جست‌وجوی استاد و پژوهشگر

Bruna G. Palmمشاهده پروفایل

مدرس

• Statistical Modeling
• Signal Processing
• Remote Sensing
• +۴ مورد دیگر

Bruna G. Palm is a Lecturer at the Department of Mathematics and Natural Sciences, Blekinge Institute of Technology (BTH), Sweden. She holds a BA (2014) and PhD (2020) in Statistics from Brazilian institutions and has held visiting and research fellow positions at BTH and the Aeronautics Institute of Technology (ITA). Education: BA in Statistics (2014), PhD in Statistics (2020) Current Role: Lecturer at BTH Collaborations: Saab AB, Sweden Her research bridges statistical modeling with remote sensing and signal processing , focusing on SAR image analysis , change detection , and machine learning applications. She has also contributed to environmental data forecasting and educational pedagogy in STEM. Key trends include Bayesian inference , autoregressive models , and Rayleigh distribution techniques for SAR imagery. Her work on resin composite masking extends into dentistry , highlighting interdisciplinary reach. She has no listed scientific awards but maintains active profiles on LinkedIn , Google Scholar , and ORCID .

• Wireless Communications
• Mathematics
• Artificial Intelligence
• +۵ مورد دیگر

Keivan Navaie is a Professor of Intelligent Networks at Lancaster University’s School of Computing and Communications. He serves as a member of the Independent Scientific Advisory Committee at the Alan Turing Institute, overseeing the £100 million BridgeAI programme, and previously as Principal AI Technology Advisor to the UK Information Commissioner’s Office (ICO). He is recognized with Fellowships from the Institution of Engineering and Technology (IET), Chartered Engineer status in the UK, Senior Fellowship of the Higher Education Academy (HEA), and the IEEE Young Investigator Award. Research Focus: Wireless communications, mathematics, artificial intelligence, 6G networks, blockchain technology, edge computing, cognitive radio networks, and non-orthogonal multiple access (NOMA). Supervision: Actively supervises PhD students in areas like wireless communications and mathematical modeling. Projects: Involved in distributed learning, blockchain integration, 6G research, and spectrum sharing systems. Awards: IEEE Young Investigator Award, Fellow of IET, Chartered Engineer, Senior Fellow of HEA.

• Task-Oriented Knowledge Acquisition
• Online Learning and Control
• Human-Robot Interaction
• +۴ مورد دیگر

Mahsa Ghasemi is an Assistant Professor at the Elmore Family School of Electrical and Computer Engineering, Purdue University, located in West Lafayette. She holds a B.Sc. in Mechanical Engineering from Sharif University of Technology (2014), an M.S.E. in Mechanical Engineering from The University of Texas at Austin (2017), and a Ph.D. in Electrical and Computer Engineering from The University of Texas at Austin (2021). Her research focuses on task-oriented knowledge acquisition, online learning and control, human-robot interaction, trustworthy AI, and socially beneficial autonomy. She is affiliated with the Materials and Electrical Engineering Building at Purdue. Education: B.Sc., Mechanical Engineering, Sharif University of Technology (2014) M.S.E., Mechanical Engineering, UT Austin (2017) Ph.D., Electrical and Computer Engineering, UT Austin (2021) Her research interests span interdisciplinary areas including reinforcement learning, causal inference, control systems, and human-autonomy collaboration. Recent work emphasizes resilient cyber-physical systems, privacy-preserving multi-agent learning, and causal discovery in decision-making frameworks. Her articles address challenges in sensor selection, no-regret learning in bandits, and formal methods for autonomous systems. Publications highlight contributions to submodular optimization in hypothesis testing, robust sensor scheduling in intrusion detection, and adaptive experimental design for causal discovery. Her work bridges theoretical foundations with practical applications in robotics, cybersecurity, and AI ethics. No scientific awards or grants are explicitly listed in the provided data. She advises no listed students but collaborates on projects involving diverse planning and decision-making in constrained environments.

Joshua I Glaserمشاهده پروفایل

استادیار

• Computational Neuroscience
• Neural Control of Movement
• Interpretable Machine Learning
• +۷ مورد دیگر

Joshua I Glaser is an Assistant Professor at Northwestern University, holding dual appointments in the Department of Neurology (Feinberg School of Medicine) and the McCormick School of Engineering. His research focuses on computational neuroscience, particularly the neural control of movement and interpretable machine learning. Glaser earned a BS in Physics and Math from the University of Illinois Urbana-Champaign (2011), a PhD in Neuroscience from Northwestern University (2018), and completed postdoctoral training at the University of Pennsylvania and Columbia University. Research Interests: Glaser’s lab develops interpretable machine learning tools to understand neural computations underlying movement and behavior. Key areas include neural population interactions, flexible behavior control, and disease-related neural dynamics. Projects include interpretable dimensionality reduction, decoding models linking neural activity to behavior, and multi-population neural interaction analysis. Lab & Collaborations: The Glaser Lab collaborates with experimental neuroscientists and statisticians. Current projects involve developing novel methods for analyzing neural data while maintaining interpretability. They host a team of PhD, master’s, and undergraduate students. Grants & Awards: While no specific awards are listed, his work reflects sustained funding in computational neuroscience and engineering. Code & Resources: Lab-developed tools include Neural_Decoding (machine learning for neural decoding), SSM (state-space models), and Sparse Component Analysis (interpretable latent factor analysis). These are publicly available on GitHub.

• Interaction Design
• Music Technology
• Live Coding
• +۳ مورد دیگر

Professor Palle Dahlstedt is affiliated with the University of Gothenburg's Interaction Design department. His work bridges music technology, live coding, and interdisciplinary performance. He specializes in gestural interactions, algorithmic creativity, and systems for collaborative improvisation. Key projects include the Bucket System, OtoKin, and research on live coding frameworks. Research interests focus on creative technologies in music and performance, with emphasis on real-time systems, human-computer interaction, and artistic collaboration. Dahlstedt has published extensively in venues like NIME, ICLC, and Evolutionary Intelligence, addressing topics ranging from hybrid piano design to generative storytelling. Notable contributions include the Biosphere Code Manifesto (2015), exploring algorithms in environmental contexts, and the Electroacoustic Modular Ecosystem (2020). His work often involves cross-disciplinary collaborations with dancers, musicians, and technologists. Performance highlights include jury-selected NIME performances (2015) and collaborations with artists like Gino Robair and Tim Perkis. Dahlstedt actively participates in international festivals and conferences, advancing the field of computational creativity and artistic research.

Eun-Suk Seoمشاهده پروفایل

استاد

• Cosmic Ray Physics
• Astrophysics
• Particle Physics
• +۴ مورد دیگر

Eun-Suk Seo is a Professor of Physics and member of the Institute for Physical Science and Technology (IPST) at the University of Maryland. Her research focuses on cosmic ray origins, acceleration, and propagation, with an emphasis on detecting exotic matter like antimatter and dark matter using balloon-borne and spacecraft instruments. She leads the CREAM collaboration and contributes to AMS, BESS, and ATIC projects. Key collaborations include the Alpha Magnetic Spectrometer (AMS) on the International Space Station, the Cosmic Ray Energetics and Mass (CREAM) program, and the Balloon-borne Experiment with a Superconducting magnet Spectrometer (BESS). Her work integrates advanced instrumentation design, such as the ISS-CREAM calorimeter, with numerical modeling to study cosmic ray spectra and interactions. Research interests span cosmic ray propagation mechanisms, spallation effects, and the role of supernova remnants and black holes as particle accelerators. Publications highlight analyses of lithium isotopes, antiprotons, and elemental composition using data from space-based experiments.

Purba Dasمشاهده پروفایل

مدرس

• Stochastic Analysis
• Mathematical Finance
• Financial Mathematics
• +۳ مورد دیگر

Dr. Purba Das is a Lecturer in Financial Mathematics at King's College London's Department of Mathematics, part of the Faculty of Natural, Mathematical & Engineering Sciences. She holds a DPhil from the University of Oxford (2022), specializing under Prof. Rama Cont, and prior degrees from Chennai Mathematical Institute (M.Sc. 2018, B.Sc. 2016). Her research focuses on stochastic analysis with applications to mathematical finance, particularly pathwise methods and rough volatility modeling. Education: DPhil (Oxford, 2022), M.Sc. (Chennai Mathematical Institute, 2018), B.Sc. (Chennai Mathematical Institute, 2016). Research interests include rough path theory, microstructure noise analysis, and functional Itô calculus. Recent work explores rough volatility's empirical validity, depositor behavior in digital banking systems, and Hölder continuity in function spaces. She co-organizes conferences on stochastic calculus and financial stability. Grants include £10k from KCL-IIT Madras Partnership and £5k from KCL's NMES Fund. Active in teaching financial mathematics and stochastic processes at both King’s and the University of Michigan.

• Mathematical Statistics
• Machine Learning
• Kernel Methods
• +۳ مورد دیگر

Prof. Dr. Nicole Mücke is a Professor in the Institute for Mathematical Stochastics at the Carl-Friedrich-Gauss Faculty of Technische Universität Braunschweig. Her research focuses on mathematical statistics, machine learning, kernel methods, and statistical inference. She explores topics such as neural network theory, inverse problems, optimization, and regularization techniques. Her work bridges theoretical foundations with practical applications in areas like distributed computing and uncertainty quantification. Prof. Mücke’s research portfolio includes contributions to empirical risk minimization, neural operator learning, and gradient-based optimization. She investigates the interplay between overparameterization and generalization in machine learning models, as well as the design of efficient algorithms for large-scale problems. Her publications span topics ranging from distributed stochastic gradient descent to localized kernel regression techniques. Her recent work emphasizes theoretical guarantees for learning algorithms, including convergence rates, statistical performance in high-dimensional settings, and the role of regularization in inverse problems. She also explores methodological advancements in spectral methods, algorithm unfolding, and data-splitting strategies to enhance statistical efficiency. Prof. Mücke’s research is characterized by a strong emphasis on rigorously analyzing machine learning algorithms through the lens of statistical theory and functional analysis. Her contributions address challenges in both classical and modern machine learning paradigms, with a focus on bridging the gap between abstract mathematical frameworks and practical implementation.

Abolfazl Safikhaniمشاهده پروفایل

استادیار

• Network Modeling
• High-Dimensional Statistics
• Spatiotemporal Models
• +۵ مورد دیگر

Abolfazl Safikhani is an Assistant Professor in the Department of Statistics at George Mason University. He holds a PhD in Statistics and Probability from Michigan State University and has held prior positions at Columbia University and the University of Florida. His research focuses on network modeling, high-dimensional statistics, spatiotemporal models, and applications in urban planning, neuroscience, and smart cities. He is an Associate Editor for Technometrics , Statistica Sinica , and Data Science in Science . He has contributed to advancements in statistical methodologies for time series analysis, including change point detection, transfer learning, and spatiotemporal modeling. His work bridges theoretical statistics with practical applications in urban growth prediction, healthcare (e.g., cancer drug response modeling), and transportation systems. Recent research trends include leveraging explainable AI for land use modeling, longitudinal omics data analysis, and structural break detection in high-dimensional systems. His publications span theoretical developments and real-world case studies, such as subway ridership during the pandemic and New York City’s taxi demand dynamics. Despite no listed awards, his editorial roles highlight his influence in statistical science. He actively mentors students and collaborates on interdisciplinary projects, emphasizing data-driven solutions for complex societal challenges.

Aniket Bhattacharyaمشاهده پروفایل

استاد

• Theoretical Condensed Matter Physics
• Soft Matter Systems
• Polymer Physics
• +۵ مورد دیگر

Aniket Bhattacharya is a Professor in the Department of Physics at the University of Central Florida (UCF), specializing in theoretical and computational studies of soft condensed matter physics and biophysics. His work spans DNA translocation through nanopores, intrinsically disordered proteins, and confined biopolymers, with applications in biomedical materials. PhD in Theoretical Condensed Matter Physics (University of Maryland, 1992) BS – Physics Honors (Presidency College, Kolkata) MS in Physics (University College of Science, Kolkata) Research interests: Scaling theory and simulations of soft matter systems Polymer physics in crowded environments Biologically inspired physics, particularly DNA transport through nanopores Physics of intrinsically disordered proteins linked to disease Development of soft materials for biomedical applications Recent publications focus on machine learning strategies for protein characterization, nanopore DNA barcoding, and polymer dynamics in confined spaces. His work has been supported by grants from the NSF, NASA, and NIH. He holds multiple patents related to nanopore-based DNA analysis and mutation prediction in disordered proteins. United States Patent 12,049,663B2 (2024) Non-Provisional Patent Application 19/232,158 (2025) His lab collaborates on interdisciplinary projects involving computational modeling, nonequilibrium transport phenomena, and biomedical applications.

James Fouldsمشاهده پروفایل

دانشیار

• Machine Learning
• Artificial Intelligence
• Ethics in AI
• +۴ مورد دیگر

James Foulds is an Associate Professor in the Department of Information Systems at the University of Maryland, Baltimore County (UMBC). His research focuses on socially conscious machine learning and artificial intelligence, emphasizing fairness, privacy, and computational social science using probabilistic models and Bayesian inference. He holds an NSF CAREER Award and a runner-up prize from the SoCal Machine Learning Symposium (2016). His work includes developing fair AI systems, privacy-preserving techniques, and Bayesian modeling approaches for intersectional fairness. Dr. Foulds earned his postdoctoral training at UC San Diego and UC Santa Cruz, and completed his PhD at UC Irvine. He teaches courses in artificial intelligence, ethical AI, and data mining. His research has been supported by grants such as the NSF CISE Research Initiation Initiative (CRII) and the NSF EAGER grant for healthcare resource fairness. Notable contributions include the Data Mining textbook (under revision), the Stochastic-CVB0 Julia package for topic modeling, and collaborations on AI fairness frameworks like the IEEE DEB Special Issue. He advises students on projects in fair machine learning, including equitable healthcare resource allocation and bias mitigation in resume filtering systems.

Zafeiria Roumeliotiمشاهده پروفایل

استادیار

• Seismology
• Geophysics
• Earthquake Source Mechanics
• +۳ مورد دیگر

Zafeiria Roumelioti is an Assistant Professor at the Department of Geology, University of Patras. Her research focuses on seismology, geophysics, and earthquake engineering, with a strong emphasis on earthquake source mechanics, ground motion simulation, and seismic hazard assessment. She leads projects such as ARGONET and PROION, involving advanced geophysical instrumentation and multidisciplinary monitoring networks. Education: BSc in Geology MSc in Geophysics PhD in Seismology from Aristotle University of Thessaloniki Research Interests: Kinematic modeling of earthquake sources Site effect studies using vertical arrays Strong ground motion simulation (deterministic/stochastic) Real-time earthquake monitoring systems Nonlinear soil response during seismic events Regional seismicity and tectonic dynamics Her work integrates geophysical data from accelerometers, GNSS, and distributed sensors to improve earthquake early warning systems and infrastructure resilience. Key contributions include the ARGONET vertical array and PROION project’s multi-parametric monitoring framework.

Scott D Bartonمشاهده پروفایل

دانشیار

• Robotics Engineering
• Music Perception and Cognition
• Audio Production
• +۳ مورد دیگر

Scott D Barton is an Associate Professor in the Department of Humanities & Arts at Worcester Polytechnic Institute (WPI), with affiliations in Robotics Engineering, Psychology, and Interactive Media & Game Development. His work bridges music, robotics, and cognition, focusing on robotic musical instruments, human-robot interaction, and perceptual studies. He holds a BA from Colgate University (1998), an MMus from Brooklyn College (2006), and a PhD from the University of Virginia (2012). Research interests include designing automated musical instruments, exploring how cognitive processes shape musical perception, and applying AI to music creation. Key projects involve the Music, Perception and Robotics Lab , where he develops systems like Cyther (a self-tuning robotic zither) and Parthenope (a robotic siren). His work has been showcased in venues like the International Conference on New Interfaces for Musical Expression. Barton emphasizes interdisciplinary collaboration, teaching students to merge technical skills with creative expression. Media coverage includes interviews in The New York Times and The Telegram & Gazette , discussing topics like urban sound phenomena and robot-human concerts. His performances blend algorithmic composition with live robotics, exemplifying WPI's focus on innovative engineering and art. Grants and collaborations are central to his work, though specific funding details are not provided. Advising focuses on guiding students in creating novel musical technologies and understanding perceptual frameworks. Upcoming events include performances at the International Computer Music Conference and workshops on computer music technology.

Hye-Won Kangمشاهده پروفایل

دانشیار

• Stochastic Modeling
• Mathematical Biology
• Biochemical Reaction Networks
• +۷ مورد دیگر

Hye-Won Kang is an Associate Professor in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County (UMBC). Her research focuses on stochastic modeling of biochemical systems, particularly in understanding complex biological processes such as enzyme clustering, microRNA signaling pathways in lung cancer and COPD, and multiscale reaction-diffusion systems. She holds a Visiting Research Fellowship from Merton College, University of Oxford. Her work integrates mathematical analysis, simulation, and experimental data to address challenges in systems biology. Key research areas include: Multiscale approximations for stochastic chemical networks Stochastic analysis of reaction-diffusion processes Modeling enzyme kinetics in metabolic diseases Gene regulatory networks and pattern formation Recent contributions include studies on glucosome condensate formation (2024), chemical systems with limit cycles (2023), and the role of microRNAs in lung cancer (2013). Her research has been published in high-impact journals such as Physical Biology , Bulletin of Mathematical Biology , and SIAM Multiscale Modeling and Simulation . She has advised PhD student Luan Chip Nguyen and has taught advanced courses in mathematical biology, statistics, and differential equations at UMBC since 2013.

Erik Van Vleckمشاهده پروفایل

استاد

• Applied Mathematics
• Numerical Analysis
• Numerical Methods for PDEs
• +۳ مورد دیگر

Erik Van Vleck is a Professor of Mathematics at the University of Kansas, affiliated with the College of Liberal Arts & Sciences. His research focuses on applied mathematics, numerical analysis, and numerical methods for partial differential equations (PDEs), with particular emphasis on data assimilation and reaction-diffusion systems. His work bridges theoretical advancements and practical applications in computational dynamics, environmental modeling, and ecological systems. Van Vleck’s research interests include multi-scale data assimilation techniques, adaptive mesh methods, and the numerical analysis of traveling wave solutions in discrete and continuous systems. He has contributed to understanding pattern formation in ecological models, particularly in savanna ecosystems and fire-driven dynamics. His methodological innovations span numerical methods for PDEs, reduced-order modeling, and stochastic filtering approaches for complex systems. His recent work highlights trends in developing robust adaptive frameworks for data assimilation, integrating advanced numerical techniques with real-world applications in geophysics and environmental science. This includes projects on metric tensor-based mesh adaptation and projected data assimilation using sliding window methods. His contributions to theoretical foundations include spectral stability analyses for discrete dynamical systems and bifurcation studies in reaction-diffusion equations. Van Vleck has collaborated extensively across disciplines, applying mathematical tools to ecological, climatological, and biomedical systems. His research often addresses computational challenges in high-dimensional spaces, emphasizing efficiency and accuracy in modeling complex phenomena. Despite the breadth of his contributions, no specific grants or advising roles are detailed in the provided materials.