جست‌وجوی استاد و پژوهشگر

Jane Wangمشاهده پروفایل

استاد

• Biological Physics
• Theoretical Condensed-Matter Physics
• Computational Fluid Dynamics
• +۴ مورد دیگر

Affiliations and Roles Professor Wang holds dual appointments as Professor of Physics and Mechanical and Aerospace Engineering at Cornell University. She is affiliated with the Sibley School of Mechanical and Aerospace Engineering and the College of Arts and Sciences. Education B.S. in Physics, Fudan University, Shanghai, China (1989) Ph.D. in Physics, University of Chicago (1996) NSF-NATO Postdoctoral Fellow, Theoretical Physics, Oxford University (1997) Visiting Member, Courant Institute of Mathematical Sciences, NYU (1997-1999) Research Her research focuses on the physics of living organisms, particularly insect flight dynamics , biophysics , and computational modeling . Key projects include: Dragonfly righting reflex mechanisms Neuro-mechanical control in fruit flies Unsteady aerodynamics and fluid-structure interactions Awards and Honors Simons Fellowship in Theoretical Physics (2020) Radcliffe Fellowship (2007) Cornell Provost's Award for Distinguished Scholarship (2005) David and Lucile Packard Fellowship (2002) Labs and Collaborations Her work integrates experimental and computational approaches, often conducted in collaboration with institutions like the Janelia Research Campus (HHMI) and the Joint Texas Experimental Tokamak (J-TEXT).

Sueda SAYLANمشاهده پروفایل

استادیار

• Memristive Devices
• Photovoltaics
• Light-Matter Interactions
• +۳ مورد دیگر

Dr. Sueda Saylan is an Assistant Professor at the Faculty of Engineering, Özyeğin University, since 2024. Her academic journey includes a Ph.D. in Interdisciplinary Engineering (2016) from Masdar Institute (now Khalifa University), postdoctoral research at Khalifa University (2016-2022), and an MSCA Postdoctoral Fellowship at Bilkent University (2022-2024). She has also held visiting researcher positions at MIT (2014) and the University of Tokyo (2016). Education Doctorate: Interdisciplinary Engineering, Masdar Institute of Science and Technology (2016) Master's: Microelectronic Manufacturing Engineering, Rochester Institute of Technology (2004) Bachelor's: Mechanical Engineering, Middle East Technical University (2002) Dr. Saylan's research focuses on memristive devices , photovoltaics , and light-matter interactions at micro/nanoscale . Her work bridges materials science and electronic engineering, with recent publications on memristor-based sensors, spectral filtering in silicon, and machine learning integration for biomedical diagnostics. Key trends from her 15 most recent articles (2013-2025) include: Advancing memristor technology for radiation sensing and vacuum monitoring Optimizing photovoltaic efficiency through light management and antireflection coatings Developing compact, low-power diagnostic devices for pathogen detection Exploring nanoscale electrode materials and switching mechanisms Applying Fourier transforms and interferometry in optical systems Scientific Awards Marie Skłodowska-Curie Actions (MSCA) Postdoctoral Fellowship (2022-2024) Dr. Saylan has received research support from prestigious programs and has contributed to interdisciplinary projects involving semiconductor physics, optical engineering, and biomedical diagnostics. Her collaborations span institutions like Khalifa University, MIT, and the University of Tokyo.

• Nuclear Engineering
• Reactor Design and Safety
• Thermal-Hydraulics
• +۳ مورد دیگر

Tomasz Kozlowski is an Associate Professor and Associate Head for Undergraduate Programs at the University of Illinois at Urbana-Champaign's Grainger College of Engineering, Department of Nuclear, Plasma, and Radiological Engineering (NPRE). He holds additional positions as Associate Professor at Poland's National Centre for Nuclear Research (NCBJ) and Affiliated Professor in Computational Science and Engineering at UIUC. His research focuses on multi-physics modeling, reactor design/safety, computational methods, and thermal-hydraulics. He has taught courses like NPRE 200 (Mathematics), NPRE 455 (Neutron Transport), and advanced modeling topics. Education: B.S., M.S., and Ph.D. in Nuclear Engineering from Purdue University (2000–2005), followed by a Docent Habilitation in Nuclear Power Safety from the Royal Institute of Technology (KTH, 2011). He has collaborated on a $2M DOE grant for fuel storage solutions and contributed to UIUC's submission for a micro-reactor license application. His work includes advanced reactor design, uncertainty quantification, and computational tools like TRACE and MCNP-ORIGEN. Research emphasizes reactor analysis methods, numerical solver development, and inverse uncertainty quantification. Over 100 publications span topics like TRISO fuel performance, BWR instability, and hydrogen production integration with microreactors. He serves as Associate Editor for Nuclear Technology and actively engages in international benchmarks (e.g., BEAVRS, OECD/NEA).

Cory Simonمشاهده پروفایل

دانشیار

• Machine Learning
• Bayesian Optimization
• Computational Materials Science
• +۳ مورد دیگر

Cory Simon serves as Associate Professor in the Department of Chemical, Biological, and Environmental Engineering within Oregon State University's College of Engineering. His research integrates machine learning, optimization, and chemical engineering to advance materials discovery and environmental sensing systems. His academic foundation includes a Ph.D. in Chemical Engineering from the University of California, Berkeley and a B.S. in Chemical Engineering from The University of Akron. Simon's work centers on Bayesian methodologies for scientific challenges, featuring: Bayesian optimization for adaptive materials synthesis Statistical inversion of physical systems with uncertainty quantification Computational design of nanoporous sensor arrays Stochastic algorithms for robotic environmental monitoring Recent publications demonstrate accelerating focus on multi-fidelity optimization for molecular design and atmospheric water harvesting, bridging chemical engineering with computational science through data-driven approaches. Leading The Simon Ensemble research group, Simon champions a versatile 'buffet-style' research philosophy—drawing from mathematics, statistical mechanics, and machine learning to address interdisciplinary problems across chemistry, materials science, and environmental engineering.

Clayton Scottمشاهده پروفایل

استاد

• Statistical Machine Learning
• Machine Learning Theory
• Artificial Intelligence
• +۵ مورد دیگر

Clayton Scott is a Professor of Electrical Engineering and Computer Science (EECS) at the University of Michigan, with a courtesy appointment in Statistics. He holds a joint appointment in the College of Engineering and is affiliated with MIDAS, AI Lab, and the Center for Computational Medicine and Bioimaging (CCMB). His research focuses on statistical machine learning theory and algorithms, with applications in medical imaging, nuclear engineering, climate science, and clinical diagnostics. He actively collaborates with researchers in psychiatry, nuclear engineering, and radiology. Education: PhD in Electrical Engineering (Rice University, 2004), MS (Rice, 2000), AB in Mathematics (Harvard, 1998). He teaches courses in machine learning (EECS 545), signal processing, and statistical methods. His work emphasizes developing scalable algorithms with theoretical guarantees, particularly in domains like functional neuroimaging, nuclear particle classification, and sepsis prediction. He advises ~1 PhD student annually and has mentored over 20 students. His grants include NSF, NIH, and DOE funding, focusing on topics like domain adaptation, label noise, and medical image registration. His lab develops open-source tools for robust kernel methods, mixture proportion estimation, and partial mixture modeling.

Mingyi Hongمشاهده پروفایل

دانشیار

• Optimization
• Machine Learning
• Signal Processing
• +۷ مورد دیگر

Mingyi Hong is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Minnesota , where he leads the OptimAI-Lab . His work bridges optimization theory , machine learning , and signal processing , with a focus on foundation models like LLMs and diffusion models. Education : Not explicitly mentioned in the text Current Projects : NSF grants on bilevel optimization, LLM unlearning, and inverse reinforcement learning Research Themes : Bilevel Optimization : Applications in LLM alignment, unlearning, and wireless systems LLM Safety : Unlearning, alignment with human feedback, robustness Diffusion Models : Inference-time alignment, adversarial training Distributed Optimization : Privacy-preserving algorithms, federated learning Recent Publications highlight trends in LLM unlearning (BLUR, LUME), optimization theory (Barrier Functions, νSAM), and diffusion models (Direct Noise Optimization). His group has secured NSF , AWS , Cisco , and Open Philanthropy grants. Scientific Recognition : IEEE Fellow (2025) SPS Best Paper Award (2022, 2021, 2018) Doctoral Dissertation Fellowship (2024) IBM Pat Goldberg Memorial Award (2022) He mentors PhD students like Siliang Zeng and Xinwei Zhang , and collaborates with institutions including Michigan State University , Amazon , and NIH on projects spanning UHF MRI technology to climate-smart agriculture .

• Plasma Physics
• Fusion Plasmas
• Applied Mathematics
• +۴ مورد دیگر

Nathan Garland is a Lecturer in Applied Mathematics and Physics at Griffith University, Australia. He is affiliated with the Queensland Quantum and Advanced Technologies Research Institute (QUATRI) and the Centre for Quantum Dynamics. Prior to joining Griffith, Garland conducted postdoctoral research at Los Alamos National Laboratory and served as sessional teaching staff at James Cook University. Education: PhD in Electrical and Electronic Engineering and Mathematics from James Cook University B.Eng (Hons) and B.Sc in Electrical and Electronic Engineering and Mathematics from James Cook University His research focuses on computational plasma modeling, with applications in low-temperature plasmas, tokamak fusion, electron transport in liquids, and deep learning integration for plasma simulations. He combines advanced numerical methods with experimental validation to address challenges in energy systems and plasma medicine. Recent publications highlight trends in plasma physics, machine learning-driven cross-section determination, and electron transport across gas-liquid interfaces. Garland contributes to fusion energy discourse through media appearances and peer review roles in journals like Plasma Sources Science and Technology and European Physical Journal D . Grants: Quantum Mechanics: The Missing Link? - $1.2M LANL LDRD grant (2019-2021) Digitally Disrupted Demos - $7.5K Griffith Sciences grant (2022) Supervision: Principal Supervisor for PhD project 'Better Modelling of Solvents' Associate Supervisor for PhD projects on landscape evolution modeling and non-equilibrium electron scattering Collaborations: Member of Tokamak Disruption Simulation (TDS) SciDAC Center IAEA Fusion Energy Conference Program Committee member

Mingfeng Wangمشاهده پروفایل

مدرس ارشد

• Continuum Robotics
• Legged Robotics
• Soft Robotics
• +۵ مورد دیگر

Dr. Mingfeng Wang is a Senior Lecturer in Robotics and Autonomous Systems at Brunel University London, affiliated with the Department of Mechanical and Aerospace Engineering within the College of Engineering, Design and Physical Sciences. His research focuses on specialized robotic systems including continuum, legged, soft, precision farming, and miniaturized robots. Chartered Engineer (CEng) with Engineering Council UK Fellow of the Higher Education Academy (FHEA) Member of IEEE, IEEE-RAS, IMechE, and IFToMM Editorial roles: Associate Editor of International Journal of Advanced Robotic Systems (JCR-Q3); Associate Editor of Frontiers in Robotics and AI (JCR-Q2); Editor of Information Processing in Agriculture (JCR-Q1), Biomimetic Intelligence and Robotics (JCR-Q1), and STEM Education Research expertise includes: Continuum Robotics : Design of extra-slender continuum robots (diameter-to-length ratio Legged Robotics : Parallel mechanism-based biped and hexapod robots for extreme environments Miniaturized Robotics : Active locomotion and drug delivery in capsule endoscopes Soft Robotics : Compliant end-effectors and bio-inspired designs Precision Farming : Laser weeding systems and agricultural automation Key scientific awards: BRIEF award (2022) TAROS Best Paper Post Nomination (2022) IFToMM Asian-MMS Best Paper Award (2014) Recent publications focus on: Cochlear implant surgery robotics Passive compliance in train fluid servicing Snake-biomimetic sealing surfaces Parallel kinematic manipulators Capsule endoscope image enhancement Professional services include conference organization (TAROS 2023/2024 Steering Committee; TAROS 2024 Programme Chair) and journal refereeing for IEEE-ASME Transactions on Mechatronics and Scientific Reports.

Bjorn Baumeierمشاهده پروفایل

دانشیار

• Multiscale Simulation
• Electronic Transport
• Computational Chemistry
• +۵ مورد دیگر

Bjorn Baumeier is an Associate Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e). His research group is part of the Centre for Analysis, Scientific Computing and Applications (CASA) and the Institute for Complex Molecular Systems (ICMS). He also participates in several research groups including Scientific Computing, ICMS Core, Eindhoven Hendrik Casimir institute, and Computational Quantum & Molecular Dynamics. His educational background includes: Diploma in Theoretical Solid State Science from the University of Münster PhD in Theoretical Solid State Science from the University of Münster Baumeier's research focuses on the development and application of multiscale simulation techniques for studying electronic transport processes in soft matter. His work combines approaches from computational chemistry, statistical physics, and mathematics to analyze the interplay between molecular electronic structure and material morphology. Additional research lines include studies of disordered biomolecular assemblies and super-coarse-grained modeling of soft granular materials. His group employs large-scale computer simulations linking quantum chemistry, classical Molecular Dynamics at various levels, and rate-based models. Recent publications (2024-2025) demonstrate a strong focus on charge transport phenomena in complex materials, with particular emphasis on interface effects in polymer composites, trap identification in molecular networks, and embedded many-body Green's function methods. His work bridges fundamental physics with practical applications in energy materials and opto-electronic devices. Scientific awards include: Vidi grant from NWO (The Netherlands Organisation for Scientific Research) in 2017 (€800,000) Baumeier has received significant research funding, most notably the Vidi grant focusing on understanding mechanisms underlying long-distance and spin-selective electronic transport in complex molecular systems. His research is often conducted in collaboration with multiple institutions and research groups within TU/e, indicating a strong interdisciplinary approach. His work has practical applications in opto-electronic devices and bio-molecular processes. His research group operates within the Computational Quantum & Molecular Dynamics group, which is part of several larger research initiatives at TU/e including ICMS and the Eindhoven Hendrik Casimir institute. This positioning allows for strong collaboration across physics, chemistry, and engineering disciplines.

Nicholas Warrenمشاهده پروفایل

استاد

• Polymer Science
• Flow Chemistry
• Online Monitoring
• +۲ مورد دیگر

Professor Nicholas Warren is a Chair in Sustainable Materials at the School of Chemical, Materials and Biological Engineering at the University of Sheffield. With a PhD from Sheffield and academic experience at Leeds University (2016-2024), his research integrates polymer chemistry with automation technologies. Education: University of Bristol (2005), University of Sheffield (PhD) Academic Positions: Postdoc at Sheffield (2005-2016), University Academic Fellow at Leeds (2016-2024), Associate Professor (2021-2024) Current Role: Chair in Sustainable Materials (2024-present) Research focuses on polymer science with flow chemistry , online monitoring , and artificial intelligence to advance sustainable materials. Key article trends include self-driving laboratories , multi-objective optimization , and nanostructured polymer systems . Scientific recognitions include: 2022 Macro Group UK Young Researchers Medal 2023 RSC Reaction Chemistry & Engineering Outstanding Early Career Paper Award Advisees span current and alumni PhD students like Dr Stephen Knox , Anna Morrell , and Dr Charlotte Pugsley . His team employs self-driving lab platforms that combine robotics, AI, and online analytics for accelerated materials discovery.

Scott E. Parkerمشاهده پروفایل

استاد

• Plasma Physics
• Magnetic Fusion Energy
• Computational Physics
• +۴ مورد دیگر

Scott E. Parker is a Professor of Physics at the University of Colorado Boulder and a Fellow at both the Renewable and Sustainable Energy Institute (RASEI) and the Center for Integrated Plasma Studies (CIPS). His expertise lies in theoretical and computational plasma physics for magnetic fusion energy. Education: Ph.D. in Engineering Science, University of California, Berkeley B.S. in Nuclear Engineering and Mathematics, University of Wisconsin, Madison His research centers on plasma turbulence simulation using advanced computational methods. Key areas include gyrokinetic theory, tokamak edge physics, and quantum information science applications to plasma modeling. The group develops particle-based simulations in five-dimensional phase space to study transport in magnetically confined plasmas, utilizing massively parallel computing and scientific visualization. Analysis of his publications reveals consistent focus on numerical techniques for plasma turbulence, particularly trapped electron modes and zonal flows. His work bridges fundamental kinetic theory with practical fusion reactor challenges, demonstrating qualitative agreement with experimental transport observations in tokamaks. Awards: RASEI Fellow CIPS Fellow Professor Parker leads the Parker Research Group, mentoring graduate students in computational plasma physics. The group collaborates on extreme-scale computing projects for fusion energy research, though specific grant details are not provided in source materials. Their work is integral to advancing predictive capabilities for next-generation fusion devices. The research group maintains active projects in magnetic fusion energy, utilizing GEM simulations and multiscale kinetic methods to address edge turbulence and transport phenomena critical for reactor viability.

Andrew Jason Christliebمشاهده پروفایل

استاد

• Computational Mathematics
• Data Science
• Nuclear Fusion Research
• +۳ مورد دیگر

Andrew Jason Christlieb is an MSU Research Foundation Professor in the Department of Computational Mathematics, Science and Engineering (CMSE) at Michigan State University. He serves as the founding director of the Center of Hierarchical and Robust Modeling of Non-Equilibrium Transport (CHaRMNET), a DOE-funded initiative advancing clean energy through fusion research. Previously, he was the founding chair of CMSE, leading its growth into a multidisciplinary department with undergraduate, graduate, and PhD programs in data science and computational methods. Under his leadership, CMSE hired 25 faculty and established programs that attracted 16 early-career awards from NSF, NIH, DoE, and DoD. Christlieb's research focuses on computing innovations for energy and education equity through initiatives like the Spelman College data science partnership. Leadership Roles: Founding Director, CHaRMNET Founding Chair, CMSE Department Research Themes: Development of advanced computational tools for fusion energy Integration of data science into STEM problem-solving Pioneering education pathways for underrepresented students Awards & Recognition: University Foundation Professor (2014) Recipient of multi-agency early-career program mentorship success Institutional Contributions: Established CMSE's academic framework including minors, bachelor’s, master’s, and PhD programs Developed faculty mentoring programs yielding 16+ early-career awards Launched Spelman College partnership for data science education equity

• Nuclear Reactor Safety
• Thermal-Hydraulics
• Computational Fluid Dynamics (CFD)
• +۳ مورد دیگر

Satya Prakash Saraswat is a Postdoctoral Researcher at KTH Royal Institute of Technology's Nuclear Science and Engineering Unit in Stockholm, Sweden. He holds a Ph.D. from the Indian Institute of Technology Kanpur, with expertise in thermal-hydraulics, nuclear reactor safety, computational fluid dynamics (CFD), and system code development. His work spans fission and fusion reactor analysis, including contributions to the VALIDATIO project (University of Pisa) for fusion safety tools and the ATLAS project (Khalifa University) for advanced reactor safety enhancements. Research interests focus on computational modeling, AI integration in nuclear safety, and experimental validation of safety systems. He has developed skills in both experimental and numerical techniques, addressing challenges in multiphase flow, reactor core dynamics, and material compatibility. Key projects include validation of ASYST and SIMMER codes for condensation phenomena and lead-lithium interaction studies. Publications highlight advancements in burn-up wave characterization, code stability analysis (RELAP5/SIMMER), and thermal-hydraulic safety assessments for reactors like ESBWR and ITER systems. His work emphasizes enhancing safety tools through rigorous validation and innovative methodologies.

Luca Guazzottoمشاهده پروفایل

استاد

• Plasma Physics
• Magnetohydrodynamics (MHD)
• Tokamak Equilibria
• +۳ مورد دیگر

Dr. Luca Guazzotto is a Professor in the Department of Physics at Auburn University, affiliated with the College of Sciences and Mathematics. He holds a Ph.D. from the University of Rochester (2005) and a Laurea in Nuclear Engineering from Politecnico di Torino (2000). His research focuses on plasma physics and magnetohydrodynamics, particularly equilibrium and stability properties of plasmas in magnetic-confinement devices like tokamaks, with an emphasis on macroscopic flow effects. He has developed influential codes such as FLOW2 and contributed to analytic equilibrium solutions for fusion applications. His work includes studies on ignition conditions, burning plasmas, and multi-fluid modeling. Guazzotto has held roles at Auburn since 2014, including Associate Professor (2014–2023) and visiting positions at Princeton Plasma Physics Laboratory and MIT's Plasma Science and Fusion Center. His research integrates computational tools (e.g., FLOW2, SIM2D) and analytical methods to advance fusion energy science. Key contributions include two-fluid equilibrium modeling, analytic solutions for the Grad-Shafranov equation, and multi-dimensional plasma stability analysis. Guazzotto's publications span journals like Phys. Plasmas and Journal of Plasma Physics , addressing topics ranging from tokamak ignition to pedestal formation mechanisms. Guazzotto's research outputs emphasize plasma dynamics and fusion reactor design, with recent works exploring realistic analytic equilibria and burning plasma profiles. His freely available codes (FLOW2, analytic equilibrium tools) facilitate broader scientific collaboration. While no explicit awards are noted, his extensive publication record and code development highlight significant academic impact.

Rong Chenمشاهده پروفایل

استاد

• Nonlinear Time Series Analysis
• Multivariate Time Series
• Monte Carlo Methods
• +۵ مورد دیگر

Rong Chen is a Distinguished Professor and Chair of the Department of Statistics at Rutgers University, within the School of Arts and Sciences. With a Ph.D. from Carnegie Mellon University, Professor Chen has established himself as a leading researcher in statistical time series analysis, Monte Carlo methods, and their applications across various fields. Professor Chen's research focuses on: Nonlinear and Multivariate Time Series Analysis Monte Carlo Methods, Statistical Computing and Bayesian Analysis Statistical Applications in Science, Engineering and Business His research trajectory has evolved significantly over the years, beginning with foundational work on nonlinear time series and moving toward more complex high-dimensional tensor time series analysis. Recent publications show a strong emphasis on matrix and tensor factor models for high-dimensional time series, reflecting the growing importance of analyzing complex structured data in modern applications. His work bridges theoretical statistical innovation with practical problem-solving across finance, engineering, and computational biology. Professor Chen has been recognized for his contributions to the field with prestigious fellowships: ASA Fellow (American Statistical Association) IMS Fellow (Institute of Mathematical Statistics) As Chair of the Department of Statistics, Professor Chen oversees academic programs including the Master in Financial Statistics and Risk Management (FSRM) and the Master in Data Science (Statistics Track) programs. His leadership extends to guiding research directions in the department and fostering collaborations across disciplines. Professor Chen has secured numerous research grants supporting work in time series analysis, statistical computing, and applications in finance, engineering, and bioinformatics. Professor Chen's research group maintains active collaborations with researchers in finance, engineering, and computational biology, applying statistical innovations to real-world problems including financial time series analysis, protein folding studies, HIV infection dynamics modeling, wind power forecasting, and nuclear material detection systems.