جست‌وجوی استاد و پژوهشگر

• Biomechanics
• Tissue Engineering
• Prosthetics Design
• +۵ مورد دیگر

Professor Mark Thompson is an Associate Professor in Engineering Science at the University of Oxford and a Tutor/Fellow at Wadham College. He holds an MEng in Engineering and Materials Science (Oxford, 1997) and a PhD in Biomechanics from the University of London (2001). His research focuses on biomechanical engineering with emphasis on tissue repair, prosthetic design, and trauma modeling. Key projects include the OxVent ventilator and in vitro microvasculature systems for traumatic injury studies. His work bridges clinical and engineering challenges, addressing issues like prosthetic maintenance, musculoskeletal modeling, and biomaterials for regenerative medicine. Thompson collaborates with institutions like the Institute of Biomedical Engineering and has contributed to open-source frameworks for motion capture analysis. Current research trends involve mechanobiology of soft tissues and bioprinting for trauma research. Labs/Teams: Affiliated with the Institute of Biomedical Engineering and Oxford e-Research Centre. His work aligns with translational medicine and emergency medical device development. Future directions include optimizing 3D-printed bio-artificial tissues and advancing tactile feedback systems for prosthetics.

Noah Simonمشاهده پروفایل

دانشیار

• Machine Learning
• High-dimensional Estimation
• Clinical Trial Design
• +۴ مورد دیگر

Noah Simon is an Associate Professor in the Department of Biostatistics at the University of Washington School of Public Health. His research focuses on high-dimensional statistical methods, machine learning, and their applications in biomedicine. He develops computational tools for genomic and clinical data analysis, including penalized regression techniques and adaptive clinical trial designs. Education: B.A. Mathematics, Pomona College (2008) Ph.D. Statistics, Stanford University (2013), advised by Robert Tibshirani Research Interests: Dr. Simon specializes in high-dimensional estimation, algorithm optimization, and clinical trial methodology. His work addresses challenges in biomarker discovery, imaging-based diagnostics, and genomic data analysis. Key areas include sparse-group lasso regularization, adaptive enrichment designs for personalized medicine, and scalable computational methods for big data. Grants & Funding: NIH Director's Early Independence Award ($250k/year, 2014–2019) Amazon and Google Cloud Computing Grants for biomarker research Awards: Forbes 30 Under 30 in Science (2015) NSF Graduate Research Fellowship Honorable Mention (2010) Weiland Fellowship (2011–2013) Advising: He mentors PhD and MS students in biostatistical methodology and data science, with current advisees including Jean Feng, Brayan Ortiz, and Jeremy Roth. Notable collaborations include work on neural activity detection via calcium imaging (SCALPEL) and nonparametric variable importance assessment using neural networks. Lab & Affiliations: Based at the Hans Rosling Center for Population Health, his group develops open-source software (e.g., sgl , standGL ) and contributes to biomedical data science initiatives at UW.

Gina R. Baiمشاهده پروفایل

استادیار

• Software Engineering
• Computer Science Education
• Unit Testing
• +۵ مورد دیگر

Gina R. Bai is an Assistant Professor of the Practice in the Department of Computer Science at Vanderbilt University's School of Engineering. Her research focuses on software engineering and computer science education, particularly addressing student challenges in software testing and programming practices. She holds a B.S. in Computer Science from Wake Forest University (2016) and a Ph.D. from North Carolina State University (2022), advised by Kathryn T. Stolee. Her work explores testing checklists, generative AI's role in education, fairness in machine learning, and DEI in computing communities. Education: B.S. Computer Science (Wake Forest), Ph.D. Computer Science (NC State) Research Themes: Testing pedagogy, AI in education, DEI, code search Notable Projects: Testing checklist interventions, analysis of ChatGPT in CS1, fairness metrics Her publications emphasize student testing practices, the impact of educational tools, and ethical considerations in software development. She teaches courses like CS 1101 and CS 2201 at Vanderbilt, integrating research into curriculum design.

• Scientific Computing
• Computational Fluid Dynamics
• Finite Element Methods
• +۳ مورد دیگر

Johan Jansson is an Associate Professor in Scientific Computing at KTH Royal Institute of Technology and BCAM (Basque Center for Applied Mathematics). He leads research in predictive Direct FEM Simulation (DFS) for aerodynamics and multiphase flows, and co-founded Icarus Digital Math as CEO. His work includes the FEniCS open-source finite element software project and MOOC-HPFEM educational initiatives. He holds roles as Director of the Center for Digital Math and collaborates internationally in computational science. Research focuses on high-performance computing (HPC), fluid-structure interaction (FSI), biomedical modeling, and renewable energy systems. Notable contributions include adaptive FEM frameworks for turbulent flow, vocal fold simulations, and wave energy converter modeling. His work bridges academic research with industrial applications, leveraging FEniCS-HPC and Unicorn solvers. Key achievements include election to the IVA Royal Swedish Academy of Sciences 100-list and securing the Severo Ochoa Center of Excellence Award. He has pioneered open-source tools like SimTek and contributed to major projects like the Salter Sink and vocal production modeling. Teaching responsibilities include courses on database technology, computational fluid mechanics, and research methodology. He actively engages in large-scale simulation projects involving marine energy, cardiac ablation protocols, and aerodynamic optimization.

Ana Paivaمشاهده پروفایل

استاد

• Social Robotics
• Affective Computing
• Virtual Agents
• +۵ مورد دیگر

Ana Paiva is a Full Professor in the Department of Computer Science and Engineering at the University of Lisbon's Instituto Superior Técnico (IST), and coordinator of the GAIPS research group at INESC-ID, now focused on AI for People and Society. She holds a Katherine Hampson Bessell Fellowship at Harvard University's Radcliffe Institute for Advanced Study. Her work centers on creating socially intelligent AI and robots through agent-based approaches, emphasizing emotional and cultural competencies. Key research areas include Social Robotics, Affective Computing, and Pro-social Computing. She leads projects like the EU-funded ANIMATAS ITN network and Portugal's AMIGOS/AGENTS initiatives. Notable contributions include the FAtiMA Toolkit for developing emotional AI agents and pioneering work in hybrid human-machine societies. She has received awards such as the EurAI Fellowship (2019) and the Blue Sky Ideas Award at AAAI’18. Her research explores how machines can foster prosocial behaviors like altruism and cooperation, with applications in education, therapy, and ethical AI design. Her advising includes PhD students like Fernando Santos (Victor Lesser Award winner) and Elmira Yadollahi (ACM award recipient). Current projects focus on inclusive robotics for mixed-ability classrooms and ethical frameworks for human-robot collaboration.

Francisco Correia dos Santosمشاهده پروفایل

استاد

• Computer Science
• Evolutionary Biology
• Economics
• +۱۰ مورد دیگر

Francisco C. Santos is a Full Professor at Instituto Superior Técnico (IST), University of Lisbon, and currently serves as Vice-President of the Portuguese Science Foundation (FCT). He holds a PhD in Computer Science from Université Libre de Bruxelles (ULB), Belgium, where he was a Marie Curie Fellow. His research spans computer science, evolutionary biology, economics, and physics, focusing on cooperation dynamics, environmental governance, network science, and AI's societal impact. He leads the GAIPS and ATP research groups at INESC-ID, Lisbon. Education PhD in Computer Science, Université Libre de Bruxelles (ULB), Belgium (Marie Curie Fellowship) FNRS Senior Researcher at ULB's Machine Learning Group Research Interests His work bridges AI, complex systems, and social dynamics. Key areas include: - Evolution of cooperation and social norms - Climate governance and collective-risk dilemmas - Network science and urban planning - Machine learning and robotics applications Awards Young Scientist Award (German Physical Society) CGD/ULisbon Prize in Computer Science AAAI Blue Sky Award IST Teaching Excellence Awards (multiple) Advising & Grants Supervised over 30 PhD and MSc students, many of whom have gone on to prestigious postdocs and academic roles. Co-headed research groups and led projects funded by FCT-Portugal and EU initiatives. Labs & Teams Leads the Group of Artificial Intelligence for People and Society (GAIPS) and the ATP interdisciplinary group at INESC-ID. Collaborates internationally on AI ethics and environmental policy.

Hantao Cuiمشاهده پروفایل

دانشیار

• Power Electronics
• Renewable Energy Systems
• Power System Simulation
• +۳ مورد دیگر

Hantao Cui is an Associate Professor in the Department of Electrical and Computer Engineering at NC State University. He previously served as an Assistant Professor at Oklahoma State University (2021–2024) and earned his Ph.D. in Electrical Engineering from the University of Tennessee, Knoxville (2018). His research focuses on applying mathematical and computational methods to model, simulate, and analyze modern power systems, particularly those dominated by renewable energy sources and inverter-based technologies. He is the author of the ANDES simulation tool and a recipient of the NSF CAREER Award (2024) and the 2020 R&D 100 Award for his work on the CURENT Large-Scale Testbed. His expertise spans power electronics, grid stability, and cyber-physical system integration. Education: Ph.D. in Electrical Engineering, University of Tennessee, Knoxville (2018) M.S. in Electrical Engineering, Southeast University, China (2013) B.S. in Electrical Engineering, Southeast University, China (2011) Research Interests: Large-scale power system simulation and modeling Inverter-based resource control and grid stability Renewable energy integration and optimization Cyber-physical system testbed development Open-source software tools for power systems analysis Key Contributions: Developed ANDES, an open-source framework for power systems modeling Advanced virtual inertia scheduling (VIS) techniques for microgrids Pioneered data-driven adaptive control strategies for grid-forming inverters Awards & Honors: NSF CAREER Award (2024) R&D 100 Award (2020) Best Paper Award at IEEE PES General Meeting (2022) Senior Member of IEEE (2020) Grants & Labs: Principal Investigator for NSF-funded research on inverter-based grid stability Core contributor to the CURENT Large-Scale Testbed project

Eirik Keilegavlenمشاهده پروفایل

پژوهشگر

• Mathematical Modeling
• Numerical Methods
• Porous Media
• +۵ مورد دیگر

Eirik Keilegavlen is a Researcher at the Department of Mathematics, University of Bergen. His primary research focuses on developing mathematical models, numerical methods, and simulation tools for multiphysics processes in porous media, particularly in geothermal energy, CO 2 storage, and subsurface energy systems. He leads the development of the open-source software PorePy, designed for simulating processes in fractured porous media. His work emphasizes coupled problems involving fluid flow, heat transfer, and mechanical deformation. Key research interests include: Mathematical modeling of coupled thermal-hydro-mechanical processes Numerical discretization methods for fractured media Development of open-source simulation tools Applications in geothermal energy extraction and carbon sequestration Recent publications highlight advancements in: Uncertainty quantification for CO 2 leakage Viscous fingering in fractured reservoirs Automated solver selection for multiphysics systems Collaborations involve interdisciplinary teams addressing challenges in geothermal reservoir stimulation, fault mechanics, and high-performance computing. His work bridges theoretical developments with practical applications in energy and environmental systems.

Peter Jansenمشاهده پروفایل

دانشیار

• Natural Language Processing
• Artificial Intelligence
• Cognitive Science
• +۵ مورد دیگر

Peter Jansen is an Associate Professor at the University of Arizona's College of Information Science with a joint appointment at the Allen Institute for Artificial Intelligence (Ai2). He specializes in natural language processing (NLP), cognitive artificial intelligence, and automated scientific discovery. His research focuses on virtual environments for scientific reasoning, such as ScienceWorld and DiscoveryWorld, and methods for explainable AI through projects like the Explanation Bank. Education: PhD in Psychology and Neuroscience from McMaster University, and a Bachelor of Independent Studies from the University of Waterloo. His interdisciplinary background combines NLP, computer science, physics, and electrical engineering. Key projects include open-source hardware initiatives like the science tricorder (featured in over 50 media outlets and exhibited at the German Museum of Technology in Berlin) and TextWorldExpress, a text-game simulator. His work emphasizes grounding science education through sensing and developing AI systems capable of systematic reasoning and explanation generation. Current roles include advising students in automated scientific discovery and contributing to cross-listed courses in Computer Science and Linguistics. He maintains an active presence in academic outreach through teaching, conferences, and publications.

Pierre Kennepohlمشاهده پروفایل

استاد

• Electronic structure & bonding
• X-ray absorption and emission spectroscopy
• Molecular electron spin qubits
• +۳ مورد دیگر

Pierre Kennepohl is a Professor and Associate Dean (Innovation) in the Faculty of Science at the University of Calgary, Department of Chemistry. He holds a PhD in Physical Inorganic Chemistry from Stanford University (2002) and a BSc in Chemistry from Concordia University. His research focuses on electronic structure & bonding, X-ray absorption/emission spectroscopy, molecular electron spin qubits, and data integrity in chemical analysis. He leads initiatives in Energy Innovations for Today and Tomorrow (2013-2021). His recent work spans catalyst design, halogen bonding mechanisms, and spectral analysis tools like SpectraFit. He advises on chemical education through courses like CHEM 201 and contributes to interdisciplinary collaborations. Research highlights include studies on CO2 reduction catalysts, copper-based antimicrobial materials, and computational modeling of reactive intermediates. His articles bridge theoretical insights with experimental validation, emphasizing sustainable chemistry and advanced spectroscopic techniques. He maintains active labs in SB231 (research office) and SB234 (laboratory).

David Ryan Koesمشاهده پروفایل

دانشیار

• Computational Drug Discovery
• Deep Learning
• Discrete Algorithms
• +۳ مورد دیگر

David Ryan Koes is an Associate Professor in the Department of Computational and Systems Biology at the University of Pittsburgh, affiliated with the Joint CMU-Pitt PhD Program in Computational Biology. His research focuses on computational drug discovery, deep learning, and discrete algorithms, aiming to develop novel methods for rapid drug development and open-source software tools. He holds an office at 3064 Biomedical Science Tower 3 and 748 Murdoch Building. Key roles include Associate Director of the CPCB program and leadership in initiatives like CompBio Academy. His software contributions include libmolgrid, gnina, and 3Dmol.js, which advance molecular modeling and visualization. Research interests emphasize AI-driven drug discovery, including molecular docking, pharmacophore modeling, and generative models for molecule design. Recent work involves deep learning for protein structure prediction and pharmacophore elucidation. He has secured NIH grants (e.g., R35GM140753) and collaborations with institutions like CMU and industry partners. Advising over 25 students in computational biology, biotech, and data science programs, Koes bridges academia and industry through projects like Pharmit and the Teach-Discover-Treat initiative. His lab's work spans from foundational ML research to applied drug discovery, with a focus on open science and reproducibility.

• Geochemistry
• Geophysics
• Thermal Modeling
• +۳ مورد دیگر

Dr. Derrick Hasterok is a Lecturer in the School of Physics, Chemistry and Earth Sciences at the University of Adelaide within the Faculty of Sciences, Engineering and Technology. His research focuses on thermal structure of Earth's lithosphere, integrating laboratory measurements and data analytics to model heat flow and crustal properties. He leads projects on crustal hydration, metamorphic processes, and geophysical modeling, with collaborations on global geochemical databases and open-source software development. Research Interests: Thermal modeling of lithospheric processes, geochemistry, data analytics, crustal dynamics, and the interplay between heat production and tectonic systems. His work addresses continental-scale heat flow patterns, metamorphic evolution, and the impact of fluid flow on crustal radioactivity. Publications highlight advancements in thermal conductivity models, Antarctic geothermal studies, and continental tectonic evolution. He advises on Honours and PhD projects, emphasizing geochemical database analysis, crustal composition modeling, and physical property measurements. His professional activities include contributions to the Global Heat Flow Database and GitHub repositories for data tools. Research locations span Antarctica, Australia, and the western United States, with a focus on linking geochemical and geophysical datasets.

James Bradburyمشاهده پروفایل

مدرس

• Machine Learning
• Sound Art
• Computer Music
• +۳ مورد دیگر

James Bradbury is a Music Lecturer at the Conservatorium of Music, University of Western Australia. He holds a PhD in Music Technology (2021) from the same institution. His research focuses on integrating machine learning into music creation and performance, with expertise in sound art, experimental art, and software development for creative applications. Previously, he served as a Post-Doctoral Research Fellow in Creative Coding at the University of Huddersfield (2021–2022). Education: Doctor of Philosophy in Music Technology, University of Western Australia (2021) Research Interests : Bradbury explores machine learning's role in shaping contemporary music through real-time interactive systems, sound art installations, and experimental music performance. His work emphasizes computational tools for creative processes, including the Fluid Corpus Manipulation (FluCoMa) platform, which enables novel compositional workflows. He investigates how algorithmic approaches can expand artistic expression while maintaining human creativity at the core. Research Trends : Recent works highlight interdisciplinary collaborations between music and computer science, particularly in developing open-source tools like FluCoMa. His performances and publications demonstrate a focus on non-narrative musical forms and real-time algorithmic generation. The 2025 Audible Edge performance exemplifies this through live machine learning-driven sample selection. Awards and Grants : No specific awards or grants mentioned in the provided texts. His contributions are primarily reflected in creative outputs and academic publications. Labs and Teams : Active within the Conservatorium of Music, Bradbury collaborates with institutions like the University of Huddersfield and participates in festivals such as Audible Edge. His work is closely tied to the FluCoMa research collective, emphasizing open-source software development for artistic innovation.

• Signal Processing
• Image Processing
• Smart Environments
• +۶ مورد دیگر

Manuel Reis is an Associate Professor with Agregação at the University of Trás-os-Montes e Alto Douro (UTAD), Portugal, in the Electrical Engineering Department. His research focuses on signal/image processing, smart environment systems, and multimedia education technologies. He is affiliated with the Institute of Electronics and Telematics Engineering of Aveiro (IEETA) and collaborates with Brazil's IFPA-Santarém in computing education research. Education: Bachelor's in Electrical Engineering (University of Trás-os-Montes e Alto Douro, 1991) Master's in Electronics and Telecommunications (University of Aveiro, 1996) PhD in Electrical Engineering (University of Aveiro, 2001) Research Interests: Signal & Image Processing Cybersecurity for IoT and Smart Environments Education Technology (e-learning, multimedia tools) 5G and Edge Computing Applications Artificial Intelligence in Agriculture and Healthcare Recent articles highlight his work on cybersecurity in connected vehicles, federated learning for IoT security, and IoT-based systems for drowsiness detection. He has contributed to projects involving smart city infrastructure, sustainable waste management, and low-cost biomedical devices. His educational research explores e-learning frameworks, student engagement metrics, and innovative teaching methodologies in engineering education. Lab/Affiliations: Institute of Electronics and Telematics Engineering of Aveiro (IEETA) Multidisciplinary Research Group IFPA-Santarém-Brazil (Computing in Education)

John Hughesمشاهده پروفایل

دانشیار

• Biostatistics
• Health Data Science
• Statistical Computing
• +۶ مورد دیگر

John Hughes, PhD, is an Associate Professor and Chair of the Department of Biostatistics and Health Data Science at Lehigh University's College of Health. With nearly 30 years of experience in higher education, he has held positions at institutions including Frostburg State University, the University of Minnesota, and Pennsylvania State University. His methodological research focuses on statistical models for dependent data, Bayesian methods, and spatial and spatiotemporal analysis. He has developed numerous software packages for R and Perl, including copCAR , ngspatial , and krippendorffsalpha . Dr. Hughes' interdisciplinary work spans environmental health, bioimaging, vaccine hesitancy, and spatial epidemiology of HPV-related cancers. He has consulted for organizations such as the Courage Kenny Research Center and Temple University. His education includes a PhD in Statistics from Penn State University and an MS in Applied Computer Science from Frostburg State University. His research emphasizes statistical computing and the application of advanced models to health data. Recent work includes methodologies for agreement coefficients, environmental noise measurement, and spatial analysis of vaccination refusal patterns. Dr. Hughes teaches courses in biostatistics, data science, and programming, reflecting his dual role as a teacher-scholar. Professional contributions include software development, collaborative research projects, and academic leadership. His work bridges computational innovation and real-world health challenges, positioning him as a key figure in modern biostatistical research.