Title: Generalized Mean and Variance for Sample-Efficient Data Science

Bio: Zoltan Szabo is a Professor of Data Science at the Department of Statistics, London School of Economics. Zoltan's research interest is statistical machine learning with focus on kernel methods, information theoretical estimators, scalable computation, and their applications. 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 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 Management Committee Member of the Data Science Institute, (v) the Programme Director of MSc Data Science, (vi) the Program 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 in machine learning and statistics, (ix) a reviewer of European (ERC), Israeli (ISF) and Swiss (SNSF) grant applications, (x) a mentor of newcomers (NeurIPS, ICML).