Professor Zoltan Szabo

Professor Zoltan Szabo

Professor of Data Science

Department of Statistics

Room No
COL 5.14
Connect with me

Languages
English, Hungarian
Key Expertise
Kernel Methods, Information Theory, Scalable Computation

About me

Zoltan Szabo's research interest is statistical machine learning with focus on kernel methods, information theory (ITE), scalable computation, and their applications in safety-critical learning, style transfer, shape-constrained prediction, hypothesis testing, distribution regression, dictionary learning, structured sparsity, independent subspace analysis and its extensions, Bayesian inference, finance, economics, analysis of climate data, criminal data analysis, collaborative filtering, emotion recognition, face tracking, remote sensing, natural language processing, and gene analysis.

Zoltan enjoys helping and interacting with the machine learning (ML) and statistics community at various levels. He serves/served as (i) an Area Chair of the most prestigious ML conferences including ICML, NeurIPS, COLT, AISTATS, UAI, IJCAI, ICLR, (ii) the moderator of statistical machine learning (stat.ML) on arXiv, (iii) the Program Chair of the Data Science Summer School, (iv) an editorial board member of JMLR and associate editor of the journal Mathematical Foundations of Computing, (v) a reviewer of various journals (such as Annals of Statistics, Journal of the American Statistical Association, Journal of Multivariate Analysis, Statistics and Computing, Electronic Journal of Statistics, Annals of Applied Probability, IEEE Transactions on Information Theory, Information and Inference: A Journal of the IMA, Foundations of Data Science, Foundations of Computational Mathematics, or Machine Learning), (vi) a reviewer of European (ERC), Israeli (ISF) and Swiss (SNSF) grant applications, (vii) a mentor of newcomers (NeurIPS, ICML).

For further details, please see his website.

My research