جست‌وجوی استاد و پژوهشگر

Weimin Huangمشاهده پروفایل

استاد

• Remote Sensing
• Radar Technology
• Oceanography
• +۳ مورد دیگر

Weimin Huang is a Full Professor in the Department of Electrical and Computer Engineering at Memorial University's Faculty of Engineering and Applied Science. His research focuses on ocean remote sensing technologies including HF radar, GNSS, and marine radar systems for measuring oceanic surface parameters. He holds a PhD in Radio Physics from Wuhan University and an M.Eng from Memorial University. Research interests span electromagnetic wave propagation, digital signal processing for marine applications, and machine learning applications in remote sensing. Key areas include: High-frequency radar ocean surface mapping Satellite-based navigation systems for earth observation Microwave radar signal analysis AI-driven environmental monitoring Recent publications demonstrate strong focus on neural network applications for sea state parameter retrieval and multi-sensor data fusion techniques. Research consistently addresses challenges in harsh marine environments and climate impact assessment. Honors include: IEEE GRSS Letters Prize Paper Award (2019) PEGNL Teaching Award (2018) NSERC Discovery Accelerator Supplement (2017) Leads the Radar Remote Sensing Lab (RRSL) and mentors graduate researchers in electromagnetic theory and remote sensing methodologies.

Paola Leccaمشاهده پروفایل

استادیار

• Graph Theory
• Dynamical Networks
• Control Theory
• +۴ مورد دیگر

Paola Lecca is an Assistant Professor at the Free University of Bozen-Bolzano's Faculty of Engineering, specializing in theoretical and applied research on graph theory, dynamical networks, control theory, and causal inference. She is a Senior Professional Member of the Association for Computing Machinery and leads projects at the Smart Data Factory laboratory, focusing on technology transfer to industry. Her research integrates mathematical methods for complex systems in biological contexts, including systems biology, biophysics, and biochemistry. She develops computational approaches for network dynamics, simulation, and analysis, aiming to understand emergent properties in multi-agent systems. Courses taught include Mathematics and Statistics for Data Science and Preparatory Mathematics. She actively contributes to conferences and editorial boards, including Frontiers in Bioinformatics and Big Data Analysis for Medical Sciences.

Mingjian Wenمشاهده پروفایل

استادیار مدعو

• Data-driven methods
• Machine learning
• Materials design
• +۳ مورد دیگر

Mingjian Wen is an Adjunct Assistant Professor in the William A. Brookshire Department of Chemical and Biomolecular Engineering at the University of Houston, affiliated with the Cullen College of Engineering. He holds the Presidential Frontier Faculty Fellow title. His research focuses on developing data-driven and machine learning methods, molecular simulations, and open cyberinfrastructure to design materials and chemical reactions for renewable energy. He completed a postdoc at Lawrence Berkeley National Lab, a Ph.D. in Aerospace Engineering & Mechanics from the University of Minnesota, and a B.S. in Chemical Engineering from Tianjin University. Education: Ph.D., Aerospace Engineering and Mechanics, University of Minnesota-Twin Cities, 2019 B.S., Chemical Engineering, Tianjin University, 2012 Postdoc, Energy Technologies Areas, Lawrence Berkeley National Laboratory, 2022 Research interests emphasize integrating machine learning with computational chemistry to address challenges in renewable energy, materials design, and sustainable processes. His work spans uncertainty quantification in atomistic models, graph neural networks for molecular properties, and open-source tools like Atomate2 and Jobflow for materials informatics. Scientific awards include the University of Minnesota Doctoral Dissertation Fellowship and the UH Alternative Textbook Incentive Award. His lab ( wengroup.github.io ) explores interdisciplinary approaches to accelerate materials discovery through AI-driven methods. Dr. Wen’s contributions span computational workflows, hydrolysis energy predictions (HEPOM framework), and equivariant neural networks for crystal elasticity. His research bridges theory and application, aiming to advance sustainable energy technologies through advanced modeling and automation.

Tony Schmitzمشاهده پروفایل

استاد

• Manufacturing
• Machining Dynamics
• Vibrations
• +۶ مورد دیگر

Tony Schmitz is the Richard Rosenberg Distinguished Professor at the University of Tennessee, Knoxville (UTK), with a joint faculty appointment at Oak Ridge National Laboratory (ORNL). He leads the Machine Tool Research Center (MTRC), focusing on machining dynamics, precision engineering, and smart manufacturing. His roles include Director of the Southeastern Advanced Machine Tools Network (SEAMTN) and President of the American Society for Precision Engineering (ASPE). Education: BS (Temple University, 1993), MS & PhD (University of Florida, 1996/1999). Postdoctoral work at NIST (1999–2000). Previous appointments include UNC Charlotte (2011–2019) and the University of Florida (2002–2011). Research emphasizes machining stability, vibration analysis, and hybrid manufacturing, leveraging machine learning and physics-based models. Research Interests: - Machining Dynamics & Stability - Additive/Subtractive Hybrid Manufacturing - Precision Engineering & Metrology - Data-Driven Manufacturing Processes - Chatter Prediction & Process Optimization Awards: - 2020 ASPE College of Fellows - 2019 SME Frederick W. Taylor Medal - 2019 Teaching Excellence Awards - 2003 NSF CAREER Award Labs/Teams: Machine Tool Research Center (MTRC), Oak Ridge National Lab collaboration, SEAMTN Industry Consortium.

Marco Aielloمشاهده پروفایل

استاد

• Service Computing
• Artificial Intelligence
• Energy Systems
• +۹ مورد دیگر

Marco Aiello is a Professor and Vice Dean at the University of Stuttgart's Faculty of Computer Science, Electrical Engineering and Information Technology. He leads the Institute of Architecture of Application Systems, focusing on Service Computing and AI-driven technologies. His research spans energy systems optimization, smart grids, IoT integration, and AI planning in robotics and automation. He has contributed to frameworks for carbon-efficient computing, blockchain-based IoT security, and human-aware robotics systems. Key research areas include: Service Composition & Large Language Model Integration Energy Smart Buildings & Sustainable Data Centers Robot Task Planning & Human-Computer Interaction Decentralized Energy Markets & Peer-to-Peer Trading Recent work emphasizes: - AI planning methodologies in real-world domains - Carbon-aware scheduling for Kubernetes deployments - Multimodal LLMs for human behavior prediction Awards: None explicitly mentioned. Grants/Advising: Active in doctoral supervision and industry collaborations, though specific grants are not detailed here. Labs/Teams: Leads the Institute's work on service-oriented systems and energy-aware computing architectures.

Nicolas Blanchardمشاهده پروفایل

استاد پژوهشی

• Organic Chemistry
• Synthetic Chemistry
• Natural Products Chemistry
• +۳ مورد دیگر

Nicolas Blanchard is a Research Professor at the University of Strasbourg and Director of Research at CNRS, leading the Biomolecules, Synthesis & Methods (BSM) team within the Laboratory of Molecular Innovation and Application (LIMA). His research focuses on synthetic methodologies for bioactive natural products, particularly Mycobacterium ulcerans toxins (mycolactones), and advanced fluorine-containing compounds. He holds a PhD from J. Cossy's laboratory (2000) and completed postdoctoral studies at Paris 6 University and Michigan University before joining CNRS in 2002. Blanchard has been recognized with prestigious awards including the CNRS Bronze Medal (2012) and the Guy Ourisson Award (2014). His teaching responsibilities include master-level courses on heteroelement chemistry (Si, S, P), pericyclic reactions, asymmetric synthesis, and retrosynthetic analysis. Research interests span metal-mediated transformations, pericyclic reactions, and the design of SF5-containing molecules. Over 100 peer-reviewed articles, book chapters, and patents reflect his contributions to organic synthesis and medicinal chemistry. Blanchard’s lab (LIMA) collaborates on projects involving mycolactone biology, photopolymerization initiatives, and sustainable synthesis methods. His work bridges fundamental chemical research with applications in drug discovery and materials science.

Zarko Boskovicمشاهده پروفایل

استادیار

• Medicinal Chemistry
• Drug Discovery
• Synthetic Organic Chemistry
• +۳ مورد دیگر

Zarko Boskovic is an Assistant Professor in the Department of Medicinal Chemistry at the University of Kansas School of Pharmacy. His research focuses on synthetic chemistry approaches to drug discovery, emphasizing data-driven strategies to identify biologically active compounds and understand molecular mechanisms of disease. He leads the Boskovic Group, which explores structural complexity in synthetic molecules to target proteins and biological pathways. Education: BSc in Chemistry, University of Niš, Serbia (2006) PhD in Organic Chemistry, University of California, Santa Barbara (2011) Postdoctoral Researcher, The Broad Institute of MIT and Harvard (2017) Research Interests: Synthesizing structurally complex compounds to investigate their biological activities, particularly in cancer and drug-resistant pathogens. His work integrates computational modeling, phenotypic screening, and target identification to advance therapies. Key areas include photochemical reactions, catalytic strategies, and molecular mechanisms underlying disease states. Awards: BroadNext10 Scientific Frontiers Award ($40,000, 2016) Howard Hughes Medical Institute Postdoctoral Fellowship (2011) Dean’s Fellowship, UC Santa Barbara (2010) Advising & Grants: Supervises graduate students like Victor Fadare and Manvendra, focusing on synthetic chemistry and drug discovery. Active in securing research funding, including NIH grants and industry partnerships. His group collaborates on projects involving novel antimalarial compounds and cancer therapy strategies. Labs/Teams: Director of the Boskovic Group at KU, specializing in medicinal chemistry and synthetic biology. Collaborates with multidisciplinary teams to bridge chemistry and biomedical applications.

Nanasaheb Thoratمشاهده پروفایل

دانشیار

• Nanomedicine
• Cancer Therapy
• Magnetic Nanoparticles
• +۷ مورد دیگر

Nanasaheb Thorat is an Associate Professor at the University of Limerick, affiliated with the Department of Physics, Bernal Institute, and Limerick Digital Cancer Research Centre. His research focuses on nanomedicine, cancer therapy, magnetic nanoparticles, and exosome-based applications. He is actively involved in developing advanced drug delivery systems, cancer imaging techniques, and nanotheranostics. His work integrates materials science, biomedical engineering, and oncology to address challenges in cancer treatment and diagnosis. Research interests include magnetic nanoparticle synthesis for hyperthermia, exosome engineering for targeted therapies, and the development of smart nanomaterials for drug delivery. He has contributed to advancements in photothermal therapy, cancer biomarker discovery, and the use of PROTACs for viral infections. His interdisciplinary approach bridges physics, chemistry, and medicine, with a strong emphasis on translating nanotechnology into clinical applications. Thorat’s recent work highlights trends in 3D tumor models for drug screening, AI-driven cancer diagnostics, and the clinical potential of nanovaccines. He collaborates globally on projects related to cancer nanomedicine, magnetic hyperthermia, and exosome-based diagnostics. His research aligns with UN Sustainable Development Goals targeting health and well-being through innovative biomedical solutions. He is open to supervising PhD students in areas such as nanomedicine, cancer therapeutics, and biomaterials. His contributions to the field are evident in over 116 peer-reviewed publications, emphasizing the application of nanotechnology in healthcare and biomedical innovation.

Elizabeth A. Barnesمشاهده پروفایل

استاد

• Climate Science
• Climate Variability
• Climate Change
• +۸ مورد دیگر

Elizabeth A. Barnes is a Professor of Atmospheric Science at Boston University, having relocated from Colorado State University (CSU) in summer 2025. Her research focuses on climate variability, predictability, and data science methods, including explainable AI and causal discovery. She holds affiliations with the Walter Scott, Jr. College of Engineering and has been recognized with prestigious awards like the PECASE and AGU Macelwane Medal. Barnes has advised numerous students and contributed to interdisciplinary projects on climate intervention and subseasonal-to-decadal prediction. Education: Ph.D. in Atmospheric Science, University of Washington (2012) B.S. in Mathematics and Physics, University of Minnesota (2007) Research Interests: Climate change mechanisms, jet-stream dynamics, Arctic-midlatitude connections, and novel data science tools for Earth systems. Her group explores explainable AI applications, stratospheric aerosol injection (SAI) scenarios, and robust climate prediction frameworks. Awards: 2025 PECASE (President Biden) 2021 AGU Macelwane Medal 2018 NSF CAREER Award 2006 Goldwater Scholar Grants & Leadership: Sponsors include DOE, NSF, NOAA, and NASA. She leads roles in NCAR predictability boards, CESM committees, and the US CLIVAR Working Group. Barnes is an associate editor for journals like Journal of Climate and Artificial Intelligence for the Earth Systems . Labs/Teams: Leads the Barnes Group at BU, focusing on climate intervention, S2D predictability, and data-driven methods. Collaborates on interdisciplinary projects with institutions like Sandia National Labs and the National Academies.

Kevin Charles Leonardمشاهده پروفایل

استاد

• Electrocatalysis
• Sustainable Energy
• Data Science
• +۳ مورد دیگر

Kevin C. Leonard is Professor of Chemical and Petroleum Engineering at the University of Kansas, with joint appointment at the Center for Environmentally Beneficial Catalysis. He leads research on sustainable electrochemical technologies for fuel and chemical production, with focus areas in electrocatalysis, scanning electrochemical microscopy, CO₂ utilization, and machine learning for catalyst discovery. His research develops novel electrochemical systems for renewable hydrogen production, CO₂ conversion, and sustainable chemical synthesis. Current projects include federated learning approaches for catalyst discovery, CO₂-expanded electrolytes for chemical production, and scanning electrochemical microscopy for catalyst characterization. Professor Leonard has received significant research funding from NSF, Department of Energy, and Cisco Systems. Awards include the ACS Sustainable Chemistry & Engineering Lectureship Award and Army Research Office Young Investigator Award. He co-founded Avium, LLC to commercialize hydrogen production technology. He teaches undergraduate and graduate courses in chemical engineering fundamentals, kinetics, catalysis, and data science. Professor Leonard currently advises 7 graduate students working on electrocatalysis, machine learning applications, and electrochemical characterization techniques.

Duc Nguyenمشاهده پروفایل

دانشیار

• Machine Learning
• Drug Discovery
• Mathematical Biology
• +۵ مورد دیگر

Dr. Duc Nguyen is an Associate Professor at the Department of Mathematics , University of Tennessee, Knoxville . His work bridges mathematics, molecular bioscience, and data science, focusing on mathematical modeling of biomolecular systems, machine learning architectures for drug discovery, and high-order numerical methods. He leads the Nguyen Lab for AI & Mathematics , which develops tools like DG-GL and AGL-Score for drug design and systems pharmacology. Dr. Nguyen has received funding from NSF grants (DMS-2245903, DMS-2151802, DMS-2053284) , Pfizer, and Bristol-Myers Squibb. His lab's methods ranked first in multiple D3R Grand Challenges , a top competition in computational drug design. He has advised 9 undergraduate students , 5 PhD students , and a postdoc , advancing AI-driven drug discovery. Key research areas include topological data analysis , differential geometry , and graph theory applied to biomolecular systems. His lab offers Research Assistant (RA) positions for Math PhD students with interests in topology, geometry, and AI. Collaborative projects span material science, biosciences, and quantitative systems pharmacology. Notable achievements include developing FRI (Flexibility-Rigidity Index) servers and being recognized among the top 2% of most-cited researchers . His lab emphasizes interdisciplinary collaboration, with opportunities for publications and industry-academia career pathways.

• Mathematical Biology
• Antimicrobial Resistance
• Microbial Pathogenesis
• +۳ مورد دیگر

David White is a Term Lecturer in the Department of Mathematics at the University of Tennessee, Knoxville, part of the College of Arts and Sciences. His academic work bridges mathematical methodologies with microbiological and public health challenges. He is affiliated with the university's mathematics department and contributes to interdisciplinary research initiatives. Research focuses on antimicrobial resistance mechanisms, bacterial pathogenesis, and stochastic modeling applications in biology and medicine. Key areas include mathematical analysis of resistance evolution, computational methods for genomic data interpretation, and statistical approaches to infectious disease dynamics. His work spans disciplines such as immunology, veterinary science, environmental policy, and game design through algorithmic approaches. Publications highlight expertise in antibiotic resistance surveillance (e.g., Salmonella, Campylobacter studies), MCMC method development, and interdisciplinary analyses of land-use tradeoffs. His recent work (2022) explores autoimmune responses triggered by bacterial pathogens, demonstrating integration of mathematical modeling with clinical immunology. No academic awards or grants are explicitly listed in provided materials. Advising and mentorship roles are not detailed in the text. His research often involves collaboration across departments and institutions, though specific lab affiliations are not noted.

• Machine Learning
• Materials Science
• Physics
• +۵ مورد دیگر

Qian Yang is an Assistant Professor in the Computer Science and Engineering Department at the University of Connecticut, Storrs. Her research focuses on machine learning applications in materials science, physics, and chemistry, bridging computational methods with physical sciences. She holds a Ph.D. from Stanford University's Institute for Computational and Mathematical Engineering and a B.A. in Applied Mathematics/Computer Science from Harvard College. Prior to UConn, she was a postdoctoral scholar at Stanford's Materials Computation and Theory group. Her research projects include developing machine learning models for chemical reaction networks, model reduction of nonlinear dynamical systems, and addressing challenges in scientific data (small datasets, imbalance, mixed fidelity). She co-hosts the Materials and Megabytes podcast, exploring interdisciplinary machine learning in materials science, featuring experts like Prof. Gábor Csányi (Cambridge) and Dr. Patrick Riley (Google). Awards: Best Student/Postdoc Presentation Award (2017), Best Instructor Award (Stanford) Teaching: Teaches courses like CSE 5095 (Machine Learning for Physical Sciences) and CSE 3666 (Computer Architecture). Known for integrating theory with practical applications. Grants/Advising: No explicit grants listed, but her research involves DOE and computational initiatives. Advising details not provided in text. Labs/Teams: Leads interdisciplinary teams focusing on machine learning in materials and physical sciences, as evidenced by podcast collaborations and course projects. Publications emphasize data-driven methods, uncertainty quantification, and algorithm development for scientific challenges, with recent work in defect analysis, battery materials discovery, and TEM data automation.

• Quantum Physics
• Superconductivity
• Bayesian Statistics
• +۳ مورد دیگر

Wolfgang von der Linden is a full Professor at TU Graz, affiliated with the Institute of Theoretical Physics - Computational Physics. He holds the title of Univ.-Prof. and serves as Dean of Studies, with office hours on Tuesdays 11-12. His research spans quantum many-body systems, superconductivity under extreme conditions, Bayesian statistical methods, and interdisciplinary applications in biomedical engineering and materials science. Key research areas include: quantum transport using neural-network quantum states, high-pressure hydride superconductors (Superhydra project), Bayesian uncertainty quantification in medical diagnostics, and computational discovery of novel materials. He has pioneered methods such as the auxiliary master equation approach for nonequilibrium quantum systems and Bayesian encoder-decoder surrogates for stochastic modeling. His work bridges theoretical physics with practical applications, including aortic dissection detection via impedance cardiography and machine learning-driven material synthesis. Over 100 peer-reviewed articles demonstrate contributions to quantum physics, computational materials science, and statistical methodology. Active in academic governance, he maintains the research group 'Many-Body Physics' at TU Graz.

• Theoretical Condensed Matter Physics
• Quantum Mechanics
• Machine Learning in Materials Science
• +۳ مورد دیگر

Alexandre Tkatchenko is a Full Professor of Theoretical Condensed Matter Physics at the University of Luxembourg, leading the Department of Physics and Materials Science and the Theoretical Chemical Physics group. His work integrates quantum mechanics, statistical mechanics, and machine learning to model complex materials, with over 150 peer-reviewed articles (h-index 58). He has held roles including Alexander von Humboldt Fellow at the Max Planck Institute (2008–2016) and has secured five ERC grants, including Advanced and Proof of Concept awards. Education: Bachelor in Computer Science and PhD in Physical Chemistry from Universidad Autónoma Metropolitana, Mexico City. Research focuses on quantum mechanical methods for molecular interactions, machine learning force fields, and computational tools like FHI-aims and libMBD. Key contributions include non-covalent interaction modeling, van der Waals forces, and AI-driven drug design. Awards include the 2020 Dirac Medal and APS Fellowship (2019). Grants include ERC Starting (2011), Consolidator (2017), and Advanced (2022) grants. His labs develop software for electronic structure calculations and explore materials for energy and drug discovery. Recent work addresses lithium diffusion in solid electrolytes and AI explainability in ADMET modeling.