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Charles G HoogstratenView profile

Associate Professor

• RNA Biochemistry
• Structural Biology
• Biophysics
• +3 more

Charles G Hoogstraten is an Associate Professor at the Department of Biochemistry & Molecular Biology, Michigan State University, and serves as Assistant Director for Undergraduate Education. His research focuses on RNA conformational dynamics and molecular function, utilizing advanced biophysical techniques such as NMR spectroscopy, surface plasmon resonance (SPR), and molecular dynamics simulations. Ph.D. (1995) - University of Wisconsin-Madison B.S. (1990) - Michigan State University, Chemistry & Biochemistry Previous appointments: UC Davis (1998-2002), University of Colorado, Boulder (1995-98) His work investigates the relationship between RNA structural flexibility and catalytic function, particularly in ribozymes like the hairpin ribozyme. Key contributions include developing novel isotope labeling methods and chemically constrained nucleotides to study RNA backbone dynamics and docking mechanisms. Collaborations with computational biologists enhance atomic-level understanding of these processes. Recent publications highlight his expertise in RNA tertiary structure, metal ion coordination, and spectroscopic analysis. His lab applies techniques such as SPR for docking kinetics, CD difference assays for equilibrium studies, and MD simulations to map RNA energy landscapes. Howard Hughes Medical Institute Predoctoral Fellowship Helen Hay Whitney Postdoctoral Fellowship Hoogstraten teaches advanced biochemistry courses (BMB 461, BMB 829) and leads the Hoogstraten Lab, which bridges experimental biophysics with computational modeling to unravel RNA-catalyzed reactions.

Yang YangView profile

Assistant Professor

• Quantum Chemistry
• Electronic Structure Theory
• Nuclear Quantum Effects
• +3 more

Yang Yang is an Assistant Professor of Chemistry at the University of Wisconsin-Madison , focusing on electronic structure theory and nuclear quantum effects in molecular systems. He leads the Yang Group , a theoretical and computational team specializing in the Constrained Nuclear-Electronic Orbital (CNEO) framework for accurate quantum simulations. B.S. in Chemistry and Physics, Peking University (2011) Ph.D., Duke University (2016) Postdoctoral Associate, University of Illinois at Urbana-Champaign and Yale University (2016–2019) His research spans method development in multicomponent quantum theory , excited states theory , and practical quantum chemistry calculations . Recent work includes landmark papers in Journal of Chemical Theory and Computation on CNEO-based reaction rate calculations and quantum computation applications. The Yang Group has secured NSF funding (2024) for CNEO integration into computational software. Publications emphasize nuclear quantum delocalization effects , vibrational spectra modeling , and nonadiabatic dynamics . NSF Grant (2024) for CNEO software integration Awards at 2024 ACS Fall Meeting Current advisees include graduate students Haoran Chen , Lin Han , Zhe Liu , Yiwen Wang , and Yuzhe Zhang . The group also collaborates with postdoctoral researchers like Zehua Chen and Tanner Culpitt .

• Topological Data Analysis
• Stochastic Geometry
• Large Deviations Theory
• +4 more

Christian Hirsch is an Associate Professor for Data Science and Statistics at Aarhus University, where he studies random networks motivated from biology and health sciences through techniques from topological data analysis and stochastic geometry. He is a member of the Stochastics group at the Department of Mathematics and holds additional affiliations as an Associate Fellow of the Aarhus Institute for Advanced Studies, and with the AU DIGIT Centre and the AU Quantum Campus. Current Position: Associate Professor for Data Science and Statistics, Aarhus University Previous Positions: Assistant Professor at University of Groningen and University of Mannheim Postdoctoral Experience: Aalborg University, LMU Munich, WIAS Berlin Education: PhD from Ulm University Christian Hirsch's research focuses on the statistical foundations of topological data analysis, large deviations theory in stochastic geometry, and percolation theory of spatial random networks. His work bridges theoretical mathematics with practical applications in data science, particularly in analyzing complex structures through topological methods. He investigates how topological features form and disappear in growing data structures, developing statistical tests to determine whether observed patterns are significant or merely random occurrences. His recent publications reveal a strong trend toward applying topological data analysis to increasingly complex structures, with significant focus on statistical validation of topological features. Hirsch has made substantial contributions to understanding the probabilistic behavior of persistent homology, developing functional central limit theorems and large deviation principles for topological functionals. His work spans theoretical foundations in stochastic geometry while finding applications in materials science, neural networks, and wireless communication systems. As an educator, Hirsch teaches graduate courses including Topological Data Analysis, Stochastic Geometry, Monte Carlo Simulation, Markov Decision Processes, Probability Theory, and Stochastic Processes. He has supervised numerous PhD, MSc, and BSc students, with several of his former students securing academic positions at institutions like University of Leiden, Tokyo Institute of Technology, and Budapest University of Technology. Hirsch leads a research group within the Stochastics group at Aarhus University, collaborating extensively with researchers across Europe and North America. His work demonstrates how topological methods can provide rigorous statistical insights into complex data structures, making significant contributions to both theoretical mathematics and practical data analysis techniques.

• Climate
• Climate change
• Climate dynamics
• +9 more

Jost-Diedrich Graf Von Hardenberg is a Full Professor in the Department of Environmental, Land and Infrastructure Engineering (DIATI) at the Polytechnic University of Turin. He serves as the Energy and Climate Change Area Coordinator and is a Scientific Advisor of the HPC-AI Advisory Council. His research focuses on climate science and geophysical fluid dynamics, with significant contributions to understanding climate change impacts and Earth system modeling. Professor Von Hardenberg's research interests span multiple areas of climate science, including climate dynamics, geophysical fluid mechanics, hydrological cycle analysis, numerical climate modeling, and precipitation downscaling. His work particularly emphasizes climate tipping points and extremes, Rayleigh-Bénard convection in geophysical contexts, high-resolution Earth-system climate modeling, and stochastic approaches to precipitation downscaling. His research integrates theoretical, computational, and observational approaches to address fundamental questions about climate system behavior and change. His recent publications demonstrate a strong focus on Atlantic Meridional Overturning Circulation (AMOC) dynamics, climate extremes in Alpine regions, urban climate effects, and interdisciplinary applications of climate science to ecological and conservation challenges. His work bridges fundamental climate dynamics with practical applications for climate adaptation and environmental management. Among his notable recognitions are the Research in Paris award from the Maire de Paris (2009) and Fellowships at the London School of Economics and Political Science (2001-2002, 2004-2006). He has participated in numerous research networks including the European Geosciences Union section 'Nonlinear Processes in Geophysics' (2004-2012), the EC-Earth Consortium (2012-present), and COST Action ES0805 TERRABITES 'The terrestrial biosphere in the earth system' (2010-2014). Professor Von Hardenberg actively supervises PhD students including Marianna Albanese, Maria Clara Corda, Sara Filippini, and Jacopo Grassi, among others. He leads multiple significant research projects such as ROTurb (Resolving Ocean Macroscale Turbulence), LocClima (Impact of LOCal conditions on Italian microCLIMAtes), CRAWL (Carbon Release in A Warming cLimate), and CliMOC (Climate Impacts of the Atlantic Meridional Overturning Circulation), with funding from EU, national programs, and commercial contracts. He is a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory, where he contributes expertise in climate data analysis and modeling. His collaborative work extends to multiple institutions through non-commercial agreements with the National Research Council and various international research consortia focused on climate system understanding.

Chun-Hua GuoView profile

Professor

• Matrix analysis
• Scientific computing
• Numerical linear algebra
• +3 more

Chun-Hua Guo is a Professor in the Department of Mathematics and Statistics at the University of Regina, Faculty of Science. His research focuses on matrix analysis, scientific computing, and applications in tensor computations and nonlinear matrix equations. He teaches advanced courses such as MATH 869 Numerical Analysis. Dr. Guo’s recent work emphasizes iterative methods for solving eigenvalue problems of nonnegative tensors, matrix equations arising in nano research, and convergence analysis of numerical algorithms. His publications address topics like Newton-Noda iteration, modified Newton methods for Z-eigenpairs, and algebraic Riccati equations associated with M-matrices. His research trends highlight advancements in computational techniques for matrix functions (e.g., matrix pth root), tensor Perron pairs, and stability analysis of iterative algorithms. These contributions bridge theoretical linear algebra with practical computational challenges in engineering and scientific domains. Dr. Guo’s advising and grants involve developing efficient numerical methods for complex systems. His work has implications for fields requiring high-precision matrix computations, such as control theory, stochastic modeling, and nano-material simulations.

Valerie WelbornView profile

Assistant Professor

• Computational Chemistry
• Biophysical Chemistry
• Molecular Dynamics Simulations
• +7 more

Valerie Welborn is an Assistant Professor in the Department of Chemistry within the College of Science at Virginia Tech. Her research program focuses on multiscale simulation of condensed phase systems, particularly examining the role of electric fields in biological interfaces and biological-like systems. She leads an active research group that bridges computational chemistry with experimental validation through multiple collaborations. Dr. Welborn's research interests span protein dynamics and function, characterization of structural and functional water, polysaccharides in solution, and polymer design for metal chelation. Her work combines morphological, structural, dynamic and electronic factors to develop new models of biological interfaces, with particular emphasis on how water interacts at a fundamental molecular level with biological entities such as proteins and bone tissues. She specializes in electric field calculations to understand protein flexibility in catalysis and ion transport, seeking to reconcile protein dynamics with electrostatic preorganization theory. Her recent publications demonstrate strong activity across multiple domains, with particular emphasis on electric field analysis in protein function, water dynamics at biological interfaces, and polymer design for metal chelation. Her work shows a consistent trajectory toward increasingly complex biological systems and more sophisticated computational approaches, including polarizable force field methods and multiscale modeling techniques. Centre for Doctoral Training (CDT) on Theory and Simulation of Materials (TSM) Ph.D. Prize for Research Excellence, 2014 Outstanding Contribution to Outreach and Public Engagement, CDT TSM, 2014 Engineering and Physical Sciences Research Council (EPSRC) fully-funded Ph.D. Fellowship, CDT TSM, 2011 Editor-selected as '2021 Hot PCCP article' Front cover article in Phys. Chem. Chem. Phys. Dr. Welborn actively mentors a diverse group of researchers, including multiple postdoctoral associates, graduate students across chemistry and related disciplines, and undergraduate researchers. Her lab participates in the NSF Materials Innovation Platform GlycoMIP (DMR-1933525), focusing on polysaccharide research. She collaborates extensively with experimental groups, particularly with Professor Michael Schulz on polymer design for metal chelation projects. Her lab develops computational tools like the ELECTRIC software package for electric field calculations in biomolecular systems. The Welborn group maintains active research programs in four main areas: protein dynamics and function, characterization of structural and functional water, polymer design for metal chelation, and polysaccharides in solution. Each program employs specialized computational approaches to address fundamental questions in biological chemistry, with particular emphasis on how electric fields govern molecular behavior at biological interfaces.

• Noise Abatement in Machines and Vehicles
• Numerical Modeling in Mechanics
• Automotive Structures and Dynamics
• +12 more

Prof. Wim Desmet is a full professor at the Faculty of Engineering Science and head of the Department of Mechanical Engineering at KU Leuven . His research focuses on advanced modeling techniques for mechanical systems, including: noise and vibration control in automotive and industrial systems computational acoustics and interval field uncertainty modeling metamaterials for broadband vibroacoustic performance AI-driven diagnostic systems in renewable energy and manufacturing Current research projects address challenges in electric vehicle drivetrains, wind turbine monitoring, and multi-physical digital twin development. He actively contributes to academic governance as: Managing Director of KU Leuven Head of Subdivision HIST Chair of multiple executive committees Member of 15+ academic and administrative councils

• Cyber-physical system security
• Threat modelling
• Critical infrastructure resilience
• +3 more

Dr. Carmen Cheh is a Research Scientist in the Department of Computer Science at the University of Illinois at Urbana-Champaign. She holds a Ph.D. in Computer Science from UIUC and a Bachelor of Computing (Hons) from the National University of Singapore. Her research focuses on cyber-physical system security, threat modeling, and critical infrastructure resilience. She has served as Co-Principal Investigator on two major grants: the 2023-2024 project on threat modeling for government systems and the 2022-2025 initiative on real-time fraud detection in e-commerce platforms. Her work bridges theoretical computer security with practical applications in critical infrastructure protection. Dr. Cheh has contributed to tools like CyberSAGE, which aids in automated security argument evaluation, and frameworks for detecting business logic flaws in software systems. She collaborates extensively with industry and government entities to advance cybersecurity practices in both digital and physical domains. Education: Ph.D., Computer Science, University of Illinois at Urbana-Champaign Bachelor of Computing (Hons), National University of Singapore Her research interests emphasize real-world security challenges in interconnected systems, including automated threat modeling, vulnerability discovery in business logic, and compliance frameworks for financial services. She has pioneered constraint-based methodologies for identifying security flaws in complex systems and developed novel approaches to insider threat detection using physical access data. Recent work explores the intersection of machine learning and cybersecurity in fraud detection systems. Her publications span top venues like ACM Transactions on Modeling and Computer Simulation, IEEE Secure Development Conference, and the IEEE International Conference on Computer Communications. Collaborations include projects with institutions like Argonne National Laboratory and the National Science Foundation.

Alan RooneyView profile

Assistant Professor

• Geochemistry
• Geochronology
• Climate Science
• +3 more

Alan Rooney is an Assistant Professor in the Department of Earth & Planetary Sciences at Yale University, affiliated with the Yale School of Arts and Sciences. He leads the Rooney Geochronology and Geochemistry Group, which is part of the Yale Metal Geochemistry Center. His research integrates radiogenic isotope geochemistry (e.g., Re-Os, Sr, Nd) with field-based methods like sedimentology and stratigraphy to investigate tectonic, climatic, and biologic transitions in Earth history. Current projects include refining Neoproterozoic chronology, studying Mid-Pleistocene ice sheet dynamics, and advancing EARTHTIME’s Re-Os geochronometer standards. His research interests are centered on three main areas: 1) Proterozoic tectonics and eukaryotic diversification, 2) ice sheet dynamics over the last 5 million years using multiple geochemical proxies, and 3) radiogenic isotopes as tracers of crustal-mantle processes. He collaborates with researchers at Dartmouth College, Oxford University, and other institutions to address these topics. The Rooney Lab emphasizes experimental approaches, such as simulating seafloor weathering of mafic rocks, to better understand isotopic fluxes into the sedimentary record. His articles highlight a focus on geochronology and isotopic analysis to unravel climate-tectonic interactions, with recent work emphasizing the Great Oxidation Event, Ediacaran biogeochemical shifts, and Mid-Pleistocene glacial variability. These studies often combine field observations with laboratory experiments to deconvolve complex Earth system processes. Dr. Rooney has no listed scientific awards. He advises three graduate students: Gryphen Goss, Sam Shipman, and Carey Ciaburri. The lab’s NSF-funded involvement in EARTHTIME underscores its commitment to advancing geochronological standards. The Rooney Geochronology Lab operates within ultra-clean facilities equipped with advanced mass spectrometers (e.g., Thermo Fisher Neptune-Plus MC-ICP-MS, Triton-Plus TIMS), adjacent to the Microprobe Facility’s electron microprobe resources, enabling precise geochemical and petrological analyses.

Alexander RodríguezView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Time Series Analysis
• +7 more

Alexander Rodríguez is an Assistant Professor in the Department of Computer Science and Engineering at the University of Michigan. His research focuses on advancing AI methods for modeling complex spatiotemporal dynamics, particularly in applications related to population health and community resilience. He specializes in machine learning, time series analysis, uncertainty quantification, and multi-agent systems, with an emphasis on scientific modeling and data-driven decision-making. Recent contributions include keynote talks at AAMAS 2025 (Autonomous Agents for Social Good workshop), presentations at the US National Academies Symposium, and invited talks at AAAI 2025 on topics like knowledge-guided machine learning and public health prediction. He co-organizes AAMAS 2025 as sponsorship co-chair and leads initiatives in AI for science and epidemic forecasting. His publications emphasize neural networks for time series forecasting, biomedical foundation models, and epidemic surveillance systems. Notable work includes 'Neural Conformal Control for Time Series Forecasting' (AAAI 2025) and 'Deepcovid: An operational deep learning-driven framework for explainable real-time forecasting' (2021). No scientific awards explicitly listed in available texts. His research group actively collaborates on grants related to AI applications in public health and infrastructure resilience, with a focus on data-centric methodologies and multi-agent systems.

• Psychometrics
• Network Theory
• Psychopathology
• +2 more

Denny Borsboom is Professor in the Psychological Methods Group at the University of Amsterdam, where he directs the Social and Behavioural Data Science Centre. His research program develops network theories of psychopathology, novel psychometric methods, and formalized psychological theory through initiatives like the Psychosystems Project and Theory Methods Lab. Research focuses on conceptual foundations of measurement, complex systems approaches to mental disorders, and computational modeling of psychological phenomena. Recent publications advance network psychometrics, validity theory, and integration of intra/interindividual dynamics. Articles demonstrate strong methodological innovation through computational psychiatry applications, network theory development, and philosophical engagement with psychological foundations. Recurring themes include psychopathological networks, measurement realism, system transitions, and collaborative theory-building frameworks.

Andrew M. OlneyView profile

Professor

• Natural Language Processing
• Intelligent Tutoring Systems
• Dialogue Systems
• +5 more

Andrew M. Olney is Professor in both the Institute for Intelligent Systems and Department of Psychology at the University of Memphis. His work bridges artificial intelligence, cognitive science, and education, with a primary focus on natural language interfaces for learning. His educational background includes a Ph.D. in Computer Science from the University of Memphis (2006), an M.S. in Evolutionary and Adaptive Systems from the University of Sussex (2001), and a B.A. in Linguistics with Cognitive Science from University College London (1998). Dr. Olney's research centers on intelligent tutoring systems and natural language processing applications in education. His specific interests include vector space models, dialogue systems, unsupervised grammar induction, and computational representations of meaning. He has made significant contributions to conversational intelligent tutoring systems and task-oriented natural language interfaces, with particular emphasis on dyadic interaction and engagement in learning contexts. Analysis of his recent publications reveals a consistent focus on leveraging AI for educational enhancement, particularly through automated content creation, question generation, and adaptive learning systems. His work spans multiple disciplines including computer science, cognitive science, linguistics, and education, demonstrating strong interdisciplinary integration. Scientific Awards and Recognition: Best Paper Award, Proceedings of Empowering Education with LLMs (2023) Associate Editor, International Journal of Artificial Intelligence in Education (Impact Factor 4.9) Dr. Olney has been actively involved in mentoring graduate students and securing research funding. He has served as Principal Investigator on $6M of federal grants within a total of $18M in federal grant funding. His service includes former editorship of the Journal of Educational Data Mining (2017-2022) and former leadership roles as Director/Associate Director of the Institute for Intelligent Systems (2006-2017). He currently coordinates both the Cognitive Science Graduate Certificate and Undergraduate Minor in Cognitive Science at the University of Memphis. His BrainTrust project, funded by the NSF, addresses the knowledge engineering bottleneck for intelligent tutoring systems through innovative approaches to virtual student simulation and knowledge representation.

Carmen MoraruView profile

Professor

• Food Processing and Engineering
• Food Safety Engineering
• Nanotechnology
• +5 more

Carmen Moraru is a Professor in the Department of Food Science at Cornell University's College of Agriculture and Life Sciences (CALS). Her research focuses on enhancing food safety, quality, and shelf life through advanced processing methods such as membrane separation, light-based technologies, high-pressure processing (HPP), and microwave vacuum drying. She also studies intermolecular interactions in foods and nanoscale surface engineering to prevent microbial contamination. Dr. Moraru teaches courses like Food Processing and Engineering (FDSC 4250) and co-instructs Advanced Food and Bioprocessing Systems (FDSC 6650) . Her lab, the Moraru Lab, explores emerging technologies in food safety and processing, with recent projects addressing microbial inactivation, nanomaterials, and sustainable drying methods. She advises multiple graduate students and has supervised notable projects, including studies on pea protein functionality and antimicrobial surfaces. Education: Doctorate in Food Science (PhD details not explicitly stated in provided texts). Lab Affiliations: Moraru Lab at Cornell University. Her scientific awards include the Kosi Award in Food Science (2019) and recognition for student achievements like Andreea Beldie's 2019 ADSA Poster Competition win. Recent publications highlight innovations in UV-C light disinfection, microwave drying, and nanotopography-based antimicrobial surfaces. Research collaborations span industry and academia, addressing challenges in dairy processing, plant-based foods, and food packaging safety. Her work frequently integrates computational modeling, experimental validation, and interdisciplinary approaches to solve complex food engineering problems.

Syukuro ManabeView profile

Researcher

• Climate Science
• Climate Modeling
• Atmospheric Science
• +3 more

Syukuro Manabe is a renowned climate scientist and Senior Meteorologist at Princeton University's Program in Atmospheric and Oceanic Sciences. He is a Nobel laureate (2021) for pioneering global climate modeling. Affiliated with the Geophysical Fluid Dynamics Laboratory (GFDL), his work laid foundations for understanding climate change mechanisms. Education: B.A., M.A., and D.Sc. in Meteorology from the University of Tokyo (1953-1959). Career highlights include developing the first global climate model (1960s) and demonstrating CO2's role in global warming. Collaborations include Kirk Bryan on coupled ocean-atmosphere models and later work on transient climate responses. Research focuses on climate system interactions, numerical modeling, and paleoclimate studies. Awards include the Nobel Prize, Crafoord Prize, and Blue Planet Prize. His models remain foundational for modern climate research, predicting temperature and hydrological changes linked to greenhouse gases.

Jan-David LiebeView profile

Professor

• Implementation Science
• Digital Health
• Human-Centered Design
• +3 more

Prof. Dr. Jan-David Liebe is a Professor of Digital Society at Osnabrück University of Applied Sciences, leading the Digital Health Management field. He holds a PhD in Business Informatics from the University of Osnabrück (2013–2018). His research focuses on implementation science in healthcare, human-centered design, and AI-driven healthcare innovations. He is a co-founder of innnow GmbH and an associated researcher at UMIT’s Institute for Medical Informatics. Key roles include heading the GMDS AG mwmKIS and contributing to national IT reports in healthcare. Research Interests: His work spans digital transformation in healthcare, evaluating healthcare IT maturity models, and fostering innovation through design thinking. Notable projects include benchmarking health IT systems and developing frameworks for AI-supported diagnostics. Professional Roles: Prof. Liebe serves as the Study Director for the Health Management MA program and Entrepreneurship Officer at the WiSo faculty. He actively contributes to research networks like the German Society for Medical Informatics and advises on healthcare IT strategies. Grants & Projects: Leads projects on cloud readiness of hospitals, VR-based medical training, and AI in echocardiography. His work is published in over 50 peer-reviewed articles, emphasizing practical applications of digital tools in healthcare workflows and policy. Affiliations: Member of the Research Commission and Study Committee at Osnabrück University of Applied Sciences. Collaborates with institutions like the Medical School Hamburg and Novia University of Applied Sciences.