About

I am an R&D specialist with over 15 years of experience in applied AI, optimization and decision science. I advise startup founders and lead engineering teams, turning complex, uncertain problems into solutions people can use. I abide by a strong work ethic and a pragmatic philosophy of using AI tools to work faster without cutting corners on quality. You can read more about how I work in my Values page.

I am currently a freelance Consultant & Strategic Advisor in Toronto, Ontario, Canada, helping startups make better decisions with AI and data by combining decision math (operations research) with machine learning. From April to September 2026, I was Chief Technology Officer & Founding Engineer at Synod IntelliCare in Toronto, a startup building tools that check healthcare AI for bias, where I took a fairness-checking platform from first design to live use on AWS in 5 weeks, built and led the engineering team, and secured research and accelerator partnerships with the Vector Institute and Sheridan College. Before that, I was a Researcher at the University of Ottawa (2024-2025), where I created a decision guide for when AI should act on its own, when a person should review, and when a person should stay in charge, and defined responsibility between people and AI to reduce overreliance on AI in high-stakes work. Earlier, I was a Postdoctoral Appointee and then Research Consultant in the Mathematics and Computer Science division at Argonne National Laboratory (2018-2023), where I built the open-source tools MÆSTRO and Apprentice for tuning slow, noisy simulations to match real experiment data.

In more detail: at Synod IntelliCare (Toronto, ON) I architected and led development of DDFA, a fairness auditing platform for healthcare AI, built on a FastAPI/Python and React/TypeScript stack with OAuth2/PKCE authentication, RBAC, audit logging, and Canadian census-based bias benchmarking, deployed on AWS (EC2, RDS, S3, IAM). I built and directed a 5-person engineering team across backend, infrastructure, and frontend functions, and partnered closely with executive leadership on company strategy, research and accelerator partnerships, and product positioning and go-to-market priorities. As an independent consultant I continue to work on an AI modeling paradigm for efficient asset allocation and portfolio optimization using operations research and machine learning techniques. At the University of Ottawa I designed a context-sensitive Human-AI orchestration framework for assigning optimal collaboration modes (HITL, HOTL, HITLFE, HOOTL) based on decision criticality and latitude, and a Human-AI responsibility allocation methodology using the 4C model (Communication, Coordination, Cooperation, Collaboration) to distribute tasks across interaction layers. At Argonne National Laboratory I was a Research Consultant (2022-2023) and a Postdoctoral Appointee (2018-2021), investigating mathematical and algorithmic techniques for approximating expensive functions and optimization for tuning derivate-free Monte Carlo simulators. I am a member of the Institute for Operations Research and the Management Sciences (INFORMS) and Society for Industrial and Applied Mathematics (SIAM). I received my Ph.D. in computer science from George Mason University in 2018. My doctoral dissertation involved investigating architectures that model and optimize stochastic closed-form arithmetic simulation models of manufacturing processes with work-in-process inventories over multiple intervals. I was a Research Assistant (RA) in GMU from 2013-2018 and a Guest Researcher at National Institute of Standards and Technology from 2015-2018. Before that, I worked at Los Alamos National Laboratory for 4 years. I also have a Masters in Computer Science from Rochester Institute of Technology and a Bachelors in computer engineering from Mumbai University.

Areas of Interest

• Algorithm Engineering
• Machine Learning & Optimization
• Decision Science
• Responsible AI
• Human-AI Orchestration
• Scientific Computing

Skills

• Technical: Machine learning, decision guidance, AI-fluent building, mixed integer linear programming, stochastic optimization, Monte Carlo simulation, surrogate modeling, backtesting and walk-forward testing, fairness auditing, data quality assessment, responsible AI, human-AI orchestration, algorithm engineering
• Languages: Python, MATLAB, HTML, CSS
• Tech stack: FastAPI, React, pandas, NumPy, SciPy, statsmodels, scikit-learn, PyTorch, Pyomo, Gurobi, MLflow, Matplotlib, AWS (EC2, RDS, S3, IAM), Docker, Git, cron, Claude Code
• Leadership: Technology leadership, engineering team leadership, architecture and security direction, company strategy and go-to-market, strategic advisory, founder advisory, partnership building, cross-functional collaboration