Jan G. Voelkel is an Assistant Professor at the Jeb E. Brooks School of Public Policy and the Department of Sociology at Cornell University. His research explores how micro-level preferences for equality and unity translate into macro-level political decisions, focusing on democratic attitudes, partisan dynamics, and moral framing. Ph.D. and M.A. in Sociology, Stanford University M.S. in Social and Behavioral Sciences, Tilburg University B.S. in Social Sciences, University of Cologne Voelkel’s work spans political psychology, metascience, and social policy, with a focus on interventions to reduce anti-democratic attitudes, partisan animosity, and gender bias in political leadership. His recent articles emphasize large-scale collaborations, reproducibility, and cross-partisan empathy. Scientific awards include: New Investigator Award (Behavioral Science & Policy Association) Public Sociology Award (American Sociological Association) Open Science Innovator Award (Stanford) Centennial Teaching Assistant Award (Stanford)
Reihaneh Rabbany is an Assistant Professor at the School of Computer Science, McGill University, and a core faculty member of Mila - Quebec's artificial intelligence institute. She holds the Canada CIFAR AI Chair and is affiliated with the Center for the Study of Democratic Citizenship. Her research focuses on complex data analysis at the intersection of network science, data mining, and machine learning. Research Interests: Network Science Data Mining Graph Representation Learning Unsupervised and Self-supervised Learning Anomaly Detection Social Good Applications Publication Trends show emphasis on temporal graph analysis, community detection, misinformation identification, and interdisciplinary collaborations with political science and criminology experts. Notable Awards Canada CIFAR AI Chair CAIAC 2021 Best Master's Thesis Award (co-supervisor) Advising includes mentoring PhD and MSc students across multiple institutions, with graduated students transitioning to roles at Microsoft Research, Mila, Yale, and Google. Labs & Collaborations: Leads the Complex Data Lab at McGill, collaborates with Mila, and contributes to community evaluation frameworks like CommunityEvaluation and TopLeaders algorithm.
David Yarowsky is a Professor in the Department of Computer Science at Johns Hopkins University. He leads the Low-Resource Languages Lab and is a member of the Center for Language and Speech Processing. Harvard University - Bachelor of Arts in Computer Science (1987) University of Pennsylvania - Master of Science in Engineering (1993) and PhD in Computer and Information Science (1996) Research Interests : Natural Language Processing, particularly focusing on word sense disambiguation, minimally supervised induction algorithms, multilingual NLP, and machine translation for low-resource languages. His work bridges theoretical linguistics with practical applications in information retrieval, spoken language systems, and very large text databases. Article Trends : His publications emphasize cross-lingual transfer learning, universal morphology, and low-resource language technologies. Key themes include morphological analysis, computational etymology, and adversarial speech recognition. Scientific Awards : ACL Fellow (2013-present) Professional Service : Served as Treasurer and Executive Committee Member of the Association for Computational Linguistics, Secretary-Treasurer of SIGDAT, and chair/co-chair of major conferences including EMNLP 2013, IJCNLP 2011, and ACL 2014. Labs & Teams : Director of the Low-Resource Languages Lab at JHU and active member of the Center for Language and Speech Processing.
Professor Hassan Rivaz is a Full Professor and Concordia University Research Chair in Medical Imaging with Deep Learning at Concordia University's Gina Cody School of Engineering and Computer Science. He holds appointments in the Department of Electrical and Computer Engineering and is cross-appointed to the Department of Computer Science & Software Engineering. Dr. Rivaz serves as the Founding Director of the IMPACT Lab and actively supervises PhD students in Electrical and Computer Engineering and Computer Science programs. Dr. Rivaz received his PhD from Johns Hopkins University in 2011, Master's degree from the University of British Columbia, and Bachelor's degree from Sharif University, followed by postdoctoral training at McGill University. His academic journey includes prestigious awards such as the NSERC Post-Doctoral Fellowship and Jeanne Timmins Costello Post-Doctoral Award. His research focuses on advancing medical image analysis through deep learning techniques, particularly in ultrasound imaging applications. Dr. Rivaz has made significant contributions to quantitative ultrasound, cancer detection, lymphedema assessment, and ultrasound elastography. His work bridges theoretical algorithm development with practical clinical applications, addressing challenges in medical image denoising, segmentation, registration, and tissue characterization. The IMPACT Lab under his direction develops innovative solutions for medical imaging problems with direct clinical relevance. Analysis of his recent publications reveals a strong emphasis on deep learning applications for ultrasound image processing, with particular focus on denoising techniques, elastography improvements, and segmentation algorithms. His work consistently addresses the challenge of working with real clinical data rather than simulated environments, contributing to more practical medical imaging solutions. Dr. Rivaz has received numerous prestigious awards including: Concordia University Research Chair in Medical Imaging with Deep Learning (2023–2028) QBIN/RBIQ Rising Star in Bio-Imaging in Quebec (2022) Concordia University Research Chair in Medical Image Analysis (2018-2023) Petro-Canada Young Innovator Award (2016–2018) He actively mentors graduate students, with many recipients of competitive scholarships including NSERC CGS, FRQNT, and FRQS awards. Dr. Rivaz serves on editorial boards for top journals including IEEE Transactions on Medical Imaging (since 2017), Medical Image Analysis (since 2025), and IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control (since 2018). He has organized major conferences including IEEE EMBC 2020, ISBI 2021, and IEEE IUS 2023, and served as Area Chair for MICCAI from 2017 to 2024. As Founding Director of the IMPACT Lab, Dr. Rivaz leads a multidisciplinary research team focused on innovative medical imaging solutions. The lab maintains strong collaborations with hospitals and research institutions to translate imaging technologies into clinical practice. Current projects include developing AI-powered ultrasound analysis tools, quantitative imaging biomarkers for cancer diagnosis, and advanced techniques for ultrasound elastography with applications in tissue characterization and disease detection.
Pierre-Philippe Combes is a CNRS Research Professor and Head of Doctoral Studies in Economics at Sciences Po since 2021. He also serves as Co-Editor of the Journal of Urban Economics and Director of Graduate Programs in Economics at Sciences Po. Previously, he held positions at Aix-Marseille University (2003-2016), Boston University (2001-2003), and CERAS-École des Ponts et Chaussées (1998-2001). He holds a PhD in Economics from CREST (Paris School of Economics). Education PhD in Economics, Paris School of Economics (CREST), 1996 Habilitation à Diriger des Recherches (HDR), Aix-Marseille University, 2004 Research Interests : Urban economics and economic geography, focusing on local labor markets, housing markets, firm and household location choices, and resulting spatial disparities. His work uses machine learning for historical data recovery and examines agglomeration economies, migration externalities, and discrimination in urban contexts. Scientific Output Trends : Recent publications analyze urbanization in Sub-Saharan Africa, agglomeration economies, real income inequality in China, and historical land use through machine learning. Collaborative work spans France, China, and international institutions, addressing housing costs, peer effects in research, and urban poverty metrics. Scientific Awards : NATO Advanced Fellowship Grant (2001-2002) PhD Thesis Award - Association Française de Science Économique (AFSE) PhD Thesis Award - Association de Science Régionale de Langue Française (ASRDLF) Mention très honorable avec félicitations du jury (PhD thesis) Teaching & Editorial Roles : Teaches urban economics at Sciences Po and ENS Lyon. Serves on editorial boards of the Journal of Economic Geography , Regional Science and Urban Economics , and Journal of Urban Economics . Participates in evaluation committees for PhDs and habilitations across Europe. Policy Contributions : Advises on urban poverty measurement for the World Bank and contributes to French regional policy reports, including the Grand Paris Express project and CNIS (National Statistical Council).
Giuseppe Carlo Marano is a Full Professor at the Department of Structural, Building and Geotechnical Engineering at Politecnico di Torino. He is also a component of the SISCON Interdepartmental Center for Infrastructure Safety. With expertise in civil and structural engineering, his work focuses on machine learning applications, seismic risk reduction, and sustainable structural optimization. Education Graduated cum laude in Structural Engineering from Polytechnic University of Bari PhD in Structural Engineering from University of Florence (2000) Research Interests Marano's research spans structural optimization, seismic engineering, and machine learning applications in civil infrastructure. He develops advanced computational models for: Seismic retrofitting of existing structures Optimization of steel and masonry structures Recycled materials in concrete production AI-driven structural health monitoring Multiobjective design methodologies Publication Trends His recent work emphasizes: Machine learning for concrete mix design and damage assessment Optimization of gridshells and arch structures Seismic isolation systems and vibration control Sustainable construction practices with recycled materials Multiobjective genetic algorithms for structural design Scientific Recognitions National Scientific Qualification - First Band (2013, MIUR Italy) Certificate of Appreciation for Outstanding Lecture (2012, China) Academic Contributions As an educator, he teaches: Consolidamento Strutturale (Structural Consolidation) Dinamica delle Vibrazioni Random (Random Vibration Dynamics) Progettazione Generativa (Generative Design) He also leads Challenge@PoliTo initiatives and contributes to national infrastructure safety regulations. Research Projects ADAPT4CE - Adaptive Digital Systems for Circular Economy (2025-2028) AI-ENVISERS - AI for Seismic Retrofit Environmental Impact (2023-2025) ADDOPTML - Additive Manufacturing Optimization (2021-2025)
Daniel A. Spielman is a Sterling Professor of Computer Science, Statistics & Data Science, and Mathematics at Yale University. He serves as the inaugural James A. Attwood Director of the Institute for Foundations of Data Science (FDS) and was previously co-Director of the Yale Institute for Network Science (YINS). He is also a member of TILOS, the NSF Institute for Learning-Enabled Optimization at Scale. His research interests span Spectral Graph Theory, Algebraic Graph Theory, Laplacian Matrices, Expander Graphs, and Random Walks on Graphs. Spielman has made fundamental contributions to understanding the mathematical foundations of data science, including work on smoothed analysis of algorithms, spectral sparsification, and solutions to the Kadison-Singer problem. Spielman's publications demonstrate a consistent focus on developing efficient algorithms with strong theoretical foundations. His work bridges pure mathematics, theoretical computer science, and practical applications in network analysis and machine learning. His research has evolved from foundational work on smoothed analysis to recent contributions in experimental design using discrepancy theory. His scientific honors include: Nevanlinna Prize for contributions to mathematical aspects of computer science Two Gödel Prizes (2008 and 2015) for outstanding papers in theoretical computer science MacArthur Fellowship ('Genius Grant') Simons Investigator award Membership in the National Academy of Sciences and American Academy of Arts and Sciences Spielman has advised numerous PhD students who have gone on to prominent positions in academia and industry. His teaching includes advanced courses in Spectral Graph Theory and Computation and Optimization. He has developed important software packages like Laplacians.jl for solving Laplacian linear equations and related problems.
Professor Michael C.L. CHAU serves as Professor and Deputy Area Head of Innovation and Information Management at The University of Hong Kong's Faculty of Business and Economics. He also holds the position of Associate Director at the HKU HKJC Centre for Suicide Research and Prevention, and serves on the HKU Senate and Court. His academic journey includes a Ph.D. in Management Information Systems from the University of Arizona and a B.Sc. in Computer Science (Information Systems) from the University of Hong Kong. Dr. Chau's research spans business analytics, artificial intelligence, web mining, fintech, smart health, and security informatics. His work focuses on applying data/text/web mining techniques to business, education, and social domains. He leads the Artificial Intelligence Research Group at HKU Business School, which has secured significant funding from the Hong Kong Research Grants Council, Food and Health Bureau, and other agencies for projects including 'The Invisible Hand in Crowdfunding' and 'Factors Moderating the Predictive Power of Social Media Sentiment on Stock Returns'. His publication portfolio includes over 150 articles in premier journals like MIS Quarterly, JMIS, and IEEE Transactions, with recent work exploring large language models, hate speech detection, and disaster-related social media analysis. His research demonstrates strong interdisciplinary connections across computer science, information systems, and business applications. INFORMS ISS Design Science Award (2020) IEEE ITSS Leadership Award in Intelligence and Security Informatics (2020) AIS Sandra Slaughter Service Award (2016) HKU Outstanding Young Researcher Award (2014) Faculty Research Postgraduate Supervision Award (2020 & 2025) Cited over 9,400 times (h-index = 47) Dr. Chau actively mentors doctoral students and has supervised numerous PhD graduates who now hold academic positions at institutions including Fudan University, Xi'an Jiaotong-Liverpool University, and the University of Maryland. His research grants portfolio demonstrates sustained funding success across diverse areas including blockchain, mental health applications, and social media analytics. The Artificial Intelligence Research Group he leads maintains strong industry connections through projects with Hong Kong Red Cross and the Hospital Authority.
Ann B. Lee is a Professor and Co-Director of the PhD Program in Statistics at Carnegie Mellon University , with a joint appointment in the Department of Statistics & Data Science and the Machine Learning Department. Prior to joining CMU, she held positions as a J.W. Gibbs Assistant Professor at Yale University and a visiting research associate at Brown University. PhD in Physics, Brown University MSc/BSc in Engineering Physics, Chalmers University of Technology, Sweden Her research focuses on statistical methodology for complex data in the physical sciences , emphasizing trustworthy inference, uncertainty quantification, and integration of classical statistics with machine learning. Recent work includes likelihood-free inference, calibrated forecasting, and diagnostics for generative models. The STAMPS research group , which she co-founded in 2018, hosts weekly meetings and public webinars. In Fall 2024, STAMPS will transition into a CMU Research Center. Recent publications span likelihood-free inference , climate modeling , and astronomy . Notable collaborations include applications to hurricane intensity guidance , galaxy redshift estimation , and cosmological parameter biases . She mentors PhD students and has advised multiple award-winning researchers, including ASA Best Student Paper Award winners. Her teaching includes advanced courses on probability, regression, and AI for climate sciences.
Dr. Yongjia Song is an Associate Professor in the Department of Industrial Engineering at Clemson University's College of Engineering, Computing and Applied Sciences. His research focuses on optimization under uncertainty, stochastic programming, and network interdiction with applications in disaster logistics, energy systems, and humanitarian operations. BS in Computational Mathematics (2009), Peking University MS in Industrial Engineering (2012), University of Wisconsin-Madison MS in Computer Sciences (2012), University of Wisconsin-Madison PhD in Industrial Engineering (2013), University of Wisconsin-Madison His work addresses complex systems under uncertainty through: Stochastic and robust optimization frameworks Integer programming for discrete decision problems Applications in disaster response and transportation networks Evacuation planning and shelter management Human trafficking disruption modeling Recent publications demonstrate trends in: Multistage stochastic programming for dynamic disaster response Bayesian preference elicitation for complex design problems Network interdiction models for security and trafficking disruption Integration of logistics and evacuation planning under uncertainty Adaptive algorithms for large-scale optimization Professional affiliations include: Institute for Operations Research and the Management Sciences (INFORMS) Mathematical Optimization Society (MOS) Society for Industrial and Applied Mathematics (SIAM) He teaches graduate courses in risk modeling (IE 8090) and actively works on practical implementations of optimization techniques in real-world systems.
Farnoush Banaei-Kashani is an Associate Professor (Tenured) in the Department of Computer Science and Engineering at the University of Colorado Denver. She also holds an Adjunct Associate Professor position in the Department of Mathematical and Statistical Sciences. As the founder and director of the Big Data Management and Mining Lab (BDLab), she leads multiple GAANN Fellowship Programs, including BDSE (Big Data Science and Engineering), DDC (Data-Driven Cybersecurity), and II (Infrastructure Informatics). She directs the 'Data Science in Biomedicine' MS Track and focuses on data-driven decision systems (DDSs), integrating machine learning and big data analytics into healthcare, energy, transportation, and environmental applications. Education: Details not explicitly provided in the text. Her research spans data management cycles for DDSs, addressing challenges like big data volume, velocity, and variety. Key projects include iWatch (crime surveillance), POCM (mobility monitoring), and GeoSIM (urban texture documentation). She teaches courses such as Machine Learning Systems, Big Data Science, and Data Mining. Publications highlight advancements in sea ice classification, federated learning, proteomic networks, and privacy-preserving AI. Her work is funded by NSF, NIH, DOT, and industry partners like Google and IBM. She has advised numerous students and contributes to academic leadership as editor, conference chair (ACM SIGSPATIAL 2018/2019), and program committee member for venues like SIGMOD and KDD.
Mingchen Gao is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo, SUNY. He serves as Program Director for the Engineering Sciences (Artificial Intelligence) MS Program and is affiliated with the Institute for Artificial Intelligence and Data Science. Previously, he was a Postdoctoral Fellow at the NIH Clinical Center's Radiology and Imaging Science Department (2014–2017). His research focuses on medical imaging informatics, computer vision, and machine learning applications in healthcare. Notable projects include NSF-funded work on continual learning and federated domain adaptation. He teaches advanced courses like CSE674 (Advanced Machine Learning) and CSE703 (Deep Learning for Medical Imaging). Dr. Gao earned his Ph.D. in Computer Science from Rutgers University (2014), advised by Dimitris N. Metaxas, and a B.S. from Southeast University, China (2007). His lab develops AI systems for medical diagnosis, with recent work on robust neural networks and federated learning frameworks. His team has produced impactful algorithms for segmentation, classification, and domain adaptation in imaging tasks. Current research includes NSF CAREER Award (2023–2028) for deployable medical diagnosis systems and collaborations on drug discovery and toxicity prediction. He advises four PhD students and has authored over 60 peer-reviewed publications in top venues like NeurIPS, CVPR, and MICCAI.
Emmanuel J. Candès is the Barnum-Simons Chair in Mathematics and Statistics at Stanford University, with joint appointments in the Institute of Computational and Mathematical Engineering and as Professor of Statistics and Electrical Engineering (by courtesy). His research spans mathematical signal processing , high-dimensional statistics , and data science , focusing on compressive sensing, inverse problems, and applications to imaging sciences. 2021 IEEE Jack S. Kilby Signal Processing Medal 2020 Princess of Asturias Award for Technical and Scientific Research 2017 MacArthur Fellow His recent work on conformal prediction and uncertainty quantification has advanced machine learning reliability, particularly in high-dimensional settings. Publications include breakthroughs in medical imaging, gravitational wave detection, and AI validation frameworks. Key collaborations include Terence Tao (UCLA) and Justin Romberg (Georgia Tech) for IEEE Kilby Medal recognition. He serves as Co-chair of Stanford's Data Science Institute and previously as Statistics Department Chair (2016–2019).
Bryan A. Plummer is an Assistant Professor in the Department of Computer Science at Boston University, affiliated with the IVC Group and the Artificial Intelligence Research (AIR) initiative at the Rafik B. Hariri Institute. He holds a PhD from the University of Illinois at Urbana-Champaign, specializing in computer vision. His research focuses on multimodal machine learning, efficient neural architectures, explainable AI, and robust ML systems. Plummer's work bridges vision and language, addressing challenges in domain generalization, synthetic data utilization, and model efficiency. Notable contributions include the Flickr30K Entities dataset and advancements in vision-language model robustness against web artifacts. He has advised over 20 students, with several securing roles at top institutions like NVIDIA and Google. His recent awards include the 3M Foundation Fellowship and NSF GRFP honorable mention. Plummer actively serves on conference committees (NeurIPS, CVPR, ICCV) and leads initiatives like the 1st Findings Workshop at ICCV'25.
Mark Lee is an Adjunct Professor in the People Analytics department at NYU’s Tandon School of Engineering, specializing in Technology Management and Innovation. He holds a Ph.D. in Engineering Psychology from Georgia Institute of Technology (1996). Currently, he serves as Head of Research, Analytics, and Business Development at UL ComplianceWire, focusing on pharmaceutical and medical device manufacturing training. His research leverages large datasets to improve healthcare safety through regulatory compliance and best practices. Courses taught include Human Factors Engineering, Workplace Design, and Predictive Analytics. Education: Ph.D. in Engineering Psychology, Georgia Tech (1996) Key Roles: Adjunct Professor, Head of Research at UL ComplianceWire Research Focus: Human Factors, Training Systems Design, Healthcare Compliance His work spans auditory display systems for aviation (e.g., 3D audio cockpit interfaces) and ergonomic design for industrial products. Recent projects emphasize data-driven solutions for regulatory challenges in life sciences. Publications highlight studies on visual search strategies, age-related cognitive performance, and application of signal detection theory in decision-making. He actively collaborates with industry and government entities, exemplified by the FDA-UL Cooperative Research Agreement.
