Adam Bonica is an Associate Professor in the Department of Political Science at Stanford University . His research integrates data science and politics , focusing on money in politics , campaigns and elections , judicial politics , and political methodology . Research Interests : Data Science, Political Ideology, Campaign Finance, Judicial Polarization, Quantitative Modeling Contact : Encina Hall West, Room 307, Stanford, CA 94305-6044 | Email : bonica@stanford.edu | Phone : (650) 725-7987 His work has been published in leading journals such as the American Journal of Political Science , Political Analysis , Journal of Economic Perspectives , Journal of Law, Economics, and Organization , and JAMA Internal Medicine . Bonica co-authored The Judicial Tug of War , which explores the politicization of the American judiciary. He has developed comprehensive databases like DIME (Database on Ideology, Money in Politics, and Elections) to advance research on political ideology and campaign finance. Recent publications include analyses of ideological persuasion in U.S. elections, judicial reform dynamics, and supervised machine learning applications in political methodology. His research spans topics such as clerk hiring as ideological indicators , professional networks in fundraising , and polarization trends among physicians and legal professionals .
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.
Richard E. Turner is a Professor of Machine Learning at the University of Cambridge's Department of Engineering and Research Lead for AI for Weather Prediction at the Alan Turing Institute. He serves as Cambridge Lead for the EPSRC Probabilistic AI Hub and previously held roles including Visiting Researcher at Microsoft Research, Co-Director of the AI4ER CDT, and Course Director for the Machine Learning and Machine Intelligence MPhil program. Current research focuses on probabilistic machine learning fundamentals, environmental prediction (weather/climate), and spatio-temporal modeling combining deep learning with Bayesian methods Supervised 26 PhD students (13 graduated) and 7 research assistants/associates Secured over £30M in research funding from EPSRC, Microsoft, Toyota, Google, DeepMind, Amazon, and Improbable Featured in BBC Radio 5 Live's The Naked Scientist, BBC World Service's Click, and Wired Magazine His recent publications demonstrate expertise in diffusion models for PDE simulations, Gaussian Processes for environmental applications, and Bayesian methods for spatio-temporal forecasting. Key trends include climate modeling using ML, neural PDE solvers, and scalable probabilistic inference. Awards : Cambridge Students' Union Teaching Award for Lecturing; supervised Qualcomm Innovation Fellowship winner Collaborations : Microsoft Research (AI4Science), Alan Turing Institute, EPSRC Probabilistic AI Hub Turner leads the Turner Group within Cambridge's Machine Learning Group, focusing on uncertainty-aware ML for scientific applications. Current research assistants work on topics like meta-learning, Bayesian inference, and climate science applications.
Amir Ali Ahmadi is a Professor at Princeton University's Department of Operations Research and Financial Engineering (ORFE), with affiliations across multiple disciplines including PACM, Computer Science, Mechanical & Aerospace Engineering, Electrical Engineering, and the Center for Statistics and Machine Learning. He serves as Director of Princeton's Optimization and Quantitative Decision Science Certificate Program and has taken temporary roles at Citadel GQS (2021-2022) and Google Brain (2020-2021). His research bridges optimization theory , dynamical systems , and control theory , focusing on scalable algorithms for complex problems in robotics, autonomous systems, and machine learning. He has pioneered DSOS/SDSOS relaxations as alternatives to traditional sum-of-squares methods, enabling faster solutions through linear/second-order cone programming. Recent publications explore: Higher-order Newton methods for socially responsible investment Data-efficient learning of dynamical systems Computational complexity of local minima Robust-to-dynamics optimization frameworks Award highlights include: 2024 Egon Balas Prize in Optimization 2024 Princeton Engineering Council Teaching Award 2023 Distinguished Teaching Award (Princeton SEAS) 2019 NSF CAREER Award 2017 DARPA Young Faculty Award 2017 Sloan Fellowship in Computer Science He advises prominent researchers like Georgina Hall (Tucker Prize finalist) and Bachir El Khadir (Goldstine Fellow), and leads the Princeton Optimization Seminar and MURI project on Control-Oriented Learning on the Fly.
Mohamed Noureldin is an Assistant Professor at the Department of Civil Engineering, Aalto University , Finland, with prior academic roles at Sungkyunkwan University, South Korea (2015–2022). His expertise lies in integrating Artificial Intelligence (AI) with Structural Health Monitoring (SHM) , Structural Digital Twin , Predictive Maintenance , and Seismic Retrofitting . Research Focus : AI-powered sustainable structural design, smart retrofitting, predictive maintenance, structural material innovation, and next-generation performance-based seismic/wind design. Industrial Experience : 20+ years in offshore/onshore structural engineering (Hyundai Heavy Industries, Samsung Engineering, Arab-Swiss Engineering Company, Zuhair Fayez Partnership). Teaching : Courses in structural analysis, seismic design, dynamics, and reinforced concrete at Aalto and Sungkyunkwan Universities. Laboratory : Leads the Structural Design AI Lab (SDAI), focusing on AI-driven resilient infrastructure. Contact : mohamed.noureldin@aalto.fi , +358504544861. His publications explore cutting-edge applications of AI, ML, and DL in seismic retrofitting, structural durability, soil stabilization, and hybrid damping systems. Collaborative work emphasizes life-cycle cost assessment and augmented reality for predictive maintenance.
Claus Lamm is a Full Professor of Biological Psychology at the University of Vienna , where he leads the Social, Cognitive and Affective Neuroscience Unit (SCAN-Unit) . He serves as Vice Dean for Research and Advancement of Early Career Researchers at the Faculty of Psychology and holds affiliations with the Vienna Cognitive Science Hub , Environment & Climate Change Hub , and Austrian Academy of Sciences . His academic career spans international collaborations and formative research experience abroad. Scientific Focus: Lamm investigates the neural underpinnings of empathy and prosocial behavior , employing multi-modal approaches combining neuroimaging, psychopharmacology, and psychoneuroendocrinology . His work extends to comparative studies with ravens and dogs, and explores environmental social neuroscience through climate change decision-making research. Recent publications show trends in cross-cultural psychology , machine learning applications , and neurobiological pathways related to social behavior. Awards & Grants: Recipient of the APS Mentor Award for his support of early career researchers. Funded by European Research Council , Austrian Science Fund , Vienna Science and Technology Fund , and intramural grants exceeding €10 million. Key projects include "Unravelling the opioid system in empathy" and "Comparative dog-human fMRI" . Media Engagement: A prominent public science communicator, Lamm has appeared in Nature , Science Magazine , and Austrian media outlets like Ö1 Mittagsjournal and ORF2 , discussing topics from pandemic psychology to social media effects . He maintains active outreach through Science TV and educational programs .
Zakia Hammal is an Assistant Research Professor with dual appointments at Carnegie Mellon University, holding positions in the Robotics Institute within the School of Computer Science and the Department of Biomedical Engineering in the College of Engineering. Her work bridges computer science, machine learning, artificial intelligence, and social/behavioral psychology to advance computational models for human behavior analysis. Dr. Hammal's educational background includes a PhD in Computer Science, a Master of Artificial Intelligence and Algorithmic with specialization in Image Processing, and an Engineer's degree in Computer Science with specialization in Computer Systems. Her academic journey has positioned her at the intersection of technical expertise and healthcare applications. Her research focuses on multimodal human behavior modeling in social interaction, with particular emphasis on health informatics and affective computing (Emotion AI). Dr. Hammal's work has pioneered computational models for multimodal assessment of psychiatric disorders, including depression severity evaluation, automatic pain intensity measurement, assessment of expressiveness in children with facial abnormalities, analysis of non-verbal communication in mother-infant interaction, and identification of behavioral markers in autism spectrum disorder. Her approach integrates computer vision, machine learning, and behavioral psychology to create systems that can objectively measure human behaviors that are often subjective in clinical settings. Analysis of her recent publications reveals a consistent trajectory toward more sophisticated multimodal approaches to healthcare challenges, particularly in pain assessment and mental health diagnostics. Her work increasingly emphasizes interpretable AI models that can translate complex behavioral patterns into clinically meaningful insights, with growing attention to applications for vulnerable populations including infants, elderly patients, and those with craniofacial abnormalities or autism spectrum disorder. Women in AI Awards North America 2023 – AI Researcher of the Year Award Outstanding Reviewer Award at FG 2015 Best Paper award at ACII 2015 Outstanding Paper award at ICMI 2012 Dr. Hammal has secured significant research funding, primarily from the U.S. National Institutes of Health, including an R01 grant for developing a Multimodal Behavioral AI platform for pain assessment and management, and additional grants for automatic pain assessment in older adults with dementia. Her leadership extends to mentoring through her involvement in organizing workshops and conferences that train the next generation of researchers in affective computing and health informatics. As an active leader in her field, Dr. Hammal serves as ACM ICMI Steering Board Committee Member, Associate Editor for IEEE Transactions on Affective Computing and IEEE Transactions on Multimedia, and has organized numerous influential workshops including the International Workshop on Automated Assessment of Pain and Face and Gesture Analysis for Health Informatics. She is set to serve as Program Chair for FG 2025, ACII 2025, and ICMI 2026, demonstrating her growing influence in shaping the future direction of research in multimodal interaction and affective computing.
Elizabeth A. Simpson is an Associate Professor in the Department of Psychology at the University of Miami's College of Arts and Sciences. She serves as Associate Director of the Child Division and directs the Social Cognition Laboratory, focusing on infant social and cognitive development through interdisciplinary methodologies. Education: Ph.D. in Psychology (unspecified institution) Key Research Areas: Developmental Psychology, Autism, Social Cognition, Primate Studies, Visual Attention, Infant Behavior Her research investigates individual differences in infant visual attention, social motivation, and physiological markers like salivary oxytocin. Recent studies demonstrate: Stability of attentional patterns from newborn to 14 months Sex differences in early face detection Links between oxytocin levels and social affect in infants Neurophysiological markers in newborns later diagnosed with ASD Scientific awards include the NSF CAREER grant for neonatal imitation research. She contributes to open science initiatives through the ManyBabies consortium and develops remote eye-tracking methods for broader accessibility. Current lab work explores: Parent-infant affective interactions during still-face episodes Predictive power of early attentional biases Development of pathogen avoidance behaviors Cross-species comparisons of social cognition
Arno De Caigny is an Associate Professor at IÉSEG School of Management in France, specializing in Marketing Analytics. He holds a Ph.D. in Sales and Marketing from the University of Lille and Masters in Economics/Mathematics and Finance from Ghent University. His professional experience includes work as a Business Analyst at Deloitte. His primary research interests include customer churn prediction, AI applications in marketing, explainable AI for business, and life event-based marketing. He develops advanced machine learning models for customer behavior prediction and retention strategies. De Caigny's recent publications demonstrate strong focus on developing interpretable machine learning models for business applications, particularly in customer churn prediction and financial decision support. His work increasingly incorporates deep learning and natural language processing techniques.
Wing Lam is an Associate Research Scientist in the Department of Pharmacology at the Yale School of Medicine. He holds a BSc in Molecular Biology and a PhD in Biochemical Pharmacology from City University of Hong Kong, followed by postdoctoral training at Yale. His research focuses on developing traditional Chinese medicine (TCM) formulations as adjuvants for cancer therapy, notably YIV-906, which enhances chemotherapy efficacy and mitigates intestinal toxicity. Lam also pioneered the STAR database for herbal drug discovery and the Mechanism-Based Quality Control (MBQC) platform for botanical drug standardization. Education: BSc (Hons) Molecular Biology, City University of Hong Kong, 1995 PhD Biochemical Pharmacology, City University of Hong Kong, 1999 Postdoc, Pharmacology, Yale University, 1999-2002 His research interests span cancer pharmacology, TCM modernization, and mitochondrial toxicity mechanisms. Key projects include YIV-906’s role in enhancing anti-PD1 and CAR T-cell therapies, developing L-nucleoside analogs like troxacitabine, and investigating tylophorine analogs’ antitumor effects. Lam has co-chaired sessions at multiple Consortium for Globalization of Chinese Medicine (CGCM) meetings and contributed to patents on herbal drug formulations and quality control methods. Recent work explores YIV-906’s potential for inflammatory bowel disease (IBD) and phase II clinical trials for colon and liver cancers. Lam’s publications highlight synergistic drug interactions, mitochondrial DNA depletion mechanisms, and TCM’s evidence-based application in chronic diseases. His grants include studies on PHY906 as an adjuvant in rectal cancer therapy and collaborations with Yiviva, Inc. He maintains active roles in editorial boards, including a special issue on herbal drug quality control in Frontiers in Pharmacology . Lam’s lab is embedded within Dr. Yung-Chi Cheng’s group, focusing on translational pharmacology and botanical drug innovation.
Yin Tat Lee is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering , University of Washington, and a Senior Principal Researcher in Microsoft AI. His research spans convex optimization , convex geometry , graph algorithms , online algorithms , and differential privacy , with applications in machine learning and theoretical computer science.
Cristian Gómez Canela is a Full Professor in the Department of Analytical and Applied Chemistry at the School of Engineering, Ramon Llull University (IQS). He serves as Coordinator of the Master's Degree in Analytical Chemistry and is an active member of the Catalan Chemical Society (SCQ), representing SCQ in EuChems-EYCN. His academic journey includes a PhD in Chemistry from the University of Barcelona (2014), followed by postdoctoral research at IDAEA-CSIC and King's College University. Dr. Gómez Canela's research focuses on environmental analytical chemistry, particularly the optimization and validation of analytical methods based on liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) and high-resolution mass spectrometry (HRMS) for determining organic pollutants in environmental samples. His work extends to metabolomics applied to aquatic organisms and the analysis of neurotoxic compounds in water systems. His research fingerprint reveals strong expertise in zebrafish models (100%), neurotransmitter analysis (66%), Daphnia magna studies (64%), and neurotoxicity assessment (21%). His recent publications (2024-2025) demonstrate a clear trend toward environmental neurotoxicology, with emphasis on the effects of pharmaceuticals and industrial pollutants on aquatic organisms. His work integrates advanced analytical techniques with biological endpoints to assess environmental risks, particularly focusing on neurological and cardiovascular impacts. The research spans method development for pollutant detection, environmental monitoring, and mechanistic studies of neurotoxic effects. Dr. Gómez Canela leads multiple significant research projects including CHEMIPARK (2024-2027) on passive sampling methodologies for environmental pollutants, GESPA (2022-2025) as part of the Environmental Process Engineering and Simulation Group, and several projects on neuroactive compounds in water systems. He has an impressive research output with 91 scientific publications from 2011-2025 and an h-index of 27 with over 2,000 citations. As a dedicated educator, he contributes to multiple academic programs including the Master in Analytical Chemistry, Master in Pharmaceutical Chemistry, and undergraduate degrees in Chemistry and Chemical Engineering. His research group GESPA represents a multidisciplinary team combining chemical engineering, biotechnology, and chemical analysis to advance environmental sustainability through theoretical and experimental approaches.
Tobias Bolch is a full Professor at the Institute of Geodesy, Graz University of Technology (TU Graz), Austria, specializing in cryospheric research with a focus on High Mountain Asia and the Tibetan Plateau. His work integrates advanced geodetic and remote sensing methodologies to address critical climate change impacts on glaciers and related systems. His research spans: Glacier dynamics and mass balance monitoring using InSAR and optical satellite data Rock glacier kinematics and permafrost interactions Glacial lake evolution and outburst flood hazards Climate-glacier-hydrology linkages in Central Asian river basins Long-term cryosphere reconstruction using historical satellite archives Analysis of his 2023-2025 publications reveals dominant trends in quantifying ice loss acceleration, particularly from lake-terminating glaciers, and the application of multi-sensor approaches (Sentinel, ASTER, UAV) for decadal-scale change detection. His work consistently emphasizes the hydrological consequences of glacier retreat for downstream water security across High Mountain Asia. No scientific awards or specific grant programs were documented in the provided materials. While student supervision details are absent, his position at TU Graz's Institute of Geodesy indicates active mentorship within geospatial and cryospheric research programs. His homepage (https://ifg.tugraz.at) serves as the primary hub for research dissemination through the university's PURE portal.
Prof. Dr. Mirko Kremer is a Professor of Supply Chain Management at the Frankfurt School of Finance & Management since July 2014. Prior to this, he was an Assistant Professor at Pennsylvania State University (2008-2014) and held visiting roles at London Business School, Kellogg School of Management, and INSEAD. He earned a Ph.D. in Business Administration from the University of Mannheim (2008), with prior studies in Business Administration at Mannheim (1999-2003) and Economics at Ruhr-Universität Bochum (1997-1999). Research Focus: Micro-behavioral foundations of supply chains, inventory management, sales forecasting, and service systems. Editorial Roles: Department Editor for Production and Operations Management , Associate Editor for Operations Research . Teaching: Courses in Operations Management, Supply Chain Strategy, and quantitative decision modeling at Frankfurt School, INSEAD, London Business School, and Kellogg School of Management. Research Trends: His work bridges behavioral economics with supply chain operations, emphasizing how human decision-making (e.g., managerial biases, consumer behavior) impacts inventory systems, pricing strategies, and forecasting accuracy. Recent articles focus on diagnostic errors under congestion, strategic sourcing biases, and dynamic pricing experiments.
Daniel Adelman is the Charles I. Clough, Jr. Professor of Operations Management at the University of Chicago Booth School of Business. He joined the faculty in 1997 after completing his PhD in industrial engineering and operations research at Georgia Tech. Adelman is a leading expert in Business Analytics and Management Analytics, helping companies deploy data and decision analysis to build world-class strategic and tactical management capabilities. Adelman received his PhD in industrial engineering and operations research in 1997, along with a bachelor's degree in industrial engineering and a master's degree in operations research, all from the School of Industrial and Systems Engineering at the Georgia Institute of Technology. Daniel Adelman's research focuses on applying analytical models to solve complex business problems across multiple industries. He has worked with firms from diverse sectors including internet services, chemical distribution, airlines, third party logistics, fiber-optics manufacturing, semiconductor manufacturing, oil, and healthcare. His research integrates real-world data with analytical models to bring structure and discipline to decision and control processes, enabling firms to achieve higher profits with lower risk. Adelman's recent work has concentrated heavily on healthcare analytics, where he leads the Healthcare Analytics Laboratory at Chicago Booth. This lab works with teams of doctoral and MBA students on projects with major healthcare institutions to optimize clinical, operational, and financial outcomes. His research spans foundational operations research including approximate dynamic programming, inventory theory/supply chain management, and revenue management/pricing optimization, as well as examining the linkage between operational performance metrics and financial performance of firms. Adelman's publications show a clear trend toward increasing focus on healthcare applications while maintaining strong theoretical foundations in operations research. His earlier work focused more on general operations management problems like inventory control and supply chain optimization, while his recent publications demonstrate a strategic shift toward healthcare analytics, particularly examining surgical team dynamics, hospital performance metrics, and resource allocation during public health emergencies like the COVID-19 pandemic. George B. Dantzig Prize (1998) for the best dissertation in operations research and management sciences that is innovative and relevant to practice Adelman regularly advises doctoral and MBA students through the Healthcare Analytics Laboratory at Chicago Booth. He has served as Associate Editor for Management Science, currently serves as Associate Editor for Manufacturing and Service Operations Management, and is the Area Editor for Operations and Supply Chain at Operations Research. His industry collaborations include significant projects with Akamai on internet pricing, with GE Global Research Labs on the electricity smart grid, with BP on gasoline supply contract portfolio optimization, and with Symantec on software release planning. Adelman leads the Healthcare Analytics Laboratory at Chicago Booth, which brings together interdisciplinary teams of doctoral and MBA students to work on a portfolio of projects with major healthcare institutions. The lab focuses on optimizing clinical, operational, and financial outcomes through advanced analytics and decision modeling.
