Dr. Leman Akoglu is the Heinz College Dean's Associate Professor of Information Systems at Carnegie Mellon University. She holds courtesy appointments in the Computer Science Department (CSD) and Machine Learning Department (MLD) of School of Computer Science (SCS). She has also received her Ph.D. from CSD/SCS of Carnegie Mellon University in 2012.
Dr. Akoglu’s research interests broadly span machine learning and data mining, and specifically graph mining, pattern discovery and anomaly detection, with applications to fraud and event detection in diverse real-world domains. At Heinz, Dr. Akoglu directs the Data Analytics Techniques Algorithms (DATA) Lab.
Dr. Akoglu is a recipient of the SDM/IBM Early Career Data Mining Research award (2020), National Science Foundation CAREER award (2015) and US Army Research Office Young Investigator award (2013). Her early work on graph anomalies has been recognized with the The Most Influential Paper (PAKDD 2020), which was awarded the Best Paper (PAKDD 2010). Dr. Akoglu's other research awards include the Best Research Paper (SIAM SDM 2019), Best Student Machine Learning Paper Runner-up (ECML PKDD 2018), Best Paper Runner-up (SIAM SDM 2016), Best Research Paper (SIAM SDM 2015), Best Paper (ADC 2014), and Best Knowledge Discovery Paper (ECML PKDD 2009). She holds 3 U.S. patents filed by IBM T. J. Watson Research Labs. Her research has been supported by the NSF, US ARO, DARPA, Adobe, Capital One Bank, Facebook, Northrop Grumman, PNC Bank, PwC, and Snap Inc.
University of Florida, USA
Dr. Jiang Bian is the Chief Data Scientist of the University of Florida Health (UF Health) system, and Professor in the Department Health Outcomes and Biomedical Informatics, College of Medicine at the University of Florida (UF). He also serves as the Chief Data Scientist for the OneFlorida+ Clinical Research Consortium, the Associate Director of the Biomedical Informatics program of the UF Clinical and Translational Science Institute (CTSI), and the Director of the Cancer Informatics Shared Resource (CISR) for the UF Health Cancer Center. His expertise and background serve an overarching theme: data science with heterogeneous data, information and knowledge resources. His research can be divided into three logical sections under this overarching theme: (1) data-driven medicine—applications of informatics techniques, including machine learning methods in medicine on solving big data problems; (2) mining the Internet, including the social web, to provide insights into health-related behavior and health outcomes of various populations and finding ways to develop interventions that promote public and consumer health; and (3) development of novel informatics methods, tools and systems to support clinical and clinical research activities such as tools for data integration, clinical trial generalizability assessment, and cohort discovery.
Dr. Rich Caruana is a senior principal researcher at Microsoft Research. Before joining Microsoft, Rich was on the faculty in the Computer Science Department at Cornell University, at UCLA’s Medical School, and at CMU’s Center for Learning and Discovery. Rich’s Ph.D. is from Carnegie Mellon University, where he worked with Tom Mitchell and Herb Simon. His thesis on Multi-Task Learning helped create interest in a new subfield of machine learning called Transfer Learning. Rich received an NSF CAREER Award in 2004 (for Meta Clustering), best paper awards in 2005 (with Alex Niculescu-Mizil), 2007 (with Daria Sorokina), and 2014 (with Todd Kulesza, Saleema Amershi, Danyel Fisher, and Denis Charles), co-chaired KDD in 2007 (with Xindong Wu), and serves as area chair for NIPS, ICML, and KDD. His current research focus is on learning for medical decision making, transparent modeling, deep learning, and computational ecology.
Amazon Alexa AI, USA
Dr. Dilek Hakkani-Tur is a senior principal scientist at Amazon Alexa AI focusing on enabling natural dialogues with machines. Prior to joining Amazon, she was leading the dialogue research group at Google (2016-2018), a principal researcher at Microsoft Research (2010-2016), International Computer Science Institute (ICSI, 2006-2010) and AT&T Labs-Research (2001-2005). She received her BSc degree from Middle East Technical Univ, in 1994, and MSc and PhD degrees from Bilkent Univ., Department of Computer Engineering, in 1996 and 2000, respectively. Her research interests include conversational AI, natural language and speech processing, spoken dialogue systems, and machine learning for language processing. She has over 80 patents that were granted and co-authored more than 300 papers in natural language and speech processing. She received several best paper awards for publications she co-authored on conversational systems, including her earlier work on active learning for dialogue systems, from IEEE Signal Processing Society, ISCA and EURASIP. She served as an associate editor for IEEE Transactions on Audio, Speech and Language Processing (2005-2008), member of the IEEE Speech and Language Technical Committee (2009-2014), area editor for speech and language processing for Elsevier's Digital Signal Processing Journal and IEEE Signal Processing Letters (2011-2013), and served on the ISCA Advisory Council (2015-2019). She is currently the Editor-in-Chief of the IEEE/ACM Transactions on Audio, Speech and Language Processing, an IEEE Distinguished Industry Speaker (2021) and a fellow of the IEEE (2014) and ISCA (2014).
Stanford University, USA
Dr. Monica Lam has been a Professor of Computer Science at Stanford University since 1988, and is the Faculty Director of the Stanford Open Virtual Assistant Laboratory. She leads the Genie open virtual assistant project, which aims to advance and democratize voice assistant technology, keep the voice web open, and protect the privacy of consumers.
Prof. Lam is a member of the National Academy of Engineering and an ACM Fellow. She has won numerous best paper awards, and has published over 150 papers on many topics: natural language processing, machine learning, HCI, compilers, computer architecture, operating systems, and high-performance computing. She is a co-author of the "Dragon Book", the definitive text on compiler technology. She received a B.Sc. from University of British Columbia (1980) and a Ph.D. from Carnegie Mellon University (1987).
Facebook AI, USA
Dr. Wang-Chiew Tan is a research scientist at Meta AI. Prior to joining Meta AI, she was at Megagon Labs where she led the research efforts on building advanced technologies to enhance search by experience. Prior to Megagon Labs, she was a Professor of Computer Science at University of California, Santa Cruz and spent two years at IBM Research - Almaden. Her research interests include data integration and exchange, data provenance, and natural language processing. She is a co-recipient of the 2014 ACM PODS Alberto O. Mendelzon Test-of-Time Award, the 2018 ICDT Test-of-Time Award, and the 2020 Alonzo Church Award. She received the 2019 VLDB Women in Database Research Award and she is a Fellow of the ACM.
Google Research, USA
Dr. Andrew Tomkins joined Google Research in 2009, where he serves as a Director in Research and Machine Intelligence, currently working on machine learning and understanding of geo data. Prior to these projects, he worked on measurement, modelling, and analysis of content, communities, and users on the World Wide Web. Before joining Google, he spent four years at Yahoo! serving as Vice President within the Research organization, and Chief Scientist of Yahoo’s search organization. Before this, he spent eight years at IBM's Almaden Research Center, where he served as co-founder and Chief Scientist on the WebFountain project. Dr. Tomkins has authored over 100 technical papers and 90 issued patents. His technical publications include two best paper awards from the International World Wide Web Conference (2000, 2003), plus one best paper award from the ACM International Conference on Web Search and Data Mining (2015). He has served on the editorial boards of Iee Computer, ACM Transactions on the Web, and Internet Mathematics. He has co-chaired the technical program for the International World Wide Web Conference (2008), the ACM Conference on Knowledge Discovery and Data Mining (2010), and the ACM International Conference on Web Search and Data Mining (2017). He received Bachelors degrees in Mathematics and Computer Science from MIT, and a PhD in CS from Carnegie Mellon University.
Berkeley University, USA
Dr. Bin Yu is Chancellor's Distinguished Professor and Class of 1936 Second Chair in the Departments of statistics and EECS, and Center for Computational Biology at UC Berkeley. She obtained her BS Degree in Mathematics from Peking University, and MS and PhD Degrees in Statistics from UC Berkeley. She was Assistant Professor at UW-Madison, Visiting Assistant Professor at Yale University, Member of Technical Staff at Lucent Bell-Labs, and Miller Research Professor at Berkeley. She was a Visiting Faculty at MIT, ETH, Poincare Institute, Peking University, INRIA-Paris, Fields Institute at University of Toronto, Newton Institute at Cambridge University, and the Flatiron Institute in NYC. She was Chair of the Department of Statistics at UC Berkeley
She has published more than 170 publications in premier venues and these papers not only investigate a wide range of research topics from practice to algorithms and to theory, but also seek deep insights. The breadth and depth of her research experience enabled unique and novel solutions to interdisciplinary data problems in audio and image compression, network tomography, remote sensing, neuroscience, genomics, and precision medicine.
Currently, she is championing collaborative research with experts in the subject knowledge and leading research in statistical machine learning (e.g. boosting, sparse modeling, kernel methods, and spectral clustering and decision trees and deep learning) and causal inference to design algorithms such as iterative random forests (iRF), agglomerative contextual decomposition (ACD) and adaptive wavelet distillation (AWD) for interpreting deep neural networks and X-learner for heterogeneous treatment effect estimation in causal inference.
She is a Member of the U.S. National Academy of Sciences and of the American Academy of Arts and Sciences. She is Past President of the Institute of Mathematical Statistics (IMS), Guggenheim Fellow, Tukey Memorial Lecturer of the Bernoulli Society, Rietz Lecturer of IMS, and a COPSS E. L. Scott Prize winner. She has been selected to deliver the Wald Memorial Lectures of IMS at JSM in 2023.
Previously, she pioneered Vapnik-Chervonenkis (VC) type theory needed for asymptotic analysis of time series and spatio-temporal processes, and made fundamental contributions to information theory and statistics through work on minimum description length (MDL) and entropy estimation. With her students and collaborators, she developed a highly cited spatially adaptive wavelet image denoising method and a low-complexity low-delay perceptually lossless audio coder that was incorporated in Bose wireless speakers, and developed a fast and well-validated Arctic cloud detection algorithm using NASA's MISR data. With the Jack Gallant Lab and her students, she developed predictive models of fMRI brain activity in vision neuroscience that made "mind-reading" possible (or reconstruction of movies using only fMRI signals).
She served on editorial boards including Annals of Statistics, Journal of American Statistical Association, and Journal of Machine Learning Research. Her leadership roles included co-chairing the National Scientific Committee of the Statistical and Applied Mathematical Sciences Institute (SAMSI), and serving on the scientific advisory committee of SAMSI and IPAM, and on the board of trustees of ICERM and the Board of Governors of IEEE-IT Society. She recently served on the scientific advisory committee for the IAS Special Year on optimization, statistics and theoretical machine learning, and the inaugural scientific advisory committees of the UK Turing Institute for Data Science and AI. She is serving on the editorial board of PNAS, the Scientific Advisory Boards of Canadian Statistical Sciences Institute (CANSSI), of the AI Policy Hub at UC Berkeley, and the Scientific Advisory Committee of the Department of Quantitative and Computational Biology at USC.
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