جست‌وجوی استاد و پژوهشگر

• Ultrasonic Technology
• Nondestructive Testing
• Smart Materials and Structures
• +۵ مورد دیگر

Professor Ningqun Guo holds the dual roles of Professor in Mechanical Engineering and Head of School for both the Malaysia School of Engineering and School of Information Technology at Monash University Malaysia. He previously served at Nanyang Technological University, Singapore. His academic journey includes a B.Eng from Nanjing University of Aeronautics and Astronautics (China) and a PhD from Imperial College London (UK). His research focuses on stress wave propagation, ultrasound applications, smart materials, nondestructive testing, and civil infrastructure analysis. He has published over 130 papers, secured S$2 million in research grants, and supervised over 10 PhD students. Key research areas include ultrasonic technology for material characterization, smart material systems, and image processing for infrastructure monitoring. His work aligns with UN Sustainable Development Goals, emphasizing sustainable infrastructure and innovation. Notable collaborations span global institutions, with recent projects exploring AI-driven pavement crack detection, transparent object reconstruction, and 3D imaging systems. His contributions to magnetorheological fluid applications and nanofluidics further underscore his interdisciplinary impact. Grants and funding have supported projects in sensor development, structural health monitoring, and advanced imaging technologies. His advisory work has produced impactful PhD graduates in mechanical engineering and materials science. Prof. Guo leads research teams focused on smart materials, nondestructive evaluation, and computational imaging. His lab integrates experimental and theoretical approaches to solve challenges in infrastructure, photonics, and nanotechnology.

Purushottam Dixitمشاهده پروفایل

استادیار

• Cellular Signaling
• Population Heterogeneity
• Ecological and Evolutionary Dynamics of Bacterial Communities
• +۵ مورد دیگر

Purushottam Dixit is an Assistant Professor of Biomedical Engineering at Yale University, specializing in computational systems biology and biophysics. He holds a Ph.D. in Chemical and Biomolecular Engineering from The Johns Hopkins University and a B.S. in Chemical Engineering from Indian Institute of Technology, Bombay. His research focuses on integrating machine learning and statistical physics to analyze high-dimensional biological data, particularly inspired by the principle of maximum entropy. Key themes include cellular signaling, population heterogeneity, bacterial community dynamics, and metabolic responses to environmental constraints. He has received the Maximizing Investigator's Research Award (NIGMS 2022-2026) and has published extensively in journals like Nature Metabolism , Cell Systems , and Nature Methods . His work addresses challenges in modeling cellular heterogeneity, lipid metabolism in cancer cells, and microbiota variability. Scientific Awards: Maximizing Investigator's Research Award, NIGMS 2022-2026 He advises doctoral students, including Hoda (first Ph.D. graduate from his lab) and Andrew, and actively recruits new candidates. The lab moved to Yale in July 2023, following previous affiliations with other institutions.

Terry Sooمشاهده پروفایل

دانشیار

• Probability Theory
• Ergodic Theory
• Statistical Science

Terry Soo is an Associate Professor in the Department of Statistical Science at University College London (UCL), a position he has held since 2020. His academic background includes postdoctoral roles at the University of Warwick and the University of Victoria, followed by an Associate Professorship at the University of Kansas. He holds degrees from Simon Fraser University (undergraduate) and the University of British Columbia (PhD). His research focuses on probability theory and ergodic theory , with a particular emphasis on topics such as nonsingular systems, Bernoulli shifts, and stochastic processes. His work often explores structural properties of dynamical systems and their isomorphisms, with applications to measure-preserving transformations and entropy theory. Soo teaches advanced courses such as Stat 0009 (Probability and Statistics) and LTCC Stochastic Processes . His research has been published in prestigious journals like Annals of Probability and Ergodic Theory and Dynamical Systems , and he maintains an active presence in the academic community through external profiles on arXiv, ZbMATH, and AMS.

Marco Baiesiمشاهده پروفایل

دانشیار

• Nonequilibrium systems
• Polymers
• Biopolymers
• +۵ مورد دیگر

Marco Baiesi is an Associate Professor in the Department of Physics and Astronomy at the University of Padua. His research focuses on nonequilibrium systems, polymers, biopolymers, topology, and machine learning applications in physics and biophysics. He has contributed to understanding the statistical mechanics of complex systems, including polymer dynamics, topological effects, and non-equilibrium thermodynamics. His work spans interdisciplinary areas such as biophysics, soft condensed matter, and machine learning for medical diagnostics. Notable contributions include studies on knotted polymer behavior, entropy production in non-equilibrium systems, and the application of AI to EEG-based dementia classification. Baiesi’s publications frequently explore topics like fluctuation theorems, stochastic processes, and the interplay between topology and material properties. His research has been published in high-impact journals such as Science , Physical Review Letters , and New Journal of Physics .

Simon Huntمشاهده پروفایل

پژوهشگر ارشد

• Mineral Physics
• High Pressure Experiments
• Geophysics
• +۳ مورد دیگر

Simon Hunt is a Presidential Fellow (Academic) in the Department of Materials at the University of Manchester. His research focuses on high-pressure experiments to study Earth's deep interior, including mantle convection, earthquake mechanisms, and anelasticity. He has affiliations with the University of Manchester at Harwell and Diamond Light Source in Didcot, Oxfordshire. Education: BSc/MSc in Natural Sciences (2004), PhD in Earth Sciences (2009, University College London). Postdoctoral roles at UCL and Stony Brook preceded his NERC Fellowships and current position. Developed the DT-Cup deformation apparatus expanding high-pressure experimental capabilities. Expertise in mineral physics, seismic wave attenuation, and high-pressure material property measurements. Notable awards include the NERC Independent Research Fellowship (2017) and the University of Manchester Presidential Fellowship (2019). Active in organizing conferences and peer review for journals like Frontiers Media S.A. . Current project involvement includes University of Manchester at Harwell focusing on advanced materials characterization techniques.

• Variational Calculus
• Partial Differential Equations
• Large Deviations Theory
• +۳ مورد دیگر

Dr. Michiel Renger is a researcher at the Department of Mathematics, Technische Universität München (TUM), within the School of Computation, Information and Technology. His research focuses on variational calculus, partial differential equations, large deviations theory, non-equilibrium thermodynamics, and chemical reaction networks. He has contributed to advancing the understanding of macroscopic fluctuation theory, gradient flows, and their applications in stochastic systems. Teaching responsibilities include courses on higher mathematics for engineering students at TUM and specialized lectures on large deviations and convex analysis at TU Berlin. His work bridges theoretical mathematics with applications in physics and engineering, emphasizing interdisciplinary approaches. Renger’s publications span peer-reviewed journals in mathematics and physics, with a focus on rigorous probabilistic and analytical methods. He holds a PhD in Mathematics from Technische Universiteit Eindhoven (2013) and has collaborated on projects in collaboration engineering, addressing challenges in collaborative modeling and organizational design. His research also extends to applied problems like node counting in wireless networks and statistical consulting for industry.

Raphael D. Levineمشاهده پروفایل

استاد

• Physical Chemistry
• Theoretical Chemistry
• Quantum Computing in Chemistry
• +۴ مورد دیگر

Raphael D. Levine is a Professor at the Department of Chemistry, University of California, Los Angeles (UCLA). He holds affiliations in the Chemistry and Biochemistry departments, Molecular & Medical Pharmacology, and is a member of the California NanoSystems Institute and the JCCC Signal Transduction and Therapeutics Program Area. Primary Affiliation: Department of Chemistry, UCLA Secondary Affiliations: Chemistry and Biochemistry, Molecular & Medical Pharmacology Research Institutes: California NanoSystems Institute, JCCC Program Area Levine's research spans interdisciplinary domains such as: Information-theoretic approaches to gene networks and carcinogenesis Quantum and molecular computing via quantum dots, DNAzymes, and redox systems Entropy analysis in biochemical signaling and cellular dynamics Ultrafast spectroscopy for molecular logic devices Isotope effects in planetary and nebular chemistry His work emphasizes the convergence of physical chemistry, computational biology, and nanotechnology. Key methodologies include surprisal analysis, maximal entropy inference, and electrochemical spectroscopy. Levine's publications demonstrate expertise in translating quantum phenomena into practical computing frameworks, with applications in cancer biology, molecular electronics, and astrochemical modeling. Notable collaborations include James R. Heath and Françoise Remacle. Contact: rafi@chem.ucla.edu | Office: Geology 3608A | Mailing: Department of Chemistry, UCLA

• Additive Manufacturing
• Materials Design
• Metal 3D Printing
• +۳ مورد دیگر

Dr. Minh-Son Pham is a Senior Lecturer in Materials at Imperial College London, specializing in additive manufacturing and materials design. He obtained his Doctor of Science from ETH Zurich and leads research on metal 3D printing, material degradation, and meta-materials. His research investigates microstructure-property relationships in additively manufactured alloys, particularly nickel superalloys and high-entropy alloys. Recent work explores damage-tolerant architected materials inspired by crystal microstructures, with applications in aerospace and biomedicine. Publications demonstrate consistent focus on process-microstructure-property relationships in additive manufacturing, combining experimental characterization with computational modeling. Research spans fundamental materials science to industrial applications with aerospace and medical device partners. Awards include the 2024 TMS Young Innovator Award. He teaches fracture mechanics and additive manufacturing courses while coordinating international exchange programs. Current projects involve industrial collaborations on alloy design for 3D printing and meta-material development.

Masoud Ghalaiiمشاهده پروفایل

مدرس

• Quantum Information
• Quantum Communications
• Quantum Computing
• +۴ مورد دیگر

Dr. Masoud Ghalaii is a Lecturer in Quantum Information at the Department of Computing and Mathematics, Manchester Metropolitan University (UK). He holds a PhD in Electrical Engineering (Quantum Communications) from the University of Leeds (2019), and has conducted postdoctoral research at the University of York and the University of Leeds, focusing on quantum technologies. His academic career includes visiting research at Institut Polytechnique de Paris-Telecom ParisTech (2015). Education: PhD, University of Leeds (2019) MSc, Sharif University of Technology (2014) BSc, Isfahan University of Technology (2011) Research Interests: Dr. Ghalaii’s work centers on quantum information science and quantum optics, with applications to quantum communication networks, quantum key distribution (QKD), and quantum repeaters. He has organized workshops (e.g., White Rose Quantum Information Science and Technologies Workshop 2024) and contributed to editorial roles (e.g., Special Issue on Quantum Communication Networks for Entropy Journal). His research emphasizes secure quantum communication protocols, hybrid quantum-classical systems, and turbulence-resistant quantum networks. Publications: His recent articles explore topics like CV-MDI-QKD, satellite-based QKD, and quantum communication in turbulent environments. These contributions highlight advancements in secure quantum communication frameworks and their practical implementation across diverse networks. Affiliations & Roles: He serves as a reviewer for international journals/conferences in quantum information and optics. His expertise bridges theoretical developments with applied quantum technologies, positioning him as a key figure in advancing quantum communication infrastructure.

H. David Sheetsمشاهده پروفایل

استاد

• Data Science
• Forensics
• Paleontology
• +۳ مورد دیگر

H. David Sheets is a Program Director and Instructional Professor at Merrimack College, affiliated with the Center for Innovation and Research. His research focuses on interdisciplinary applications of data science to fields such as geology, paleontology, biology, and forensics. He holds a B.S. in Physics from SUNY College at Fredonia and a Ph.D. in Physics from the University of Buffalo. His work includes co-authoring the textbook *Geometric Morphometrics for Biologists: A Primer* and pioneering methods for analyzing forensic examiner performance, art surface textures, and graptolite extinction patterns. Key research interests include geometric morphometrics, statistical analysis in forensics, and paleontological data integration. He has advised Master’s student Shannon Connolly and collaborates on projects such as surface metrology for art conservation and stratigraphic correlation methods. Recent publications explore forensic firearm identification, Late Ordovician mass extinction dynamics, and lacquer material characterization. Teaching specialties include Machine Learning, AI programming, statistics, and databases. His contributions bridge academia and practice through interdisciplinary data science applications in both natural and forensic sciences.

Wangzhong Muمشاهده پروفایل

مدرس ارشد

• Sustainable Metallurgy
• Microstructure Physics
• Alloy Design
• +۳ مورد دیگر

Prof. Wangzhong Mu is a Senior Lecturer (Docent) in the Department of Materials Science and Engineering at KTH Royal Institute of Technology, Stockholm. His research focuses on sustainable metallurgy, microstructure physics, and alloy design. He leads the thermo-physical property analysis section in the Hultgren Lab and is affiliated with Digital Futures at KTH. Educations: PhD in Materials Science, KTH Royal Institute of Technology (2015) MSc/Bachelor's in Materials Science, Northeastern University, China Research Interests: Inclusion engineering and microstructure-property correlations in steels High-entropy alloy design using digital tools (AI/thermodynamic modeling) In-situ characterization via confocal microscopy and multiscale analysis Recycling-oriented steel production and CO2 reduction strategies Grants/Projects (selected): SSF Strategic Mobility Grant (2023-2024): Clean steel for sustainable future VINNOVA Mobility Grant (2022-2024): Hydrogen-based metallurgy STINT Project (2022-2023): Inclusion engineering for green steel EIT RawMaterials (ENDUREIT, 2019-2021): Durable steels at intermediate temperatures Labs/Teams: Hultgren Lab (materials characterization), Digital Futures (AI-driven metallurgy), and international collaborations with Hanyang University (South Korea), IIT Bombay (India), and Tohoku University (Japan).

• Electrocatalysis
• CO2 Reduction
• Energy Conversion Devices
• +۳ مورد دیگر

Kazuyuki Iwase serves as Associate Professor at Tohoku University's Institute of Multidisciplinary Research for Advanced Materials since April 2025, following progressive appointments as Senior Assistant Professor (2023-2025) and Assistant Professor (2019-2023). His academic journey includes postdoctoral research at Paul Scherrer Institute (Switzerland) and multiple JSPS Research Fellowships. He maintains active collaborations with prominent researchers including Prof. Itaru Honma and Prof. Takaaki Tomai. Dr. Iwase's research focuses on electrocatalysis for sustainable energy conversion, specializing in carbon dioxide reduction reaction (CO2RR) and oxygen evolution reaction (OER) systems. His work spans nanomaterials engineering, electrocatalyst design, and device integration for renewable energy applications. Key methodologies include supercritical hydrothermal processing, mechanical alloying, and machine learning optimization of electrochemical systems. His publication record demonstrates consistent high-impact output with 36 accepted articles through 2025, featuring 11 as corresponding author and 15 as first/equal-first author. Recent work explores manganese nanospinels for OER, Ag-Sn intermetallics for CO2RR, and machine learning approaches for reaction optimization, showing strong interdisciplinary connections between materials science, electrochemistry, and sustainable engineering. The 5th Symposium for The Core Research Clusters for Materials Science and Spintronics Poster Award (2021) Student Presentation Award, Chemical Society of Japan (2016) International Exchange Support Award, Electrochemical Society of Japan (2016) SIEMME Best Oral Presentation Award (2014) Dr. Iwase has secured significant research funding as Principal Investigator, including a JST PRESTO grant (¥40,000,000) for CO2 conversion research and multiple JSPS Grants-in-Aid totaling over ¥59,000,000. His academic service includes peer review for prestigious journals including Angewandte Chemie and Nature Sustainability. He maintains active international engagement through invited lectures in Japan, India, and Switzerland, focusing on nanomaterials for electrocatalysis.

André Schlichtingمشاهده پروفایل

استاد

• Applied Mathematics
• Partial Differential Equations
• Stochastic Analysis
• +۵ مورد دیگر

André Schlichting is a Full Professor of Applied Analysis at the University of Ulm, leading the Institute of Applied Analysis since October 2024. Previously, he served as an Associate Professor for Applied Mathematics at the University of Münster (2020–2024) and held postdoctoral and visiting professor roles at institutions including the University of Bonn and RWTH Aachen. His research focuses on the qualitative analysis of complex systems, combining methods from partial differential equations, stochastic analysis, and numerical analysis. Key interests include metastability in statistical mechanics, phase transitions, variational methods, and the longtime behavior of dissipative systems. Education: Habilitation (Facultas Docendi), University of Bonn, 2020 (Phase Transitions in Interacting Systems) PhD in Mathematics, Universität Leipzig, 2012 Diploma in Mathematics, TU Freiberg, 2008 Studies in Mathematics at University of Pavia (Italy) and TU Freiberg Research Interests: His work bridges applied mathematics and theoretical physics, addressing problems in statistical mechanics, stochastic processes, and machine learning. Key areas include: Metastability in molecular and statistical mechanics systems Coarsening and nucleation phenomena in interacting particle systems Variational methods for dissipative evolution equations Entropy methods and functional inequalities (spectral gap, log-Sobolev) Gradient flows and their limits in continuum and discrete settings Discrete and nonlocal dynamics on graphs and data sets Recent Article Trends: His recent work emphasizes gradient flow structures, discretization schemes for PDEs, and metastability in stochastic systems. Notable contributions include analysis of the exchange-driven growth model, McKean-Vlasov equations on manifolds, and covariance-modulated optimal transport. These studies highlight interdisciplinary approaches to bridging discrete and continuum dynamics. Labs/Teams: He leads the Applied Analysis group at Ulm University, focusing on collaborative research in PDEs, stochastic processes, and numerical methods. Active participation in initiatives like the Hausdorff Trimester Program (Bonn) underscores his role in fostering interdisciplinary research networks.

Jibran Khaliqمشاهده پروفایل

دانشیار

• Smart Materials
• Ceramics
• Polymers
• +۷ مورد دیگر

Dr. Jibran Khaliq is an Associate Professor in the Department of Mechanical and Construction Engineering at Northumbria University. He specializes in smart materials, including ceramics, polymers, and composites, with a focus on functional properties and advanced material design. His work spans energy materials, biomedical applications, and nanocomposites. Education: Ph.D. in Advanced Energy Materials, Queen Mary University of London (2014) MSc in Materials and Surface Engineering, National University of Science and Technology, Islamabad (Pakistan) BEng in Metallurgical and Materials Engineering, University of Engineering and Technology Lahore (Pakistan) Research Interests: Development of advanced dielectric ceramics for microwave and electronic applications Smart materials for sensing and actuation Thermoelectric and piezoelectric composites Nanostructured materials for biomedical and energy storage systems Grants and Funding: Total research income exceeding £1M Notable projects include Royal Society (£12,000, PI), Innovate UK (£291,919 and £228,223), and British Council Institutional Link (£297,286) Labs/Teams: While specific lab names are not mentioned, his work involves collaborations in advanced materials synthesis and characterization, likely through Northumbria's research facilities.

Ryan T. Whiteمشاهده پروفایل

دانشیار

• Deep Learning
• Machine Learning
• Artificial Intelligence
• +۱۱ مورد دیگر

Ryan T. White is an Associate Professor at Florida Institute of Technology in the Department of Mathematics and Systems Engineering within the College of Engineering and Science. He serves as Director of the NEural TransmissionS (NETS) Lab, focusing on deep learning, computer vision, and data science. He is also an Affiliate Faculty member in Electrical Engineering and Computer Science. Ph.D. in Applied Mathematics (2015) from Florida Tech His research bridges deep learning and computer vision with applications in autonomous satellite operations , physics-informed neural networks for biomedical and geoscience problems, and NLP in aerospace domains. Projects include real-time edge computing , stochastic process analysis , and generative AI for synthetic data. The NETS Lab he directs has produced 15+ recent publications in conferences like IEEE Aerospace, AIAA SCITECH, and AAS/AIAA, with funding from the U.S. Space Force, Air Force Research Lab, and NSF. His teaching spans graduate/undergraduate courses in deep learning , machine learning , probability , and honors calculus . Current advisees include Ph.D. candidates and M.S. students working on topics like 3D object detection , information-theoretic neural analysis , and geophysical signal processing . The lab’s scientific contributions include real-time satellite feature detection , physics-guided neural networks for blood flow modeling, and entropy-based visual explanations for AI interpretability. Collaborations span Georgia Tech , Mulitscale Cardiovascular Fluids Laboratory , and Engage-AI for global development projects analyzing UNDP Sustainable Development Goals.