Dr. Marcel Dettling is a Group Lead in Data Analysis and Statistics at the ZHAW School of Engineering , focusing on predictive analytics, applied statistics, and complex data analysis. He also serves as a Lecturer at ETH Zurich , teaching advanced statistical methods. Education : PhD in Mathematics (2000-2004), ETH Zurich Postdoc in Applied Statistics (2004-2006), Johns Hopkins University His research spans predictive analytics (regression, classification, time series), data mining, and applications in health economics, transportation safety, social sciences , and business analytics . Recent work includes pharmaceutical cost group analysis for Swiss healthcare and predictive maintenance for marine vessels. Selected publications highlight his expertise in flight trajectory modeling , deep learning error mitigation , and statistical frameworks for rehabilitation finance . His projects address diverse fields like crowdworking in nursing, energy optimization for shipping, and customer behavior prediction.
Scott E. Denmark is the Reynold C. Fuson Professor of Chemistry at the University of Illinois, Department of Chemistry, within the College of Liberal Arts & Sciences. He earned his S.B. from MIT (1975) and D.Sc. Tech. from ETH-Zürich (1980) under Albert Eschenmoser. His research focuses on synthetic organic chemistry, organoelement systems (Si, P, Sn, S, Li), palladium catalysis, and chemoinformatics. He has pioneered methods in asymmetric catalysis, total synthesis of natural products, and tandem cycloaddition reactions. Denmark chairs editorial boards for top journals and leads the Denmark Group, mentoring over 100 students. Awards include the Paracelsus Prize (2020), Noyori Prize (2019), and membership in the National Academy of Sciences (2018). Education: S.B., Massachusetts Institute of Technology, 1975 D.Sc. Tech., ETH-Zürich, 1980 (Advisor: Albert Eschenmoser) Research Interests: Design of new organic reactions and catalysts Structure-reactivity relationships in organoelement systems Total synthesis of alkaloids, polyenes, and glycosides Machine learning for catalyst optimization Asymmetric phase transfer catalysis Green chemistry using water-based systems Recent Research Trends: Recent work emphasizes indium-catalyzed allylations of carbohydrates (Nature, 2025) and chemoinformatics-driven catalyst design. Collaborations with MIT and industry (e.g., Pfizer, Amgen) highlight applied impact. Awards: Paracelsus Prize (2020) Ryoji Noyori Prize (2019) Member, National Academy of Sciences (2018) Member, American Academy of Arts and Sciences (2017) Advising & Grants: Mentored 40+ PhD students and 60+ postdocs. Active in funding initiatives for chemoinformatics and sustainable catalysis. Recent grants include Pines Fellowship (2025) for student Matthew Albritton. Labs/Teams: Leads the Denmark Group at UIUC, known for interdisciplinary projects merging organic synthesis with computational methods. Collaborates globally, including with ETH-Zürich and Hiroshima University.
Matthew Wills is Professor of Evolutionary Palaeobiology in the Department of Life Sciences at the University of Bath. He is affiliated with the Milner Centre for Evolution and the Centre for Mathematical Biology, where he leads research into macroevolutionary processes using fossil and molecular data. His work integrates phylogenetics, morphological disparity, and stratigraphic congruence to understand large-scale evolutionary patterns. His research interests focus on the role of fossils in building phylogenies, the evolution of morphological complexity, and the testing of macroevolutionary trends such as early high disparity and increasing complexity. He investigates how developmental shifts underpin major evolutionary transitions and how fossilization biases affect our understanding of the Tree of Life. His lab conducts projects on arthropod supertrees, molluscan ontogeny, and the phylogeny of Eumalacostraca using molecules, morphology, and fossils. His recent publications reveal a strong focus on quantifying morphological complexity, vertebral evolution in mammals, species richness in birds, and the impact of boundaries on trait evolution. These works frequently appear in high-impact journals like Nature Communications and Nature Ecology & Evolution , indicating a trend toward integrative, data-rich evolutionary analyses combining paleontological, morphological, and phylogenomic approaches. Principal Investigator, Biodiversity and The Sixth Mass Extinction (Royal Commission for the Exhibition of 1851) Principal Investigator, Susceptibility to Mass Extinctions: Ammonites as a Case Study (NERC) Principal Investigator, PLUTO Project (BBSRC) Principal Investigator, Arthropod Supertree of Life (BBSRC) He has supervised 17 research students and contributes to public discourse through platforms like The Conversation . His work supports UN Sustainable Development Goals related to life on land and climate action through deep-time biodiversity research.
Dr. Rong Fan is the Harold Hodgkinson Professor of Biomedical Engineering and Professor of Pathology at Yale University. His research focuses on developing and applying single-cell and spatial omics technologies to study immune systems, cancer, and aging. His lab has pioneered technologies like the IsoCode microchip for high-throughput protein profiling, and spatial multi-omics platforms (e.g., DBiT-seq, spatial-ATAC-seq) to analyze tissue complexity at cellular resolution. He co-founded IsoPlexis, Singleron Biotechnologies, and AtlasXomics to commercialize these innovations. Education: PhD in Chemistry from UC Berkeley (2006), B.S. in Applied Chemistry from University of Science and Technology of China (1999). Postdoctoral training at Caltech before joining Yale in 2010. Research interests include CAR-T cell therapy optimization, spatial epigenomics, and multi-omics integration. Key achievements include discovering biomarkers predictive of CAR-T efficacy and defining spatial genomic landscapes in cancer and neuroinflammation. Awards: NSF CAREER Award, Packard Fellowship, election to AIMBE, CASE, and NAI. Serves on advisory boards for Bio-Techne and Yale Ventures. Active in training future scientists via the Yale Biomedical Engineering and Yale School of Medicine programs.
Eric Collet is a University Professor and Head of Department at the Institute of Physics of Rennes (IPR), a joint research unit of CNRS and Université de Rennes. His work centers on ultrafast photoinduced phase transitions, spin-crossover materials, and the control of functional materials using light and THz excitation. He leads a dynamic research team and is deeply involved in international collaborations, particularly through the IM-LED International Laboratory with Japan. University: University of Rennes School: Institute of Physics of Rennes Department: Department of Materials and Light Academic Rank: Professor Email: eric.collet@univ-rennes.fr Professor Collet's research explores the ultrafast dynamics of molecular and condensed matter systems, especially those exhibiting multistability and photoresponsiveness. His work combines femtosecond optical and X-ray techniques to probe structural and electronic changes at atomic scales. Key areas include spin-crossover phenomena, photomagnetism, ferroelasticity, and nonlinear phononics. He investigates how light can trigger cooperative responses in materials, leading to persistent phase transitions with applications in photonics and memory devices. The recent publications of Eric Collet reveal a strong focus on ultrafast structural dynamics, photoinduced charge and spin transitions, and the coupling of electronic states with lattice distortions. His team frequently employs advanced X-ray methods at large-scale facilities like ESRF and LCLS. The research spans from fundamental quantum dynamics to applied materials science, with recurring themes in symmetry breaking, cooperative switching, and room-temperature photoresponse in molecular systems. Scientific awards and recognitions include: CNRS Silver Medal (2020) Louis Ancel Prize, French Physical Society Hot Paper selection in PCCP (2019) Very Important Paper recognition in Eur. J. Inorg. Chem. (2019) News & Views feature in Nature Chemistry (2020) Eric Collet actively mentors PhD students and postdoctoral researchers, and has supervised numerous publications in top journals. He has led major scientific initiatives such as the UCM2018 symposium and JMC2021 conference. His research is supported by grants from ANR, CNRS, and the Institut Universitaire de France. He collaborates extensively with institutions in France, Japan, and beyond. His research is conducted primarily within the Department of Materials and Light at the Institute of Physics of Rennes. He co-directs the IM-LED International Laboratory with Prof. Shin-ichi Ohkoshi (University of Tokyo) and collaborates with groups in Bordeaux, Lebanon, and Japan. His lab specializes in time-resolved X-ray diffraction, ultrafast spectroscopy, and nonlinear optical control of materials.
Krishna Gummadi is a Scientific Director and Professor at the Max Planck Institute for Software Systems (MPI-SWS) in Germany, where he leads the Networked Systems Research Group. He also holds a professorship at the University of Saarland, demonstrating his dual commitment to research and academic instruction in computer science. His educational background includes: Ph.D. in Computer Science and Engineering from the University of Washington (2005) B.Tech. in Computer Science and Engineering from the Indian Institute of Technology, Madras (2000) Gummadi's research spans networked and distributed computer systems with a current focus on social computing systems. His work addresses critical challenges in algorithmic fairness, privacy in social media, trustworthiness of online identities, and information dissemination in social networks. He approaches these problems through interdisciplinary methods combining user-centric studies, data-centric analysis, and systems-centric design to create practical solutions that enhance fairness, transparency, and user control in online platforms. His methodology integrates large-scale observational studies, computational modeling, and system implementation to tackle complex human-computer interaction challenges at societal scale. His recent publications reveal a strong emphasis on fairness in algorithmic decision making, with significant contributions to quantifying and addressing discrimination in machine learning systems. His work bridges computer science, social science, and ethics, creating frameworks for fair classification, understanding media bias, and developing privacy-preserving techniques that maintain functionality while protecting user data. The research demonstrates a progression from technical system design to addressing societal implications of computing systems. Among his notable scientific achievements: ERC Advanced Grant in 2017 for 'Foundations for Fair Social Computing' Test of Time Awards at ACM SIGCOMM and AAAI ICWSM Casper Bowden Privacy Enhancing Technologies (PET) and CNIL-INRIA Privacy Runners-Up Awards IW3C2 WWW Best Paper Honorable Mention Multiple Best Paper awards across prestigious conferences Gummadi has advised numerous PhD students and postdoctoral researchers who have gone on to prominent positions in academia and industry. His ERC Advanced Grant has supported extensive research into fair social computing, while his leadership in major conferences (including serving as General Chair for ICWSM 2016 and Program Chair for WWW 2015) has shaped research directions in the field. His teaching portfolio includes courses on Distributed Systems, Human-Centered Machine Learning, and Social Media Analysis. He leads the Networked Systems Research Group at MPI-SWS, which has developed several publicly available systems including tools for fair classification, privacy risk assessment, trust evaluation in social media, and information diet management. The group's work bridges theoretical advances with practical implementations that address real-world challenges in social computing, with numerous software releases and datasets made available to the research community.
Dr. Anett Hoppe is a research staff member at the Leibniz Information Centre for Science and Technology (TIB) in Hannover, Germany, where she works in the Visual Analytics research group. Her research focuses on the intersection of artificial intelligence, education technology, and information science, with particular emphasis on how people learn through search processes and educational video consumption. Dr. Hoppe completed her academic journey with: Ph.D. in Semantic Web technologies for online user profiles from the University of Burgundy, Dijon, France Her primary research interests span Search as Learning, software-based support for scientific reproducibility, and ethical considerations in computer-based decision making. She investigates how visual elements, reading sequences, and AI technologies impact knowledge acquisition during web search and educational video consumption. Her work bridges human-computer interaction, educational psychology, and information retrieval to create more effective learning experiences, with recent publications examining the role of large language models, vision-language models, and visual complexity in educational contexts. Analysis of her recent publications (2024-2025) reveals a strong interdisciplinary focus combining computer science, educational psychology, and information science. Her research examines video-based learning effectiveness, knowledge gain prediction, educational resource discovery, and the impact of visual elements on learning outcomes. She consistently explores how AI technologies can be leveraged to enhance educational experiences while maintaining attention to ethical considerations and scientific reproducibility. Dr. Hoppe maintains active collaborations with researchers across multiple institutions, with frequent co-authorship patterns indicating strong research partnerships, particularly with Ralph Ewerth and other members of the Visual Analytics group at TIB. Her work supports TIB's mission to advance knowledge infrastructure and scholarly communication through innovative technological solutions while directly addressing practical challenges in educational technology and information retrieval.
Kay Severin is a full professor at the Laboratory of Supramolecular Chemistry (LCS) within École Polytechnique Fédérale de Lausanne (EPFL) , Switzerland. His research focuses on the design and reactivity of metal-ligand assemblies, including coordination cages, metalloligands, and supramolecular receptors. He has pioneered the use of metalloligands for constructing heterometallic architectures and developed systems for anion extraction and stimuli-responsive hydrogels. Key funder: Swiss National Science Foundation (FNS) Collaborative work with Rosario Scopelliti and Farzaneh Fadaei Tirani Research Interests: Severin's work spans supramolecular chemistry, organometallic synthesis, and functional materials. Recent projects include: Dynamic palladium-based hydrogels with anion-responsive crosslinks Gold(I)-driven nano-onion structures via π-stacking Triazene-derived ligands for Sandmeyer-type reactions Metalloligand assembly of Fe/Pd/Au heterotrimetallic cages Publication Trends: Over 300 publications since 1994, with recent emphasis on: Coordination-driven self-assembly (2024: 6 articles) Triazene and diazoolefin reactivity (2025: 4 articles) Metal-ligand interactions in nanogels and vesicles (2024-2025: 3 articles) Environmental applications in anion extraction (2025: 1 article)
Prof. Tine De Moor is a leading scholar at Rotterdam School of Management, Erasmus University Rotterdam , holding the Chair of Social Enterprise and Institutions for Collective Action . With a PhD in History from Ghent University (2003), she specializes in long-term institutional analysis of collective action across historical and modern contexts. Department of Business-Society Management ERIM Research Member Former Utrecht University Professor (2012-2020) Research Focus: Her work examines institutions for collective action from medieval commons to modern cooperatives, with key contributions to understanding energy cooperatives, citizen collectives, and social enterprises. She leads innovative citizen science projects and investigates labor market participation patterns over the past millennium. Publication Trends: Recent articles focus on platform cooperatives in the gig economy , energy prosumerism motivations , and institutional grammar applications . She explores paradoxes in cooperative governance and develops computational models for historical institutional analysis. Awards & Grants: Recipient of prestigious honors including ERC Starting Grant NWO-VIDI Grant International Association for Study of the Commons Leadership Advising & Collaboration: She has co-authored with scholars like Daan van Weeren and David Bunders, with significant citations in environmental and social science domains. Her work informs policy on collective resource management and sustainable transitions. Labs & Teams: Founding editor of the International Journal of the Commons , she leads the CollectieveKracht knowledge platform and collaborates with Triodos Bank stakeholders. Her research integrates GIS, archival analysis, and agent-based modeling.
Eduardo Rocha is a Professor and Head of the Microbial Evolutionary Genomics laboratory at the Institut Pasteur, within the Department of Genomes and Genetics. His research integrates bioinformatics, molecular evolution, and genomics to understand bacterial genome organization and dynamics, particularly focusing on mobile genetic elements and their role in adaptation and antibiotic resistance. His research interests include microbial evolutionary genomics, genome organization, horizontal gene transfer, mobile genetic elements (plasmids, phages, integrons), bacterial pathogen evolution, and computational biology. His work lies at the intersection of molecular evolution, population genetics, and molecular epidemiology, with strong translational implications for understanding antimicrobial resistance and infectious disease emergence. The recent publications highlight a consistent focus on mobile genetic elements, genome plasticity, and bacterial adaptation. Key themes include the role of integrons and CRISPR-Cas systems in bacterial immunity, plasmid-mediated spread of antibiotic resistance, phage-plasmid interactions, and the development of bioinformatics tools for microbial genomics. These works frequently appear in high-impact journals such as Science , Nature Microbiology , and PLoS Biology , reflecting significant contributions to the field. Eduardo Rocha leads multiple funded research projects, including ERC-2011-StG EVOMOBILOME, ANR Magisbac, and ANR SHAPE. He has developed and maintains several widely used bioinformatics software tools: IntegronFinder, MacSyFinder, PanACoTA, SatelliteFinder, CapsuleFinder, TXSScan, and others. He mentors a large team of PhD students, postdoctoral researchers, and engineers, and has supervised numerous former students who now hold independent research positions worldwide. Eduardo Rocha has received research funding from major agencies including the European Research Council (ERC) and the French National Research Agency (ANR). His work is central to the LabEx IBEID and the INCEPTION convergence program, where he serves on the steering committee, promoting interdisciplinary research in infectious disease emergence. His laboratory, part of the Genomes and Genetics department, actively contributes to microbial evolutionary genomics through both methodological development and biological discovery. The team participates in networks such as Phages.fr, GDR BIM, and GDR AIEM, reinforcing its collaborative and integrative approach.
Olaf Steinbach is a University Professor (Univ.-Prof.) at the Institute of Applied Mathematics at Graz University of Technology. His academic career spans over three decades with continuous research activity from 1992 to the present, including publications scheduled for 2026. He serves as a project manager for several research initiatives including the Special Research Area (SFB) F90 Computational Electric Machine Laboratory, which runs from 2022 to 2026. Professor Steinbach's research interests primarily focus on Numerical Analysis and Computational Mathematics . His work centers around developing and analyzing advanced numerical methods, particularly Finite Element Methods (FEM) and Boundary Element Methods (BEM), for solving partial differential equations (PDEs) and optimal control problems. His research spans both theoretical aspects (such as error analysis, stability, and convergence) and practical applications (including electric machines, electromagnetics, and biomechanics). He has made significant contributions to space-time finite element methods, which treat time as an additional dimension in the discretization process, leading to more robust and efficient solvers for time-dependent problems. Analysis of his recent publications (2021-2026) reveals a strong focus on optimal control problems governed by partial differential equations, with particular emphasis on elliptic, parabolic, and hyperbolic PDEs. His work demonstrates a consistent pattern of developing robust numerical methods with rigorous error analysis, often incorporating regularization techniques to handle challenging constraints. The applications span computational electromagnetics (particularly electric machines), fluid dynamics, and wave propagation problems. His research increasingly incorporates advanced computational techniques including parallel computing and isogeometric analysis. Professor Steinbach has supervised numerous doctoral students and has been actively involved in organizing academic events, including summer schools on Boundary Element Methods. His collaborative network extends across multiple disciplines and institutions, reflecting the interdisciplinary nature of his work in computational mathematics. His research has been supported through multiple significant projects including DK-W1244 Doctoral Program on Partial Differential Equations, the EU CASOPT project on optimization of industrial devices, and the ongoing Special Research Area on Computational Electric Machine Laboratory. These projects demonstrate his leadership in establishing research frameworks that bridge theoretical mathematics with practical engineering applications. Professor Steinbach maintains an active research group within the Institute of Applied Mathematics, collaborating closely with researchers in computational engineering, electrical engineering, and biomechanics. His work on the Computational Electric Machine Laboratory represents a particularly strong interdisciplinary effort combining mathematical theory with electrical engineering applications.
Netina Tan is University Scholar and Associate Professor of Political Science at McMaster University, where she conducts research on authoritarian resilience, digital technology, and political representation of women and ethnic minorities, with a focus on East and Southeast Asia. Her scholarly work has gained international recognition, with publications referenced in 24 Wikipedia pages and covered by 32 news outlets. Dr. Tan earned her PhD in Comparative Politics from the University of British Columbia, a Master's in Political Science from the University of Regina, a Master's in Southeast Asian Studies from the National University of Singapore, and a Bachelor of Arts in Political Science and Sociology from the National University of Singapore. She was also a Social Sciences and Humanities Research Council (SSHRC) Postdoctoral Fellow at the University of Toronto. Her research interests center on authoritarianism, democratization, and representation in Asian contexts, with particular attention to how digital technology affects political systems. She investigates how women and ethnic minorities gain political representation in authoritarian and semi-authoritarian regimes across Southeast Asia, examining electoral systems, quota mechanisms, and party strategies that either facilitate or hinder inclusion. Analysis of her recent publications reveals consistent focus on democratic backsliding in Southeast Asia, especially Singapore; the relationship between digital technology and authoritarian resilience; electoral manipulation tactics in Asian contexts; and gender representation through quotas and electoral systems. Her work bridges comparative politics, gender studies, and technology studies with regional expertise in Southeast Asia. University Scholar at McMaster University Dr. Tan has advised graduate students including Cassandra Preece, with whom she has co-authored publications. Her research has attracted significant scholarly attention with hundreds of readers on academic platforms like Mendeley. She regularly contributes expert commentary to media outlets on Southeast Asian politics, particularly regarding elections in Cambodia, Malaysia, and Singapore. Her scholarly activities include directing research projects on electoral malpractice in Asia and gender equality in Myanmar politics, with recent books including 'Putting Women Up Gender Equality and Politics in Myanmar' (2024) and 'Electoral Malpractice in Asia Bending the Rules' (2023).
Melanie Weber is an Assistant Professor of Applied Mathematics and Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS), leading the Geometric Machine Learning Group. Her research focuses on leveraging geometric structures in data for designing efficient machine learning and optimization algorithms with theoretical guarantees. She holds a PhD from Princeton University (2021) and has held fellowships at the Mathematical Institute of Oxford, Brasenose College, and the Simons Institute. Her work bridges geometry, optimization, and machine learning, with funding from NSF, Sloan Foundation, and Harvard initiatives. Education : PhD in Applied Mathematics, Princeton University (2021) BSc/MSc in Mathematics and Physics, University of Leipzig (2016) Research Interests : Dr. Weber's research integrates geometric principles into machine learning and optimization, focusing on non-Euclidean spaces, graph structures, and manifold-based methods. Key areas include optimization on Riemannian manifolds, curvature-based analysis (e.g., Ricci curvature), and developing algorithms resilient to data geometry challenges like over-smoothing in graph neural networks. Her work emphasizes theoretical foundations while addressing practical scalability in high-dimensional data. Awards & Recognition : 2024 Sloan Research Fellowship 2023 Leslie Fox Prize in Numerical Analysis 2023 NSF Grant for Geometric Optimization Grants & Funding : Supported by National Science Foundation (NSF), Alfred P. Sloan Foundation, Aramont Foundation, Harvard Dean’s Fund, and Harvard Data Science Initiative. Labs & Collaborations : Leads the Geometric Machine Learning Group at SEAS, collaborating with institutions like MIT, Max Planck Institute, and industry labs (Facebook, Google, Microsoft). Active in organizing workshops on geometric methods and curvature analysis.
Stephanie Gil is an Assistant Professor of Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences. Her research focuses on artificial intelligence, robotics, and distributed systems, particularly addressing challenges in multi-agent coordination, resilience to adversarial attacks, and wireless communication for autonomous systems. She leads the REACT Lab, advancing research in resilient multi-robot networks and cyber-physical systems. Her work integrates machine learning, control theory, and wireless sensing to solve problems such as whale tracking via autonomous robots, proactive multi-robot routing, and decentralized exploration without explicit information exchange. She has received prestigious awards, including the DARPA Young Faculty Award (2024) and the Amazon Research Award (2021). Key research areas include resilient distributed optimization, trust-centered coordination in multi-agent systems, and leveraging wireless signals (e.g., WiFi-CSI) for sensing and bearing estimation. Her contributions span both theoretical frameworks and practical implementations, with a focus on real-world applications like autonomous rideshare routing and environmental monitoring. Dr. Gil’s research also explores trust and cybersecurity in dynamic networks, with publications on crowd vetting, malicious robot detection, and adaptive communication strategies. She collaborates on interdisciplinary projects, such as Project CETI, combining AI and robotics for ecological studies.
Clyde Kruskal is an Associate Professor in the Department of Computer Science at the University of Maryland, College Park. His research focuses on parallel architectures, models, and algorithms. He earned a Ph.D. from New York University in 1981 and a bachelor’s degree from Brandeis University in 1976. His work includes foundational contributions to parallel computing, such as the read–modify–write concept in distributed systems. Kruskal’s research spans topics like interconnection networks, synchronization mechanisms, and algorithm design for parallel systems. Education: Bachelor’s Degree: Brandeis University, 1976 Master’s Degree: New York University (Courant Institute), 1978 Ph.D.: New York University (Courant Institute), 1981 Research Interests: Parallel computing architectures, parallel algorithms design, multiprocessor synchronization, interconnection networks, and computational geometry problems like graph coloring and visibility analysis. His work emphasizes theoretical foundations and practical implementations in parallel systems. Notable Contributions: Kruskal co-authored the book Problems With A Point: Exploring Math And Computer Science (2019), and his research includes foundational papers on parallel prefix operations, sparse matrix algorithms, and synchronization protocols. His publications span over three decades, reflecting sustained contributions to parallel computing theory and practice. Advising & Outreach: He has mentored students through programs like the Summer Combinatorial Algorithms REU at UMD, fostering undergraduate research in algorithm design and parallel computing.
