Karthik Bhaskar

Karthik Bhaskar

Machine Learning Researcher | Data Scientist | Software Engineer

University of Toronto

Vector Institute


I am a Senior Data Scientist 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 !).


  • Deep Learning
  • Privacy Preserved Trustworthy Machine Learning
  • Deep Reinforcement Learning
  • Natural Language Processing
  • Recommendation Systems


  • M.A.Sc in Electrical and Computer Engineering/Machine Learning, 2020

    University of Toronto

  • B.Eng in Electronics and Communication Engineering, 2016

    Anna University



Data Scientist


Nov 2020 – Apr 2022 Toronto
  • Built a Semantic Search Engine using Transformers to get relevant data for downstream ML tasks like building Dashboard.
  • Deployed the Search Engine using Streamlit, Docker, Docker Compose and other MLOps tools.
  • Built Transformer based Topic Modelling on Deficiency data and used the results in downstream Classification Model to predict the category of deficiencies.
  • Built a DistilGPT-2 based Transformer neural network to generate synthetic financial data for downstream ML models.

Machine Learning Researcher

Vector Institute

Aug 2020 – Jan 2021 Toronto
  • WangLab is affiliated with Vector Institute and University Health Network - Advised by Bo Wang
  • Worked on a project at the intersection of Computational Biology, Deep Learning and Natural Language Processing.
  • Applied Self Supervised Learning, Weak Supervision and Data Programming on MIMIC III database.
  • Built Transformer based architecture model to improve the accuracy of massively multi-task classification problem.
  • Our Team BeatCovid designed and built a DL model to predict the COVID-19 cases across worldwide in XPrize’s Pandemic Response Challenge and secured 16th place out of 250 teams participated in this challenge worldwide.

Machine Learning Engineer


May 2020 – Aug 2020 Toronto
  • Builta text classifier using Word Embedding, LSTM, and Transformers.
  • Created a Diversity and Inclusion score that reflects employee experience within organizations.
  • Designed a Deep Learning based Recommendation System using the diversity and inclusion score.

Machine Learning and Data Science Intern

TD Canada Trust

May 2019 – Aug 2019 Toronto
  • Worked with cybersecurity professional and built a defense mechanism using deep learning and unsupervised learning techniques to prevent cyberattacks.
  • Built Unsupervised Auto Tagging algorithm and Automatic Rule Synthesis for Octavius.
  • Investigated an Automatic Rule synthesis algorithm for Octavius using Deep Reinforcement Learning to improve overall defense mechanism.

Graduate Machine Learning Researcher

University of Toronto

Aug 2018 – Jan 2020 Toronto
  • 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.


Machine Learning and Data Science Intern

TD Canada Trust

May 2018 – Aug 2018 Toronto
  • Worked with cybersecurity professionals and built machine learning-based classifiers to prevent cyberattacks.
  • Devised a Deep Learning model that detects and prevents the cyberattack before it happens (ProjectX).
  • Used NLP, Neural Networks, Knowlege Graphs for Keyword Extraction, etc.

Software Engineer


May 2016 – Jul 2017 Chennai
  • Project deals with retail side applications supported by Apple open to outside as well as Internal to Apple.
  • Performed root cause analysis and carried out recommended changes.
  • Conducted impact analysis on new changes before moving the code to production systems.
  • Integrated changes in the production environment with minimal impact to existing functionalities.
  • Extensively monitored the system to make sure business users are not impacted.




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