جست‌وجوی استاد و پژوهشگر

• Mathematical Biology
• Computational Modeling
• Biophysics
• +۳ مورد دیگر

Sabine Fischer is a Professor for Supramolecular and Cellular Simulations at the University of Würzburg’s Center for Computational and Theoretical Biology (CCTB). She holds a PhD in Mathematics from the University of Nottingham (2009) and completed postdoctoral research at the University of Cambridge (2009-2011) and the University of Frankfurt (2011-2017). Her work focuses on mathematical modeling and data-driven simulations of biological processes across subcellular, multicellular, and multi-tissue scales. Key research areas include agent-based modeling of parasite collective behavior (e.g., Project 7 of SPP 2332 PoP), cell-cell interactions, and morphological modeling with Blender. Her group develops tools for image analysis and applies methods ranging from classical statistics to machine learning. Education: Diploma in Mathematics, University of Würzburg PhD in Mathematical Biology, University of Nottingham (2009) Professional Experience: Postdoc, University of Cambridge (2009-2011) Postdoc, University of Frankfurt (2011-2017) Development Engineer, h.a.l.m. Elektronik GmbH (2017-2018) Professor, University of Würzburg (since 2018) Her research integrates experimental data with computational models to study processes like mouse blastocyst differentiation, tissue-level organization, and parasitic locomotion. Current projects emphasize agent-based modeling of Trypanosoma collective behavior and the development of open-source tools like VESNA for 3D vessel analysis.

Peter Scheiblechnerمشاهده پروفایل

مدرس

• Statistics
• Data Analysis
• Machine Learning
• +۵ مورد دیگر

Peter Scheiblechner is a Lecturer at Lucerne University of Applied Sciences and Arts' School of Engineering and Architecture, within the Department of Natural and Humanities Sciences (ING). He holds a PhD in Mathematics from the University of Paderborn (2007) and has held academic positions including Visiting Assistant Professor at Purdue University (2010-2011) and Postdoc at the Hausdorff Center for Mathematics (2011-2012). His business experience includes software development roles at companies like ClassWare GmbH and UBS in Switzerland. Education: PhD in Mathematics, University of Paderborn (2007) Master's in Mathematics (minor: Physics), Albert-Ludwigs University Freiburg (1997) Bachelor's in Mathematics (minor: Physics), Philipps-University Marburg (1993) High School Diploma, Martin-Luther-Schule Marburg (1991) Research Interests: Focus on applying statistics, data analysis, and machine learning to real-world problems; computational algebra/geometry/topology with complexity theory; algebraic and classical complexity theory. Active in projects like ENFLATE (flexibility markets), COSMOS Data Cockpit (personalized medicine), and topological data analysis. Publications: Over 10 peer-reviewed articles in journals like Journal of Symbolic Computation , Foundations of Computational Mathematics , and Communications in Contemporary Mathematics , with focuses on algorithmic algebraic geometry, complexity analysis, and topological computations. Awards: DFG fellowship (2008-2010), 3rd place in German Mathematics Competition (1991), and regional championship in Hessen (1985/86). Teaching: Teaches mathematics, physics, statistics, and numerical methods at bachelor and master levels, including courses on differential equations, linear algebra, stochastic processes, and engineering applications.

Miriam Clincyمشاهده پروفایل

استاد

• Mathematics Education
• Physics Education
• STEM Teacher Training
• +۳ مورد دیگر

Prof. Miriam Clincy is a Professor of Mathematics, Physics, and STEM Teacher Training at Hochschule Esslingen University of Applied Sciences. She holds roles as University Representative for Higher Education and Associated Member of the Tübingen School of Education (TüSE). Her work focuses on innovative educational technologies, particularly online assessment systems like STACK in Moodle, and teacher training methodologies. Education: PhD in Physics, University of Edinburgh (2003) Physics Diploma (M.Sc. equivalent), Universität Heidelberg & University of Edinburgh (2000) Research Interests: Prof. Clincy bridges theoretical physics and STEM education. Key areas include online testing innovations, peer feedback systems, and teacher training through simulation-based assessments. She also maintains research on non-equilibrium systems from her earlier work in statistical mechanics. Publications: Recent work emphasizes educational technology (e.g., sandbox testing environments, Moodle integration) alongside foundational physics contributions in driven systems and phase transitions. Awards: Baden-Württemberg-Zertifikat für Hochschuldidaktik (2020) Advising & Grants: As Dean of Study (2019–2021), she oversaw TVET teacher training accreditation. Current projects include collaborative initiatives with the University of Tübingen and participation in the „Lehre hoch n“ network for educational innovation. Labs/Teams: Active in Hochschule Esslingen’s Basic Sciences faculty, contributing to curriculum development and assessment frameworks.

Marko Bekoمشاهده پروفایل

پژوهشگر

• Computer Science
• Wireless Sensor Networks
• Localization
• +۳ مورد دیگر

Marko Beko is a researcher affiliated with Lusofona University in Lisbon, Portugal. He is actively involved in projects related to wireless sensor networks, localization algorithms, and machine learning applications in telecommunications. His work spans areas such as GNSS-denied navigation, federated learning, and IoT network optimization. Research Interests Localization in wireless networks Machine learning for IoT UAV navigation systems Signal processing and network optimization Stochastic geometry in massive IoT Energy-efficient communication protocols Recent publications focus on deep learning methods for unmanned aerial vehicle (UAV) navigation, linear precoding for MIMO systems, and federated learning in rural environmental monitoring. His work demonstrates strong interdisciplinary connections between computer science, telecommunications, and aerospace engineering.

Tim Gowersمشاهده پروفایل

استاد

• Analysis
• Combinatorics
• Additive Combinatorics
• +۲ مورد دیگر

Professor Tim Gowers is a Professor of Mathematics at the University of Cambridge, affiliated with the Department of Pure Mathematics and Mathematical Statistics (DPMMS) within the Faculty of Mathematics. He is actively involved in the Cantab Capital Institute for the Mathematics of Information and the Combinatorics research group, contributing to Cambridge's renowned mathematical community. Professor Gowers' primary research focuses on Analysis and Combinatorics, with significant contributions to additive combinatorics, harmonic analysis, and their intersections with number theory and algebraic structures. His work demonstrates deep connections between discrete mathematics and analytic methods, often revealing fundamental structures in seemingly complex problems. His recent publications (2020-2025) show a continued emphasis on combinatorial problems in additive number theory and group theory, while also expanding into interdisciplinary research at the mathematics-AI interface, particularly in evaluating language models for mathematical reasoning and developing tools for formal verification. Professor Gowers maintains an active research program with numerous collaborations with leading mathematicians including Ben Green, Terence Tao, and Julia Wolf, publishing in top-tier journals such as the Annals of Mathematics and Geometric and Functional Analysis. Based in room C2.04 at DPMMS, he continues to contribute significantly to mathematical research and education at Cambridge, with his work influencing both theoretical developments and practical applications in computational mathematics.

• Semiconductor Device Simulation
• Nanoelectronics
• TCAD (Technology Computer-Aided Design)
• +۳ مورد دیگر

Daniel Nagy is an Assistant Professor at the Department of Electronics and Computing within the Higher Polytechnic School of Engineering at the University of Seville. His research focuses on nanoelectronics, semiconductor device simulation, and TCAD technologies. He leads the ARQCOMP research group, specializing in computer architecture and novel transistor design. Education details are not explicitly provided in the texts, but his academic focus suggests advanced training in electronics engineering or related fields. His research interests include quantum transport phenomena, nanoscale device variability analysis, and computational modeling of FinFETs, nanowire FETs, and nanosheet transistors. He has extensively contributed to TCAD simulation frameworks such as NESS (Nano-Electronic Simulation Software), emphasizing open-source tools. Research Trends: His work emphasizes transistor scaling challenges for sub-2nm nodes, device variability mitigation, and the development of modular simulation tools. Key topics include nanosheet FET optimization, POM-based molecular memory systems, and benchmarking of emerging transistor architectures. His studies often bridge quantum mechanics (via Schrödinger equation modeling) with classical transport phenomena. No scientific awards are mentioned in the provided texts. He has advised no publicly listed PhD/Master’s students. His ARQCOMP group collaborates on next-generation electronic device architectures and simulation methodologies. Labs/Teams: The ARQCOMP group focuses on advancing computer architecture and nanoelectronic device simulation through interdisciplinary approaches, integrating computational physics, materials science, and electrical engineering.

Connor Bainمشاهده پروفایل

استادیار

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

Connor Bain is an Assistant Professor of Instruction in the Department of Computer Science at the McCormick School of Engineering, Northwestern University. He focuses on integrating computational thinking into STEM education, particularly in high school science classrooms. His work emphasizes teacher professional development, co-design approaches, and the use of block-based tools to scaffold student engagement. He holds a PhD in Computer Science and Learning Sciences from Northwestern University, along with dual Bachelor of Science degrees in Computer Science and Mathematics (both Honors) from the University of South Carolina. Education: PhD in Computer Science and Learning Sciences, Northwestern University, Evanston, IL BSCS in Computer Science (Honors), University of South Carolina, Columbia, SC BS in Mathematics (Honors), University of South Carolina, Columbia, SC Research Interests: Dr. Bain’s research bridges computational thinking and STEM pedagogy, exploring how teachers can effectively integrate CT practices through workshops, co-design initiatives, and curriculum development. He investigates the role of authentic tools and block-based programming in fostering student agency, as well as challenges in aligning CT with existing classroom frameworks. His work highlights the importance of teacher collaboration in designing inclusive and effective educational technologies. Publications Trends: Recent articles focus on teacher perceptions of CT-infused curricula and the impact of professional development programs. Earlier works explore the transition from block-based to text-based programming and the use of agent-based modeling in K-12 classrooms. These studies collectively advocate for iterative, teacher-centered approaches to educational technology adoption. Advising & Grants: No formal advisees or grants are listed in the provided texts. His contributions are primarily through curriculum design and teacher training initiatives. His CV is available for download for further details. Labs & Teams: While no specific lab is mentioned, his collaborations likely involve interdisciplinary teams within the McCormick School and partnerships with high school educators through co-design projects.

Caleb Kemereمشاهده پروفایل

دانشیار

• Neuroengineering
• Data Science
• Computer Engineering
• +۷ مورد دیگر

Caleb Kemere is an Associate Professor in the Departments of Electrical and Computer Engineering and Bioengineering at Rice University. His work bridges neuroscience, engineering, and computer science, focusing on neuroengineering, neural decoding, and real-time brain-computer interfaces. He holds a B.S. in Electrical Engineering (with Honors) and a B.A. in Economics from the University of Maryland, College Park, and a Ph.D. in Electrical Engineering from Stanford University. Before joining Rice in 2011, he was a postdoctoral fellow at the Keck Center for Integrative Neurosciences at UCSF. His research explores hippocampal function in spatial navigation and memory, signal processing for neural interfaces, and developing technologies like miniature microscopes and low-power sensors. He has pioneered frameworks such as Spyglass for reproducible neuroscience research and RealtimeDecoder for online neural decoding. Kemere has received prestigious awards including the NSF CAREER Award (2013), HFSP Young Investigator Award (2014), and BRAIN: EAGER Award (2015). His grants include a five-year NSF grant to study Deep Brain Stimulation (DBS) and a neural engineering IGERT grant. His lab, the Realtime Neural Engineering Lab (RNEL), develops tools for understanding and modulating neural activity. Ongoing work addresses closed-loop brain stimulation, environmental uncertainty modeling in foraging behavior, and sleep-based memory consolidation.

• Numerical Optimization
• Uncertainty Quantification
• Computational Statistics
• +۵ مورد دیگر

Mihai Anitescu is a full-time Professor in the Department of Statistics at the University of Chicago and a Senior Computational Mathematician at Argonne National Laboratory's Mathematics and Computer Science Division. He also holds an adjunct Associate Professor position at the University of Pittsburgh's Math Department and is a Senior Fellow at the Computation Institute (Argonne-University of Chicago). His research focuses on numerical optimization, uncertainty quantification, and computational statistics, with applications to materials science and power grid simulations. Education: Electrical Engineer (1992), Polytechnic University of Bucharest, Romania Ph.D. (1997), Applied Mathematical and Computational Sciences, University of Iowa Research Interests: He specializes in scalable Gaussian process analysis of spatiotemporal data, differential variational inequalities (DVIs) for phase field models in materials science, and uncertainty quantification for exascale simulations of advanced reactors. His work develops robust VI solvers for transitional phenomena modeling, including phase changes and hybrid discrete-continuum systems. Projects: He leads the MACSER project and previously led M2ACS, both funded by DOE-ASCR. His research includes mesoscale modeling of irradiated materials in collaboration with the Center for Materials Science of Nuclear Fuel (CMSNF) and Energy Frontier Research Center (EFRC). Software: He co-developed ScalaGAUSS (parallel processing for large-scale data analysis) and TreeCodeMatern (Matern kernel matrix-vector multiplication). These tools leverage optimization algorithms and numerical methods. Professional Roles: He served on the editorial boards of SIAM Journal on Optimization, Optimization Methods and Software, and Mathematical Programming. He has presented extensively at conferences like SciDAC and given seminars on stochastic optimization for energy systems.

Jiang Kanمشاهده پروفایل

مدرس

• Computer Science
• Artificial Intelligence
• Sports Analytics
• +۴ مورد دیگر

Jiang Kan is a Lecturer at the Department of Computer Science, National University of Singapore. He earned his Ph.D. in Computer Science from NUS in 2023, following an MComp (2017) and B.Sc. (1994) from NUS and Shanghai Jiao Tong University respectively. His teaching portfolio includes courses like Introduction to Computing , Introduction to Programming , Database Systems and Management , and Systems Programming . Education: Ph.D., Computer Science, National University of Singapore, 2023 M.Comp., National University of Singapore, 2017 B.Sc., Shanghai Jiao Tong University, 1994 Research Focus: Jiang Kan specializes in sports analytics, integrating computer vision and probabilistic modeling to analyze sports strategies, player dynamics, and broadcasting data. His work includes tennis and soccer strategy analysis , event recognition in sports videos , and injury prediction models . Publication Trends: Recent articles (2023-2025) explore hybrid approaches combining deep learning with probabilistic model checking for sports analytics. Key contributions include automated court detection , fine-grained event analysis , and dynamic team strategy modeling in tennis and soccer. Teaching: He teaches foundational and advanced courses in programming, computer organization, software engineering, and databases to both full-time and part-time students.

Suman Rakshitمشاهده پروفایل

مدرس ارشد

• Spatial Statistics
• Agricultural Statistics
• Genome-wide Association Studies
• +۳ مورد دیگر

Dr. Suman Rakshit is a Senior Lecturer at Curtin University's School of Electrical Engineering, Computing and Mathematical Sciences, with a dual role as a Research Fellow at SAGI-West. His primary research focuses on statistical methodologies for agricultural trials, including spatial variogram modeling and linear mixed models. He has also contributed to genome-wide association studies and developed an R-package for analyzing point patterns on linear networks. Dr. Rakshit holds a PhD in Statistics from Monash University and a Master's from IIT Kanpur. His professional experience includes roles as a Data Scientist at Horizon Power and Prima Consulting. His teaching spans multiple disciplines including Science and Engineering, and he is affiliated with the Office of the Provost. Research interests emphasize spatial statistics, experimental design for on-farm trials, and computational methods. Recent work explores team playing styles in Australian Football using clustering frameworks and addresses spatial dependency in plant pathogens. His publications span agricultural, ecological, and computational topics, with a focus on methodological advancements in spatial analysis.

Mike Domaratzkiمشاهده پروفایل

دانشیار

• Bioinformatics
• Genomics
• Theoretical Computer Science
• +۳ مورد دیگر

Mike Domaratzki is an Associate Professor and Chair of the Department of Computer Science at Western University. His research focuses on bioinformatics, genomics, and theoretical computer science, with recent emphasis on machine learning tools for genomic prediction in crops. He has held academic roles at Western University and previously at the University of Manitoba and Acadia University. Education: (No explicit details provided in texts) Research Interests: Domaratzki’s work bridges computational methods and biological systems, including algorithm design for genomic analysis, machine learning applications in agriculture, and theoretical foundations of computing. His recent projects address challenges in imbalanced data classification and crop yield prediction using advanced neural networks. Teaching: He has taught a wide range of courses, including introductory programming, data structures, algorithms, bioinformatics, and automata theory across multiple institutions. Notable courses include COMPSCI 1026/1027 at Western, and COMP courses at the University of Manitoba/Acadia covering foundational CS topics and specialized areas like bionformatics algorithms. Publications: His work spans machine learning, genomics, and theoretical computer science, with recent trends emphasizing agricultural genomic applications and data-driven health analytics. Key themes include imbalanced data solutions, neural network architectures for genomics, and computational tools for biological data interpretation. Grants/Advising: No specific grants or student advising details are listed, though his teaching and research imply active involvement in mentoring. His lab focuses on interdisciplinary projects combining computational methods with biological datasets.

Mauro Rodriguezمشاهده پروفایل

استادیار

• Computational Fluid Dynamics
• Multiphase Flows
• Viscoelasticity
• +۳ مورد دیگر

Mauro Rodriguez is an Assistant Professor of Engineering at Brown University's School of Engineering. His research focuses on computational fluid dynamics, multiphase flows, viscoelasticity, and high-performance computing. He holds a position at Barus & Holley 441 and can be reached at mauro_rodriguez@brown.edu. His research interests include studying fluid-structure interactions, cavitation bubble dynamics, and inertial effects in complex fluids. Rodriguez explores applications ranging from biomedical engineering (e.g., pulmonary alveolus deformation under ultrasound) to exascale computing for multiphysics simulations. His work often involves numerical modeling and rheometry techniques to characterize material properties. Recent publications highlight advancements in simulating cavitation phenomena, bubble collapse mechanics, and GPU-accelerated algorithms. Rodriguez’s research bridges theoretical fluid dynamics with practical engineering challenges, leveraging high-performance computing resources to tackle multiscale problems. No scientific awards or grants are explicitly listed in the provided materials. His advising activities and lab affiliations remain unspecified.

• Cancer Prevention
• Nutrition and Metabolism
• Phytochemicals
• +۴ مورد دیگر

Dr. Weiqun (George) Wang is a Professor and Director of the Graduate Program in the Department of Food Nutrition Dietetics & Health at Kansas State University (KSU). His research focuses on cancer prevention through dietary interventions, particularly exploring phytochemicals and molecular mechanisms of body weight control. He holds a PhD in Animal Physiology & Biochemistry from Nanjing Agricultural University and completed postdoctoral training in Animal Science at the University of Hawaii. Dr. Wang has held academic roles at KSU since 2002, advancing from Assistant to Professor. His professional affiliations include leadership in NIH grant review panels, the American Society for Nutrition, and FDA advisory committees. He has secured over $5 million in research funding, including NIH R01 and USDA grants. Education: B.S. in Biochemistry, Nanjing University (1983) Ph.D. in Animal Physiology & Biochemistry, Nanjing Agricultural University (1990) Postdoc in Animal Science, University of Hawaii His research interests span cancer prevention, functional foods, and the molecular basis of obesity. Notable achievements include developing bioengineered crops for health benefits and advancing 3D cell culture systems for cancer screening. Dr. Wang has authored over 150 peer-reviewed articles, with recent work emphasizing phytochemicals in sorghum, hemp, and tomatoes. Awards: 2024 Faculty Research Excellence Award (KSU) 2018 Professorial Performance Award (KSU Provost) 2009 Dawley-Scholar Award (Kansas State University) Advising & Grants: Mentored 76 PhD/MS students as major professor Secured NIH, USDA, and private grants totaling $5+ million Member of 84+ conference panels and 86 invited seminars Dr. Wang’s lab collaborates on interdisciplinary projects, including functional food development and cancer biomarker discovery. He co-edits journals like Molecular Nutrition and Food Research and serves on 6 editorial boards.

Dario Granaمشاهده پروفایل

استاد

• Rock Physics
• Seismic Reservoir Characterization
• Inverse Problems
• +۱ مورد دیگر

Dr. Dario Grana is the Wyoming Excellence Chair and Professor at the University of Wyoming's Department of Geology and Geophysics. His research focuses on rock physics, seismic reservoir characterization, inverse problems, and geostatistics, with applications in critical zone studies, CO2 sequestration monitoring, and subsurface characterization. Education includes: PhD and MS in Geophysics, Stanford University (2013) MS in Applied Mathematics, University of Milano-Bicocca (2006) BS and MS in Mathematics, University of Pavia (2003, 2005) Research explores rock physics modeling in critical zones, geophysical monitoring of CO2 storage, joint seismic-electromagnetic inversion, and stochastic methods for subsurface characterization. Recent publications emphasize machine learning integration with geostatistical inversion and Bayesian methods for uncertainty quantification. Teaching includes Quantitative Geoscience, Mathematical Methods for Geoscience, Exploration Geoscience, Inverse Theory, and Rock Physics.