Organizers

Mihajlo Grbovic, Ph.D. is a Machine Learning Scientist at Airbnb. He holds a PhD in Machine Learning from Temple University in Philadelphia. He has more than 15 years of technical experience in applied Machine Learning, acting as a Science Lead in a portfolio of projects at Yahoo and Airbnb. During his time at Yahoo, from 2012 to 2016, he worked on integrating Machine Learning in various Yahoo Products, such as Yahoo Mail, Search, Tumblr & Ads. Some of his biggest accomplishments include building Machine Learning-powered Ad Targeting for Tumblr, being one of the key developers of Email Classification for Yahoo Mail and introducing the next generation of query-ad matching algorithms to Yahoo Search Ads. Dr. Grbovic joined Airbnb in 2016 as a Machine Learning Scientist, specializing in Machine Learning. He works mostly on Search & Recommendation problems for Airbnb Homes and Experiences. Some of his key accomplishments include building the first Airbnb Search Autocomplete algorithm, building out Machine Learning-powered Search for Airbnb Experiences, building algorithms that power Airbnb Categories that are currently showcased on Airbnb Homepage. Currently, he is working on building an AI Travel Concierge at Airbnb. Dr. Grbovic published more than 60 peer-reviewed publications at top Machine Learning and Web Science Conferences, and co-authored more than 10 patents (h-index: 25; citations: 3073; i10-index: 37). He was awarded the Best Paper Award at KDD 2018 Conference. His work was featured in Wall Street Journal, Scientific American, MIT Technology Review, Popular Science and Market Watch.

Vladan Radosavljevic is a Machine Learning Chapter Lead at Spotify. Currently, Vladan is leading a team that builds foundational Generative AI models at Spotify. Prior to Spotify, Vladan was a Head of Data Science at OLX Group where his team built solutions for two-sided marketplace platforms. Before OLX, he was a Senior Scientist at Uber ATG working on systems for autonomous driving. Prior to Uber, he was a Research Scientist at Yahoo Labs where he worked on computational advertising problems. Vladan received his PhD from Temple University in Philadelphia in 2011. His work was featured in Market Watch, VentureBeat, IEEE Innovation at Work, and other news outlets across the world. Vladan previously co-organized a series of successful and well attended workshops such as AdKDD Workshops at KDD from 2017-2023, Podrecs Workshop at RecSys 2020, and ML4SM Workshop at TheWebConf 2023. 

Minmin Chen is a senior staff research scientist from Google Deepmind. She received her Ph.D. in computer science from Washington University in St. Louis. She leads a team working on RL, exploration and LLMs for recommender systems. Her passion lies in innovating and realizing RL and other ML techniques to improve long term user experience/journey on recommendation platforms. She leads both fundamental and applied research, delivered ~50 publications and ~100 landings within different Google recommendation products since 2017.

Katerina Iliakopoulou Zanos is a senior machine learning engineer at Meta. She is working on the machine learning models that power content recommendations on the Facebook home feed and Facebook Reels, with an emphasis on cold start problems. Previously, she was a Staff Software Engineer at The New York Times, where she helped build their personalization platform as well as scaling the newsroom messaging platform so that it could send 50 million emails in under 1 minute. She holds a dual Master's degree in Computer Science and Journalism from Columbia University and a D.Eng in Electrical and Computer Engineering from Aristotle University of Thessaloniki. She is passionate about building intelligent machines for the media and exploring how technology can reshape information consumption. 

Thanasis Noulas is a seasoned VP of Engineering at Bitvavo, Europe's premier crypto exchange, overseeing both Pricing and Retail trading divisions. With a wealth of experience, he serves as a trusted advisor to European startups on pricing strategies and marketplace optimization. Thanasis has contributed to a diverse set of marketplaces across retail investing, content, travel, and transportation sectors. He has held key roles at companies such as Booking.com, Uber, Airbnb, Netflix, and Trade Republic. Passionate about cutting-edge research, Thanasis focuses on areas like causal inference, pricing dynamics, and ranking algorithms, driving innovation in the digital marketplace landscape.

Amit Goyal is a Senior Applied Scientist at Amazon Music where he focuses on customer acquisition, engagement, and retention by collaborating across product, marketing, and industry teams. He specializes in causal inference based on observational data, personalization, content valuation,  and long-term value business metrics with applications in streaming media. Amit has past experience as co-organizer of workshop on Machine Learning for Streaming Media at The Web Conference 2023 and workshop on Query Understanding for Search on All Devices at WSDM 2016. He has published more than 20 papers in premium conferences, such as NAACL, EMNLP, EACL, CVPR, ACL, Neurips, WWW, SIGIR, AAAI. He also served on program committees for Neurips, ACL, EMNLP, NAACL, ICLR, ICML, KDD,  WWW, WSDM, COLING.

Fabrizio Silvestri (h-index: 45; citations: 6,655; i10-index: 104) is a Full Professor at the Department of Computer, Control and Management Engineering at Sapienza University of Rome. His research interests focus on Artificial Intelligence, particularly machine learning applied to web search problems and natural language processing. He has authored more than 150 papers in international journals and conference proceedings and holds nine industrial patents. Silvestri has been recognized with a ``test-of-time'' award at the ECIR 2018 conference for an article published in 2007. He also received three best paper awards and other international recognitions. Silvestri spent eight years in industrial research laboratories, including Yahoo! and Facebook. At Facebook AI, he directed research groups to develop artificial intelligence techniques to combat malicious actors who use the Facebook platform for malicious purposes, such as hate speech, misinformation, and terrorism. Recently, Silvestri has also worked as a consultant for Spotify Research. Silvestri has experience in organizing numerous workshops and conferences, and he will be one of the General Chairs of ECIR 2025 in Lucca and one of the Program Committee Chairs of CIKM 2026 in Rome. Silvestri holds a Ph.D. in computer science from the University of Pisa, with a thesis on “High-Performance Issues in Web Search Engines: Algorithms and Techniques''.

Tony Qin is Co-founder and Chief Scientist of foreva.ai, an early-stage startup specializing in voice AI agentic systems for business operations. He is also a Board Member of the INFORMS San Francisco Bay Area Chapter. Previously, he was Principal Scientist at Lyft Rideshare Labs and Director of the Decision Intelligence group at DiDi AI Labs, spearheaded the development of reinforcement learning (RL) for rideshare marketplace optimization. Tony received his Ph.D. in Operations Research from Columbia University. He is Associate Editor of the ACM Journal on Autonomous Transportation Systems. He has served as Area Chair/Senior PC of KDD, AAAI, and ECML-PKDD, and a referee of top journals.  He is an INFORMS Senior Member, an Outstanding Area Chair of ECML-PKDD 2024, a Franz Edelman Award Finalist and Laureate, and received the INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice. Tony previously co-organized the KDD Workshop on Decision Intelligence and Analytics for Online Marketplaces in 2022 and 2023, as well as other workshops on RL and transportation at AAAI, ICML, and IJCAI.

Rui Song is a senior principal scientist at Amazon. She got her PhD in Statistics from University of Wisconsin in 2006 and has been a faculty member at North Carolina State University since 2012. Her research interests include generative AI, reinforcement learning and causal inference. Her research has been supported as principal investigator by National Science Foundation (NSF) including the NSF Faculty Early Career Development (CAREER) Award. She has served as an associate editor for several statistical journals. She is an elected Fellow of the American Statistical Association and Institute of Mathematical Statistics.

Dr. Hongtu Zhu is a Professor of Biostatistics, Statistics, Radiology, Computer Science, and Genetics at the University of North Carolina at Chapel Hill. He previously served as a DiDi Fellow and Chief Scientist of Statistics at DiDi Chuxing (2018–2020) and held the Endowed Bao-Shan Jing Professorship in Diagnostic Imaging at MD Anderson Cancer Center (2016–2018). Dr. Zhu is a Fellow of IEEE, ASA, and IMS and currently serves as the Coordinating Editor of JASA and the Editor of JASA Applications and Case Studies. He has received several prestigious awards, including the Established Investigator Award from the Cancer Prevention Research Institute of Texas (2016) and the INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice (2019). Dr. Zhu has authored over 350 publications in top journals such as Nature, Science, Cell, and Nature Genetics as well as all major statistical journals, and more than 55 conference papers at leading machine learning and AI conferences, including NeurIPS, KDD, AAAI, ICML, and ICLR.