Welcome to my personal page! I am a 3rd year PhD student at MIT, in the department of Electrical Engineering and Computer Science, working in LIDS under the supervision of Tamara Broderick. My main research interests are machine learning (in particular generative modeling) and Bayesian inference. I am also very interested (and eager to learn more!) in optimal transport, and the way in which this is used in generative modeling, statistics, and probability.
Last summer, I interned as a Machine Learning researcher at Apple, working with Nick Foti in the Health AI team. The main goal of the internship was to develop generative models for solving discrete combinatorial optimization problems relevant to Apple Health AI team.
Before joining MIT, I completed a MSc in Data Science at Bocconi University advised by Igor Prünster, with a thesis focused on random measure approaches to Bayesian nonparametrics. Prior to that, I obtained a BSc in Economics and Computer Science advised by Massimo Marinacci, where I worked on neuroeconomics and statistical models for decision making.
Besides research, I have always been very passionate about having an active role in the communities I am part of. I am the (co-)President of the MIT EECS Graduate Students’ Association, a board member of MITaly, and a mentor at Lead The Future.
Feel free to contact me via email at renb at mit dot edu.
- Bayesian Inference
- Machine Learning
- Statistical optimal transport
-
PhD in Electrical Engineering & Computer Science, 2026 (expected)
Massachusetts Institute of Technology
-
MSc in Data Science & Business Analytics, 2021
Bocconi University
-
BSc in Economics & Computer Science, 2019
Bocconi University
Recent news
-
June 2023: I have been selected as a Student Poster Award winner at the 36th New Englang Statistics Symposium.
-
May 2023: I am excited to share I will be in Seattle for the summer interning at Apple, doing research in the Health AI team!
-
February 2023: I won the Best Presentation Award for the talk on ‘‘Gaussian Processes at the Helm(holtz)’’ at 28th Annual LIDS Student Conference, Optimization and Algorithms Session :)
-
July 2022: I have been selected as a Poster Award winner in the category BayesComp/j-ISBA at ISBA 2022.
-
June 2022: I won the Microsoft Award for the best talk at BAYSM 2022!
Publications
Recent publications
-
Berlinghieri, R.; Trippe, B. L.; Burt, D. R.; Giordano, R.; Srinivasan, K.; Özgökmen, T.; Junfei, X.; & Broderick, T. Gaussian processes at the Helm (holtz): A more fluid model for ocean currents. In International Conference on Machine Learning (ICML 2023).
-
Berlinghieri, R.; Krajbich, I.; Maccheroni, F.; Marinacci, M.; Pirazzini, M. Measuring utility with diffusion models. Science Advances 9 (34), 2023.
-
Jing, B.*, Corso, G.*, Berlinghieri, R., & Jaakkola, T. (2022). Subspace diffusion generative models. In European Conference on Computer Vision (ECCV 2022).
Conferences
Talks
- Gaussian processes at the Helm(holtz):
- AGU Annual Meeting 2023 - physics-informed machine learning oral session. San Francisco, December, 2023. [Invited Talk]
- Approximation Methods in Bayesian Analysis workshop. CIRM Marseille, June 2023.
- New England Statistics Symposium 2023. Boston, June 2023.
- MIT Machine Learning Advances Symposium. Cambridge, May 2023. [Spotlight Talk]
- 28th Annual LIDS Student Conference. Cambridge, February 2023.
- 2022 NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems. New Orleans LA, December 2022.
- 13th International Conference on Bayesian Nonparametrics (BNP13). Puerto Varas, Chile, October 2022.
- Bayesian Young Statisticians Meeting (BAYSM 2022). Montreal, Canada, June 2022.
- Machine Learning for Submesoscale Characterization, Ocean Prediction, and Exploration (ML-SCOPE) meeting. Online, May 2022.
Posters
- Gaussian processes at the Helm(holtz):
- Bayesian Nonparametrics Networking Workshop. Melbourne, December 2023.
- EnviBayes workshop on complex environmental data. Fort Collins, September 2023.
- 40th International Conference on Machine Learning. Honolulu, July 2023.
- Approximation Methods in Bayesian Analysis workshop. CIRM Marseille, June 2023.
- New England Statistics Symposium 2023. Boston, June 2023.
- MIT Machine Learning Advances Symposium. Cambridge, May 2023.
- World Meeting of the International Society for Bayesian Analysis (ISBA 2022). Montreal, July 2022.
- MIT Statistics and Data Science conference (SDSCon). Cambridge, April 2022.
Contact
- renb [at] mit [dot] edu
- 32-D740 - 32 Vassar Street, Cambridge, MA 02139
- Contact me