جستجوی استاد راهنمای دانشگاهی

Jakub Kostalمشاهده پروفایل

دانشیار

• Computational Chemistry
• Green Chemistry
• Predictive Toxicology
• +۳ مورد دیگر

Jakub Kostal is an Associate Professor of Chemistry and Director of the MS Environmental and Green Chemistry Program at George Washington University. He leads the Kostal Research Group, focusing on computational chemistry, green chemistry, and predictive toxicology. His work aims to develop computer models to predict chemical toxicity and design safer, sustainable chemicals. Education: PhD from Yale University (2012), B.A. from Middlebury College (2006). He collaborates with the Lapkin Group at the University of Cambridge on sustainable process engineering using AI. His research team includes graduate students Jillian Brejnik, Diana Garnica Acevedo, and Geetesh Devineni. Research interests center on reducing chemical hazards through computational methods, including predicting environmental persistence, optimizing chemical reactions for sustainability, and advancing machine learning tools for toxicity prediction. His team’s contributions include refining models for pesticide safety and designing bio-based alternatives. Publications highlight advancements in quantum mechanics modeling, in silico toxicity prediction, and sustainable chemical design. He actively engages with policymakers, including speaking at the White House on sustainable chemistry strategies.

Possu Huangمشاهده پروفایل

استادیار

• Protein Design
• Computational Biology
• Structural Biology
• +۴ مورد دیگر

Possu Huang serves as Assistant Professor of Bioengineering in the Department of Bioengineering at Stanford University's School of Engineering. His research group operates from the Shriram Center for Bioengineering, sharing laboratory space with the Bintu Lab while maintaining an active interdisciplinary program bridging computational and experimental protein engineering. His educational journey includes: B.A. in Molecular and Cell Biology (Biochemistry) from UC Berkeley Ph.D. in Biochemistry and Molecular Biophysics from Caltech Postdoctoral training as Senior Fellow at University of Washington Dr. Huang's research focuses on atomic-precision protein design through integrated computational-experimental approaches. His lab has pioneered breakthroughs including the first computationally designed protein-protein interface, TIM barrel fold design principles, and the eOD HIV immunogen. Current work leverages machine learning , structural biology , and library optimization to develop therapeutic proteins and nanotechnology platforms, with particular emphasis on achieving natural-level complexity in engineered systems. Analysis of his 2023-2025 publications reveals three dominant trends: (1) generative AI for all-atom protein design (e.g., Protpardelle, SHAPES), (2) immunology applications targeting MHC complexes for HIV vaccine development, and (3) optogenetic tools through engineered fluorescent proteins. These works consistently integrate deep learning with wet-lab validation to solve biomedical challenges. Dr. Huang has mentored nine graduate students to completion, including PhD candidates Carla (defended June 2025), Christian (defended May 2025), and master's students Shankara and Kirsten. His lab maintains active social media presence (@possuhuanglab) and regularly publishes high-impact research while training the next generation of protein engineers. The Huang Lab operates as a dynamic interdisciplinary hub where computational biologists collaborate with experimentalists to push the boundaries of protein design. Current projects include developing tetravalent nanobody platforms, photoswitchable binders for temporal protein control, and AI-driven solutions for HIV immunogen design, all conducted within Stanford's state-of-the-art bioengineering facilities.

Doug L. Jamesمشاهده پروفایل

استاد

• Computer Graphics
• Computer Sound Synthesis
• Physically Based Modeling and Animation
• +۳ مورد دیگر

Doug L. James is a Full Professor of Computer Science at Stanford University since 2015, following roles as Associate Professor at Cornell University (2006-2015) and Assistant Professor at Carnegie Mellon University (2002-2006). He holds a PhD in Applied Mathematics from the University of British Columbia (2001), alongside earlier degrees from the same institution and the University of Western Ontario. His research focuses on computer graphics, sound synthesis, and physically-based modeling, with notable contributions to fluid simulation, cloth animation, and medical modeling. Key achievements include the 2012 Technical Achievement Award from the Academy of Motion Picture Arts and Sciences for 'Wavelet Turbulence,' and the 2013 Katayanagi Prize. He serves as a consulting Senior Research Scientist at Pixar Animation Studios and has led roles like Technical Papers Chair at SIGGRAPH 2015. His work integrates physics-based principles with interactive systems, emphasizing real-time applications and data-driven methods. Research interests span sound synthesis for animations (e.g., cloth, water, impact sounds), deformable models for medical simulation, and tools like 'svMorph' for virtual surgery planning. His publications reflect a blend of algorithmic innovation and practical applications in film, gaming, and healthcare.

Joseph Ramseyمشاهده پروفایل

پژوهشگر

• Causal Inference
• Machine Learning
• Computational Epistemology
• +۴ مورد دیگر

Joseph Ramsey is a Researcher in the Department of Philosophy at Carnegie Mellon University , affiliated with the Dietrich College of Humanities and Social Sciences . He serves as Director of Research Computing and has been instrumental in developing computational infrastructure and algorithms for causal inference. Core projects: Tetrad (causal search algorithms), AProS (proof generator for logic), Causality Lab , and Laboratory for Symbolic and Educational Computing . His research spans causal modeling, algorithm design, and applications in neuroscience, bioinformatics, and education. He has contributed to software tools like Causal-learn and Py-Tetrad , enabling scalable causal discovery in high-dimensional datasets. He has received funding from NASA, NSF, and the University of Pittsburgh for projects ranging from Martian rover software to glaucoma detection models. His work integrates philosophy, computer science, and applied statistics.

Neville Hoganمشاهده پروفایل

استاد

• Rehabilitation Engineering
• Robotics
• Motor Neuroscience
• +۳ مورد دیگر

Neville Hogan is a Professor of Mechanical Engineering and Brain & Cognitive Sciences at MIT, and Director of the Newman Laboratory for Biomechanics and Human Rehabilitation. He holds a Ph.D. from MIT and has been a faculty member since 1979, leading roles in the Mechanical Engineering department's System Dynamics and Control division. His research bridges biology and engineering, focusing on motor neuroscience, rehabilitation robotics, and human-robot interaction. He co-founded Interactive Motion Technologies, Inc. and is a board member at Advanced Mechanical Technologies, Inc. Education: Dip. Eng. (Dublin College of Technology), M.S., M.E., and Ph.D. (MIT) Research interests include developing robots for neurological rehabilitation, such as gentle physiotherapy devices for stroke patients. His work emphasizes physically cooperative machines and human balance control analysis. Awards include Honorary Doctorates from Delft University and Dublin Institute of Technology, and the Silver Medal from Ireland’s Royal Academy of Medicine. Awards: Honorary Doctorates, Silver Medal, and other distinctions Led the Newman Lab, focusing on biomechanics and human rehabilitation. Advised numerous students (not listed here). His publications explore dynamic primitives, impedance control, and human-robot interaction, with applications in exoskeletons and rehabilitation therapies.

Nick Heardمشاهده پروفایل

استاد

• Computational Bayesian Inference
• Clustering
• Changepoint Analysis
• +۶ مورد دیگر

Nick Heard is a Professor and Chair in Statistics at the Department of Mathematics, Faculty of Natural Sciences, Imperial College London. His research focuses on computational Bayesian inference, clustering, and changepoint analysis applied to dynamic networks (e.g., computer networks, social networks) and bioinformatics. He leads the EPSRC-funded NeST project on Network Stochastic Processes and Time Series, collaborating with universities including Bristol, Oxford, and LSE. His work bridges statistical theory with applied problems in cyber-security and neuroscience. Research Interests - Modelling large dynamic networks - Changepoint analysis and anomaly detection - Statistical methods for cyber-security - Bayesian computation and inference - Spectral clustering and graph embeddings Grants & Collaborations - Co-leads the NeST project on Dynamic graph embeddings: procedures and inference - EPSRC Programme Grant (EP/T004870/1) supporting Network Stochastic Processes and Time Series research Software & Tools - Developed open-source packages for Bayesian changepoint analysis (e.g., changepoints ) - Code for p-value combination methods ( standardised_partial_product )

Philippe Dixonمشاهده پروفایل

استادیار

• Biomechanics
• Neuroscience
• Machine Learning
• +۳ مورد دیگر

Dr. Philippe Dixon is an Assistant Professor in the Department of Kinesiology & Physical Education at McGill University, with a division in Biomechanics and Neuroscience. He holds adjunct professor roles at the University of Montreal (School of Kinesiology and Physical Activity Sciences) and the University of Laval (Department of Kinesiology). His research focuses on human movement biomechanics using motion capture systems and wearable sensors, combined with machine learning for health and athletic performance optimization. He has expertise in gait analysis, muscle coactivation patterns in cerebral palsy, and predictive modeling of physiological states. Dr. Dixon earned a Post-doctoral fellowship in Public Health at Harvard University, a PhD in Engineering Science from the University of Oxford, and dual degrees in Biomechanics and Physics from McGill University. His education includes a Bachelor of Education in Mathematics and Physics (McGill), a Master of Science in Biomechanics (McGill), and a PhD in Engineering Science (Oxford). He has received grants such as the NSERC Discovery Grant (2022–2027) and the FRQSC AUDACE Grant (2022). His work emphasizes wearable sensor integration, with contributions to datasets like NACOB and tools like OpenOFM. He currently supervises Master’s and PhD students in biomechanics and machine learning applications. Key research themes include gait adaptations on uneven surfaces, machine learning for cough detection via smart garments, and musculoskeletal coordination in clinical populations. His articles span biomechanical modeling, wearable sensor validation, and neuro-musculoskeletal analysis, reflecting interdisciplinary innovation in human movement science.

• Biotechnology
• Systems Engineering in Bioprocessing
• Model-Based Tools
• +۴ مورد دیگر

Professor Cleo Kontoravdi is a Professor of Biological Systems Engineering at the Department of Chemical Engineering, Imperial College London, within the Faculty of Engineering. Her roles include Director of Postgraduate Studies (2021–present) and Postgraduate Admissions Tutor (2018–2021). She holds affiliations with key research centers such as the Centre for Process Systems Engineering, Centre for Synthetic Biology, and Future Vaccine Manufacturing Research Hub. Her research focuses on applying systems engineering principles to bioprocessing, integrating model-based tools like sensitivity analysis and optimization with experimental work on mammalian cell cultures and vaccine production. Key areas include metabolic flux analysis, media optimization, and multiscale modeling. Her recent publications highlight advancements in hybrid modeling frameworks, mRNA vaccine process design, and metabolic engineering of CHO cells. She actively contributes to vaccine manufacturing strategies and sustainable biopharmaceutical supply chains. Education: PhD (2007) and MEng (2002) in Chemical Engineering from Imperial College London. Professional experience spans academic roles (since 2007) and industry R&D at Lonza Biologics (2006–2007). Her work bridges computational biology, bioprocess engineering, and systems biology, addressing challenges in vaccine development and production efficiency. Awards: Not explicitly listed in the provided text. Research grants and collaborations are implied through her involvement in high-impact projects like the Future Vaccine Manufacturing Hub. She advises on bioprocess optimization, glycoengineering, and biomanufacturing sustainability.

Patrick Jennyمشاهده پروفایل

استاد

• Computational Fluid Dynamics
• Turbulent Reactive Flows
• Multiphase Flow Modeling
• +۳ مورد دیگر

Prof. Patrick Jenny is a Full Professor at the Department of Mechanical and Process Engineering and Head of the Institute of Fluid Dynamics at ETH Zurich. His research focuses on computational fluid dynamics (CFD), numerical methods for turbulent and multiphase flows, and reservoir simulation. He has held positions at ChevronTexaco and Cornell University, and received the National Latsis Prize 2005. PhD in CFD from ETH Zurich (1997) Postdoctoral work at Cornell University (1997–1999) Senior Researcher at ChevronTexaco (1999–2003) Research interests include: turbulent reactive flows, PDF modeling, multi-scale reservoir simulation, and data assimilation in engineering systems. He teaches courses on fluid dynamics, turbulence, and computational methods. Over 100 peer-reviewed publications span topics like fracture modeling, LES/RANS coupling, and particle-laden flows. His work bridges academia and industry, addressing challenges in energy systems, environmental engineering, and numerical algorithms. Winner: National Latsis Prize 2005 Led over 20 PhD projects and collaborates with institutions globally. His lab develops open-source tools for CFD and energy systems analysis.

Benno Liebchenمشاهده پروفایل

استاد

• Soft Matter Physics
• Active Matter
• Colloidal Systems
• +۳ مورد دیگر

Prof. Benno Liebchen holds a faculty position at the Technische Universität Darmstadt within the Institute for Condensed Matter Physics , part of the Faculty of Physics. He leads the Liebchen Group , dedicated to advancing research in the Theory of Soft Matter , focusing on active matter, colloidal systems, and non-equilibrium phenomena. His work explores collective behavior in self-propelled particles, phase transitions in active fluids, and adaptive strategies in smart materials. Research Interests include: Active matter dynamics and pattern formation Non-equilibrium statistical mechanics Biophysical systems and biomimetic design Computational modeling of soft matter Recent publications highlight breakthroughs in intelligent active particles , self-reverting vortices , and motility-induced phase coexistence . His lab develops tools like the AMEP Python package to analyze active systems. Teaching responsibilities include advanced modules in soft matter physics. Collaborative projects involve interdisciplinary approaches to microswimmer behavior and machine learning-driven optimization of collective systems. Contact: +49 6151 16-24509 / Office: S2|04 104

Ed Chienمشاهده پروفایل

استادیار

• Computer Science
• Graphics
• Computational Engineering
• +۳ مورد دیگر

Ed Chien is an Assistant Professor at Boston University's Department of Computer Science within the College of Arts & Sciences. He specializes in applying differential geometry and topology to graphics, computational engineering, and machine learning. Previously, he was a postdoctoral researcher at MIT's CSAIL and Bar-Ilan University. His work focuses on mathematically rigorous solutions to problems in geometric data processing and optimal transport. Education PhD in Mathematics, Rutgers University (2015) A.B. in Mathematics & Physics, Dartmouth College (2009) Research Highlights Dr. Chien's research includes fundamental studies on hexahedral mesh topology for Finite Element Modeling, optimal transport applications in machine learning, and singularity-free geometric algorithms. His work bridges theoretical mathematics with computational tools for engineering and graphics. Publications span top venues like NeurIPS, Eurographics, and SIGGRAPH, reflecting contributions to geometry processing and machine learning intersections. Program committee roles include Eurographics SGP (2019) and AAAI (2020). Awards & Recognition No specific awards listed, but notable service includes program committee memberships and peer-reviewed contributions to leading conferences. Grants & Advising Active in mentoring through academic positions but no explicit grant details provided. Research focuses on advancing geometric algorithms and optimal transport methodologies.

Lillian Chongمشاهده پروفایل

استاد

• Computational Biophysics
• Biomolecular Simulations
• Protein Binding Pathways
• +۳ مورد دیگر

Lillian T. Chong is a Professor in the Department of Chemistry at the University of Pittsburgh, affiliated with the Kenneth P. Dietrich School of Arts and Sciences. She leads the Chong Lab, focusing on computational biophysics and biomolecular simulations. Her research emphasizes developing advanced simulation methods like weighted ensemble (WESTPA) for studying rare events in biomolecules, such as protein folding, binding pathways, and conformational switches. Research Interests: - Development of weighted ensemble algorithms for long-timescale simulations - Protein-protein binding kinetics and unbinding pathways - Design of switchable proteins with enhanced dynamic properties - Integration of experimental data (e.g., NMR, EPR) with simulations Recent Article Trends: Recent work explores ligand unbinding mechanisms, glycan-mediated spike protein dynamics, and force field validation. The lab’s methods are applied to drug discovery, viral entry mechanisms, and enzyme catalysis. Awards & Honors: Gordon Bell Special Prize for HPC-Based COVID-19 Research (2020) NSF CAREER Award (2009-2014) Bellet Teaching Excellence Award (2017) Advising & Grants: Advised students including Darian Yang (PhD 2023) and Jeremy Leung (PhD 2023). Funded by NSF and industry grants, including work on SARS-CoV-2 spike protein dynamics and force field development. Labs & Teams: The Chong Lab collaborates with groups at CMU and NIH, developing open-source tools like WESTPA and LPATH . Research spans Pittsburgh’s computational biophysics community, with interdisciplinary projects in drug design and protein engineering.

• Computational Social Science
• Public Health
• Injury Prevention
• +۴ مورد دیگر

Associate Professor Jason Thompson holds an Associate Professor position in the Department of Psychiatry at the University of Melbourne. He is affiliated with the Faculty of Medicine, Dentistry and Health Sciences and previously served as Co-Director of the Transport, Health and Urban Systems (THUS) Research Laboratory at the Melbourne School of Design. He earned a PhD in Medicine (2015) from Deakin University, a Master's in Clinical Psychology, and a Bachelor of Science with Honours. His research focuses on computational social science applied to injury rehabilitation, compensation systems, and healthcare design. He has attracted over $5M in research funding and published over 100 articles. Key areas include agent-based modeling, systems dynamics, and policy analysis for public health challenges such as pandemic response and urban mobility. Thompson currently leads the NHMRC Centre of Excellence in Compensable Injury after Road Crashes. Grants: ARC Future Fellowship (2022), DECRA (2017) Awards: Best Paper Award (Computational Social Science Society of the Americas, 2017) Labs: THUS Research Lab (until 2024) His work bridges epidemiological modeling (e.g., influencing Victoria's 2020 pandemic exit strategy) and complex systems analysis for injury prevention and health system design.

Celia Reinaمشاهده پروفایل

دانشیار

• Materials Science
• Computational Mechanics
• Non-equilibrium Thermodynamics
• +۳ مورد دیگر

Celia Reina is an Associate Professor in the Department of Mechanical Engineering and Applied Mechanics at the University of Pennsylvania’s School of Engineering and Applied Science (SEAS). Her research focuses on multiscale modeling of materials, bridging statistical mechanics, thermodynamics, and machine learning. She develops novel frameworks for predicting non-equilibrium material behavior using data-driven methods and uncertainty quantification. Her work emphasizes integrating computational tools like neural networks (Stat-PINNs, VONNs) with physical principles to model dissipative systems, phase transitions, and mesoscale dynamics. Key areas include coarse-graining techniques, epistemic uncertainty analysis, and predictive modeling of complex materials under dynamic loading. Recent publications highlight advancements in stochastic systems, resonant metamaterials, and the derivation of thermodynamic models from particle-level fluctuations. She leads efforts in experimental-simulation co-design to enhance predictive capabilities in materials science.

Ruming Zhangمشاهده پروفایل

دانشگاهی

• Partial Differential Equations
• Inverse Problems
• Numerical Methods
• +۴ مورد دیگر

Prof. Dr. Ruming Zhang is a Tenure-Track Professor at TU Berlin's Faculty II - Mathematics and Natural Sciences, leading the Analysis and Applications group since May 2023. He specializes in numerical methods for partial differential equations and inverse problems, with a focus on wave scattering and periodic structures. His research bridges theoretical analysis and computational techniques, addressing challenges in areas like photonic crystals and non-destructive testing. Education & Career: PhD in Mathematics (Chinese Academy of Sciences, 2014) Postdoctoral Researcher at Michigan Technological University Marie-Curie Fellow (University of Bremen, 2015-2018) Junior Group Leader at KIT (Karlsruhe Institute of Technology, 2018-2023) Research Interests: Analysis and numerical methods for PDEs, inverse scattering problems, waveguide analysis, periodic structures, and their applications in nanotechnology and engineering. His work emphasizes high-order numerical schemes and theoretical frameworks for ill-posed problems. Key Contributions: Development of nonuniform mesh methods for periodic surface scattering, high-order numerical techniques for bi-periodic structures, and monotonicity-based shape reconstruction in waveguides. His methods address challenges in computational wave physics and mathematical modeling. Awards: Richard-von-Mises Prize (GAMM, 2023) Marie-Curie Fellowship (EU FP7-PEOPLE, 2015-2017) Teaching & Mentorship: Offers courses on inverse problems, scattering theory, boundary element methods, and applied analysis. Advises students on thesis topics in mathematical theory for photonic crystals and wave propagation. Actively promotes interdisciplinary collaboration between mathematicians and engineers. Grants & Projects: DFG Grant (2019-2024): Higher-order methods for acoustic scattering in periodic structures Marie-Curie COFUND Fellowship (Bremen TRAC, 2015-2017) Labs/Teams: Leads the Analysis and Applications research group at TU Berlin, focusing on interdisciplinary projects combining mathematical theory with computational tools for real-world applications.