University of Toronto, MASc.

Department: Materials Science & Engineering (2021-2023)
Thesis: Machine Learning and Data Driven Materials Design
Lab: Computational Materials Lab
Supervisor: Chandra Veer Singh
CGPA: 3.9/4.0

• Group Website

University of Waterloo, BASc.

Degree: Nanotechnology Engineering Honours (2016-2021)
Achievements: Dean's List, 2 years of co-op experience in startup building, quantitative analysis and microfabrication
CGPA: 3.9/4.0

Business Intelligence Data Analyst
GameAddik

• Duration - 07/2023 - 02/2024
• Reporting & automation - Conversion metrics discrepancy investigation, reporting, insights and anomoly detection automation between client and internal databases connected via Looker and REST API
• BI & dashboarding - Developing live data Tableau dashboards for key website conversion metrics. Identifying primary conversion pipelines to guide future business and graphical design decisions
• Data integration & analytics - Enhanced functionality of key client AWS pipelines (Glue/Lambda) by integrating additional metrics and optimizing connectivity for seamless integration with BI software solutions.

Data Scientist Graduate Co-op
IPEX Technologies

• Duration - 04/2022 - 10/2022
• Database unification - Automated extraction and processing of siloed formulation repositories for streamlined data queries and accelerated DOE generation
• Polymer formulation optimization - Discovered new top performing formulations through bayesian optimization. Experimental polymers yielded 80-160% improved mechanical properties
• High-throughput testing guidance - Guided technician test plans through principal component analysis of formulations to identify gaps in formulation combinations below a given synthesis costs

MASc. Machine Learning Thesis Researcher
University of Toronto, Computational Materials Lab

• Duration - 05/2021 - 06/2023
• Graph neural network representation - Developped and published a graph representation algorithm for GNNs that outperform Meta AI algorithms at a 800% reduced computational cost and 25% accuracy improvement on sample databases
• Bayesian modeling and techno-economic optimization - Created a framework that applied synthetic minority over-sampling and gaussian regression to a DFT based Hydrogen electrode dataset generated with high-throughput computations. Random sample optimization yielded a 90% improved overpotential compared to industry benchmark materials at a 15% reduced synthesis cost
• Empirically derived mechanistic research - Published a MLP NN decomposition and reconstruction technique to reduce model abstraction and analyze KPIs and feature space importance on datapoint prediction.This method helped design a best in field CO₂ conversion catalyst

Process Engineer Co-op
MIT, Grossman Group

• Duration - 1/2020 - 10/2020
• Business development & product design - Scaled the fabrication process of a semiconductor membrane technology and applied to incubator program for early stage startup company (Currently SiTration). Pitched funding to investors resulting in admittance to Greentown Labs and $2.35 million in Pre-Seed Funding
• Microfabrication process engineering - Metal assisted chemical etching for first ever steric filtration membranes via largest aspect ratio (200:1) pore etching in literature
• Failure analysis - MD LAMMPS modeling of novel filtration technology for biofouling mechanics and failure modes

• Group Website
• Startup Website

Product Design - Polymer Engineer Co-op
Apple Inc.

• Duration - 08/2018 - 04/2019
• Project coordination - Spearheaded prototype material development as main coordinator to international collaborators for large scale biomaterial testing and analysis efforts resulting in 25% increase in recycled plastic content of iPhone 12 antenna splits
• Rapid prototyping - Audio product hand carry program manager for emergency road-block problem solving through failure analysis and chemical reformulation of production phase coatings
• Project reporting - High level reporting and direction advice on multiple product lines presenting to senior management, design teams and international vendors

Clean Energy Research Assistant
University of Waterloo, Yu Group.

• Duration - 04/2017 - 08/2017
• Process optimization - Improved green graphene synthesis methods for improved economic and environmental feasibility through gum arabic intercalation and thermal shocking
• Advanced materials characterization - 2D material testing with Raman, SEM, TEM, FTIR, and more
• SOP writing - Lab SOP documentation and verification of ASTM/ISO standards for publication data

• Group Website

NAM28 - Sponsored Speaker (2023)

• Conference: North American Catalysis Society (NAM) bi-annual meeting
• Presentation topic: Network decomposition technique for data driven design of high entropy alloy catalysts for the CO₂RR
• Audience: Commercial sector, academia (professors & graduate students), government agencies (national labs & institutes)

• Conference Website

NRC - AI4D Initiative (2021-2023)

• Aim: AI powered design of high entropy alloy catalysts for the conversion of CO₂ into fuels
• Collaborators: NRC Research Officers, University of Toronto, University of Ottawa, University of Waterloo
• Outcome: 2 publications detailing the creation of a FeCoNiCuMo HEA catalyst with best in field CO₂ conversion to CH₄ kinetics

• Government Page

Thesis Presenter - Ursula Franklin Research Seminar (2022)

• Presentation topic: Graph Neural Network frameworks to leverage public databases
• Audience: UofT professors & engineering graduate students