I’m a Research Scientist at Google working on graph learning and social networks. In 2017 I received a Ph.D. in Statistics from the Department of Statistics and Operations Research (STOR) at UNC Chapel Hill, with a dissertation focusing on graph ML and computational genomics. My thesis advisors were Andrew Nobel and Shankar Bhamidi. Throughout my degree I was an active member of a working group in the GTEX Project, and part of the Probability Group at UNC.
Publications
- Yoon, Minji; Wu, Yue; Palowitch, John; Perozzi, Bryan; Salakhutdinov, Russ. “Graph generative model for benchmarking graph neural networks” ICLR 2023
- Yoon, Minji; Palowitch, John; Zelle, Dustin; Hu, Ziniu; Salakhutdinov, Russ; Perozzi, Bryan. “Zero-shot Domain Adaptation of Heterogeneous Graphs via Knowledge Transfer Networks” NeurIPS 2022
- Palowitch, John; Tsitsulin, Anton; Mayer, Brandon; Perozzi, Bryan. “GraphWorld: Fake Graphs Bring Real Insights For GNNs” KDD 2022
- Huang, Di; Bartel, Jacob; Palowitch, John. “Recurrent Graph Neural Networks for Rumor Detection in Online Forums” KDD MIS2 ‘21 (arxiv)
- Tsitsulin, Anton; Benedek, Rozemberczki; Palowitch, John; Perozzi, Bryan. “Synthetic Graph Generation to Benchmark Graph Learning” WWW GLB ‘21
- Dewaskar, Miheer; Palowitch, John; He, Mark; Love, Michael; Nobel, Andrew B. “Finding Stable Groups of Cross-Correlated Features in Multi-View data” arxiv:2009.05079
- Tsitsulin, Anton; Palowitch, John; Perozzi, Bryan; Müller, Emmanuel. “Graph Clustering with Graph Neural Networks” Journal of Machine Learning Research, 24(127), 2023
- Palowitch, John; Perozzi, Bryan; “MONET: Debiasing Graph Embeddings via the Metadata-Orthogonal Training Unit” 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
- Palowitch, John; “Computing the statistical significance of optimized communities in networks.” Nature Scientific Reports, 9, 2019
- Palowitch, John; Bhamidi, Shankar; Nobel, Andrew B. “Significance-based community detection in weighted networks.” Journal of Machine Learning Research, 18(188), 2018
- Palowitch, John; Zhou, Yihui; Shabalin, Andrey; Zhou, Yihui; Nobel, Andrew B.; Wright, Fred A. “Estimation of Interpretable eQTL Effect Sizes Using a Log of Linear Model.” Biometrics, 74(2), 2017
- Wilson, James D.; Palowitch, John; Bhamidi, Shankar; Nobel, Andrew B. “Significance Based Extraction in Multilayer Networks with Heterogeneous Community Structure.” Journal of Machine Learning Research, 18(149), 2017
- Aguet, Francois; Wright, Fred A.; Lappalainen, Tulli; Ardlie, Kristin G.; Dermitzakis, Emmanouil T.; Brown, Christopher D.; Montgomery, Stephen D; Palowitch, John; the GTEx Consortium. “Genetic effects on gene expression across human tisues.” Nature, 550, 2017
- Saha, Ashis; …; The GTEx Consortium; Engelhardt, Barbara E.; Battle, Alexis. “Co-expression networks reveal the tissue-specific regulation of transcription and splicing.” Genome Research, 28(7), 2018
- Yang, Fan; Wang, Jiebia; The GTEx Consortium; Pierce, Brandon L.; Chen, Lin S. “Identifying cis-mediators for trans-eQTLs across many human tissues using genomic mediation analysis.” Genome Research, 28(7), 2018
Past Conference Presentations
- ASONAM 2020, December 7-10 2020
- Graph Mining & Learning @ NEURIPS, December 6 2020
- SIAM NS20, July 9-10 2020
- Data Institute SF Annual Conference, March 11-12 2019
- SIAM NS18, July 12-13 2018
- Data Institute SF Annual Conference, October 16-17 2017
- SIAM NS17, July 13-14 2017
- Conference on Statistical Learning and Data Science, June 6-8 2017
- Joint Statistical Meetings, July 30-August 4 2017
Awards
- Winner of 2017 Duke Datathon (sponsored by Citadel, Citadel Securities, and Correlation One)
- SSPA/Monsanto Student Grant, 2015