Professional & Research Experience
Machine Learning Engineer — YouTube Shorts
- Generative Sequential User Modeling (TokenMinds): Co-authored and deployed an LLM-adapted encoder-decoder user model (370M MoE encoder / 370M dense decoder in JAX) unifying short-form, long-form, and search histories via hierarchical RQ-VAE Semantic IDs; serves billions of users asynchronously (1.44M read QPS, 96.4% cache hit rate), cutting upstream training and serving compute by 50% and 31% while driving statistically significant live A/B gains on YouTube Shorts (+0.13% Satisfied Engagement, +0.11% Engaged Users, +0.33% Freshness Engagement).
- Long-Context Compression & Representation Stability: Extended sequential user modeling to long interaction horizons using soft-token compression, sparse cross-attention bottlenecks, and multi-resolution recency compression. Established longitudinal representation-stability diagnostics (cosine drift, perturbation invariance, split-history self-retrieval, and effective-rank monitoring) and non-autoregressive auxiliary reconstruction objectives to prevent representation collapse under continuous daily training.
- Causal Point-Process Objectives, Variance Reduction & Cold-Start Estimation: Designed multi-task sequence objectives for noisy implicit-feedback streams, incorporating right-censored survival/hazard modeling, count-process supervision, deterministic importance/loss weighting, and C++ streaming input filters with strict request-time causality checks against lookahead label leakage; developed empirical-Bayes shrinkage representations for cold-start candidate scoring.
- Optimization Symmetries & Low-Precision Training Numerics: Investigated orthogonal gauge symmetries and outlier-mitigation techniques for low-precision (FP8/INT4) quantization-aware training under matrix-sign and adaptive optimizers; profiled end-to-end XLA/TPU HBM, VMEM, and operator-fusion rooflines on large-scale multi-task ranking architectures.
- Scaling Laws & Experimentation Infrastructure: Conducted Iso-FLOPS capacity, batch-size scaling, Maximal Update Parameterization (μP), and low-compute proxy transfer studies to predict full-scale ranking performance; led multi-launch model integration cycles and built automated training-lifecycle governance and paired statistical evaluation tooling for continuous TPU training pipelines.
Graduate Researcher (Adviser: Prof. Daniel M. Roy)
- Loss-Landscape Geometry & Non-Local Model Merging: Established simultaneous linear mode connectivity modulo permutation symmetries across neural network populations (ECML-PKDD 2024; CPAL Spotlight 2024); identified deep-layer variance collapse in non-local multi-task model merging across distinct foundation models and open-sourced task-specific statistical repair (TACT).
- Sparse Optimization Dynamics: Analyzed normalization-induced gradient skew in sparse training from scratch (SparseOpt, ICML 2026) and weight-symmetry permutation alignment for random-initialization lottery-ticket masks (ICML 2025).
- Nonasymptotic Monte Carlo & Heavy-Tailed Random Graphs: Derived compositional nonasymptotic error bounds for nested MCMC in probabilistic programs and consistent bootstrap tail-index estimators for sparse exchangeable power-law graphs (Electronic Journal of Statistics, 2021).
Amazon.com
Applied Scientist Intern — Sponsored Display Ads (Manager: Ben Allison)
- Counterfactual Evaluation & Contextual Bandits: Developed counterfactual off-policy evaluation estimators for ad-selection policies to quantify conversion lift while reducing live A/B exploration cost; evaluated Upper Confidence Bound (UCB) contextual bandit algorithms in production A/B experiments measuring click-conversion rate.
Education
Ph.D. in Computer Science
University of Toronto
- Adviser: Prof. Daniel M. Roy
- Thesis: Exploiting linear connectivity modulo permutations in neural networks for parsimonious machine learning
M.Sc. in Computer Science
University of Toronto
- Adviser: Prof. Daniel M. Roy
- Thesis: Sparse exchangeable bipartite graphs
B.A.Sc. with Honors in Electrical Engineering
University of Toronto
- Minor in Robotics and Mechatronics
Technical Skills
- Languages & Systems: Python, C++, C, SQL, Bash; multi-slice TPU/GPU distributed training, custom C++ streaming data & sparse coordinate kernels, XLA/Pallas profiling, HBM/VMEM roofline analysis.
- ML & Optimization: JAX/Flax, PyTorch, TensorFlow; LLM/Transformer sequence modeling, non-Euclidean/matrix-sign & sparse optimization, low-precision (FP8/INT4) training numerics, model merging, μP scaling.
- Statistical & Quantitative Methods: Survival & point-process hazard modeling, counterfactual and off-policy evaluation, contextual bandits, empirical-Bayes shrinkage, heavy-tailed asymptotics, nonasymptotic concentration, MCMC, low-SNR paired/bootstrap inference.
Teaching Experience
CSC2515H: Introduction to Machine Learning
· Teaching Assistant
Fall 2022, Fall 2021, Fall 2019
CSC263H1: Data Structures and Analysis
· Teaching Assistant
Fall 2018
CSC240H1: Enriched Introduction to the Theory of Computation
· Teaching Assistant
Winter 2018
CSC236H1: Introduction to the Theory of Computation
· Teaching Assistant
Winter 2017, Fall 2016
Honors & Academic Service
Awards & Fellowships
- Vector Research Grant (2018–2025)
- Department of Computer Science 50th Anniversary Graduate Scholarship (2017)
- University of Toronto Excellence Award (2013, 2015)
- Dean's Honors List (2011–2016)
Peer Review & Service
- Reviewer: NeurIPS (2021–2023, 2025–2026)
- Reviewer: ICML (2023–2025)
- Reviewer: ICLR (2020–2023)