- Machine learning algorithms
- Sensitivity analysis of machine learning models
- Distributed machine learning
My research is about tradeoff between the amount of computations and the accuracy when using machine learning techniques on data streams with concept drift. I also work on distributed monitoring of machine learning models as well as optimization of deep neural networks.
AutoMon: Automatic Distributed Monitoring for Arbitrary Multivariate Functions. H. Sivan, M. Gabel, and A. Schuster. Proceedings of the 2022 ACM SIGMOD International Conference on Management of Data (SIGMOD), 2022.
GitHub, Paper, Slides, Video (see Supplemental Material), Poster
Incremental Sensitivity Analysis for Kernelized Models. H. Sivan, M. Gabel, and A. Schuster. The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2020.
Paper, Slides, Code
Online linear models for edge computing. H. Sivan, M. Gabel, and A. Schuster. The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2019.
Paper, Slides, Poster
Online Linear Models for Edge Computing. Hadar Sivan, Moshe Gable, Assaf Schuster. In AI week, Tel Aviv University, 2019.
Automatic Distributed Monitoring for Arbitrary Multivariate Functions
Israeli Networking Day 2023 (second place best talk award)
M.Sc. Thesis Seminar
Slides, September 2019
Awards and Scholarships
- Faculty excellence award, spring 2022
- Excelling TA award, Network Security 236350, winter 2020/21
- MLIS student research prize for Cross-PI Collaboration in Data Science in funding of PBC (VATAT) for 2020-2021
- CS Faculty Capture the Flag competition, won the first place in 2021 and the second place in 2020
- Excelling TA award, Network Security 236350, spring 2019
- Hasso-Plattner Institute (HPI) scholarship, August 2020
- Faculty excellence award, spring 2020
- Excellence scholarship dean’s support, winter 2018
- 2011/12 and 2010/11 CS Excellent Students (SAMBA) Award
- Presidents list for excellent students; Deans list for excellent students, 2008-2012