Hi, I'm Shlomi.

I’m a third-year CS Ph.D. student at Boston University, being supervised by Prof. Ran Canetti.

I’m interested in Responsible AI, particularly:

1. Societal impact of algorithms and machine learning systems
2. Interpretable machine learning
3. Differentially private synthetic data for government data

I’m also an Associated Researcher at the Alexander von Humboldt Institute for Internet and Society (HIIG) in Berlin. Last summer I interened at Twitter Cortex where I leverged human-in-the-loop research to improve toxicity models. In summer 2019, I did a research internship at the Center for Human-Compatible AI at UC Berkeley, working on neural network interpretability.

For the last years, I teach courses in Responsible AI, Law, Ethics & Society in various institutes including Boston University, Cornell Tech, Tel Aviv University and the Technion. Our materials are available for faculty here.

In my previous life, I was a social entrepreneur - co-founder of the Israeli Cyber Education Center. There I led the development of nationwide educational programs in computing for kids and teens. The center aims to increase the social mobility of underrepresented groups in tech, such as women, minorities, and individuals from the suburbs of Israel. I co-authored a Computer Network textbook in a tutorial approach (in Hebrew). I taught also a new academic course on Problem Solving using Python. Before that, I was an algorithmic research team leader in cybersecurity.

Publications

Shlomi Hod, Karni Chagal-Feferkorn, Niva Elkin-Koren and Avigdor Gal. “Data Science Meets Law: Learning Responsible AI Together”. Communications of the ACM (2022). Featured on the journal cover.

*Gavin Brown, *Shlomi Hod, *Iden Kalemaj. “Performative Prediction in a Stateful World”. International Conference on Artificial Intelligence and Statistics - AISTATS(2022). Preliminary version at NeurIPS Workshop on Consequential Decision Making in Dynamic Environments, with contributed talk (2020).

*Shlomi Hod, *Stephen Casper, *Daniel Filan, Cody Wild, Andrew Critch and Stuart Russell. “Detecting Modularity in Deep Neural Networks”. arXiv preprint arXiv:2110.08058 (2021).

*Daniel Filan, *Stephen Casper, *Shlomi Hod, Cody Wild, Andrew Critch, and Stuart Russell. “Clusterability in Neural Networks”. arXiv preprint arXiv:2103.03386 (2021).

*Daniel Filan, *Shlomi Hod, Cody Wild, Andrew Critch, and Stuart Russell. “Neural Networks are Surprisingly Modular”. arXiv preprint arXiv:2003.04881 (2020).