جست‌وجوی استاد و پژوهشگر

• Materials Science
• Computational Chemistry
• Artificial Intelligence
• +۹ مورد دیگر

Dr. Jed Pitera is an Adjunct Assistant Professor at the University of California, San Francisco (UCSF) Department of Pharmaceutical Chemistry and currently serves as the strategy co-lead for Accelerated Discovery in Sustainable Materials at IBM Research - Almaden. He has spent over two decades at IBM Research, applying computational tools and machine learning to materials R&D challenges. Caltech (Biology, Chemistry) University of California, San Francisco (Ph.D. in Biophysics) ETH Zurich (Postdoctoral work in computational physical chemistry) His research focuses on leveraging AI, machine learning, high-performance computing, and quantum computing for advanced materials discovery, particularly in sustainability applications such as carbon capture, energy storage, and PFAS replacement. He also works on improving the sustainability of existing materials in semiconductor manufacturing and directed self-assembly techniques. His work spans computational physical chemistry, polymer science, and AI-driven approaches to material design. His publications demonstrate a focus on AI-driven materials discovery (6 papers), directed self-assembly applications (4 papers), semiconductor manufacturing (4 papers), computational modeling (4 papers), and sustainability-focused research (5 papers). Notable trends include integrating robotics with AI for materials discovery and developing lifecycle assessment tools for sustainable design. Dr. Pitera leads the Accelerator Technologies project at IBM and contributes to the IBM Safer Materials Advisor initiative. He has collaborated with researchers across multiple institutions, including Dan Sanders, Brandi Ransom, Seiji Takeda, and Teodoro Laino.

François Raymond J Cornetمشاهده پروفایل

پژوهشگر

• Organometallic Chemistry
• Computational Chemistry
• Catalyst Design
• +۳ مورد دیگر

François Raymond J Cornet is a Postdoctoral Researcher in the Department of Energy Conversion and Storage and a PhD Student in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His dual affiliation bridges energy conversion research and computational science, focusing on AI-driven molecular design. His research spans Organometallic Chemistry , Computational Chemistry , and Machine Learning , with specialization in catalyst design through diffusion models and inverse design methodologies. Key areas include metallocene chemistry, density functional theory applications, and generative modeling for chemical space exploration, targeting organometallic complexes like Vaska's complex. Cornet's publication trajectory reveals a concentrated effort in advancing equivariant diffusion models for molecular generation, particularly addressing small-data challenges in catalyst design. His work consistently integrates quantum chemistry with deep generative architectures, establishing new paradigms for inverse-design pipelines in computational chemistry. No scientific awards were documented in the source material. He recently completed the PhD project Machine learning for electronic scale inverse design of enzymatic catalysts (2021-2025) under supervisors M. N. Schmidt (primary), A. Bhowmik, and O. Winther, with examiners W. K. Boomsma and S. Olsson. Collaborators include P. Deshmukh, B. Benediktsson, and C. A. Naesseth across multiple publications. Research operations occur within DTU's interdisciplinary framework connecting the Department of Energy Conversion and Storage and Department of Applied Mathematics and Computer Science, leveraging computational infrastructure for molecular simulations and AI model training.

Federico Beccaمشاهده پروفایل

دانشیار

• Theoretical Physics
• Condensed Matter Physics
• Strongly Correlated Systems
• +۴ مورد دیگر

Federico Becca is an Associate Professor in the Department of Physics at the University of Trieste . He is a theoretical condensed matter physicist specializing in strongly correlated electronic systems , with a focus on spin liquids , superconductivity , and topological phases . His research employs quantum Monte Carlo and exact diagonalizations to study lattice models like the Hubbard and Heisenberg models . Education: Laurea in Physics, Università La Sapienza, Roma (1996) PhD in Condensed Matter, ISAS-SISSA, Trieste (2000) Research Interests: Prof. Becca’s work centers on Mott insulators , spin-liquid phases , and superconductivity in systems with magnetic frustration and strong electron correlations. He investigates unconventional electronic states in reduced dimensions and explores the interplay between spin-phonon interactions and quantum phase transitions . Scientific Collaborations: Alberto Parola (Insubria University) Michele Fabrizio (SISSA) Roser Valenti (University of Frankfurt) Didier Poilblanc (University of Toulouse) Yasir Iqbal (Indian Institute of Technology Madras) Current Projects: Unravelling the intertwined correlated states of matter in moiré superlattices (funded by MINISTERO DELL'UNIVERSITA' E DELLA RICERCA). He also leads the "Studio computazionale del modelli di Kane and Mele" project as a Scholar at risk.

Sirojan Tharmakulasingamمشاهده پروفایل

مدرس

• Machine Learning
• Artificial Intelligence
• Data Science
• +۶ مورد دیگر

Dr. Sirojan Tharmakulasingam serves as a Lecturer and Research and Development Coordinator at the Signals, Information & Machine Intelligence lab within the Faculty of Engineering at the University of New South Wales (UNSW) Sydney. His work bridges theoretical machine learning with practical applications in edge computing and high-performance systems. His research spans multiple cutting-edge domains including machine learning, artificial intelligence, data science, edge computing, and high-performance computing. Dr. Tharmakulasingam specializes in developing next-generation inference models by integrating machine learning, signal processing, mathematical modeling, and computing across diverse data types including images, video, audio, and quantum molecular data. His work has significant implications for scientific computing, telecommunications, and healthcare applications. Analysis of his publication trends reveals a strong focus on practical AI implementations, with increasing emphasis on edge computing solutions, quantum applications, and energy-efficient models. His recent work demonstrates progression from foundational machine learning techniques toward specialized applications in scientific computing and real-time systems. Dr. Tharmakulasingam holds a Doctor of Philosophy from UNSW Sydney and a Bachelor of Science of Engineering from the University of Moratuwa in Sri Lanka. His academic journey reflects a strong foundation in both theoretical and applied engineering principles. As Research and Development Coordinator for the Signals, Information & Machine Intelligence lab, he oversees critical research infrastructure and collaborations. His work location in Room 447 of the EE&T Building (G17) places him at the heart of UNSW's engineering research ecosystem, with access to the Mark Wainwright Analytical Centre's extensive facilities.

• Trustworthy Machine Learning
• Algorithmic Fairness
• Robust Distributed Learning
• +۵ مورد دیگر

Sanmi (Oluwasanmi) Koyejo is an Assistant Professor in the Department of Computer Science at Stanford University and an adjunct Associate Professor at the University of Illinois at Urbana-Champaign. He leads Stanford Trustworthy AI Research (STAIR), working to develop the principles and practice of trustworthy machine learning with applications to neuroscience and healthcare. Koyejo holds affiliations with multiple Stanford institutes including SAIL, HAI, CRFM, AIMI, AI Safety, Machine Learning Group, and Bio-X. Koyejo completed his Ph.D. at the University of Texas at Austin followed by postdoctoral research at Stanford University. His research bridges theoretical machine learning with practical healthcare applications, focusing on developing robust and fair AI systems that can be trusted in critical domains. His work spans algorithmic fairness, robust distributed learning, metric elicitation, and applications to medical imaging and neuroscience. His recent publications demonstrate a strong focus on emerging challenges in AI including emergent abilities in large language models, fairness in medical AI, federated learning, and robustness against adversarial attacks. His work has increasingly addressed real-world healthcare challenges through deep learning applications to medical imaging, particularly chest radiographs for disease detection. Scientific Awards: NSF CAREER Award 2021 Skip Ellis Early Career Award Sloan Research Fellowship Frederick E. Terman Faculty Fellow (2022) Best Paper Award from UAI Kavli Fellowship IJCAI Early Career Spotlight Koyejo actively mentors a large research group with numerous PhD students and postdocs. His research has been supported by significant grants including NSF funding for projects like 'Fair Federated Representation Learning for Breast Cancer Risk Scoring.' He serves in leadership roles including as General Co-chair for NeurIPS 2022 and President of the Black in AI organization. His STAIR research group focuses on developing trustworthy AI principles and practices, with applications to healthcare and neuroimaging. The group collaborates extensively with healthcare institutions including OSF Healthcare and participates in major initiatives like the NIH-funded MIDRC and the NSF AI research institute AIFARMS.

Matthew John M. Kraneمشاهده پروفایل

استاد

• Materials Process Modeling
• Solidification of Metal Alloys
• Thermal Processing
• +۳ مورد دیگر

Dr. Matthew John M. Krane is a Professor in the Department of Materials Engineering at Purdue University and a member of the Purdue Center for Metal Casting Research. His work focuses on the design, development, and modeling of materials processes , particularly solidification and thermal processing of metal alloys, with strong emphasis on defect prevention and uncertainty quantification in numerical models. Current projects include grain-refined particle transport in DC casting , exergy optimization in copper smelting , and boron segregation in continuous casting . Past research includes through-process modeling of Al alloys , microsegregation studies , and laser hardening techniques . Scientific awards include an invited keynote lecture at the 2015 International Symposium on Liquid Metal Processing and Casting and multiple invited papers in high-impact journals.

Angelos Georghiouمشاهده پروفایل

دانشیار

• Stochastic Optimization
• Robust Optimization
• Operations Management
• +۴ مورد دیگر

Angelos Georghiou is an Associate Professor of Operations Management at the Department of Business and Public Administration, University of Cyprus. He previously held academic positions as Assistant Professor at McGill University (2016–2019), Post-Doctoral Researcher at MIT (2012–2013), and Postdoctoral Researcher at ETH Zurich (2013–2016). His work bridges computational methods in stochastic and robust optimization with applications in healthcare, energy systems, and operations management. Ph.D. in Operations Research, Imperial College London (2012) M.Sci. in Mathematics, Imperial College London (2008) His research focuses on stochastic optimization , robust optimization , and decision rules , addressing challenges in healthcare analytics , energy-efficient control systems , and machine learning integration . Recent publications in Management Science and Operations Research highlight his contributions to decision-dependent information discovery and computational frameworks. Key trends in his 15 most recent articles include: Advancing robust optimization techniques for multistage problems Applications in healthcare (psychiatric risk prediction) and energy systems Integration of machine learning with stochastic programming Development of tractable algorithms for complex decision environments Scientific Awards : Esdras-Minville Best Student Paper Award (2023) He serves on editorial boards of Operations Research Letters (Area Editor) and Management Science , and collaborates with institutions like MIT, ETH Zurich, and McGill University. His work with undergraduate students includes a 2021 paper in SIAM Undergraduate Research Online .

David Hyten, Jr.مشاهده پروفایل

استاد

• Plant Genetics
• Soybean Genomics
• Molecular Breeding
• +۴ مورد دیگر

David Hyten, Jr. serves as the Haskins Professor of Plant Genetics within the Department of Agronomy & Horticulture at the University of Nebraska-Lincoln, where he leads research advancing soybean genomics and breeding methodologies for global food security. Education: Ph.D., University of Maryland, 2005 M.S., University of Tennessee, 2002 B.A., Southern Illinois University, 1999 Research Focus: Dr. Hyten's program integrates high-throughput genotyping with quantitative genetics to dissect complex traits including disease resistance (cyst nematode, stem borer), seed composition (protein/oil), and yield stability. His work pioneers genomic selection pipelines and recombination hotspot analysis to accelerate breeding cycles, with emphasis on translating genomic discoveries into practical cultivar development through marker-assisted selection and predictive modeling. Publication Trends: Recent outputs (2021-2025) reveal evolving emphasis from foundational genomics (recombination mapping, QTL identification) toward applied breeding solutions, including cost-effective DNA extraction methods, pedigree-based imputation techniques, and community strategic planning for soybean genomics. His work increasingly addresses genotype-environment interactions and stability of key traits across diverse production systems.

Roberto Tronمشاهده پروفایل

استادیار

• Control Theory
• Robotics
• Computer Vision
• +۴ مورد دیگر

Roberto Tron is an Assistant Professor in the Mechanical Engineering and Systems Engineering departments at the Boston University College of Engineering , with his office located at 110 Cummington Mall. His research integrates control theory, robotics, and computer vision to solve complex multi-agent coordination problems. His primary research interests focus on Riemannian geometry applications , distributed multi-agent systems , and safety-critical control . Key methodologies include Control Barrier Functions (CBFs), Riemannian optimization, and distributed consensus algorithms, with applications spanning autonomous aerial vehicles, robotic manipulation, and multi-robot security systems. Analysis of his recent publications reveals a strong emphasis on safety verification and real-time optimization for autonomous systems. His work consistently bridges theoretical foundations in nonlinear control with practical implementations in robotics, particularly addressing challenges in limited sensor fields of view, distributed task allocation, and noise-robust navigation. The research shows increasing integration of formal methods like Signal Temporal Logic with learning-based approaches. Tron received his Ph.D. from The John Hopkins University and previously conducted post-doctoral research at the GRASP Lab, University of Pennsylvania. His work demonstrates significant contributions to provably safe autonomous systems through frameworks like the Control Barrier Function Toolbox.

• Applied Geophysics
• Glaciology
• Geophysical Data Integration
• +۳ مورد دیگر

Alberto Godio is a Full Professor at the Politecnico di Torino, affiliated with the Department of Environmental, Land and Infrastructure Engineering (DIATI). He coordinates Latin America relations under the University Strategic Plan and is a member of the Interdepartmental Center Photonext for Applied Photonics. His research focuses on Applied Geophysics, Geophysical Data Integration, and Glaciology. He graduated in Mining Engineering (1988) and earned a PhD in Underground Resources Engineering (1993). He has been an Associate Professor (2005-2024) and Full Professor (2024-present) at PoliTO, leading projects funded by EU (FP7, LIFE, Horizon), MIUR (PRIN, FIRB), and regional bodies. His recent publications explore geophysical methods for subsurface modeling, glacial systems, and environmental remediation, with keywords spanning Geophysics, Hydrology, and Climate Science. His work emphasizes ground-penetrating radar, seismic noise analysis, and hybrid modeling techniques. Scientific awards include the Best Paper Award at the Near Surface Geoscience Conference (2008). He has supervised PhD students on fiber optic sensors and GPR optimization, advised projects on digital twins, and led EU-funded research on biogas enhancement in landfills.

Marco A.R. Ferreiraمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Multi-Scale Modeling
• Spatial-Temporal Models
• +۴ مورد دیگر

Marco A.R. Ferreira is an Associate Professor in the Department of Statistics at Virginia Polytechnic Institute and State University (Virginia Tech), affiliated with the College of Science. He holds a Ph.D. in Statistics from Duke University (2002), with a dissertation on Bayesian multi-scale modeling under M. West. He also earned an M.Sc. (1994) and B.Sc. (1993) in Statistics from the Federal University of Rio de Janeiro. Research Interests: Ferreira specializes in Bayesian statistics, multi-scale modeling, spatial-temporal models, computational methods (e.g., MCMC), and applications in environmental science, genomics, and epidemiology. His work emphasizes hierarchical models, inverse problems, and high-dimensional data analysis. Publications Trends: His research spans advanced statistical methodologies for environmental monitoring, civil unrest modeling, and genomic data analysis. Key themes include Bayesian hierarchical models, spatiotemporal fusion, and computational algorithms for optimal experimental design. Awards & Honors: OBAYES Poster Prize (2009) CNPq Fellowship (2003–2006) WNAR/COBAL 2 Award (2005) Springer Poster Prize (2003) Finalist, Savage Award (2003) Best Contributed Paper (JSM 2000) Professional Activities: He serves as an Associate Editor for Bayesian Analysis and is a member of the American Statistical Association and the International Society for Bayesian Analysis.

Nicholas Ossiمشاهده پروفایل

استادیار مهمان

• Nonlinear Waves
• Integrable Systems
• Partial Differential Equations
• +۲ مورد دیگر

Nicholas Ossi is a Visiting Assistant Professor in the Department of Mathematics at the University at Buffalo's College of Arts and Sciences. He holds a PhD in Applied Mathematics from Florida State University (2024) and a BS in Mathematics and Physics from the University of Central Florida (2018). His research focuses on integrable nonlinear partial differential and difference equations modeling wave propagation in dispersive media, particularly within the nonlinear Schrödinger (NLS) family. His work explores inverse scattering transforms for space-time-shifted integrable equations and investigates non-Hermitian systems with applications in wave dynamics and topological properties. Recent publications address discrete integrable systems, breather interactions, and effects of complex potentials. Office hours: Tuesday/Thursday 10:00-11:00am. Contact: 326 Mathematics Building, UB North Campus, Buffalo NY, 14260-2900; (904) 891-0164; nossi@buffalo.edu .

Alexandra Andreeva Soskovaمشاهده پروفایل

استاد

• Computability Theory
• Computable Structure Theory
• Mathematical Logic
• +۴ مورد دیگر

Alexandra Andreeva Soskova is a Full Professor in the Department of Mathematical Logic and its Applications at Sofia University's Faculty of Mathematics and Informatics. She holds a PhD in Mathematics (1990) and has held academic roles since 1979, including Head of Department (2008-2016) and Vice Dean (2015-2016). Her research focuses on computability theory, computable structure theory, and enumeration reducibility, with notable contributions to degree spectra and abstract structures. Education: BS (1978), MS (1979) in Mathematics, PhD (1990) in Mathematical Logic (Sofia University) Award: Honorary badge 'St. Kliment Ohridski' (2020) Her work explores effective properties of abstract structures, including coding in graphs, cohesive powers, and jump inversion theorems. She has supervised multiple PhD and MS students, including Stefan Vatev. Key grants include collaborations with institutions in Austria, Russia, and the US. She actively participates in academic service, including roles with the Association for Symbolic Logic and organizing international conferences.

Niels Otaniمشاهده پروفایل

دانشیار

• Cardiac Electrophysiology
• Computational Biology
• Biomechanical Imaging
• +۹ مورد دیگر

Niels Otani is an Associate Professor in the School of Mathematics and Statistics at the College of Science, Rochester Institute of Technology (RIT). He holds a BA from the University of Chicago and a Ph.D. from the University of California, Berkeley. His research focuses on cardiac electrophysiology, computational biology, and biomechanical imaging, with a particular emphasis on understanding and controlling cardiac arrhythmias such as ventricular fibrillation. Dr. Otani has pioneered methods for visualizing action potential propagation using ultrasound and developed novel defibrillation strategies that reduce energy requirements. He also investigates the role of ephaptic coupling in cardiac dynamics and the mechanisms underlying spiral wave formation and termination. His work bridges mathematics, physics, and cardiology, with applications in clinical therapies and biomedical engineering. Dr. Otani's research has been supported by grants such as an NSF award for developing diagnostic tools for cardiac disease. He teaches advanced courses in multivariable calculus, linear algebra, and research thesis supervision. Collaborations span interdisciplinary teams, including veterinary cardiology and biomedical imaging groups. His contributions include over 50 publications on topics ranging from action potential dynamics to computational modeling of cardiac tissue, with a focus on translating theoretical findings into clinical solutions.

Shujuan Maoمشاهده پروفایل

استادیار

• Environmental Seismology
• Hydrogeophysics
• Geothermal Energy
• +۵ مورد دیگر

Shujuan Mao is an Assistant Professor in the Department of Earth and Planetary Sciences at the University of Texas at Austin, part of the Jackson School of Geosciences. She holds a B.S. from Peking University, a Ph.D. from MIT, and conducted postdoctoral research at Stanford University and the Institut des Sciences de la Terre in France. Her research focuses on environmental seismology, hydrogeophysics, and geothermal energy, with an emphasis on understanding subsurface fluid dynamics using seismic interferometry. Key areas include groundwater monitoring, carbon sequestration, and volcanic unrest. She leads the 'Seismo4D' research group, which develops cutting-edge seismic techniques for 4D subsurface imaging. Mao's work spans academic contributions (over 15 peer-reviewed articles), student supervision (two current advisees), and international collaborations. Her group actively recruits PhD students and postdocs interested in environmental seismology and energy transition challenges. Her mailing address is Jackson School of Geosciences, Austin, TX 78712-1692, with contact via smao@jsg.utexas.edu or shujuan.c.mao@gmail.com.