Students

I have had the opportunity to work with many wonderful students on problems in probability, random graphs, network science, statistics, and machine learning. My advising has included PhD students, master’s students, and broader dissertation mentoring across the department.

Current PhD Students

Current doctoral students and ongoing supervision.

Akshay Sakanaveeti

2023– · Co-advised with Amarjit Budhiraja

Former PhD Students

Former PhD students, with dissertation topics and subsequent positions where available.

Xuan Wang

2009–2014 · Co-advised with Amarjit Budhiraja

Dissertation: Some Asymptotic Problems for Dynamic Random Graphs

First job after graduate school: postdoctoral fellow in probability at Georgia Tech. Later Data Scientist at Databricks.

James Wilson

2010–2015 · Co-advised with Andrew Nobel

Dissertation: Statistical Analysis of Relational Data: Mining and Modeling Complex Networks

Assistant Professor, University of San Francisco.

John Palowitch

2012–2017 · Co-advised with Andrew Nobel

Dissertation: Testing-based Community Detection Methods for Complex Networks.

Data Scientist, Internet Archive.

Jimmy Jin

2012–2017 · Co-advised with Andrew Nobel

Dissertation: Testing-based Community Detection Methods for Complex Networks.

Staff Data Scientist, LaunchDarkly.

Suman Chakraborty

2013–2018 · Co-advised with Andrew Nobel

Dissertation: Dense Graph Limits and Applications

Postdoctoral Scholar, University of Michigan.

Iain Carmichael

2015–2019 · Co-advised with Steve Marron

Dissertation: Probabilistic and Geometric Approaches to the Analysis of Non-standard Data.

NSF Postdoctoral Fellow, University of Washington. Currently a faculty in the School of Data Science and Society at UNC Chapel Hill.

Ruituo Fan

2017–2020 · Co-advised with Andrew Nobel and Nico Fraiman

Dissertation: Learning Latent Community Structures in Network-based Data

Quantitative Researcher, Inno Asset Management.

Miheer Dewaskar

2017–2021 · Co-advised with Amarjit Budhiraja

Dissertation: High-dimensional Problems in Statistics and Probability: Correlation Mining and Distributed Load Balancing.

Postdoctoral Scholar, Duke University. Currently faculty at the University of New Mexico.

Mark He

2017–2021 · Co-advised with Andrew Nobel

Dissertation: Community Detection in Multimodal Networks.

Postdoctoral Scholar, Columbia University. Currently senior applied statistician at NYU Medical Center.

Aman Barot

2017–2022 · Co-advised with Andrew Nobel

Dissertation: Techniques in Network Embedding and Gaussian Comparison for High-dimensional Statistics

Manager, American Credit Acceptance.

Dhruv Patel

2019–2023 · Co-advised with Vladas Pipiras

Dissertation: Community Dynamic Factor Models: Correlation Networks, Clustering Guarantees and Vector Autoregressions.

Data Scientist, IDA.

Hui Shen

2019–2023 · Co-advised with Yufeng Liu

Dissertation: Consistency of Some Statistical Learning Techniques: Unsupervised Learning and Network Change Point.

Postdoctoral Scholar with Eric Kolaczyk, McGill University.

Sumit Kumar Kar

2019–2025 · Co-advised with Nilay Argon and Serhan Ziya

Dissertation: Three Essays on Influence Maximization in Networks.

Principal Biostatistician, Novartis (India).

Master’s Students

  • Joe Zappa (2015) — Convex Geometry and Concentration Inequalities
  • Jessica Pearlman (2014) — The Influence of Technical, Social and Financial Factors on Water Pump Sustainability in Mozambique
  • Feng Bill Shi (2012) — Diffusion through Information Networks
  • Yueming Xing (2012) — Statistical Analysis of Alumni Donations at UNC
  • Luxi Zheng (2010) — Measures of Importance of Nodes in Social Networks

Broader Mentoring

In addition to formal primary supervision, I have served on PhD dissertation committees across a few other departments.