I’m a Research Scientist at Google working on natural language, graph learning, and social networks. In general I’m interested in what can be learned from where & when information travels on the web. After an undergrad in math I did a doctorate degree in statistics at UNC Chapel Hill with a dissertation focused on community detection. My advisors were Andrew Nobel and Shankar Bhamidi. For much of my degree I was an active member of a working group in the GTEX Project led by Andrew and Fred Wright.
I made this site in grad school to showcase my main project, CCME. Nowadays there are much better ways to do graph clustering, and I maintain this site mainly as a way to maintain the SEO of my name. The lists of publications and talks below are out-of-date and will probably stay that way. The full list of my work is of course on Google Scholar, and you can find me in Google corp spaces such as my persons page and this blog post.
I also have a second life as a musician. If you’re in the Bay Area and you like jazz, R&B, and hip-hop, come see me play: jpalomusic.com.
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