جست‌وجوی استاد و پژوهشگر

Iwan de Eschمشاهده پروفایل

استاد

• Drug Design
• Fragment-Based Drug Design (FBDD)
• Computer-Aided Drug Design (CADD)
• +۵ مورد دیگر

Prof. Iwan de Esch is a Full Professor in the Department of Chemistry and Pharmaceutical Sciences at VU University Amsterdam, leading the Drug Design & Synthesis division and serving as Head of Department. He holds dual appointments in the Faculty of Science and AIMMS (Amsterdam Institute for Molecular and Life Sciences). His research focuses on understanding molecular interactions for drug discovery, employing CADD and FBDD approaches. He co-founded three biotech companies (De Novo Pharmaceuticals, IOTA Pharmaceuticals, Griffin Discoveries) and coordinates the EU-funded FRAGNET network. PhD from VU University (1998) Postdoc at University of Cambridge (Drug Design Group) Recipient of the 2011 Galenus Research Prize Received grants from EU-FP7, IMI, and Dutch STW Research interests span fragment-based drug design, receptor-ligand interactions, and pre-clinical drug candidate development. His work impacts GPCRs, PDEs, kinases, and protein-protein interactions. He teaches courses on drug discovery methodologies and computational tools. Over 200 peer-reviewed publications and 8 datasets/software contributions highlight his impactful career.

Michael Gallaugherمشاهده پروفایل

استادیار

• Clustering and Classification
• Machine Learning
• Matrix and Tensor Variate Data Analysis
• +۵ مورد دیگر

Dr. Michael Gallaugher is an Assistant Professor of Statistical Science at Baylor University. He holds a Ph.D., M.S., and B.S. in Statistics from McMaster University. His research focuses on clustering and classification methodologies, particularly in matrix/tensor variate data, mixed-type data, clickstream analysis, and outlier detection. He has been recognized with prestigious awards including the Vanier Canada Graduate Scholarship and the Banting Postdoctoral Fellowship. Education: Ph.D., Statistics, McMaster University (2020) M.S., Statistics, McMaster University (2017) B.S., Statistics, McMaster University (2015) Research Interests: Dr. Gallaugher's work emphasizes advanced clustering techniques for complex data structures, including high-dimensional datasets, clickstream behavior analysis, and skewed distribution modeling. His contributions span statistical methodology development and applications in fields like bioinformatics and sports science. His recent work explores hidden Markov models for time series and robust co-clustering algorithms. Publications Trends: His publications reflect a strong focus on matrix-variate distributions, skewed data modeling, and algorithmic innovation in clustering. Recent work (2022-2025) highlights advancements in contaminated normal mixtures, co-clustering for high-dimensional data, and spatial regression models. Awards: Vanier Canada Graduate Scholarship Banting Postdoctoral Fellowship Advising & Grants: While no advisees are listed, his research has been supported by grants from the Natural Sciences and Engineering Research Council of Canada. He contributes to statistical consulting services at Baylor and collaborates internationally on methodological projects. Labs & Teams: He is affiliated with Baylor's Department of Statistical Science and collaborates with research groups focused on machine learning and statistical computing.

Fernando Camelliمشاهده پروفایل

دانشیار

• Computational Fluid Dynamics (CFD)
• Urban Environmental Modeling
• Contamination Dispersion Modeling
• +۳ مورد دیگر

Fernando Camelli is an Associate Professor in the Physics & Astronomy Department at George Mason University, holding dual roles as Instructional Faculty and Faculty. His research focuses on computational fluid dynamics (CFD), urban environmental modeling, and high-performance computing. He specializes in simulating complex fluid flows in urban environments, subway systems, and industrial applications, with particular emphasis on turbulence modeling, fluid-structure interaction, and GPU-accelerated algorithms. Key research areas include: CFD for urban airflow and contamination dispersion Meshless and immersed boundary methods Integration of geographic information systems (GIS) with CFD Large-scale simulations using parallel computing His work addresses practical challenges such as subway ventilation optimization, emergency contaminant dispersion prediction, and urban infrastructure design. Recent studies emphasize scalability improvements for fluid-structure interaction simulations and GPU-based code modernization.

Weiwen Jiangمشاهده پروفایل

استادیار

• Quantum Computing
• AI Accelerators
• Hardware-Software Co-Design
• +۳ مورد دیگر

Weiwen Jiang is a tenure-track Assistant Professor in the Department of Electrical and Computer Engineering at George Mason University (GMU), affiliated with the College of Engineering and Computing (CEC). He leads the JQub lab, focusing on hardware/software co-design for computing systems, spanning classical (FPGAs, ASICs) and quantum computing applications in AI-driven fields like medical imaging and geophysics. Prior to GMU, he held a postdoctoral position at the University of Notre Dame and earned his PhD in Computer Science from Chongqing University with a joint PhD in Electrical and Computer Engineering from the University of Pittsburgh. His research emphasizes quantum computing, AI accelerators, and domain-specific computing. Notable achievements include the 2025 NSF CAREER Award, ACM Sigda Meritorious Service Award (2024), and IEEE QuantumWeek Best Paper Award (2023). His work is funded by NSF, DoE, ARO, Meta, and Leidos. He co-chaired IEEE QuantumWeek (2023–2025) and created workshops like StableQ at ESWEEK 2023. Key contributions include developing frameworks like QuPAD for quantum learning and JQub's AI-driven geophysical and medical imaging tools. His lab graduated Dr. Yi Sheng (now at University of South Florida) and Dr. Zhepeng Wang (Amazon Applied Scientist). Current research explores quantum machine learning, noise mitigation, and fairness in AI for edge devices.

• Design for Policy
• Sustainable Development
• Climate Transitions
• +۴ مورد دیگر

Ramia Mazé is a Professor of Design for Social Innovation and Sustainability at the London College of Communication (University of the Arts London, UAL), affiliated with the Service Futures Lab. Her research focuses on design’s role in policy, sustainable development, and climate justice, emphasizing participatory, critical, and politically-engaged practices. She leads international projects such as the Sustainable Transitions through Democratic Design doctoral network and the Design|Policy UKRI/AHRC Research Network. Education: PhD in Interaction Design, Malmö University (2007) MA(Hons) in Computer Related Design, Royal College of Art (2000) BA (Magna Cum Laude) in Architecture, Barnard College, Columbia University (1996) Research Interests: Mazé explores design’s intersection with governance, feminist epistemologies, and education. Her work addresses climate transitions, public policy engagement, and equity in design practices. Methodologically, she prioritizes practice-based research and interdisciplinary collaboration. Grants & Collaborations: Key grants include the €4M EU Horizon Europe-funded Sustainable Transitions through Democratic Design (CD4T) and UKRI/AHRC-funded Design|Policy Network. She collaborates with institutions like the Service Futures Lab, Policy Design Community, and Nordic Design Research Society. Advising & Career Development: Currently supervising 9 doctoral candidates (7 completed previously). Active in shaping academic promotion criteria and supporting unconventional research career paths, particularly gender-inclusive pathways. Labs & Networks: Core member of the Service Futures Lab and steering group member of the Design|Policy Network. Former leadership roles at Aalto University (Finland) and Konstfack University (Sweden).

Robert Sean McCannمشاهده پروفایل

استادیار

• Malaria Vector Ecology
• Medical Entomology
• Vector Control Strategies
• +۵ مورد دیگر

Dr. Robert Sean McCann is an Assistant Professor in the Department of Medicine at the University of Maryland School of Medicine (UMSOM), with a secondary appointment in Microbiology and Immunology. He is affiliated with the Center for Vaccine Development and Global Health (CVDGH), leading the Malaria Research Program. His research focuses on malaria vector ecology, transmission dynamics, and intervention strategies. Education & Training: B.S. in Biology, University of North Dakota (2002–2006) Ph.D. in Entomology & EEBB (Ecology, Evolutionary Biology & Behavior), Michigan State University (2009–2013) Postdoctoral Researcher, Wageningen University & Research (2014–2019) Postdoctoral Fellow, UMSOM (2019–2020) Research Interests: Dr. McCann investigates spatial and temporal patterns of malaria vectors (Anopheles mosquitoes) and parasite transmission using field, lab, and computational tools. Key areas include: Vector habitat use and environmental drivers Genomic analysis of Anopheles populations Evaluation of vector control interventions (e.g., larval source management, house improvements) Human-to-mosquito transmission determinants Integration of geostatistical methods for high-resolution monitoring Advising & Grants: He leads the IRSDA/K01 Fogarty International Center award studying vector diversity impacts on intervention efficacy. His work emphasizes interdisciplinary collaboration to translate research into scalable malaria control strategies. Labs/Teams: Active in the Malaria Research Program at CVDGH, collaborating with global teams in Africa (e.g., Malawi, Kenya) on field-based studies.

Mourad Zeghalمشاهده پروفایل

استاد

• Geotechnical Engineering
• Soil Mechanics
• Seismic Response Analysis
• +۳ مورد دیگر

Dr. Mourad Zeghal is a Professor in the Department of Civil and Environmental Engineering at Rensselaer Polytechnic Institute (RPI). His research focuses on computational geomechanics, seismic response monitoring, and geotechnical system identification. He leads projects addressing liquefaction mitigation, multiscale modeling of geosystems, and development of advanced computational tools for geotechnical analysis. Dr. Zeghal collaborates with RPI's Center for Network for Earthquake Engineering Simulation (CEES), Scientific Computation Research Center (SCOREC), and Inverse Problems Center (IPRPI). His work emphasizes reducing risks from natural hazards through improved design tools and model validation. Key projects include the Liquefaction Experiments and Analysis Projects (LEAP), which use centrifuge testing and machine learning to analyze soil behavior under seismic loads. Dr. Zeghal's research integrates experimental data with numerical simulations to enhance understanding of soil-structure interaction and lateral spreading during earthquakes. Research Interests: Soil liquefaction, multiscale modeling, inverse problem methods, computational geomechanics Key Collaborations: CEES, SCOREC, IPRPI, and global research networks Focus Areas: Centrifuge testing, model validation, seismic hazard mitigation His recent publications (2023–2025) highlight advancements in quantifying uncertainty in soil response, analyzing LEAP centrifuge experiments, and developing machine learning approaches for model calibration. Dr. Zeghal actively engages with industry and government labs to translate research into practical engineering solutions.

Mikros Emmanuelمشاهده پروفایل

استاد

• NMR Spectroscopy
• Molecular Structure Analysis
• Structure Based Drug Design
• +۴ مورد دیگر

Mikros Emmanuel is a Professor in the Section of Pharmaceutical Chemistry at the University of Athens , affiliated with the Faculty of Pharmacy and Department of Pharmaceutical Chemistry . His academic career spans decades, with significant contributions to pharmaceutical chemistry and metabolomics. Education : Ph.D. and M.Sc. in Organic Chemistry (1988, 1984), Université PARIS XI; Diploma in Chemistry (1983), University of Athens. His research focuses on NMR spectroscopy, molecular structure analysis of biologically active compounds, structure-based drug design, and metabolomics. He has pioneered methods integrating NMR with computational tools for drug discovery and biomarker identification. Recent publications highlight his work in NMR-MS heterocovariance for drug screening, metabolomics in diagnostics, virtual screening for kinase inhibitors, and computational approaches in understanding metabolic disorders. Scientific Awards : DAAD Fellowship (Germany, 1996), Marie Curie Fellowship (European, 1994), CIES Fellowship (France, 1993), National Foundation Scholarship (ΙΚΥ Greece, 1985-88). He has taught undergraduate and graduate courses such as Inorganic Pharmaceutical Chemistry, Organic Spectroscopy, and Drug Design, while leading research initiatives in pharmaceutical chemistry and related fields.

Christian Liebscherمشاهده پروفایل

استاد

• Advanced Transmission Electron Microscopy
• Materials Science
• Energy-Related Materials
• +۶ مورد دیگر

Prof. Christian Liebscher is a Professor of Advanced Transmission Electron Microscopy at the Ruhr University Bochum , affiliated with the Faculty of Physics and Astronomy and the Research Center Future Energy Materials and Systems (RC FEMS). His work focuses on developing cutting-edge TEM techniques to understand energy-related materials' atomic-scale structure-functionality relationships. He combines aberration-corrected scanning TEM (STEM), 4D-STEM, and in-situ microscopy with machine learning to analyze complex material datasets. Education and Career: 2000–2006: Study of Materials Science at the University of Bayreuth. 2006–2010: PhD at the University of Bayreuth (summa cum laude) with a thesis on phase and dislocation analysis in superalloys. 2011–2014: Postdoc at the University of California, Berkeley, and the National Center for Electron Microscopy (Lawrence Berkeley National Laboratory). 2014–2015: Staff scientist at the University of Duisburg-Essen. 2015–2024: Group leader at the Max Planck Institute for Sustainable Materials in Düsseldorf. Research Interests: Prof. Liebscher’s research bridges microscopy innovation and materials understanding. He emphasizes atomic-scale characterization of interfaces, defects, and grain boundaries in metals and alloys using advanced STEM and 4D-STEM. His work addresses how structural features—like segregation, strain, and phase transitions—impact material properties. He also pioneers machine learning tools to automate data analysis from microscopy and tomography, advancing materials dataspaces. Key topics include energy materials (e.g., PEM fuel cells), high-entropy alloys, and nanomaterials for applications like semiconductors and electromagnetic absorption. Scientific Contributions: His publications highlight trends in grain boundary phase transitions, microstructure-property correlations, and integration of AI into microscopy. For example, recent work explores how grain boundary complexions affect mechanical strength in alloys and how in-situ TEM reveals deformation mechanisms under realistic conditions. He has contributed significantly to methodologies like scanning precession electron diffraction tomography and unsupervised machine learning for atomic-resolution datasets. Labs and Collaborations: Prof. Liebscher leads the Advanced Transmission Electron Microscopy group at RUB, building on his previous leadership at the Max Planck Institute. His lab collaborates with institutions like the Lawrence Berkeley National Laboratory and integrates interdisciplinary approaches combining experimental microscopy with computational modeling.

Susan E. Brennanمشاهده پروفایل

استاد

• Cognitive Science
• Psycholinguistics
• Human-Computer Interaction
• +۳ مورد دیگر

Susan E. Brennan is a Distinguished Professor in the Department of Psychology at Stony Brook University, with joint appointments in Computer Science and an affiliation in Linguistics. She holds a Ph.D. from Stanford University (1990) and has been a faculty member since 1990. Her research focuses on the cognitive science of language use, particularly interactive spoken dialogue, eye-tracking as a communication tool, and human-AI interaction. She is also involved in projects exploring how technology interfaces with communication, including speech and language systems. Her academic roles include serving as Director of Graduate Studies and Associate Chair in Psychology. Brennan’s work has been continuously supported by the NSF, totaling over $3.5M in grants. Notable awards include the Chancellor’s Award for Excellence in Scholarship (2003) and the Dean’s Award for Excellence in Service (2012). Research interests span the psychology of language, multimodal communication, and the neural basis of dialogue. She has published extensively on topics like common ground in conversation, coordination signals, and the role of gaze in collaborative tasks. Her lab integrates behavioral experiments, eye-tracking, and computational models to study real-world communication dynamics.

Laura Na Liuمشاهده پروفایل

استاد

• Nanophotonics
• DNA Nanotechnology
• Plasmonics
• +۴ مورد دیگر

Prof. Laura Na Liu is a Professor and Director at the 2nd Physics Institute, University of Stuttgart, with a dual affiliation at the Max Planck Institute for Solid State Research. Her research bridges nanophotonics, DNA nanotechnology, and plasmonics, focusing on dynamic systems for biomedical applications, optical metamaterials, and synthetic biology. Key contributions include DNA-templated plasmonic architectures, reconfigurable metasurfaces, and synthetic cell components using DNA nanotechnology. Academic training includes advanced work in physics and materials science, with a career spanning leading institutions. Research interests emphasize the interplay between nanoscale structures and optical/chemical functionalities. Recent publications highlight innovations in programmable nanomaterials, real-time molecular tracking, and high-performance holography systems. Her work integrates experimental and theoretical approaches, addressing challenges in biophotonics, nanoelectronics, and smart materials. Awards and recognitions are listed in institutional records, while her lab actively collaborates with industry on applied photonics solutions.

Shiva Jahangiriمشاهده پروفایل

استادیار

• Big Data Management Systems
• Databases for AI/ML
• Knowledge Graphs
• +۶ مورد دیگر

Shiva Jahangiri is an Assistant Professor in the Department of Computer Science and Engineering at Santa Clara University's School of Engineering. His research focuses on Big Data Management Systems, Databases for AI/ML, and Query Optimization. He leads the DBIS Lab, which explores database internals, vectorized data processing, and open-source projects like Apache AsterixDB. Education: Ph.D. in Computer Science from the University of California, Irvine; M.S. in Computer Science (Data Science) from the University of Southern California. Current courses taught include Advanced Programming, Advanced Database Systems, and Introduction to Database Systems. He advises Ph.D. and Master’s students on topics like Vector Databases, Query Scheduling, and Resource Management. Recent research trends involve optimizing group-by/aggregation operators, schema inference for semi-structured data, and memory management in complex join queries. His work bridges theoretical advancements with practical implementations in open-source systems. DBIS Lab activities include student participation in senior design projects, directed research, and volunteer roles. The lab emphasizes industry collaboration for hands-on experience in database systems development.

Wilfried Siegمشاهده پروفایل

استاد

• Foundations of Mathematics
• Proof Theory
• History of Logic
• +۳ مورد دیگر

Wilfried Sieg serves as the Patrick Suppes Professor of Philosophy at Carnegie Mellon University within the Dietrich College of Humanities and Social Sciences, where he has been a core faculty member since 1985. He previously held appointments at Columbia University (1977-1985) and co-founded CMU's interdisciplinary Pure and Applied Logic program. His educational background includes: Ph.D. in Philosophy, Mathematical Logic, and Foundations of Mathematics from Stanford University (1977) M.S. in Mathematics and Logic from Westfälische-Wilhelms-Universität, Münster (1971) B.S. in Mathematics and Physics from Freie Universität, Berlin (1969) Sieg's research fundamentally explores the Foundations of Mathematics through proof theory, with significant contributions to the History of Modern Logic and Philosophy of Mathematics. His work critically examines Hilbert's Program, Gödel's incompleteness theorems, and the historical development of computability concepts. He emphasizes the dialectic between mathematical practice and philosophical reflection, revealing how foundational positions shape and are shaped by mathematical developments. Analysis of his recent publications shows a sustained trajectory from historical-philosophical investigations toward computational implementations, particularly through the AProS project. This evolution reflects growing integration of proof-theoretic insights with automated reasoning systems, focusing on heuristic search algorithms and cognitive modeling of mathematical reasoning. Sieg directs the Laboratory for Symbolic & Educational Computing (LSEC), which develops pedagogical tools like the AProS proof tutor. His grant-funded projects include editorial work on Hilbert, Gödel, and Bernays supported by the German National Science Foundation, Sloan Foundation, and National Endowment for the Humanities, alongside NSF-funded development of web-based logic education. He has advised numerous graduate students through research seminars while maintaining active collaboration with computer science and mathematics departments.

Francesco Stellacciمشاهده پروفایل

استاد

• Nanomaterials
• Nanoparticles
• Soft Materials
• +۷ مورد دیگر

Francesco Stellacci is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL), where he holds the Alcan EP Chair and leads the Supramolecular Nanomaterials and Interfaces Laboratory (SUNMIL). He is affiliated with the School of Engineering (STI) and the Institute of Materials (IMX), with additional appointments in the Institute of Bioengineering (IBI-STI). Stellacci also serves on the EPFL Academic Strategic Committee and the Research Awards Commission, and is a committee member for the Doctoral Program in Materials Science and Engineering. Stellacci's research focuses on the complex interactions between supramolecular assemblies and their molecular surroundings, with particular emphasis on: Nanomaterials and Nanoparticles Soft Materials and Supramolecular Interactions Solid-Liquid Interfaces Nanomedicine and Antiviral Applications His recent work demonstrates a strong trend toward biomedical applications of nanomaterials, particularly in antiviral therapies, drug delivery systems, and diagnostic tools. Stellacci's laboratory has pioneered research on supramolecular interfaces with nanoscale domains, leading to innovative approaches in viral inhibition, protein delivery, and sustainable materials. His publications reveal a consistent focus on the physical and chemical properties of nanomaterials and their interactions with biological systems, with increasing emphasis on practical applications in healthcare and sustainability. Among his notable scientific achievements are: Technology Review TR35 "35 Innovators under 35" award (2005) Popular Science Magazine "Brilliant 10" award (2007) Packard Fellowship (2005) Stellacci has been instrumental in developing novel methodologies for nanoparticle characterization and application, with particular expertise in supramolecular nanostructures. His work bridges fundamental materials science with practical biomedical applications, as evidenced by numerous patents and translational research projects. He has supervised multiple doctoral students whose theses reflect the interdisciplinary nature of his research program, spanning from fundamental nanoscience to applied biomedical engineering. Stellacci's laboratory (SUNMIL) serves as a hub for interdisciplinary research, bringing together expertise from materials science, chemistry, biology, and engineering to address challenges in nanomedicine and sustainable technologies.

Chun Wangمشاهده پروفایل

استاد

• Quantitative Research Methods
• Item Response Theory (IRT)
• Computerized Adaptive Testing (CAT)
• +۳ مورد دیگر

Chun Wang is a Professor in the Department of Measurement & Statistics at the University of Washington's College of Education. His research focuses on advancing quantitative methods in educational and psychological measurement, with expertise in item response theory (IRT), computerized adaptive testing (CAT), and cognitive diagnostic modeling. He holds affiliate faculty status at the Center for Statistics and the Social Sciences. Education: B.S. in Psychology, Peking University (China) M.S. and Ph.D. in Quantitative Psychology, University of Illinois at Urbana-Champaign Research Interests: Development and validation of multidimensional/mixture IRT models Computerized adaptive testing optimization Cognitive diagnostic modeling for classroom applications Health measurement and bias detection in assessments Recent Trends in Articles: His work bridges statistical innovation with practical applications, emphasizing fairness and efficiency in assessments. Notable areas include: - Healthcare : Predictive models for discharge disposition and functional outcomes - Education Technology : Adaptive learning systems and diagnostic feedback mechanisms - Methodology : Bias detection (DIF), Bayesian estimation techniques, and computational efficiency Scientific Awards : Includes the Anne Anastasi Award (2020), McKnight Presidential Fellowship (2017), and multiple best reviewer recognitions from leading psychometrics journals. Advising & Grants: Supervised students including Xiao J., Zhu R., and Lu J.* in high-impact projects. Co-led a $10M NIH grant (AmplifyGAIN Center) to advance Gen AI in STEM education. Published extensively in Psychometrika , Journal of Educational and Behavioral Statistics , and other top outlets. Labs/Teams: Directs the Pmetrics Lab ( https://sites.uw.edu/pmetrics/ ), collaborating on cutting-edge measurement tools for education and healthcare.