جست‌وجوی استاد و پژوهشگر

Andrew D. Whiteمشاهده پروفایل

دانشیار

• Large language models
• Deep learning in chemistry
• Molecular dynamics
• +۵ مورد دیگر

Andrew D. White is an Associate Professor of Chemical Engineering at the Hajim School of Engineering & Applied Sciences, University of Rochester. He holds a PhD from the University of Washington (2013). His research focuses on automating scientific discovery through AI, particularly leveraging large language models (LLMs) and deep learning techniques in chemistry. His lab develops agents that integrate literature analysis, hypothesis generation, and experimental design to advance fields like molecular dynamics and drug discovery. Education: PhD in Chemical Engineering, University of Washington, 2013 BS/MS (not explicitly stated in text, inferred from career timeline) Research Interests: Large language models for scientific automation Deep learning applications in chemistry and materials science Molecular dynamics simulations Scientific agents and autonomous systems Publications: His work includes groundbreaking studies on closed-loop AI systems for chemistry, federated learning in molecular property prediction, and multi-agent systems for drug discovery. Recent highlights include the Robin system and ChemCrow tools. Awards: Recipient of the NSF Career Award (2018), NIH Outstanding Investigator Award (2020), and the Curtis Teaching Award (2019). He also advises biotech companies and serves on the National Academy of Sciences' Chemical Sciences Roundtable. Grants & Funding: Supported by DOE, NSF (multiple grants including CBET-1751471), NIH (R35GM137966), and LLNL projects. Collaborates with institutions like Argonne National Lab and Qubit Pharmaceuticals. Labs & Teams: Leads the White Lab at Rochester and co-founded FutureHouse, a nonprofit advancing AI-driven scientific discovery. Supervises a multidisciplinary team of PhD students and postdocs in computational chemistry, AI, and biophysics.

• Materials Science
• Computational Materials Science
• Battery Technology
• +۶ مورد دیگر

R. Edwin García is a Professor at the School of Materials Engineering at Purdue University, where he has been faculty since 2005. He holds appointments in the Materials Engineering department within Purdue's College of Engineering, specifically in the School of Materials Engineering located in the Neil Armstrong Hall of Engineering at Purdue's West Lafayette campus. His educational background includes: B.S. in Physics from the National University of Mexico (1996) M.S. in Materials Science and Engineering from Massachusetts Institute of Technology (2000) Ph.D. in Materials Science and Engineering with a minor in Applied Mathematics from Massachusetts Institute of Technology (2003) Professor García's research focuses on the design of materials and devices through the development of a fundamental understanding of the solid state physics of individual phases, their short and long range interactions, and associated microstructural properties and time evolution. His current research emphasizes establishing relationships between material properties and resultant performance and degradation in electrochemical systems. He integrates computational approaches ranging from kinetic Monte Carlo, phase field and level set methods, to finite elements, finite volumes, and symbolic computing. His work particularly addresses microstructure design, crystallographic texture, and grain boundary science and engineering to control the topology of underlying phases and establish practical relations between processing, microstructure, and material properties. His recent publications demonstrate a strong focus on lithium-ion battery technology, ferroelectric materials, and computational modeling of material behaviors. The research trends show increasing integration of machine learning with traditional computational methods, exploration of novel sintering techniques like flash sintering, and deeper investigation into the fundamental mechanisms of material degradation in energy storage systems. His work spans multiple length scales from atomistic to continuum modeling, reflecting a comprehensive approach to materials design and analysis. Professor García teaches several courses including MSE 230 (Structure and Properties of Materials), MSE 350 (Thermodynamics of Materials), MSE 597G (Modeling and Simulation of Materials), MSE 597I (Introduction to Computational Materials), and MSE 597N (Physical Properties of Crystals). He mentors graduate students in areas related to computational materials science, battery technology, and microstructural evolution. His research group, the Laboratory of Computational Microstructures, focuses on developing home-grown analytical theories and algorithms to resolve relevant time and length scales in materials systems. The group's work has significant implications for portable power sources, including rechargeable batteries and fuel cells, as well as for ferroelectric ceramic applications.

Christoph Schmidtمشاهده پروفایل

استاد

• Biophysics
• Cell Mechanics
• Microrheology
• +۵ مورد دیگر

Christoph F. Schmidt is the Hertha Sponer Distinguished Professor of Physics at Duke University with cross-appointments in the Thomas Lord Department of Mechanical Engineering and Materials Science, Biology, and Biomedical Engineering. He serves as Co-Director of the Duke Materials Initiative and leads an active research program at the intersection of physics and biology. His educational background includes a D.R. from the Technical University of Munich (Germany) in 1988. Schmidt has established himself as a leading researcher in biophysics through decades of innovative work. Professor Schmidt's research spans multiple scales of biological organization, from single molecules to whole organisms. His lab investigates cellular mechanics using advanced techniques including optical trapping, atomic force microscopy, and microrheology. A significant innovation from his group involves single-walled carbon nanotubes for high-bandwidth intracellular tracking. Current research focuses on cardiomyocyte mechanics, Drosophila tissue dynamics, and computational analysis of complex biological systems. His work on motor proteins like Eg5 and ncd has provided fundamental insights into cellular division mechanics. His recent publications (2021-2025) demonstrate increasing integration of computational approaches with experimental biophysics, particularly in analyzing cardiac tissue mechanics and Drosophila sensory systems. The work shows progression from fundamental biophysical measurements toward applications in understanding disease mechanisms and biological function. Professor Schmidt teaches several courses including PHYSICS 995 (Graduate Training Internship), PHYSICS 493 (Research Independent Study), PHYSICS 415 (Biophysics II), PHYSICS 174 (Introduction to Frontiers of Biophysics), and BIOLOGY 425 (Biophysics II). He has successfully mentored numerous graduate students to completion, including recent PhD graduates Dr. Mingru Li and Dr. Xiaoxuan Jian. The Schmidt Lab, part of Duke's Physics Department and the Duke Soft Matter Center, maintains state-of-the-art equipment for optical trapping, atomic force microscopy, and advanced light microscopy. The lab participates in the Triangle Soft Matter Workshop, fostering collaborations with researchers from Duke, UNC Chapel Hill, and NC State University. Current research directions include mechanical responses of suspended cells, tracking non-equilibrium cellular fluctuations, nuclear mechanics, and bacterial membrane mechanics under turgor pressure.

Lucia Carichinoمشاهده پروفایل

استادیار

• Mathematical Biology
• Fluid Dynamics
• Numerical Methods
• +۵ مورد دیگر

Lucia Carichino is an Assistant Professor in the School of Mathematics and Statistics at Rochester Institute of Technology (RIT). She holds a PhD in Mathematics from Purdue University and a BS/MS in Mathematical Engineering from Politecnico di Milano, Italy. Her research focuses on mathematical and computational models of multiscale biological systems, particularly fluid-structure interaction in biological contexts like ocular blood flow and microswimmers. She emphasizes integrating experimental data with mathematical models to advance medical understanding. Carichino teaches courses such as Differential Equations, Linear Algebra, and oversees undergraduate research projects. In 2023, she received the National Science Foundation LEAPS-MPS award for her work on computational modeling of eye-contact lens interactions. Her research has been published in high-impact journals and presented at conferences. She actively collaborates on projects addressing glaucoma, ocular hemodynamics, and biomedical applications. Education: PhD in Mathematics, Purdue University BS and MS in Mathematical Engineering, Politecnico di Milano, Italy Research interests include fluid dynamics, numerical methods, and mathematical biology. Her work bridges theoretical models with biomedical applications, such as optimizing gene therapy delivery and analyzing ocular physiology under varying environmental conditions (e.g., altitude). She explores topics like sperm motility, computational simulations of biological systems, and the interplay between fluid dynamics and biological structures. Her recent articles highlight advancements in ocular pharmacokinetics, contact lens interactions, and altitude effects on intraocular pressure. These studies underscore her expertise in multiscale modeling and fluid-structure interaction. Carichino also contributes to educational initiatives, fostering a collaborative classroom environment. Notable awards include the NSF LEAPS-MPS award (2023). She advises student research projects and collaborates with colleagues, such as Maki, on interdisciplinary studies. Her work is supported by grants and has led to presentations at ophthalmology and mathematics conferences. Carichino’s lab focuses on computational modeling of biological systems, particularly in ophthalmology and microscale fluid dynamics. Her team develops tools to simulate complex physiological processes, aiding in medical diagnostics and treatment strategies.

• Numerical Modeling
• Computational Simulation
• Materials Science
• +۵ مورد دیگر

Assoc Prof Ng Teng Yong is an Associate Professor at the School of Mechanical & Aerospace Engineering (NTU), specializing in numerical modeling and simulation. With a background as Research Manager at A*STAR Institute of High Performance Computing, his work spans materials science, nanotechnology, and aerospace engineering. Current focus on graphene-based desalination membranes Expertise in molecular dynamics simulations Investigates nanoscale fluid mechanics and structural dynamics Recent publications highlight advancements in energy-efficient electrodialysis, smart robotics, and nonlinear vibration analysis. His interdisciplinary approach integrates computational methods with experimental validation in additive manufacturing and soft material mechanics.

Long Caiمشاهده پروفایل

استاد

• Spatial Genomics
• Cellular Biology
• Developmental Biology
• +۴ مورد دیگر

Long Cai is a Professor at the California Institute of Technology, affiliated with the Biology and Biological Engineering department. He pioneered the field of spatial genomics and co-developed transformative technologies such as seqFISH and MEMOIR. Research Interests: His work focuses on decoding biological systems through spatial genomics, integrating molecular imaging with computational analysis to uncover cellular organization in tissues. Key areas include developmental biology, neuroscience, kidney regeneration, and cancer biology. Publications: Recent studies highlight applications of spatial transcriptomics in kidney disease, brain nuclear architecture, and multi-omics tissue mapping. His research emphasizes creating high-resolution atlases of cellular dynamics. Scientific Awards: NIH Director’s Pioneer Award (2022) Labs & Collaborations: He leads the Cai Lab, which develops cutting-edge imaging tools in collaboration with the Elowitz Lab and other interdisciplinary teams.

Anil K. Jainمشاهده پروفایل

استاد

• Pattern Recognition
• Biometrics
• Machine Learning
• +۳ مورد دیگر

Anil K. Jain is a University Distinguished Professor at Michigan State University, where he has taught and conducted research for over 50 years. His work focuses on Pattern Recognition , Biometrics , and Machine Learning , with foundational contributions to fingerprint, face, and palmprint recognition. B.S., Indian Institute of Technology, Kanpur (1969) M.S. and Ph.D., The Ohio State University (1970, 1973) in Electrical Engineering His research spans Computer Vision , Deep Learning , and Biometric Security , addressing challenges in adversarial robustness , demographic bias , and generative models . Recent publications emphasize transformer-based architectures , domain adaptation , and contactless biometric systems . Scientific awards include: Inductee, National Academy of Engineering (2016) Inductee, The World Academy of Sciences (2019) BBVA Foundation Frontiers of Knowledge Award (2025) Fellowships: Guggenheim, Humboldt, Fulbright Doctor Honoris Causa: 3 universities He has authored seminal works like Introduction to Biometrics and Handbook of Face Recognition , and served as Editor-in-Chief of IEEE Transactions on Pattern Analysis and Machine Intelligence . His leadership in Forensic Science includes roles on the Defense Science Board and AAAS study teams.

• Biomaterials
• Tissue Engineering
• Regenerative Medicine
• +۱۲ مورد دیگر

Professor Hala Zreiqat AM is a leading biomedical engineer at The University of Sydney , serving as the Director of the ARC Training Centre for Innovative BioEngineering . A Fellow of all major Australian academies (AAS, ATSE, FAHMS, FRSN), she develops 3D printed bioceramics for bone regeneration while championing diversity through initiatives like the IDEAL Society and BIOTech Futures mentorship program. Her work bridges academia, clinical practice, and industry in musculoskeletal research . Research Focus: Her lab creates synthetic bone scaffolds that mimic natural bone architecture, strength, and porosity, enabling non-rejected bone regeneration via patient-matched implants. Key applications include orthopaedic, dental, and maxillofacial repair , with over $18M in competitive funding and multiple patents. Current projects explore AI-driven scaffold performance prediction and anti-senescence strategies for aging-related bone loss. Scientific Trends: Recent publications highlight 3D printed nanovoxelated ceramics , antisenescence biomaterials , and multifunctional theranostic platforms . Her team integrates machine learning for scaffold design, atom probe tomography for interface analysis, and two-photon imaging for cellular monitoring in 3D environments. 2021-2022 Fulbright Senior Scholar 2018 NSW Premier's Woman of the Year 2019 Eureka Prize for Innovative Use of Technology Fellow of Australian Academy of Science (2021) Over $18M in research funding Teaching & Leadership: She designed core courses like Tissue Engineering and Nanomaterials in Medicine , mentoring 158 students in 2020 alone. As Chair of CAAR (2020-2023), she strengthens Australia-Arab collaborations. Her lab trains early-career researchers , with alumni now in academia and industry.

Ronnie Sircarمشاهده پروفایل

استاد

• Financial mathematics
• Stochastic models
• Market volatility
• +۵ مورد دیگر

Ronnie Sircar is the Eugene Higgins Professor of Operations Research and Financial Engineering at Princeton University , where he contributes to the Department of Operations Research and Financial Engineering (ORFE). His work spans financial mathematics, stochastic modeling, and applied probability, with a focus on market volatility, optimal investment strategies, and dynamic game theory. Email: sircar@princeton.edu Office: Sherrerd Hall, Room 208, Princeton, NJ 08544 His research interests include: Stochastic Volatility: Asymptotic analysis, calibration, and impact on option pricing and portfolio optimization. Mean Field Games: Applications to cryptocurrency mining, energy markets, and interbank network formation. Portfolio Theory: Forward performance processes, drawdown constraints, and risk-averse strategies. Credit Risk: Multi-name credit derivatives, CDO valuation, and risk measures. Energy Systems: Renewable reliability, unit commitment, and electricity market design. Recent publications emphasize mean field games in energy and blockchain, stochastic volatility in portfolio optimization, and machine learning applications for financial engineering. He has advised graduate students such as Giulia Crippa, Nicolas Garcia, and Burak Aydin, often collaborating with researchers including M. Soner, P. Chan, and A.M. Reppen.

Dane Morganمشاهده پروفایل

استاد

• Computational materials science
• Ab initio electronic structure
• Multiscale methods
• +۴ مورد دیگر

Dane Morgan is a Professor in the Department of Materials Science & Engineering at the University of Wisconsin-Madison, College of Engineering. His research focuses on computational materials science for materials design, including ab initio electronic structure modeling, multiscale methods, and machine learning applications in materials discovery. His work spans nuclear materials, battery and fuel cell electrodes, and electronic materials. Education : PhD, 1998, University of California, Berkeley MS, 1994, University of California, Berkeley BA, 1992, Swarthmore College Research Interests : Computational materials science, ab initio methods for electronic structure and thermokinetics, machine learning for materials discovery, electrochemical systems modeling, and applications in nuclear materials, batteries, and electronic materials. His work integrates advanced computational techniques with experimental validation. Scientific Awards : 2024 APL Materials, Editors Pick 2023 Microscopy and Microanalysis Best Paper Award (Instrumentation and Software category) 2023 IEEE Transactions on Plasma Science Best Paper Award 2023 Kellet Mid-Career Award 2015 TMS Materials Genome Initiative Ambassador 2006 3M Technical Nontenured Faculty Grant

• Computational Mathematics
• Numerical Analysis
• Tsunami Modelling
• +۵ مورد دیگر

Stephen Roberts is a Professor at the Australian National University (ANU) in the College of Science, Department of Mathematics. He is the lead developer of the ANUGA open-source hydrodynamic modeling software, which simulates dam breaks, floods, and tsunamis for governments and engineers. Roberts has made significant contributions to computational mathematics, particularly in numerical methods for partial differential equations, sparse grid data fitting, and finite element approximations scaling to millions of data points. MSc, Flinders University (1980) PhD, University of California, Berkeley (1985) His research interests include: Computational methods for shallow water wave equations Development of Python-based scientific computing frameworks Global sensitivity analysis and uncertainty quantification Adaptive mesh algorithms for fluid dynamics Recent publications focus on energy-stable numerical schemes, multiscale flood simulation, and convergence analysis in sensitivity methods. Roberts actively collaborates with environmental agencies and has led major computational science education programs at ANU. Stephen serves as Treasurer of the Computational Mathematics Group (ANZIAM) and leads projects in: Parallelization of hydrodynamic models CO2 leak detection via atmospheric measurements Optimization of sparse grid combinations His work combines theoretical advancements with real-world applications in disaster risk reduction and climate policy.

Quoc Thong Le Giaمشاهده پروفایل

دانشیار

• Numerical Analysis
• Approximation Theory
• Partial Differential Equations
• +۵ مورد دیگر

Quoc Thong Le Gia is an Associate Professor in the School of Mathematics & Statistics at the University of New South Wales (UNSW), Sydney. He holds a PhD in Mathematics from Texas A&M University (2003), an MS in Mathematics from Texas A&M University (2000), and a BSc in Mathematics and Computer Science from UNSW (1998). His research focuses on Numerical Analysis , Approximation Theory , Partial Differential Equations , and Stochastic Processes , with particular expertise in problems on spherical domains. His work bridges theoretical mathematics with practical applications in computational science, data science, and machine learning. Le Gia's recent publications demonstrate a strong focus on numerical methods for PDEs on spheres, stochastic analysis, and machine learning applications. His work shows consistent progression from theoretical foundations to practical implementations, with increasing interdisciplinary applications in recent years. L. F. Guseman Prize in Mathematics, Texas A&M University (2003) As a dedicated academic mentor, Le Gia has supervised numerous PhD, Master's, and Honours students across computational mathematics and data science topics. He has secured significant research funding through ARC Discovery Projects including DP220101811 (2022-2024) and DP180100506 (2018-2020). Professionally, he serves as External Associate Editor for Frontiers in Applied Mathematics and Statistics , Secretary for ANZIAM's Computational Mathematics Group, and Co-chair of Mathematics of Computation and Optimisation (AustMS Special Interest Group).

• Advanced Materials
• Composite Materials
• Smart Materials
• +۸ مورد دیگر

Prof. Raul Fangueiro is a Full Professor and Vice-Dean at the School of Engineering, University of Minho, Portugal. As a Senior Researcher, he leads FIBRENAMICS – Institute of Innovation on Fiber-Based Materials and Composites. His work spans advanced materials (nano, smart, composites) and structures (3D, auxetic, multiscale) with applications in defense, healthcare, construction, and automotive sectors. Supervised over 20 PhD and Post-Doc researchers Scientific coordinator of 20+ national/international research projects Author of 200+ journal papers (H-index: 47), 500+ conference publications, 36 books, 40 patents Research focuses on nanotechnology , electrospinning , and sustainable material systems : Auxetic composites for personal protection Biodegradable nanofibers for medical use Smart textiles for biological/chemical resistance Recycled mineral/wood-based composites Graphene-reinforced green materials Multiscale fiber architectures Scientific recognition includes: Top 2% most influential scientist (Elsevier/Stanford 2020) Founder of AUXDEFENSE and ICNF conferences Editorial board member of leading composite journals Advisor to European Defense Agency/NATO working groups Active in industry-academia partnerships through spin-offs (Sciencentris, B4Logic, Beyond Composites, Givaware, Pixartidea) and collaborative projects with institutions like Instituto Superior Técnico, University of Aveiro, and international universities.

• Fluid Mechanics
• Multiphase Flow Modeling
• Numerical Simulation
• +۳ مورد دیگر

Marcus Herrmann is a Professor of Aerospace and Mechanical Engineering at Arizona State University's School for Engineering of Matter, Transport and Energy. He is also affiliated with the Center for Negative Carbon Emissions. His research focuses on fluid mechanics, multiphase flows, atomization processes, and numerical methods for discontinuous interfaces. Herrmann holds a PhD in Mechanical Engineering from RWTH Aachen University (2001) and a Diplom (1995). His career includes a postdoctoral fellowship at Stanford University's Center for Turbulence Research (CTR) and a visiting scientist position at the University of Technology Eindhoven, Netherlands. He has secured major grants from NASA, NSF, and industry partners like Honeywell, focusing on atomization modeling, supersonic crossflows, and turbulence simulations. Research interests span computational fluid dynamics, multiphase flow simulation, and LES/DNS methodologies. His recent work emphasizes high-fidelity numerical techniques for particle-resolved simulations and phase interface dynamics. Teaching includes courses like MAE 561 (Computational Fluid Dynamics) and MAE 384 (Advanced Math Methods for Engineers). He actively advises students through research and dissertation roles. Notable projects include modeling wax deposition in pipelines and developing novel approaches for interface dynamics in turbulent flows. His work bridges fundamental fluid mechanics with industrial applications like combustion systems and porous media modeling.

Nicolas Perkowskiمشاهده پروفایل

استاد

• Stochastic Analysis
• Probability Theory
• Stochastic Partial Differential Equations
• +۳ مورد دیگر

Prof. Nicolas Perkowski is a Professor in the Department of Mathematics at Freie Universität Berlin, specializing in Stochastic Analysis and Probability Theory. He holds roles such as Vice Spokesperson of DFG CRC/TRR 388 (since 2024) and Chair of the Master of Mathematics Examination Board. His research focuses on stochastic partial differential equations (SPDEs), rough paths, and applications in mathematical physics. Notable contributions include work on singular SPDEs, fractional processes, and the KPZ equation. Perkowski has authored/co-authored numerous publications in top journals like the Annals of Probability and Communications in Mathematical Physics, and he serves as an associate editor for several journals. His institutional responsibilities include leadership in research collaborations and academic governance. Education: PhD and diploma in stochastic population models (details not explicitly provided in text). Research Interests: Stochastic Analysis, Probability Theory, SPDEs, Mathematical Physics, Nonlinear Filtering. Recent Articles: Focus on fractional processes, SPDEs with singular terminal conditions, and stochastic sewing lemmas. His work bridges theoretical probability with applications in physics and engineering, with a strong emphasis on rigorous mathematical frameworks for complex stochastic systems.