Mark Liberman is a Trustee Professor at the University of Pennsylvania , holding appointments in the Department of Linguistics and Department of Computer and Information Science . He serves as Director of the Linguistic Data Consortium and Faculty Director of Ware College House . His career spans linguistics, speech technology, and computational methods. Education: Harvard University (1965-1969), MIT (M.S. 1972, Ph.D. 1975) Professional Experience: AT&T Bell Laboratories (1975-1990), University of Pennsylvania (1990-present) His research interests include: Corpus-based Phonetics : Analyzing speech patterns via large-scale datasets. Clinical Applications : Developing speech biomarkers for neurodegenerative diseases. Tonal Phonology : Studying lexical tone and intonation in languages like Yoruba and Mandarin. Formal Annotation Models : Creating frameworks for linguistic data standardization. Recent publications highlight automated speech analysis, cross-linguistic prosody, and digital biomarkers for conditions like ALS-FTD and Alzheimer’s. His collaborations span computational linguistics , neurology , and cognitive science . Scientific awards include the IEEE James L. Flanagan Award (2017), Antonio Zampolli Prize (2010), and fellowships from the AAAS and Linguistic Society of America . He advises PhD students May Chan and Jonathan Him Nok Lee and contributes to editorial boards for journals like Cognition and Annual Review of Linguistics . His work bridges speech science , language technology , and neurocognitive research .
Sunita Sarawagi is a Professor at Computer Science and Engineering , IIT Bombay, and a member of AI Labs@CSE . She is also associated with the Center for Machine Intelligence and Data Science (CMInDS), which she founded in 2020. Education: PhD in Computer Science from UC Berkeley (Thesis: Query Processing in Tertiary Memory Databases), BTech in Computer Science from IIT Kharagpur Research Interests span machine learning , data analytics , graphical models , and structured learning , with applications in text segmentation, sequence modeling, domain adaptation, and human-in-the-loop systems. Her publications reveal a strong focus on integrating data mining with database systems , temporal data analysis , and information extraction using probabilistic methods. Professional Activities include serving on the IEEE John Von Neumann Medal committee (2017-), VLDB 2011 Research Track Co-chair , and multiple program committee roles at top conferences like ICML, KDD, and SIGMOD. Labs & Teams : Leads the SS Lab , a research group focused on probabilistic graphical models, sequence modeling, and data integration techniques.
Rui Zhang is an Associate Professor in the Department of Computer and Information Sciences at the University of Delaware. He holds a PhD in Electrical Engineering from Arizona State University (2013) and has held academic positions at the University of Hawaii and industry roles at UTStarcom. His research focuses on cybersecurity, wireless networks, and privacy-preserving technologies, with notable work on secure edge computing and mobile authentication systems. Education: PhD, Electrical Engineering, Arizona State University, 2013 M.E., Communication and Information Systems, Huazhong University of Science & Technology, 2005 B.E., Communication Engineering, Huazhong University of Science & Technology, 2001 Research Interests: Rui’s work spans secure wireless systems, mobile crowdsourcing, accessibility technologies for visually impaired users, edge computing, and social network privacy. He has led NSF-funded projects on trustworthy hierarchical edge computing and secure mobile cloud sensing. Grants & Awards: Recipient of the NSF CAREER Award (2017) and multiple NSF grants totaling over $1M. Current focus includes NSF-funded research on edge computing security and privacy-preserving data aggregation. Teaching: Has taught courses in algorithms, network security, and computer networks at both University of Delaware and University of Hawaii. Labs & Teams: Leads research projects in secure edge computing and collaborates with Arizona State University on federally funded initiatives. Active in organizing academic conferences like ISC 2016 and serving on editorial boards of IEEE journals.
Nina Balcan is a Professor at Carnegie Mellon University and holds the Cadence Design Systems Professorship in Computer Science. She is affiliated with the School of Computer Science, specifically the Machine Learning Department (MLD) and Computer Science Department (CSD). Her research spans foundational aspects of machine learning, artificial intelligence, theoretical computer science, algorithmic game theory, and interdisciplinary connections in learning theory. Machine Learning Artificial Intelligence Theoretical Computer Science Algorithmic Game Theory Multi-Agent Systems Data-Driven Algorithm Design Her recent work focuses on advancing algorithm design through machine learning, robustness in adversarial environments, and economic modeling. Key contributions include Learning to Branch (JACM 2024), Regret Minimization in Stackelberg Games (NeurIPS 2024), and Learning Accurate Decision Trees (UAI 2024, Outstanding Student Paper Award). She has pioneered novel approaches to data-driven optimization, semi-supervised learning, and privacy-preserving clustering. Nina has received prestigious accolades including ACM Fellow AAAI Fellow Simons Investigator 2019 ACM Grace Murray Hopper Award Her teaching at CMU includes graduate courses on machine learning, advanced machine learning, and specialized topics like algorithmic game theory.
Andrés Buxó-Lugo serves as an Assistant Professor of Psychology at the University at Buffalo, where he directs the Language Processing and Computation Lab. His research investigates the cognitive mechanisms underlying language production, comprehension, and acquisition with a specialized focus on speech prosody—the rhythm, intonation, and intensity patterns in speech—and their role in human communication. His primary research interests include psycholinguistics, cognitive psychology, speech prosody, language production, language comprehension, language acquisition, and computational linguistics. He examines how listeners integrate diverse linguistic cues during speech processing, how individuals learn unfamiliar constructions like non-native pronunciations or novel prosodic patterns, and the cognitive basis of durational changes in speech. His work also explores how communicative context shapes prosodic production and how higher-level linguistic information aids prosodic structure parsing. Analysis of his 15 most recent publications (2019-2025) reveals consistent interdisciplinary work bridging cognitive science, linguistics, and computational modeling. Key trends include phonological representation studies, speech planning mechanisms, intonation adaptation across talkers, lexical representation structures, and the integration of input expectations in syntactic parsing. His research demonstrates significant methodological diversity, incorporating experimental paradigms, computational modeling, and acoustic analysis to unravel language processing complexities. As director of the Language Processing and Computation Lab at the University at Buffalo, Buxó-Lugo leads research initiatives focused on developing computational models of language processing while investigating the cognitive foundations of speech and prosody through empirical experimentation and theoretical innovation.
Flavio Vasconcellos Comim is a Full Professor at IQS School of Management in Barcelona, Spain, holding the Chair of Ethics and Christian Thought (intercenter). His research focuses on the intersections of Sustainability, Economics, and Ethics. Fields of Interest : Human Development, Capability Approach, Social Exclusion, Regional Development, AI Ethics, Poverty Stigma (Aporophobia), Sustainable Development, Social Welfare, Justice, and Happiness. Recent research includes publications on youth HDI in Spain (2025), AI bias against the poor (2024), and critiques of Sen's social choice theory (2024). He leads projects like Big data applied to the study of aporophobia and Population Medicine and Sustainable Development collaborations with China. His academic output spans journals like Regional Studies , AI and Society , and Cambridge Journal of Regions, Economy and Society , with keywords spanning Economics, Machine Learning, Human Development, and Social Justice. No formal students or awards are listed in available records.
Martin T. Wells is the Charles A. Alexander Professor of Statistical Sciences at Cornell University, with joint appointments in the Department of Statistical Science, Department of Biological Statistics and Computational Biology, Department of Social Statistics, and as Professor of Clinical Epidemiology and Health Services Research at Weill Medical School. He serves as Editor-in-Chief of the ASA-SIAM Book Series and Co-Editor of the Journal of Empirical Legal Studies. Cornell University, Ithaca, NY Weill Cornell Medical College Research Interests span applied and theoretical statistics, Bayesian methods, biostatistics, clinical epidemiology, and computational biology. His work bridges disciplines like finance, legal studies, and health services research. Article Trends highlight advancements in Bayesian modeling, quantum cognition machine learning, tensor analysis, and misclassification correction, with applications in genomics, finance, and public health. Fellow of the American Statistical Association Fellow of the Royal Statistical Society Contributions include developing statistical software (e.g., rTensor), methodological innovations in clinical trials, and empirical legal studies on civil rights and the death penalty.
Ron Steinfeld is an Associate Professor in the Department of Software Systems & Cybersecurity at Monash University, Australia. He holds editorial roles in Designs Codes and Cryptography (since 2017) and has served on technical committees for top-tier conferences like ASIACRYPT, CRYPTO, and EUROCRYPT. His research focuses on quantum-safe cryptography, lattice-based cryptography, and blockchain security, with over 80 refereed publications and AUD$4M+ in research funding. Education: BSc Mathematics and Physics (Monash University, 1998) BE (Hons., First Class) Electrical and Computer Systems (Monash University, 2000) PhD Computer Science (Monash University, 2003) Research Interests: Design and analysis of cryptographic algorithms, quantum-safe protocols, lattice-based security foundations, and blockchain applications. His work underpins NIST standard algorithms like Kyber and Dilithium through structured lattice problem research. Recent Articles Trends: Focus on privacy-preserving blockchain protocols, post-quantum signature schemes, encrypted data search, and cryptographic applications in adversarial AI. Recent work includes fair Bitcoin watchtower schemes and genomic database privacy solutions. Awards: ASIACRYPT 2015 Best Paper Award Advising & Grants: Supervises PhD projects on quantum-safe cryptography and blockchain security. Leads initiatives like the AUD$4M ARC-funded Quantum Information Technology project. Collaborates with industry partners including CSIRO/Data61. Labs/Teams: Key contributor to Monash’s cybersecurity and cryptography research groups, involving interdisciplinary projects on materials discovery ( Æinstein initiative) and post-quantum blockchain security.
Huamin Qu is a Chair Professor in the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST). He serves as the Founding Dean of the Academy of Interdisciplinary Studies (AIS), Founding Head of the Division of Emerging Interdisciplinary Areas (EMIA), and was the Founding Acting Head of Computational Media and Arts (CMA) at HKUST(GZ). Qu directs the VisLab and coordinates the Human-Computer Interaction (HCI) group. He obtained his BS in Mathematics from Xi'an Jiaotong University and MS/PhD in Computer Science from Stony Brook University. Qu's research integrates Data Visualization , Human-Computer Interaction , and Human-Centered AI , with applications in urban informatics, social networks, and explainable AI. His work focuses on developing interactive systems for big data analytics, visual storytelling, and AI-driven decision support. Research extends to multimodal communication, fintech, and augmented reality applications. His publications emphasize visual analytics for complex datasets (mobility, social media, financial), interaction techniques for immersive environments, and AI-enhanced visualization tools. Recent works explore explainable AI interfaces and large-scale data communication frameworks. IEEE Visualization Academy (2020) IEEE VGTC Technical Achievement Award AI 2000 Most Influential Scholar (2019, 2023, 2024) 21 Best Paper/Honorable Mention awards IBM Faculty Award (2009) APICTA Merit Award (2015) Yelp Dataset Grand Prize (2018) Qu has advised 48 PhD graduates (21 now faculty at institutions like UC Davis, University of Minnesota, Texas A&M) and 30 MPhil students. He secured major grants including RGC theme-based projects (digital citizenship, air pollution), UGC AoE (slope safety), and China's 973 Program. As VisLab director, he leads 20+ researchers in visualization/HCI projects adopted by Microsoft, IBM, Huawei, and Tencent.
Phuong H. Nguyen is an Associate Professor at the Department of Electrical Engineering at Eindhoven University of Technology (TU/e). He leads the digital Power & Energy Systems (digi-PES) lab, focusing on smart energy systems, distributed control, and IoT integration for future energy grids. His research addresses energy transition challenges, including microgrid optimization, flexibility markets, and data-driven grid management. Nguyen holds a PhD from TU/e (2010) and has held visiting researcher positions at Clemson University. His work contributes to UN SDG goals related to affordable and clean energy. Education: Bachelor's in Electrical Engineering, Hanoi University of Science and Technology (2002) Master's in Electrical Engineering, Asian Institute of Technology (2004) PhD in Electrical Engineering, TU/e (2010) Key Research Areas: Smart Grids and Cyber-Physical Systems Distributed Energy Resource Management Local Energy Markets and Flexibility Provision IoT and Big Data in Energy Systems Awards & Recognition: Best Paper Award SEST 2020 Projects & Grants: REACT-D: Reactive Power Management (Project Manager) Al-driven Applications to Smarten Power System Operation (Project Lead) UNIversity Campus as a Self-Regulated Network (Co-Manager) Labs & Teams: The digi-PES lab develops cyber-physical tools for energy transition, including microgrid simulations, flexibility markets, and grid resilience strategies.
Daniel M. Kane is a Professor at the University of California, San Diego (UCSD), holding a joint appointment in the Department of Mathematics and the Department of Computer Science and Engineering (CSE). His research spans mathematics and theoretical computer science, with a focus on number theory, combinatorics, complexity theory, and computational statistics. He earned a Ph.D. in Mathematics from Harvard University (2011) and dual BS degrees in Mathematics with Computer Science and Physics from MIT (2007). Prior to UCSD, he was a postdoctoral researcher at Stanford University (2011–2014) on an NSF fellowship. His research interests include robust statistics, machine learning, polynomial threshold functions, and algorithmic methods for high-dimensional data. Notable achievements include co-authoring the book Algorithmic High-Dimensional Robust Statistics (Cambridge University Press, 2023) and receiving the Best Paper Award at the Conference on Computational Complexity (2013), as well as gold medals at the International Mathematical Olympiad (2002 and 2003). Current teaching includes courses such as Math 96 (Putnam Seminar), Math 154 (Graph Theory), CSE 101 (Algorithms), and CSE 203A (Randomized Algorithms). He has consulted for companies like CASPER Labs and AIble, and his work extends to cryptographic protocols, including quantum money schemes based on quaternion algebras. Key contributions include breakthroughs in robust mean estimation, list-decodable learning, and the development of efficient algorithms for statistical problems. His research often bridges foundational theory with practical applications in machine learning and data analysis.
Lam James is a Chair Professor of Control Engineering at the University of Hong Kong (HKU), affiliated with the Faculty of Engineering. He holds a BSc (1st Hons.) in Mechanical Engineering from the University of Manchester, and MPhil/PhD degrees from the University of Cambridge. His academic journey includes roles as a Croucher Fellow, Lecturer at the University of Melbourne, and faculty member at City University of Hong Kong before joining HKU in 1993. Professor Lam serves as Editor-in-Chief for four international journals including IET Control Theory and Applications and Journal of The Franklin Institute . His research focuses on networked control systems, vibration control, control theory, and multi-agent systems. With 600+ peer-reviewed publications, he maintains an H-index of 102 (Web of Science) and 108 (Scopus), recognized as a Highly Cited Researcher in multiple fields. Education: BSc Manchester (Mechanical Engineering), MPhil/PhD Cambridge Editorial Roles: 20+ journals, including leadership roles since 2012 His awards include Foreign Membership of Academia Europaea (2024), National Academy of Artificial Intelligence membership (2025), and two State Natural Science Awards (2015, 2019). He is a Fellow of multiple institutions including IEEE, IMechE, and HKIE.
Kevin C. Zhou is an Assistant Professor in the Department of Biomedical Engineering at the University of Michigan. His research focuses on developing high-performance computational optical imaging systems with unprecedented spatiotemporal throughput, integrating advanced optical instrumentation with machine learning-driven algorithms to analyze big data in biology and medicine. His lab specializes in creating imaging systems capable of capturing high-resolution, high-speed, and high-dimensional datasets. Dr. Zhou holds a Ph.D. in Biomedical Engineering from Duke University (NSF GRFP Fellow) and a B.S. in Biomedical Engineering from Yale University (Barry Goldwater Scholar). Prior to joining U-M, he was a Schmidt Science Fellow and postdoctoral researcher at UC Berkeley. Key research areas include: High-throughput microscopy (gigapixel-scale systems) 3D tomographic imaging Light field and Fourier-based imaging modalities Machine learning for image reconstruction and analysis Biomedical applications in cellular/molecular imaging His recent work has advanced technologies like multi-camera array microscopes (MCAM/MCAS) and Fourier light field mesoscopes, achieving video-rate 3D imaging of freely moving organisms. These innovations enable applications in digital cytopathology, behavioral tracking, and high-content biological studies. Notable awards include the NSF Graduate Research Fellowship and Barry Goldwater Scholarship. His research has been featured in top journals and conferences with a focus on advancing optical imaging hardware and computational pipelines.
Vladimir Spokoiny is a Professor at the Departments of Mathematics and Economics of the Humboldt University of Berlin and Head of the Research Group "Stochastic Algorithms and Nonparametric Statistics" at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) in Berlin, Germany. His research spans multiple areas of statistics, machine learning, and financial mathematics, with significant contributions to nonparametric statistics, high-dimensional data analysis, and statistical methods in finance. Spokoiny received his M.Sc. in applied mathematics from the Moscow Institute of Railway Engineering in 1981 and his Ph.D. in mathematics from Lomonosov Moscow State University in 1988. He completed his Habilitation at Humboldt University in 1996. His academic career includes positions at the All-Union Institute of Railway Transport in Moscow, the Institute for Information Transmission Problems in Moscow, and the Institute for Applied Analysis and Statistics in Berlin before joining the Weierstrass Institute and Humboldt University where he has been a professor since 2002. Spokoiny's research focuses on adaptive nonparametric smoothing and hypothesis testing, high dimensional data analysis, statistical methods in finance, image analysis with applications to medicine, classification, and nonlinear time series. His work often addresses the challenges of nonstationarity in time series data and develops innovative methods for volatility estimation and risk management. He has made significant contributions to the development of adaptive weights smoothing procedures, which have applications in image processing, community detection, and manifold learning. His recent work has expanded into high-dimensional statistics, Bayesian inference, and optimization methods for machine learning, with publications demonstrating novel approaches to Gaussian approximation, Laplace methods, and statistical inference in non-Euclidean spaces. Spokoiny has supervised numerous PhD students including Oliver Reiss, Danilo Mercurio, Ying Chen, Elmar Diederichs, and Mstislav Elagin, whose research has focused on mathematical finance, time series analysis, and statistical methods. He serves as an Associate Editor for The Annals of Statistics (since 2004) and Statistics and Decisions (since 2002), and has previously served on the editorial board of the Journal of Statistical Planning and Inference. His professional activities include reviewing for major statistical journals including Annals of Statistics, Bernoulli, Econometrica, and Journal of American Statistical Association, as well as reviewing grant proposals for the National Science Foundation (USA), German Research Foundation, and Netherlands Organisation for Scientific Research. Spokoiny is a member of several professional societies including the International Statistical Institute, American Statistical Association, Institute of Mathematical Statistics, and Bernoulli Society. He is fluent in Russian (mother tongue), English, and German, and has good knowledge of French. His research group at WIAS focuses on developing novel statistical methodologies with applications across various scientific domains, particularly emphasizing adaptivity and robustness in complex data environments. The group's work has significant implications for financial risk management, medical imaging, and machine learning applications, with recent publications addressing fundamental questions in high-dimensional statistics and nonparametric inference.
Univ.-Prof. Torsten Möller, PhD is a Professor at the University of Vienna and serves as Head of the Research Group Visualization and Data Analysis and Head of the Research Network Data Science. His work spans data visualization, visual analytics, and human-computer interaction, with a focus on biomedical, environmental, and societal data applications. Academic rank: Professor Research group: Visualization and Data Analysis Network: Data Science Email: torsten.moeller@univie.ac.at Research interests include: Visual data analysis for complex systems Interdisciplinary applications in climate science and medicine Human-computer interaction in data exploration Image processing and computer graphics Recent publication trends show expertise in: Visualizing climate change and pandemic data Multi-volumetric and network analysis Algorithmic transparency and user-centered design Interdisciplinary collaborations (e.g., astrophysics, medical imaging) Statistical and uncertainty visualization Design frameworks for visualization recommendation Teaching includes courses in: Computer graphics and visualization Image processing and analysis Human-computer interaction Data analysis projects Doctoral research seminars
