Zoltan Szabo is a Professor of Data Science at the Department of Statistics, LSE. Zoltan's research interest is statistical machine learning with focus on kernel methods, information theory (ITE), scalable computation, and their applications. These applications include 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 in various forms. 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) a Turing Academic Liaison, (iv) a DSI Management Committee Member, (v) the Programme Director of MSc Data Science, (vi) the Programme Chair of the Data Science Summer School, (vii) an editorial board member of JMLR, a senior associate editor of the journal ACM Transactions on Probabilistic Machine Learning, and an associate editor of the journal Mathematical Foundations of Computing, (viii) 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), (ix) a reviewer of European (ERC), Israeli (ISF) and Swiss (SNSF) grant applications, (x) a mentor of newcomers (NeurIPS, ICML).

For further details, please see his website.