جست‌وجوی استاد و پژوهشگر

James Smithمشاهده پروفایل

دانشیار

• Male Reproductive Health
• Fertility Preservation
• Urology
• +۳ مورد دیگر

Dr. James Smith is an Associate Professor at the UCSF School of Medicine and a urologist at the University of California San Francisco (UCSF) Center for Reproductive Health. As Director of the Male Reproductive Health Center, he specializes in fertility preservation counseling, sperm retrieval techniques, microsurgical vasectomy reversal, and management of male reproductive disorders like Peyronie's disease and hypogonadism. UCSF School of Medicine graduate (MD/MS 2002) Completed surgery/urology residency at University of Utah (2007) Board-certified by the American Board of Urology His research focuses on advancing male reproductive biology understanding through clinical studies on semen analysis methodologies, fertility preservation strategies, and biomarker development for conditions like prostate cancer. Recent publications examine mail-in fertility testing compliance, sperm cryopreservation techniques, and environmental impacts on reproductive health. Dr. Smith maintains professional affiliations with six key medical societies including the American Urological Association and Sexual Medicine Society of North America. As a clinical innovator, he advocates for expanded access to fertility care through remote testing solutions while balancing clinical practice with research leadership at UCSF's pioneering reproductive health program.

Alexander Korotinمشاهده پروفایل

استادیار

• Generative Modeling
• Unpaired Learning
• Optimal Transport
• +۲ مورد دیگر

Alexander Korotin is an Assistant Professor at the Skolkovo Institute of Science and Technology (Skoltech) where he heads the Generative AI research group. He is also a senior research scientist at the Artificial Intelligence Research Institute (AIRI), leading the "Foundations of Generative AI" group. His academic journey includes a PhD in Math & Physics from Skoltech (2023), an MSc in Computer Science from the Higher School of Economics (HSE), and a BSc in Mathematics also from HSE. Dr. Korotin's research focuses on generative modeling, with particular emphasis on developing novel algorithms based on Optimal Transport and Schrodinger Bridges. His work bridges theoretical mathematics with practical machine learning applications, contributing significantly to the field of generative artificial intelligence. He has pioneered approaches to make Schrodinger Bridge solvers more efficient and practical, most notably with his "Light Schrödinger Bridge" framework that simplifies complex computational procedures while maintaining theoretical rigor. His publication record shows a clear progression toward making advanced generative modeling techniques more accessible and computationally efficient. Recent work demonstrates increasing sophistication in handling complex distribution matching problems through physics-inspired approaches (like electrostatic field matching) and novel distillation techniques that accelerate inference. The research spans from theoretical foundations to practical applications in image processing, semi-supervised learning, and reinforcement learning. Dr. Korotin has received recognition for his contributions to neural optimal transport and Schrodinger Bridges, with his papers frequently appearing in premier machine learning venues. His work on efficient computational methods for optimal transport has established him as a rising expert in these specialized areas of machine learning. As an academic leader, Dr. Korotin advises research students and collaborates extensively with colleagues across institutions, contributing to the advancement of generative AI through both theoretical developments and practical implementations. His work continues to push the boundaries of what's possible in generative modeling, with recent publications focusing on making advanced mathematical approaches more computationally efficient for real-world AI applications.

• Environmental Fluid Mechanics
• Wildfire Propagation Modeling
• Computational Wind Dynamics
• +۵ مورد دیگر

Professor Rob Stoll at the University of Utah's Mechanical Engineering Department specializes in environmental fluid dynamics, wildfire modeling, and urban/agricultural canopy flows. His work bridges computational modeling with field measurements across diverse domains. Develops GPU-accelerated QES simulation tools Focuses on wildfire-atmosphere coupling Advances low-cost sensor networks for environmental monitoring Research Trends revealed through 15 recent publications include: Wildfire propagation under heterogeneous terrain/fuel 3D wind modeling in vineyards and urban environments Particle transport in sparse and row-organized canopies Turbulence characterization in stable boundary layers Open-source hardware for atmospheric sensing Scientific Contributions 2008: Best PhD Dissertation (University of Minnesota) 2017-2022: NSF Grant for wildfire modeling 2020-2025: Systems approach for carrot blight management 2023-2026: Vineyard smoke exposure modeling grants Teaching Activities include graduate courses in Numerical Methods for Engineering Systems and advising PhD/Master's theses since 2015.

• Geometric and probabilistic functional analysis
• Random matrices, random graphs, and random tensors
• Free probability and high dimensional phenomena
• +۱۰ مورد دیگر

Djalil CHAFAÏ is a University Professor of Mathematics at Université Paris-Dauphine - PSL, with dual affiliation at CEREMADE (Centre de Recherche en Mathématiques de la Décision) and DMA (Département de Mathématiques et Applications) at École normale supérieure (Paris) - PSL. He currently serves as Directeur des études du DMA (2021-2026) and Directeur scientifique du RNBM (2021-2025). His extensive research spans multiple areas of probability theory, mathematical physics, and applied mathematics. CHAFAÏ's research interests center around geometric and probabilistic functional analysis, random matrices, random graphs, free probability, and high-dimensional phenomena. His work connects mathematical theory with applications in biology, physics, and data science. He has made significant contributions to understanding cutoff phenomena in high-dimensional diffusions, Riesz energy problems, and Coulomb gases. His research often combines theoretical analysis with visual illustrations created using computational tools like Octave, Python, and Julia. Analysis of his recent publications reveals a strong focus on cutoff phenomena in various stochastic processes, equilibrium measures in potential theory, and the mathematical properties of random matrix ensembles. His work demonstrates a consistent pattern of bridging abstract mathematical concepts with concrete physical phenomena, particularly in statistical physics. The interdisciplinary nature of his research is evident in the diverse applications ranging from mathematical biology to data science. CHAFAÏ has successfully advised numerous doctoral students whose work continues to influence the field. His current doctoral students include Samuel Chan-ashing, Rémi Bonnin, and Kewei Pan, while his former students have gone on to positions at prestigious institutions worldwide. He is actively involved in the mathematical community through organizing conferences, seminars, and workshops, including the Matrices Et Graphes Aléatoires (MEGA) project and the Conviviality project funded by ANR.

Yoann Le Hénaffمشاهده پروفایل

پژوهشگر

• Numerical analysis
• Mathematical physics
• Partial differential equations
• +۲ مورد دیگر

Yoann Le Hénaff is a postdoctoral researcher at the Numerical Analysis Department of the University of Tübingen, Germany. He works within the CRC TRR 352's B4 project and collaborates with C. Lubich's group. His research focuses on computational mathematics and physical modeling through numerical analysis. Education: PhD in Mathematics (2024), University of Rennes, France Master's degree in Mathematics, University of Rennes, France Engineering degree in applied mathematics, INSA Rennes, France Research Interests: Specializing in partial differential equations and numerical simulations, Le Hénaff develops grid-free particle methods for the Vlasov-Poisson system and studies modulated soliton solutions for nonlinear Schrödinger equations. Key subfields include spectral concentration problems, Dirac-Frenkel variational principles, and computational complexity optimization. Publication Trends: Recent work demonstrates expertise in hybrid numerical methods combining classical PDE approaches with modern computational techniques. 2023-2025 publications show progression from particle method convergence proofs to advanced soliton-based algorithms and spectral concentration generalizations across multiple dimensions. Collaborations: Actively involved in the CRC TRR 352 consortium, participating in annual retreats and contributing to the B4 project's objectives. Regularly presents at international seminars including the NAG seminar in Tübingen and IRMAR doctoral meetings in Rennes.

Jeremy Hoskinsمشاهده پروفایل

استادیار

• Numerical Analysis
• Fast Algorithms
• Quantum Optics
• +۴ مورد دیگر

Jeremy Hoskins is an Assistant Professor in the Department of Statistics at the University of Chicago. His research focuses on fast algorithms and numerical analysis with diverse applications including quantum optics, lens design, glaciology, computational biology, and inverse problems. Research Interests: Specializing in Numerical Analysis and Fast Algorithms , he applies computational methods to cross-disciplinary challenges in Quantum Optics , Lens Design , Glaciology , and Computational Biology . His work addresses Inverse Problems requiring high-performance computing solutions. Contact: Email: jeremyhoskins@uchicago.edu Institution: University of Chicago Department: Department of Statistics

• Data Analytics
• Causal Machine Learning
• Policy Evaluation
• +۷ مورد دیگر

Anthony Strittmatter is a Professor of Applied Econometrics at UniDistance Suisse in Brig/Valais. His research integrates causal machine learning and microeconometric policy evaluation to analyze experimental and non-experimental data in labor economics, business economics, and health economics. He is affiliated with the Institut des Politiques Publiques (IPP) , Swiss Institute for Empirical Economic Research (SEW-HSG) , and the CESifo Network . Education : Diploma in Economics (2009, Albert-Ludwigs University Freiburg); Ph.D. in Economics and Finance (2013, University of St. Gallen) under Prof. Bernd Fitzenberger and Prof. Michael Lechner. His research focuses on heterogeneous policy effects , optimal allocation rules , and causal machine learning applications. Recent work includes studies on gender pay gaps using big data, labor market sorting , and ramadan fasting’s impact on terrorism . Articles often employ Lasso-type estimators , changes-in-changes methodologies , and empirical Monte Carlo simulations . Current projects involve training voucher allocation , decision-making speed in professional chess , and causal effects of public health care systems on economic development. His GitHub repositories include educational materials for machine learning in economics and econometrics.

Dong Anمشاهده پروفایل

استادیار

• Quantum Computing
• Applied Mathematics
• Quantum Algorithms
• +۳ مورد دیگر

Dong An is an Assistant Professor at the Beijing International Center for Mathematical Research (BICMR) , Peking University, since September 2024. He previously held a QuICS Hartree Postdoctoral Fellowship at the University of Maryland (2021-2024). He earned his Ph.D. in Applied Mathematics (2021) from UC Berkeley under Professor Lin Lin and a B.S. in Computational Mathematics (2016) from Peking University. Research Focus: Quantum algorithms for classical scientific computing problems and classical algorithms for quantum many-body problems. Key Areas: Quantum simulation, Hamiltonian dynamics, quantum linear systems, differential equations, and adiabatic quantum computing. Publications highlight advancements in quantum differential equation solvers, linear combinations of Hamiltonian simulations, and multi-product methods with explicit commutator scaling. His work bridges theoretical quantum computing and practical scientific computation, emphasizing optimal parameter dependence and error bounds. Scientific Awards: Co-Principal Investigator of NSF Robust Quantum Simulation Seed Grant QuICS Hartree Fellowship Bernard Friedman Memorial Prize in Applied Mathematics Multiple honors from Peking University Teaching: Currently instructs graduate-level quantum algorithms courses at Peking University. Previously served as a teaching assistant for differential equations and calculus at UC Berkeley. Service: Organizer for workshops on quantum differential equations and SIAM special sessions. Active reviewer for journals like PRX Quantum and conferences including QIP and TQC.

Hiroshi Ozawaمشاهده پروفایل

استادیار

• Hypersonic Aerothermodynamics
• Optical Flow Diagnostics
• Shock Wave Interactions
• +۳ مورد دیگر

Hiroshi Ozawa is an Assistant Professor in the Department of Mechanical Engineering at the University of Hawaii. His research focuses on hypersonic aerothermodynamics, optical flow diagnostics, and shock wave boundary layer interactions. He utilizes advanced experimental techniques like temperature-sensitive paint for flow visualization and heat transfer analysis in high-speed environments. University: University of Hawaii Department: Mechanical Engineering Academic Rank: Assistant Professor Email: hozawa9@hawaii.edu His work addresses transient flow phenomena in supersonic and hypersonic regimes, with particular emphasis on boundary layer transition and combustion dynamics. Recent publications highlight applications of fast-response diagnostics in Mach 5.2 flows and shock tube experiments. Research interests span non-stationary high-speed aerothermodynamics, transient optical diagnostics, and supersonic combustion processes. He contributes to academic discourse through experimental studies on shock interactions and laminar-turbulent transition mechanisms.

John S. Lyonsمشاهده پروفایل

استاد

• Child and Adolescent Mental Health
• Public Mental Health Systems
• Child Welfare Policy
• +۴ مورد دیگر

Dr. John S. Lyons is a Professor of Health Management and Policy and Director of the Center for Innovation in Population Health at the University of Kentucky College of Public Health. He is a leading expert in the intersection of mental health, child welfare, education, and justice systems. Role: Director, Center for Innovation in Population Health Academic Rank: Professor Key Focus Areas: Mental health systems, child welfare policy, trauma-informed care, and outcome assessment Dr. Lyons specializes in developing evidence-based assessments such as the Child and Adolescent Needs and Strengths (CANS), Family Advocacy and Support Tool (FAST), and Adult Needs and Strengths Assessment (ANSA). His research emphasizes resilience factors, trauma impact, and systemic integration of mental health practices. Recent publications highlight his work in trauma screening, telehealth implementation, and predictive modeling for behavioral health services. He actively promotes trauma-informed systems change and has contributed to national initiatives like the National Partnership for Child Safety (NPCS).

Mikhail Shapiroمشاهده پروفایل

استاد

• Biomedical Engineering
• Synthetic Biology
• Neuroscience
• +۳ مورد دیگر

Mikhail Shapiro is the Max Delbrück Professor of Chemical Engineering and Medical Engineering at the California Institute of Technology , and an Howard Hughes Medical Institute Investigator since 2021. His research laboratory develops acoustic and magnetic biomolecular tools for imaging and controlling cellular function in vivo. Chemical Engineering Faculty Medical Engineering Affiliation Howard Hughes Medical Institute Investigator Research focuses on noninvasive control of biological systems through: Acoustic reporter genes (ARG 2.0) for real-time tumor monitoring Ultrasound neuroimaging via acoustically transparent cranial windows Gas vesicle engineering for cellular manipulation Microrobotics with acoustic hydrogels His 2024-2025 publications demonstrate breakthroughs in: Nonlinear ultrasound for deep-tissue visualization Acoustic percolation switches for targeted drug delivery SEMPER systems for multi-gene expression control Scientific recognition includes: HHMI Investigator (2021-) Vilcek Foundation Prize in Biomedical Innovation Roger Tsien Award for Chemical Biology Packard Fellowship in Science and Engineering

Shiv Chandrasekaranمشاهده پروفایل

استاد

• Numerical Analysis
• Numerical Linear Algebra
• Structured Matrix Decomposition
• +۳ مورد دیگر

Shiv Chandrasekaran is a Professor and Vice Chair in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara, with additional affiliation to the Department of Computer Science. He leads the Scientific Computing Group (SCG), focusing on advanced numerical algorithms for structured matrices and differential equations. His office is located in Harold Frank Hall. Education: He holds a Ph.D. in Numerical Analysis from Yale University. Research Interests: Professor Chandrasekaran's work spans numerical linear algebra, structured matrix decompositions, and efficient solvers for integral/differential equations. Key areas include: Development of fast numerical algorithms High-order discretization methods Applications in computational physics and machine learning Publications: His recent articles (2015–2018) emphasize scalable methods for interpolation, clustering, and PDE solutions, reflecting a consistent focus on optimizing computational efficiency for large-scale mathematical problems. Student Advising: He mentors doctoral candidates in computational fields. Current advisees include Vamshi Chowdary (computer vision) and Ethan Epperly (structured matrices). Alumni hold positions at institutions like KU Leuven, Apple, and Qualcomm.

Andreas Paul Eberhard Kloecknerمشاهده پروفایل

دانشیار

• Scientific Computing
• Parallel Computing
• Numerical Techniques
• +۷ مورد دیگر

Andreas Paul Eberhard Kloeckner is an Associate Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), where he has been serving since 2013 (promoted to Associate Professor in 2019). He also holds an affiliate faculty appointment in the Department of Electrical and Computer Engineering since 2016. His academic journey includes a PhD in Applied Mathematics from Brown University (2010), an MSc from Brown University (2006), and a Diplom in Applied Mathematics from Universität Karlsruhe (2005). Prior to joining UIUC, he was a Courant Instructor at the Courant Institute of Mathematical Sciences at New York University. Dr. Kloeckner's research focuses on high-order accurate integral equation methods, fast algorithms for elliptic boundary value problems, and code transformation for high-performance scientific computing. His work bridges mathematical theory with practical implementation, with particular emphasis on GPU computing and parallel architectures. He has made significant contributions to the development of open-source scientific software, most notably PyCUDA and PyOpenCL, which have become widely used tools in the scientific computing community. His publication record shows a consistent focus on advancing numerical methods for scientific computing, with recent work emphasizing code generation techniques, fast integral equation solvers, and optimization of algorithms for modern hardware architectures. The trajectory of his publications reveals a progression from foundational work on GPU-based discontinuous Galerkin methods toward increasingly sophisticated approaches for integral equation methods and automatic code generation. Among his notable recognitions is the 2017 National Science Foundation CAREER Award, which supports his work on general-purpose, high-order integral equation methods for computer simulation in engineering. His research has been published in prestigious venues including SIAM Journal on Scientific Computing and has influenced both academic research and practical applications in scientific computing. Dr. Kloeckner has advised numerous graduate students through completion of their PhD and MS degrees, with alumni moving to positions at Apple, NVIDIA, Rice University, and other leading institutions. His research group maintains an active portfolio of open-source software projects that advance the state of scientific computing infrastructure.

Louis Morge-Rolletمشاهده پروفایل

مدرس

• Radiofrequency
• Signal Processing
• Artificial Intelligence
• +۴ مورد دیگر

Louis Morge-Rollet is a Lecturer at Grenoble INP's Esisar school. He holds a PhD in Radio Fingerprint Authentication for IoT (defended September 2023) and served as a Temporary Teaching and Research Assistant at UBO Brest (seconded to ENSTA Bretagne) from October 2022 to August 2024. PhD from ENSTA Bretagne (2019-2023) Engineering degree from Grenoble INP – Esisar Specialization in Digital Communications Systems His research focuses on radiofrequency signal analysis under embedded constraints , with emphasis on: Signal detection and communication standard identification Radio fingerprint authentication for IoT devices Drone signal detection and classification Embedded implementation on CPU, GPU, and FPGA platforms Statistical fingerprinting techniques Micro-Doppler sensing in radio communications Recent publications cover: Dynamic calibration of PVDF gauges (2024) Intrusion detection system characterization (2023) Multiple RF fingerprinting approaches (2022) Cognitive radio detection methods (2022) Micro-Doppler modeling (2022) Laboratory affiliations: LCIS (Laboratoire de Conception et d'Intégration des Systèmes) ENSTA Bretagne UBO Brest

• Adaptive Signal Processing
• Wireless Communication Systems
• CDMA Technologies
• +۳ مورد دیگر

Prof. Dr. Aykut Hocanin is a faculty member in the Department of Electrical and Electronics Engineering at Eastern Mediterranean University (EMU). He holds a PhD in Electrical and Electronics Engineering from Bogazici University, an MS in Electrical Engineering from Texas A&M University, and a BS in Electrical and Computer Engineering from Rice University. PhD: 1994-2000, Bogazici University MS: 1992-1993, Texas A&M University BS: 1988-1992, Rice University Research Interests: Dr. Hocanin specializes in adaptive signal processing algorithms, wireless communication systems, and sparse system identification. His work focuses on developing efficient algorithms for impulsive noise environments and improving CDMA system performance through robust detection and interference cancellation techniques. He has contributed significantly to adaptive filtering methods, including recursive inverse algorithms and variable step-size LMS approaches. Scientific Contributions: His recent research (2020-2024) includes fast quasi-Newton adaptive algorithms and data-reuse extended NLMS techniques. Earlier work (2011-2015) explored entropy-based subspace separation, 2D recursive inverse filtering, and mobility modeling in wireless networks. He has supervised numerous graduate theses on these topics. Professional Role: As a professor at EMU, Dr. Hocanin teaches courses like INFE362 and EENG461 while maintaining active research in digital signal processing and wireless communication. He serves as an associate editor and reviewer for academic journals and conferences.