Karan Bhanot, Ph.D.

About me

I am Karan Bhanot, a Computer Science Ph.D. from Rensselaer Polytechnic Institute (RPI). My Ph.D. Thesis is titled "Synthetic Data Generation and Evaluation for Fairness", completed under the guidance of my advisor, Dr. Kristin P. Bennnett. My research interests include Machine Learning, Deep Learning, Deep Generative Models, Synthetic Data Generation, Trustworthy AI, Fairness in Machine Learning, Ethical AI, Robustness, Generative AI, Natural Language Processing, Large Language Models (LLMs), Health Informatics & Engineering, Medical Data Analysis, Healthcare AI, Predictive Modeling, Generative Adversarial Networks, Transfer Learning, and Data Privacy. In addition to my research experience, I have software development expertise across several Data Science and Full-Stack products.

I have worked with many experts from academia and industry including Dr. Isabelle Guyon (ChaLearn, Google), Dr. John S. Erickson (RPI), Dr. Ioana Baldini (IBM), Dr. Dennis Wei (IBM), Dr. Jiaming Zeng (formerly IBM, now AKASA), Dr. Yooyoung Park (formerly IBM, now Moderna), and Thilanka Munashinghe (RPI).


  • Doctor of Philosophy (Ph.D.) in Computer Science, Rensselaer Polyechnic Institute, 2019-2023
  • Master of Science (M.Sc.) in Computer Science, Rensselaer Polyechnic Institute, 2019-2021
  • Bachelor of Technology (B.Tech.) in Computer Science, Punjab Engineering College, 2014-2018


  • Programming: Python, R, JavaScript (JS), CSS, HTML, React, Java, C++, MySQL, PostgreSQL, MongoDB, REST API, JSON, Linux, Jira.
  • Machine/Deep Learning (ML/DL): Supervised Learning, Unsupervised Learning, Classification, Regression, Clustering, Generative AI, Synthetic Data Generation, Natural Language Processing (NLP), Data visualization, Feature Engineering, Exploratory Data Analysis (EDA), Model Training, Scikit-learn (Sklearn), Numpy, Pandas, Matplotlib, Seaborn, Tensorflow, PyTorch, R Shiny, Leaflet, GeoPandas.
  • CS Knowledge: Algorithms, DBMS (Relational Databases & NoSQL), Data Structures, Object Oriented Programming (OOPs).
  • Interpersonal Skills: Management, Leadership, Collaboration, Communication, Teamwork, Active Learning, Contructive Feedback, Time-management.


  • AI Research Scholar, IBM, 2022-2023
    • Spearheaded the end-to-end development of a Python framework platform with modular, extendable, and maintainable code, improving the process of Machine Learning algorithm fairness evaluation across 81 data scenarios by 60%.
    • Authored a successful two-year grant of $400,000 funding for research and development with IBM, facilitating architecture design, software development, and research dissemination for robust and responsible AI applications.
    • Published 5 first-author papers on Machine Learning and Data at peer review conferences/journals, presenting complex technical ideas to multiple inter-disciplinary audiences (in-person and remote).
    • Collaborated with academic and industry ML engineers, researchers, and experts to identify and integrate open-source libraries, reducing overall development time by 80%.
  • Graduate Research Assistant, Rensselaer Polytechnic Institute, 2019-2021
    • Collaborated and communicated with cross-functional teams in data science, engineering, privacy, and fairness for developing 4 production-ready software applications, managing 40% of the development cycle.
    • Analyzed large-scale private Electronic Healthcare Records (EHRs) data on secure cloud servers for statistical analysis, exploratory data analysis, ML model training, and data visualization of 300,000 records and 100 features.
    • Managed a team of 30 students for the development, deployment and monitoring of the award-winning MortalityMinder visualization application, earning the third-position prize of $15,000.
    • Mentored 50 students on programming language fundamentals, code design, code reviews, and comprehensive documentation, resulting in a 30% decrease in code-related issues across 6 months.
    • Performed data collection, feature engineering, exploratory data analysis, and data aggregation of multiple datasets, facilitating access to real-world data for 120 students for projects and research.
  • AI Fairness Research Extern, IBM, 2021-2021
    • Created two comprehensive datasets after data aggregation of 20 CSV files to evaluate bias of ML models, identifying variability of 20% across multiple experiments.
    • Summarized findings from 30 articles on AI ethics and fairness, informing decision-making processes within the team.
    • Contributed actively to weekly team meetings by presenting research findings, proposing innovative solutions to mitigate bias, and fostering collaboration among team members.
  • Software Engineer, Cvent, 2018-2019
    • Developed and implemented 100 new features for the Appointments product, resulting in a 20% increase in product functionality.
    • Collaborated with 4 cross-functional teams in agile development, ensuring seamless integration of new features into the software.
    • Conducted thorough code reviews, resulting in a 40% decrease in bugs and improved overall code quality.
    • Engaged in 50 community-building sessions and product demos, fostering collaboration across 5 departments.


  • K. Bhanot, I. Baldini, D. Wei, J. Zeng, K. P. Bennett, "Stress-testing Bias Mitigation Algorithms to Understand Fairness Vulnerabilities", AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2023. (Paper)
  • K. Bhanot, I. Baldini, D. Wei, J. Zeng, K. P. Bennett, "Stress-testing Fairness Mitigation Techniques under Distribution Shift using Synthetic Data", Knowledge Discovery in Databases (KDD), 2022. (Paper)
  • K. Bhanot, I. Baldini, D. Wei, J. Zeng, K. P. Bennett, "Downstream Fairness Caveats with Synthetic Healthcare Data", Conference on Health, Inference, and Learning (CHIL), 2022. (Paper)
  • K. Bhanot, I. Baldini, D. Wei, J. Zeng, K. P. Bennett, "Evaluating Fairness of Synthetic Healthcare Data Models", AMIA Annual Symposium, 2022. (Paper)
  • J. S. Franklin, K. Bhanot, M. Ghalwash, K. P. Bennett, J. McCusker, D. L. McGuinness, "An Ontology for Fairness Metrics", AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2022. (Paper)
  • K. Bhanot, J. Pedersen, I. Guyon, K. P. Bennett, "Investigating synthetic medical time-series resemblance", Neurocomputing, 2022. (Paper)
  • K. Bhanot, S. Dash, J. Pedersen, I. Guyon, K. P.Bennett, "Quantifying Resemblance of Synthetic Medical Time-Series", European Symposium on Artificial Neural Networks (ESANN), 2021. (Paper)
  • K. Bhanot, M. Qi, J. S. Erickson, I. Guyon, K. P. Bennett, "The problem of fairness in synthetic healthcare data", Entropy Journal 2021 Special Issue - Representation Learning: Theory, Applications and Ethical Issues, 2021. (Paper)
  • K. Bhanot, J. McConnon, S. Jacobson, L. Ngweta, J. S. Erickson, K. P. Bennett, "Investigating social determinants of premature mortality in the united states", Institute for Data Exploration and Applications (IDEA), 2021. (Paper)
  • T. Munasinghe, A. N. Maheshwarkar, K. Bhanot, "Socioeconomic and Geographic Variations that Impacts the Spread of Malaria", AAAI Fall 2020 Symposium on AI for Social Good, 2020. (Paper)
  • K. P. Bennett, L. Ngweta, K. Bhanot, J. S. Erickson, "MortalityMinder: A Web Tool for Visualizing and Investigating Social Determinants of Premature Mortality in the United States", AMIA Annual Symposium, 2020.
  • K. Bhanot, D. Schroeder, I. Llewellyn, N. Luczak, T. Munasinghe, "Dengue Spread Information System (DSIS)", International Conference on Medical and Health Informatics (ICMHI), 2020. (Paper)
  • L. Ngweta, K. Bhanot, A. Maharaj, I. Bogle, T. Munasinghe, "Identifying the Relationship between Precipitation and Zika Outbreaks in Argentina", IEEE International Conference on Big Data, 2019. (Paper)