Kai Leonhard is an Adjunct Professor at the Chair of Technical Thermodynamics , RWTH Aachen University. His research focuses on computational chemistry, thermodynamics, and molecular modeling, particularly in solvent design and reactive chemical processes. Department: Chair of Technical Thermodynamics Email: kai.leonhard@ltt.rwth-aachen.de Prof. Leonhard's work integrates quantum chemistry with computer-aided molecular and process design (CAMD/CAPD), emphasizing solvation thermodynamics, reaction kinetics, and machine learning applications. His projects span biofuel combustion, microgel synthesis, and sustainable solvent development. Recent publications highlight advancements in COSMO-RS-based solvent screening, reaction network exploration via ChemTraYzer-TAD, and multi-fidelity modeling for partition coefficients. He employs machine learning to enhance predictive thermodynamic models and optimize chemical processes.
Prof. Dr.-Ing. Jürgen Teich is a full Professor and Chair for Hardware-Software Co-Design at the Department of Computer Science, Friedrich Alexander University Erlangen-Nuremberg (FAU). He serves as Head of Department Computer Science and Vice Dean of the Technical Faculty since August 2024, and has been Speaker of the FAU Research Center Embedded System Initiative (FAU ESI) since 2023. His educational background includes: Diploma degree in Electrical Engineering, University of Kaiserslautern (1989) Dr.-Ing. degree in Electrical Engineering, University of Saarland (1993) Habilitation (PD Dr.-Ing.) entitled "Synthesis and Optimization of Digital Hardware/Software Systems" (1996) Prof. Teich's research focuses on Embedded Systems , Invasive Computing , Hardware-Software Co-Design , and Reconfigurable Computing . His work spans from theoretical foundations to practical implementations, with particular emphasis on resource-constrained systems, many-core architectures, and energy-efficient computing. He has pioneered research in invasive computing paradigms that enable more efficient use of many-core processors by allowing applications to dynamically claim resources. His recent publications reveal a strong trend toward energy-efficient AI deployment on embedded devices , security of embedded systems , and novel memory technologies . There's a clear focus on practical implementations of machine learning on microcontrollers (TinyML), hardware acceleration for data processing, and innovative approaches to power management in self-powered systems. Among his notable scientific awards are: IEEE Fellow (since 2018) Member of Academia Europaea, Section Informatics (since 2011) Member of the National Academy of Science and Engineering (acatech) (since 2018) Member of the German Society of Humboldtians (since 2021) Prof. Teich has been Principal Investigator for numerous DFG-funded projects including SFB/Transregio 89 "Invasive Computing" (2010-2022), SFB 694, and multiple priority programs. He has coordinated large collaborative research efforts across Germany and internationally, with significant funding from DFG and other sources. His research group has produced influential work in embedded systems design and co-design methodologies. He leads the Hardware-Software Co-Design research group at FAU, which focuses on innovative approaches to embedded system design, invasive computing architectures, and efficient implementation of machine learning on resource-constrained devices. The group maintains strong collaborations with industry partners including Intel, Xilinx, and automotive companies.
Peng Gao is a Professor in the Department of Geography and the Environment at Syracuse University, affiliated with the Maxwell School of Citizenship and Public Affairs. His work bridges river geomorphology and urban geospatial analysis, leveraging GIS, remote sensing, and UAV technologies to address environmental and social challenges. Education: Ph.D., Physical Geography, State University of New York at Buffalo (2003) M.S., Physical Geography, Lanzhou University (1993) B.S., Solid Mechanics, Lanzhou University (1990) Professor Gao specializes in river morphodynamics—particularly in the Qinghai-Tibet Plateau—and geospatial applications for urban planning. His research examines braided/meandering river systems, peatland hydrology, and how urban built environments influence social inequities and public health outcomes through spatial analysis. His 2020-2024 publications reveal a dual focus: (1) fluvial processes in high-altitude regions (e.g., neck cutoff dynamics, braided river discharge estimation using Landsat), and (2) urban applications (e.g., green building design, lead poisoning exposure mapping). This reflects a strategic integration of field geomorphology with computational geospatial modeling. Professor Gao actively mentors through SOURCE undergraduate research grants and PhD committees. Current funded projects include peatland mapping in the Andean Altiplano, I-81 Viaduct impact analysis in Syracuse, and studies on urban built environments affecting childhood lead poisoning. His work utilizes UAVs for BVLOS operations and collaborates with Syracuse CoE on urban environmental simulations, emphasizing technical innovation in geospatial data acquisition and analysis.
Prof. Dr. Hüseyin Yapıcı is a faculty member in the Department of Mechanical Engineering at Başkent University . His research focuses on Nuclear Energy Systems , Accelerator Technology , and Thermodynamics . Nuclear Reactor Design Energy Systems Optimization Heat Transfer Analysis His work involves numerical simulations , neutronic analysis , and nuclear waste transmutation . Recent publications highlight three-dimensional power density modeling in accelerator-driven systems and tritium production studies. Prof. Yapıcı has supervised numerous students, including Gizem Bakır , Alper Buğra Arslan , and Büşra Durmaz , across diverse projects from fusion-fission hybrids to renewable energy systems .
Siew, Shu Qin Cynthia is an Assistant Professor at the National University of Singapore, specializing in psycholinguistics and cognitive science. She holds a Ph.D. and M.A. from Kansas University (KU) and a B.Soc.Sci. (Hons.) from NUS. Her research focuses on applying network analysis to study cognitive structures like the mental lexicon and semantic memory. Education: Ph.D. in Psychology, KU M.A. in Psychology, KU B.Soc.Sci. (Hons.) in Linguistics, NUS Her work integrates cognitive psychology experiments, computational modeling, and linguistic corpora to explore two core themes: (1) How lexicon structure influences processing (e.g., phonological/orthographic similarity affecting word recognition), and (2) How lexicon structure evolves over time (e.g., language acquisition across monolinguals and bilinguals). Recent publications highlight her innovative use of network science to model phonological and semantic networks and software tools like spreadr for simulating spreading activation. This work bridges computational methods with empirical studies on lexical retrieval and memory organization.
Peter J. Thomas is a Professor in the Department of Mathematics, Applied Mathematics, and Statistics at Case Western Reserve University's College of Arts and Sciences, with secondary appointments in Electrical Engineering and Computer Science, Cognitive Science, and Biology. He serves as Co-Editor-in-Chief of Biological Cybernetics and leads the Computational Biomathematics Laboratory. Primary Affiliation: Department of Mathematics, Applied Mathematics, and Statistics Secondary Affiliations: Department of Electrical Engineering and Computer Science, Department of Cognitive Science, Department of Biology Leadership: Co-Editor-in-Chief of Biological Cybernetics Thomas earned his B.A. in Physics and Philosophy from Yale University (1990), M.S. in Mathematics from the University of Chicago (1994), and both M.A. in Conceptual Foundations of Science and Ph.D. in Mathematics from the University of Chicago (2000). His research spans mathematical neuroscience, theoretical biophysics, and information theory applications to biological systems. Thomas specializes in understanding how noise and stochasticity affect neural coding, developing mathematical frameworks for gradient sensing in cells, and applying graph theory to biological networks. His work on stochastic shielding has provided novel approaches to simplifying complex stochastic models while preserving essential dynamics. His research bridges theoretical mathematics with experimental neuroscience through collaborations with the Chiel laboratory and others. Thomas's recent publications demonstrate a strong focus on stochastic oscillators, sensory feedback mechanisms, and information theory applications to biological systems. His work consistently develops novel mathematical frameworks to address specific biological questions, with significant contributions to understanding phase dynamics in neural oscillators and information processing in biochemical signaling. Core Fulbright Scholar Program (2013) Simons Fellow in Mathematics Program (2014) Multiple NSF grants as Principal Investigator Co-Editor-in-Chief of Biological Cybernetics Thomas has mentored numerous students at all levels, from undergraduates to postdoctoral researchers. His laboratory has produced successful scholars who have gone on to faculty positions at institutions like New Jersey Institute of Technology and the University of Nevada, Reno. He has actively organized workshops at the Banff International Research Station and served on editorial boards for leading journals in computational neuroscience. The Computational Biomathematics Laboratory focuses on developing mathematical frameworks to understand neural dynamics, cellular signaling, and pattern formation. The lab maintains strong collaborations with experimental neuroscience groups and has made significant contributions to understanding rhythmic neural systems, respiratory control mechanisms, and information processing in biological systems.
Fred Feinberg is the Joseph and Sally Handleman Professor of Marketing and Professor of Statistics (by courtesy) at the University of Michigan, where he is also an Affiliated Faculty member of the Center for the Study of Complex Systems. His work integrates advanced Bayesian methods with large-scale marketing data to illuminate how people make choices under uncertainty. Education Ph.D., Sloan School of Management, Massachusetts Institute of Technology (1989) Doctoral program in Mathematics, Cornell University (1983–84) S.B. Mathematics & S.B. Philosophy, Massachusetts Institute of Technology (1983) Research Focus Feinberg’s scholarship centers on discrete choice models that leverage real-world decisions to infer latent attributes such as demographics, product appeal, and socioeconomic status. Methodologically, he employs Hierarchical Bayes (HB) models and cutting-edge MCMC algorithms to handle massive data sets, while theoretically he advances dyadic utility theory and optimal search under uncertainty. Applications span click-through behavior, menu-based choice, online dating preferences, spatial marketing, and consumer reactions to intangible or aesthetic product features. Recent empirical studies explore the wearout versus weariness effects of online advertising, the impact of data breaches on consumer behavior, and dynamic pricing for digital media subscriptions. Across these projects, Feinberg couples rigorous statistical innovation with actionable managerial insights, bridging marketing science, operations, and engineering. Scientific Awards & Leadership Joseph and Sally Handleman Endowed Professorship Past President, INFORMS Society for Marketing Science Departmental Editor, Production and Operations Management Former Co-Editor, Marketing Science Co-author (with T. Kinnear & J. Taylor) of the textbook Modern Marketing Research: Concepts, Methods, and Cases Grants & Collaborations While explicit grant lists are not provided, Feinberg’s prolific publication record in top-tier journals (e.g., Journal of Marketing Research , Marketing Science , Management Science ) and editorial board service imply sustained external funding and interdisciplinary partnerships, particularly with operations, engineering, and computer-science groups. Laboratories & Teams Feinberg is formally affiliated with the Center for the Study of Complex Systems (CSCS) at the University of Michigan, where he collaborates on network-based choice frameworks and large-scale behavioral data analytics. He maintains active ties to the Ross Marketing faculty and the Department of Statistics, fostering joint workshops and doctoral training initiatives.
Daniel Rabosky is a Professor in the Department of Ecology and Evolutionary Biology at the University of Michigan, where he also serves as Curator at the Museum of Zoology. His research program spans macroevolution, speciation dynamics, and phylogenetic comparative methods, with particular expertise in Australian reptiles and squamate evolution. Rabosky maintains an active laboratory and is currently seeking new graduate students and postdoctoral fellows to join his research team. Rabosky's research interests focus on macroevolutionary patterns and processes, particularly the connections between microevolutionary dynamics and large-scale biodiversity patterns. His work integrates phylogenetic comparative methods with ecological and morphological data to understand speciation processes, adaptive radiations, and the evolutionary dynamics of reptile communities, especially Australian skinks. He has made significant contributions to methodological developments in evolutionary biology through software tools like BAMM (Bayesian Analysis of Macroevolutionary Mixtures) and BAMMtools for analyzing evolutionary rate heterogeneity across phylogenetic trees. Analysis of Rabosky's recent publication record reveals a strong focus on evolutionary theory, methodological development, and empirical studies of reptile diversification. His work spans theoretical macroevolution, phylogenetic comparative methods, Australian herpetology, and the connections between population-level processes and macroevolutionary patterns. The research demonstrates increasing integration of genomic data with traditional morphological and ecological approaches, reflecting broader trends in evolutionary biology. Rabosky actively mentors graduate students including Matheus Januário and Tristan Schramer, and supervises postdoctoral fellows Michael Harvey, Jonathan Mitchell, Sonal Singhal, and Rudolf von May. His laboratory receives research funding supporting multiple projects in macroevolutionary dynamics, with recent grants likely supporting work on the connections between metapopulation ecology and speciation rates, as evidenced by his 2025 Ecology Letters paper. The Rabosky Lab maintains a strong presence in both theoretical and empirical evolutionary biology, with particular strengths in phylogenetic methods development, squamate reptile evolution, and the interface between micro- and macroevolution. The lab actively collaborates with researchers across institutions and contributes to major initiatives like the openVertebrate project for 3D imaging of museum specimens.
Professor Serdar Özoğuz is a full faculty member at the Department of Electronics and Communication Engineering , Istanbul Technical University . Holding a Ph.D. from ITU (2000) and a M.Sc. from ITU (1993) , he has taught courses like Active Network Synthesis , Basics of Electrical Circuits , and Scientific Research Ethics since 2014. His research focuses on Active RC filters Nonlinear electronic circuits Analog integrated circuit design Network synthesis . His recent publications emphasize machine learning applications in RF/microwave design , quantum computing for CAD tools , and emerging memory devices . The department's Devreler ve Sistemler Laboratuvarı Çok Geniş Ölçekli Tümdevre (VLSI) Tasarımı Laboratuvarı likely support his work. Despite no explicit awards listed, his 15+ recent articles in high-impact journals underscore his technical contributions.
Amir Bahadori serves as Professor and Nuclear Engineering Program Director in the Department of Mechanical and Nuclear Engineering at Kansas State University's Carl R. Ice College of Engineering, holding the Hal and Mary Siegele Professorship in Engineering. He directs the Radiological Engineering Analysis Laboratory (REAL) and established the Institute for Radiation Health Studies (IRHS) in 2024, focusing on radiation protection, space radiation environments, and radiation health effects. His educational background includes: Ph.D. in Biomedical Engineering, University of Florida (2012) M.S. in Nuclear Engineering Sciences, University of Florida (2010) B.S. in Mechanical Engineering and Mathematics, Kansas State University (2008) Bahadori's research spans radiation transport modeling, dosimetry, and risk assessment with applications in space exploration, medical physics, and radiation epidemiology. He develops computational frameworks for radiation exposure scenarios and biological response prediction, emphasizing space radiation protection for Artemis missions and chronic exposure studies through the Million Person Study collaboration. Analysis of his recent publications reveals dominant themes in space radiation measurement (Artemis missions), radiation epidemiology (Million Person Study innovations), and advanced detection systems (miniaturized neutron spectrometers). His work increasingly integrates big data approaches for radiation risk assessment and electrostatic shielding concepts for deep-space exploration. His scientific recognition includes: NASA Graduate Student Research Fellowship (2009) Certified Health Physicist designation Big 12 faculty fellowship (2022-2023) NCRP council election (2024) Two USPTO patents Bahadori secures substantial research funding from NASA for space radiation instrumentation, Department of Energy projects via the Kansas City National Security Campus, and collaborative epidemiological studies. He mentors nuclear engineering graduate students while leading interdisciplinary teams developing radiation protection solutions for aerospace and medical applications. His laboratory infrastructure includes the REAL with Beocat high-performance computing resources, radiation detectors, and a 3D printer, plus the IRHS with a Precision X-ray XRad320 irradiator and radon chamber. These facilities support collaborations across K-State colleges and external organizations for radiation health effect studies.
Prof. Dr. Matthias Weidlich is a faculty member at Humboldt University of Berlin within the Institute of Computer Science under the Faculty of Mathematics and Natural Sciences . His research focuses on Process Mining , Complex Event Processing , and Data Privacy with applications in Business Process Management and Scientific Workflows . Research Interests: Business Process Management and Process Mining Complex Event Processing and Stream Data Analysis Data Privacy and Security in Process Systems Scientific Workflow Systems and User Behavior Heterogeneous Network Embeddings Algorithm Design and Optimization Recent Publications (2023-2025) demonstrate expertise in: Efficient stream processing techniques Privacy-preserving process mining frameworks Scientific workflow analysis tools Graph neural network applications Multi-modal data integration Adaptive querying systems Contact: Office: Unter den Linden 6, 10099 Berlin Phone: 030 2093-41277 Email: matthias.weidlich@hu-berlin.de Web: hu.berlin/data
Charity Nyelele is an Assistant Professor in the Environmental Sciences department at the University of Virginia. Her research bridges human well-being and environmental systems, focusing on biodiversity, climate change, and ecosystem services through the lens of environmental justice and equity. Specializes in urban forestry and socio-ecological synthesis Active in climate justice, carbon sequestration, and stormwater management Nyelele's recent work integrates machine learning and social media data to map recreational ecosystem services and optimize tree planting frameworks. She has developed multi-objective decision support tools to address urban ecosystem service trade-offs and leads research in fire-driven ecosystem restoration across Western US forests. She teaches courses on Environmental and Climate Justice , Management of Forest Ecosystems , and co-instructs Politics, Science, and Values . Contact: hbt3mb@virginia.edu
Dr. Thomas Lancaster is a Principal Teaching Fellow in the Department of Computing at Imperial College London, part of the Faculty of Engineering. He specializes in academic integrity, generative AI's impact on education, and combating contract cheating. His roles include Associate Dean at Staffordshire University and leadership positions at Coventry University and Birmingham City University. His research spans ethical AI use, plagiarism detection, and educational policy. He has authored numerous articles on cheating prevention and technology's role in academic integrity. His Orcid identifier is 0000-0002-1534-7547, and he can be reached at t.lancaster@imperial.ac.uk. Research Interests: Lancaster focuses on the intersection of technology and academic ethics, including generative AI's implications for student work, digital watermarking, and social media's role in enabling cheating. He advocates for staff-student partnerships to strengthen integrity frameworks and has pioneered methodologies for detecting source code plagiarism from online repositories. Publications: His recent work highlights global comparisons of cheating industries, the evolution of AI-driven cheating threats, and policy development to address historical misconduct. He emphasizes practical solutions for institutions, such as leveraging AI tools ethically and enhancing detection systems. Professional Contributions: As a leader in computing education, Lancaster has improved placement-year support for students and developed strategies to address transnational education challenges. His work on the SEEPAI project in Southeast Europe underscores his global impact.
Xujie Si is an Assistant Professor in the Department of Computer Science at the University of Toronto. He is also a faculty affiliate at the Vector Institute and an affiliate member at Mila - Quebec AI Institute, holding a Canada CIFAR AI Chair. Previously, he served as an Assistant Professor at McGill University's School of Computer Science. Education: Ph.D., Computer and Information Science, University of Pennsylvania (advised by Mayur Naik) M.S., Computer Science, Vanderbilt University B.E. (with Honors), Nankai University Research Focus: His work bridges AI and program reasoning, emphasizing the integration of statistical and logical methods. Key areas include: Static analysis and verification using deep learning/reinforcement learning Neuro-symbolic systems for urban simulation (e.g., LogiCity) Automated theorem proving via LLMs and symbolic reasoning Program repair and compiler fuzzing Recent Article Trends: Recent work focuses on synergizing LLMs with symbolic reasoning (e.g., Olympiad inequality proving), advancing SAT solving with graph neural networks, and applying neuro-symbolic methods to Euclidean geometry formalization. Awards: Canada CIFAR AI Chair (2023) Lab/Teams: Leads research teams exploring program analysis, neuro-symbolic AI, and formal verification at the University of Toronto and Vector Institute.
Hayretdin Bahsi is an Assistant Professor at the School of Informatics, Computing, and Cyber Systems at Northern Arizona University . His research focuses on cybersecurity, with expertise in malware detection, IoT security, and machine learning applications in defense mechanisms. He collaborates internationally on maritime cybersecurity, healthcare systems, and critical infrastructure protection. Research Interests include Android malware analysis, botnet detection, explainable AI in intrusion detection, and threat modeling for AI-driven systems. His work addresses challenges like concept drift in malware detection and privacy-preserving techniques for IoT networks. Publications span 66 scholarly works since 2009, emphasizing cybersecurity trends in AI, IoT, and healthcare. Recent contributions explore large language model (LLM) applications in vulnerability detection and cyber threat modeling for healthcare systems. Collaborations include projects on maritime cyber-insurance, cyber incident management in low-income countries, and datasets like MedBIoT for IoT botnet analysis. His work bridges theory and practice, addressing real-world cybersecurity challenges.
