جست‌وجوی استاد و پژوهشگر

• Microelectromechanical Systems (MEMS)
• Porous Silicon
• Infrared Systems
• +۷ مورد دیگر

Adrian Keating is a Professor in the School of Mechanical Engineering at The University of Western Australia (UWA), specializing in Microelectromechanical Systems (MEMS), porous silicon, and infrared sensors. He holds roles such as Mechatronics Course Advisor and Laser Safety Officer. His career spans over 20 years, including industrial experience at Calient Networks as Fiber Optics Technology Manager, where he developed high-yield MEMS-based products. Dr. Keating earned a Bachelor of Engineering (Honors) from The University of Melbourne and a Ph.D. from Telecom Research Laboratories, focusing on photonic communication networks. His research emphasizes optical sensors, MEMS, and IoT technologies. Notable contributions include patents for fiber-collimator designs and work on MEMS-based infrared sensors. He actively supervises students and leads projects in areas like soil and grain parameter assessment via microspectrometers. Key research interests include porous silicon materials, thermal sensing, and MEMS fabrication. He has secured grants totaling millions, such as the 2004 ARC Discovery Project on MEMS/NEMS technologies. Teaching responsibilities include units like Mechatronic Systems and Engineering Dynamics. Current projects explore infrared thermal imagers, microfluidics, and IoT-enabled beehive monitoring systems. His work aligns with UN Sustainable Development Goals through innovations in agriculture and environmental monitoring. Dr. Keating collaborates internationally, with recent projects in Japan (NTT) and the U.S. (UC Santa Barbara). His lab focuses on developing low-cost, high-performance sensors and imaging systems. Patents and peer-reviewed publications reflect his expertise in optical systems, micromachining, and material science. Future research aims to advance infrared imaging and MEMS-based sensor technologies for industrial and environmental applications.

Hongsong Fengمشاهده پروفایل

استادیار مهمان

• Machine Learning
• Topological Data Analysis
• Computational Biology
• +۳ مورد دیگر

Hongsong Feng is a Visiting Assistant Professor in the Department of Mathematics at Michigan State University (MSU), located in C309 Wells Hall. His research focuses on interdisciplinary applications of mathematics and machine learning to problems in computational biology, drug discovery, and topological data analysis. He specializes in combining advanced mathematical frameworks like persistent homology, algebraic topology, and differential geometry with machine learning techniques to analyze complex biological systems and molecular interactions. Key research interests include protein flexibility analysis, drug-target interaction networks, and the development of AI-driven methods for predicting protein-ligand binding affinities. His work often bridges theoretical mathematics with practical biomedical challenges, such as opioid use disorder treatment and cocaine addiction drug development. Recent projects involve leveraging transformers, knot theory, and generative network models to advance drug discovery and systems pharmacology. Notable contributions include methodologies like Persistent Sheaf Laplacian analysis, CAML (Commutative Algebra Machine Learning), and TIDAL (Topology-Inferred Drug Addiction Learning). These approaches integrate multiscale geometric learning and topological signal processing to model molecular dynamics and network complexities. His research also extends to numerical methods for solving elliptic interface problems and parabolic partial differential equations, reflecting a strong foundation in applied mathematics. Despite his extensive publication record, no specific academic awards or grants are explicitly listed in the provided materials. No formal advisees are mentioned, suggesting he may be early in his academic career or focused primarily on research collaborations.

Steve Bruntonمشاهده پروفایل

استاد

• Machine Learning
• Fluid Dynamics
• Control Systems
• +۵ مورد دیگر

Steve Brunton is the James B. Morrison Endowed Career Development Professor in Mechanical Engineering at the University of Washington, with an adjunct appointment in Applied Mathematics. He leads the Brunton Lab, focusing on data-driven discovery of dynamical systems, machine learning, and control strategies for complex systems. His research integrates techniques like dimensionality reduction, sparse sensing, and adaptive controllers in an equation-free context. Key areas include fluid dynamics (turbulence control, bio-locomotion, renewable energy), neuroscience, medical data analysis, and networked systems. Education: PhD in Mechanical and Aerospace Engineering (Princeton University, 2012); B.S. in Mathematics (Caltech, 2006). Awards: APS Fellow (2020) for contributions to fluid dynamics modeling and control. Publications: Over 100 peer-reviewed articles, including influential works on SINDy (Sparse Identification of Nonlinear Dynamics), PySINDy software, and textbooks like Data-Driven Science and Engineering . Lab & Teams: The Brunton Lab develops algorithms for scientific computing and control, with applications in aerospace, energy systems, and biomedical engineering.

Cui Ying Toeمشاهده پروفایل

مدرس

• Renewable Energy
• Photocatalysis
• Green Hydrogen
• +۳ مورد دیگر

Dr. Cui Ying Toe is a Lecturer in Renewable Energy Engineering at the University of Newcastle's School of Engineering, affiliated with the College of Engineering, Science and Environment. She holds a PhD in Advanced Manufacturing from the University of New South Wales and a Bachelor of Engineering from the University of Malaya. Her research centers on solar energy conversion technologies, including photocatalytic hydrogen generation, photoelectrochemical CO₂ reduction, and scalable photoreactor systems for sustainable fuel production. Her work bridges fundamental catalyst design and industrial applications, emphasizing techno-economic feasibility and AI-driven material discovery. Recent projects explore hybrid photocatalysis-photoelectrocatalysis systems and waste-derived chemical synthesis. Key research themes include: Photocatalytic reforming of organic waste for simultaneous H₂ and value-added chemical production Nanoscale engineering of kesterite and ferroelectric materials for enhanced solar fuel efficiency Machine learning optimization of photocatalyst performance Her 15 most recent publications (2021-2025) demonstrate a consistent focus on renewable energy materials, with emerging trends in defect engineering, single-atom catalysts, and solar-driven ammonia synthesis. Earlier work established foundational insights into plasma-functionalized coatings and biomimetic implant interfaces. She has received the Best Poster Award (2021) and Future Women Leaders Conference Award (2019) . Current industry partnerships target photocatalytic system upscaling and techno-economic validation. She coordinates courses in Bioenergy and Geothermal Systems, supervising PhD candidates in photoelectrochemical reactor design.

Raina M. Maierمشاهده پروفایل

استاد

• Microbial Ecology
• Bioremediation
• Environmental Microbiology
• +۳ مورد دیگر

Raina M. Maier is a Professor and Director of the UA Superfund Research Program at the University of Arizona. Her research focuses on microbial ecology in stressed environments, bioremediation of mining sites, and biosurfactant discovery. She holds a PhD in Microbiology from Rutgers University. Her work emphasizes the interplay between microbial communities, soil health, and plant adaptation in arid ecosystems, particularly in metal-contaminated environments. Key research areas include the microbial ecology of caves, deserts, and dust; phytoremediation strategies for mine tailings; and the development of glycolipid-based technologies for environmental cleanup. She has contributed to understanding plant-microbe interactions in metal tolerance and drought resistance, with applications in restoring degraded lands. Her research spans biogeochemical transformations in mine tailings, soil microbiome dynamics, and the impact of environmental stressors on plant and microbial systems. Collaborations involve interdisciplinary approaches to address environmental health disparities and sustainable remediation practices. Dr. Maier’s projects often integrate field studies with lab-based experiments, leveraging tools like machine learning for ecological monitoring and novel surfactants for metal recovery. She advocates for ecoengineering solutions to landscape restoration and community-driven environmental science initiatives.

• Microfluidics
• Nanotechnology
• Biomedical Engineering
• +۳ مورد دیگر

Vania Silverio is an Assistant Professor at the Department of Physics within the Instituto Superior Técnico of the University of Lisbon. Her research focuses on microfluidics, nanotechnology, and biomedical engineering applications, with particular expertise in metrology standards for microfluidic systems. She teaches courses such as Microfluidics and Micro and Nanofabrication Techniques. Her work integrates experimental methods with computational modeling to address challenges in fluid dynamics and device fabrication. Education: Vania holds a PhD in Mechanical Engineering from Instituto Superior Técnico (2016) and a 5-year Bachelor's Degree in Technological Chemistry from the Faculty of Sciences of the University of Lisbon (2003). Her doctoral research investigated micro-cooling technologies for power systems, receiving Distinction. Her undergraduate work included reaction calorimetry studies, earning top grades. Research interests span microfluidic device development, magnetic cytometry, and thermal management solutions. Her recent projects include portable bioaerosol samplers (SOLARIS), high-throughput screening systems, and metrology standards for lab-on-a-chip technologies. She collaborates with INESC Microsystems and Nanotechnologies, advancing applications in biomedical diagnostics and environmental monitoring. Notable contributions include pioneering leakage testing protocols for microfluidics and integrating neural networks with magnetic sensors for cytometry. Her work bridges fundamental research with practical applications, emphasizing reproducibility and precision in microscale systems.

• Analytical Chemistry
• Chemometrics
• Proteomics
• +۳ مورد دیگر

Lutgarde Buydens is a Full Professor and head of the Analytical Chemistry department at Radboud University in the Netherlands, within the Faculty of Sciences and Institute for Molecules and Materials. She has maintained a continuous academic career at Radboud University since 1989 and served as Vice Dean of the Faculty of Science from 2012-2015. Her educational background includes a Pharmacy degree (1978, cum laude) and PhD (1986, cum laude) from the Brussels Free University in Belgium, with doctoral research on "Structure activity relationships" under Prof. Luc Massart and Prof. Paul Geerlings. Prior to joining Radboud, she completed postdoctoral work at the University of Illinois Medical Center in Chicago (1982-1983) and research at the Free University of Brussels (1984-1988). Professor Buydens specializes in developing novel methods for chemometric data handling and interpretation of complex multivariate data in chemical and biological fields. Her research philosophy emphasizes methodological work valuable across application areas, with focus on application-oriented research that validates methods for pioneering science. Her work spans analytical chemistry, chemometrics, proteomics, metabolomics, and chemical data sciences, with particular attention to data that doesn't exhibit well-behaved statistical characteristics. Her publication record includes 264 scientific papers, 4 handbooks, and 270 conference papers, with 30% published in top 10% impact factor journals. Her work has received over 12,100 citations (Google Scholar), with an h-index of 51. Notably, her publications rank her 6th among chemometricians and 24th among analytical chemists globally, placing her in the top 2% of researchers worldwide in her field. EAS award for outstanding achievements in Chemometrics (2012) Christophe Plantin Award for outstanding accomplishments of a Belgian citizen abroad (2000) Honorary member of Italian Chemometric Society (1995) First Elsevier Chemometrics Award for young scientists (1992) Ranked 24th in The Analytical Scientist's Power List 2013 and 2015 (top 100 most influential people in analytical sciences) Professor Buydens has led research through numerous (inter)national collaborative projects, including five European Collaboration Consortia. Her iconic "blue chemometrics books" (Handbook of Chemometrics and Qualimetrics) serve as reference materials in the field with over 3,300 citations. Her work bridges theoretical method development with practical applications in chemistry and biology, demonstrating consistent excellence that has established her as a world leader in chemometrics and analytical chemistry.

Hamid Usefiمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Computer Vision
• +۷ مورد دیگر

Hamid Usefi is a Professor of Mathematics and Statistics at Memorial University of Newfoundland, with a cross-appointment to the Department of Computer Science. He earned his PhD in Mathematics from the University of Western Ontario in 2006, followed by postdoctoral fellowships at the University of British Columbia and the University of Toronto. His research focuses on algebra, machine learning, cryptography, and their applications in bioinformatics, genetics, finance, and industry. He has organized sessions on machine learning at Canadian Mathematical Society meetings and serves as an editor for Nature: Scientific Reports . Dr. Usefi's research interests include deep learning, computer vision, and medical imaging, alongside theoretical algebraic studies. His work bridges pure mathematics and applied domains, with contributions to biomarker discovery, CO2 adsorption modeling, and disease prediction using genomic data. He has been recognized with an NSERC Postdoctoral Fellowship and a MITACS NCE Fellowship. As an educator, he has supervised numerous graduate and undergraduate students in mathematics, statistics, and computer science, mentoring over 20 students since 2010. His publications span journals like Computational Statistics & Data Analysis , Scientific Reports , and Journal of Algebra , demonstrating expertise in both theoretical and applied research. Awards: NSERC Postdoctoral Fellowship (UBC), MITACS NCE Fellowship (University of Toronto) Service: Guest Editor for Scientific Reports (2024 collection on population health modeling) Conferences: Co-organizer of CMS sessions on mathematics of machine learning (2021–2024)

Ulrich Egertمشاهده پروفایل

استاد

• Neurotechnology
• Neuronal Network Dynamics
• Epilepsy Mechanisms
• +۳ مورد دیگر

Prof. Ulrich Egert is a Professor for Biomicrotechnology at the Department of Microsystems Engineering, Faculty of Engineering at the University of Freiburg. He also serves as Director of the Bernstein Center Freiburg. His research focuses on mechanisms underlying neuronal network dynamics, epilepsy, and neurotechnological systems. He holds a PhD in Biology from Eberhard-Karls University (1995) and a Habilitation from the Faculty of Biology, University of Freiburg (2005). Education: PhD in Biology, Eberhard-Karls University (1995) Habilitation in Biology, University of Freiburg (2005) Graduate studies in Biology/Zoology (Tübingen/Duke University) Research interests include spatial/temporal dynamics of neuronal networks, epilepsy mechanisms, neurotechnological hybrid systems, and electrophysiological recording techniques. His work combines experimental approaches (MEA recordings, optogenetics, calcium imaging) with computational modeling. Collaborations involve developing high-density recording devices and closed-loop stimulation systems. Key contributions include studies on hippocampal network reorganization in epilepsy, adaptive stimulation techniques, and nanostructured electrode coatings. His lab explores how network structure influences robustness and epileptiform activity, with applications in seizure control and drug discovery. Techniques used: Intracellular patch-clamp, MEA recordings, optogenetics, calcium imaging, microelectrode array technology.

• Robotics
• Human-Robot Collaboration
• Robot Manipulation
• +۸ مورد دیگر

Roel Pieters is a Professor in Automation Technology and Mechanical Engineering, specializing in robotics and human-robot collaboration. His research focuses on advancing automation in industrial settings through innovations in robotic manipulation, sensor-based systems, and cognitive architectures. He has contributed to the development of datasets like the 6DAPose synthetic assembly dataset and the Engine Assembly Dataset, aiding advancements in pose estimation and robotic control. His work aligns with UN Sustainable Development Goals, particularly in fostering innovation and industrial efficiency. Pieters has organized events such as the IEEE Future of Robotics in Finland Seminar and serves as a reviewer for journals like IEEE Transactions on Automation Science and Engineering. His research emphasizes safety, adaptability, and human-centric design in robotics applications, including collaborative tasks and industrial automation. Key themes in his work include multimodal human-robot interaction, cognitive systems for exploration and learning, and the integration of AI with robotics. He explores topics like gesture-based commands, neural curiosity mechanisms, and the personalization of robotics for Industry 5.0. His contributions span both theoretical advancements and practical deployments in manufacturing and service robotics.

Peter C. Amadioمشاهده پروفایل

استاد

• Hand Surgery
• Orthopedics
• Tendon Biology
• +۵ مورد دیگر

Peter C. Amadio, M.D. is a Professor of Orthopedics and Professor of Biomedical Engineering at Mayo Clinic College of Medicine and Science in Rochester, Minnesota. He holds the distinguished position of Lloyd A. and Barbara A. Amundson Professor of Orthopedics and serves as Principal Investigator of the Tendon and Soft Tissue Biology Laboratory. Dr. Amadio previously served as Dean for Research Academic Affairs (2012-2022) and is an active member of the Division of Hand Surgery in the Department of Orthopedic Surgery. Dr. Amadio's educational background includes an MD from Jefferson Medical College, Thomas Jefferson University, and a BS from Pennsylvania State University. His clinical training encompassed a Hand Fellowship at Thomas Jefferson University, Chief Residency in Orthopaedics at Massachusetts General Hospital (Harvard Medical School), and an Orthopaedic Residency at Harvard Combined Orthopaedic Residency Program. His research focuses on tendon biology and carpal tunnel syndrome, with particular emphasis on Tendon lubrication and adhesion prevention Ultrasound biomarkers for carpal tunnel syndrome Novel treatments including senolytic therapies for carpal tunnel syndrome Development of a comprehensive 3D atlas of hand anatomy Dr. Amadio collaborates extensively with the Center for Regenerative Biotherapeutics and the Robert and Arlene Kogod Center on Aging at Mayo Clinic. Analysis of Dr. Amadio's recent publications reveals a strong focus on translating basic science discoveries into clinical applications. His work spans biomechanics, tissue engineering, ultrasound imaging, and senolytic therapies, with an emphasis on developing non-surgical alternatives to traditional hand surgery procedures. The research demonstrates a progression from fundamental tendon biology toward clinical translation and commercialization. Among his notable scientific achievements: Distinguished Mayo Investigator (2018) CORR ORS Richard A. Brand Award for Outstanding Orthopaedic Research (2014) Fellow, American Institute for Medical and Biological Engineering (2021) President, Orthopaedic Research Society (2021-2022) Dr. Amadio has secured significant research funding including multiple NIH grants and Congressionally Directed Medical Research Programs funding. His laboratory has developed treatments that have already changed clinical practice in tendon surgery. Current projects include the FITCATS clinical trial investigating fisetin for carpal tunnel syndrome and development of a biological product to prevent tendon adhesions. The Tendon and Soft Tissue Biology Laboratory, directed by Dr. Amadio, employs a multidisciplinary approach combining orthopedics, biomechanics, biochemistry, dynamic ultrasound, and molecular and cell biology to improve outcomes in tendon repair and carpal tunnel syndrome treatment.

• accelerated materials science
• high-throughput synthesis
• machine learning
• +۳ مورد دیگر

Prof. Helge S. Stein is an Associate Professor (W3, tenured) at the Technical University of Munich (TUM), leading the Chair of Digital Catalysis within the TUM School of Natural Sciences. His research focuses on accelerating the discovery and optimization of materials and processes for energy storage and conversion through integration of machine learning, autonomous robotics, and high-throughput experimentation. Education & Career: 2013: MSc Physics, Georg August University Göttingen 2017: Doctor of Engineering, Ruhr-University Bochum 2017–2020: Postdoctoral Researcher at Caltech (Joint Center for Artificial Photosynthesis) 2020–2023: Tenure Track Professor at Karlsruhe Institute of Technology Since 2023: Associate Professor at TUM Research Interests: Stein’s group develops high-throughput systems for synthesis, characterization, and recycling of materials, with a focus on batteries and catalysts. They leverage data science and robotics to optimize materials-in-systems and bridge the gap between theoretical models and experimental validation. Collaborations include the Munich Data Science Institute (MDSI) and Munich Institute for Robotics and Machine Intelligence (MIRMI). Key Projects: Development of combinatorial synthesis machines and lab automation systems Bayesian optimization and explainable machine learning models Materials acceleration platforms (MAPs) for rapid material discovery Grants & Labs: Labs include CRC 2004-6 and CH631XX offices. Stein’s work is supported by initiatives like the Munich Catalysis Alliance (MuniCat) and international collaborations. Advising: Prospective students are encouraged to contact Stein or group representatives for project opportunities in areas like high-throughput robotics, data management, or electrode manufacturing.

Miguel Molina Solanaمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Data Science
• Energy Management
• +۷ مورد دیگر

Dr. Miguel Molina-Solana is an Associate Professor (Prof. Titular) at the Department of Computer Science and AI, Universidad de Granada (Spain) and Honorary Research Fellow at Imperial College London's Data Science Institute. He holds a PhD in Computer Science and AI from Universidad de Granada (2008-2012) and an MSc in Computer Science from the same institution (2002-2007). His academic journey includes roles as Marie Curie Research Fellow (2017-2019 at Imperial College), Research Associate at the Data Science Institute (2015-2017), and postdoctoral researcher at UGR (2012-2015). Research focuses on Artificial Intelligence , Data Science , Energy Management , and Disinformation Analysis . Notable projects include SINERGY (deep learning for energy simulation), IBERIFIER (EU-funded disinformation observatory), and IA4TES (AI applications for sustainable energy transition). He co-leads the SAIL Lab at UGR and has led research groups in visualization and energy systems. Publications span Angewandte Chemie , Energy and Buildings , and Applied Soft Computing , covering topics like physics-informed neural networks, HVAC control optimization, and Telegram-based disinformation analysis. His work bridges machine learning with domain-specific challenges in energy, music informatics, and smart built environments. Outreach activities include science communication projects (e.g., 'The Sound of Mars' art installation) and education initiatives through Native Scientist. Current roles involve advising PhD students on energy and AI projects, with active grants from Spanish and EU programs. Labs/Teams: SAIL Lab (Universidad de Granada), Data Science Institute (Imperial College London collaborations), and interdisciplinary teams in energy and disinformation research.

Amanda Barnardمشاهده پروفایل

استاد

• Applied machine learning and artificial intelligence
• Data science and eResearch
• Computational methods for physical sciences
• +۲ مورد دیگر

Professor Amanda Barnard is a Senior Professor of Computational Science at the ANU College of Engineering and Computer Science. She leads research in computational modeling, high-performance supercomputing, and AI applications in materials science. With a BSc (Hons) in applied physics (2000) and PhD in theoretical condensed matter physics (2003) from RMIT University, she has held prestigious roles including Distinguished Postdoctoral Fellow at Argonne National Lab (USA) and Violette & Samuel Glasstone Fellow at Oxford University (UK). Board member at BioViS (Garvan Institute), CTCMS (AIBN), Our Health in Our Hands (ANU), and NeSI (New Zealand eScience). Former Chair of the Australian National Computational Merit Allocation Scheme (NCMAS) and current Chair of the Australasian Leadership Computing Grants (ALCG). Research focuses on materials informatics, nanoinformatics, and AI-driven material discovery. Awards include the 2009 Malcolm McIntosh Physical Scientist of the Year, 2014 Feynman Prize in Nanotechnology, and 2019 AMMA Medal. Her work bridges computational science with real-world applications in energy storage, carbon removal, and hydrogen economy technologies. Collaborates with industry through ChoiceFlows Inc. and Data61 (CSIRO).

Melina Freitagمشاهده پروفایل

استاد

• Numerical Linear Algebra
• Data Assimilation
• Inverse Problems
• +۶ مورد دیگر

Melina Freitag is Professor for Data Assimilation at the Institute for Mathematics, University of Potsdam. Her research spans numerical linear algebra, inverse problems, and model order reduction, with applications in geophysics, image processing, and machine learning. She leads the Data Assimilation Group and contributes to the SFB 1294 Collaborative Research Center. Education : Prof. Freitag earned her Diplom in Mathematics at TU Chemnitz (2004) and PhD in Mathematical Sciences from University of Bath (2007). Research Themes : Her work focuses on Krylov subspace methods, low-rank approximations, and preconditioning for large-scale systems. She bridges classical numerical analysis with modern data assimilation, addressing challenges in: Bayesian inverse problems with unstable systems Optimized spectral sampling in X-ray imaging Physics-informed neural networks for Navier-Stokes inversion Parameter-dependent eigenvalue analysis Collaborations & Grants : She collaborates with institutions like KTH Stockholm and Arizona State University. Her group secures funding through SFB 1294 and participates in INI Cambridge networks. Leadership : Co-Chair of GAMM Activity Group on Applied Numerical Linear Algebra SIAM Activity Group on Linear Algebra Chair (2022) Teaching : Delivers courses on numerical optimization, matrix methods in data science, and inverse problems, integrating computational theory with real-world applications.