About

I am currently an independent Consultant & Strategic Advisor in the Greater Toronto Area, 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 a live cloud product in a few 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