I will join the Department of Statistics and Data Science (SDS) at the University of Texas at Austin as an assistant professor in Summer 2021.

I am a Data Science Fellow with the Michigan Institute for Data Science (MIDAS) at the University of Michigan. Before that, I was McWilliams postdoctoral research fellow at the McWilliams Center for Cosmology in Carnegie Mellon University. I received my Ph.D. in Scientific Computing and Physics from the University of Michigan. My research contributes to the fields of astroinformatics and urban informatics; and is focused on understanding and mitigating the unexpected and not-well understood consequences of AI models, including algorithmic bias and uncertainty quantification, in real-world settings. My Ph.D. training was inastroinfomatics with focus on developing computational and algorithmic solutions for learning and inference in uncertain settings. In the past four years, I simultaneously pursued urban informatics as a new research direction. In this direction, I design and deploy AI solutions that enable data-informed decision making and resource allocation in government. My research actively contributes to both fields and have led to a number of novel research initiatives.

I am currently leading two research teams. Danai Koutra and I co-founded the Michigan-Data Informed Cities for Everyone (M-DICE) in early 2020. M-DICE is a research team that partners with city governments to design and deploy end-to-end AI- and decision-support systems. I am also in the leadership team of Baryon Pasters (BP). BP is an international research team that designs and deploys computational and algorithmic solutions to problems in modern survey astronomy. Besides science, I am interested in implementing novel educational models to engage with students. With my colleagues at the University of Michigan, I built the first data science “Service learning” program. Last but not least, I am an active member of the data science for social good communitiy, a growing community of data scientists from all disciplines who employs AI solutions to meaningfully change society by enhancing equitable access to resources.

I was a Schmidt Science Fellow finalist, recipient of the best student paper award in KDD’18, an awardee of the Michigan Institute for Computational Discovery and Engineering (MICDE) fellowship, and recipient of >$400k grant funding. I am an active member of several international projects and collaborations, including the Dark Energy Survey(DES), the COsmostatistics INitiative (COIN), and XMM-XXL Consortium, among others. I am also a Statistics Without Borders (SWB) volunteer.

World Economic Forum Report: I contributed to a report from the World Economic Forum featuring a data science project co-funded by MIDAS, U-M Transportation Research Institute (UMTRI), U-M College of Engineering and the Knight Foundation. The project is part of a larger Seamless Integrated Mobility effort that aims to transform mobility systems in Detroit, Ann Arbor and Windsor, MI. The project is one example of how data science can make a significant impact on policy making.

Undergraduate/graduate students: I am continually looking for dedicated students at the University of Michigan (you) who are interested in taking part in data science with social impact or astronomy projects. These projects involve a balance of theoretical, methodological, and data analysis work. Experience in theoretical physics, astronomy, statistics, or computer science is a plus but not a requirement. If you are looking for a project, feel free to email me.

General Research Interest

My general research interest goes beyond what I am currently focused on. Here is a non-exhaustive list of my research interests:

Main Research

  • Urban informatics – AI-support systems for decision-making and resource allocation tasks
  • Astroinformatics – Data-driven and machine learning solutions for problems in astronomy
  • Computation – Computational and approximate methods in statistical inference
  • Methodology – Fair and trustworthy AI systems in uncertain settings
    • Algorithmic fairness, identifying and mitigating data and algorithmic bias
    • Uncertainty quantification in machine learning models

Data Science for Social Good

  • data-driven public policy and decision making,
  • service-learning,
  • education, and public outreach.


  • topological and statistical properties of random fields,
  • network theory and its application,
  • non-perturbative methods.