جست‌وجوی استاد و پژوهشگر

Tanya P. Garciaمشاهده پروفایل

دانشیار

• Biostatistics
• Neurodegenerative Diseases
• High-Dimensional Variable Selection
• +۵ مورد دیگر

Tanya P. Garcia, PhD is an Associate Professor of Biostatistics at the Gillings School of Public Health and Research Faculty in the UNC Neurology Huntington Disease Program at the University of North Carolina at Chapel Hill . She leads the Methods for INcomplete Data (MIND) Lab , focusing on statistical methods for handling censored, missing, or incomplete data in neurodegenerative disease progression studies. Education: PhD in Statistics, Texas A&M University MS in Statistics, University of Western Ohio MS in Industrial Engineering and Operations Research, UC Berkeley Research Interests: Specializing in High-Dimensional Variable Selection , Longitudinal Data Analysis , and Neurodegenerative Disease Modeling , her work develops reproducible statistical methods for Huntington's disease progression, improving clinical trial design and biomarker identification. Scientific Awards: American Statistical Association Fellow (2024) Landis Award for Outstanding Mentorship (2024) Roy R. Kuebler Award (2024) Gertrude M. Cox Award (2024) Leadership & Mentorship: Director of the MIND Lab, Chair-Elect of the Biometrics Section of ASA, and Tyson Academic Leadership Fellow (2023–2024). Her lab alumni have secured prestigious positions at institutions like Wake Forest University and Baylor University.

Kai Gongمشاهده پروفایل

استادیار

• Sustainable Infrastructure Materials
• Environmental Sustainability
• Materials Science
• +۴ مورد دیگر

Dr. Kai Gong is an Assistant Professor of Civil and Environmental Engineering at Rice University, with affiliations at the Rice Advanced Materials Institute and Ken Kennedy Institute. His research focuses on sustainable infrastructure materials, environmental sustainability, and materials science. He holds a Ph.D. in Civil & Environmental Engineering and Materials Science from Princeton University, an MEngSci from Monash University (Australia), and dual B.S. degrees from Monash University and Central South University (China). Research Interests: Development of durable, sustainable infrastructure materials Waste encapsulation and conversion to value-added products Carbon mineralization and utilization Advanced characterization techniques (synchrotron/neutron scattering) Data-driven modeling and atomistic simulations Notable Awards: 2023 Le Chatelier Medal (Cement and Concrete Research) 2024 Giatec Award for Best Paper in Sustainability Walbridge Fund Graduate Award (2019) His work integrates computational methods (e.g., molecular dynamics) with experimental techniques to address decarbonization challenges in infrastructure. The Gong Research Group actively seeks motivated researchers for opportunities in sustainable materials innovation.

Peter K. Kitanidisمشاهده پروفایل

استاد

• Groundwater Flow
• Hydrologic Modeling
• Inverse Problems
• +۳ مورد دیگر

Peter K. Kitanidis is a Professor in the Department of Civil and Environmental Engineering and the Institute for Computational and Mathematical Engineering at Stanford University . His research focuses on groundwater flow , hydrologic forecasting , and stochastic inverse modeling , with applications to pollutant remediation and CO₂ storage monitoring . Education : Diploma, National Technical University of Athens (1974) M.S., MIT (1976) Ph.D., MIT (1978) Research Interests : Groundwater modeling and contaminant transport Hydraulic tomography and aquifer characterization Stochastic methods for uncertainty quantification Bioremediation and enhanced in-situ pollutant decay Dilution and mixing processes in heterogeneous media Real-time river flow forecasting Scientific Awards : L.G. Straub Award (1979) W.L. Huber Research Prize (1994) ISI Highly Cited Researcher (2001) AGU Hydrologic Sciences Award (2011) ASCE Pioneers in Groundwater Lecturer (2011) Advising and Grants : Advised 20+ PhD and MS students (1978–2018) Principal investigator on NSF, EPA, and DOE-funded projects Developed software for groundwater data analysis and CO₂ monitoring Contributed to bioremediation protocols and hydraulic tomography algorithms Labs and Teams : Kitanidis Laboratory for groundwater crisis solutions Collaborated with Oak Ridge National Laboratory and Stanford Hydrogeology Group Mentored postdocs (2000–2017) in reactive transport and inverse modeling

Alexandra M. Schmidtمشاهده پروفایل

استاد

• Biostatistics
• Bayesian Inference
• Spatial Statistics
• +۳ مورد دیگر

Professor Alexandra M. Schmidt is a leading academic in Biostatistics at McGill University, holding an endowed University Chair. She specializes in spatial and spatio-temporal modeling, particularly in epidemiology and environmental health. Previously, she served as a Full Professor at the Federal University of Rio de Janeiro (2012–2016). Her research focuses on Bayesian methodologies for analyzing complex processes, including disease spread, environmental hazards, and socio-economic disparities. She has authored influential books such as Spatio-Temporal Methods in Environmental Epidemiology with R (2023) and contributed to over 150 peer-reviewed articles. Key awards include the ISBA Fellowship (2024), ASA Fellowship (2020), and the Abdel El-Shaarawi Award (2008). Education: PhD in Statistics (2001, University of Sheffield, UK), MSc and BSc in Statistics (Federal University of Rio de Janeiro, Brazil). Research interests span Bayesian inference, spatial statistics, and environmental epidemiology. She has advised numerous PhD/MSc students and collaborated on projects linking statistical methods to public health challenges, such as modeling dengue outbreaks and air pollution impacts. Active in academic service, she has chaired major conferences (e.g., 2022 ISBA World Meeting) and serves on editorial boards of top journals like Bayesian Analysis and Canadian Journal of Statistics . Teaching includes advanced courses on generalized linear models, spatial epidemiology, and Bayesian analysis. Her work bridges theoretical statistics with practical applications, addressing global health issues through innovative spatio-temporal modeling techniques.

• Heterogeneous Catalysis
• Nanomaterials
• In Situ Spectroscopic Techniques
• +۷ مورد دیگر

Dr. Simon Beaumont is an Associate Professor in the Department of Chemistry at Durham University , with additional responsibilities as Associate Dean (PGR) in the Faculty of Science. His research program integrates heterogeneous catalysis , nanomaterials , and in situ spectroscopic techniques to develop sustainable chemical processes. BA & MSci Natural Sciences, University of Cambridge (2003-2007) PhD in Heterogeneous Catalysis, University of Cambridge (2010) Postdoctoral Fellowship at UC Berkeley (2010-2012) Research foci include mechanistic studies of catalytic processes, nanoparticle synthesis , and in situ characterization via X-ray absorption (NEXAFS), DRIFTS, and Raman. His work addresses challenges in CO2 hydrogenation , biomass conversion , and environmental remediation , supported by national/EU/industrial funding. Recent publications highlight trends in selective hydrogenation (furfural), multi-functional catalysts (acid-base systems), and nanoparticle stability under reactive conditions. All studies emphasize molecular-level understanding for practical catalyst design. Scientific awards include Leverhulme Trust and Addison Wheeler fellowships. Teaching portfolio spans first-year laboratories , organic chemistry tutorials , and advanced catalysis lectures . Supervision of five research postgraduates and leadership of industry-funded projects further demonstrate his academic impact.

• Energy Systems Optimization
• Conceptual Process Design
• Life Cycle Assessment
• +۸ مورد دیگر

Prof. Dr. André Bardow is a Full Professor in Energy and Process Systems Engineering at ETH Zurich , leading research at the intersection of thermodynamics, machine learning, and sustainable energy systems. Previously, he held professorships at RWTH Aachen University (2010-2020) and TU Delft (2007-2010). He also served as part-time director at Forschungszentrum Jülich (2017-2022) and visiting professor at UC Santa Barbara (2015/16). His work focuses on energy systems optimization , computer-aided molecular design , and CO2 capture & utilization . PhD from RWTH Aachen University Current ETH Zurich affiliation Former roles at RWTH Aachen, TU Delft, Jülich Research Center His research integrates machine learning with thermodynamic modeling to optimize processes like crystallization and electrochemical cooling . Recent publications demonstrate advancements in solvent design, CO2 transport LCA, and ORC working fluid optimization. He chairs the VDI Technical Committee for Thermodynamics (2016-2024) and has received multiple awards including the Covestro Science Award and Arnold-Eucken-Award . Current projects address carbon circular economies , electrified chemical production , and AI-driven process optimization . His lab at ETH Zurich develops cutting-edge technologies like ML-CAMPD frameworks for sustainable separation processes and photoacid-based CO2 capture systems. Funding from the H2020 Systemic Expansion of Circular Ecosystems (grant 101036854) supports these initiatives. 2024 Clarivate Highly Cited Researcher 2022 Inaugural Lecture: "To sustainability and beyond: A computer-animated story on energy & chemicals" Recipient of multiple teaching and research excellence awards

David S. Eisenbergمشاهده پروفایل

استاد

• Protein Aggregation
• Amyloid Structures
• Prion Diseases
• +۵ مورد دیگر

David S. Eisenberg is a Professor of Chemistry and Biochemistry and Biological Chemistry at the University of California, Los Angeles, where he also serves as Director of the UCLA-DOE Institute for Genomics and Proteomics and as an HHMI Investigator. His research focuses on protein interactions, particularly the structural basis for conversion of normal proteins to the amyloid state and conversion of prions to the infectious state. Dr. Eisenberg earned his undergraduate degree in biochemical sciences from Harvard College and his D.Phil. degree in theoretical chemistry from Oxford University on a Rhodes Scholarship. His postdoctoral research was on ice and water with Walter Kauzmann at Princeton and in protein crystallography with Richard Dickerson. He joined the UCLA faculty after his postdoctoral studies. Dr. Eisenberg and his research group focus on protein interactions in amyloid and prion diseases. These diseases involve protein aggregation where normal functional proteins convert to abnormal aggregated forms. Systemic amyloid diseases like dialysis-related amyloidosis result from fiber accumulation until organ failure, while neurodegenerative diseases like Alzheimer's, Parkinson's, ALS, and prion conditions appear to be caused by smaller oligomers. In 2005, his team determined the atomic-level structure for the amyloid fiber spine, revealing a 'steric zipper' of two parallel beta sheets packed across a dry interface. Since then, they've determined approximately 90 amyloid spines from 15 disease-related proteins. In 2010, they identified the structure of a toxic amyloid-related oligomer consisting of six anti-parallel beta strands forming a cylindrical barrel. His recent publications demonstrate continued innovation in amyloid research, with focus areas including structural prediction of amyloid formation, mechanisms of tau fibril disassembly in Alzheimer's disease, cryo-EM analysis of amyloid polymorphism, and structure-based design of inhibitors for amyloid toxicity. His work integrates computational, structural, and biochemical approaches to understand protein aggregation across multiple disease contexts. Dr. Eisenberg has received numerous prestigious awards and honors: National Academy of Sciences Member American Philosophical Society Member Institute of Medicine Member Howard Hughes Medical Institute Investigator Biophysical Society Emily M. Gray Award Harvard Westheimer Medal UCLA Seaborg Medal Technion - Israel Institute of Technology Harvey Prize in Human Health As Director of the UCLA-DOE Institute for Genomics and Proteomics and an HHMI Investigator, Dr. Eisenberg leads significant research initiatives in protein structure and aggregation. His laboratory combines X-ray crystallography, bioinformatics, and biochemical techniques to investigate protein interactions, with particular emphasis on amyloid-forming proteins and their role in disease. The Eisenberg Lab, located in Boyer Hall at UCLA, maintains an active research program investigating the structural basis of protein aggregation. The lab continues to build on its landmark discoveries of amyloid structures while exploring new frontiers in understanding protein misfolding diseases and developing potential therapeutic interventions.

Mohamed Amaraمشاهده پروفایل

استاد

• Partial Differential Equations
• Mechanics in Porous Media
• Fluid Mechanics
• +۵ مورد دیگر

Mohamed Amara is a full-time Professor at the University of Pau and the Pays de l'Adour (UPPA) since 1996, affiliated with the Laboratory of Mathematics and their Applications (CNRS-UMR 5142). He served as its director (1999-2007), Director of the Doctoral School of Exact Sciences (ED211, 2007-2008), and UPPA's Scientific Council Vice-President (2008-2012). He has been UPPA's President since 2012 (re-elected until 2020). Education: Mathematics from University of Algiers (1973), Pierre and Marie Curie University (DEA 1974, Doctorate 1978, State Doctorate 1983) Academic Roles: Research Associate at Ecole Polytechnique (1978-1982), Algerian Electricity and Gas Company (1983-1992), Professor in Algiers (1988-1994), Tunis (1994-1995), and Associate Professor at Paris 6 (1995-1996) His research focuses on numerical simulation of partial differential equations for environmental/energy applications, including mechanics in porous media (petroleum engineering, geoscience), fluid mechanics (aerodynamics, estuarine hydrodynamics), non-Newtonian flows, and wave propagation. Articles highlight expertise in discontinuous Galerkin methods, Helmholtz problems, finite element discretization, and multiphysics systems. He managed 20 doctoral theses and led national mathematics programs at ANR (2007-2011). He chairs the Cocktail association for higher education IT systems and collaborates with INRIA's Magique 3D team (since 2006).

Harri Lähdesmäkiمشاهده پروفایل

دانشیار

• Probabilistic Machine Learning
• Deep Generative Models
• Artificial Intelligence
• +۴ مورد دیگر

Harri Lähdesmäki is an Associate Professor (tenured) at the Department of Computer Science, Aalto University, where he leads the Computational Systems Biology research group. His work focuses on probabilistic machine learning and deep generative models with applications in biomedicine and molecular biology. Key Research Interests: Probabilistic machine learning, deep generative models, computational biology, bioinformatics, longitudinal data modeling Contact: harri.lahdesmaki@aalto.fi | Konemiehentie 2, 02150 Espoo, Finland His recent publications highlight advancements in: Gaussian process priors for scalable deep generative models Single-cell analysis of immune repertoires in leukemia and diabetes Probabilistic deconvolution methods for RNA-seq data Epigenetic analysis using hidden Markov and mixed models Transformer-based survival prediction and missing data handling Harri’s work integrates mechanistic modeling with Bayesian inference, particularly applied to immunology, cancer biology, and early disease prediction.

Ying Chenمشاهده پروفایل

دانشیار

• Financial Statistics
• Machine Learning
• Quantitative Finance
• +۷ مورد دیگر

Assoc Prof Ying Chen is an Associate Professor at the National University of Singapore , affiliated with the Department of Mathematics, Asian Institute of Digital Finance (as Academic Director of PhD Program in Digital FinTech 2022–2024), Risk Management Institute (2019–2023), Department of Statistics and Data Science (2019–2023), and Department of Economics (2018–2023). She also contributes to NUS Graduate School for Integrative Sciences and Engineering since 2016. Research Interests include: AI forecasting and quantum computing for finance Nonstationary time series and functional data analysis Energy data analytics and precision medicine Network autoregression and spatial-temporal modeling Explainable AI and citation metrics Portfolio liquidation and market-making algorithms Article Trends demonstrate expertise in: Adaptive forecasting for gas flows and electricity prices Blockchain network influence detection Quantum computing applications in finance Functional autoregression with mixed predictors Credit rating fairness and explainability High-resolution implied volatility modeling Scientific Awards include: ISI Elected Member (2016–) International Statistical Institute Council (2023–2027) IASC Scientific Secretary (2017–2019, 2023–2025) Advisory roles for EU FIN-TECH and xAIM projects

Ira Kemelmacher-Shlizermanمشاهده پروفایل

استاد

• Computer Vision
• Computer Graphics
• Generative AI
• +۶ مورد دیگر

Ira Kemelmacher-Shlizerman is a Full Professor of Computer Science at the Paul G. Allen School of Computer Science & Engineering at the University of Washington and Director of the UW Reality Lab. She also serves as a Principal Scientist at Google, where she leads the Shopping Gen AI visuals teams focusing on Virtual Try-On, 3D, and product videos. Her research spans computer vision, computer graphics, and Generative AI, with particular contributions to virtual try-on technology, 3D modeling, and augmented reality applications. Professor Kemelmacher-Shlizerman's research interests focus on Generative AI applications in visual computing. Her work bridges the gap between theoretical computer vision and practical applications, particularly in e-commerce and virtual reality. She has made significant contributions to virtual try-on technology, 3D editing with generative models, and AI applications for shopping experiences. Her research combines deep learning with traditional computer vision techniques to solve challenging problems in image and video synthesis. Her recent publications demonstrate a strong trend toward Generative AI applications for visual shopping experiences, virtual try-on technology, and 3D content creation. The work spans multiple top conferences including CVPR, SIGGRAPH, and ICCV, with a focus on practical applications of computer vision and graphics. Her research has evolved from foundational work in face reconstruction and aging to current applications in virtual shopping and 3D content generation. Google faculty award Madrona prize GeekWire Innovation of the Year Award Covers of CACM and SIGGRAPH Best student paper honorable mention at CVPR'21 Best demo runner up MobiSys'22 Senior member of IEEE Distinguished Member of ACM Professor Kemelmacher-Shlizerman has successfully tech-transferred multiple research projects to industry. She founded Dreambit, a startup acquired by Meta, and previously built and launched the Face Movies feature at Google. She currently leads Google's Shopping Gen AI visuals teams, focusing on 10x improvements to shopping journeys. Her UW Reality Lab serves as a hub for AR/VR research with industry partnerships. She has mentored numerous PhD students who have become researchers in both academia and industry, with several publications featuring student co-authors receiving recognition at top conferences. Professor Kemelmacher-Shlizerman leads the Graphics and Imaging Laboratory (GRAIL) and the UW Reality Lab, which focuses on augmented and virtual reality research with industry partnerships including Google. The labs work on cutting-edge projects in virtual try-on, 3D modeling, and immersive experiences, bridging academic research with real-world applications.

Andrew D. Whiteمشاهده پروفایل

دانشیار

• Large language models
• Deep learning in chemistry
• Molecular dynamics
• +۵ مورد دیگر

Andrew D. White is an Associate Professor of Chemical Engineering at the Hajim School of Engineering & Applied Sciences, University of Rochester. He holds a PhD from the University of Washington (2013). His research focuses on automating scientific discovery through AI, particularly leveraging large language models (LLMs) and deep learning techniques in chemistry. His lab develops agents that integrate literature analysis, hypothesis generation, and experimental design to advance fields like molecular dynamics and drug discovery. Education: PhD in Chemical Engineering, University of Washington, 2013 BS/MS (not explicitly stated in text, inferred from career timeline) Research Interests: Large language models for scientific automation Deep learning applications in chemistry and materials science Molecular dynamics simulations Scientific agents and autonomous systems Publications: His work includes groundbreaking studies on closed-loop AI systems for chemistry, federated learning in molecular property prediction, and multi-agent systems for drug discovery. Recent highlights include the Robin system and ChemCrow tools. Awards: Recipient of the NSF Career Award (2018), NIH Outstanding Investigator Award (2020), and the Curtis Teaching Award (2019). He also advises biotech companies and serves on the National Academy of Sciences' Chemical Sciences Roundtable. Grants & Funding: Supported by DOE, NSF (multiple grants including CBET-1751471), NIH (R35GM137966), and LLNL projects. Collaborates with institutions like Argonne National Lab and Qubit Pharmaceuticals. Labs & Teams: Leads the White Lab at Rochester and co-founded FutureHouse, a nonprofit advancing AI-driven scientific discovery. Supervises a multidisciplinary team of PhD students and postdocs in computational chemistry, AI, and biophysics.

Carolyn Conner Seepersadمشاهده پروفایل

استاد

• Additive Manufacturing
• Design for Additive Manufacturing
• Materials Design and Simulation
• +۳ مورد دیگر

Dr. Carolyn Conner Seepersad is a Woodruff Professor in the George W. Woodruff School of Mechanical Engineering at Georgia Institute of Technology. She leads the Digital Design and Manufacturing research group and previously founded the Center for Additive Manufacturing and Design Innovation at The University of Texas at Austin. Her research focuses on additive manufacturing, materials design, and process innovation. She holds editorial roles, including Editor-in-Chief of the ASME Journal of Mechanical Design, and has received numerous awards for research and teaching. Education: PhD, Mechanical Engineering, Georgia Tech, 2004 MS, Mechanical Engineering, Georgia Tech, 2001 BA, Philosophy, Politics, and Economics, Oxford University, 1998 BS, Mechanical Engineering, West Virginia University, 1996 Her research interests span design for additive manufacturing, simulation-based materials and structures, and metamaterials. She emphasizes manufacturing-aware design and sustainability. Key contributions include lattice structure optimization, negative stiffness composites, and process-aware manufacturing techniques. Her publications reflect advancements in additive manufacturing processes, materials characterization, and design methodologies. Awards include the ASME Design Automation Award and recognition as a University of Texas System Academy of Distinguished Teachers. Seepersad has advised on grants such as the LEAP-HI GOALI project and contributed to initiatives like the Solid Freeform Fabrication Symposium. Her work bridges academia and industry, emphasizing practical applications and innovation. Labs/Teams: Leads the Digital Design and Manufacturing group at Georgia Tech, previously directed the UT Austin Additive Manufacturing Center.

• Environmental Impact Assessment
• Life Cycle Assessment (LCA)
• Industrial Ecology
• +۴ مورد دیگر

Prof. Dr. Stefanie Hellweg serves as a Full Professor for Ecological Systems Design at the ETH Zürich , Department of Civil, Environmental and Geomatic Engineering. She is Deputy Head of the Institute of Environmental Engineering and leads groundbreaking research at the intersection of industrial ecology, life cycle assessment, and circular economy systems. PhD in Environmental Engineering (ETH Zürich, 2000) Industrial Engineering Degree (Karlsruhe University, 1996) Her research develops advanced methodologies for environmental impact assessment of products and technologies, focusing on: Life Cycle Assessment (LCA) with correlated uncertainty modeling Circular economy implementation in chemical and energy sectors Industrial symbiosis optimization for heat and nutrient flows Urban wastewater systems redesign for resource recovery Chemical pollution analysis in plastic materials Key publication trends reveal expertise in: Hybrid wastewater treatment systems Energy-efficient greenhouse cultivation Exergy analysis for sustainability metrics Machine learning applications in environmental modeling Policy-relevant industrial ecology studies She actively participates in global sustainability initiatives as: President of the International Society of Industrial Ecology (ISIE) Member of UN Environment's International Resource Panel Editorial board member for leading environmental journals

• Multimodal Discourse Analysis
• Systemic Functional Linguistics
• Digital Humanities
• +۶ مورد دیگر

Professor Kay O'Halloran serves as Chair Professor and Head of Department of Communication and Media within the School of the Arts at the University of Liverpool since August 2019. She concurrently holds the position of Co-Director for the Digital Media and Society Institute (DMSI), demonstrating significant leadership across academic and research domains. Her career spans prestigious institutions including Curtin University (2013-2019) and National University of Singapore (1998-2013), where she directed the Multimodal Analysis Lab and served as Deputy Director of the Interactive & Digital Media Institute. PhD from Murdoch University (1996) Postdoctoral position at Martin Luther University (1997-1998) Visiting Distinguished Professor at Shanghai Jiaotong University (2017-2020) Professor O'Halloran's research focuses on multimodal discourse analysis, particularly the interaction of language with visual and mathematical resources. Her pioneering work in systemic functional multimodal discourse analysis (SF-MDA) has significantly impacted mathematics education and digital communication studies. Current research emphasizes digital tools for multimodal analysis and mixed methods approaches to big data analytics, addressing contemporary challenges in misinformation and public health communication. Her recent publications reveal strong thematic concentration in pandemic-related communication analysis, misinformation resistance mechanisms, and multimodal argumentation across social and cultural contexts. The 15 most recent articles demonstrate consistent application of multimodal frameworks to urgent societal issues including public health crises, political discourse, and digital literacy challenges. Founding editor of Routledge Studies in Multimodality book series (57+ volumes) Over 150 publications in leading international journals 50+ plenary/keynote presentations globally Competitive funding from National Research Foundation, MOE Singapore, US Air Force, ARC, NHMRC, and others Professor O'Halloran has directed interdisciplinary research teams comprising social scientists, computer scientists, and designers. Her leadership extends to developing commercial multimodal analysis software (Multimodal Analysis Image and Video) adopted internationally. Current projects include Eurovision 2023 wellbeing evaluation and COVID-19 misinformation immunity development, demonstrating continued relevance to contemporary societal challenges.