- Upcoming talks:
- Information Theory and Applications (ITA) Workshop, San Diego, CA
- Maryland State Bar Association 2019 Mid-Year Meeting, Towson, MD
- Legal Hackers Baltimore meeting, University of Baltimore School of Law, Baltimore, MD
- RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan
- I gave a public talk on fairness in machine learning at the Maryland AI Meetup, on Nov 14, 2018, at Emerging Technology Centers (ETC), Baltimore. Here are the slides!
- I was panelist/presenter at a Federal Trade Commission hearing, Competition and Consumer Protection in the 21st Century, "Perspectives on Ethics and Common Principles in Algorithms, Artificial Intelligence, and Predictive Analytics" panel, on Nov 13, 2018, at Howard University.
- I was panelist/presenter at the, AAAI Fall Symposium on AI for Government and Public Sector Applications, "Towards Mission-Based Research Roadmap to Address Unwanted Bias" panel, on October 19, 2018, Arlington, VA.
- I gave a talk on fairness at the Networking and Information Technology Research and Development (NITRD) Program's Interagency Working Group on Privacy R & D, at the National Coordination Office, Washington D.C., on September 7, 2018.
I am an assistant professor in the Department of Information Systems at UMBC. Previously, I was a postdoctoral scholar at the University of California, San Diego under the Data Science Postdoctoral Fellowship program, co-sponsored by ITA, Calit2, the Qualcomm Institute, CSE and ECE. Before that, I was a postdoctoral scholar in Lise Getoor's LINQS research group at UCSC, and I graduated from Padhraic Smyth's DataLab group at UCI. I go by Jimmy personally, and James professionally.
For more information, see my:
- Publications, including links to code and slides,
- CV (or email for a more up to date version),
- PhD thesis, which has a lot of tutorial-style introductory material on probabilistic latent variable modeling that you may find useful,
- Github page.
Research InterestsMy research interests are broadly in the area of socially conscious machine learning and artificial intelligence. My work aims to improve AI’s role in society regarding fairness and privacy, and to promote the practice of computational social science, using probabilistic models and Bayesian inference.
Below is an overview of my research, as of 2018.
- IS 733 Data Mining (Spring 2019, UMBC) [Course webpage]
- IS 698/800 Special Topics in IS: Probabilistic Machine Learning (Fall 2018, UMBC) [Course webpage]
- IS 733 Data Mining (Spring 2018, UMBC)
- IS 733 Data Mining (Fall 2017, UMBC) [Student evaluations]
- CSE291D Latent Variable Models (Spring 2017, UCSD) [Course webpage] [Student evaluations]
- CSE291D Latent Variable Models (Spring 2016, UCSD) [Course webpage] [Student evaluations]
- Generative Models for Social Media Analytics: Networks, Text, and Time. ICWSM 2018 tutorial, with Kevin Xu [Slides] [Github page]
- Generative Models for Social Network Data. SBP-BRiMS 2016 tutorial, with Kevin Xu [Slides]
- See publications for links to software associated with my papers.
- At the behest of the University of Waikato Department of Mathematics, I created Tuatara Turing Machine Simulator , a graphical Turing machine simulator and construction tool for teaching purposes.
- I also worked on the GUI for the Boundary Visualizer, a classification visualization tool in WEKA , a popular java open source data mining toolkit developed at the University of Waikato.
- I was a member of the Japanese taiko drumming groups Watsonville Taiko and Waitaiko , and the Korean drumming group Hansori at UC Irvine.
- I played guitar in the rock group 4 Second Fuse.
- In 2007-2008, I was part of the problem reviewing/writing team for the ACM South Pacific Programming Contest.
- For a number of years, I was involved with the executive committee of the Waikato ACM Student Chapter .
- My Erdös number is two (James R. Foulds - Leslie R. Foulds - Paul Erdös).
- New arXiv preprint on fairness in machine learning, with Shimei Pan!
- Kevin Xu and I presented a tutorial on generative models for social media analytics, at ICWSM 2018, Stanford, June 25, 2018. Check out the slides, and the Github page, with python code for demos!
- I gave a talk, with Shimei Pan, at NIST's Information Technology Laboratory, on June 4, 2018, about fairness in machine learning and artificial intelligence systems.
- Three extended abstracts by my students accepted at the Mid-Atlantic Student Colloquium on Speech, Language and Learning (MASC-SLL 2018). Congrats Taif Ghiwaa, Rashidul Islam, and Kamrun Keya (who got a talk slot)!
- Java code for the mixed membership word embeddings training algorithms uploaded to GitHub. Check it out!
- Fast code for SCVB0 in the Julia language uploaded to GitHub (finally!)
- A new paper accepted at AISTATS 2018: Mixed Membership Word Embeddings for Computational Social Science! See the arXiv preprint.
- ICWSM 2018 tutorial accepted, Generative Models for Social Media Analytics: Networks, Text, and Time, with Kevin Xu! To be presented at Stanford on June 25.
- I am teaching IS-733 Data Mining in Fall 2017, and again in Spring 2018.
- I have accepted a tenure-track position at the University of Maryland, Baltimore County (UMBC), Information Systems Department, beginning Fall 2017!
- Our paper, Dense Distributions from Sparse Samples: Improved Gibbs Sampling Parameter Estimators for LDA, was accepted at JMLR! See the ArXiv preprint.
- New preprint uploaded to the arXiv: Mixed Membership Word Embeddings for Computational Social Science.
- I am teaching CSE291D Latent Variable Models again in Spring 2017. See last year's course webpage.
- Our paper on differentially private EM was accepted at AISTATS!
- I gave a talk at the UCSD AI seminar (Winter 2017).
- I am a co-chair for the 2017 Information Theory and Applications Workshop.
- Winner of the SoCal Machine Learning Symposium 2016 runner-up prize for best presentation!
- SoCal Machine Learning Symposium extended abstract on mixed membership word embeddings accepted for oral presentation!
- New NIPS workshop paper on privacy-preserving topic modeling.
- I co-presented a tutorial on generative models for social network data at SBP-BRiMS 2016 with Kevin Xu. [Slides]
- New preprint on practical privacy for EM on arXiv.
- Our paper on privacy-preserving Bayesian inference was accepted to UAI.
- In Spring 2016, I taught an advanced graduate-level course in probabilistic machine learning, CSE291D Latent Variable Models.
- I was a workshop co-chair for the 2016 Information Theory and Applications Workshop.
- I accepted a postdoctoral scholar position at UCSD, affiliated with Calit2, ITA, and CSE.
- I gave 5 oral presentations at 2015 summer conferences: ICML, KDD, RecSys and ACL (x2).