CAN) messages.

Education

Publications

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