I am currently a second year MASc student studying at the University of Toronto, supverised by Prof. Scott Sanner. My research direction is on Conversational Recommender Systems (CRSs). This is focused on building and evaluating CRSs using machine learning tools such as Bayesian approach, deep learning, and language models
I am working at Loblaw Digital as a Data Scientist intern. Before that, I interned at the Vector Institute as an applied machine learning intern, also interned as a data engineer intern at RBC , and I worked as a Java Developer at Intact Financial Corporation during my professional experience yar.
Prior to that, I completed my BASc. in Industrial Engineering at the University of Toronto, where I got chance to work with Prof. Birsen Donmez as a research assistant and completed my capstone project with Prof. Scott Sanner in the final year, which won the first-place capstone design award.
MASc | Machine Learning Researcher
our paper "Towards Understanding and Mitigating Unintended Biases in Language Model-driven Conversational Recommendation" got accepted by Information Processing & Management Conference 2022.
I am joining Loblaw Digital as a full-time Machine Learning Engineer, working for the design and implementation of recommender systems used by Loblaw.
I successfully completed my MASc Thesis Defense as of August 9th, 2022.
our paper "Mitigating the Filter Bubble while Maintaining Relevance: Targeted Diversification with VAE-based Recommender Systems" got accepted by The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR2022), which has 20% acceptance rate.
our paper "Distributional Contrastive Embedding for Clarification-based Conversational Critiquing" got accepted by TheWebConf2022 (WWW2022), which has 17.7% acceptance rate.
I am starting my full-time internship at Loblaw Digital as a Data Scientist, working for the design and implementation of recommender systems used by Loblaw.
I am starting my part-time internship at the Vector Institute as an applied AI engineer, working as a part of Project Pensive. My work mainly focuses on conversational recommender systems and enabling user agency for the systems.
Our paper "Bayesian Critiquing with Keyphrase Activation Vectors for VAE-based Recommender Systems" got accepted by SIGIR2021, which has a 27.6% acceptance rate.
I am now pursuing a MASc degree in Industrial Engineering at the University of Toronto under the supervision of Prof. Scott Sanner. My main research area is Modeling Uncertainty in Conversational Recommender Systems.
I am working now working at RBC as a Data Engineer intern. I have also won the RBC tomorrow challenge award during my internship.
I am now graduated from the University of Toronto with a BASc degree and Honor (GPA 3.81).
Our capstone project, won the first place at the 2019-2020 MIE Capstone Design Showcase.
I am now a full-time co-op student at Intact, working as a Software Developer / Test Automation Engineer.