About CV Resume

Advanced Computer Scientist

• ARPA-H PARADIGM - Leading the medical object detection and segmentation effort, and working towards a LLM-based task guidance and planning system to help inexperienced healthcare providers in low-resource rural settings.
• DARPA CCU - Developed automated modules for facial analysis, scene understanding and LLM-based dialogue assessment to help improve operator situational awareness and interactional effectiveness in various cross-cultural settings.
• DARPA EDGE - Developed an emotion recognition and feedback pipeline to help design better human machine interfaces (HMI) for better operator situational awareness in off-nominal situations.
• NSF - Served as the co-PI and developed automated student group collaboration assessment and recommendation systems by modeling individual-level and group-level behaviors in online and in-person classroom settings.
• Commercial - Created and implemented proof-of-concepts and functional prototypes that helped Fortune 500 clients explore new product opportunities and drive innovation.

Research Intern

• Developed deep-learning-based algorithms for multimodal behavior analysis and assessment of student group collaboration in classroom and online settings. The deep-learning models were designed with the objective of providing meaningful information to teachers and actionable feedback to students.

Digital Pathology Intern

• Developed deep-learning-based image segmentation pipelines to identify regions of necrosis in multiplex immunofluorescence digital pathology images. Worked closely with the software platform team to integrate the trained deep-learning model on the internal research software platform for enabling Field-Of-View (FOV) and Whole-Slide-Image (WSI) inference.

Computation Intern

• Developed time-series-based deep-learning algorithms to model electrocardiogram (ECG) signals of different heart disease conditions. Proposed and implemented several strategies to handle the data-imbalance problem across the different heart disease categories.

Graduate Intern

• Developed image processing and machine learning pipelines for identifying cancerous tissue regions in CT and MRI images.

Ph.D., Electrical Engineering

• Thesis - Building Invariant, Robust and Stable Machine Learning Systems Using Geometry and Topology
• Coursework - Statistical Machine Learning, Computational Image Understanding & Pattern Analysis, Digital Image & Video Processing, Optimization, Random Signal Theory, Transform Theory, Detection & Estimation Theory

• Research Recognition Award - Center for Vision Technologies Group, SRI

• Facilitators’ Choice Award - 2021 NSF STEM for All Video Showcase

• Travel Award - Harvard CRCS workshop on AI for Social Impact

• Most Innovative Award - Poster Symposium, Roche Diagnostics

• First Position in the 2019 Digital Pathology Hackathon - Roche Diagnostics