جست‌وجوی استاد و پژوهشگر

Rayan Saabمشاهده پروفایل

استادیار

• Compressed Sensing
• Signal Processing
• Applied and Computational Harmonic Analysis
• +۵ مورد دیگر

Rayan Saab is an Assistant Professor in the Department of Mathematics at the University of California, San Diego. His research focuses on the mathematics of information, data science, and signal processing, with an emphasis on quantization, compressed sensing, and machine learning algorithms. Education: Ph.D. in Electrical and Computer Engineering from the University of British Columbia (2010). His work bridges theoretical mathematics and practical applications in data acquisition, digitization, and processing. Recent publications analyze quantization methods for neural networks and compressed sensing systems. He has taught graduate and undergraduate courses in numerical analysis, optimization, and mathematical methods in data science. Research trends include: Developing quantization algorithms with provable guarantees. Applications in high-dimensional data and machine learning. Stochastic frameworks for neural network compression. Scientific awards: Banting Postdoctoral Fellowship (2011-2013). Hellman Fellowship. Advising and grants: Advised students in thesis projects related to signal processing and data science. Received funding for research in mathematical data acquisition. Contact: rsaab@ucsd.edu | Office: AP&M 5157.

Hanadi Sleimanمشاهده پروفایل

استاد

• Chemical Biology
• Materials Chemistry
• Nanotechnology
• +۳ مورد دیگر

Professor Hanadi Sleiman is a renowned academic in the Department of Chemistry at McGill University, specializing in DNA-based nanomaterials and their applications in drug delivery and supramolecular chemistry. She holds leadership roles, including Director of the NSERC CREATE training program in Nucleic Acids and President of the International DNA Nanotechnology Society (ISNSCE). Her research focuses on engineering DNA nanostructures for targeted therapies, such as cancer treatments, and advancing materials chemistry through DNA-functionalized systems. Education: Ph.D. in Chemistry, Stanford University (1990) Postdoctoral Fellow, University of Louis Pasteur (1993) Research Interests: Professor Sleiman’s work combines synthetic chemistry with DNA self-assembly to create programmable materials. Her lab designs DNA cages for drug encapsulation, explores DNA-minimal approaches to scalable materials, and integrates DNA with nanoparticles, polymers, and metals for biomedical applications. Key areas include cancer therapy, biosensors, and enzyme mimics. Awards & Honors: Fellow of the Royal Society of Canada (2017) Killam Research Fellowship (2018) R. U. Lemieux Award in Organic Chemistry (2018) William Dawson Scholar Award (2004–2012) Grants & Collaborations: She directs the NSERC CREATE program and collaborates with institutions like the Quebec Centre for Advanced Materials (QCAM) and the McGill Centre for Structural Biology (CRBS). Her training initiatives emphasize nucleic acid therapeutics and diagnostics. Labs & Teams: Her Sleiman Group at McGill develops innovative DNA architectures in the Otto Maass laboratory, with a focus on translational research for clinical applications.

Felix Krahmerمشاهده پروفایل

استادیار

• Compressed Sensing
• Signal Processing
• Optimization
• +۴ مورد دیگر

Prof. Felix Krahmer is a TUM Tenure Track Assistant Professor of Optimization and Data Analysis at the Department of Mathematics, Technische Universität München (TUM), part of the School of Computation, Information and Technology. Previously, he held a Junior Professorship (W1) for Mathematical Data Analysis at the University of Göttingen (2012–2015). His research focuses on mathematical foundations of data science, including compressed sensing, signal quantization, uncertainty quantification, and optimization algorithms. He has led an Emmy Noether research group and contributed to the ZeMat interdisciplinary project. Education : PhD in Mathematics (2009), New York University, advisors: Percy Deift & Sinan Güntürk MSc in Mathematics (2007), New York University BSc in Mathematics (2004), Jacobs University Bremen Research Interests : Krahmer’s work bridges theoretical mathematics and applied data science. Key areas include compressed sensing algorithms, high-dimensional signal reconstruction, quantization theory for analog-to-digital conversion, and optimization methods for inverse problems. He also explores applications in imaging (e.g., MRI reconstruction) and stochastic processes. Key Contributions : His research on phase retrieval, sigma-delta quantization, and compressed sensing recovery has advanced signal processing techniques. Recent efforts focus on uncertainty quantification for high-dimensional inverse problems and the mathematical analysis of consensus-based optimization. Grants & Awards : Emmy Noether Research Group Grant (DFG) Funding from BMBF (ZeMat project) Teaching & Outreach : Krahmer has taught advanced courses on compressed sensing, functional analysis, and random matrix theory. He co-organized workshops on probabilistic techniques and clinical risk prediction, emphasizing interdisciplinary collaboration. Labs/Teams : Member of the TUM Data Science Research Group, focusing on optimization, imaging, and uncertainty quantification. Active in the Munich Center for Quantum Science and Technology (MCQST).

• Materials Science
• Nanotechnology
• Photochemistry
• +۶ مورد دیگر

Elina Vuorimaa-Laukkanen is a University Lecturer at Tampere University's Faculty of Engineering and Natural Sciences, Department of Materials Science and Environmental Engineering, and a Docent in Pharmaceutical Nanotechnology at the University of Helsinki's Faculty of Pharmacy. She leads the research team Supramolecular Chemistry of Bio- and Nanomaterials , focusing on light-driven studies of biological processes, solid-phase behavior, and drug release activation. Her work spans multidisciplinary collaboration with chemists, pharmacists, biologists, and physicists. Education : Doctor of Philosophy (Technology), Tampere University, 1994 Licentiate of Philosophy (Chemistry), University of Helsinki, 1993 Research Interests : Her expertise includes Photochemistry and Nanotechnology of self-assembling materials (phospholipids, polymers, proteins, oligo/polynucleotides), Time-resolved Spectroscopy , Fluorescence Lifetime Microscopy , and Langmuir-Blodgett Films . She develops methods to track drug nanocarriers interacting with living cells and investigates Extracellular Vesicles for theranostic platforms. Recent Publications highlight advancements in Chitosan-hyaluronate polyplexes for oligonucleotide delivery, Fluorescence Anisotropy for nanocarrier analysis, and Self-assembly of copoly(2-oxazoline)s for drug encapsulation. Collaborations : She works within Tampere University's Chemistry & Advanced Materials Research Cluster , PREIN Photonics Flagship, GeneCellNano, and the EVE Extracellular Vesicle Ecosystem projects. Teaching : Responsible for Physical Chemistry and Lab Safety courses in the chemistry curriculum.

Marc Schneiderمشاهده پروفایل

استاد

• Colloidal drug delivery systems
• Nanoparticle and microparticle engineering
• Nanotechnology in pharmacy
• +۳ مورد دیگر

Prof. Dr. Marc Schneider holds a professorship in Biopharmaceutics and Pharmaceutical Technology at Saarland University's College of Pharmacy . His research focuses on colloidal drug delivery systems, particularly nanostructured and non-spherical particle engineering for overcoming biological barriers in pulmonary and transdermal applications. He leads an internationally recognized lab in Saarbrücken, collaborating with Helmholtz Institute for Pharmaceutical Research Saarland (HIPS) and trinational institutions. Research Highlights: Development of inhalable nano/microparticle systems Surface modification of gelatin nanoparticles Characterization of mucus-penetrating particles 3D printing for microneedle fabrication Atomic Force Microscopy (AFM) for nanoparticle analysis Selected Scientific Awards: European Journal of Pharmaceutics and Biopharmaceutics Best Paper Award (2018) for mucus-penetrating nanoparticles Recognized in 'Ausgezeichnete Orte im Land der Ideen' competition (2018) for 'Nano-Mais' drug delivery system Collaborative Networks: Co-editor for Advanced Drug Delivery Reviews special issue on biological barriers Key participant in trinational Master's program in Biomedicine with Strasbourg, Mainz, and Luxembourg Active in Controlled Release Society (CRS) conferences and local chapters

Guido Montúfarمشاهده پروفایل

استاد

• Deep Learning Theory
• Mathematical Machine Learning
• Graphical Models
• +۴ مورد دیگر

Guido Montúfar is a Professor in the Departments of Mathematics and Statistics & Data Science at the University of California, Los Angeles (UCLA), effective since 2024. He also leads the Mathematical Machine Learning Group at the Max Planck Institute for Mathematics in the Sciences (MPI MIS) in Leipzig, Germany since 2018. His academic journey includes a PhD in Mathematics from Leipzig University (2012), and Diplom degrees in Physics and Mathematics from TU Berlin (2009 and 2007). Montúfar's research focuses on the theoretical foundations of deep learning, mathematical machine learning, and the interplay between geometry and learning. Key areas include neural network architecture theory, optimization landscapes, and information geometry. His work bridges algebraic statistics, graphical models, and topological data analysis. His grants and awards include an ERC Starting Grant (2018-2023), a Sloan Research Fellowship (2022), and an NSF CAREER Award. He has advised numerous PhD students and postdocs, contributing to significant advancements in machine learning theory and applications. Montúfar teaches courses on applied mathematics, optimization, and machine learning at UCLA. His research also explores topics like oversquashing in graph neural networks and the geometry of policy gradients in reinforcement learning.

Peter Horakمشاهده پروفایل

دانشیار

• Nonlinear Optics
• Quantum Optics
• Photonics
• +۴ مورد دیگر

Dr Peter Horak is an Associate Professor at the Optoelectronics Research Centre (ORC) of the University of Southampton. With expertise in theoretical and computational photonics, his research spans nonlinear optics, quantum technology, and fiber optics. He obtained his MSc in Theoretical Physics (1993) and PhD in Theoretical Quantum Optics (1997) from the University of Innsbruck, followed by postdoctoral positions in Paris and Glasgow before joining the ORC in 2001. Current roles: ORC Exam Officer Admissions Tutor, EPSRC CDT in Quantum Technology Engineering Dr Horak leads the Computational Nonlinear Optics group, focusing on theoretical and numerical modeling of photonics systems across scales—from single photons to gigawatt laser pulses. His work addresses fundamental physics and device optimization in hollow-core fibers, quantum devices, and high-harmonic generation. Recent publications highlight hollow-core fiber gas dynamics, nonlinear optical sensing, and microresonator alignment challenges. Collaborations include the UK National Quantum Technology Programme and EPSRC-funded projects like QCI3 and HiPPo. External roles: Editorial Board, Physical Review A (2017–2022) Peer reviewer for EPSRC (since 2012) and ESF (since 2017) He supervises PhD students in nonlinear and quantum optics, and teaches optical fiber and computational modeling topics.

Federico Toschiمشاهده پروفایل

استاد

• Fluid Dynamics
• Statistical Physics
• Soft Condensed Matter
• +۵ مورد دیگر

Federico Toschi is a Full Professor at Eindhoven University of Technology (TU/e), holding joint appointments in Applied Physics and Mathematics and Computer Science departments. His research focuses on multi-scale transport phenomena, combining statistical physics, fluid dynamics, and computational methods. He leads projects in the 4TU Centre for Multiscale Phenomena and EAISI. Education: PhD in Physics (University of Pisa, 1998) and academic background at Scuola Normale Superiore di Pisa. Interdisciplinary expertise in fluid dynamics turbulence, Lagrangian turbulence, crowd dynamics, and Lattice Boltzmann methods. Recipient of APS Fellow (2015), Euromech Fluid Mechanics Fellow (2012), and Ig Nobel Prize for Physics (2021). Research emphasizes turbulence modeling, pedestrian dynamics, and active matter, with applications in environmental flows and crowd management. His work bridges computational innovations with experimental validations. Recent articles explore kinetic data-driven turbulence modeling, pedestrian flow optimization, and turbulence effects in biological systems. Projects include digital twins for seismicity modeling and rarefied gas dynamics. Teaches fluid mechanics, computational physics, and chaos theory courses. Founded Flow Matters Holding BV, applying research to practical solutions.

Efstathios (Stathis) Michaelidesمشاهده پروفایل

استاد

• Advanced Energy Systems
• Energy Conversion and Conservation
• Geothermal Energy
• +۹ مورد دیگر

Efstathios (Stathis) Michaelides is the W.A. "Tex" Moncrief, Jr. Founding Chair of Engineering at Texas Christian University (TCU). He holds a Ph.D. and M.S. in Engineering Science from Brown University (1980, 1979) and a B.A. in Engineering Science and Economics from Oxford University (1977). His research focuses on advanced energy systems, multiphase flow, and renewable energy transitions. Ph.D. , Engineering Science, Brown University, 1980 M.S. , Engineering Science, Brown University, 1979 B.A. , Engineering Science and Economics, Oxford University, England, 1977 Michaelides' work spans energy conversion , geothermal systems , nanofluidics , and particle-fluid dynamics . His recent publications analyze energy storage requirements for renewable transitions, drag force correlations in complex flows, and thermodynamic implications of carbon sequestration. His research trends include decarbonization strategies , nanoparticle-enhanced phase transitions , and smart microfluidic systems . He has also contributed to foundational texts like Particles, Bubbles and Drops (2006) and the Multiphase Flow Handbook (2017).

Miaoyan Wangمشاهده پروفایل

دانشیار

• Statistical Machine Learning
• Matrix/Tensor Data Analysis
• High-Dimensional Statistics
• +۳ مورد دیگر

Miaoyan Wang is an Associate Professor in the Department of Statistics at the University of Wisconsin-Madison, part of the School of Computer, Data & Information Sciences. She holds early tenure and is a faculty affiliate in the Mathematical Foundations of Machine Learning, Institute for Foundations of Data Science (IFDS), and Center for Demography of Health and Aging (CDHA). She is currently on sabbatical as a visiting associate professor at Stanford University and Lawrence Livermore National Laboratory. Education: PhD in Statistics from the University of Chicago (2015), BS in Mathematics from Fudan University (2010). Postdoctoral training included positions at UC Berkeley (Computer Science) and the University of Pennsylvania (Math+X). Research focuses on statistical machine learning, with emphasis on matrix/tensor data analysis, high-dimensional statistics, nonparametric learning, and applications in genetics. Her work bridges theory and practice, addressing challenges in computational efficiency and statistical optimality for complex data structures. Awards include the prestigious NSF CAREER Award (2022), multiple best paper awards (ASA, IMS, NEURIPS), and recognition from ASHJ and IGES. Her group has secured grants totaling $3.4 million, including NSF funding for foundational machine learning research and collaborative projects in population genomics. Advising includes PhD students Chanwoo Lee and Jiaxin Hu, with former students Yuchen Zeng and Zhuoyan Xu. She teaches advanced statistical methods and computational courses, emphasizing rigorous theoretical foundations and practical applications. Key collaborations include work on tensor decomposition algorithms, statistical genetics, and interdisciplinary projects with biology and computer science departments. Her lab contributes open-source software tools for data analysis, including packages for tensor block models and multiway clustering.

Ming-Jun Laiمشاهده پروفایل

استاد

• Multivariate Splines
• Sparse Solutions of Underdetermined Linear Systems
• Wavelet Theory
• +۸ مورد دیگر

Ming-Jun Lai is a Professor in the Department of Mathematics at the University of Georgia. His career spans decades, focusing on multivariate splines, sparse solutions of linear systems, wavelet theory, and their applications in numerical analysis and machine learning. Education: Lai received his Ph.D. from Texas A&M University and completed postdoctoral training at the University of Utah. He has supervised 22 Ph.D. students and two current Ph.D. candidates. Multivariate Splines: Applied to scattered data fitting, numerical PDE solutions, image enhancement, and surface design. Sparse Solutions: Used in compressed sensing, low-rank matrix recovery, and graph clustering. Wavelet Theory: Construction of biorthogonal and tight wavelet frames for image edge detection. Optimal Transport: Numerical solutions for Monge-Ampère equations. Research Trends (2025–2023): Recent work includes interpolating space curves with geometric continuity, spherical spline smoothing, and applications in machine learning, particularly graph clustering and optimal control in biological systems. Scientific Awards: UGA Research Medal (2002) McCay Award (2013) Advisees: Lai has mentored 24 Ph.D. students, including Zhaiming Shen (2024), Jinsil Lee (2023), and current students Valerio Palamra and Ye Tian. Laboratory & Collaborations: He collaborates with institutions like Georgia Tech, UCLA, and Zhejiang University, applying splines in aerospace engineering and biomedical imaging.

Xin Ningمشاهده پروفایل

استادیار

• Soft electronics for aerospace engineering
• Bio-inspired aerospace structures
• In-space manufacturing and assembly
• +۱ مورد دیگر

Xin Ning is an Assistant Professor in the Department of Aerospace Engineering at the University of Illinois at Urbana-Champaign (UIUC), where he also holds appointments in the Materials Research Laboratory. Previously, he served as an Assistant Professor at Pennsylvania State University from 2018 to 2023. His academic journey includes postdoctoral research at UIUC and California Institute of Technology (Caltech). Educated at Caltech, he earned a Ph.D. in Aeronautics in 2015 and an M.S. in Aeronautics in 2010. His research focuses on soft electronics for aerospace engineering , bio-inspired aerospace structures , in-space manufacturing and assembly , and multifunctional space structures . These interests drive innovations in lightweight, adaptive, and multifunctional materials and systems for aerospace applications. For instance, his work explores bio-inspired cellular materials for aircraft wings and bistable composite booms for deployable structures. He has been recognized with several honors, including the 2022 Young Investigator Award from the Office of Naval Research, 2019 Haythornthwaite Foundation Research Initiation Award, 2015 William F. Ballhaus Prize from Caltech, and the 2012 Dow-Resnick Fellowship. In addition to research, Professor Ning teaches courses such as AE 323: Applied Aerospace Structures and AE 498: Space Structures . While specific grants are not detailed here, his work has been supported through awards like the ONR Young Investigator grant. He collaborates with the Materials Research Laboratory at UIUC and engages in interdisciplinary projects involving soft electronics and bio-inspired engineering.

Job Boekhovenمشاهده پروفایل

دانشیار

• Synthetic Chemistry
• Biophysics
• Biochemistry
• +۷ مورد دیگر

Prof. Dr. Job Boekhoven is an Associate Professor at the Department of Bioscience , TUM School of Natural Sciences , Technical University of Munich . His research focuses on synthetic life , chemically fueled self-assembly , and supramolecular materials , aiming to synthesize life from scratch. Research Interests include creating synthetic cells that compete for resources, replicate, and undergo Darwinian evolution . His lab designs molecules like lipids , peptides , and nucleic acids that self-assemble into active compartments regulated by chemical energy. These systems exhibit life-like hallmarks such as emergence , self-division , and controllable lifetimes . Scientific Awards include: ERC Consolidator Grant (2024) Lecturer Award by Association of the Chemical Industry (2024) ERC Starting Grant (2019) Volkswagen Foundation 'Life?' Grant (2019) Max Planck Fellow (2019) VCI Dozentenpreis (2021) Thieme Chemistry Journal Award (2017) Rubicon Postdoctoral Fellowship (2013) Publications highlight trends in nonequilibrium materials , dynamic combinatorial libraries , and protocell engineering . His work bridges synthetic chemistry with biophysics to explore life's origins and applications in materials science .

• Precision Machine Design
• Medical Device Design
• Renewable Energy Systems
• +۵ مورد دیگر

Professor Alexander Slocum holds the Walter M. May (1939) and A. Hazel May Chair in Emerging Technologies at MIT's Department of Mechanical Engineering within the School of Engineering. A distinguished educator and researcher, Slocum has made significant contributions across precision machine design, medical device innovation, and renewable energy systems. His research interests span precision machine design for medical devices and energy industry applications, with particular focus on offshore renewable energy storage systems and kinematic couplings. Slocum's work bridges theoretical mechanical engineering principles with practical applications that address real-world challenges in healthcare and sustainable energy. His recent publications demonstrate a strong emphasis on bio-inspired engineering, underwater energy storage systems, and medical device innovation. The articles reveal a consistent pattern of applying fundamental mechanical engineering principles to solve problems in healthcare delivery and renewable energy storage, often with a focus on practical implementation in resource-constrained environments. NSF Presidential Young Investigator (1987) MacVicar Faculty Fellow (1999) Massachusetts Professor of the Year Award (2000) Multiple R&D 100 Awards (1994-2010) ASME Leonardo da Vinci Award (2004) ASME Machine Design Award (2008) ASME Ruth and Joel Spira Outstanding Design Educator Award (2018) National Academy of Inventors Fellow (2021) Slocum actively mentors students through MIT's Experimental Study Group (which he directs) and his renowned 2.75/2.750 Precision Machine Design courses. His educational approach emphasizes hands-on learning and real-world problem solving, particularly through medical device design projects developed in collaboration with Boston-area clinicians. His research has been supported by significant grants from the NSF, Department of Energy, and military research agencies. His PERG (Precision Engineering Research Group) lab fosters interdisciplinary collaboration, bringing together mechanical engineers, materials scientists, and medical professionals to develop innovative solutions for healthcare and energy challenges. The lab is particularly known for its work on kinematic couplings, hydrostatic bearings, and bio-inspired engineering solutions.

• Continuum Mechanics
• Metamaterials
• Composite Materials
• +۵ مورد دیگر

William Parnell is a Professor of Applied Mathematics at the University of Manchester's School of Mathematics. His research focuses on continuum mechanics, metamaterials, and industrial composites, with applications in soft tissue mechanics and acoustic wave manipulation. He leads the Mathematics of Waves and Materials (MWM) group and co-founded the Manchester Materials Modelling Centre (M3C). He has held roles including EPSRC Fellowship 'NEMESIS' (2014-2019) and its extension, contributing to transformative materials science. Education: BSc Mathematics (First Class), University of Bristol (1996-1999) MSc Mathematical Modelling and Scientific Computing (Distinction), University of Oxford (1999-2000) PhD in Applied Mathematics, University of Manchester (2001-2004) His research interests span elastic wave propagation, cloaking, and viscoelastic modeling. He has pioneered hyperelastic cloaking techniques and developed mathematical methods for metamaterials. His work contributes to UN Sustainable Development Goals related to advanced materials and digital innovation. Key achievements include the 2019 Whitehead Prize and over 80 publications. His grants include funding for microstructured material design and collaborations with Thales UK and the National Physical Laboratory. Grants & Awards: EPSRC Fellowships (NEMESIS and extension) Whitehead Prize (2019) Labs/Teams: MWM Group (focusing on waves and materials) M3C (Manchester Materials Modelling Centre)