جست‌وجوی استاد و پژوهشگر

• Data Acquisition
• Trigger Systems
• Real-time Monitoring
• +۵ مورد دیگر

Dr. Andreas Kopmann serves as Deputy Director of the Institute for Process Data Processing and Electronics (IPE) at Karlsruhe Institute of Technology (KIT) and leads the Process Data Processing group. With over two decades of experience in experimental physics and data systems, he plays a pivotal role in major international research collaborations including the KATRIN neutrino experiment and PANDA detector project. PhD in Electrical Engineering, University of Hannover (2000) Diploma in Electrical Engineering, University of Hannover (1994) Dr. Kopmann's research focuses on data acquisition systems, trigger systems, real-time monitoring, GPU computing, and data management for large-scale physics experiments. His work bridges experimental physics requirements with advanced computing technologies, particularly in high-data-rate applications for particle physics and synchrotron radiation facilities. He has pioneered novel detector technologies and data processing frameworks that enable cutting-edge scientific discoveries in neutrino physics and accelerator science. Analysis of Dr. Kopmann's recent publications reveals a strong trajectory toward higher data rates, sophisticated real-time processing, and integration of machine learning techniques. His work spans neutrino physics through KATRIN, detector development for PANDA and other experiments, and innovative data acquisition systems like KALYPSO and UFO. The interdisciplinary nature of his research combines particle physics, computing science, and electronics engineering to solve complex experimental challenges. KIT Program Lead for "Matter and Technologies" (2021-present) Coordinator of Helmholtz Program Topic "Detector Technologies and Systems" Principal Investigator in Karlsruhe School for Elementary Particle Physics (KSETA) Project Leader for Data Acquisition in KATRIN experiment As Deputy Director of IPE, Dr. Kopmann oversees research groups developing critical technologies for experiments at KIT, DESY, CERN, and other international facilities. His team's work on high-speed data acquisition, detector electronics, and computing infrastructure supports groundbreaking research in particle physics, neutrino physics, and materials science.

Angela Woodمشاهده پروفایل

استاد

• Biostatistics
• Health Data Science
• Cardiovascular Epidemiology
• +۵ مورد دیگر

Professor Angela Wood serves as Professor of Biostatistics and Health Data Science in the Department of Public Health and Primary Care at the University of Cambridge, with her research centered in the Cardiovascular Epidemiology Unit. She also holds the prestigious position of Vice-Master at Darwin College where she serves as College Tutor. Dr. Wood earned her BSc (Hons) in Mathematics and Statistics in 1998 and completed her doctorate in 2001 under the supervision of Profs Peter Diggle and Robin Henderson at the University of Lancaster. Following post-doctoral research with Prof Ian White at the MRC Biostatistics Unit in Cambridge, she joined the University of Cambridge in 2006 as a University Lecturer in Biostatistics. Her research interests focus on advancing statistical methods for epidemiological research, particularly in cardiovascular disease. Professor Wood specializes in health data science applications for complex disease prediction, with expertise in large-scale data integration and analysis. Her work bridges biostatistical methodology development with practical applications in population health. Professor Wood holds significant leadership positions including co-Lead of the Big Data for Complex Disease Driver Programme for HDR UK; BHF Data Science Centre Associate Director and Theme Lead for Structured Data; co-Lead of the NIHR Cambridge BRC Data Science and Population Health theme; Regional co-Lead for Health Data Research UK Cambridge; Programme Leader in the NIHR Blood and Transplant Research Unit; Turing Fellow at the Alan Turing Institute; and Steering Group Member of the European Society of Cardiology Cardiovascular Risk Collaboration Unit. As an active supervisor, Professor Wood takes PhD students through the Department's doctoral training program in Public Health and Primary Care. Her leadership extends to major national health data initiatives that shape research directions in cardiovascular epidemiology and population health sciences. She contributes to the Cardiovascular Epidemiology Unit's work in health data science, molecular epidemiology, and systems genomics, with particular emphasis on methodological development and translational research.

Kevin Hongمشاهده پروفایل

استاد

• Information Systems
• Online Platforms
• Privacy Design
• +۴ مورد دیگر

Kevin Hong is a Professor and Miami Herbert Centennial Endowed Chair at the University of Miami's Miami Herbert Business School , where he also serves as Associate Dean for Research. His work focuses on information systems and digital platform design. Current roles: Professor, Centennial Endowed Chair, Associate Dean for Research Email: yxh1034@miami.edu ORCiD: 0000-0002-0577-7877 Research Interests: Kevin Hong's work explores digital privacy, user engagement, and behavioral economics across online platforms. His studies examine: Temporal cues in online learning environments Privacy-enhancing designs in dating applications AI implementation in customer service Content generation incentives in knowledge platforms Labor dynamics in gig economy marketplaces Publication Trends: Recent studies demonstrate expertise in field experimentation, behavioral analytics, and platform economics. Key methodologies include: Randomized controlled trials Difference-in-differences modeling Large-scale observational studies

Dong Liمشاهده پروفایل

دانشیار

• High performance computing
• Non-volatile memory
• Memory heterogeneity
• +۲ مورد دیگر

Dong Li is an Associate Professor at the University of California, Merced , where he directs the Parallel Architecture, System, and Algorithm Lab (PASA) and co-directs the High Performance Computing Systems and Architecture Group . He co-founded Yotta Labs Inc. and previously held research roles at Oak Ridge National Laboratory (2011-2014) and a PhD from Virginia Tech. Research Interests: Dong's work focuses on High performance computing (HPC) Memory heterogeneity and non-volatile memory Systems for machine learning and AI Fault tolerance in large-scale systems His innovations include heterogeneous memory optimization for recommendation models and GNNs, CXL memory integration, and persistent memory debugging tools. Recent Publications highlight advancements in CXL-based inter-node communication Memory tiering for laminography reconstruction ML-guided memory optimization for DLRM and GNN Fault tolerance benchmarks and error analysis Awards & Recognition: NSF CAREER Award (2016) Oracle Research Award (2022) ASPLOS Distinguished Artifact Award (2021) Virginia Tech Early Career Alumni Award (2023) Advising & Funding: Dong has mentored 22 students (8 PhD, 6 Master’s, 8 undergraduates) and secured grants from NSF, NVIDIA, Meta, and national labs (Argonne, Lawrence Berkeley, Lawrence Livermore). Collaborations include Microsoft (DeepSpeed, Intel PMDK), AMD, SK Hynix, and Intel/MICRON hardware donations.

Leila De Florianiمشاهده پروفایل

استاد

• Geometric Modeling
• Data Visualization
• Spatial Data Representation and Processing
• +۶ مورد دیگر

Leila De Floriani is a Professor at the University of Maryland, with appointments in the Department of Geographical Sciences and the University of Maryland Institute for Advanced Computer Studies (UMIACS). She previously served as a professor at the University of Genova (Italy) since 1990, where she developed Italy's first undergraduate and graduate curricula in computer graphics and directed the Ph.D. program in Computer Science for eight years. Her professional activities include serving as the 2020 President of the IEEE Computer Society and currently as IEEE Division VIII Director for 2023-24. Professor De Floriani's research spans geometric modeling, data visualization, spatial data representation and processing, computer graphics, shape analysis, and topological data analysis. Her work focuses on developing mathematical models and algorithms for representing, analyzing, and visualizing complex spatial data, particularly through hierarchical models, mesh-based representations, and topology-based approaches. Her research group, the GeoVis group, investigates applications in terrain modeling, environmental data analysis, and forest structure mapping using LiDAR technology. Analysis of her recent publications reveals a strong focus on terrain representation and processing, with increasing emphasis on topological data analysis, machine learning integration, and efficient algorithms for large-scale spatial data. Her work bridges theoretical foundations in computational topology with practical applications in geospatial sciences, demonstrating consistent innovation in data structures and visualization techniques. Scientific Awards & Recognitions Fellow of IEEE (2016) for contributions to geometric modeling and scientific visualization Fellow of International Association for Pattern Recognition (IAPR) (1998) for contributions to geometric modeling and image analysis Fellow of Eurographics Association (2020) for outstanding contributions to computer graphics and visualization Pioneer of Solid Modeling Association (2017) for seminal work in solid and feature-based modeling Inducted Member of IEEE Visualization Academy (2020) IEEE Computer Society Golden Medal Award (2018) Inducted Member of IEEE Honor Society Eta Kappa Nu (2019) Multiple best paper awards at major conferences including Shape Modeling International (2015), IEEE/EG Symposium on Volume and Point-Based Graphics (2008), and ACM SIGSPATIAL (2008) Professor De Floriani has successfully advised numerous PhD students including Xin Xu, Yunting Song, and Noel Dyer, whose recent dissertations focused on topology-based individual tree mapping, efficient terrain analysis, and bathymetric data visualization respectively. Her research has been funded by prestigious agencies including the National Science Foundation, NASA, and the European Commission. As the leader of the UMD GeoVis group, she oversees a research program that develops open-source tools for spatial data analysis available on GitHub, with current projects focusing on forest point cloud processing and topology-based geospatial data visualization. The GeoVis group, affiliated with the Department of Geographical Sciences, UMIACS, and the Center for Geospatial Information Sciences, maintains a strong collaborative environment with ongoing projects in geometric modeling, spatial data structures, topology-based machine learning, and mesh-based terrain modeling. The group has received recent funding from NASA's HPOSS program for developing an open-source library for forest point cloud processing based on topological data analysis.

• Physics Education
• Computational Science Education
• STEM Education
• +۳ مورد دیگر

Danny Caballero is a Professor at Michigan State University with joint appointments in the Department of Physics & Astronomy and the Department of Computational Mathematics, Science and Engineering . He holds the Lappan-Phillips Chair of Physics Education and co-directs the Physics Education Research Lab , while also serving as a principal investigator for the Learning Machines Lab and research faculty at the University of Oslo’s Center for Computing in Science Education. His research focuses on: How computational tools and scientific practices influence learning Cognitive and sociocultural theories in STEM education Departmental valuation of computational methods WCAG-compliant open educational resource development Recent publications highlight: Statistical modeling in physics education Computational literacy assessment Graduate admissions policy analysis Machine learning applications in educational assessment Scientific recognitions include: Lappan-Phillips Chair (2025) $300,000 NSF grant for computational education tools Additional roles: Founder of OER-Forge platform Union of Tenure System Faculty organizer Editor for upcoming Computing in Physics Education book

• Machine Learning
• Statistical Methodology
• Computational Biology
• +۵ مورد دیگر

Eric P. Xing is a Professor at the Language Technologies Institute of Carnegie Mellon University , and currently serves as President of the Mohamed bin Zayed University of Artificial Intelligence . His work bridges machine learning methodology with computational biology and large-scale AI systems . Research Focus: Developing machine learning theory for high-dimensional, dynamic data Building foundation models for biology (AIDO, scLong, ProteinAligner) Designing scalable AI architectures (Pollux, LLM360, PAN) Advancing interpretable and controllable NLP systems Scientific Leadership: Founded the SAILING Lab at CMU Co-chaired ICML 2014 and ICML 2019 Recipient of the Jay Lepreau Best Paper Award (OSDI 2021) Education & Mentorship: Advises PhD students across machine learning and computational biology Alumni include faculty at ETH Zurich, University of Chicago, and UC San Diego

Hannah Leslieمشاهده پروفایل

استادیار

• Health Systems Research
• Epidemiology
• Health Care Quality Measurement
• +۵ مورد دیگر

Hannah Leslie serves as an Assistant Professor in the Division of Prevention Sciences at the University of California, San Francisco (UCSF), focusing on health systems research to improve care for marginalized populations globally. Her work spans HIV quality measurement, non-communicable disease management, and pandemic-responsive health service delivery across Latin America and South Africa. Education: PhD in Epidemiology, University of California, Berkeley Postdoctoral Fellowship in Health Systems, Harvard T.H. Chan School of Public Health Dr. Leslie develops innovative methodologies for health care quality assessment and identifies scalable health system interventions targeting inequities. Her research examines hypertension management, maternal-child health, HIV prevention (PrEP), and housing instability impacts on mental health, emphasizing context-specific solutions in resource-limited settings. She employs mixed-methods approaches including large-scale facility surveys, implementation science frameworks, and community-engaged co-creation processes. Recent publications (2024-2025) reveal consistent focus on health system measurement in Latin America and South Africa, with emerging themes around digital health solutions, post-pandemic recovery, and multi-level barriers to equitable care. Her work bridges epidemiological rigor with practical health policy applications, particularly through the QuEST LAC network for evidence-based policy in Latin America. Scientific Awards: CFAR Mentored Scientist Award (2021) for "Identifying clinic factors that shape population health outcomes in times of crisis" Dr. Leslie actively mentors through UCSF's CFAR program and collaborates with international networks including the QuEST LAC initiative. Her research is supported by competitive awards focusing on health systems strengthening during crises, with current projects examining hypertension management in Peru, PrEP delivery in South Africa, and digital interventions for breast cancer care in Mexico. Affiliated with UCSF's Center for AIDS Research (CFAR), she leads cross-national collaborations in Mexico, Peru, South Africa, and Nepal. Her work integrates health systems analysis with community engagement, particularly through the People's Voice Survey initiative measuring patient perspectives on care quality across Latin America.

• Operational Research
• Logistics
• Stochastic Optimization
• +۳ مورد دیگر

Michel Gendreau is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He holds a B.Com. from McGill University, and both an M.Sc. and Ph.D. from the University of Montreal. His research focuses on operational research with applications in logistics, transportation, energy systems, and telecommunications. He is affiliated with several prestigious research centers including the Institute for Data Valorization (IVADO), the Trottier Energy Institute (IET), and the Interuniversity Research Center on Enterprise Networks, Logistics and Transport (CIRRELT). Professor Gendreau's research interests span operational research, with particular emphasis on stochastic optimization methods applied to transportation and logistics problems, energy systems management, and telecommunications. His work often addresses real-world challenges through mathematical modeling and algorithm development, with applications ranging from bike-sharing systems to emergency response planning and electricity grid management. The analysis of his recent publications reveals a strong focus on vehicle routing problems under uncertainty, maintenance optimization, and the integration of stochastic programming with machine learning techniques for improved decision making. Professor Gendreau has received numerous prestigious awards recognizing his contributions to the field of operations research. In 2022, he was named a Fellow of the International Federation of Operational Research Societies (IFORS). In 2010, he was awarded Fellow status by INFORMS (Institute for Operations Research and the Management Sciences). Most notably, in November 2015, he received the Robert M. Herman Lifetime Achievement Award from the Transportation Science and Logistics Society of INFORMS, which is considered the most prestigious distinction for operational researchers working in logistics and transportation. Throughout his career, Professor Gendreau has supervised 25 doctoral students and 18 master's students, contributing significantly to the development of the next generation of operations research experts. His research has been supported by numerous grants from organizations including NSERC (Natural Sciences and Engineering Research Council of Canada), with expertise recognized in Operational Research and Management Science (NSERC subject 1601) and Logistics (NSERC subject 1603). Professor Gendreau is actively involved in several research teams and laboratories, particularly those focused on data valorization, energy systems, and transportation logistics. His current work continues to push the boundaries of stochastic optimization and its applications to complex real-world problems, with recent publications addressing challenges in urban transportation, energy management, and emergency response systems.

Jon Barkerمشاهده پروفایل

استاد

• Noise-Robust Speech Processing
• Machine Listening
• Computational Modeling of Human Hearing
• +۴ مورد دیگر

Professor Jon Barker is a faculty member at the University of Sheffield , where he holds a Personal Chair in the School of Computer Science . He leads the Speech and Hearing (SpandH) research group and co-founded the CHiME international workshop series on robust speech recognition. Education : PhD in Computer Science (University of Sheffield, 1999); BA in Electrical and Information Sciences (Cambridge University). Research Focus : His work bridges machine listening and human auditory perception , with key contributions to noise-robust speech recognition , speech intelligibility prediction , and hearing aid signal processing for speech and music. Recent projects include the Clarity Challenges and Cadenza Challenges , large-scale machine learning initiatives to improve accessibility for hearing-impaired users. Publication Trends : Recent articles emphasize machine learning for hearing aid optimization , dysarthric speech recognition , audio-visual integration , and music demixing algorithms . Collaborations span speech processing, psychoacoustics, and biomedical engineering. Scientific Awards : EURASIP Best Paper Award (2009) ISCA Best Paper Award (2008) Grants and Leadership : He has secured major EPSRC grants including EnhanceMusic (2022-2026) and Challenges to Revolutionise Hearing Device Processing (2019-2025). He co-led the TAPAS Marie Curie Training Network (2017-2022) and led projects like AV-COGHEAR (2015-2018) and CHiME (2009-2012). Labs and Teams : Barker collaborates closely with the Speech and Hearing Research Group and contributes to international initiatives like the CHiME Workshop . His lab develops open datasets such as the Clarity Speech Corpus and Audio-Visual Lombard Corpus .

• Metallurgical Process Engineering
• Metal Recycling
• Extractive Metallurgy
• +۷ مورد دیگر

Professor Karl Bernhard Friedrich serves as Universitätsprofessor and Chair of Metallurgical Process Engineering and Metal Recycling at RWTH Aachen University's Faculty of Georesources and Materials Engineering. He leads the Institute for Metallurgical Science and Electrometallurgy (IME), with his office located in Building 1401, Room 108 at Intzestraße 3 in Aachen. His leadership extends to numerous EU-funded research initiatives focused on sustainable metallurgy and circular economy principles. Professor Friedrich's research interests span metallurgical process engineering, metal recycling, extractive metallurgy (pyrometallurgy and hydrometallurgy), battery recycling technologies, lithium recovery processes, and vacuum metallurgy. His work emphasizes resource efficiency and sustainable practices in metal production and recycling, with particular focus on developing environmentally sound processes for electronic waste and battery recycling. The institute under his leadership has pioneered several innovative recycling concepts for lithium-ion batteries and electronic scrap. Analysis of his recent publications reveals a strong focus on battery recycling technologies, particularly lithium recovery from spent batteries through both pyrometallurgical and hydrometallurgical routes. His research group has developed advanced processes for black mass treatment, slag valorization, and metal recovery optimization. The work demonstrates increasing integration of digital technologies and multi-objective modeling in metallurgical process development. Honorary doctorate from Montanuniversität Leoben (2025) recognizing outstanding scientific achievements and commitment to promoting young scientists Long-standing collaboration with Montanuniversität Leoben through the Aachen-Leoben Workshop for non-ferrous metallurgists Professor Friedrich actively supervises numerous doctoral students and has established several collaborative research programs including the DFG priority program 'engineered artificial minerals,' and doctoral programs 'circular electronics' and 'circular e-cars.' His institute maintains strong industry connections, with growing demand for TBRC and VAR trial campaigns. The institute recently commissioned a new large-scale arc furnace and is preparing for leadership transition in early 2027. The IME under Professor Friedrich's leadership operates extensive research facilities focused on vacuum metallurgy, pyrometallurgy, and hydrometallurgy. The institute collaborates with industry partners through the GDMB Zinc & Lead Technical Committee and participates in numerous European research networks. Current research activities focus on developing sustainable processes for battery recycling, electronic waste treatment, and critical metal recovery with emphasis on circular economy principles.

• Integrated Circuits
• Signal Processing
• Data Analytics
• +۴ مورد دیگر

Xin Li is a Professor in the Department of Electrical and Computer Engineering at Duke University and serves as the Associate Vice Chancellor at Duke Kunshan University. He holds a Ph.D. from Carnegie Mellon University (2005) and has held leadership roles in research consortia like the FCRP Focus Research Center and the Center for Silicon System Implementation (CSSI). His research bridges integrated circuits , machine learning , and cyber-physical systems , with applications in autonomous driving, battery lifetime prediction, and smart buildings. Education : Ph.D., Carnegie Mellon University (2005); M.S., Fudan University (2001); B.S., Fudan University (1998) His work emphasizes robust design methodologies for analog/RF circuits, data-driven predictive modeling , and Bayesian inference for high-dimensional variation spaces. Recent publications focus on generative adversarial networks for circuit design, multi-view imputation for incomplete data, and knowledge-driven autonomous systems . He has received numerous accolades, including the NSF CAREER Award (2012) , IEEE Donald O. Pederson Best Paper Awards (2013, 2016) , and IEEE Fellow (2017) . He has served as Editor for journals like IEEE Transactions on Biomedical Engineering and as Chair for conferences including ISVLSI and CAD/Graphics.

Kaius Sinnemäkiمشاهده پروفایل

استاد

• Linguistics
• Language Typology
• Comparative Linguistics
• +۹ مورد دیگر

Kaius Sinnemäki is a Professor of Quantitative and Comparative Linguistics at the University of Helsinki, affiliated with the Department of Languages under the Faculty of Arts. Since 2025, he has directed the strategic profiling action Diversity in Society and Life (DIVSOL) , funded by the Research Council of Finland, and previously led the ERC Starting Grant project Linguistic Adaptation (2019-2024). His research focuses on large-scale comparative linguistics, integrating language typology with sociolinguistics. Key areas include core argument marking, statistical methods in typology, linguistic complexity, language contact, and the interplay between language and religion. He teaches courses spanning language typology, evolution, sociolinguistics, and quantitative methods. Recent publications address replication in linguistic research, adaptation in contact ecologies, and complexity measures across phonological, morphosyntactic, and syntactic domains. His work often bridges theoretical linguistics with empirical data analysis, utilizing open datasets and computational tools. Scientific Awards: Prize for the Humanities (2021) He actively supervises academic theses, serves on editorial boards (e.g., Linguistic Typology ), and participates in international collaborations, peer reviews, and media engagements on linguistic diversity and secularism.

• Power Systems
• Microgrids
• Inverter Control
• +۳ مورد دیگر

Xiaonan Lu is an Associate Professor of Electrical Engineering Technology at Purdue University's School of Engineering Technology, with a courtesy appointment in the Elmore Family School of Electrical and Computer Engineering. His research focuses on critical challenges in modern power systems dominated by inverter-based resources, particularly stability and control in microgrids and renewable-integrated grids. His research interests span power systems engineering with emphasis on small-signal stability analysis, dynamic modeling of hybrid AC/DC microgrids, and advanced control strategies for grid-forming and grid-following inverters. He investigates AI-assisted modeling techniques, resilience enhancement through hydrogen integration, and data-driven optimization of microgrid operations to address challenges in low-inertia power systems and distributed energy resource coordination. Analysis of his recent publications (2024-2025) reveals dominant trends toward AI-aided stability assessment, seamless control transitions between inverter modes, and quantifiable trade-offs in voltage regulation and power sharing. His work consistently addresses practical implementation challenges including communication delays, cyber resilience, and standardized testing methodologies for inverter-dominated systems.

Philipp Koehnمشاهده پروفایل

استاد

• Machine Translation
• Natural Language Processing
• Artificial Intelligence
• +۲ مورد دیگر

Philipp Koehn is a Professor in the Department of Computer Science at Johns Hopkins University, with additional affiliation at the University of Edinburgh. His primary research focuses on statistical and neural machine translation, specifically developing methods to leverage large-scale digital information for cross-lingual communication. He leads the Machine Translation Research Group and maintains key resources like the Moses toolkit and Europarl corpus. His research interests span: Core machine translation techniques (statistical/neural approaches) Low-resource and unsupervised translation methods Cross-lingual representation learning Speech-to-speech translation systems Large-scale parallel data mining and alignment Evaluation methodologies for generated text Koehn's recent publications demonstrate strong focus on improving translation efficiency (dynamic compression, streaming models), robustness (noise handling, error correction), and accessibility (low-resource languages, radio speech processing). Key trends include multilingual generalization, document-level coherence, and human-centered evaluation. Significant scientific recognition includes: ACL Fellow (2024) IAMT Award of Honor (2015) European Inventor Award Finalist (2013) He currently advises PhD students Rachel Wicks, Elina Baral, Bismarck Odoom, and Weiting Tan. His Machine Translation Group develops widely-used open-source tools and organizes major conferences including WMT and MT Marathon.