جستجوی استاد راهنمای دانشگاهی

Ryan L. Hartmanمشاهده پروفایل

استاد

• Chemical Reaction Engineering
• Continuous-Flow Manufacturing
• Flow Chemistry
• +۳ مورد دیگر

Ryan L. Hartman is a Full Professor in the Department of Chemical and Biomolecular Engineering at the NYU Tandon School of Engineering. He holds leadership roles, including Chairman of NYU's Tenured/Tenure-Track Faculty Senators Council and Chair of the CBE Doctoral Recruiting Committee. His academic journey includes a Ph.D. from the University of Michigan, postdoctoral research at MIT, and prior positions at the University of Alabama and Schlumberger. Hartman’s research focuses on sustainable chemical processes using microfluidics, machine learning, and reaction engineering to advance energy, materials, and environmental applications. Education: Ph.D., Chemical Engineering, University of Michigan (2006) M.S., Chemical Engineering, University of Michigan (2003) B.S., Chemical Engineering, Michigan Technological University (2001) Postdoctoral Research, MIT Chemical Engineering (2010) Research Interests: His work combines microfluidics with AI to optimize chemical reactions, reduce environmental impact, and enable high-throughput experimentation. Key areas include laser-induced nucleation, CO2 sequestration, and plasma-driven methane conversion. Recent projects explore mechanisms of crystal formation and sustainable manufacturing. Publications: Over 50 peer-reviewed articles highlight his contributions to microfluidic systems, hydrate crystallization, and AI-driven catalysis. Notable work includes NSF-funded studies on laser-induced nucleation mechanisms and CO2 capture. His research bridges academia and industry, emphasizing practical applications. Awards: NSF CAREER Award, National Academy of Inventors membership, and Schlumberger Inventor Award reflect his impactful contributions. He leads the Hartman Research Lab, collaborating with institutions like Columbia University and Anadarko Petroleum. Grants & Leadership: Principal investigator on NSF grants, senior personnel in MRSEC and CBET projects. Serves on editorial boards and professional societies (AIChE, ISCRE). Thrust lead in DC-MUSE (decarbonizing chemical manufacturing). Labs & Teams: Directs the Hartman Lab, focusing on sustainable chemical processes. Collaborates with NYU Tandon Future Labs and industry partners to translate innovations into real-world solutions.

Roger Narayanمشاهده پروفایل

استاد

• Biomedical Materials
• Microneedle-Based Sensors
• Additive Manufacturing
• +۳ مورد دیگر

Roger Narayan is a Distinguished Professor in the Department of Materials Science and Engineering at North Carolina State University’s College of Engineering. His expertise spans biomedical materials, microneedle-based sensors, and additive manufacturing. He holds editorial roles at Biomaterials Forum and Applied Physics Reviews, and has edited influential texts like Biomedical Materials and Encyclopedia of Biomedical Engineering . His research focuses on advanced materials for medical devices, including 3D-printed microneedles for drug delivery and biosensing, electrochemical sensors for transdermal monitoring, and diamond-like carbon coatings to enhance biocompatibility. Recent work includes innovations like ChocOmega-3, a 3D-printed omega-3-enriched chocolate, and machine learning-integrated biosensors for real-time health monitoring. Dr. Narayan has secured grants for projects such as antimicrobial microneedle technologies, laser-annealed battery materials, and 3D-printed ceramics evaluation. His research bridges nanotechnology, materials science, and clinical applications, aiming to advance wearable diagnostics and precision medicine. Editorial Roles: Biomaterials Forum (Executive Editor), Applied Physics Reviews (Associate Editor) Grants: Over 10 funded projects including NSF/FDA collaborations and FDA grants Labs/Teams: Active in NC State’s Materials Science and Engineering Department labs, focusing on biomedical materials innovation Future Work: Expanding AI-driven sensor platforms and scalable biomedical manufacturing

Qing Guمشاهده پروفایل

دانشیار

• Quantum Photonics
• Plasmonics
• Non-Hermitian Topological Photonics
• +۴ مورد دیگر

Qing Gu is an Associate Professor in the Department of Physics at North Carolina State University (NC State), with a joint appointment in the Department of Electrical and Computer Engineering (ECE). She holds a Ph.D. in Electrical Engineering from the University of California, San Diego (2014) and a Bachelor's from the University of British Columbia, Canada (2008). Her research focuses on quantum-inspired nanophotonics, active and topological hyperbolic metamaterials, and perovskite optoelectronics. She is a member of the Chancellor's Faculty Excellence Cluster in Carbon Electronics and authored the book Semiconductor Nanolasers (Cambridge University Press, 2017). Key research themes include non-Hermitian topological photonics, plasmonic nanoantennas, and nanoscale light sources. Her work intersects electrical engineering, physics, and materials science, with applications in quantum information science, neuromorphic photonic computing, and high-speed optical communication. Education : Ph.D., Electrical Engineering, UC San Diego (2014) Bachelor's, Electrical Engineering, University of British Columbia (2008) Awards : NSF CAREER Award (2020) Bennett Faculty Fellow Award (2024) ARO Young Investigator Award (2019) Grants & Leadership : Leads projects in topological photonics and perovskite optoelectronics Principal Investigator on NSF, ARO, and industry-funded grants Her recent publications explore topics like Purcell-enhanced lasing, plasmonic polarization control, and topological edge-mode lasers. She collaborates with industry partners and academic institutions globally, advancing nanophotonic technologies for next-generation optoelectronic systems.

Milad Abolhasaniمشاهده پروفایل

استاد

• Self-Driving Labs
• Autonomous Fluidic Labs
• Flow Chemistry
• +۷ مورد دیگر

Milad Abolhasani is the ALCOA Professor and University Faculty Scholar at North Carolina State University's Department of Chemical and Biomolecular Engineering, serving as Director of Graduate Programs and Director of Accelerated Technologies for the Integrative Sciences Initiative. He holds a B.Sc. from Sharif University of Technology, M.A.Sc. from the University of British Columbia, and Ph.D. from the University of Toronto, with postdoctoral training at MIT. His research focuses on Self-Driving Fluidic Labs—autonomous robotic platforms for accelerating materials discovery and manufacturing. Notable contributions include innovations in flow chemistry, microfluidics, and AI-driven experimentation. His work has been recognized with awards such as the NSF CAREER Award and AIChE Early Career Investigator Award. He leads a multidisciplinary team advancing sustainable technologies and has pioneered Self-Driving Lab systems for next-generation materials science. Education: B.Sc., Mechanical Engineering, Sharif University of Technology (2008) M.A.Sc., Mechanical Engineering, University of British Columbia (2010) Ph.D., Mechanical Engineering, University of Toronto (2014) NSERC Postdoctoral Fellow, Chemical Engineering, MIT (2014–2016) Research Interests: Self-Driving Labs and Autonomous Experimentation Flow Chemistry and Microfluidics Process Intensification and Nanomanufacturing AI Integration in Materials Discovery Awards: NSF CAREER Award 2024 AIChE CRE Early Career Investigator Award Camille & Henry Dreyfus Foundation Machine Learning Award AIChE 35 Under 35 (2020) Advising & Grants: Directed over 15+ graduate students Funded by NSF, Dreyfus Foundation, and industry partnerships Recipient of Chancellor’s Innovation Fund and Goodnight Award Labs & Collaborations: Abolhasani Lab: Focuses on Self-Driving Fluidic Labs Integrative Sciences Initiative: Accelerating cross-disciplinary innovation Center for Accelerated Photocatalysis (CAPs)

Robert Landersمشاهده پروفایل

استاد

• Additive Manufacturing
• Control Systems Engineering
• Alternative Energy Systems
• +۳ مورد دیگر

Robert Landers is the Associate Faculty Director of iNDustry Labs and a Collegiate Professor in the Department of Mechanical Engineering at the University of Notre Dame's College of Engineering. His research focuses on advanced manufacturing processes, including laser powder bed fusion, digital glass forming, and control systems for alternative energy systems. He holds a Ph.D. from the University of Michigan, an M.S. from Carnegie Mellon University, and a B.S. from the University of Oklahoma. Landers' research interests include thermal modeling of additive manufacturing processes, in-situ monitoring techniques for quality control, and energy systems optimization for hydrogen fuel cells and lithium-ion batteries. His work bridges mechanical engineering with mechatronics, emphasizing precision control in industrial robotics and machine tools. His recent publications emphasize defect prediction in additive manufacturing, kinematic error compensation in robotics, and novel methods for glass-metal integration. Landers collaborates on interdisciplinary projects such as digital twin systems for manufacturing and sensor-driven process optimization. Key areas of contribution include: Development of layer-to-layer control strategies for additive manufacturing Integration of LIBS and thermal imaging for real-time material analysis Advances in wire saw machining for semiconductor fabrication Optimization of lithium-ion battery degradation models for microgrid applications His research has been applied in aerospace, energy storage, and medical device manufacturing sectors.

Edward Maginnمشاهده پروفایل

استاد

• Molecular Simulations
• Ionic Liquids
• Battery Electrolytes
• +۳ مورد دیگر

Edward Maginn is the Associate Vice President for Research and the Keough-Hesburgh Professor of Engineering at the University of Notre Dame's Department of Chemical and Biomolecular Engineering. He holds a B.S. from Iowa State University and a Ph.D. from UC Berkeley. His research focuses on molecular simulations of ionic liquids, battery electrolytes, and sustainable materials. He leads the Molecular Simulation Design Framework (MoSDeF) project, emphasizing reproducible computational methods. Research interests include: molecular dynamics modeling of solvation phenomena, design of green refrigerants, and understanding ionic liquid behavior in energy storage systems. Notable contributions involve machine learning applications for material discovery and thermophysical property prediction. Recent work explores solubility of noble gases in molten salts, structural dynamics in deep eutectic solvents, and optimizing hydrofluorocarbon separation processes. Maginn's interdisciplinary approach bridges computational chemistry, materials science, and environmental engineering.

Alexandra Boltassevaمشاهده پروفایل

استاد

• Nanophotonics
• Plasmonics
• Optical Metamaterials
• +۳ مورد دیگر

Alexandra Boltasseva is a Professor of Electrical and Computer Engineering and Materials Engineering at Purdue University, affiliated with the Birck Nanotechnology Center and the Purdue Quantum Science and Engineering Institute. A leading expert in nanophotonics and plasmonics, her research focuses on optical metamaterials, nanofabrication, and quantum information technologies. She has held editorial roles at Optical Materials Express and is recognized for advancing machine-learning-assisted photonic design. Moscow Institute of Physics and Technology (B.Sc., M.Sc.) Technical University of Denmark (Ph.D. in nanophotonics) Her research explores the discovery and optimization of nanophotonic structures, including plasmonic materials and metamaterials, for applications in quantum information, on-chip circuitry, and sustainable technologies. Key themes span low-loss photonic devices, tunable optical platforms, and nonlinear optical phenomena. Recent publications emphasize quantum photonics, machine learning for optical design, 2D materials, and time-varying photonic systems. Her work bridges fundamental physics and practical implementation across quantum computing, sensing, and energy conversion. Awarded by multiple societies, including: Fellow of the American Physical Society (2023) Fellow of the Materials Research Society (2021) Fellow of the National Academy of Inventors (2020) Blavatnik National Award Finalist (2018) IEEE Photonics Society Young Investigator (2013) Boltasseva leads the development of platforms for nanophotonic applications, with significant contributions to silicon nitride quantum emitters, plasmonic color printing, and photonic time crystals. Her lab’s work integrates experimental and theoretical approaches for next-generation photonic technologies.

Samuel Ippolitoمشاهده پروفایل

دانشیار

• Nanotechnology
• Chemical Sensing
• Materials Engineering
• +۳ مورد دیگر

Associate Professor Samuel Ippolito is affiliated with the School of Engineering at RMIT University, Melbourne. His research focuses on nanotechnology, chemical sensing, and materials engineering, particularly in acoustic-based sensors, nano-structured thin films, and mercury sensing technologies. He leads the Mercury Management and Industrial Chemical Sensors (MMICS) group within the Centre for Advanced Materials and Industrial Chemistry (CAMIC). Ippolito has coordinated courses in real-time systems engineering, software engineering design, and engineering computing. His recent work includes developing advanced functional nanomaterials for industrial gas sensing and exploring light-assisted sensing mechanisms. Research interests span nanomaterials synthesis, electrochemical biosensors, and spectroscopic techniques applied to environmental and medical diagnostics. Supervised projects highlight innovations in neuromorphic learning, spiking neural networks, and sensor fabrication. His contributions include high-impact studies on gas sensors, biosensor protocols, and AI-driven medical diagnostics. Ippolito collaborates across disciplines, integrating materials science with engineering and computational methods to address challenges in environmental monitoring and healthcare. Academic Roles: Associate Professor (2023–present), Lecturer (2014–2022) Labs/Teams: MMICS Group, CAMIC Centre Supervision: Over 17 research projects focusing on nanotechnology applications

Carlos Garcia-Cerveraمشاهده پروفایل

استاد

• Nonlinear Partial Differential Equations
• Materials Science
• Computational Physics
• +۵ مورد دیگر

Carlos Garcia-Cervera is a Professor of Mathematics at the University of California, Santa Barbara (UCSB), affiliated with the College of Letters and Science. He holds a B.S. from Universitat de València (1994) and a Ph.D. from New York University's Courant Institute (1999). His research focuses on nonlinear PDEs, materials science, and computational physics, particularly in micromagnetics, liquid crystals, and electronic structure theory. Key roles include Vice-Chair of the UCSB Mathematics Department (2009-2015) and leadership in NSF-funded initiatives like the IGERT program in Computational Science and Engineering. Notable awards include the NSF CAREER Award (2007-2012) and Bizkaia Talent Award (2014-2016). Research interests span: 1) Micromagnetics (domain wall dynamics, spin-polarized transport), 2) Liquid crystals (smectic phases, magnetic field effects), 3) Polymers (block copolymer self-assembly), and 4) Electronic structure methods (orbital-free DFT, multiscale modeling). Over 150 publications include seminal work on micromagnetic simulations and smectic A layer undulations. Advised 14 Ph.D. students and supervised 6 postdocs. Active in professional societies (AMS, SIAM) and editorial work. Current projects include magnetoelectric nanodevices and physics-informed neural networks for PDE control.

Greg Hirthمشاهده پروفایل

استاد

• Geophysics
• Tectonics
• Volcanology
• +۴ مورد دیگر

Greg Hirth is the Vice President for Research and Professor of Earth, Environmental, and Planetary Sciences at Brown University. He holds affiliations within the Department of Earth, Environmental & Planetary Sciences and has served as a researcher at institutions like Woods Hole Oceanographic Institution and MIT. His research focuses on experimental rock mechanics, deformation mechanisms in crustal and mantle lithologies, and structural geology. Recent awards include the 2024 Harry Hess Medal from the American Geophysical Union. Education: PhD (1991), ScM (1987), and BS (1985) from Brown University and Indiana University, respectively. Honors include Fellowships from the Mineralogical Society of America (2006) and AGU (2008). Key research areas include tectonics, volcanic processes, and the application of experimental flow laws to geophysical observations. He advises graduate students such as Nicholas Dygert and Brooks Proctor. His grants include studies on mantle dynamics, lower crustal viscosity, and plume-trench interactions. Labs/Teams: Not explicitly listed, but his work involves collaborations with institutions like WHOI and MIT, focusing on mantle rheology and deformation mechanisms.

Roman Koteckýمشاهده پروفایل

استاد

• Probability
• Statistical Physics
• Theory of Phase Transitions
• +۳ مورد دیگر

Roman Kotecký is a Professor of Mathematics at the University of Warwick's Mathematics Institute and a Senior Research Fellow at the Center for Theoretical Study (CTS) in Prague, with a joint appointment at Charles University's Department of Theoretical Physics. His research focuses on probability theory, statistical physics, and the mathematical analysis of phase transitions. He has contributed extensively to Pirogov-Sinai theory, cluster expansions, and the study of phase coexistence in lattice models. Education: Advanced degrees in theoretical physics and mathematics (details not explicitly listed). His work explores topics such as surface tension, nonlinear elasticity, and critical phenomena in systems like the Ising model and Potts models. Recent publications include studies on phase transitions in antiferromagnetic systems and the mathematical foundations of statistical mechanics. He has collaborated with leading researchers, including Marek Biskup and Alan D. Sokal, on groundbreaking analyses of partition function zeros and droplet formation. His contributions have advanced understanding of equilibrium crystal shapes and first-order phase transitions. Kotecký’s expertise bridges probability, analysis, and physics, making him a pivotal figure in interdisciplinary mathematical research.

James Kermodeمشاهده پروفایل

استاد

• Materials Modelling
• Multiscale Modelling
• Machine Learning in Materials Science
• +۵ مورد دیگر

Prof. James Kermode is a Professor of Materials Modelling at the University of Warwick's School of Engineering, serving as Head of Research (Deputy Head of School). He leads the EPSRC Centre for Doctoral Training in Modelling of Heterogeneous Systems (HetSys) and the Warwick Centre for Predictive Modelling (WCPM). His research focuses on multiscale materials modelling, machine learning, and uncertainty quantification in atomistic simulations. Education: PhD in Theoretical Chemistry and Materials Modelling from the Cavendish Laboratory, University of Cambridge (2004-2007), with postdoctoral roles at King's College London and the University of Cambridge. Research Interests: Development of algorithms and software for multiscale modelling, particularly machine learning interatomic potentials, fracture mechanics, and parameter-free modelling of chemomechanical failure processes. Notable projects include QM-accurate dislocation simulations and the creation of data-driven silicon potentials. Grants and Leadership: Director of HetSys CDT since 2023, member of UKCP Consortium (2023-2026), and PI for multiple EPSRC grants. Supervised projects on radiation damage databases (ENTENTE) and hydrogen diffusion in alloys. Labs/Teams: Directs Warwick Centre for Predictive Modelling and contributes to software projects like libAtoms/QUIP and matscipy.

Jan Stühmerمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Molecular Mechanics
• +۷ مورد دیگر

Jun.-Prof. Dr. Jan Stühmer is an Assistant Professor and Principal Investigator (PI) at the Helmholtz Institute for Information Technology (HITS). His research focuses on machine learning, artificial intelligence, and their interdisciplinary applications in molecular mechanics, protein structure prediction, and materials science. He explores advanced graph neural networks, neuro-symbolic systems, and geometric algorithms to address challenges in computational biology, weather forecasting, and computer vision. Key research interests include permutation-invariant learning models (e.g., Set-LLM), error correction in machine learning systems, and the development of physics-based force fields like Grappa. His work bridges theoretical computer science with practical applications in drug design, materials discovery, and climate modeling. Recent contributions highlight innovations in protein design through geometric algebra, dynamic graph representation learning, and the optimization of crystal structure networks. Dr. Stühmer’s methodologies often integrate spatial information learning and ensemble forecasting techniques, showcasing a commitment to impactful, cross-disciplinary research. Despite extensive publications since 2009, no awards or grants are explicitly mentioned in the provided data. His academic trajectory reflects a sustained focus on advancing machine learning fundamentals and their real-world implications.

Benoit Revil-Baudardمشاهده پروفایل

استادیار

• Computational Mechanics
• Materials Modeling
• Plasticity
• +۷ مورد دیگر

Benoit Revil-Baudard is an Assistant Professor in the Department of Materials Science and Engineering at the University of Arizona, part of the College of Engineering. He joined the university in August 2023 after a decade-long tenure as a Research Scientist in the Department of Mechanical and Aerospace Engineering at the University of Florida. PhD in Computational Mechanics, École des Mines de Paris, France MS/BS in Mechanical Engineering, Institut National des Sciences Appliquées de Lyon, France His research centers on computational mechanics and the modeling of heterogeneous and metallic materials under extreme conditions. Key interests include plasticity, damage and fragmentation, anisotropic behavior, advanced manufacturing, and finite element simulations. He has developed both numerical models and experimental frameworks with applications in defense technologies. His recent publications reflect a strong focus on anisotropic yield modeling, multi-scale simulation of metallic sheets, dynamic response of additively manufactured materials, and constitutive modeling of single and polycrystalline systems. The work spans aluminum, titanium, molybdenum, and energetic materials, combining theory, simulation, and experimental validation. Benoit Revil-Baudard has co-authored over 50 refereed papers, contributed to two books, and co-edited one. His scholarly output demonstrates sustained contributions to the mechanics of materials and forming processes. He advises on research in materials modeling and advanced manufacturing, though specific students are not listed. His work involves significant computational and experimental infrastructure, suggesting leadership in a research laboratory focused on extreme loading responses. Grants and funding sources are not explicitly mentioned.

• Theoretical Physics
• Quantum Optics
• Cavity Optomechanics
• +۱۲ مورد دیگر

Florian Marquardt is the Scientific Director and Head of the Theory Division at the Max Planck Institute for the Science of Light in Erlangen, Germany, and holds a part-time professorship at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). He leads a research group focused on the intersection of quantum physics, optics, and machine learning, contributing to foundational and applied advances in quantum technologies. His educational background includes a PhD summa cum laude from the University of Basel (2002), followed by postdoctoral research at Yale University and in the Swiss National Center for Competence in Research. He served as a Junior Professor at LMU Munich (2005–2010) and Full Professor at FAU (2010–2016) before joining MPL. Marquardt's research spans cavity optomechanics , quantum many-body physics , neuromorphic computing , and machine learning for physics . His group develops minimalist theoretical models to explain experimental phenomena and explores how AI can automate scientific discovery. He is particularly interested in energy-efficient analog computing devices inspired by neural networks and in leveraging physics to improve machine learning. The recent publications reflect a strong trend toward integrating artificial intelligence with quantum science, including automated experiment design, quantum error correction via neural networks, and neuromorphic photonics. There is also sustained work in quantum measurement, topological photonics, and nonequilibrium dynamics, showing both breadth and depth in theoretical physics. Emmy-Noether Fellowship (DFG, 2007) Walter Schottky Prize (DPG, 2009) ERC Starting Grant (2011) Marquardt actively supervises research and teaches advanced courses such as 'Machine Learning for Physicists' and 'Foundations of Quantum Mechanics' at FAU. His group participates in major research networks including the Max Planck School of Photonics, ELLIS, and the European Laboratory for Learning and Intelligent Systems. The Theory Division fosters collaborations on quantum technologies and neuromorphic systems, with strong emphasis on interpretability, generalization, and hardware implementation.