Eva Cantoni is a Full Professor at the Research Center for Statistics within the Geneva School of Economics and Management , University of Geneva. Her expertise spans robust statistical methodology, model selection, and applications in ecology and medicine. Ph.D. from University of Geneva Accredited European Statistician (FENStatS) Research Interests : She specializes in Robust statistics for real-world data Variable/model selection in high-dimensional settings Nonparametric and semi-parametric regression Zero-inflated and overdispersed count models Longitudinal and spatiotemporal data analysis Her work addresses ecological challenges (fish stock assessment), medical applications (hospital congestion modeling), and housing market analysis. Recent Trends in Publications : Recent articles focus on Confidence intervals for robust mixed models Editorial leadership in robust statistics Applications to fisheries science and public health Flexible modeling frameworks for complex data Comparative studies of statistical measures Extremes modeling in healthcare Leadership & Grants : She has served as: Vice-Dean for Teaching (2020-2023) Director of Master's in Statistics (2012-2019) Director of Applied Statistics Certificate (2015-2019) President, Swiss Federal Statistics Committee (2024-2027) Specialty Chief Editor, Frontiers in Applied Mathematics (2024) Grants include projects on Robust solutions for modern data (2023-2025) Sustainable fisheries modeling (2018-2021) Advancements in state-space models (2014-2017) Software Contributions : Developed R packages for robust statistical methods: confintROB (bootstrap confidence intervals) RobSSM (robust state-space models) R2_LMM (explained variation measures)
Yuan Zhong is an Associate Professor of Operations Management at the University of Chicago Booth School of Business . He previously held positions as an Assistant Professor at Columbia University’s Department of Industrial Engineering and Operations Research and was a Postdoctoral Scholar at UC Berkeley’s Computer Science Department. Education: PhD in Operations Research, MIT (2012) MA in Mathematics, Caltech (2008) BA in Mathematics, University of Cambridge (2006) His research focuses on applied probability and stochastic system design , with applications in cloud computing , supply chain management , and e-commerce logistics . Recent work explores multi-period production systems and dynamic resource allocation in data centers and healthcare operations . Recent publications analyze cloud value chains , sparse graph design for delivery networks, and process flexibility in manufacturing. He has contributed to journals like Operations Research , Annals of Applied Probability , and Stochastic Systems . Scientific Awards: 2012 Kenneth C. Sevcik Outstanding Student Paper Award Best Student Paper Award at ACM Sigmetrics (2012) He teaches courses in business process fundamentals and queueing theory , with a future schedule including Operations Management: Business Process Fundamentals (2025–2026). No explicit student advising list was provided.
Abolfazl Asudeh is an Associate Professor in the Department of Computer Science at the University of Illinois Chicago and director of the Innovative Data Exploration Laboratory (InDeX Lab) . He is a Senior Member of ACM and IEEE , serving as Associate Editor for IEEE Transactions on Knowledge and Data Engineering , VLDB Ambassador , and VLDB Endowment Liaison to NSF . His research focuses on Algorithm Design for Data and AI problems , emphasizing efficient, accurate, and responsible solutions through Approximation Algorithms , Randomized Methods , and Computational Geometry . Recent work explores LLM optimization ( Needle ), fair data structures ( FairHash ), and responsible AI frameworks ( Chameleon ). Scientific awards include Communications of the ACM Research Highlight Google Research Scholar Award SIGMOD 2019 Research Highlight Best of VLDB 2020 SIGMOD 2017 Reproducibility Award Grants: NSF IIS-2348919 (2024-2027): Fairness-aware Data Structures NSF IIS-2107290 (2021-2024): Collaborative Fairness Research The InDeX Lab develops systems like Needle (image retrieval) and RSR (matrix multiplication). His work integrates fairness , reliability , and computational efficiency across data structures , LLMs , and responsible AI implementations.
Sean Ren is an Associate Professor in Computer Science at the University of Southern California, where he holds the Andrew and Erna Viterbi Early Career Chair. He directs the INK Research Lab and serves as Research Team Leader at USC's Information Sciences Institute. Affiliated with the USC NLP Group and Machine Learning Center, his research focuses on developing robust NLP systems through knowledge-aware architectures and data-efficient learning. His research interests include: Evaluation methods exposing NLP limitations in reasoning tasks Augmenting models with commonsense/knowledge via novel algorithms Graph neural networks for relational inference Model robustness verification and enhancement Neural-symbolic integration for interpretable AI Recent publications demonstrate strong emphases on language model reasoning, knowledge distillation, and compositional generalization. His group's ACL/NeurIPS papers frequently address robustness gaps in state-of-the-art models. Honors include: ACL Outstanding Paper (2023) MIT TR Innovator 35 Asia Pacific (2023) NSF CAREER Award (2021) Forbes 30 Under 30 (2019) ACM SIGKDD Dissertation Award (2018) Research is supported by NSF, DARPA, IARPA, and industry partners (Google, Amazon, Meta). He leads the INK Lab with focuses on label-efficient learning and knowledge-guided NLP, while actively recruiting PhD students for projects bridging symbolic and neural paradigms.
David J. Stensrud is a Professor of Meteorology and Atmospheric Science at Pennsylvania State University, where he has been a faculty member in the Department of Meteorology and Atmospheric Science within the College of Earth and Mineral Sciences. His research focuses on advancing our understanding of severe weather phenomena and improving numerical weather prediction capabilities. Dr. Stensrud received his academic training at Penn State, earning his M.S. in Meteorology in 1985 and his Ph.D. in Meteorology in 1992. His educational background has provided the foundation for his extensive research career focused on atmospheric dynamics and prediction. Dr. Stensrud's research spans several critical areas in atmospheric science, with particular emphasis on mesoscale meteorology , numerical weather prediction , and synoptic meteorology . He is internationally recognized for his work on ensemble forecasting , where he explores how groups of numerical weather prediction models can provide probabilistic forecasts of severe weather events. His research on convective-scale data assimilation aims to improve how observations from radar and satellites are incorporated into high-resolution weather models. Additional research interests include the physical processes behind severe weather phenomena like derechos and heavy rainfall events, the predictability of convective-scale phenomena, and the dynamics of the North American monsoon system. He has made significant contributions to understanding how urban environments influence thunderstorms and how convective systems interact with their larger-scale environment. Analysis of Dr. Stensrud's recent publications reveals a consistent focus on improving severe weather prediction through advanced data assimilation techniques. His work primarily centers on integrating radar and satellite observations into convection-allowing models to enhance forecasting capabilities for thunderstorms and other severe weather phenomena. A notable trend in his research is the increasing sophistication of ensemble approaches to address uncertainties in both initial conditions and model physics. His publications demonstrate a progression from fundamental studies of mesoscale phenomena to increasingly operational applications with potential for real-world forecasting improvements. Dr. Stensrud has served in several important professional capacities that highlight his standing in the meteorological community: Chair, Storm-scale Radar Data Assimilation Workshop, Norman, Oklahoma, October 2011 Member, NOAA/NWS Functional Weather Radar Requirements Integrated Working Team, 2012-2013 Guest Editor, Advances in Meteorology, Special Issue on "Storm-scale data assimilation and NWP", 2013 Commissioner, Scientific and Technological Activities Commission, American Meteorological Society, 2016-2017 Dr. Stensrud has authored more than 150 peer-reviewed publications and a textbook entitled "Parameterization Schemes: Keys to Understanding Numerical Weather Models." He has been actively involved in mentoring graduate students, though specific names of advisees are not provided in the available information. In collaboration with colleagues at Penn State, he helped create a 20-station environmental monitoring network across Pennsylvania with plans to expand to 50+ stations. His research has been supported by various grants that have enabled field campaigns such as the Mesoscale Predictability Experiment (MPEX) in 2013, where his team intercepted severe thunderstorms to collect critical observational data. Dr. Stensrud is involved with several research teams and facilities at Penn State, including work with the Joel N. Myers Weather Center and the Bob and Charlotte Landis Broadcast Room. His research group focuses on analyzing data from dual-polarization radar systems and developing improved techniques for assimilating these observations into convection-allowing models. He collaborates extensively with other researchers at Penn State and beyond, particularly in studies involving the interactions between urban environments and thunderstorms, and the upscale effects of deep convection on larger-scale weather patterns.
Andrew Gettelman is a distinguished climate scientist at Pacific Northwest National Laboratory whose research spans atmospheric sciences, climatology, and climate modeling. With a D-index of 91 and over 32,652 citations across 343 publications, he ranks 422nd globally and 194th nationally in Environmental Sciences. His research interests focus on fundamental climate processes including cloud microphysics, aerosol-cloud interactions, stratospheric dynamics, and climate model development. Gettelman has made significant contributions to understanding Arctic climate feedbacks, particularly how clouds respond to sea ice loss, and has advanced the representation of aerosols in climate models through his work on the Community Atmosphere Model (CAM). Analysis of his publication trends reveals a consistent focus on improving climate model representations of atmospheric processes, with recent work emphasizing climate sensitivity in the Community Earth System Model (CESM2) and bounding global aerosol radiative forcing. His research bridges fundamental atmospheric science with practical applications for understanding climate change. Among his recognitions, Gettelman has been named to the World's Best Scientists 2025 list. His highly cited works include foundational papers on cloud microphysics schemes and aerosol representation in climate models. Gettelman maintains extensive collaborative networks, frequently working with researchers from the National Center for Atmospheric Research, University of Colorado Boulder, and other leading climate institutions. His research has been instrumental in advancing climate modeling capabilities used in major international climate assessments.
Florian Brandl is an Argelander Professor (associate professor with tenure) at the University of Bonn, holding positions in both the Department of Economics and the Hausdorff Center for Mathematics. Previously, he was a postdoctoral research scholar at Princeton University and Stanford University. His academic career demonstrates a strong foundation in mathematical economics and game theory, with affiliations spanning multiple prestigious institutions. Brandl earned his Doctoral degree in Mathematics (summa cum laude) from the Technical University of Munich in 2018, following a Master's degree (2013) and Bachelor's degree (2011) from the same institution. His doctoral work focused on "Zero-Sum Games in Social Choice and Game Theory" under the supervision of Felix Brandt, establishing the foundation for his research trajectory. Prof. Brandl's research spans microeconomic theory with a focus on social choice theory, decision theory, and game theory. He is particularly interested in decision-making under uncertainty, connections between social choice and game theory, and dynamic processes converging to equilibrium. His work employs mathematical tools to analyze interactions of multiple entities in economic contexts, often incorporating algorithmic approaches and methods from theoretical computer science. He has made significant contributions to fair division, mechanism design, and probabilistic social choice. His publication record shows consistent contributions across multiple subfields, with recent work focusing on patience effects in fair division, social learning barriers, and axiomatic characterizations of equilibrium concepts. Brandl's research demonstrates strong interdisciplinary connections between economics, mathematics, and computer science, with publications in top journals across all three disciplines. Best Student Paper Award at WINE 2021 for "Funding Public Projects: A Case for the Nash Product Rule" Associate Editor for Theoretical Economics Co-organizer of the COMSOC Video Seminar Prof. Brandl actively contributes to academic service and community building. He co-organizes the COMSOC Video Seminar and will host a Trimester Program on "Advances in Mechanism Design" in Bonn in summer 2026. He serves on program committees for major conferences including COMSOC 2023 and EC 2023. His research has been supported through his position as a Bonn Junior Fellow at the Hausdorff Center for Mathematics since 2021. Based at the Institute for Microeconomics and affiliated with the Hausdorff Center for Mathematics, Brandl collaborates with a broad network of researchers across economics and computer science. His work often involves interdisciplinary collaboration, as evidenced by his numerous co-authored publications with researchers from various institutions worldwide. He maintains strong connections with the University of Oxford's Global Priorities Institute as a Research Affiliate.
Jonathan Cullen is Professor of Sustainable Engineering at the University of Cambridge and President of Fitzwilliam College, specializing in resource efficiency and decarbonization through top-down analysis of industrial material and energy systems. His work bridges academic research with industry applications across energy-intensive sectors. Education: Bachelor's in Chemical and Process Engineering, University of Canterbury, New Zealand MPhil in Engineering for Sustainable Development, University of Cambridge PhD in Engineering Fundamentals of Energy Efficiency, University of Cambridge His research develops metrics for quantifying energy and material consequences of production systems, focusing on circular economy implementation, minimum energy requirements, and zero-carbon transition pathways. Key applications target cement, steel, plastics, and petrochemicals where he pioneers methods like exergetic analysis and material flow accounting to expose carbon lock-ins and circularity opportunities. Recent publications reveal three dominant trends: (1) Frameworks for theoretical minimum energy requirements across industrial processes, (2) Geopolitical analysis of critical mineral flows and ownership structures, and (3) Circular economy metrics for plastics and construction materials. These consistently employ system-scale modeling validated through industry partnerships. Research Funding: Lead: C-THRU ($4M, VKRF) - carbon clarity in petrochemical supply chains Co-I: UK FIRES (£5.2M, EPSRC) - industrial decarbonization program Co-I: CirPlas (£1.25M, UKRI) - plastic waste elimination 7+ projects (EPSRC, Innovate UK, Horizon 2020) Academic Leadership: Teaching: Energy Systems and Policy (MPhil in Energy Technologies) Undergraduate supervision in Materials/Mathematics Graduate Tutor at Fitzwilliam College IPCC AR6 Lead Author (Industry Chapter) He directs the Resource Efficiency Collective, which develops open-source tools like Mat-dp for material demand projections and Starter Data Kits for energy planning. Current work focuses on scaling circular business models for construction retrofitting and quantifying geopolitical risks in critical mineral supply chains.
David Hong is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Delaware. He holds a PhD from the University of Michigan, where he was an NSF Graduate Research Fellow, and previously served as an NSF Postdoctoral Research Fellow at the University of Pennsylvania. His research focuses on developing robust methods for analyzing heterogeneous and high-dimensional data, particularly through low-rank matrix and tensor techniques. Applications span medical imaging, radar systems, genomics, and astronomy. He emphasizes theoretical guarantees and practical algorithms for signal extraction and inverse problems. Education: PhD in Electrical Engineering and Computer Science (University of Michigan), NSF Postdoctoral Research Fellowship (University of Pennsylvania). Research Interests: Low-rank matrix/tensor methods, heterogeneous data analysis, unsupervised learning, and applications in healthcare, imaging, and sensor systems. His work addresses noise robustness, scalable algorithms, and real-world deployment challenges. Scientific Awards: Recipient of the NSF Postdoctoral Research Fellowship (2020) and NSF Graduate Research Fellowship (2015). Advising & Grants: Advisor to graduate students in machine learning and signal processing (no named advisees listed). Active NSF grant recipient for foundational and applied research in data science. Labs/Teams: Engaged in interdisciplinary collaborations through the University of Delaware's Center for Computational Research and Data Science initiatives.
Marisa Carrasco-Queijeiro is the Julius Silver Professor of Psychology and Neural Science at New York University, affiliated with the Department of Psychology and the Center for Neural Science. She holds a PhD from Princeton University (1989) and has held academic positions at Wesleyan University and NYU, including department chair from 2001–2007. Her research focuses on the interplay between visual perception, attention, and neurophysiological mechanisms. She uses psychophysical methods to study visual search efficiency, spatial resolution, and how attention modulates early visual processing. Key contributions include demonstrating that attention enhances perceptual resolution at attended locations and challenging traditional serial/parallel search models through time-course analyses and cueing experiments. Her work spans visual search efficiency, eccentricity effects, perceptual organization, and contrast sensitivity. Notable findings include attention's role in reducing eccentricity-based performance declines and the modulation of spatial resolution via covert attention. Methodologies include fMRI, EEG, MEG, and computational modeling. Awards: Guggenheim Fellow (1999), NSF Young Investigator Award (1993–1999), Porter Ogden Jacobus Fellowship (1987–1988). Lab: Carrasco Lab at NYU investigates visual perception development in children and adult cognition. Located at 6 Washington Place, Room 971, NYC. Her lab integrates cognitive neuroscience and psychological approaches to understand attention's neural underpinnings. Current projects explore developmental visual perception and attention's impact on early visual processing via neuroimaging and behavioral studies.
Rob van Beers is an Assistant Professor at the Faculty of Behavioural and Movement Sciences at Vrije Universiteit Amsterdam, with affiliations to Neurocontrol, IBBA, and AMS - Sports. His research focuses on human motor control, spatial perception, and computational modeling using Bayesian approaches to understand sensory-motor integration under uncertainty. He holds ancillary roles as a Researcher at Radboud University (Nijmegen) since 2015 and serves on the Editorial Board of the Journal of Neurophysiology since 2015. His work contributes to UN Sustainable Development Goals related to health and well-being. Key research interests include motor learning dynamics, sensorimotor adaptation, and the neural basis of spatial orientation. Recent studies explore Alzheimer’s impacts on motor adaptation and Bayesian inference in vestibular path integration. Teaching responsibilities include courses on linear systems dynamics, physical measurement techniques, and motor systems regulation. His work spans 42 peer-reviewed articles, with datasets published on platforms like Dryad and Zenodo.
Thomas J. O’Dell is a Professor of Physiology and Associate Director of the Brain Research Institute at the University of California, Los Angeles (UCLA). His research focuses on synaptic plasticity mechanisms, particularly long-term potentiation (LTP) and depression (LTD), in the hippocampus. He investigates β-adrenergic signaling, NMDA receptor dynamics, and astrocyte calcium signaling in learning and memory processes. O’Dell’s work bridges molecular neurobiology with behavioral neuroscience, emphasizing how synaptic changes underlie cognitive functions. Research Interests: Neuronal plasticity mechanisms in hippocampal circuits Role of NMDA receptors in synaptic function and disease β-Adrenergic modulation of LTP Calcium signaling in astrocytes and its impact on synaptic transmission Grants & Funding: NIH R21MH115404 (Mechanisms of homeostatic plasticity) NIH R01NS060677 (Astrocyte calcium signaling in striatum) NIH R01MH060919 (NMDA receptor signaling in LTP) Labs/Teams: O’Dell leads a lab at the UCLA Brain Research Institute, collaborating on projects involving synaptic physiology, proteomics, and behavioral neuroscience.
Matthew J. Cracknell is a Senior Lecturer in Geodata Analytics at the University of Tasmania's School of Natural Sciences, specializing in Earth Sciences. He holds a PhD in Computational Geophysics (2014) and BSc (Hons) in Geophysics (2009), both from the University of Tasmania. His research integrates geoscience with machine learning to address challenges in mineral exploration, environmental remediation, and sustainable resource management. Key focuses include automated detection of geological features in drillcore imagery, decarbonization of energy systems via ore deposit discovery, and legacy mine waste characterization. Cracknell leads the CODES Research Program 6 (Geophysics and Computational Geosciences) and Module 2 of the AMIRA P1249 project. He has secured significant industry and government funding, including projects with Boliden AB, Anglo American, and the Tasmanian Government. His work emphasizes collaboration with mining partners and agencies like Geoscience Australia and Mineral Resources Tasmania. Teaching roles include developing courses on the mining value chain, climate resilience, and geodata analytics. As Graduate Research Coordinator, he promotes HDR student well-being and supervises over 20 doctoral and masters students. Awards include the 2019 Oz Minerals Explorer Challenge Prize. Key affiliations include the International Association for Mathematical Geosciences, Australian Society of Exploration Geophysicists (Tasmanian Branch Secretary), and Geological Society of Australia.
Felix A. Epp is a Postdoctoral Researcher at Aalto University's Department of Design, with an external position at the University of Helsinki (2024-2026). His work sits at the intersection of human-computer interaction, wearable technology, and design research, focusing on how interactive technologies shape human practices and future societies. Education: Doctor of Science in Technology, Aalto University (2023) Master's degree in Arts and Design, Hochschule Darmstadt (2014) Bachelor's degree in Arts and Design, Hochschule Darmstadt (2011) Epp's research explores embodied technological experiences and technology-mediated social practices, with a particular focus on wearable technologies and their impact on social-cultural practices. His doctoral work investigated how wearables shape clothing practices, and his recent work integrates critical futures studies with practice-based research to incorporate anticipation in technology innovation. He employs generative design research and qualitative fieldwork methods, including research through design in everyday contexts and participatory design approaches. His fingerprint reveals strong engagement with Wearable Technology (100%), Human-Computer Interaction (62%), and Design Research (56%). His publication record shows a clear trajectory toward anticipatory design and futures thinking in HCI, with increasing focus on how technologies can help us engage with uncertain futures. His recent work bridges design research with critical futures studies, creating methods like the Future Ripples Method to activate anticipatory capacities in innovation teams. This evolution reflects a growing recognition of the need for designers to anticipate multiple possible futures rather than designing for a single predetermined outcome. Scientific Awards: CHI 2025 Honorable Mention Award MUM 2020 Honourable Mention Award Epp has been actively involved in significant research projects including 'FutureMethods: Methodology for HCI evaluations of possible futures' (2020-2024) and 'Digital Aura: Crafting a Digital Representation of Self in the Physical World' (2017-2021). His collaborative work spans multiple institutions and disciplines, reflecting the interdisciplinary nature of his research. He serves on program committees for major conferences including ACM DIS and CHI, demonstrating leadership in the HCI community. His research contributes to Sustainable Development Goals related to technology and society, with particular relevance to responsible consumption and production through his work on smart clothing in circular economies.
John Leahy is the Allen Sinai Professor of Macroeconomics and Public Policy at the University of Michigan, holding dual appointments in the Department of Economics (College of Literature, Science, and the Arts) and the Gerald R. Ford School of Public Policy. As Chair of the Economics Department, he focuses on macroeconomic theory, monetary policy, and behavioral economics, particularly rational inattention models. His research emphasizes how cognitive limitations and information processing affect economic decisions, contrasting classical economic assumptions. Leahy has held positions at Harvard, NYU, and Boston University, and served as Coeditor of the American Economic Review and Editor of the American Economic Journal: Macroeconomics. He consults with Federal Reserve Banks, advocating for data-driven, question-first research methodologies. His work bridges theoretical rigor and practical applications, influencing policy analysis and academic discourse. Education: PhD in Macroeconomics from Princeton University; MSFS from Georgetown University; BA in Math and History. His research spans macroeconomic policy, structural change, and behavioral models of decision-making, with recent focus on wishful thinking and imperfect information processing. He collaborates widely, emphasizing interdisciplinary approaches and creative problem-solving. Key contributions include modeling rational inattention, analyzing age structure impacts on monetary policy, and exploring North-South economic disparities. His editorial leadership and academic mentorship reflect his commitment to advancing innovative economic inquiry.
