جست‌وجوی استاد و پژوهشگر

David W. Inglisمشاهده پروفایل

دانشیار

• Microfluidics
• Biomedical Engineering
• Cell Separation
• +۴ مورد دیگر

Dr. David W. Inglis is an Associate Professor in the School of Engineering at Macquarie University, Sydney. He holds a PhD in Electrical Engineering from Princeton University (2007) and a B.Sc. in Engineering Physics from the University of Alberta, Canada (2001). His research focuses on microfluidic technologies for biomedical applications, including cell separation, nanofluidics, and device fabrication. He is a founding member of the Biomedical Microdevices Group (bmmd) and affiliated with the ARC Centre of Excellence for Nanoscale BioPhotonics and MQ Photonics. Key research areas include deterministic lateral displacement (DLD) for particle sorting, viscoelastic microfluidics, and optical biosensing. His work spans engineering, biology, and medicine, with applications in healthcare diagnostics and biotechnology. Dr. Inglis teaches courses on mechatronics and advanced engineering systems at Macquarie. Recent publications highlight advancements in microfluidic device design, cell behavior analysis under fluidic conditions, and biomedical applications of microfabrication. His contributions include optimizing separation efficiencies and exploring novel methods for detecting biomarkers and contaminants in complex fluids.

Wenyuan Tangمشاهده پروفایل

دانشیار

• Power Systems
• Electricity Markets
• Data Analytics
• +۵ مورد دیگر

Wenyuan Tang is an Associate Professor in the Department of Electrical and Computer Engineering at North Carolina State University. His research focuses on electricity markets, data analytics, machine learning, and optimization for power systems. He holds a Ph.D. in Electrical Engineering from the University of Southern California and completed postdoctoral work at UC Berkeley and Stanford. Education: B.Eng. in Electrical Engineering, Tsinghua University (2008) M.S. Electrical Engineering & M.A. Applied Mathematics, University of Southern California (2010-2014) Ph.D. Electrical Engineering, University of Southern California (2015) Research interests include power systems resilience, renewable energy integration, and microgrid management. Recent work emphasizes probabilistic forecasting, battery storage optimization, and multi-energy system coordination. He has received the NSF CAREER Award (2022), R. Ray Bennett Faculty Fellow Award (2021), and Goodnight Early Career Innovators Award (2023). Awards: NSF CAREER Award (2022) R. Ray Bennett Faculty Fellow Award (2021) Goodnight Early Career Innovators Award (2023) His work includes leadership in IEEE editorial roles and collaborations on projects like the DOE Solar Forecasting Prize. Research emphasizes bridging optimization theory with practical grid operations, focusing on resilience, efficiency, and sustainability.

• Data Compression
• Indexing Data Structures
• Algorithms
• +۷ مورد دیگر

Giovanni Manzini is a Professor of Computer Science at the University of Pisa's Department of Computer Science. He holds a PhD in Mathematics from the Scuola Normale Superiore di Pisa and has held visiting roles at MIT, Johns Hopkins University, and the University of Melbourne. Previously, he served as Assistant Professor at the University of Torino and Associate Professor at the University of Eastern Piedmont. His research focuses on designing algorithms and data structures for data compression, indexing massive datasets, and computational biology. Notable contributions include advancements in the Burrows-Wheeler Transform (BWT), grammar-based compression, and matrix operations optimization. He received the ACM Paris Kanellakis Theory and Practice Award (2022) and the 2023 Test-of-Time Award from the European Symposium on Algorithms. His recent work explores two-dimensional string repetitiveness, BWT-based indexing, and green computing techniques for matrix operations. Manzini has contributed to over 100 conference program committees since 2020, including DCC, ALENEX, and SPIRE. His teaching spans courses on algorithms, programming languages, and numerical analysis. Current courses include Algorithms and Data Structures for Data-intensive Applications and an undergraduate laboratory course on programming fundamentals. He leads software projects such as MMRepair (grammar-compressed matrix multiplication), BigBWT (BWT construction via prefix-free parsing), and EZcount (microRNA quantification tools). He also mentors the Scuola Ortogonale under the Elicsir Foundation, promoting STEM education.

Simon Foucartمشاهده پروفایل

استاد

• Mathematical Data Science
• Compressive Sensing
• Approximation Theory
• +۵ مورد دیگر

Simon Foucart is a Professor in the Department of Mathematics at Texas A&M University, affiliated with the College of Arts & Sciences, and serves as Associate Director of the Institute of Data Science. He holds a PhD from the University of Cambridge (2005) and prior degrees from Ecole Centrale Paris and the University of Cambridge's Part III Math Tripos. His research focuses on Mathematical Data Science, Compressive Sensing, and Approximation Theory, with applications in Engineering and Bioinformatics. Key roles include Editorial Board memberships for major journals like Journal of Approximation Theory and Constructive Approximation . He has supervised numerous PhD students and postdocs, including current advisee Thomas Winckelman. Notable awards include the Presidential Impact Fellow (2019) and Heilbronn Distinguished Visiting Fellow (2024). His publications span optimal recovery, sparse recovery algorithms, neural network theory, and computational frameworks. Recent work emphasizes algorithmic solutions for data science challenges, including low-rank matrix recovery and graph signal processing. His software contributions include MATLAB packages like MinProj and Basc for approximation and optimization tasks. Teaching includes graduate courses on Mathematical Data Science and approximation theory, supported by his textbook Mathematical Pictures at a Data Science Exhibition . He has held visiting positions at institutions such as the Isaac Newton Institute and Los Alamos National Laboratory.

Serap Savariمشاهده پروفایل

دانشیار

• Information Theory
• Data Compression
• Network Coding
• +۳ مورد دیگر

Serap Savari is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, College of Engineering. She holds a Ph.D. from the Massachusetts Institute of Technology (1996). Her research focuses on information theory, data compression, network coding, and computing/communication systems. Her work spans applications in semiconductor manufacturing analytics, image processing (SEM imaging), and network optimization. Her research includes advancements in image registration techniques, error analysis in superresolution imaging, and deep learning applications for denoising SEM images. She has contributed to compression algorithms for lithography systems and network coding methodologies for constrained networks. Savari’s work bridges theoretical foundations (e.g., fix-free codes, entropy conservation) with practical engineering challenges in manufacturing and communication systems. Her publications reflect a trajectory from foundational coding theory (1990s) to modern applications in AI-driven image analysis (2020s). Notable contributions include optimizing multibeam mask writers and developing uncertainty quantification methods for semiconductor line-edge roughness. No scientific awards are explicitly listed in the provided text. She has advised no students listed here, though her research may involve collaborative teams. Her office is located in WEB 308G, with contact via savari@tamu.edu.

Mohammad Farazmandمشاهده پروفایل

استادیار

• Applied Dynamical Systems
• Control Theory
• Optimization
• +۴ مورد دیگر

Mohammad Farazmand is an Assistant Professor in the Department of Mathematics at North Carolina State University (NC State). His research focuses on applied dynamical systems, control theory, optimization, and data-driven methods. He holds a PhD in Mathematics from ETH Zurich (2014). His work emphasizes stochastic modeling in epidemiology and environmental systems, fluid dynamics, and the development of numerical methods for partial differential equations (PDEs). Key contributions include enforcing conservation laws in numerical discretizations, quantifying uncertainties in stochastic compartmental models, and predicting extreme events like wildfires using sparse sensor data. He also explores reduced-order modeling and shape-morphing techniques for efficient simulation of complex systems. Recent publications span journals like Nonlinear Dynamics , Journal of Computational Physics , and Proceedings of the Royal Society A , highlighting interdisciplinary applications in fluid mechanics, epidemiology, and wildfire risk assessment. Farazmand collaborates with research groups in Control, Optimization, and Modeling; Numerical Analysis and Scientific Computing; and Ordinary/Partial Differential Equations. His methods bridge deterministic and probabilistic frameworks to address challenges in state estimation and rare event prediction.

Richard Kleemanمشاهده پروفایل

استاد

• Non-equilibrium Statistical Physics
• Climate Science
• Dynamical Systems Predictability
• +۳ مورد دیگر

Richard Kleeman is a Professor of Mathematics and Atmosphere/Ocean Science at New York University's Courant Institute, part of the College of Arts and Science. He holds dual affiliations within the Department of Mathematics and Atmosphere/Ocean Science. His research focuses on non-equilibrium statistical systems and applying information theory to improve understanding of dynamical system predictability, particularly in climate and geophysical contexts. Education: PhD in Mathematical Physics from University of Adelaide (1986), BSc in Theoretical Physics from Australian National University (1980) Expertise: Non-equilibrium statistical physics, ENSO predictability, stochastic climate modeling Affiliations: Courant Institute, NYU's Climate & Atmosphere Research Groups His work explores predictability limits in mid-latitude atmospheric systems and tropical ocean-atmosphere interactions, with significant contributions to ENSO (El Niño–Southern Oscillation) dynamics modeling. Kleeman develops theoretical frameworks to analyze turbulent systems and quantifies predictability using entropy-based methods. His research bridges statistical physics, applied mathematics, and geophysical fluid dynamics. Key projects include NSF-funded studies on mid-latitude predictability and ENSO variability, as well as stochastic modeling of decadal climate oscillations. He has pioneered path integral formulations for non-equilibrium systems and information-theoretic approaches to forecast uncertainty. Active in climate model development, Kleeman's work addresses unresolved atmospheric variability impacts and spectral analysis of geophysical models. His contributions span both theoretical advancements and practical applications in climate prediction systems.

Sarah Larsonمشاهده پروفایل

دانشیار

• Climate Variability
• Climate Predictability
• Ocean-Atmosphere Interaction
• +۳ مورد دیگر

Sarah Larson is an Associate Professor in the Marine, Earth, and Atmospheric Sciences Department at North Carolina State University (NCSU). Her research focuses on climate variability, predictability, and the role of ocean dynamics in climate systems, particularly in relation to El Niño-Southern Oscillation (ENSO), Atlantic Meridional Overturning Circulation (AMOC), and coupled atmosphere-ocean modeling. She leads the Climate Modeling & Predictability Group at NCSU, which uses advanced climate models and observational datasets to explore climate processes. Dr. Larson holds a Ph.D. in Meteorology and Physical Oceanography from the University of Miami (2016) and a B.S. in Meteorology from the University of South Alabama (2011). She teaches courses in atmospheric dynamics and climate predictability. Her recent work emphasizes understanding how wind-driven ocean circulation influences climate trends, ENSO predictability, and regional climate impacts such as extreme rainfall and flash droughts. Her research highlights the importance of ocean dynamics in decadal climate variability, including studies on the North Atlantic warming hole and the asymmetric impacts of ENSO teleconnections. Collaborative efforts in her group address climate model biases, stochastic processes in ENSO dynamics, and the application of climate projections to public health and infrastructure planning. Dr. Larson’s work has been recognized through grants and collaborations, though no specific awards are mentioned in the provided texts. She actively mentors students and postdocs, emphasizing interdisciplinary approaches to climate science challenges.

Oskar Kvimanمشاهده پروفایل

پژوهشگر

• Bayesian phylogenetics
• Probabilistic machine learning
• Variational inference
• +۱۵ مورد دیگر

Oskar Kviman is a doctoral student at KTH Royal Institute of Technology working in the Lagergren Lab within the Division of Computational Science and Technology. His research bridges machine learning, statistics, and computational biology with a focus on developing and applying advanced probabilistic methods. His primary research interests include: Bayesian phylogenetics and probabilistic machine learning Variational inference, variational auto-encoders, and sequential Monte Carlo methods Generative AI techniques including flow matching, Schrödinger bridges, and diffusion models Computational cancer research focusing on differential expression testing and spatial transcriptomics Kviman's publication record demonstrates significant contributions to variational inference methodology, particularly in phylogenetics and generative modeling. His work spans top machine learning conferences including ICML, NeurIPS, and AISTATS, showing consistent development of techniques that improve efficiency and accuracy in probabilistic modeling. Recent publications focus on multi-marginal flow matching, variational resampling, and mixture learning in black-box variational inference. He has been recognized for his peer review contributions as a Top reviewer (10%) for AISTATS 2023. Kviman has supervised master's theses for Xindi Liu and Ricky Molén at KTH and serves as a lecturer for 'Statistical Methods in Applied Computer Science' since 2021, while previously working as a teaching assistant for 'Machine Learning, Advanced Course' and 'Deep Learning, Advanced Course'.

• Chemical Engineering
• Energy Systems
• Global Optimization
• +۳ مورد دیگر

Prof. Alexander Mitsos is a Universitätsprofessor for Process Systems Engineering at RWTH Aachen University's Faculty of Mechanical Engineering since 2012. He leads the Systemverfahrenstechnik group and directs the Institute for Energy and Climate Research (IEK-10) at Forschungszentrum Jülich. His research focuses on deterministic global optimization, energy systems, and sustainable chemical processes. Mitsos' academic lineage traces back to Prof. Roger W. H. Sargent via his PhD advisor Paul I. Barton. Education: 1994–1999: Diploma in Chemical Engineering from Universität Karlsruhe (KIT), specializing in Thermodynamics and Mechanics 2001–2006: PhD in Process Systems Engineering at MIT, advised by Paul I. Barton Research Interests: His work integrates advanced mathematical methods with engineering challenges. Key areas include: Optimal design and operation of chemical processes and energy systems Development of global optimization algorithms for nonlinear problems Integration of machine learning in process design and control Energy transition technologies, including power-to-X processes and CO2 utilization Publications: Over 200 articles across 2025–2024 highlight innovations in optimization, electrochemical systems, and sustainable fuels. Recent focus areas include reinforcement learning for control, graph neural networks for molecular design, and energy market scenario modeling. Labs & Teams: Leads the Process Systems Engineering Lab at RWTH and directs IEK-10’s energy systems modeling initiatives. Collaborates internationally on projects like COMANDO (energy systems optimization framework).

John Hardingمشاهده پروفایل

مدرس

• Computational Design
• Architectural Structures
• Decarbonisation & Biomaterials
• +۷ مورد دیگر

Dr. John Harding is a Lecturer in Architecture at the University of Reading, where he serves as the BSc Architecture Admissions Tutor and teaches architectural technology at both undergraduate and postgraduate levels. He is affiliated with the Department of Architecture within the School of Architecture, focusing on digital strategies for reducing embodied carbon in response to the climate emergency. PGCert in Teaching and Learning in Higher Education (2017) EngD, University of Bath, UK (2015) BA (Hons) Mathematics, The Open University, UK (2011) MSc Architecture: Computing and Design, University of East London, UK (2008) BSc (Hons) General Architectural Studies, University of Bath, UK (2002) His research lies at the intersection of computational design, architectural structures, and sustainability, with a particular emphasis on decarbonisation, biomaterials, evolutionary design, and AI applications in architecture. He explores how digital tools can bridge architecture and structural engineering, fostering innovative, low-carbon building solutions. His recent publications reflect a strong trend in computational and sustainable architecture, including work on bamboo gridshells, cellulose-based 3D printing, interactive evolution in design, and parametric modeling. These works demonstrate a consistent focus on integrating advanced computation with ecological responsibility and interdisciplinary collaboration. Dr. Harding is a Member of the International Association of Shell and Spatial Structures (IASS) and a Fellow of the Higher Education Academy (HEA), recognizing his professional standing and teaching excellence. He has contributed to significant research projects and pavilion designs, including the UWE Research Pavilion 2016 and the Ongreening Pavilion, often working in collaborative teams. His work is supported by a strong record of conference and journal publications, indicating active engagement in the academic and design communities.

• Complex Systems Modeling
• Nonlinear Dynamics
• Multiscale Computations
• +۳ مورد دیگر

Yannis G. Kevrekidis is a Hans Fischer Senior Fellow at the Technical University of Munich (TUM-IAS), hosted by Katharina Krischer. He holds a joint appointment at Johns Hopkins University’s Department of Chemical and Biochemical Engineering. His research focuses on complex systems modeling, nonlinear dynamics, and equation-free methods, integrating machine learning with multiscale computations. Education: B.Sc. in Chemical Engineering from National Technical University of Athens; Ph.D. from University of Minnesota, advised by Rutherford Aris and Lanny Schmidt. He has held positions at Los Alamos National Laboratory as a Director’s Fellow and has been a faculty member at Princeton University since 1986, where he contributed to computational dynamics and pattern formation studies. Research Interests: Pioneered the equation-free approach for multiscale systems, bridging microscopic simulations with macroscopic analysis. Recent work emphasizes combining data-driven methods (e.g., manifold learning) with traditional modeling. Key themes include complex systems, bifurcation analysis, and computational neuroscience. Awards: Humboldt Prize (1998), Packard Fellowship (1988), and numerous honors from AIChE and SIAM. Currently affiliated with TUM-IAS Focus Group on Complex Systems Modeling and Computation. Key Collaborations: Includes interdisciplinary projects with experimentalists (e.g., G. Ertl at Fritz Haber Institute) and computational mathematicians (e.g., Ronald Coifman on manifold learning).

Natalia Kushikمشاهده پروفایل

مدرس

• Model-Based Testing
• Finite State Machines
• Software-Defined Networks (SDN)
• +۷ مورد دیگر

Natalia Kushik is a Lecturer at Telecom SudParis, part of the Institut Polytechnique de Paris. Her primary affiliation is with the ACMES (Algorithmics and Computer Models for Engineering and Systems) research group. Her research focuses on model-based testing methodologies, formal verification of systems, and applications in software-defined networking (SDN), cloud computing, and distributed systems. Her work extensively employs finite state machines (FSMs) and automata theory to design test strategies for complex systems. Notable areas include deriving homing/synchronizing sequences for automata, race condition detection in distributed systems, and optimizing network configurations using timed models. She has contributed to improving the reliability and security of SDN controllers and cloud infrastructures through formal methods. Recent trends in her publications emphasize probabilistic approaches for test suite minimization, emulation-based validation of dynamic networks, and formal analysis of reactive systems. She has collaborated on projects involving QoE (Quality of Experience) evaluation for multimedia services and fault models for digital circuits. Her work often bridges theoretical computer science with practical system implementation challenges. Her articles frequently explore intersections between formal methods and real-world applications, such as applying cellular automata for network parameter modeling and using SMT solvers for configuration validation. She has also published on optimizing interpreted programming languages using state models and proactive trust assessment mechanisms for service systems. Kushik’s research is characterized by interdisciplinary collaborations, with contributions to both academic conferences (e.g., ICTSS, ENASE) and industry-oriented venues (e.g., IEEE NCA). Her work addresses challenges in system reliability, security, and scalability across diverse domains like telecommunications, cloud computing, and embedded systems.

Marko Nedeljkovمشاهده پروفایل

استاد

• Partial Differential Equations
• Mathematical Physics
• Applied Mathematics
• +۳ مورد دیگر

Dr. Marko Nedeljkov is a full professor at the University of Novi Sad's Faculty of Sciences, specializing in the Department of Analysis, Probability, and Differential Equations. His research focuses on hyperbolic conservation laws, singular solutions, and generalized function algebras, particularly in contexts involving delta shock waves, shadow waves, and nonlinear partial differential equations. He has contributed extensively to the study of fluid dynamics, gas dynamics, and stochastic processes, with applications to mathematical physics and engineering. Key areas of investigation include the Riemann problem, viscosity solutions, and admissibility conditions for systems admitting singular solutions. His work frequently involves numerical modeling, asymptotic analysis, and symmetry-based approaches to complex multi-dimensional systems. Dr. Nedeljkov has authored over 80 peer-reviewed articles, including foundational studies on the Chaplygin gas model, pressureless gas dynamics, and fractional derivative systems. His research methodologies blend analytical techniques with computational tools, addressing problems such as dam-break dynamics, energy dissipation in fluids, and quantum systems with delta potentials. Collaborations span theoretical mathematics, applied physics, and computational fluid dynamics, reflecting his interdisciplinary approach to solving nonlinear phenomena challenges.

Weiming Fengمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Discrete Probability
• Sampling Algorithms
• +۳ مورد دیگر

Dr. Weiming Feng is an incoming Assistant Professor at the School of Computing and Data Science of The University of Hong Kong. His research focuses on Theoretical Computer Science, particularly sampling and approximate counting algorithms, with applications in Statistics and Learning Theory. Prior to joining HKU, he held postdoctoral positions at The University of Edinburgh, UC Berkeley, and ETH Zürich. Education: PhD in Computer Science from Nanjing University (2021). Research interests include Discrete Probability, Markov Chain Monte Carlo (MCMC), and algorithmic developments for high-dimensional distributions. His work bridges theoretical foundations and practical applications, addressing challenges in computational efficiency and probabilistic modeling. Notable contributions include advancements in deterministic approximation of statistical distances, MCMC derandomization, and fast sampling techniques for combinatorial problems. His publications span top venues like SODA, FOCS, and STOC, reflecting contributions to algorithm design and computational theory. Lab and Team: While specific lab affiliations are not mentioned, his research is embedded within the broader computational and data science initiatives at HKU's School of Computing and Data Science.