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Adam BrandtView profile

Professor

• Energy Systems
• Greenhouse Gas Emissions
• Environmental Impact Assessment
• +3 more

Adam Brandt is a Professor of Energy Science Engineering at Stanford University and a Senior Fellow at the Precourt Institute for Energy. His research focuses on reducing environmental impacts of energy systems, particularly greenhouse gas emissions from fossil fuels. He employs life cycle assessment (LCA) and process optimization to analyze energy systems at large scales. His work addresses emissions from transportation energy supply, power systems with carbon capture, and methane leakage reduction. Brandt holds a Ph.D., M.S., and B.S. from UC Berkeley and UC Santa Barbara, respectively. **Education:** Ph.D., Energy and Resources, UC Berkeley (2008) M.S., Energy and Resources, UC Berkeley (2005) B.S., Environmental Studies (Physics emphasis), UC Santa Barbara (2003) His research interests emphasize actionable solutions for decarbonization, including: Methane emissions mitigation strategies Life cycle assessment frameworks for energy systems Optimization of energy system efficiency Climate policy design for fossil fuel industries Aircraft-based emission monitoring technologies Recent work highlights advancements in carbon intensity mapping, satellite-based methane detection, and policy mechanisms for low-cost emission reductions. His projects often involve interdisciplinary collaboration across engineering, environmental science, and economics. Brandt is affiliated with the Environmental Assessment and Optimization (EAO) Group and contributes to Google Scholar publications on energy transitions and climate mitigation.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +3 more

Hongbo Liu is a researcher at Indiana University - Purdue University Indianapolis , Department of Computer Information and Graphics. With a focus on Artificial Intelligence, Machine Learning, and Network Analysis , Liu has contributed extensively to computational intelligence through 118+ publications since 2004. Multi-disciplinary research spanning Graph Theory, Swarm Intelligence, and Deep Learning Recent work includes Robust Gated Models for Temporal Networks and Self-Adaptive Neuroevolution Systems (2024-2025) Key research themes include: Dynamic network analysis and link prediction Crowd behavior modeling and trajectory forecasting Swarm-based optimization for complex systems Fuzzy logic and granular computing applications Neural network architectures for image and text processing Liu's publications demonstrate strong collaborations with researchers like Ajith Abraham, Yu Yang, and Bo Zhang across 15+ academic journals and conferences . The work spans from theoretical graph algorithms (2015-2017) to applied systems in autonomous robotics and blockchain (2024).

• Precision Livestock Farming
• Data Analysis
• Image and Signal Processing
• +4 more

Helen Schomburg is a researcher at the Institute of Animal Welfare and Animal Husbandry (ITT), focusing on advanced technological solutions to enhance animal welfare and optimize farming practices. Her work integrates Precision Livestock Farming with Machine Learning and Image Processing to develop automated monitoring systems for livestock. Education: Dr. rer. nat. (Doctorate in Natural Sciences) Research Interests: Her research spans Precision Livestock Farming , Data Analysis , and Machine Learning , with a focus on applying Pattern Recognition and Signal Processing to assess animal behavior and health. She specializes in creating digital tools for real-time welfare monitoring in pigs and poultry. Publications & Projects: Schomburg's recent work includes designing the Bite-o-Mat device for tail biting detection in pigs and prototype systems for broiler chicken activity monitoring using elevated platforms with integrated sensors. Her contributions emphasize reducing labor intensity and improving early intervention through automated data analytics. Labs & Teams: She collaborates with colleagues like Prof. Dr. L. Schrader, Dr. J. Knöll, and Dr. A. Patt, contributing to initiatives such as the ErhEb project under the European Partnership Animal Health & Welfare. Her efforts align with the Experimental Station Celle, promoting innovation in farm animal environments.

Anna Maria EchterhölterView profile

Professor

• history of science (18th and 19th century)
• historical-political epistemology
• epistemic decolonisation
• +9 more

Anna Maria Echterhölter is Professor of Modern History: History of Science at the Department of History, University of Vienna, a position she has held since March 2018. She also serves as spokesperson of the key research area History of Science at the Faculty of Historical and Cultural Studies since March 2019. Previously, she was Visiting Professor of History of Technology at Technische Universität Berlin (2018) and Visiting Professor of Cultural History at Humboldt Universität zu Berlin (2017). Her academic journey includes fellowships at the Max Planck Institute for the History of Science and the German Historical Institute Washington DC. Echterhölter's educational background includes a Magister programme in literature and cultural studies from Freie Universität Berlin (1994-2001), with additional studies in physics. Her academic credentials were solidified with her dissertation on "Epistemic values in obituaries on scientists 1711–1860" (2009) and her habilitation thesis defended in February 2017. Her international academic experience includes semesters at Goldsmiths College London (1998), summer school at the University of Silesia in Katowice (1999), and the Goethe-Institute in Manila (1996). As a historian of science, Echterhölter specializes in social histories of quantification, particularly focusing on the measurement of resources and how units of measurement interact with patterns of justification in allocation schemes. Her research spans history of science (18th and 19th century) , historical-political epistemology , epistemic decolonisation , and the history of quantification and modern data technologies . She has made significant contributions to understanding colonialism, measurement and quantification in Oceania , technologies of money , and the history of auxiliary sciences and historical methods including metrology and chronology. Her work on Pacific perspectives through the lens of epistemic decolonization has been particularly influential in challenging colonial narratives. Echterhölter's recent publications reveal a clear trajectory toward examining the intersections of historical quantification practices with contemporary AI and data technologies. Her work increasingly focuses on how historical measurement systems inform current data practices, particularly in colonial contexts. A notable trend is her exploration of "enquétes" (colonial surveys) as serialized forms of knowledge production that shaped scientific fields and colonial administration. Her scholarship bridges historical analysis with critical examinations of modern computational methods, demonstrating how historical patterns of quantification continue to influence contemporary data practices and epistemological frameworks. Editor of Science in Context (since 2022) Editor of Historische Wissensforschung book series Founding member of ilinx magazine Reviews for ISIS, N.T.M., Centaurus, and other prestigious journals Supervisor in two sections of the Vienna Doctoral School of Historical and Cultural Studies Professor Echterhölter leads the key research area History of Science at the University of Vienna, which includes the MA History program and the joint MA programme "Epistemologies of Science and Technology." She has organized numerous significant conferences and workshops, including "Beyond Quantity: Research with Subsymbolic AI" (2022), "The Metrics of Energy: Accounting for Nature in the History of Social Sciences and Ecological Economics" (2023), and "Scales, Norms, Limit Values in Times of (Digital) Change" (2021). Her collaborative work with institutions like the Institute for Historical Research London on "Anthropocene Histories" demonstrates her commitment to international scholarly exchange and interdisciplinary approaches to the history of science.

Laszlo UjjView profile

Associate Professor

• Nonlinear Optics
• Ultrafast Laser Spectroscopy
• Coherent Raman Spectroscopy
• +7 more

Dr. Laszlo Ujj is an Associate Professor in the Department of Physics at the University of West Florida, part of the Hal Marcus College of Science and Engineering. He has been a key contributor to both undergraduate and graduate-level physics education and maintains an active experimental research program in advanced optical spectroscopy. Education: Ph.D. in Physics, University of Szeged, Hungary (2001) Ph.D. in Nonlinear Spectroscopy/Physics, JPTE University of Pecs, Hungary (1990) M.S. in Theoretical Physics, JATE University of Szeged, Hungary (1983) Dr. Ujj's research focuses on nonlinear optics , ultrafast laser spectroscopy , and coherent Raman spectroscopy , with applications in bio-photonics and material analysis . He has developed state-of-the-art laser spectroscopy systems at UWF and previously at the University of Arizona. His work bridges experimental innovation and theoretical understanding in optical science. His recent publications reflect a strong trend toward label-free biological imaging , laser-induced breakdown spectroscopy (LIBS) for elemental analysis, and ultrafast dynamics in biomolecules . These works span disciplines including biophysics, materials science, and analytical chemistry, showcasing interdisciplinary impact. Scientific Recognition: h-index of 14 (Web of Science and Google Scholar) Over 599 citations by 2019 (Web of Science) Google Scholar: 727 citations, i10-index of 17 Presentations at American Physical Society and International Conference on Advanced Vibrational Spectroscopy Dr. Ujj has supervised numerous undergraduate researchers, many of whom have pursued advanced degrees at institutions such as Harvard University , the University of New Mexico , and the University of Arizona . His educational efforts include revising core physics courses and integrating research into the undergraduate curriculum. He teaches a wide range of courses including quantum theory, thermodynamics, electromagnetism, and laser physics. He also leads the Laser Spectroscopy Lab at UWF, where students engage in hands-on research with cutting-edge optical systems. Laboratory and Research Team: Dr. Ujj has developed and maintained advanced laser spectroscopy laboratories at both the University of Arizona and UWF. His lab focuses on coherent Raman microscopy, LIBS, and ultrafast spectroscopy, providing students with direct experience in experimental design, instrumentation, and data analysis.

Andrew CaplinView profile

Professor

• Cognitive Economics
• Behavioral Macroeconomics
• Human-AI Collaboration
• +3 more

Andrew Caplin is a Silver Professor of Economics at New York University's Department of Economics. He leads the Sloan Foundation Program on Cognitive Economics at Work and has made groundbreaking contributions to cognitive economics, behavioral macroeconomics, and human-AI collaboration. NYU Department of Economics Sloan Foundation Program on Cognitive Economics at Work Copenhagen Life Panel (CLP) research His research spans cognitive economics, focusing on decision-making mistakes, AI integration in economic systems, and housing finance reforms through equity-sharing mortgages. He developed novel frameworks for measuring subjective earnings risk, economic decision-making skills, and collaborative human-AI protocols in medical and financial contexts. Scientific Awards Sloan Foundation Program leadership Caplin's work examines belief calibration, rational inattention, and career trajectory dynamics. His Copenhagen Life Panel leverages Danish administrative data to study labor market transitions and economic well-being across life cycles, while his equity-sharing mortgage research addresses housing affordability through market design innovations.

• Alcohol use
• substance use disorder recovery
• ecological momentary assessment
• +4 more

Kyler S. Knapp PhD is an Assistant Professor in the Department of Community Health and Health Behavior at the University at Buffalo, School of Public Health and Health Professions. His research focuses on alcohol use disorders, recovery processes, and psychological well-being through advanced quantitative methods and ecological momentary assessment (EMA). Education: PhD and MS in Human Development and Family Studies from Pennsylvania State University, BS in Psychology and Human Development and Family Studies from Central Michigan University. Kapp's work examines intra-individual dynamics in recovery, leveraging intensive longitudinal data to identify daily-life risk and protective factors for substance use disorders. His methodological expertise includes quantitative approaches and ambulatory assessment technologies. Scientific Awards NIAAA T32 Postdoctoral Fellowship in Alcohol Etiology and Treatment Training Pennsylvania State University Prevention and Methodology Training Program Fellowship He is affiliated with the Society of Addiction Psychology and Research Society on Alcohol.

Xuerong Meggie WenView profile

Professor

• Sufficient Dimension Reduction
• Variable Selection
• Model Selection
• +7 more

Xuerong Meggie Wen is a Professor of Statistics in the Department of Mathematics & Statistics at Missouri University of Science and Technology, where she conducts research in advanced statistical methodologies. She is affiliated with the College of Arts, Sciences, and Education and maintains an active research program in dimension reduction, variable selection, survival analysis, and longitudinal data modeling. Ph.D. in Statistics, University of Minnesota M.S. in Operations Research, Chinese Academy of Sciences B.S. in Probability and Statistics, Peking University, China Her research focuses on sufficient dimension reduction , variable and model selection , and survival and recurrent event data analysis . She develops model-free and nonparametric methods for high-dimensional and complex data structures, with applications in science and engineering. Her work emphasizes conditional independence, sparsity, and efficient estimation without restrictive parametric assumptions. The recent publications show a strong trend in sufficient dimension reduction across multi-population and multi-index settings, with increasing emphasis on sparsity , link-free methods , and conditional screening . Her research bridges theoretical statistics with practical applications, particularly in handling censored, recurrent, and high-dimensional data. Dr. Wen has not been explicitly mentioned to have received scientific awards in the provided text. She has advised and collaborated with several researchers, including Lu Li, Zhou Yu, Lei Huo, and Xuejing Liu, indicating an active role in mentoring graduate students and junior researchers. While no grants are listed, her sustained publication record suggests ongoing research support. She teaches courses in statistics, accessible through the university’s Canvas platform. Dr. Wen leads a research group focused on nonparametric and semiparametric statistical methods, particularly in dimension reduction and variable selection for complex data. Her team works on both theoretical development and computational implementation of novel statistical techniques.

Francesco V. PepeView profile

Associate Professor

• Quantum Imaging
• Quantum Information
• Quantum Optics
• +7 more

Francesco V. Pepe is an Associate Professor in the Department of Physics at the University of Bari Aldo Moro, Italy, where he is affiliated with the Dipartimento Interateneo di Fisica. He leads the Quantum Optical Technologies Laboratory (QuOT Lab) and is the Principal Investigator of the INFN project PICS (Plenoptic Imaging with Correlations), focused on advancing correlation plenoptic microscopy. His research spans quantum imaging, quantum information, and quantum optics, with a strong emphasis on correlation-based imaging techniques and their applications in high-resolution 3D microscopy. PhD in Physics (specific institution and year not specified in text) His research interests lie at the intersection of quantum optics and imaging science, focusing on quantum imaging , correlation-based microscopy , plenoptic imaging , light-field technologies , and quantum information processing . He explores the use of intensity correlations in light to overcome classical limitations in resolution, depth of field, and imaging speed. His work also extends to quantum simulation of many-body systems (e.g., Schwinger model), bound states in the continuum , and light-matter interactions in waveguide QED platforms. The recent articles (2024–2025) reveal a strong trend toward quantum-inspired imaging techniques , particularly correlation plenoptic and hyperspectral imaging, with applications in turbulence-robust and real-time volumetric imaging. There is also a significant focus on quantum simulation of gauge theories using quantum computing platforms, dimensional reduction in field theories , and non-Markovian dynamics in quantum systems. The consistent use of correlation measurements, quantum error mitigation, and GPU-accelerated processing highlights a multidisciplinary approach combining theory, computation, and experimental design. Francesco V. Pepe has supervised Master’s students in Physics and is actively involved in collaborative research projects. While no formal scientific awards are listed in the provided text, his leadership in the INFN PICS project and extensive publication record underscore his prominence in the field. He has also contributed to advancements in quantum decay dynamics , spontaneous emission in dispersive media , and nonexponential decay phenomena , often in collaboration with leading researchers in quantum optics and condensed matter physics. He is associated with the QuOT Lab, which focuses on developing quantum optical technologies for imaging and sensing. The lab engages in both theoretical modeling and experimental implementation, particularly in correlation imaging, plenoptic microscopy, and quantum simulation. The team collaborates widely across Italy and internationally, contributing to projects in quantum 3D imaging, remote sensing, and Earth observation.

Ilse C.F. IpsenView profile

Professor

• Numerical Linear Algebra
• Randomized Algorithms
• Probabilistic Numerical Analysis
• +8 more

Ilse C.F. Ipsen is a Distinguished Professor in the Department of Mathematics at North Carolina State University, with a joint appointment in the Department of Statistics. She is a Fellow of the American Mathematical Society and the Society for Industrial and Applied Mathematics (SIAM), and delivered the Olga Taussky-Todd Lecture at ICIAM 2023. Institution: North Carolina State University School: College of Sciences Department: Department of Mathematics Rank: Professor Email: ipsen@ncsu.edu Her research centers on numerical linear algebra, randomized algorithms, and probabilistic numerical analysis. She has made significant contributions to error analysis in floating-point computation, matrix perturbation theory, and the development of randomized methods for large-scale data problems. Her work bridges theoretical foundations with practical applications in data science, quantum physics, and computational statistics. She is the author of the textbook Numerical Matrix Analysis (SIAM, 2009) and serves as Editor-in-Chief of the SIAM Book Series on Data Science. The recent publications highlight a strong trend toward probabilistic numerical methods, uncertainty quantification, and randomized matrix algorithms. Themes include error analysis for inner products and summation, Bayesian solvers for linear systems, and scalable methods for trace and diagonal estimation. Applications span biobank data analysis, quantum physics, and PageRank computation. Scientific Awards: Fellow of the American Mathematical Society (AMS) Fellow of the Society for Industrial and Applied Mathematics (SIAM) Olga Taussky-Todd Lecture, ICIAM 2023 She has advised multiple graduate and undergraduate students, including J.T. Holodnak, T. Wentworth, and R. Rehman. Her collaborative research has been supported by interdisciplinary grants, particularly in quantum physics and data-intensive computing. She has served on editorial boards of leading journals such as SIAM Review , Acta Numerica , and Numerische Mathematik , and chaired SIAM activity groups, reflecting her leadership in the applied mathematics community. She is actively involved in the development of software and algorithms, including the MATLAB package kappa_SQ for randomized sampling, and has contributed to discussions on communicating applied mathematics through case studies.

Angelika PeerView profile

Professor

• Robotics
• Control Systems
• Human-Robot Interaction
• +15 more

Angelika Peer is a Full Professor in the Faculty of Engineering at the Free University of Bozen-Bolzano, Italy, a position she has held since November 2017. Previously, she served as a Full Professor at the Bristol Robotics Laboratory, University of the West of England, UK (2014–2017), and as a senior researcher and lecturer at the Technical University of Munich, Germany, where she was also a TUM-IAS Junior Fellow. Her academic base is firmly within engineering, particularly in intelligent and human-centered systems. Diploma in Electrical Engineering and Information Technology, Technical University of Munich, 2004 Doctor of Engineering, Technical University of Munich, 2008 Her research interests are centered on intelligent systems that bridge humans and machines. Key areas include robotics, control systems, human-robot interaction (HRI), emotion recognition through physiological signals, shared control, and intelligent manufacturing. She investigates how dynamic models and machine learning can improve safety, adaptability, and efficiency in human-machine collaboration. Her work spans both theoretical control design and practical applications in Industry 5.0, sustainable production, and assistive technologies. Her recent publications (2023–2025) reflect a strong trend toward data-driven and model-based intelligent systems. Topics include energy-based safety control in HRI, neural-network-based trajectory adaptation in powder compaction, emotion intensity estimation using NARX models and appraisal theory, and task prediction in digitalized shop floors using support vector regression. These works demonstrate expertise in integrating dynamic modeling, machine learning, and real-time control with applications in manufacturing and human-centered computing. Angelika Peer has advised PhD students including Ehtisham Ul Hasan and Isabel Francisca Sota Machado Barradas. She is involved in research initiatives such as the Human-centered Intelligent Systems (HCIS) macro area at her university, promoting interdisciplinary work at the intersection of computer science, engineering, and human factors. Her research is supported by collaborations and publications in high-impact journals and conferences, with a strong emphasis on open access and peer-reviewed dissemination.

• Machine Learning
• Remote Sensing
• Artificial Intelligence
• +9 more

Jordi Muñoz Mari is an Associate Professor in the Department of Electronic Engineering at the School of Engineering, University of Valencia. He is a key member of the Image and Signal Processing (ISP) research group within the Image Processing Laboratory (IPL), a recognized research institute (ERI) at the university. His research focuses on the intersection of artificial intelligence, remote sensing, and environmental science. Key areas include machine learning for Earth observation, biophysical parameter retrieval, crop yield forecasting, climate modeling, and causal inference in ecological systems. He also explores applications of AI in education, particularly generative models for assessment and digital learning tools. The most recent publications highlight a strong trend toward physics-aware machine learning, uncertainty quantification, and large-scale applications using platforms like Google Earth Engine. His work spans climate extremes (medicanes, hurricanes, droughts), agricultural monitoring, biodiversity, and educational technology, demonstrating interdisciplinary innovation. Jordi Muñoz Mari completed his PhD at the University of Valencia in 2004 on telemonitoring systems for cardiac patients. His ongoing research involves advanced signal processing, kernel methods, and AI-driven geoinformation systems.

Anastasia AilamakiView profile

Professor

• Data Systems
• Database Systems
• Resource Optimization
• +4 more

Anastasia Ailamaki is a Professor at the Swiss Federal Institute of Technology in Lausanne (EPFL), affiliated with the School of Computer and Communication Sciences and the Department of Computer Science. She is also a member of the Board of Directors of the Technical University of Crete (TUC), where she is a distinguished alumna, having earned her Master of Science in Electrical and Computer Engineering. Her leadership in data systems research has been recognized with the prestigious ERC Advanced Grant. Her research focuses on foundational approaches to optimizing system resources through declarative data systems. This work lies at the intersection of database systems, computer architecture, and performance modeling, aiming to improve efficiency and scalability in modern data-intensive applications. Her interests include query processing, data storage optimization, and the integration of hardware-aware techniques into database design. The awarded ERC project, "A Foundational Approach to Resource Optimization through Declarative Data Systems," represents a significant advancement in the field, funded with 2.5 million euros, highlighting the relevance and impact of her research agenda. ERC Advanced Grant Professor Ailamaki has advised numerous students throughout her career and has led major research initiatives, though specific names and grants beyond the ERC are not detailed in the current text. Her role on the External Advisory Committee and Board of Directors at TUC reflects her ongoing commitment to academic leadership and institutional development. She is actively involved in shaping the future of data systems research at EPFL and through her collaborations with institutions like the Technical University of Crete. Her lab or research group likely focuses on systems for data science and performance-aware computing, although specific team details are not provided.

Mohammad GhassemiView profile

Assistant Professor

• Machine Learning
• Data Mining
• Natural Language Processing
• +7 more

Mohammad Ghassemi is an Assistant Professor in the Department of Computer Science and Engineering at Michigan State University's College of Engineering. His work focuses on developing AI systems that integrate human and machine intelligence to address complex challenges in healthcare, decision-making, and social behavior. He has affiliations with MIT's Institute for Medical Engineering and Science, Standard & Poor's Financial Services, and Ghamut Corporation, and serves on multiple startup advisory boards. Education: PhD from MIT (Electrical Engineering and Computer Science), M.Phil. from University of Cambridge (Information Engineering), B.Sc. from New Mexico State University (Electrical Engineering & Applied Mathematics). Research: Combines machine learning, natural language processing, and crowdsourcing to predict health outcomes, enhance medical decision support, and design wearable AI for social interaction. Awards: Multiple US patents, media features in BBC, NPR, Wall Street Journal, and publications in Nature Scientific Data, Science Translational Medicine, and AAAI. Key projects include the Connect platform for health data sharing, large-scale medical databases, and an AI-driven wearable for social coaching. His lab emphasizes end-to-end systems for real-world impact in healthcare and human behavior analysis.

• Social Sciences
• Humanities
• Data Management
• +3 more

Elina Late is a Senior Research Fellow at the Department of Communication Sciences, University of Tampere. Her work bridges social sciences, humanities, and computer science through research data management, image data interaction, and academic collaboration frameworks. Key Research Areas: Data interaction in humanities, open science, learned societies, image data modeling Recent Publications: Focus on collaborative workflows, FAIR data principles, and cross-border academic networks Activities: Conference presentations at CHIIR '25, STI2024, and invited lectures on digital image research Datasets: Principal contributor to national and European learned society datasets (2018-2023)