Proposed innovative solutions to US DOTs ‘AI in Transportation’ RFI through a methodology aimed at equity in micro-mobility solutions using randomization in deep neural networks for bias elimination in fleet positioning.Performed traffic analysis using City of Toronto’s signalised intersection traffic volume data to optimise traffic light timings in attempts to cut travel delay times on popular downtown corridors in Toronto.Created an NLP scraping tool to analyse University survey data for curriculum and space-planning optimization.

Leading the quantitative finance team working on projects involving quant trading, machine learning and quant research. Helping upcoming undergraduates build solid fundamentals in quantitative methods of analysis including reinforcement learning based trading algorithms, research paper presentations and quant interview preparation.

Leading a team of aspiring machine learning engineers on projects involving Monte Carlo simulations, LSTM-GAT based stock price predictions, options pricing models using unusual indicators and traditional quantitative finance portfolio optimization algorithms.

Data Scientist and Quantitative Systems Engineer at St. George Capital: UofT's first Investment Engineering Design Team. Driving quantitative research using a variety of machine learning, technical analysis, and time series techniques as well as supporting the project compatibility for enhanced results. Educating members about algorithms and financial models.

Created a CNN-based supervised text detection model to classify traffic signs in Hollywood.Applied OCR and machine vision models to supplement deep learning process for increased text detection performance.

Created a mechanically adjustable (via Arduino controlled linear actuators) silicone biofidelic limb that replicates actual muscle fibre and bone structure placement for stress testing (through cell patch examination).Incorporated 3D printing to analyse distal connection strength to improve user experience and comfort. Explored modifying the design of adjustable trans-tibial residual limb sockets by testing their adaptability to limb volume fluctuations.

Led the creation of multiple vehicle mounting solutions (SolidWorks) for perception sensors inculding radars and LIDARs; and created risk prediction models for an all autonomous future while working on university-designed level 5 autonomous vehicle (Chevy Bolt EUV).Won overall 2nd place and 1st in static challenges as the mechanical lead at GM and SAE's Autodrive Challenge 2023 among 72 teams from across North American universities, the competition was held in June 2023 in Michigan, USA.

Used risk prediction and financial analysis tools to assess the compatibility of numerous autonomous vehicle solutions across regions like highways and cities with Synchro models.

Teaching assistant for Praxis I, an engineering design course enabling students to implement fundamentals concepts of engineering to design and communicate proposed ideas or prototypes. I was responsible for encouraging students to think outside of the box, help with any technical aspects of design as well as providing them feedback on communication strategies after assessments.

Worked under the supervision of Dr. Qin Liu (ISTEP) to improve undergraduate engineering student experience by analyzing student survey data (university resources, educational experience, and skill development) across years.Used tools like Kruskal-Wallis test, Spearman correlation test and Pandas/Scipy to perform predictive analytics and recommend suggestions for improving the academic curriculum, university resources and overall student experience.Presented my findings at Undergraduate Engineering Research Day (UnERD), and to ISTEP faculty/department, Data Science Institute (DSI).

Learned the use of Camunda-BPM to create processing models for sales of insurance services, and implemented them on multiple other service networks.