جست‌وجوی استاد و پژوهشگر

• Quantum Computing
• Machine Learning
• Combinatorial Optimization
• +۳ مورد دیگر

GOH Siong Thye is an Adjunct Lecturer at the Lee Kong Chian School of Business, Singapore Management University. He teaches courses such as Decision Analysis and focuses on interdisciplinary research spanning quantum computing, machine learning, and optimization techniques. His work addresses challenges in high-dimensional data analysis, combinatorial optimization, and algorithm design for quantum systems. Education: Not explicitly stated in the provided text. Research interests include quantum algorithms for differential equations, interpretable density estimation methods, and applications of machine learning in industrial systems such as gas turbine failure detection. He explores hybrid quantum-classical frameworks for solving complex combinatorial problems, leveraging QUBO formulations and variational approaches. His articles highlight contributions to areas like quantum volunteer’s dilemma in game theory, sparse coding methodologies, and surrogate model-based optimization techniques. Notable topics include enhancing QUBO solvers through recommendation systems and developing frameworks for intelligent crime response scheduling. No scientific awards or grants are mentioned in the provided texts. He advises no formally listed students, though his collaborative work may involve academic partnerships.

• Crowd Simulation
• Motion Planning
• Game Technology
• +۴ مورد دیگر

Roland Geraerts is an Associate Professor in the Department of Information and Computing Sciences at Utrecht University, specializing in geometric computing and crowd simulation. His research focuses on motion planning, navigation meshes, and high-performance computing for virtual environments. He co-founded the annual Motion in Games conference and the startup uCrowds, which develops crowd simulation software (SimCrowds) used in urban planning and emergency management. Education: He teaches a master's course on crowd simulation and serves as program director for the Computer Science bachelor's program. His PhD research (2006) centered on sampling-based motion planning and path quality optimization. Research Interests: His work spans crowd behavior modeling, augmented reality interfaces, and real-time simulation engines. Notable projects include the CrowdAR Table for interactive crowd visualization and collaboration with Schiphol Airport and Rotterdam on evacuation simulations. Articles Trends: His publications emphasize practical applications of algorithms in game development, emergency planning, and urban design. Recent work explores reinforcement learning for social distancing simulations and multi-layered environment navigation. Scientific Awards: Winner of the 2020 Academic Startup Competition (uCrowds). Impact & Grants: Member of the Faculty of Science Impact Committee, contributing to long-term policy and vision for computer science applications. Collaborative projects include simulations for the Dutch parliamentary elections and the Tour de France Grand Départ. Labs/Teams: Leads the Geometric Computing group at Utrecht University, developing the SimCrowds platform and AR interfaces for real-time crowd interaction.

Natalie Knoxمشاهده پروفایل

استاد مدعو

• Public Health Genomics
• Microbial Genomics
• Bioinformatics
• +۳ مورد دیگر

Dr. Natalie Knox is an Adjunct Professor in the Department of Medical Microbiology and Infectious Diseases at the University of Manitoba. She also leads the Computational Biology Unit at the Public Health Agency of Canada’s National Microbiology Laboratory. Her research integrates microbial genomics and bioinformatics for public health applications, including pandemic surveillance (e.g., SARS-CoV-2) and gut microbiome studies in immune-mediated diseases like multiple sclerosis and inflammatory bowel disease (IBD). She collaborates internationally to advance genomic epidemiology and diagnostics. Education: PhD in Animal Sciences, University of Manitoba BSc in Human Nutritional Sciences, University of Manitoba Research Interests: Public health metagenomics for pathogen detection Genomic surveillance of infectious diseases (e.g., Helicobacter pylori in Indigenous populations) Microbiome influences on immune-mediated diseases Implementation of genomic technologies in public health labs Awards: Nakano Citation Award (2014) PHAC Research Merit Award (2012) Labs/Teams: Leads the Computational Biology Unit at the National Microbiology Laboratory and contributes to the Visual and Automated Disease Analytics (VADA) Graduate Training Program.

Chen Wangمشاهده پروفایل

استادیار

• Random Matrix Theory
• Time Series Analysis
• High-dimensional Data Analysis

Dr. Chen Wang serves as an Assistant Professor in the Department of Statistics and Actuarial Science at the University of Hong Kong, with visiting appointments at the University of Cambridge Faculty of Economics during June-August 2024, July-August 2022, and July-December 2019. His academic position and active research output confirm his status as a current faculty member engaged in interdisciplinary statistical research. His core research focuses on: Random Matrix Theory for high-dimensional covariance estimation Time Series Analysis in complex stochastic systems High-dimensional Data Analysis methodologies These statistical frameworks provide foundational tools for modern data-intensive scientific domains. Analysis of his 2022-2025 publications reveals a strategic expansion into biomedical AI applications, particularly in single-cell genomics and spatial biology. His work demonstrates consistent innovation in developing AI agents for biological experimentation (e.g., PerTurboAgent for Perturb-seq, SpatialAgent) and advancing molecular design through diffusion models. This trajectory shows a deliberate integration of his statistical expertise with cutting-edge computational biology challenges. No scientific awards or honors were documented in the provided materials. The available information contains no details regarding graduate student supervision, research grant funding, or laboratory affiliations. His visiting positions at Cambridge suggest collaborative international research activities, but specific advising relationships or grant mechanisms remain unreported in the source text.

Debra McGivneyمشاهده پروفایل

استادیار

• Magnetic Resonance Imaging
• Magnetic Resonance Fingerprinting
• Computational Inverse Problems
• +۴ مورد دیگر

Debra McGivney, PhD, is an Assistant Professor in the Department of Biomedical Engineering at the Case School of Engineering, Case Western Reserve University. She also serves as Associate Chair of the Case School of Engineering. Her research focuses on mathematical modeling of biomedical applications, particularly in medical imaging. She specializes in magnetic resonance fingerprinting (MRF), inverse problems using Bayesian frameworks, and computational solutions for imaging challenges. Education: PhD in Applied Mathematics, Case Western Reserve University (2013) MS in Mathematics, John Carroll University (2006) BS in Mathematics, University of Notre Dame (2004) Her research interests include MRF techniques for quantifying tissue properties, tackling the partial volume effect, and improving imaging accuracy through pattern matching and statistical methods. Recent work emphasizes optimizing pulse sequences, reducing artifacts, and applying MRF to brain tumors and epilepsy diagnostics. Her publications highlight advancements in MRF simulation, error estimation, and integration with quantum-inspired algorithms. She has contributed to 3D imaging techniques, radiomic analysis, and automated sequence design. Labs and collaborations involve interdisciplinary teams focused on medical imaging innovation, though specific lab names are not mentioned.

• Computational Statistics
• Time Series Analysis
• Forecast Evaluation
• +۳ مورد دیگر

Dr. Marc-Oliver Pohle is a Visiting Scientist at the Chair of Computational Statistics , part of the School of Business and Information Technology at Karlsruhe Institute of Technology (KIT). His work focuses on advanced statistical methodologies and their applications in forecasting and time series analysis. Research Interests Time Series Analysis Forecast Evaluation Multivariate Statistical Methods Long Memory Processes Statistical Calibration Recent Publications 2025: Simultaneous Inference Bands for Autocorrelations 2024: Testing Quantile Forecast Optimality 2024: Measuring Dependence between Events 2023: Generalised Covariances and Correlations 2023: Forecasting under long memory 2022: Score-based calibration testing for multivariate forecast distributions 2022: Unlucky Number 13? Manipulating Evidence Subject to Snooping 2021: Essays on Forecasting and Forecast Evaluation 2020: The Murphy decomposition and the calibration-resolution principle

Thomas S Richardsonمشاهده پروفایل

استاد

• Graphical Models
• Causal Inference
• Bayesian Inference
• +۳ مورد دیگر

Thomas S Richardson is a Professor at the University of Washington, holding adjunct appointments in Economics and Electrical Engineering. He is also affiliated with the eScience Steering Committee and Center for Advanced Studies in the Behavioral Sciences at Stanford University (Fellow, 2004). His research focuses on graphical models, algorithmic model selection, Bayesian inference, causal models, and their applications in economics and biostatistics. BA, University of Oxford MS and PhD, Carnegie Mellon University Key research areas include: Causal Inference: Instrumental variable models, counterfactuals, SWIGs (Single World Intervention Graphs), and identification of direct effects. Graphical Models: Acyclic directed mixed graphs, nested Markov properties, marginal log-linear parametrizations, and latent variable modeling. Applications: Epidemiology, econometrics, bone biology, and ecological inference. His recent publications emphasize causal effect bounds, finite population testing, and structural nested mean models. Articles span topics like SWIGs, IV model analysis, and high-dimensional causal discovery. Scientific Awards: Fulbright Fellowship (2014) Institute for Advanced Studies, University of Bologna (2007) Center for Advanced Studies in Behavioral Sciences, Stanford (2004) Rosenbaum Fellow, Isaac Newton Institute (1997) Rousseeuw Prize for Statistics (2022)

• Plasma Physics
• Computational Physics
• Physics Education
• +۴ مورد دیگر

Christopher M. Orban is an Associate Professor at The Ohio State University Marion Campus in the Department of Physics. His research spans plasma physics, cosmological simulations, and innovative physics education methods. He leads the STEMcoding Project and develops free VR apps through the BuckeyeVR Initiative . Academic Rank: Associate Professor Department: Physics University: The Ohio State University Campus: Marion Campus Research Interests: Orban investigates high-intensity laser-plasma interactions for ion acceleration, develops computational tools for astrophysical simulations, and pioneers the use of virtual reality and programming exercises in physics education. His work bridges theoretical modeling with experimental validation. Laser-Produced Plasmas Cosmological Perturbation Theory Virtual Reality Education Computational Thinking Code Validation (FLASH, Gadget2) Electromagnetic Field Simulations Notable Contributions: He developed open-source educational tools like the Arduino-based pressure sensor and created browser-based physics simulations using classic video games. His 2020 High Energy Density Physics paper validated FLASH code for astrophysical jet modeling, while 2019 Physics Education work explored computational thinking in introductory courses.

Munir Eberhardt Hiabuمشاهده پروفایل

دانشیار

• Interpretable Machine Learning
• Nonparametric Statistics
• Statistical Machine Learning Methods
• +۷ مورد دیگر

Munir Eberhardt Hiabu is an Associate Professor of Data Science and Actuarial Mathematics at the Department of Mathematical Sciences, University of Copenhagen. His research focuses on interpretable machine learning, nonparametric statistics, and statistical methods for high-dimensional data, particularly in risk quantification and visualization. University: University of Copenhagen Department: Mathematical Sciences Academic Rank: Associate Professor Email: mh@math.ku.dk Research Interests : Hiabu develops statistical machine learning techniques to exploit low-dimensional structures in high-dimensional data for enhanced prediction accuracy and interpretability. His work bridges actuarial mathematics, survival analysis, and additive models, with applications in non-life insurance reserving and fairness in insurance pricing. Scientific Awards : Not documented in the provided text. Advisory Opportunities : PhD and postdoc positions are available through annual calls. Prospective students must apply via institutional portals mentioning his name.

Shaojie Tangمشاهده پروفایل

استاد

• Machine Learning
• Responsible AI
• Combinatorial Optimization
• +۶ مورد دیگر

Shaojie Tang is a Professor in the Management Science and Systems department at the School of Management, University at Buffalo. He serves as the Associate Director of the AI and Management Science Institute for Artificial Intelligence and Data Science. His research focuses on Machine Learning, Responsible AI, and Combinatorial Optimization, with applications in business and data science. PhD from Illinois Institute of Technology His work spans interdisciplinary domains including Federated Learning, Recommender Systems, and Optimization techniques. Key subfields involve Personalized AI, Tensor Completion, and Algorithm Design for collaborative systems. Recent publications from 2025 and 2024 highlight his leadership in Federated Learning, Neural Network Decoupling, and Strategic AI Deployment. Trends include balancing personalization with collaboration, enhancing model robustness, and cross-modal adaptation in distributed environments. Professional affiliations include IEEE, ACM, and INFORMS. He advises graduate students in theoretical and applied AI research, though specific advisees are not listed publicly. His research teams operate under the Institute for Artificial Intelligence and Data Science, advancing scalable solutions for dynamic business challenges.

Johanna S. Hardinمشاهده پروفایل

استاد

• Statistical Analysis
• High-Throughput Genetic Data
• Machine Learning
• +۴ مورد دیگر

Johanna S. Hardin is a Professor of Mathematics and Statistics at Pomona College, holding the Hardison Chair of Analytical Thinking. She has been affiliated with the institution since 2002, specializing in statistical methods for high-throughput genetic data, machine learning, and education equity in STEM. Her work emphasizes collaborations across disciplines, including biology, physics, and geology, and she is committed to open-access educational materials and initiatives such as the PAYS and Pomona Scholars of Math programs. Education: She earned a B.A. in Mathematics from Pomona College, followed by an M.S. and Ph.D. in Statistics from the University of California, Davis. Research Interests: Her areas include statistical normalization of complex data, clustering methodologies, and promoting equity in STEM through accessible education. She co-authored the open-access textbook Introduction to Modern Statistics and advocates for dynamic data integration in pedagogy. Publications: Her recent work spans statistical education reforms, bioinformatics applications, and machine learning frameworks. Notable contributions include a unified framework for random forest prediction error estimation and studies on tumor-specific networks in glioma. Awards : Elected Member of the International Statistics Institute (2018) Wig Distinguished Professor Award for Excellence in Teaching (2016) MAA Hogg Award for Excellence in Teaching Introductory Statistics (2014) Advising & Grants: She has mentored numerous undergraduate researchers, evident in co-authored papers like those with Lu B. (2021) and Coleman J. (2016). Her work with Project NExT (2002) and national Posse initiatives highlights her dedication to fostering accessible STEM pathways. Labs/Teams: Affiliated with Pomona’s PAYS and PSM programs, focusing on lowering barriers to STEM education, particularly in statistics and data science.

• Mathematical Finance
• Probability Theory
• Statistics
• +۷ مورد دیگر

Patrizia Semeraro is a Full Professor at the Department of Mathematical Sciences "GL Lagrange" (DISMA), Polytechnic University of Turin. She is a member of the Interdepartmental Center R3C - Responsible Risk Resilience Center and the Master's and Continuing Education School. Her research and teaching are centered in mathematical finance, probability, and statistics, with applications in financial modeling and risk analysis. Polytechnic University of Turin, Department of Mathematical Sciences (Current) Her primary research interests include mathematical finance, stochastic processes, multivariate modeling, and statistical methods in economics and finance. She focuses on advanced topics such as multivariate Lévy models, tempered stable processes, subordination, and high-dimensional Bernoulli distributions. Her work bridges theoretical probability with practical applications in credit risk, derivative pricing, and financial engineering. Recent publications demonstrate a strong trend in developing and calibrating multivariate financial models, analyzing dependence structures, and applying probabilistic methods to real-world economic problems. Her work spans journals in operations research, statistics, probability, and financial economics, reflecting interdisciplinary expertise. Scientific recognitions include: Outstanding paper awarded by Emerald Literati Network, Italy (2016) Fellow - INDAM, Italy (2019–present) Fellow - AMASES, Italy (2017–present) She supervises PhD students including Chen Zhang, Alessandro Mutti, and Giovanni Amici. She has been involved in nationally funded research projects such as PRIN and SUP-CHAIN-DIS (2023–2025), focusing on supply chain disruptions and financial losses. She teaches in the Financial Engineering program and has previously taught courses such as Complements of Mathematics and Fundamentals of Physics and Mathematical Principles. She is a member of the Probability and Applications research group and contributes to the Mathematical Models in Finance research area at DISMA.

• Computer Science
• Artificial Intelligence
• Molecular Dynamics
• +۳ مورد دیگر

David Ryan Glowacki is a Researcher at the Department of Electronics and Computing, University of Seville, affiliated with the Center for Research in Intelligent Technologies (CITIUS) and the Intangible Reality Laboratory (IRLab). His work bridges computational chemistry, virtual reality (VR), and human-centered design. Key research focuses include VR-driven molecular dynamics simulations, AI-assisted drug discovery pipelines, and the psychological impacts of immersive VR experiences on mental health and consciousness. Glowacki has pioneered frameworks like Narupa iMD and Omg-vr gloves for multiuser molecular manipulation in VR environments. His research explores how VR can enhance scientific collaboration through tactile interactions with molecular systems, enabling real-time drug binding pathway analysis and citizen scientist participation. Notable contributions include applying VR to study protein conformational changes (e.g., SARS-CoV-2 M pro), developing sonification tools for molecular data, and investigating the psychological effects of group-based VR experiences that induce ego dissolution akin to psychedelic states. He also explores the intersection of computational chemistry with artistic and philosophical domains, such as 'aesthetic paradigms beyond everyday phenomenology.' Glowacki’s work integrates open-source software development with experimental design, emphasizing democratizing complex scientific tools through interactive technologies. His projects often involve cross-disciplinary collaborations between computer scientists, chemists, artists, and psychologists.

Thomas Denneyمشاهده پروفایل

استاد

• Magnetic Resonance Imaging (MRI)
• Cardiovascular MRI
• Ultra High Field MRI
• +۱۰ مورد دیگر

Thomas Denney is the Mr. & Mrs Bruce Donnellan & Family Endowed Professor in the Department of Electrical and Computer Engineering at Auburn University's Samuel Ginn College of Engineering. He serves as Director of the Auburn University Neuroimaging Center (also known as the MRI Research Center), leading cutting-edge research in biomedical imaging and MRI technology. His educational background includes a Ph.D. in Electrical and Computer Engineering from Johns Hopkins University, an M.S., and a B.S. from Auburn University in the same discipline. Dr. Denney’s research focuses on ultra-high field MRI , cardiovascular MRI , image processing and analysis , image reconstruction , and modeling of multi-dimensional stochastic processes . His work spans critical areas in biomedical engineering, particularly in improving diagnostic imaging through advanced signal and image processing techniques. Additional interests include motion estimation, inverse problems, computer/robot vision, and numerical methods for medical imaging applications. His research has contributed to Auburn’s leadership in neuroimaging, including housing the world’s first field-installed, clinically approved parallel transmit 7T MRI scanner. He is involved in interdisciplinary projects exploring brain plasticity through art and PTSD biomarker identification in older adults. Dr. Denney maintains active research profiles through PubMed and Google Scholar , and his full curriculum vitae is available online. He has not been mentioned as receiving any specific scientific awards in the provided texts. Dr. Denney advises graduate students through his research center and leads a team at the Neuroimaging Center, though no individual students are named. He has not received any named fellowships or grants in the provided information, but his endowed professorship and directorship indicate significant institutional support and recognition. He leads the Auburn University MRI Research Center , a major interdisciplinary facility advancing MRI technology and applications in clinical and research settings. The center plays a pivotal role in Auburn’s engineering and medical research ecosystem, particularly in neuroscience and cardiovascular imaging.

Spencer LaVere Smithمشاهده پروفایل

دانشیار

• Neuroengineering
• Computational Neuroscience
• Neural Circuitry
• +۳ مورد دیگر

Spencer LaVere Smith is an Associate Professor in the Department of Electrical and Computer Engineering at UC Santa Barbara (UCSB). He leads the SLAB (Spencer Lab) focusing on neuroengineering and computational neuroscience. His research combines novel instrumentation design with experimental insights into neural circuit function. Dr. Smith holds a B.S. in Physics and Mathematics from the University of Iowa, and a Ph.D. in Neuroscience and Neuroengineering from UCLA. After postdoctoral work at University College London, he established his laboratory in the US in 2011, moving to UCSB in 2018. His honors include the Klingenstein-Simons Fellowship (2013), McKnight Technological Innovation Award (2015), and Presidential Early Career Award for Scientists and Engineers (2019). Research interests center on developing tools for measuring/manipulating neural activity, including multiphoton microscopy innovations and deep-brain recording systems. The lab explores mesoscale neural circuit architecture, visual processing dynamics, and neurotechnology applications for understanding brain-behavior relationships. Key areas of focus include: Developing next-generation multiphoton imaging systems with expanded field-of-view capabilities Exploring neural manifold topology in population activity Investigating cortical communication through discrete information channels Advancing neurophotonic tools for high-speed neural recording His work bridges engineering and neuroscience, with recent advancements in Air Objective Imaging and Neuroformer generative models for brain data analysis. Current projects emphasize real-time neural circuit visualization and translational applications of neurotechnology.