جست‌وجوی استاد و پژوهشگر

Alexander Bastounisمشاهده پروفایل

مدرس

• Artificial Intelligence
• Computational Mathematics
• Optimization
• +۵ مورد دیگر

Alexander Bastounis is a Lecturer in Applied Mathematics at King's College London, affiliated with the Department of Mathematics and the King’s Institute for Artificial Intelligence. His research focuses on computational mathematics, optimization, and the trustworthiness of AI systems. He holds a PhD from the University of Cambridge and has held academic roles at institutions including Leicester University, City University of Hong Kong, and TU Berlin. Education: PhD in Applied Mathematics from DAMTP, University of Cambridge (2018). Earlier academic qualifications not specified. Research interests include foundational aspects of computational mathematics, AI limits and robustness, adversarial attacks, and inverse problems. His work explores computational barriers in estimation and learning, with recent attention on stealth attacks in AI models and feature selection reliability. Received the Leslie Fox Prize (2019) for work on inverse problems Contributed to SIAM News articles on compressed sensing and AI challenges Advising and grants: Currently supervises the EPSRC-funded project '50:50 Haleon/EPSRC DLA Studentship' (2025–2029). No listed students. Labs/teams: Active in King’s Institute for Artificial Intelligence and collaborates on interdisciplinary projects across computational mathematics and AI security.

Maria Christina Marianiمشاهده پروفایل

استاد

• Applied Mathematics
• Nonlinear partial differential equations
• Stochastic differential equations
• +۵ مورد دیگر

Maria Christina Mariani is a Professor and Department Chair in the Department of Mathematical Sciences at the University of Texas at El Paso (UTEP). Her interdisciplinary research bridges mathematics with applications in public health, geophysics, physics, and finance, with a focus on developing novel mathematical models for complex data analysis. Dr. Mariani earned her Ph.D. in Mathematics from the University of Buenos Aires in 1992, where she received an Outstanding dissertation award. She also holds an M.S. in Physics (1996) and an M.S. in Mathematics (1987), both from the University of Buenos Aires with highest honors. Her research interests span Applied Mathematics, Nonlinear partial differential equations, Stochastic differential equations, Machine Learning techniques, Mathematical Finance, Mathematical Physics, and Numerical Methods. She has developed mathematical models for medical data analysis (particularly breast cancer, heart disease, and prostate cancer), seismic and explosive data, and financial markets. Her work emphasizes the development of mathematical models to enhance understanding of medical data and extreme events in various phenomena. Dr. Mariani's recent research focuses on applying machine learning and stochastic models to complex data sets across multiple domains. Her work demonstrates consistent innovation in developing novel algorithms for medical diagnosis and prognosis, analyzing seismic data, and modeling financial markets using Levy processes, Ornstein-Uhlenbeck models, and wavelet techniques. She has mentored numerous students throughout her career, including PhD candidates, MS students, and post-doctoral researchers, demonstrating her commitment to academic development and knowledge transfer. Dr. Mariani has served as Department Chair and holds the Shigeko K. Chan Distinguished Professor title in Mathematical Sciences, reflecting her significant contributions to the field and institution.

HanQin Caiمشاهده پروفایل

استادیار

• Mathematical Optimization
• Data Science
• Machine Learning
• +۵ مورد دیگر

Dr. HanQin Cai is the Paul N. Somerville Endowed Assistant Professor in the Department of Statistics and Data Science at the University of Central Florida (UCF), also serving as Director of the Data Science Lab. He holds a joint appointment with the Department of Computer Science. His research focuses on theoretical and algorithmic foundations of mathematical optimization, data science, and machine learning, with emphasis on non-convex algorithms, adversarial attacks, signal/image processing, and deep learning integration. He has secured NSF grants totaling over $2.6M, including a $121K single-PI grant and a $2.49M co-PI grant. His work has been recognized with the UCF OSCaR Award (2025) and IEEE Senior Member status (2024). Education: PhD in Applied Mathematical and Computational Sciences from University of Iowa (2018), with M.S. in Mathematics (2014) and M.C.S. in Computer Science (2017). Previously served as a Postdoc at UCLA Mathematics Department under Dr. Wotao Yin. Research highlights include: query-efficient zeroth-order optimization, robust signal processing with corrupted data, adversarial attacks on neural networks, and tensor-based methods for high-dimensional data analysis. His recent publications explore advanced techniques in matrix recovery, tensor decompositions, and explainable AI. Grants & Awards: NSF DMS-2304489 (2022–2025), NSF DUE-2321986 (2024–2029), UCF OSCaR Award, IEEE Senior Membership. Labs & Teams: Directs UCF's Data Science Lab, collaborates across disciplines in statistics, computer science, and engineering.

Matthew Siegfriedمشاهده پروفایل

دانشیار

• Subglacial hydrology
• Ice-ocean interaction
• Grounding zone dynamics
• +۴ مورد دیگر

Matthew Siegfried is an Associate Professor in the Department of Geophysics at Colorado School of Mines, where he leads the Mines Glaciology Laboratory . His work combines satellite remote sensing with field and airborne geophysical methods to study Earth's glaciers and ice sheets, focusing on subglacial hydrology, ice-ocean interactions, and grounding zone dynamics. PhD in Earth Sciences (2015) - Scripps Institution of Oceanography M.S. in Earth Sciences (2010) - Dartmouth College A.B. in Earth Sciences (2008) - Dartmouth College His research explores subglacial hydrology (subglacial lakes, groundwater), ice-ocean interactions (grounding zones, ice shelves), and geophysical surveying using air/spaceborne altimetry (ICESat-2, Operation IceBridge). Recent projects examine West Antarctic ice streams, Arctic coastal dynamics, and Ross Ice Shelf stability. Publications span topics in subglacial lake evolution , ICESat-2 algorithm development , and ice-ocean feedback mechanisms . Collaborative work includes biogeochemical studies of Mercer Subglacial Lake and Arctic permafrost-wildfire interactions. 2023 Mines Excellence in Research Award 2022 Undergraduate Student Government Faculty Award 2020 AGU Outstanding Reviewer He mentors through the Mines Glaciology Laboratory , supported by NASA and NSF grants (CAREER program, ICESat-2 projects). Outreach includes podcast appearances (e.g., Historians at the Movies) and K-12 collaborations.

Clayton Scottمشاهده پروفایل

استاد

• Statistical Machine Learning
• Machine Learning Theory
• Artificial Intelligence
• +۵ مورد دیگر

Clayton Scott is a Professor of Electrical Engineering and Computer Science (EECS) at the University of Michigan, with a courtesy appointment in Statistics. He holds a joint appointment in the College of Engineering and is affiliated with MIDAS, AI Lab, and the Center for Computational Medicine and Bioimaging (CCMB). His research focuses on statistical machine learning theory and algorithms, with applications in medical imaging, nuclear engineering, climate science, and clinical diagnostics. He actively collaborates with researchers in psychiatry, nuclear engineering, and radiology. Education: PhD in Electrical Engineering (Rice University, 2004), MS (Rice, 2000), AB in Mathematics (Harvard, 1998). He teaches courses in machine learning (EECS 545), signal processing, and statistical methods. His work emphasizes developing scalable algorithms with theoretical guarantees, particularly in domains like functional neuroimaging, nuclear particle classification, and sepsis prediction. He advises ~1 PhD student annually and has mentored over 20 students. His grants include NSF, NIH, and DOE funding, focusing on topics like domain adaptation, label noise, and medical image registration. His lab develops open-source tools for robust kernel methods, mixture proportion estimation, and partial mixture modeling.

• Stochastic Analysis
• Mathematical Finance
• Option Pricing
• +۵ مورد دیگر

Blanka Horvath is a Lecturer at King's College London and Honorary Lecturer at Imperial College London's Department of Mathematics (Faculty of Natural Sciences). Her research focuses on stochastic analysis and mathematical finance, particularly in numerical methods, machine learning applications, and volatility modeling (e.g., SABR and rough volatility models). She holds a PhD from ETH Zurich (2015), a Diplom in Mathematics from the University of Bonn, and an MSc in Economics from the University of Hong Kong. She has organized major conferences such as the SIAM MMF 2017 mini-symposium and co-organized the Rough Volatility Meeting at Imperial College. Her honors include the 2019 Risk Rising Star Award and the 2024-25 LMS Emmy Noether Fellowship. She collaborates with institutions like UBS, The Alan Turing Institute, and Quantennium LTD. Her teaching includes courses on numerical methods in finance and Python/R programming. She supervises PhD and MSc students in areas like rough volatility, machine learning, and quantitative finance. Recent work explores quantum GANs for option pricing and regime detection using Wasserstein distances.

• Formal Verification and Control
• Safety-Critical Systems
• Machine Learning
• +۵ مورد دیگر

Dr. Yulong Gao is an Assistant Professor in the Department of Electrical and Electronic Engineering at Imperial College London, affiliated with the Control and Power Research Group. He holds a B.E. in Automation from Beijing Institute of Technology (2013), M.E. in Control Science & Engineering (2016), and a joint Ph.D. in Electrical Engineering from KTH Royal Institute of Technology and Nanyang Technological University (2021). He has held postdoctoral positions at Oxford University and KTH. His research focuses on formal verification and control, machine learning, and their applications to safety-critical systems, including autonomous systems and control synthesis under uncertainty. His work emphasizes robust control strategies, data-driven optimization, and formal methods to ensure safety in dynamic systems. Recent publications address challenges in autonomous vehicle motion planning, risk-aware Bayesian neural networks, and adaptive task planning using temporal logic. He has contributed to stochastic modeling, distributed MPC, and resilient control under cyber-physical threats. Research affiliations include the Control and Power Research Group at Imperial College London, leveraging interdisciplinary collaboration to advance theoretical and applied control systems research.

• Sparse signal processing
• Signal processing for machine learning
• Wireless communications
• +۴ مورد دیگر

Dr. Wei Dai is a Senior Lecturer (Associate Professor) in the Department of Electrical and Electronic Engineering at Imperial College London, part of the Faculty of Engineering. He holds affiliations with the EPSRC Centre for Maths of Precision Healthcare and the Communications and Signal Processing group. His research focuses on sparse signal processing, machine learning applications in signal processing, linear and bilinear inverse problems, wireless communications, and random matrix theory. Notably, he contributed to the first compressive sensing DNA microarray prototype and has a highly cited 2009 paper on compressive sensing reconstruction. Dr. Dai's educational background includes a Ph.D. in Electrical and Computer Engineering from the University of Colorado at Boulder (2007) and postdoctoral research at the University of Illinois at Urbana-Champaign (2007-2010). His work bridges theoretical signal processing with practical applications in sensing, communication systems, and biomedical signal analysis. He leads research initiatives in gridless DOA estimation, robust beamforming, and cortico-muscular coupling analysis using advanced optimization techniques. His research outputs span topics like spectral compressed sensing, Bayesian methods for integrated sensing-communication systems, and dictionary learning for causal discovery. Ongoing work emphasizes low-rank matrix recovery, distributed compressed sensing, and mathematical frameworks for super-resolution localization. Dr. Dai collaborates across disciplines, leveraging signal processing innovations for healthcare technology and next-generation wireless systems.

Xin Liمشاهده پروفایل

استاد

• Mathematical Analysis
• Wireless Communication Systems
• Blockchain Technology
• +۳ مورد دیگر

Professor Xin Li is the Chair of Mathematical Analysis at the School of Mathematics & Statistics, University of Glasgow. His research focuses on interdisciplinary areas at the intersection of mathematical analysis, wireless communication systems, and blockchain technology. He holds a faculty position with expertise in reconfigurable intelligent surfaces (RIS), signal processing, and network optimization. Recent publications highlight his work on RIS-aided information-sensing integrated systems (ISAC), blockchain-based consensus networks in cellular environments, and adaptive beamforming techniques for multipath communication. His research emphasizes practical applications of theoretical mathematical models in telecommunications and distributed systems. Prof. Li's work demonstrates trends in integrating mathematical analysis with emerging technologies like millimeter-wave systems and Byzantine fault tolerance mechanisms. His contributions bridge pure mathematical rigor with real-world communication challenges, addressing both theoretical and applied aspects of modern wireless networks. He currently oversees academic activities within the School of Mathematics & Statistics and maintains an active research program supported by interdisciplinary collaborations. His contact information includes Xin.Li@glasgow.ac.uk and ORCID 0000-0002-2243-3742.

• Mathematical Biology
• Biophysics
• Stochastic Processes
• +۳ مورد دیگر

Professor Kenneth A Lindsay is an Honorary Senior Research Fellow at the University of Glasgow's School of Mathematics & Statistics. His work bridges mathematical biology, biophysics, and financial econometrics, with a focus on stochastic processes and computational modeling. Key contributions include advancements in compartmental modeling of neurons, Fokker-Planck equation solutions for stochastic differential equations, and applications in energy market forecasting. He has authored/co-authored over 25 peer-reviewed articles and edited the influential 2005 book Modeling in the Neurosciences . Collaborations with researchers like David Brillinger, Adrian Hurn, and Jonathan Rosenberg highlight interdisciplinary expertise. His research addresses topics ranging from dendritic branching patterns to econometric optimization techniques. Publications span journals such as Biological Cybernetics , Journal of Financial Econometrics , and European Journal of Heart Failure . Notable work includes modeling electricity price spikes and analyzing magnetic field effects on nerve excitability. Lindsay's edited volumes and book chapters further underscore his role in advancing neuroscience and quantitative finance methodologies. His academic contributions reflect a commitment to mathematical rigor applied to real-world systems, with applications in biomedical engineering, energy economics, and computational neuroscience.

Caglar Oskayمشاهده پروفایل

استاد

• Multiscale Computational Modeling
• Composite Materials
• Structural Failure Analysis
• +۷ مورد دیگر

Caglar Oskay is the Cornelius Vanderbilt Professor of Engineering and Chair of the Department of Civil and Environmental Engineering at Vanderbilt University. He is also a Professor of Mechanical Engineering. His research focuses on multiscale computational modeling of material and structural systems under extreme conditions, with expertise in composite materials, failure mechanisms, and computational mechanics. Dr. Oskay earned his Ph.D. in Civil Engineering from Rensselaer Polytechnic Institute (2003) and has held academic roles there before joining Vanderbilt in 2006. He was honored as an ASME Fellow in 2017 and as a Chancellor Faculty Fellow in 2016. Key research areas include multiscale failure modeling, life prediction of heterogeneous materials, and computational methods for composites and multiphysics systems. His work integrates advanced simulation techniques with experimental validation, addressing challenges in infrastructure resilience, additive manufacturing defects, and quantum computing applications in engineering. Education: Ph.D., Civil Engineering, Rensselaer Polytechnic Institute (2003) M.S., Civil Engineering, Rensselaer Polytechnic Institute M.S., Applied Mathematics, Rensselaer Polytechnic Institute B.S., Civil Engineering, Middle East Technical University Recent research highlights include stochastic modeling of geotechnical infrastructure failures, quantum-enhanced finite element methods, and predictive analytics for additive manufacturing defects. He leads interdisciplinary efforts on backward erosion piping in flood protection systems and has secured grants for multiscale modeling of titanium alloys and composites. Awards: ASME Fellow (2017) Chancellor Faculty Fellow (2016) Advising and grants: Dr. Oskay’s grants include NSF-funded studies on backward erosion piping and quantum computing applications. His research group collaborates with industry partners on materials for aerospace and energy sectors, emphasizing computational tools for failure prediction and material design. His work bridges computational mechanics with practical engineering challenges, advancing methods for infrastructure resilience, advanced materials, and sustainable design through multiscale modeling innovations.

Alex Mogilnerمشاهده پروفایل

استاد

• Computational Biology
• Cell Biophysics
• Mathematical Biology
• +۵ مورد دیگر

Alex Mogilner is a Professor of Mathematics and Biology at New York University's Courant Institute and Department of Biology. His research focuses on computational and mathematical modeling of cellular processes, including cell motility, mitosis, actin dynamics, and galvanotaxis. He collaborates closely with experimentalists to bridge theoretical and empirical cell biology. Education: Ph.D., Applied Mathematics, University of British Columbia, Canada (1995) Ph.D., Physics, USSR Academy of Sciences (1990) M.Eng., Engineering Physics, Ural Polytechnic Institute, USSR (1985) Research Interests: Mathematical modeling of molecular machines in cells Mitotic spindle dynamics and chromosome segregation Actin-myosin contraction mechanisms Galvanotaxis and electrotaxis in cell migration Cellular biophysics and mechanochemical processes Selected Publications Highlights: Developed models for mitotic spindle assembly and error correction Investigated mechanisms of cell polarization and turning Studied actin network contraction and cytoskeletal dynamics Explored galvanotactic cell migration in electric fields Labs/Teams: Leads a computational modeling lab at NYU, collaborating with experimental groups globally in cell biology, biophysics, and systems biology.

Jacquelien Scherpenمشاهده پروفایل

استاد

• Automation & Control Systems
• Nonlinear Control Systems
• Smart Energy Systems
• +۳ مورد دیگر

Jacquelien Scherpen is a Professor at the University of Groningen, currently serving as Rector Magnificus. She holds a position in the Engineering and Technology institute Groningen (ENTEG) within the Faculty of Science and Engineering. Her academic journey includes a PhD in Applied Mathematics from the University of Twente (1994) and post-doctoral roles at Delft University of Technology before joining Groningen in 2006. She has been a leader in systems and control research, with roles as Scientific Director of ENTEG (2013–2019) and Director of the Groningen Engineering Center (2016–2023). Her research focuses on model reduction, nonlinear control, smart energy systems, and distributed control applications in smart grids. Key achievements include the Automatica Best Paper Prize (2020), Knight of the Order of the Netherlands Lion (2019), and the Prince Friso Engineer of the Year Award (2023). She has held leadership roles in organizations like the European Control Association (EUCA) and is a Fellow of IEEE. Her professional activities include editorial roles in journals like the IEEE Transactions on Automatic Control and the International Journal of Robust and Nonlinear Control. She has organized major conferences, including the European Control Conference (ECC) 2021 and 2023. Her work bridges theoretical advancements with practical applications, emphasizing sustainability and engineering innovation.

Tony Lindebergمشاهده پروفایل

استاد

• Computer Vision
• Computational Neuroscience
• Medical Image Analysis
• +۴ مورد دیگر

Tony Lindeberg is a Professor of Computer Science—Computational Vision at KTH Royal Institute of Technology, affiliated with the Division of Computational Science and Technology. He teaches the course Image Analysis and Computer Vision (DD2423). His research focuses on scale-space theory, early vision, and computational modeling of biological and auditory vision systems. Key contributions include theories on receptive fields, time-causal spatio-temporal models, and feature detection algorithms. Research interests span computational neuroscience, medical image analysis, and spatio-temporal recognition. Lindeberg has pioneered work on scale-invariant image features, affine transformations, and Galilean diagonalization for motion analysis. He is the author of the foundational book Scale-Space Theory in Computer Vision (1993). His work bridges computer vision and biological vision systems, with applications in gesture recognition, dynamic texture analysis, and neural networks. He leads the Vision Lab and Computational Brain Science Lab at KTH, emphasizing theoretical rigor and practical algorithms for visual perception tasks.

Paul Franzonمشاهده پروفایل

استاد

• Quantum Information Science
• Machine Learning & AI Hardware
• 3D Integration
• +۴ مورد دیگر

Paul Franzon is the Cirrus Logic Distinguished Professor and Associate Department Head for Graduate Affairs at the Department of Electrical and Computer Engineering, North Carolina State University. He holds a PhD and Bachelor's in Electrical Engineering and a Bachelor's in Physics/Mathematics from the University of Adelaide, Australia. His research focuses on quantum information science, machine learning-driven hardware design, 3D integration, and high-speed systems. Education: PhD in Electrical Engineering, University of Adelaide (1988) Bachelor's in Electrical Engineering, University of Adelaide (1984) Bachelor's in Physics and Mathematics, University of Adelaide (1982) Research Interests: Quantum computing and algorithm optimization AI-driven design automation for 3D integrated circuits High-speed communication systems Hardware security and FPGA acceleration Awards & Honors: IEEE Fellow (2006) Alcoa Foundation Distinguished Engineering Research Award (2005) NC State Alumni Distinguished Undergraduate Professor Award (2003) NSW Australia Expatriate Scientist Award (2003) Advising & Grants: Advised PhD student Priyank Kashyap (2023 graduate) Recipient of NSF Young Investigators Award (1993) Labs & Collaborations: Center for Advanced Electronics Through Machine Learning (CAEML) IEEE EPS Society (Associate Editor)