Pablo Castells is an Associate Professor at the Computer Science Department of the Universidad Autónoma de Madrid, Spain, where he leads the Information Retrieval group. His research interests are in the fields of recommender systems and information retrieval, dealing with models, theory, algorithms and evaluation. His most recent research focus in these areas includes novelty and diversity, algorithmic and evaluation bias, and interactive recommendation. His research contributions can be found in the main conferences and journals in the field, where he also co-organized workshops and tutorials on the aforementioned topics (e.g. at ACM RecSys, ACM SIGIR, ACM WSDM, and WWW). Pablo served as PC co-chair of RecSys 2016, and has been appointed general co-chair of ECIR 2020 and PC co-chair of SIGIR 2021.
Dietmar Jannach is a full professor of Information Systems at AAU Klagenfurt, Austria. Before joining AAU in 2017, he was a full professor of Computer Science at TU Dortmund, Germany. In his research, he focuses on the application of intelligent system technology to practical problems and the development of methods for building knowledge-intensive software applications. In the last years, Dietmar Jannach worked on various practical aspects of recommender systems. He is the main author of the first textbook on the topic published by Cambridge University Press in 2010 and was the co-founder of a tech startup that created an award-winning product for interactive advisory solutions.
Alexandros Karatzoglou is the Scientific Director at Telefonica Research. His research focuses on Machine Learning. Alexandros received his PhD in Machine Learning from the Vienna University of Technology (TUWIEN). During his PhD he was a frequent visitor to the Statistical Machine Learning group at the ANU/NICTA in Canberra Australia. He has over 50 papers in the field and has won 3 best paper awards at the ACM RecSys and ECMLPKDD conferences. He is also the author of the core machine learning R package kernlab, and enjoys giving lectures on Machine Learning, Recommender Systems and Computational Statistics.
Joseph A. Konstan is Distinguished McKnight University Professor and Distinguished University Teaching Professor in the Department of Computer Science and Engineering, and Associate Dean for Research in the College of Science and Engineering at the University of Minnesota, where he formerly led the GroupLens Center for Social and Human-Centered Computing. His research addresses a variety of human-computer interaction issues, including recommender systems and social computing. He is best known for his work in collaborative filtering recommenders (the GroupLens project won the ACM Software Systems Award and one of its papers was recognized with the Seoul Test of Time Award), and for the creation of the MovieLens recommender system and datasets. Dr. Konstan received his Ph.D. from the University of California, Berkeley in 1993. He is a Fellow of the ACM, IEEE, and AAAS and a member of the CHI Academy. He chaired the first ACM Conference on Recommender Systems in 2007, and has served on its steering committee since its inception.
Denis Parra is Assistant Professor at the Department of Computer Science at PUC Chile, he obtained his PhD at the University of Pittsburgh, USA. Prof. Parra conducts research in Recommendation Systems as well as in applications at the crossroads of HCI and AI. His work has focused on the role of visualization and interactivity in recommender systems upon user’s perception of transparency, trust and controllability. He has published his research in conferences like ACM IUI, ACM RecSys, ECIR, and UMAP, as well as in journals such as ACM TiiS, IJHCS, ESWA, PloS One and UMUAI. Currently, Prof. Parra works on applications of explainable AI (XAI) in Recommendation Systems, as well as in Medicine and in Fake News. He is a researcher at the Millenium Institute on Data Fundamentals, a large Chilean initiative to study the impact of Data across disciplines such as Computer Science, Statistics, Political Science and Journalism. He is also member of the AI Lab at PUC and leader of the SocVis research group.
Dr. Tao Ye is a Director of Science and Principal Scientist at Pandora. She is a founding member of the Pandora science team, and has been working on personalized recommendation systems, measurements, and user modeling since 2010. Most recently, she has been leading the Personalization, Search and Voice science team that advances the machine learning and data driven innovations in many personalized music recommendation and search functionalities in Pandora. In the larger RecSys research community, she co-founded and co-chaired the Large Scale Recommender Systems workshop for 5 years (2013-2017), and gave many invited talks at conferences and RecSys workshops. She has two decades of experience in the software industry, holding research scientist and lead engineer positions in social media, networking and mobile systems. She holds 14 granted patents and has published 12 peer reviewed papers. She received her PhD from University of Melbourne in Electrical and Electronic Engineering, her MS from UC Berkeley in EECS and dual BS degrees from Stony Brook University in CS and Engineering Chemistry.