جست‌وجوی استاد و پژوهشگر

• Wireless Communication
• Communication Systems
• Cellular Networks
• +۴ مورد دیگر

Ki Won Sung is an Associate Professor at KTH Royal Institute of Technology, specializing in wireless communication systems and network optimization. His research focuses on 5G/6G technologies, integrated sensing-communication systems, cell-free massive MIMO, and stochastic network modeling. Research interests include: Wireless network architecture design and optimization Resource allocation in multi-user communication systems Integrated sensing and communication (ISAC) Stochastic modeling of ultra-dense networks Energy-efficient communication protocols Millimeter wave and massive MIMO systems His recent publications demonstrate strong emphasis on beyond-5G systems, particularly cell-free massive MIMO deployments and URLLC applications. Research trends show consistent focus on network optimization through advanced signal processing, geometric decomposition methods, and cross-layer protocol design. Teaching activities include course management and examination for multiple degree projects and core courses: Communication Systems (IK2200) Mathematical Statistics (IX1501) Mobile Networks and Services (IK2560) Radio Networks (IK2510) Stochastic Simulation (II2206) Wireless Systems (IK1330)

Chun-Biu Liمشاهده پروفایل

دانشیار

• Computational Mathematics
• Statistical and Computational Methods in Biophysical Systems
• Information Theory
• +۴ مورد دیگر

Chun-Biu Li is an Associate Professor at the Department of Mathematics , Stockholm University , specializing in computational mathematics and statistical physics of biophysical systems. His research bridges information theory, machine learning, and nonequilibrium statistical mechanics to understand complex biological processes. Education: PhD in mathematical & statistical physics from the University of Texas at Austin (supervised by Ilya Prigogine), M.S. in mathematical physics from the University of Utah. Academic Experience: Associate Professor (2016–present) at Stockholm University, Associate Professor (2008–2016) at Hokkaido University, and JST/CREST Researcher (2005–2008) at Kobe University. Research Interests focus on data-driven statistical analyses, nonequilibrium biophysical systems, AI explainability, and fluctuation theorems. His work applies these methods to plant morphogenesis, molecular motor dynamics, and machine learning applications in life sciences. Teaching includes advanced courses on deep learning, reinforcement learning, and statistical methods for life science applications. His Supervision has guided over 30 students in topics ranging from protein mutation modeling to causal inference algorithms. Scientific Awards: Professional Development Award, University of Texas at Austin (2003) Summer Research Fellowship, University of Texas at Austin (2001) National Dean's List for Outstanding College Students (1997) Publications (110+) highlight his contributions to plant morphogenesis, single-molecule analysis, and statistical methods. Recent works examine DNA barcode clustering, rotary motor protein dynamics, and shape-aware data visualization techniques.

Erik Carlssonمشاهده پروفایل

استاد

• representation theory
• algebraic geometry
• algebraic combinatorics
• +۵ مورد دیگر

Erik Carlsson is a Professor in the Mathematics department at the University of California, Davis. His research spans multiple areas of pure and applied mathematics, connecting deep theoretical concepts with practical computational applications. He received his Ph.D. from Princeton University under Professor Andrei Okounkov in 2008, and a B.S. in Mathematics with honors and a minor in Computer Science from Stanford University in 2003. Carlsson's research focuses on representation theory, algebraic geometry, algebraic combinatorics, computational topology, and connections with nonconvex optimization. He is particularly interested in connections between Goresky-Kottwitz-Macpherson (GKM) spaces and applications to Macdonald theory and combinatorics. His work also includes computational topology, especially persistent homology, which he develops in collaboration with John Carlsson. One of his recent developments is a method for constructing the alpha complex in high dimension using the powerful duality principle in mathematical optimization. His recent publications show a clear trend toward bridging theoretical mathematics with computational applications. His work spans from proving deep conjectures in algebraic combinatorics (like the shuffle conjecture) to developing practical algorithms for topological data analysis. The interdisciplinary nature of his work connects pure mathematical theory with applications in data science, computer vision, and optimization problems. Carlsson has made significant contributions to multiple fields of mathematics through his collaborations and independent work. His research has implications for both theoretical mathematics and practical computational problems, with recent publications as of 2024 demonstrating his continued active research program.

Xingjie Niمشاهده پروفایل

دانشیار

• Metasurface
• Photonics
• Plasmonics
• +۵ مورد دیگر

Xingjie Ni is an Associate Professor in the Electrical Engineering department at the Materials Research Institute (MRI) . With a focus on metasurface physics , photonics , and plasmonics , their research spans advanced optical technologies and computational imaging. Research Trends : Recent work explores metasurface design for achromatic lenses and light manipulation machine learning-enhanced polarimetric imaging with encoding metasurfaces ultrathin optical devices enabling geometric image transformations reconfigurable liquid crystal systems for dynamic photonic applications electrically tunable nonlinear optics for ensemble learning nanoscale fabrication techniques for scalable metalenses Grants & Projects : Active grants include NSF funding for Photonic Integrated Guided-Wave-Driven Metasurfaces NASA collaboration on Metalens Origami Deployable Lidar National Institute of Biomedical Imaging and Bioengineering support for Metasurface-Based Endoscope

• Machine Learning
• Statistics
• Model-Based Clustering
• +۵ مورد دیگر

Brendan Murphy is a Full Professor in the School of Mathematical Sciences at University College Dublin since 2015, previously holding a Professor role at the same institution (2007-2015) and a Lecturer position at Trinity College Dublin (1999-2007). He is a Principal Investigator at the Insight Centre for Data Analytics and actively works in Machine Learning & Statistics. Research Focus: His work centers on Model-based clustering Mixture models Applications in sports analytics, food science, microbiome studies, and public health Bayesian statistical methods High-dimensional data analysis Article Trends: Murphy's recent publications (2024-2025) emphasize soft clustering techniques, Bayesian mixture models, and their applications across diverse domains including metabolomics, political science, and oceanography. His work addresses challenges in variable selection, robust classification, and multi-omics integration.

• Space Physics
• Radar Remote Sensing
• Radio Remote Sensing
• +۶ مورد دیگر

Juha Vierinen is a Professor in Space Physics at the Department of Physics and Technology, UiT The Arctic University of Norway. His research focuses on experimental space physics, specializing in radar and radio remote sensing techniques applied to space plasma dynamics, atmospheric physics, space debris characterization, and planetary science. Develops advanced radar systems for ionospheric and mesospheric studies Works with EISCAT, EISCAT_3D, and GNSS networks Investigates Kelvin-Helmholtz Instabilities, GNSS scintillation, and space debris mapping Recent research includes interferometric imaging of ionospheric structures and machine learning applications in ionogram analysis. He collaborates with international institutions on projects like UNICube, QBDebris, and CASCADE. Based in Tromsø, Norway, he operates from Forskningsparken 1 A220. His publications span topics like solar eclipse effects on ionospheric density, smartphone-based space weather mapping, and multi-static meteor radar turbulence studies. Current work emphasizes volumetric radar analysis and fine-scale plasma irregularity characterization using novel imaging techniques.

Curtis Huttenhowerمشاهده پروفایل

استاد

• Computational Biology
• Bioinformatics
• Human Microbiome
• +۴ مورد دیگر

Dr. Curtis Huttenhower is a Professor of Computational Biology and Bioinformatics at Harvard T.H. Chan School of Public Health , with dual appointments in the Department of Biostatistics and Department of Immunology and Infectious Diseases . His research focuses on computational methods for microbial community analysis, human microbiome public health implications, and machine learning applications in genomics. Education: B.S. (2000) from Rose-Hulman Institute of Tech, M.S. (2003) from Carnegie Mellon, Ph.D. (2008) from Princeton Major grants: NIH R21CA299494 (cancer virome), U24HL175772 (HVP consortium), OT2CA297578 (early-onset colorectal cancer prevention) His work spans functional metagenomics , microbiome diagnostics , and structured biological knowledge in machine learning . Recent studies include strain-level microbiome mapping and microbiome links to depression, diabetes, and cardiovascular disease . He contributes to open-source tools like MaAsLin and WAAFLE , and leads the Human Microbiome Project sub-cohort for inflammatory bowel disease microbiome characterization.

Mark Iwenمشاهده پروفایل

استاد

• Computational Harmonic Analysis
• Mathematical Data Science
• Signal Processing
• +۱۲ مورد دیگر

Mark Iwen is a Full Professor with a dual appointment in the Department of Mathematics and the Department of Computational Mathematics, Science and Engineering (CMSE) at Michigan State University. His research bridges theoretical mathematics and computational science, focusing on high-dimensional data analysis, signal processing, and numerical algorithms. Research Interests: His work centers on computational harmonic analysis, mathematical data science, and algorithms for large-scale data. Key areas include sparse spectral methods, phase retrieval, tensor decomposition, compressive sensing, sublinear-time algorithms, and Johnson-Lindenstrauss embeddings. He develops provably accurate and efficient algorithms for problems in high dimensions, such as solving PDEs, function approximation, and manifold learning. Publications and Trends: His recent publications (2023–2025) emphasize sparse and low-rank tensor methods, phase retrieval, and high-dimensional PDE solvers. There is a strong focus on algorithmic efficiency, provable guarantees, and applications in imaging and data science. Many works involve collaborative efforts and include open-source code. Scientific Awards: Scholarship established in honor of Benjamin (Ben) Segal, reflecting his mentorship and academic impact Advising and Grants: He advises numerous students, including undergraduates and graduate researchers, many of whom are co-authors on publications and code repositories. While specific grants are not listed, his sustained research output and collaborative projects suggest active funding. He co-founded the One World MINDS Seminar, demonstrating leadership in the academic community. Labs and Teams: He leads a research group focused on algorithm development for high-dimensional problems, with strong ties to computational mathematics and data science. His team develops and shares code for sparse FFTs, phase retrieval, tensor methods, and PDE solvers, indicating a collaborative and open research culture.

Jenni Nikuمشاهده پروفایل

مدرس ارشد

• Multivariate Statistical Modeling
• Latent Variable Models
• Statistical Method Development
• +۳ مورد دیگر

Jenni Niku is a Senior Lecturer at the Department of Mathematics and Statistics, University of Jyväskylä, specializing in multivariate statistical modeling and computational methods for ecological data. She is actively involved in the Predictive Community Ecology Group and leads research projects on latent variable models for complex ecological structures. Research Focus: Latent variable models for community ecology Spatial-temporal pattern analysis Statistical methodology for multivariate data Joint species distribution modeling Computational tools for ecological datasets Recent Publications: Jenni has contributed to advancements in analyzing compositional count data, fungal dispersal dynamics, chronic disease modeling, and peatland restoration. Her work bridges statistical innovation with ecological applications, particularly in handling high-dimensional data. Collaborations: She works extensively with researchers from Biological and Environmental Science departments, focusing on interdisciplinary applications of her statistical models.

• Image processing
• Inverse problems
• Radio interferometric imaging
• +۷ مورد دیگر

Audrey Repetti is an Associate Professor in the Department of Actuarial Mathematics and Statistics within the School of Mathematical and Computer Sciences at Heriot-Watt University in Edinburgh, UK. She also holds a dual affiliation with the Institute of Sensors, Signals, and Systems in the School of Engineering and Physical Sciences, and is part of the Maxwell Institute for Mathematical Sciences - Edinburgh. Her research spans mathematical imaging, optimization, and computational methods with applications across astronomy, medical imaging, and optical engineering. Dr. Repetti's research focuses on developing advanced mathematical frameworks for solving imaging inverse problems. Her work centers on optimization algorithms, Bayesian uncertainty quantification, and the integration of machine learning with traditional mathematical approaches. She has made significant contributions to radio interferometric imaging, computational optical imaging with photonic lanterns, and uncertainty quantification in medical imaging. Her research bridges theoretical mathematics with practical applications in astronomy, healthcare, and engineering. Analysis of her recent publications reveals a clear trajectory toward integrating traditional mathematical imaging approaches with modern machine learning techniques. Her work increasingly focuses on 'hybrid' methodologies that combine data-driven models with optimization frameworks. Key themes include plug-and-play algorithms, uncertainty quantification in imaging, and the development of efficient computational methods for high-dimensional inverse problems. Her research demonstrates strong interdisciplinary connections between mathematics, signal processing, astronomy, and medical imaging. Dr. Repetti is actively involved in academic service, including co-organizing the 2026 ICMS Workshop on Imaging inverse problems and generating models. She has received research funding supporting her work in computational imaging and inverse problems, though specific grant details aren't listed in the provided materials. Her teaching portfolio includes advanced courses in scalable inference, deep learning, and statistics for sciences. She leads several research projects with associated software toolboxes including BUQO (Bayesian Uncertainty Quantification by Optimization), SARA-COIL (Compressive optical imaging with a photonic lantern), and CALIM (Self direction-dependent effect calibration and imaging in radio-interferometry). These projects demonstrate her commitment to developing practical computational tools that advance both theoretical understanding and real-world applications in imaging science.

Kartic Subrمشاهده پروفایل

مدرس ارشد

• Computer Graphics
• Monte Carlo Rendering
• Physics-based Simulation
• +۱۳ مورد دیگر

Kartic Subr is a Senior Lecturer (equivalent to Associate Professor) in the School of Informatics at the University of Edinburgh, where he conducts research at the intersection of computer graphics, machine learning, and robotics. He is affiliated with the Institute of Perception, Action and Behaviour and leads an active research group in physics-aware simulation and AI for scientific discovery. His research interests span Computer Graphics , Monte Carlo Rendering , Physics-based Simulation , Machine Learning , Robotics , and Computational Biology . He has made significant contributions to rendering theory, light transport analysis, BRDF modeling, and simulation of fluids and fractures. Recently, his group has applied deep learning to protein design, achieving notable success in international competitions. The recent publications reflect a strong trend toward interdisciplinary research, combining computer graphics with AI and biology. Key themes include explainable AI for physics-aware interfaces (e.g., CueTip), neural rendering (NeRF), protein design with CNNs, and reward learning in robotics. His group regularly publishes in top venues such as SIGGRAPH, NeurIPS, and IEEE RA-L. Royal Society University Research Fellowship (2014–2024) Lead, GCRF Impact Accelerator (2019) Lead, EPSRC First Grant (2017) Royal Society Challenge Grant (2016) Royal Society Newton International Fellow (2010) Marie-Curie Visitor (2006) Best Paper Honorable Mention, I3D 2012 Best Paper, I3D 2011 Kartic Subr actively supervises PhD students and postdocs, including Leonardo Castorina, Zhiyuan Zhang, and Kevin Denamganaï. Former students have gone on to positions at DeepMind, University of Edinburgh, and industry. He has secured multiple grants, including from the Royal Society and EPSRC. His research group collaborates across disciplines, particularly with biological sciences on protein design. He leads projects on approximate physics for robotics, protein design via deep learning, and interactive rendering systems. The group has developed frameworks like TIMED for de novo protein design and tools for explainable physics-aware assistants.

• Bayesian imaging sciences
• Statistical foundations of quantitative imaging
• Uncertainty quantification in imaging inverse problems
• +۶ مورد دیگر

Marcelo Pereyra is a Professor in Statistics at the School of Mathematical & Computer Sciences of Heriot-Watt University and the Maxwell Institute for Mathematical Sciences in Edinburgh, UK. His academic journey began with a double M.Eng. degree from ITBA (Argentina) and INSA Toulouse (France), followed by a M.Sc. from INSA Toulouse in 2009. He earned his Ph.D. in Signal Processing from the University of Toulouse in 2012, after which he served as a Research Fellow in Statistics at the University of Bristol from 2012 to 2016. In 2017, he joined Heriot-Watt University as an Assistant Professor in Statistics, was promoted to Associate Professor in 2019, and subsequently to Professor in Statistics in 2023. His educational background includes: Ph.D. in Signal Processing, University of Toulouse (2012) M.Eng. (double degree) from ITBA (Argentina) and INSA Toulouse (France), with M.Sc. from INSA Toulouse (2009) Professor Pereyra's research advances the statistical foundations of quantitative and scientific imaging. He has made important contributions to Bayesian imaging sciences and developed significant connections between statistical, variational, and machine learning approaches to imaging. His specific interests include robust uncertainty quantification in imaging inverse problems, automatic calibration and verification of statistical image models, scalable Bayesian computation algorithms derived from stochastic diffusion processes, and applications of imaging with high social or environmental value. His work sits at the intersection of statistics, computational mathematics, and imaging science, with a strong emphasis on developing mathematically rigorous methods that provide reliable uncertainty quantification alongside point estimates. His recent publications demonstrate a clear trajectory toward integrating modern machine learning techniques, particularly diffusion models and generative approaches, with traditional Bayesian statistical methods for imaging problems. The research spans applications from medical imaging to astronomical observations and industrial inspection, with consistent emphasis on uncertainty quantification. His work increasingly focuses on developing scalable computational methods that can handle the high-dimensional nature of modern imaging problems while maintaining statistical rigor. Professor Pereyra has received numerous prestigious awards throughout his career: SIAM SIGEST Award in Imaging Sciences for contributions to proximal Markov chain Monte Carlo methodology Marie Curie Intra-European Fellowship for Career Development (2013) Brunel Postdoctoral Research Fellowship in Statistics (2012) Postdoctoral Research Fellowship from French Ministry of Defence (2012) Leopold Escande PhD Thesis award from the University of Toulouse (2012) INFOTEL R&D award from the Association of Engineers of INSA Toulouse (2009) ITBA R&D award from the Buenos Aires Institute of Technology (2007) Professor Pereyra is deeply committed to developing early career talent, currently supervising five PhD students and two Postdoctoral Research Associates (PDRAs), having previously supervised four PhD students and three PDRAs to completion. His research has received significant support from Heriot-Watt University and the UK Engineering and Physical Sciences Research Council (EPSRC). He is known for fostering multidisciplinary collaboration, having organized eleven international interdisciplinary research meetings in the UK since 2012 and chaired the IMA Conference on Inverse Problems in Edinburgh (2022). As a leader in his field, Professor Pereyra has held Invited Professor positions at prestigious institutions including Institut Henri Poincaré (Paris, 2019), Ecole Normale Supérieure Lyon (2023), and Université Paris Cité (2024). He frequently delivers invited talks at leading mathematical centers worldwide (CIRM, BIRS, IHP, Flatiron, Hausdorff School, INI, and ICMS) to promote multidisciplinary collaboration in imaging sciences.

Ahmed El-Sayedمشاهده پروفایل

دانشیار

• Robotics
• Artificial Intelligence
• Fuzzy Systems
• +۴ مورد دیگر

Dr. Ahmed El-Sayed is an Associate Professor in the Department of Electrical and Computer Engineering at the School of Engineering, University of Bridgeport. He holds a Ph.D. in Computer Science and Engineering (2016) and M.Sc. degrees in Computer Engineering (2011) and Engineering Mathematics (2006). Ph.D. in Computer Science and Engineering, University of Bridgeport (2016) M.Sc. in Computer Engineering, University of Bridgeport (2011) M.Sc. in Engineering Mathematics, Alexandria University (2006) B.Sc. in Electrical Engineering, Alexandria University (2003) His academic work focuses on robotics , artificial intelligence , and computer vision , with specific emphasis on fuzzy systems , machine learning , and soft computing . His research spans theoretical developments and practical implementations in autonomous systems , medical imaging , and industrial automation . The 15 most recent publications highlight his contributions to generative models , medical diagnostics , disassembly sequencing , and high-dimensional feature analysis . These works intersect disciplines such as robotics , machine learning , and sustainable manufacturing . Professional Memberships: IEEE ASEE Phi Kappa Phi Upsilon Pi Epsilon Egyptian Engineering Syndicate (lifetime member) He teaches courses including Embedded Systems Design , Computer Vision , and Introduction to Autonomous Vehicles .

Ahmet Coskunمشاهده پروفایل

استادیار

• Spatial Biology
• Single Cell Biotechnology
• Spatial Omics
• +۹ مورد دیگر

Dr. Ahmet Coskun is an Assistant Professor of Biomedical Engineering at Georgia Institute of Technology and Emory University, where he holds the Bernie-Marcus Early-Career Professorship. He directs the Single Cell Biotechnology and Spatial Omics Laboratory, an interdisciplinary program focused on multiparameter imaging of single cells within their spatial context. His work bridges the fields of bioengineering, computational biology, and systems biology to address fundamental challenges in cancers, immunology, and pediatric diseases. Dr. Coskun received his PhD from the University of California, Los Angeles (UCLA) working with Aydogan Ozcan. He completed postdoctoral training at the California Institute of Technology with Long Cai and served as an Instructor at Stanford University with Garry Nolan. His educational background has provided him with a strong foundation in both engineering principles and biological systems. Dr. Coskun's research lies at the nexus of multiplex bioimaging, microfluidic biodynamics, and big data biocomputation. His laboratory pursues three main research thrusts: spatial genomics (using seqFISH and correlation FISH methods), spatial proteomics (using CODEX technology combined with super-resolution imaging), and spatial metabolomics (using computational and isotope barcoding approaches with MIBI). His team develops machine learning algorithms to analyze the resulting high-dimensional imaging datasets, creating image-based 'omic technologies to reveal the spatial nature of biological systems. Their work has significant implications for understanding therapeutic response variability and cellular organization in health and disease. NSF CAREER Award 2024 NIH R35 MIRA Award 2023 BMES-CMBE Rising Star Award 2023 American Lung Association Innovation Award 2022 Student Recognition of Excellence in Teaching: Class of 1934 CIOS Award NIH K25 Award Burroughs Wellcome Fund CASI Award Dr. Coskun leads an interdisciplinary research team comprising PhD students from Bioengineering, Electrical and Computer Engineering, Mechanical Engineering, and Biomedical Engineering programs. His lab has been supported by numerous federal and private grants, including funding from multiple NIH institutes (NIA, NIAID, NCI, NIDCR, OD, and ORIP), Wellcome LEAP, Burroughs Wellcome Fund, NSF CMaT, American Cancer Society IRG, Multi-cellular engineered living systems (M-CELS), and Regenerative Medicine Center. In addition to his research, Dr. Coskun leads outreach programs through BioCrowd Studio, which engages K12 and undergraduate students through interactive virtual media and distributed biokits. The Single Cell Biotechnology and Spatial Omics Laboratory is strategically positioned at the forefront of spatial biology research. The lab benefits from advanced technologies including super-resolution microscopy, imaging mass spectrometry, combinatorial molecular barcoding, and machine learning to enhance the information capacity of cellular data. The team's innovative approaches to spatial multi-omics profiling have positioned them as leaders in understanding cellular heterogeneity and organization within tissues.

Visa Koivunenمشاهده پروفایل

استاد

• Statistical Signal Processing
• Wireless Communications
• Multisensor Systems
• +۴ مورد دیگر

Visa Koivunen is a Distinguished Professor of Signal Processing at Aalto University (since 1999), with positions as Academy Professor (2010) and Aalto Distinguished Professor (2020). He holds an honorary D.Sc. (Tech.) from the University of Oulu and has held visiting roles at Princeton University, the University of Pennsylvania, and EPFL. His research focuses on statistical signal processing, wireless communications, radar systems, and integrated sensing and communications (ISAC). He has published over 490 papers, including award-winning works, and advised 31 doctoral theses. Key roles include leadership in conferences (e.g., Asilomar 2018 General Chair) and technical committees (IEEE SPS). Recognitions include the EURASIP Technical Achievement Award (2015), IEEE Signal Processing Society Best Paper Awards (2007, 2017), and EURASIP Fellow status (2020). He co-chairs NATO panels on cognitive radars and ISAC. His research interests span signal processing fundamentals and applications in radar, communications, and machine learning. Recent work emphasizes ISAC, reinforcement learning for resource allocation, and causal inference in federated systems. He has pioneered waveform design techniques using GANs and Bayesian methods for spatial signal analysis. Educations: D.Sc. (Tech.) with honors from the University of Oulu (1994), Primus Doctor Award (1989-1994). Awards: IEEE Fellow, EURASIP Fellow, Member of Academia Europaea. Service: Associate Editor for IEEE Transactions, Chair of IEEE SPAWC and Asilomar conferences. His work bridges theory and practice, addressing challenges in radar-communication coexistence, energy-efficient edge computing, and secure distributed inference. He has delivered over 50 invited talks globally and actively contributes to NATO initiatives on cognitive radar systems.