Machine Learning Engineer
About
I am a Machine Learning Engineer with 4+ years of experience building AI systems across foundation models, generative AI, agentic AI, evaluation, and machine learning infrastructure.
My work sits at the intersection of research and engineering, with a focus on making state-of-the-art AI methods accessible, reproducible, and deployable. I've contributed to foundation models, evaluation frameworks, open-source ML tooling, and deployed AI applications, while collaborating with researchers, engineers, and domain experts across a variety of projects.
Currently, I work at the Vector Institute, where I contribute to production AI applications, agentic AI evaluation, foundation models, synthetic data generation, privacy-preserving machine learning, and other applied AI initiatives.
Areas of Expertise
Selected Work
Production AI
Technical Lead for a production LLM-powered thematic analysis and reporting platform deployed within a major mental health organization. Designed the Azure-based AI infrastructure and analytics workflows that enabled automated reporting, thematic analysis of helpline interactions, and Tableau-integrated service quality monitoring.
Agentic AI
Contributed to research on explainability, transparency, and governance in agentic AI systems. Explored how autonomous AI agents can be evaluated, monitored, and understood through benchmarking, evaluation frameworks, and human-centered oversight approaches.
Foundation Models
Co-author and contributor to EHRMamba, a foundation model for electronic health records. Contributed to data preprocessing, training infrastructure, experimentation workflows, and evaluation pipelines for large-scale clinical sequence modeling.
PublicationOpen Source
Contributor to CyclOps, an open-source toolkit for healthcare machine learning research and deployment. Developed data pipelines, evaluation workflows, and applied ML use cases supporting model development, monitoring, clinical evaluation, and reproducible experimentation.
GitHubBenchmarking
Contributed to the MIDST Challenge at SaTML 2025, a benchmarking initiative focused on evaluating the privacy risks of synthetic tabular data. Supported challenge design, evaluation methodology, participant review, and competition operations, attracting 700+ submissions from 71 participants.
PublicationAudio ML
A multi-instrument music style translation system developed through graduate research in generative AI for music and audio. Combined source separation, style-content disentanglement, and autoregressive audio generation to enable audio-based re-instrumentation.
Generative Audio
Co-founded an AI startup developing a platform for generating sound sample variations for music creators. Led technical strategy, AI system design, MVP development, and customer validation, translating research in generative audio into a user-facing product.
Sound of AI AcceleratorSelected Publications
Expertise & Technologies
Education
York University
Thesis: Music-STAR: Audio-Based Multi-Instrument Music Style Translation.
Amirkabir University of Technology
Graduated ranked first among hardware engineering students.
More About Me
Outside of work, I enjoy traveling, discovering new restaurants, photography, and music. I've always been drawn to both creative and technical pursuits, which is one of the reasons I became interested in audio, music, and generative AI.
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