جست‌وجوی استاد و پژوهشگر

Axel van de Walleمشاهده پروفایل

استاد

• Computational Materials Science
• First Principles Calculations
• Thermodynamics
• +۴ مورد دیگر

Axel van de Walle is a Professor of Engineering at Brown University's School of Engineering. He holds a B.Eng. in Engineering Physics from École Polytechnique de Montréal (1995) and a PhD in Materials Science and Engineering from MIT (2000). His career includes postdoctoral research at Northwestern University and a tenure at Caltech as an Assistant Professor in Applied Physics and Materials Science. His research focuses on computational materials science, first-principles calculations, thermodynamics, and energy materials. Key areas include phase diagram modeling, high-entropy alloys, and computational studies of bulk metallic glasses. He leads the Virtual Materials Laboratory, developing tools like the Alloy-Theoretic Automated Toolkit (ATAT) for materials design and thermodynamic analysis. His work bridges computational and experimental methods, with projects such as investigating rare earth oxides under extreme conditions and optimizing alloy compositions for aerospace applications. Notable contributions include the prediction of high-melting-temperature materials and advancements in CALPHAD database integration. He has secured grants including 'Collaborative Research: Rare Earth Materials Under Extreme Conditions' (2022) and contributed to the United States COVID-19 Forecast Hub dataset (2021). His lab collaborates on software tools like DFTTK and aflow++ to automate first-principles thermodynamics and machine learning-driven materials discovery.

• Software Engineering
• Cloud Computing
• Internet of Things
• +۳ مورد دیگر

Ευριπίδης Γ.Μ Πετράκης is a Professor & Laboratory Director at the School of Electrical and Computer Engineering, Technical University of Crete (TUC) since 1999. He holds a Ph.D. in Computer Science (University of Crete, 1993) and a B.Sc. in Physics (National University of Athens, 1985). His research focuses on Cloud Computing, IoT, Semantic Web, and Image Indexing, with over 175 publications and top international recognition including the 2024 AINA Best Paper Award and Stanford's Top 2% Global Impact ranking (2021-2023). Key projects include leading roles in Horizon 2020-funded BorderUAS (2020-2023), MARS smart farming initiative (2018-2021), and FP7-funded FI-STAR (2013-2016). He has pioneered solutions for IoT security (iBoT), edge-cloud computing (DeFog), and semantic service frameworks (OASL). His academic contributions span advanced distributed systems, including Apache Flink-based real-time video processing (Video2Flink) and Kubernetes optimization strategies. He also actively contributes to standards development for IoT interoperability and semantic web services.

Joshua Schrierمشاهده پروفایل

استاد

• Physical Chemistry
• Computational Chemistry
• Machine Learning for Chemical Experiments
• +۳ مورد دیگر

Joshua Schrier is the Kim B. and Stephen E. Bepler Chair and Professor of Chemistry at Fordham University. His research focuses on Physical Chemistry, Computational Chemistry, and Machine Learning for chemical experiments. He leads a lab in JMH 620 and maintains an office in JMH 616. His work emphasizes autonomous experimentation, AI-driven materials discovery, and open-source laboratory automation. Schrier’s research bridges theoretical and experimental approaches, leveraging machine learning to accelerate discovery in areas like perovskite nanocrystals, rare-earth separation, and self-assembling vesicles. His research interests include developing self-driving labs to reduce experimental bias, optimizing synthesis workflows with active learning, and enhancing reproducibility through data science. Key projects include the 'Dark Reactions' initiative, which uses failed experiments to train predictive models, and the 'Frugal Twin' concept for low-cost automation. Schrier collaborates on the Materials Acceleration Platform (MAP) for energy materials discovery. Publications highlight themes like machine learning for crystal polymorph prediction, AI-guided synthesis protocols, and automated pH estimation. While no scientific awards are listed, his contributions to lab automation and data-driven methodologies have advanced experimental chemistry practices. Advising and grants are not detailed here, but his lab engages in collaborative, interdisciplinary research with undergraduate and graduate students. His lab’s infrastructure includes custom-built robots like Sidekick (a 3D-printed liquid handler) and software frameworks like ESCALATE for experiment management. Current work explores AI’s role in hypothesis generation, sustainable materials development, and democratizing access to advanced experimental tools.

Feng Luoمشاهده پروفایل

استاد

• Deep Learning
• Bioinformatics
• Computational Biology
• +۳ مورد دیگر

Feng Luo is a Professor at the School of Computing , Clemson University, and founding director of the Clemson AI Research Institute for Science and Engineering (AIRISE). He earned his Ph.D. in Computer Science from the University of Texas at Dallas in 2004 and held a postdoctoral position at the University of Texas Southwestern Medical Center before joining Clemson in 2006. Research Focus: Deep Learning, High-Throughput Biological Data Analysis, Computational Genomics Laboratory: BigData Analytics Research Group ( BigData@Clemson ) His research spans Deep Learning applications in bioinformatics, with emphasis on genomic data analysis and network biology . Recent work includes genome assembly algorithms, methylation detection, and AI-driven cybersecurity for IoT devices. His 15 most recent publications (2024-2025) focus on advanced AI/ML methods for 3D genome reconstruction , molecular structure generation , video denoising , and diffusion model security , reflecting interdisciplinary work bridging Computer Science with Genomics and Materials Science . Dr. Luo has advised numerous graduate students and postdocs, several of whom now hold positions at companies like Amazon , Meta , and LinkedIn . He actively serves on university and professional committees as a Senior IEEE Member and ACM Member . His funded projects include NSF , DOE , and NIFA grants addressing citrus disease resistance , off-road autonomy , and AI-enabled healthcare .

Jianhua Tongمشاهده پروفایل

دانشیار

• Sustainable Clean Energy
• Proton Conducting Ceramics
• Electrochemical Devices
• +۵ مورد دیگر

Dr. Jianhua "Joshua" Tong is an Associate Professor in the Department of Materials Science and Engineering at Clemson University, leading the Sustainable Clean Energy Laboratory (SCEL). He holds a Ph.D. in Chemistry (2002) from the Dalian Institute of Chemical Physics, Chinese Academy of Sciences. Prior to Clemson, he worked at institutions in Japan, the U.S., and China, focusing on sustainable energy and advanced materials. His research emphasizes solid-state ionic materials and devices, including protonic ceramic fuel cells, electrochemical sensors, and CO₂ utilization technologies. Education Ph.D., Dalian Institute of Chemical Physics, Chinese Academy of Science (2002) M.S., Dalian University of Technology (1998) B.S., Liaoning Shihua University (1995) Research Focus Dr. Tong’s work spans energy materials and devices, including low-temperature fuel cells, 3D-printed ceramics, laser processing, and machine learning for material discovery. His lab develops innovative solutions for hydrogen production, solar fuels, and carbon capture. Key methodologies include additive manufacturing, reactive sintering, and electrochemical synthesis. Publications His over 60 peer-reviewed articles and patents highlight advancements in proton-conducting ceramics, laser-sintered materials, and electrolysis systems. Recent trends focus on 3D-printed energy devices and AI-driven material design. Labs & Teams The SCEL lab integrates interdisciplinary approaches to address global energy challenges, emphasizing scalability and sustainability in clean energy technologies.

• Computational Biophysics
• Membrane Proteins
• Biophysics
• +۳ مورد دیگر

Blake Mertz, Ph.D., is a Researcher and Principal Scientist at Modulus Discovery, affiliated with the C. Eugene Bennett Department of Chemistry at West Virginia University as Joint/Adjunct Faculty. His research focuses on computational biophysics to study membrane proteins' structure-function relationships, combining simulations with experimental data to analyze protein behavior across time and spatial scales. Education: Ph.D. in Chemistry from Iowa State University, followed by an NIH Ruth Kirschstein Postdoctoral Fellowship at the University of Arizona. Teaching areas include Physical Chemistry and Biochemistry, with courses such as CHEM 342 (Experimental Physical Chemistry) and CHEM 593 (Computational Chemistry). Research emphasizes membrane protein dynamics, including pHLIP peptides, proteorhodopsin, and retinal-protein interactions. Publications highlight computational studies of surfactant micelles, ligand-receptor interactions, and battlefield wound treatment innovations. His Mertz Group website hosts ongoing projects and collaborations. Professional affiliations include leadership in interdisciplinary research at WVU and Modulus Discovery, with a focus on translational applications of computational biophysics.

Loukas G Astrakasمشاهده پروفایل

استاد

• Medical Physics
• Magnetic Resonance Spectroscopy (MRS)
• Functional Magnetic Resonance Imaging (fMRI)
• +۷ مورد دیگر

Loukas G. Astrakas is Professor of Medical Physics at the University of Ioannina, School of Medicine , Greece, where he also serves as Director of the Medical Physics Laboratory at the University Hospital of Ioannina. After obtaining a B.Sc. in Physics from the University of Athens (1989), he earned a Ph.D. in Magnetic Resonance Spectroscopy from the Technical University of Athens (1999), followed by an M.Sc. in Medical Physics from the University of Patras (2014). His post-doctoral training took him to the National Centre of Scientific Research “Demokritos”, Harvard University, the Athinoula A. Martinos Center for Biomedical Imaging and Massachusetts General Hospital, Boston. He was appointed Lecturer (2009), then Assistant Professor (2013), Associate Professor (2020) and finally full Professor (2024) at the University of Ioannina. Research Focus: Professor Astrakas’ work centres on the development and clinical translation of advanced magnetic-resonance techniques—particularly magnetic-resonance spectroscopy (MRS), functional MRI (fMRI), and quantitative MRI—for non-invasive characterisation of paediatric brain tumours, epilepsy, neuro-degenerative diseases, testicular pathologies and rheumatological disorders. His research bridges fundamental physics, biomedical engineering and clinical radiology, with emphasis on metabolic imaging, voxel-based morphometry and machine-learning-assisted analysis. Publications & Impact: His 105+ peer-reviewed publications (2001–2025) map an evolving trajectory from methodological innovation in MRS and fMRI to large-scale clinical validation studies. Recurring themes include paediatric neuroradiology, neuro-oncology, and integration of imaging biomarkers with genomic data to enable precision medicine. Awards & Funding: Marie Curie Fellowship, European Commission (2004–2007) Leadership & Service: Director, Medical Physics Laboratory, University Hospital of Ioannina (2019–present) Member, NMR Surgical Laboratory, Massachusetts General Hospital (2001–present) Professor Astrakas teaches core and advanced courses in medical physics, supervises graduate research, and maintains active collaborations with Harvard Medical School, MGH, and European imaging networks, driving forward next-generation MR technologies for routine clinical care.

• Computational Chemistry
• Biophysical Chemistry
• Enzymology
• +۵ مورد دیگر

Kwangho Nam is an Associate Professor in the Department of Chemistry and Biochemistry at The University of Texas at Arlington (UTA), where he has held positions since 2016. He specializes in computational biophysics, focusing on enzyme catalysis, protein dynamics, and multiscale modeling. His academic journey includes a PhD in Physical Chemistry from the University of Minnesota (2006), followed by postdoctoral research at Harvard University (2006–2011). He has held roles at Umeå University (2011–2016) before joining UTA. His research interests center on understanding enzyme mechanisms using quantum mechanical/molecular mechanical (QM/MM) simulations, with applications to drug design and biomolecular systems. Key areas include adenylate kinase, protein kinases, and DNA repair enzymes. He leads the development of the CHARMM-GUI platform for biomolecular simulations. Dr. Nam has secured grants from NIH, Q-Chem Inc., and other agencies to advance his work. He advises numerous graduate students and postdocs, contributing to their research in computational chemistry and structural biology. His teaching spans Physical Chemistry and advanced computational methods courses. Notable contributions include studies on enzyme catalysis-allostery connections, F1-ATPase mechanisms, and multiscale simulation methodologies. He actively participates in academic committees and international conferences, promoting interdisciplinary research and computational tools in chemistry.

Deepanwita Dasguptaمشاهده پروفایل

دانشیار

• Philosophy and History of Science
• Cognitive Studies of Science
• Science and Culture
• +۳ مورد دیگر

Deepanwita Dasgupta is an Associate Professor of Philosophy at the University of Texas at El Paso (UTEP), affiliated with the Department of Philosophy within the College of Liberal Arts. She holds a Ph.D. in philosophy of science from the University of Minnesota (2010) and another Ph.D. in philosophy of language from Jadavpur University, India. Her research focuses on the philosophy and history of science, particularly in non-Western contexts, with emphasis on India during the early 20th century. Her 2021 book, Creativity from the Periphery , examines the emergence of scientific creativity in peripheral contexts, proposing the concept of 'peripheral creativity.' She is currently working on a second book framing science as a cognitively diverse and wisdom-driven practice, alongside a website ( Science in the 21st Century ) promoting interdisciplinary dialogue between sciences and humanities. Educations: Ph.D. Philosophy of Science, University of Minnesota, 2010 Ph.D. Philosophy of Language, Jadavpur University, India Research Interests: Dr. Dasgupta’s work explores peripheral scientific communities, emphasizing how constraints can foster creativity. She investigates case studies like Satyendra Nath Bose and the Indian scientific community, as well as gender contributions in modern space science. Her framework of 'peripheral trading zones' examines scientific collaboration in non-Western settings. Publications Trends: Her recent articles (2020–2024) highlight peripheral creativity, gender roles in science (e.g., Rocket Women of India ), and interdisciplinary methodologies. She also engages with borderland research, such as the Maya Blue pigment project, showcasing science’s societal and cultural intersections. Awards: No scientific awards explicitly listed in available texts. Advising/Grants: No advising or grant information provided in the data. However, her research projects involve collaborations at institutions like the University of Bergen and Cardiff University. Labs/Teams: She leads the Science in the 21st Century initiative, a platform for interdisciplinary science-humanities discussions, featuring columns and future research ideas.

• Deep Learning
• Machine Learning
• Software Engineering
• +۷ مورد دیگر

Nemanja Milošević is an Assistant Professor at the Department of Mathematics and Informatics , Faculty of Sciences, University of Novi Sad, Serbia. He actively contributes to open-source communities as a Fedora Project Developer & Ambassador and maintains a personal blog related to academic topics. PhD in Computer Science Specializes in Deep Learning, Federated Learning, and Edge Computing Lectures courses: Deep Learning, Distributed Deep Learning, Business Software Development, Scientific Programming, and Mobile Application Development Research focuses on neural network architectures, system security, and toxicology applications His research spans multiple domains including: Deep Learning optimization techniques Industrial IoT security frameworks Big Data analytics across manufacturing and banking sectors Toxicology modeling with machine learning Edge computing resource management Software quality analysis methodologies Recent publications demonstrate expertise in federated learning implementations, negative feature classification, and deep learning applications in toxicology. He maintains active collaborations across Europe and contributes to IEEE and arXiv publications. As part of his academic practice, Nemanja: Develops open-source software solutions Participates in international research workshops Utilizes modern e-learning platforms (PMF Moodle) Engages in cross-disciplinary research projects Contact: nmilosev@dmi.rs | nmilosev@fedoraproject.org

Ia Iashviliمشاهده پروفایل

استاد

• Particle Physics
• High Energy Physics
• Experimental High Energy Physics
• +۳ مورد دیگر

Ia Iashvili is a Professor in the Department of Physics at the University at Buffalo, College of Arts and Sciences. Her research focuses on experimental high-energy physics, particularly within the CMS experiment at the Large Hadron Collider (LHC). BS in Physics from Tbilisi State University (1990) PhD in Physics from Humboldt University, Berlin (2000) Postdoctoral Research at UC Riverside (2000-2005) Her work centers on particle physics, aiming to uncover fundamental laws of the physical universe through precision measurements of the Standard Model (SM) and searches for physics beyond the SM. She specializes in the Higgs mechanism, electroweak symmetry breaking, and new particle searches via proton-proton collisions at the LHC. She has led efforts in hadronic jet calibration, critical for CMS physics analyses, and has contributed to upgrades of the CMS Forward Pixel Detector. Recent publications highlight her focus on jet energy calibration, pileup mitigation, and dijet/resonant ttbar searches. Keywords span High Energy Physics, CMS Experiment, and Standard Model, with subfields including Higgs Boson studies, new particle detection, and detector performance optimization. Contact: iashvili@buffalo.edu

Ming Huمشاهده پروفایل

استاد

• Thermal Transport
• Nanotechnology
• Energy Systems
• +۸ مورد دیگر

Ming Hu is a Professor in the Department of Mechanical Engineering at the Molinaroli College of Engineering and Computing, University of South Carolina. His research focuses on computational thermal science with applications in energy systems, nanotechnology, and advanced materials engineering. Education: Dr.-Ing. in Mechanics from the Institute of Mechanics, Chinese Academy of Sciences (2006) B.S. in Mechanical Engineering from the University of Science and Technology of China (2001) Dr. Hu's research centers on micro-/nano-scale thermal transport phenomena, particularly in low-dimensional materials and nanostructures. His work spans energy nanotechnology, interfacial heat transfer for thermal management, and multi-scale multiphysics modeling of complex energy systems. He employs advanced computational techniques including molecular dynamics, first-principles simulations, and machine learning to investigate phonon transport mechanisms and thermal properties of novel materials. Analysis of recent publications reveals a strategic shift toward artificial intelligence applications in thermal science. Dr. Hu has pioneered graph neural network architectures for high-throughput prediction of phonon properties and thermal conductivity, with significant focus on materials for wide bandgap semiconductor cooling, thermoelectrics, and battery systems. His current work emphasizes accelerating materials discovery through machine learning while maintaining physical interpretability. Scientific Awards: No major awards were documented in the provided information. Dr. Hu leads the Nano Energy and Engineering Laboratory, which specializes in computational thermal science research. The laboratory develops and applies advanced simulation methodologies to investigate heat transfer in next-generation materials. No information regarding graduate students or research grants was available in the provided materials.

Amin Kharrazمشاهده پروفایل

استادیار

• Cybersecurity
• Data-Driven Security
• Malware Analysis
• +۳ مورد دیگر

Dr. Amin Kharraz is Assistant Professor in the Knight Foundation School of Computing and Information Sciences at Florida International University. His research develops systems for data-driven security analysis of evolving cyber threats. Key research areas: Automated detection and mitigation of online attacks Malware behavior analysis at scale Vulnerability discovery in web ecosystems Privacy-enhancing browser technologies Social engineering in software supply chains Recent publications advance in-kernel forensics for evasive attacks and isolated browsing environments for privacy protection. Research addresses security challenges at the intersection of technology and society.

Rongying Jinمشاهده پروفایل

استاد

• Condensed Matter Physics
• Quantum Materials
• Superconductivity
• +۹ مورد دیگر

Rongying Jin is a Professor in the Department of Physics and Astronomy at the University of South Carolina's College of Arts and Sciences. She holds the SmartState Endowed Chair for Experimental Nanoscale Physics and the John M. Palms Bicentennial Chair. Her research focuses on experimental condensed matter physics, particularly on developing and characterizing quantum materials with properties like superconductivity, quantum magnetism, and topological phases. Research Interests : Quantum materials synthesis under extreme conditions Investigation of charge, spin, and heat transport Magnetic force microscopy and neutron scattering Collaboration with theorists for atomic-level understanding Recent Article Trends : Her recent publications emphasize Weyl semimetals, spin-liquid states, pressure-induced phase transitions, and machine learning-driven discovery in quantum materials. Studies include detailed electronic and magnetic characterization of compounds like EuZn2As2, UCuxBi2, and PdTe2. Labs/Teams : She leads the Center for Experimental Nanoscale Physics (CENPhys) and collaborates with interdisciplinary teams on quantum material synthesis and characterization.

Alex Azizمشاهده پروفایل

مدرس

• Computational Materials Science
• Energy Storage
• Defective Graphene
• +۲ مورد دیگر

Dr. Alex Aziz is a Lecturer in Materials/Physical Chemistry at Manchester Metropolitan University’s Joint Education Institute with Hubei University. He holds a PhD in computational chemistry (University of Reading) and completed postdocs at CICEnergiGUNE (Spain), Tohoku University (JSPS fellowship, Japan), and University of Bath. Research employs density functional theory (DFT) and molecular dynamics to optimize materials for energy storage/generation, especially defective graphene supercapacitors and battery systems. Collaborates internationally on photovoltaic and thermoelectric materials. Recent articles (2021-2024) focus on computational design of carbon nanomaterials, battery cathodes, and biomimetic hybrids, with emerging work in AI for materials discovery. Teaches thermodynamics, kinetics, and materials characterization at undergraduate/postgraduate levels. Member of Royal Society of Chemistry and JSPS Alumni. Reviews for Scientific Reports and ACS journals.