AI Research Scientist and Engineer

Agam Chopra

AI scientist and engineer with 6+ years of experience developing novel AI algorithms from theory to real-world implementation across generative AI, multimodal learning, computer vision, and medical imaging.

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6+ years AI research and engineering
h-index 5 143 citations as of May 13, 2026
2 awards Competitive AI imaging research grants
BraTS Top-ranked challenge placements, 2022-2024

Experience

Research depth with production engineering range.

Senior AI Consultant

J.S. Chopra & Associates - Remote | Full time, Remote

Jul 2024 - Present
  • Owned AI strategy and delivery across the full lifecycle for audit, fraud review, and data mining automation, from discovery and data cleaning to prototyping, deployment, and KPI monitoring; reduced review time and external expert spend, delivering an average $12,000 quarterly net profit lift.
  • Built production anomaly detection, risk scoring, and decision support systems in Python, PyQt, SQL, PyTorch, and Numba, while mentoring analysts and developers on AI workflow adoption and technical decisions.

Research Scientist, Doctoral Candidate

KurtLab, University of Washington - Seattle, WA | Full time, Hybrid

Sep 2022 - Jul 2026
  • Addressed limited access to expensive clinical biomarkers by designing novel controllable multimodal generative models for MRI to Tau-PET synthesis, including advanced vector quantization and information theory methods that improved quantitative performance metrics, interpretation, and clinician trust for Alzheimer's imaging research and technology adoption.
  • Solved reproducibility and scale bottlenecks in 3D medical AI by building efficient PyTorch/CUDA preprocessing, training, and evaluation libraries; enabled experimentation at scale across Linux and HPC environments.
  • Led model development across tumor synthesis, inpainting, classification, survival prediction, and registration workloads; achieved top placements in BraTS challenges from 2022 to 2024.
  • Collaborated with Microsoft on clinically aligned LLM/VLM evaluation for complex medical datasets and with Amazon Robotics on 3D scene generation and damage estimation pipelines; translated research into applied AI workflows across healthcare and industrial environments.
  • Secured 2 competitive research awards for AI imaging proposals, including an Amazon research grant and a University of Washington internal data science grant.

Graduate Teaching Assistant

University of Washington - Seattle, WA | Full time, Hybrid

Jan 2023 - Jun 2025
  • Closed the gap between theory and practice in computational engineering courses by teaching ME535 and ME230 through lectures, labs, and code support in Python, C, and C++; supported 200+ students.
  • Coached students on algorithm design, optimization, numerical methods, and scalable scientific programming; improved readiness for AI/ML workflows and engineering simulations.

AI Research Associate

Stevens Institute of Technology - Hoboken, NJ | Full time, In person

Jan 2021 - Jan 2022
  • Developed deep learning methods for 4D spatiotemporal registration of accelerated MRI time series, improving temporal consistency and alignment accuracy over classical baselines.
  • Designed custom architectures, preprocessing pipelines, and validation workflows for heterogeneous biomedical imaging data, increasing robustness and supporting publication quality research.

Full Stack Software Engineer

Reindeer Shuttle Inc. - West Lafayette, IN | Full time, Hybrid

May 2018 - Jun 2020
  • Redesigned and optimized the company's Progressive Web Application across desktop and mobile platforms, improving performance, reliability, customer engagement, and operational stability.
  • Built analytics and reporting pipelines for fleet operations, demand forecasting, and business intelligence using Python, SQL, Excel, and Google Analytics, contributing to a 10% increase in revenue.

Education

Biomedical AI, machine learning, and physics training.

Scheduled defense July 2026

Ph.D., Mechanical Engineering

Artificial Intelligence in Biomedical Computer Vision

University of Washington, Seattle, WA

May 2022

M.S., Computer Science and Machine Learning

Stevens Institute of Technology, Hoboken, NJ

May 2020

B.S., Physics

Purdue University, West Lafayette, IN

Publications

Selected work in multimodal synthesis, registration, and medical imaging AI.

  1. QPID-Net+: True Partial Information Decomposition for Generalizable Multimodal Medical Image Synthesis. Chopra, A.; Kurt, M. Validation study ongoing.
  2. Stress-Testing QPID-Net for Synthetic Tau-PET: Calibration, Staging, and High-Uptake Fidelity. Chopra, A.; Neher, C.; Ren, T.; Rivera, J. E. H.; Jahanian, H.; Kurt, M. Manuscript in preparation for MIRASOL.
  3. Interpretable MRI to Tau-PET Synthesis via Quantized Latent Disentanglement. Chopra, A.; Neher, C.; Ren, T.; Rivera, J. E. H.; Jahanian, H.; Kurt, M. Submission ongoing at IEEE TMI. arXiv, 2026.
  4. Here Comes the Explanation: A Shapley Perspective on Multi-contrast Medical Image Segmentation. Ren, T.; Rivera, J. E. H.; Chopra, A.; Oswal, H.; Pan, Y.; Ruzevick, J.; Kurt, M. arXiv, 2025.
  5. 3D Inception-based TransMorph: Pre- and Post-operative Multi-contrast MRI Registration in Brain Tumors. Abderezaei, J.; Pionteck, A.; Chopra, A.; Kurt, M. BrainLes / Springer LNCS, 2024.
  6. Re-DiffiNet: Modeling Discrepancies in Tumor Segmentation Using Diffusion Models. Ren, T.; Sharma, A.; Rivera, J. E. H.; Rebala, H.; Honey, E.; Chopra, A.; Ruzevick, J.; Kurt, M. arXiv, 2024.
  7. An Ensemble Approach for Brain Tumor Segmentation and Synthesis. Heras Rivera, J. E.; Chopra, A.; Ren, T.; Oswal, H.; Pan, Y.; Sordo, Z.; Walters, S. arXiv, 2024.
  8. An Optimization Framework for Processing and Transfer Learning for Brain Tumor Segmentation. Ren, T.; Honey, E.; Rebala, H.; Sharma, A.; Chopra, A.; Kurt, M. MICCAI Challenge / Springer, 2023.
  9. The Brain Tumor Sequence Registration (BraTS-Reg) Challenge. Baheti, B.; Chakrabarty, S.; Akbari, H.; Bilello, M.; Wiestler, B.; Schwarting, J.; Chopra, A.; et al. arXiv, 2021.

Technical Skills

Tools and strengths.

Languages

Python, C, C++, SQL, JavaScript, TypeScript, MATLAB

ML / AI

PyTorch, CUDA, Transformers, LLMs, VLMs, Latent Diffusion, Reinforcement Learning, Computer Vision, Multimodal Learning, Medical Imaging, Self-supervised Learning

Systems

Linux, Git, LaTeX, Docker, Singularity, Azure, HPC, MLOps, Data Pipelines, Experimentation Frameworks

Strengths

AI research, model development, production engineering, open-source tooling, technical mentoring, cross-functional collaboration