Stephanie Wilson is a Professor of Human-Computer Interaction at City St George's, University of London, and Co-Director of the Centre for HCI Design (HCID). She co-founded the EPSRC Centre for Doctoral Training in Diversity in Data Visualization (DIVERSE CDT) and contributes to the Institute for Creativity and AI. Her research emphasizes inclusive interaction design, data visualization, co-design, and innovative digital technologies for healthcare, particularly for people with aphasia. She has supervised 17 PhD students to completion and led significant projects like EVA Park and INCA, which explore accessible virtual worlds and digital tools for aphasia. Her work has earned multiple awards, including ACM SIGCHI Honorable Mention Awards and the Tech4Good Accessibility Award Finalist. Stephanie has secured over £10 million in research funding, including grants from EPSRC and Innovate UK, and actively contributes to academic governance through roles like Chair of the Research Degrees Committee and establishing the Women++ group. She advocates for participatory design and ethical research practices in healthcare technology.
Carlo Alberto Furia is an Associate Professor and Vice Dean at the Faculty of Informatics, Università della Svizzera italiana (USI). He is affiliated with the Software Institute, where he leads the ATOM research group. His academic journey includes prior roles as an Associate Professor at Chalmers University of Technology and a Senior Researcher at ETH Zurich’s Chair of Software Engineering. PhD in Computer Science, Politecnico di Milano Master of Science in Computer Science, University of Illinois at Chicago Laurea in Computer Science and Engineering, Politecnico di Milano His research centers on formal methods for software engineering, aiming to enhance software correctness, reliability, and quality through rigorous techniques. Key areas include automated program verification, contract-based development, loop invariant inference, and empirical evaluation using Bayesian data analysis. He emphasizes practical applicability and automation in formal methods. His recent publications reflect a strong focus on program analysis at the bytecode level, multilingual software analysis, automated repair of Android security issues, and empirical methodologies. These works span topics such as JVM substitutability, exception behavior in Java bytecode, and information flow security, demonstrating a consistent thread in improving software robustness through formal and automated techniques. He is actively involved in the software engineering research community as an Associate Editor of the Empirical Software Engineering (EMSE) journal and as a Program Committee member for major conferences including FASE, FM, ASE, ICSE, and CauSE. Carlo Furia has advised multiple research projects and supervised student theses. He has led and contributed to funded research initiatives, particularly in program analysis and verification. His group has developed tools such as AutoProof and other software artifacts available through the ATOM software page. He regularly teaches courses such as Software Analysis, Programming Fundamentals, and Software Design & Modeling. He leads the ATOM research group, which focuses on advancing automated techniques for software testing, analysis, and verification. The group develops practical tools and conducts empirical studies to validate research outcomes.
Brian Leung is an Associate Professor at McGill University, jointly affiliated with the Department of Biology and the Bieler School of the Environment . He holds the prestigious UNESCO Chair for Dialogues on Sustainability and serves as Director of the McGill Neotropical Environment Option (NEO) , a collaborative program with the Smithsonian Tropical Research Institute. His work bridges ecological theory, computational modeling, and environmental policy. Dr. Leung earned his PhD in Biology from Carleton University and completed postdoctoral research at the University of Cambridge and the University of Notre Dame. His academic journey at McGill began in 2004 as an Assistant Professor, advancing to Associate Professor in 2010. His research centers on predictive ecology , particularly modeling biological invasions and sustainability challenges . He develops and applies mathematical, statistical, and computational models to understand invasion dynamics across terrestrial, aquatic, and marine systems. His recent work includes the Panama Research and Integrated Sustainability Model (PRISM) , a spatially explicit framework for sustainability science in the Global South. His research spans scales from local to global and integrates ecological, economic, and social factors. His recent publications show a strong focus on invasion risk assessment , species distribution modeling , economic costs of invasions , and ecological forecasting . He frequently publishes in top journals such as Nature , Ecology Letters , and Global Ecology and Biogeography , emphasizing data-driven decision-making and policy relevance. Dr. Leung has received significant recognition through invitations to contribute to major reports and has co-edited influential works on invasive species economics. While specific named awards are not listed, his leadership roles and publication record reflect high scientific esteem. He actively mentors a dynamic research group, supervising multiple Ph.D. and M.Sc. students on projects related to invasion modeling, mangrove conservation, forest pest dynamics, and urban ecology. His lab emphasizes quantitative skills and interdisciplinary collaboration. He has secured research funding to support these projects, though specific grants are not detailed in the text. He leads the Leung Lab , which focuses on predictive modeling in ecology and sustainability. The lab collaborates with institutions such as the Smithsonian Tropical Research Institute and environmental firms like Habitat. Current projects include multi-species connectivity modeling, mangrove ecosystem services, and forecasting forest pest outbreaks.
Dermot J Hayes is the Charles F. Curtiss Distinguished Professor in Agriculture and Life Sciences and holds the Pioneer Hi-Bred International Chair in Agribusiness at Iowa State University's Department of Economics and Ivy School of Business. His expertise spans agricultural economics, financial economics, and international trade policy with a focus on commodity markets, farm policy, and China's agricultural impacts. Education: Ph.D. and M.S. in Agricultural Economics from the University of California, Berkeley (1986 and 1982). Research Interests: Includes U.S. farm policy, international trade dynamics, agribusiness strategies, crop insurance, financial derivatives, and China's role in global commodity markets. His work emphasizes policy analysis, market resilience, and emerging challenges like disease outbreaks (e.g., African Swine Fever). Awards: AAEA Fellow (2007), AAEA Enduring Quality Award (2006), and J.H. Ellis Teaching Award (2005). His research on food safety auctions remains influential. Consulting: Since 1995, he has advised the National Pork Producers Association on trade economics. His work informs policy debates on tariffs, trade deals, and market disruptions. Labs & Collaborations: Engaged with Iowa State's interdisciplinary initiatives on agriculture-environmental nexus and bioenergy systems.
Daniel Chen is an Assistant Professor of Business Analytics at the Carroll School of Management, Boston College. His research focuses on strategy in online platforms, leveraging machine learning and structural estimation to analyze two-sided markets and gig economy dynamics. He holds a Ph.D. in Operations Management from the Wharton School (University of Pennsylvania), advised by Gad Allon, and degrees from the University of Southern California, including an M.S. in Mathematical Finance and a B.S. in Economics/Mathematics. His work addresses strategic interactions between platforms and users, with a particular emphasis on data-driven solutions. Research Interests: Chen’s research spans gig economy operations, causal inference with machine learning, algorithmic transparency, and network-based rating systems. He collaborates with industry partners like driver analytics companies to study worker behavior, platform efficiency, and policy impacts. His methodologies combine theoretical modeling with empirical analysis, addressing real-world challenges in platform design and regulatory frameworks. Awards: Finalist, 2023 INFORMS Behavioral Operations Management Best Working Paper Award Labs/Teams: Collaborates with a driver analytics company to analyze gig economy datasets, contributing to both academic research and practical policy recommendations. His work bridges theoretical insights with actionable strategies for platforms and regulators.
Prof. Ruth King is the Thomas Bayes’ Professor of Statistics at the University of Edinburgh’s School of Mathematics. Her research focuses on applying Bayesian statistical methods to ecological and public health challenges, including population estimation for hidden groups (e.g., injecting drug users, modern-day slaves) and wildlife conservation. She develops computationally efficient techniques for analyzing large datasets, such as spatial capture-recapture models for animal populations and spatio-temporal abundance models for hidden human populations. Key projects include estimating survival rates of guillemots (30,000 individuals) and improving capture-recapture models to account for animal movement dynamics. Her work bridges statistical methodology with real-world applications, emphasizing rigorous inference and scalable algorithms. King’s academic contributions span Bayesian modeling frameworks, parameter clustering in neuroscientific data, and hierarchical centering in random effects models. She collaborates with biologists and policymakers to address conservation and public health issues. Notable recent projects include incorporating memory effects into spatial capture-recapture models and developing semi-complete data augmentation for state-space models. Her interdisciplinary approach addresses challenges in ecology, epidemiology, and computational statistics, with a focus on methodological innovation for large-scale data. Her scientific contributions are highlighted through over 100 peer-reviewed articles, including work on integrated population models, animal movement dynamics, and hidden Markov models for seabird behavior. King emphasizes the importance of statistics in uncovering hidden information within datasets, advocating for robust methodologies that ‘stand up in court’ when applied to critical real-world problems.
James Zou is an Associate Professor of Biomedical Data Science at Stanford University, with courtesy appointments in Computer Science and Electrical Engineering. His research focuses on advancing machine learning methodologies for healthcare applications, emphasizing reliability, fairness, and statistical rigor. He holds a Ph.D. from Harvard University and has held positions at Microsoft Research, Cambridge University (as a Gates Scholar), and UC Berkeley (Simons Fellow). Zou leads the Stanford Data4Health hub and is a Chan-Zuckerberg Investigator. His work spans AI-driven diagnostics, spatial transcriptomics, and ethical AI frameworks. Key achievements include the EchoNet AI system for echocardiography and foundational contributions to data valuation (e.g., Data Shapley). Awards include the Sloan Fellowship, NSF CAREER Award, and Google/Tencent AI awards. Education: Ph.D., Harvard University (2014); Postdoctoral roles at Microsoft Research, Cambridge, and Berkeley. Research Interests: Machine learning for healthcare, algorithmic fairness, interpretable AI, spatial omics, and translational bioinformatics. His lab develops tools like TextGrad (PyTorch for text agents) and frameworks for evaluating medical AI systems. Recent work addresses LLMs in peer review and clinical decision-making. Grants/Grants: Supported by NSF, Sloan Foundation, Chan-Zuckerberg Initiative, and industry partnerships (Google, Amazon, Adobe). Advises on over 20 doctoral students, many contributing to high-impact papers in Nature , Science , and top conferences (NeurIPS, ICML). Leads collaborations in cardiology, oncology, and veterinary medicine. Labs/Teams: Stanford AI Lab, Stanford Data4Health, and interdisciplinary groups in precision medicine. Active in open-source projects like FrugalML and MetaViz.
Simon Mak is an Assistant Professor of Statistical Science at Duke University and a Faculty Network Member of the Duke Institute for Brain Sciences. His educational background includes: Ph.D. in Statistics, Georgia Institute of Technology (2018) M.S. in Statistics, Georgia Institute of Technology (2018) B.S. in Statistics, Simon Fraser University (2013) Dr. Mak's research focuses on advanced statistical methodologies for complex scientific problems. His expertise spans statistical modeling , Bayesian inference , Gaussian process emulation , and uncertainty quantification . He applies these methods to nuclear physics (heavy-ion collisions), engineering (engine control systems), and music information retrieval, emphasizing scalability and interpretability in scientific computing. Analysis of his 2023-2025 publications reveals dominant trends in scalable Gaussian process methods for massive datasets and multi-fidelity simulations, particularly applied to high-energy physics and engineering systems. He has pioneered innovations in Bayesian optimization for expensive simulators and developed novel frameworks for online change-point detection in streaming data, demonstrating exceptional cross-disciplinary impact. Dr. Mak leads multiple significant research initiatives: Collaborative Research: Cost-Efficient and Confident Sampling for Modern Scientific Discovery (2023-2026) Science-Integrated Predictive modeLing (SCINPL) for scalable scientific computing (2022-2025) The X-SCAPE collaboration for statistically advanced nuclear collision modeling (2020-2025) These projects fund his development of statistical frameworks for scientific discovery in complex systems. He actively contributes to the JETSCAPE collaboration, developing multi-stage frameworks for studying jet quenching in heavy-ion collisions, and applies statistical methods through the Duke Institute for Brain Sciences to advance neuroscience research.
Andrew Li is an Associate Professor of Operations Research at Carnegie Mellon University's Tepper School of Business since 2024, previously serving as Assistant Professor since 2018. His research bridges statistics, optimization, and machine learning with applications to healthcare operations and retail management. Current teaching: Optimization, Business Analytics Capstone, and Topics in Optimization and Statistics PhD from MIT's Operations Research Center (2018), BS in Operations Research/Applied Mathematics from Columbia University (2012) Research Focus: Dr. Li develops data-driven decision frameworks for complex systems. Key areas include: Experience-based learning models with fairness constraints (organ allocation) Anomaly detection in low-rank matrices (retail inventory accuracy) Nanoparticle-based diagnostic systems for CAD and Alzheimer's Nonstationary demand forecasting in supply chains Publication Trends: Recent work combines bandit algorithms with healthcare applications (split liver transplants, CAD detection) and retail operations (inventory accuracy). Theoretical contributions include regret-optimal policies and entrywise anomaly detection guarantees. Scientific Honors: INFORMS Nicholson Award (2018) INFORMS Pierskalla Award (2021) NSF CAREER Award (2023) Professional Leadership: Active in INFORMS and CMU committees including MBA Analytics Curriculum, Thompson Award, and ENAiBLE AI-driven retail collaborative co-founder since 2021.
Mo Jiang is a Researcher in the Department of Chemical & Life Science Engineering at Virginia Commonwealth University's College of Engineering. His research focuses on advanced crystallization processes for energy storage materials and pharmaceutical manufacturing. He specializes in continuous manufacturing techniques such as slug-flow reactors, aiming to improve material uniformity, scalability, and process efficiency. His work bridges chemical engineering principles with practical applications in battery technology and drug substance development. Research Interests: Continuous crystallization and manufacturing systems Slug-flow synthesis of battery cathode materials Process optimization for pharmaceuticals and energy storage Scalable synthesis of uniform microcrystals His recent articles highlight advancements in low-cobalt/cobalt-free lithium-ion battery cathodes, pharmaceutical crystallization methods, and the application of computational fluid dynamics to enhance manufacturing processes. These studies emphasize improving material performance, reducing costs, and achieving sustainable production methods. While no formal academic awards are listed, his prolific publication record demonstrates expertise in interdisciplinary engineering solutions. He collaborates on projects involving process design, real-time monitoring, and the integration of advanced manufacturing technologies.
Jinjin Gu is a tenure-track Assistant Professor at Sofia University "St. Kliment Ohridski" 's INSAIT (Institute for Computer Science, Artificial Intelligence, and Technology), leading research on visual cognition and intelligence. Her work spans visual perception, processing, generation, and reasoning. Education: Ph.D. in Electrical and Computer Engineering (2024), University of Sydney B.Sc. in Computer Science and Engineering (2020), Chinese University of Hong Kong, Shenzhen Her research focuses on visual cognition , including agentic systems , diffusion models , GAN architectures , model interpretability , super-resolution , and multimodal vision-language systems . She has developed novel paradigms like HYPIR for diffusion-quality restoration at GAN speeds. Recent publications highlight advancements in image/video restoration , generative modeling , and visual reasoning . Her work addresses critical challenges in model generalization , causal interpretation , and real-world application robustness . Scientific Awards: Stanford University's World's Top 2% Scientists (2024) Yunfan Award at World Artificial Intelligence Conference (WAIC) (2023) She has advised students contributing to TPAMI, CVPR, and ICLR publications, and serves as Area Chair for ICLR 2026, NeurIPS 2025, and ICML 2025.
Christoph Stadtfeld is Associate Professor of Social Networks at ETH Zurich's Department of Humanities, Social and Political Sciences and co-director of the ETH Social Networks Lab. His research examines social network dynamics, focusing on tie formation processes, network effects on individuals, and advanced statistical methodologies for longitudinal network analysis. Education: PhD from Karlsruhe Institute of Technology (2011) Postdoctoral researcher and Marie-Curie fellow at University of Groningen, University of Lugano, and MIT Media Lab (2011-2014) His work bridges sociology, statistics, and computer science to address fundamental questions about how social structures evolve and influence behavior. Key interests include relational event modeling, co-evolution of networks and attributes, and applications in mental health, political polarization, and scientific collaboration. He develops innovative methods for analyzing dynamic networks using cutting-edge computational approaches. Recent publications reveal strong emphasis on methodological rigor in temporal network analysis, with significant contributions to relational event modeling and dynamic network actor frameworks. His work increasingly addresses societal challenges including political polarization, mental health impacts of social isolation, and innovation dynamics in healthcare. Scientific awards: Raymond Boudon Award of the European Academy of Sociology (2017) Freeman Award of the International Network for Social Network Analysis (2021) As co-director of the ETH Social Networks Lab, Stadtfeld leads interdisciplinary research teams developing novel network methodologies. His work has been supported by prestigious fellowships including Marie-Curie funding, and he actively mentors graduate students in network science methodology and applications across diverse domains. The ETH Social Networks Lab serves as a hub for advancing network theory and methodology, with ongoing projects examining student networks during crises, scientific collaboration dynamics, and innovation ecosystems through the lens of network science.
Ravi Dhar is the George Rogers Clark Professor at the Yale School of Management and holds an affiliated appointment as a Professor of Psychology at Yale University. He serves as Director of the Center for Customer Insights , focusing on consumer behavior, branding, and marketing strategy through psychological and economic frameworks. Ph.D. in Marketing, University of California at Berkeley (1992) MS, University of California at Berkeley (1990) MBA, Indian Institute of Management (1987) BTech, Indian Institute of Technology (1986) His research examines preference formation, self-regulation, and the interplay of conflicting goals in consumer decisions. Recent work explores sustainability, mobile commerce, and how guilt paradoxically enhances consumer pleasure. He has published over 50 articles and advised Fortune 100 companies across industries. Key trends in his publications include behavioral economics , eco-conscious consumption , and technology-mediated decisions . His studies address choice overload, goal systems, and the psychological drivers of indulgence versus self-control. Distinguished Scientific Contribution Award (Society for Consumer Psychology, 2012) Yale SOM Alumni Teaching Award (2012) William O'Dell Award Finalist (2004, 2008, 2012) AMA Doctoral Consortium Fellow (1991) Dhar consults firms on customer insights and has held visiting roles at HEC Paris , Erasmus University , and Stanford/NYU . He edits top journals like Journal of Consumer Research and Marketing Science , shaping academic and industry discourse.
Dr. Yanqing Hu is an Associate Professor at the Department of Statistics and Data Science, School of Science, Southern University of Science and Technology (SUSTech). With a Ph.D. in Systems Theory from Beijing Normal University (2011) and postdoctoral experience at the Levich Institute, City University of New York (2011-2013), his work focuses on big data analysis of complex systems, particularly in social media dynamics, network resilience, and graph neural network applications. Ph.D.: Beijing Normal University (Systems Theory, 2011) Postdoctoral: Levich Institute, CUNY (2011-2013) Research spans complex network analysis, information spreading mechanisms, and predictability of network structures. His work combines theoretical frameworks with real-world applications in social networks, infrastructure systems, and brain connectivity. Recent publications explore information percolation in social media, resilience quantification in interdependent networks, and intrinsic structure predictability. These studies appear in high-impact journals like Nature Human Behaviour (IF: 24.3), Nature Communications (IF: 17.7), and PNAS (IF: 10). World AI Conference Youth Outstanding Paper Nomination Beijing Outstanding Doctoral Dissertation Award Guangdong Special Support for Young Talents Guangdong Outstanding Youth Fund Collaborations include leading researchers from Boston University, King's College London, and Shenzhen-Hong Kong Institute of Microelectronics. His work informs network defense strategies and efficient navigation mechanisms in complex systems.
Callan Hummel (they/them) is an Assistant Professor in the Department of Political Science at the University of British Columbia's Faculty of Arts. Their research focuses on why and how communities with little political power organize and negotiate with their governments, with particular expertise in comparative politics, civil society, LGBTQ+ policy, labor politics, health policy, and Latin American politics. Hummel earned their Ph.D. and M.A. from the University of Texas at Austin in 2017 and 2014, respectively, and completed their B.A. at the University of Washington in 2009. As a nonbinary researcher, they bring unique perspectives to their work examining political power dynamics and marginalized communities. Dr. Hummel is the author of Why Informal Workers Organize: Contentious Politics, Enforcement, and the State (Oxford University Press 2021), which won the 2023 Riker Prize for the Best Book in Political Economy. Their current research agenda examines the expansion of trans and nonbinary rights globally, using diverse methodologies including statistical analysis, ethnography, survey, computational, experimental, and formal modeling. They conduct field research with trans-led NGOs and street vendor unions in Miami, Florida, La Paz, Bolivia, and São Paulo, Brazil. Hummel's recent scholarly output demonstrates a strong focus on transgender rights, particularly in Latin American contexts, with several 2024-2025 publications examining gender-affirming policies in Bolivia and transgender experiences in Florida. Their work consistently bridges political science with public health concerns, particularly regarding the impacts of policy on marginalized communities. The research combines comparative analysis with deep ethnographic engagement, often focusing on how informal workers and transgender communities navigate state institutions. 2023 Riker Prize for Best Book in Political Economy Publications in Lancet Global Health , BMJ Global Health , British Journal of Political Science Research funded by NIH, NSF, Department of Education, and APSA Dr. Hummel actively supervises graduate students and has contributed to understanding harassment and satisfaction among political science graduate students. Their interdisciplinary approach connects political science with public health, gender studies, and economic sociology. They are available for collaborations through research clusters and grant opportunities at UBC.
