I am a Data Scientist/ML Engineer at CIBC. Previously, I worked as a ML Researcher at WangLab affiliated with Vector Institute and University Health Network, proudly advised by Prof. Bo Wang. I completed my Master’s degree in ECE, specialized in Machine Learning at the University of Toronto advised by Prof. Deepa Kundur and by Prof. Yuri Lawryshyn. In my free time I work on projects at the intersection of Machine Learning, Natural Language Processing and Healthcare. Apart from my that, I am also interested in Deep Learning and Deep Reinforcement Learning with the focus of transfer learning, imitation learning, model-based RL. My ultimate goal is to build robust, privacy-preserved, and interpretable algorithms with human like ability to generalize in real-world environments by using data as its own supervision.
I am a Stream Owner and Discussion Group Lead of the “Machine Learning in Healthcare” stream at AISC (Aggregate Intellect), where we discuss one paper at a time every week from basics to state-of-the-art ML papers in HealthCare. I also host several live sessions with global researchers spanning the broad area of ML in HealthCare.
Throughout my life, I have approached every challenge with enthusiasm, creativity, and a ceaseless desire to achieve success. This passion and drive have paved the way to countless opportunities, unique experiences, and excellent relationships, both personally and professionally. I enjoy working with people and discussing ideas. If you would like to chat, feel free to send me a message on Twitter.
Outside of academics, I enjoy basketball, hiking, biking, running (pretty much every sport !).
M.A.Sc in Electrical and Computer Engineering/Machine Learning, 2020
University of Toronto
B.Eng in Electronics and Communication Engineering, 2016
Responsible for developing and building cutting edge state of the art deep learning based recommendation system.
Built a Deep Learning-based Recommendation System for Wolseley’s e-commerce website from scratch to production.
Dataset is massive involving more than 200,000 unique customers and 500,000 unique SKUs
Achieved a personalized NDCG score of 72.4% and improved theO ne-Product Hit Ratio to 100%.
Deep Learning, Matrix Factorization, Collaborative Filtering, NLP, Bayesian Optimization, etc.
Thoughts on AI
In this paper, several Collaborative Filtering (CF) approaches with latent variable methods were studied using user-item interactions to capture important hidden variations of the sparse customer purchasing behaviours. The latent factors are used to generalize the purchasing pattern of the customers and to provide product recommendations. CF with Neural Collaborative Filtering(NCF) was shown to produce the highest Normalized Discounted Cumulative Gain (NDCG) performance on the real-world proprietary dataset provided by a large parts supply company. Different hyperparameters were tested using Bayesian Optimization (BO) for applicability in the CF framework. External data sources like click-data and metrics like Clickthrough Rate (CTR) were reviewed for potential extensions to the work presented. The work shown in this paper provides techniques the Company can use to provide product recommendations to enhance revenues, attract new customers, and gain advantages over competitors.
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