About Me

I’m a Computer Science Ph.D. student at Princeton University.

I am fascinated by the interplay between machine learning, graphical models, causal inference, and computer vision. Recently I’ve been thinking about

  • How generative machine learning models such as VAEs and GANs can be integrated into causal inference to solve latent confounding.
  • How methods of graphical models can be incorporated into fair machine learning.
  • Large scale causal inference on time series datasets using representation learning algorithms.

Download my resumé.

  • Artificial Intelligence
  • Graphical Models
  • Computer Vision
  • Causal Inference
  • Time Series
  • Ph.D. candidate at Princeton University

  • B.S. in Computer Science with Honors and Specialization in Machine Learning & B.S. in Mathematics with Honors, 2022

    The University of Chicago

  • High School Diploma in Science and Technology, 2018

    Bahcesehir High School for Science and Technology


Booth School of Business - Advisor: Prof. Bryon Aragam
Jun 2021 – Present Chicago
Working on fundamental and applied research in graphical models, causal inference, and generative machine learning models.
Computer Vision Intern
Sep 2021 – Oct 2021 Ankara, Turkey (Remote)
Wrote the image processing software of an Infrared Vein Visualization System.
The University of Chicago - Jopa Lab
Jul 2020 – Present Chicago
  • Implemented an accurate and scalable framework in Python for representing, comparing, and indexing time series.
  • Working on Scalable Causal Inference for Time Series.
The University of Chicago - Advisor: Dr. Minh-Tam Trinh, Prof. Peter May
Jun 2019 – Aug 2019 Chicago
  • Worked on modular forms and analytic number theory for 3 months at the University of Chicago REU program.
  • Authored and published an expository paper on the orthogonal decomposition theorem of modular forms.
Robotics Intern
Jul 2018 – Aug 2018 Ankara, Turkey
Participated in the design of a robotic ultrasound localization arm product for lithotripters.