جست‌وجوی استاد و پژوهشگر

Alain Bonnevilleمشاهده پروفایل

استاد

• Geothermal Energy
• Geological CO2 Storage
• Geophysical Methods
• +۳ مورد دیگر

Dr. Alain Bonneville is a Lab Fellow and Geophysicist at Pacific Northwest National Laboratory (PNNL) and holds a Courtesy Professor appointment at Oregon State University's College of Earth, Ocean, and Atmospheric Sciences. With extensive experience in geological storage of CO2, geothermal energy, and geophysical monitoring techniques, Dr. Bonneville leads diverse research projects that bridge fundamental science and practical applications for energy and environmental challenges. Dr. Bonneville's educational background includes: PhD in Geophysics from the University of Montpellier, France MS in Petroleum Geophysics from IFP-School, Paris, France BS in Geology from the University of Lyon, France Dr. Bonneville's research spans several critical areas in Earth sciences and energy systems. His work on geothermal energy focuses on super-hot enhanced geothermal systems (EGS), site characterization, monitoring, and stimulation fluids. In geological CO2 storage, he investigates project management, site characterization, numerical modeling, and monitoring methods using potential fields and remote sensing. His expertise in geophysical methods includes heat flow measurements, gravity surveys, muon tomography development for borehole deployment, and remote sensing applications. Additional research areas encompass marine heat flow instrumentation development, thermal monitoring of active volcanoes, and intraplate volcanism studies in the Indian and Pacific Oceans. Dr. Bonneville has received significant recognition for his contributions to science, including: Membership in the Washington State Academy of Sciences Lab Fellow position at Pacific Northwest National Laboratory Executive Committee membership on the U.S. National Risk Assessment Partnership Scientific Committee membership at IFP-Energies Nouvelles, France He also holds two U.S. patents related to electrophilic acid gas-reactive fluids for enhanced fracturing and recovery of energy producing materials. Throughout his career, Dr. Bonneville has led significant research initiatives, including the PNNL Carbon Sequestration Initiative (2009-2013) and the European Marie Curie Research Training Network on Greenhouse Gas Removal (GRASP), which involved 14 academic and industrial institutions across 7 countries and supported 35 PhD students and post-docs. His work on the FutureGen 2.0 project demonstrates his leadership in large-scale carbon storage site characterization and monitoring program design. Dr. Bonneville maintains active collaborations with research teams at PNNL's Environmental Molecular Sciences Laboratory and works closely with Oregon State University's geoscience researchers. His laboratory work focuses on developing novel instrumentation for geophysical monitoring, particularly in the areas of muon tomography for subsurface characterization and thermal monitoring systems for geothermal and carbon storage applications.

Konstantinos Kalogeropoulosمشاهده پروفایل

استادیار

• Proteomics
• Snake Venom Research
• Protein Structure Prediction
• +۳ مورد دیگر

Konstantinos Kalogeropoulos is an Assistant Professor at the Department of Biotechnology and Biomedicine, Technical University of Denmark (DTU), leading research at the Cell Diversity Lab. His work bridges proteomics, computational biology, and snake venom research. Current projects: "The Proteomic Landscape during Influenza Infection" (2022-2025) Supervisor for PhD projects on protease network rewiring in psoriasis and wound exudate degradomics Research interests include: Proteomic analysis of inflammatory diseases Snake venom toxin structure prediction Extracellular matrix biomechanics De novo peptide sequencing algorithms Computational modeling of protease networks Recent article trends demonstrate his work in • Database-free proteomics (InstaNovo/InstaNexus) • Snake venom pathophysiology (V-ToCs clustering) • Inflammatory disease biomarkers (psoriasis, impaired healing) • Extracellular matrix mechanics (fibronectin tension, gut inflammation) Advising: Supervises PhD students Polhaus, C. J. M. and Haack, A. M., focusing on protease networks and wound healing.

Matthew Brookhouseمشاهده پروفایل

مدرس ارشد

• Plant physiology
• Sub-alpine ecology
• Dendrochronology
• +۲ مورد دیگر

Dr. Matthew Brookhouse is a Senior Lecturer at the Fenner School of Environment & Society, part of the Australian National University's Institute for Climate, Energy & Disaster Solutions. With a PhD in Dendroclimatology from ANU, he specializes in using forest structural complexity and tree-ring analysis to understand climate interactions and ecological responses in Australian subalpine environments. Research Focus: Sub-alpine ecology, Dendrochronology, CO2 responsiveness in eucalypt species Teaching: First-year research methods with emphasis on statistical application, advanced modeling and field botany Projects: Leading collaborative snow-gum dieback research and dendrochronological monitoring initiatives His publications span 2006-2025 with recent emphasis on machine learning applications for forest monitoring, tropical tree-ring chronologies for climate change, and climate sensitivity in Australian alpine ecosystems. Key collaborations include institutions like Australian Nuclear Science and Technology Organisation and University of Canberra researchers. Current projects focus on snow-gum woodland dieback mechanisms, high-resolution dendrometric monitoring, and integrating dendrochronology with environmental policy frameworks. He maintains active supervision of research students and contributes to both undergraduate and postgraduate curriculum development.

Dominique Forayمشاهده پروفایل

استاد

• Economics of Innovation
• Science and Technology Indicators
• Technology Policy
• +۳ مورد دیگر

Dominique Foray is a Professor at Ecole Polytechnique Fédérale de Lausanne (EPFL) and holds the Chair of Economics and Management of Innovation (CEMI). He is a member of the Swiss Council for Science, chairman of the KOF Swiss Economic Research Institute's Advisory Board, and a foreign member of Columbia University's Center of Capitalism and Society. His career includes extensive advisory roles, such as with the European Commission's 'Knowledge for Growth' group and Germany's Expert Commission for Research and Innovation. Education: Ph.D. in Economics, University Lumière of Lyon (1984) Habilitation à diriger des recherches, University Lumière of Lyon (1992) Foray's research focuses on the economics of innovation, knowledge-based economy dynamics, technology policy, smart specialization strategies, and institutional frameworks for innovation. He has authored seminal works including The Economics of Knowledge (MIT Press, 2004) and Smart Specialisation (Routledge, 2015). His recent publications explore mission-oriented innovation policies, smart specialization in EU contexts, and knowledge diffusion mechanisms. Foray's work bridges academic theory with practical policy design, influencing regional and national innovation strategies globally. Advising: Past EPFL PhD students: Aouinait Camille Marguerite, Ayoubi Charles Chadi, Baruffaldi Stefano Horst, Bednyagin Denis, Bogers Marcellus, Coffano Monica, Conti Annamaria, Cristelli Gabriele, Gaulé Patrick, Hamdan Livramento Intan Maizurah, Marino Marianna, Muñoz Tellez de Kieffer Viviana Carolina, Pellegrin Claudia, Simeth Markus Foray's institutional roles include leadership in European policy initiatives and contributions to the World Bank's studies on innovation's role in economic growth. His interdisciplinary approach integrates economics, policy analysis, and technology management to address grand societal challenges through innovation.

Xuezhe Maمشاهده پروفایل

استادیار

• Natural Language Processing
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Xuezhe Ma is an Assistant Professor in the Department of Computer Science at the University of Southern California's Viterbi School of Engineering. Previously, he was a Ph.D. student at Carnegie Mellon University's Language Technologies Institute, where he worked under the supervision of Professor Eduard Hovy. His academic journey includes a Master's degree from Shanghai Jiao Tong University's Center for Brain-like Computing and Machine Intelligence and a Bachelor's degree in Computer Science from the same institution. Ph.D. in Computer Science, Carnegie Mellon University (completed ~2020) M.S. in Brain-like Computing, Shanghai Jiao Tong University B.S. in Computer Science, Shanghai Jiao Tong University Dr. Ma's research spans multiple areas at the intersection of Natural Language Processing and Machine Learning, with particular focus on structured prediction, syntactic and semantic parsing, machine translation, language generation, and deep generative models. His recent work has expanded into vision-language models, large language model architectures, and applications across computer vision tasks. His research combines theoretical foundations with practical implementations, as evidenced by his development of tools like NeuroNLP2 and MaxParser. His publication record shows a clear trajectory from foundational NLP work during his PhD (including papers on dependency parsing and sequence labeling) to more recent contributions in generative models and large language systems. The 15 most recent publications reveal a strong focus on addressing fundamental challenges in generative modeling, context handling, and multimodal integration, with applications spanning literary translation, medical imaging, and news diffusion analysis. AI2 Outstanding Intern Award (2018) Dr. Ma has secured research funding supporting his work in generative models and language technologies, with projects focusing on improving the efficiency and capabilities of large language models. His research group at USC is actively working on next-generation language understanding and generation systems, with particular emphasis on context-aware modeling and multimodal integration. He has established collaborations with industry partners including the Allen Institute for AI and has contributed to open-source projects like Texar. At USC, Dr. Ma leads research in the Information Sciences Institute, directing projects on efficient large language model architectures and multimodal reasoning systems. His lab focuses on developing novel approaches to context handling, model efficiency, and multimodal integration, with applications across diverse domains including healthcare, literary analysis, and news media.

Mohammad Modarresمشاهده پروفایل

استاد

• Probabilistic risk assessment
• Uncertainty analysis
• Probabilistic Physics of Failure
• +۵ مورد دیگر

Mohammad Modarres is the Nicole J. Kim Eminent Professor at the University of Maryland within the A.J. Clark School of Engineering. He serves as Director of the Center for Risk and Reliability (CRR) and is a Professor of Nuclear Engineering in the Department of Mechanical Engineering. Dr. Modarres co-founded the world's first degree-granting graduate curriculum in reliability engineering at the University of Maryland and has established himself as an international expert in reliability and risk analysis. Dr. Modarres received his educational credentials from prestigious institutions: B.S. in Mechanical Engineering from Tehran Polytechnic M.S. in Mechanical Engineering from MIT M.S. and Ph.D. in Nuclear Engineering from MIT Dr. Modarres' research spans multiple critical areas in engineering risk and reliability. His primary interests include probabilistic risk assessment, uncertainty analysis, probabilistic physics of failure, and probabilistic fracture mechanics. His work encompasses both experimental investigations and sophisticated probabilistic model development. He has made significant contributions to materials degradation science, prognosis and health management systems, and nuclear safety analysis. His research bridges theoretical developments with practical applications in complex engineering systems, particularly in nuclear power and aerospace sectors. Analysis of Dr. Modarres' recent publications reveals a strong trend toward integrating advanced data science techniques with traditional reliability engineering. His work increasingly incorporates machine learning, deep learning, and entropy-based approaches to solve complex problems in prognostics and health management. There's a clear focus on multi-unit systems, particularly in nuclear power applications, and a growing emphasis on data-driven methodologies for remaining useful life estimation and failure prediction. His research maintains a strong foundation in probabilistic methods while embracing cutting-edge computational approaches. Dr. Modarres has received numerous prestigious honors and awards throughout his distinguished career: Nicole Y. Kim Eminent Professorship in Engineering Minta Martin Professorship in A.J. Clark School of Engineering University of Maryland Distinguished Scholar-Teacher (2019) Tommy Thompson Award for outstanding lifetime contributions to nuclear safety (American Nuclear Society) Fellow, American Nuclear Society Fellow, Institute of Electrical and Electronics Engineers (IEEE) Life Fellow of IEEE 1996 Maryland Inventor of the Year Award (in Information Sciences) FDA Commissioner Special Citation for Contributions to Risk Assessment Methods (2004) 2008 International Research Leadership Award (Society for Reliability Engineering, Quality and Operations Management) Honorary Doctorate from Universidad Da Vinci de Guatemala As the founding director of the Center for Risk and Reliability, Dr. Modarres has built a world-renowned program that has awarded over 500 Ph.D. and master's degrees. His research has been supported by significant grants from government agencies and industry partners, particularly in nuclear safety, aerospace reliability, and critical infrastructure protection. He has mentored numerous students who have gone on to become leaders in reliability engineering across various industries. His center collaborates extensively with the International Atomic Energy Agency (IAEA) and other international organizations on risk assessment methodologies. The Center for Risk and Reliability (CRR), which Dr. Modarres directs, serves as a hub for multidisciplinary research in risk and reliability engineering. The center has recently renovated its facilities to enhance collaboration among researchers from different engineering disciplines. CRR maintains strong partnerships with industry leaders including Amazon Lab126 (as evidenced by their collaboration on device durability research) and has been instrumental in advancing probabilistic risk assessment tools used in nuclear power plant safety analysis. The center hosts regular seminars, workshops, and international conferences, positioning itself at the forefront of risk and reliability research globally.

Robert O. Ritchieمشاهده پروفایل

استاد

• Materials Science
• Mechanical Engineering
• Fracture Mechanics
• +۷ مورد دیگر

Robert O. Ritchie is the H. T. & Jessie Chua Distinguished Professor of Engineering at the University of California, Berkeley, where he holds dual appointments as Professor of Materials Science & Engineering and Professor of Mechanical Engineering. He is also a Faculty Senior Scientist at Lawrence Berkeley National Laboratory. His distinguished career spans over four decades with significant contributions to the field of materials science and engineering. Professor Ritchie received his B.A. in Physics & Metallurgy (1969), M.A. in Materials Science (1973), Ph.D. in Materials Science (1973), and Sc.D. in Materials Science (1990), all from Cambridge University, UK. His research focuses on the mechanical behavior of advanced materials, with particular emphasis on fracture mechanics, fatigue properties, and damage tolerance. Professor Ritchie's work spans multiple domains including metallic glasses, high-entropy alloys, biomaterials, and nature-inspired structural materials. His laboratory employs cutting-edge techniques such as in situ high-temperature computed tomography to study failure mechanisms in ceramic-matrix composites and nuclear graphite. His research has significant implications for aerospace, biomedical, and energy applications. Analysis of Professor Ritchie's recent publications reveals a strong focus on advanced structural materials, particularly metallic glasses and high-entropy alloys. His work combines experimental approaches with computational modeling to understand deformation mechanisms at multiple length scales. There is a clear trend toward bioinspired materials design, with several papers examining natural structures like fish scales, horn sheaths, and bone to develop new engineering materials with exceptional mechanical properties. Member, National Academy of Sciences (2025) Foreign Fellow, Academy of Athens, Greece (2024) Robert Henry Thurston Award (ASME) (2022) ASM Gold Medal (ASM Intl.) (2021) William D. Nix Medal, inaugural winner (TMS) (2020) Fellow (Foreign Member) of the Royal Society (FRS), London, UK (2017) Morris Cohen Award (TMS) (2017) Acta Materialia Gold Medal (2014) David Turnbull Award (MRS) (2013) A. Cemel Eringen Medal (Society of Engineering Science) (2010) Professor Ritchie has advised numerous graduate students and postdoctoral researchers throughout his career. His research has been supported by various funding agencies including the Department of Energy, National Science Foundation, and industry partners such as Rolls-Royce. He has served on numerous advisory boards including the Rolls-Royce Materials & Structures Advisory Board (2011-2019) and the Scientific Advisory Board of the Advanced Light Source at LBNL (2013 to date). Professor Ritchie leads the Ritchie Group at UC Berkeley, which maintains strong collaborations with Lawrence Berkeley National Laboratory. The laboratory employs state-of-the-art techniques including electron microscopy, x-ray tomography, and mechanical testing across multiple length and time scales. His team has developed innovative in situ characterization methods that have significantly advanced the understanding of material failure mechanisms under extreme conditions.

Michael Lepechمشاهده پروفایل

استاد

• Sustainable Infrastructure Design
• Civil Engineering
• Environmental Engineering
• +۴ مورد دیگر

Michael Lepech is a Professor of Civil and Environmental Engineering and Senior Fellow at the Woods Institute for the Environment at Stanford University. His research focuses on integrating sustainability into civil engineering through quantitative assessment and multi-scale modeling, particularly via the Sustainable Integrated Materials, Structures, Systems (SIMSS) framework. He also leads the Stanford Center at the Incheon Global Campus (SCIGC) in South Korea, exploring smart city technologies for urban sustainability. Education : PhD in Civil and Environmental Engineering (2006), MBA in Finance and Strategy (2008) from the University of Michigan. Research Areas : Sustainable infrastructure design, biopolymer composites, life cycle assessment, digital twinning, smart city technologies, and multi-physics deterioration modeling. Leadership : Director of SCIGC, advancing research on smart and sustainable urban environments in Songdo, South Korea. His recent publications focus on biopolymer-bound composites, traffic signal optimization, and life cycle sustainability analysis. He has received recognition as a Senior Fellow at Stanford’s Woods Institute for environmental research.

J.D. Jansenمشاهده پروفایل

استاد

• Systems and Control Theory
• Subsurface Flow Mechanics
• Geothermal Energy
• +۴ مورد دیگر

J.D. Jansen is a full Professor at Delft University of Technology (TU Delft) in the School of Civil Engineering & Geosciences, specializing in the Reservoir Engineering department. His research focuses on systems and control theory applied to subsurface flow mechanics, with applications in oil and gas production, geothermal energy, and induced seismicity. He actively contributes to academic networks through editorial roles, keynote presentations, and international collaborations. Active in computational geosciences (modeling, simulation, data analysis) Member of the Dutch Mining Council since 2016 Recipient of two Society of Petroleum Engineers awards in 2018 His work emphasizes optimization of subsurface processes, numerical modeling of porous materials, and seismicity mitigation. Recent publications highlight fault slip mechanics, benchmarking simulations, and stress pattern analysis in geothermal systems. Collaborations span geomechanics, reservoir engineering, and data assimilation domains. Scientific awards include: Distinguished Membership of the Society of Petroleum Engineers (2018) SPE Distinguished Achievement Award for Petroleum Engineering Faculty (2018) He leads research initiatives in induced seismicity, particularly in the Groningen Field, and contributes to model-based production optimization. Media engagements since 2020 demonstrate public outreach in energy transition topics.

Matthew Santaمشاهده پروفایل

استاد

• Music Theory
• Post-Tonal Analysis
• Diatonic Set Theory
• +۴ مورد دیگر

Matthew Santa serves as Professor of Music Theory and Chair of the Music Theory and Composition Area at Texas Tech University School of Music, with prior teaching appointments at Queens College and Hunter College. His leadership shapes curriculum development and academic initiatives within the department. Academic credentials include advanced degrees from Louisiana State University and The City University of New York, establishing foundational expertise in theoretical frameworks and analytical methodologies. Research focuses on post-tonal analysis , diatonic set theory , and parsimonious voice leading , bridging complex theoretical concepts with practical pedagogy. His work integrates popular music analysis and metrical studies, emphasizing accessibility for diverse learners through innovative teaching resources. Publications demonstrate evolving scholarly trends from late-20th century set theory toward contemporary applications in musical form and rhythm. Recent textbooks synthesize Rothstein, Krebs, and Mirka's theories into unified analytical approaches for undergraduate and graduate education. Scientific recognition includes: MTSNYS Young Scholar Award (1998) Mentorship encompasses graduate and undergraduate students across composition and theory disciplines, though specific advisees aren't documented in source materials. No grant funding details appear in available records. Collaborative projects include the Flute/Theory Workout series with Lisa Garner Santa and Thomas Hughes, blending performance technique with theoretical concepts through MIDI accompaniment systems.

Kai Cieliebakمشاهده پروفایل

استاد

• Symplectic geometry
• Contact geometry
• Hamiltonian dynamics
• +۵ مورد دیگر

Prof. Dr. Kai Cieliebak is a Professor of Mathematics at the University of Augsburg, where he holds the Chair of Analysis and Geometry within the Institute of Mathematics under the Faculty of Mathematics, Natural Sciences, and Materials Engineering. He has been at Augsburg University since 2012, following a professorship at Ludwig-Maximilians-Universität München from 2001-2012. His research group includes several researchers and postdocs working on symplectic geometry and related fields. Dr. Cieliebak earned his Diplom in mathematics summa cum laude from Ruhruniversität Bochum in 1992, with thesis on "Pseudo-holomorphe Kurven und periodische Orbits auf Cotangential Bündeln" under advisor H. Hofer. He completed his PhD in mathematics at ETH Zürich in 1996, with thesis "Symplectic boundaries: closed characteristics and action spectra," also advised by H. Hofer. His academic journey included positions at Harvard University, Stanford University, and research at IBM Zürich before his professorships in Munich and Augsburg. Prof. Cieliebak's research focuses on symplectic and contact geometry , with significant contributions to understanding symplectic manifolds, Lagrangian and Legendrian knots, Stein manifolds, and string topology. His work in Hamiltonian dynamics explores variational methods, periodic orbits, and celestial mechanics problems, particularly the restricted three-body problem. In global analysis , he investigates solution spaces of elliptic PDEs and symplectic field theory. His approach often bridges differential geometry, topology, and dynamical systems, with applications to mathematical physics. Over the past decade, Prof. Cieliebak's publications reveal a consistent focus on symplectic homology, Floer theory, and their applications to geometric problems. His work shows increasing integration of algebraic structures with geometric methods, particularly in cyclic homology and string topology. Recent research demonstrates strong collaboration with Urs Frauenfelder on celestial mechanics problems, applying symplectic techniques to the restricted three-body problem and related orbital dynamics. Prof. Cieliebak has secured significant research funding throughout his career, including multiple DFG grants under project codes CI 45/1 through CI 45/12, NSF grants, and participation in European Science Foundation networking programs. His most notable grants include "Foundations of Symplectic Field Theory" (2009-2015) and the current "Rabinowitz Floer Homology" project (since 2023), both in collaboration with U. Frauenfelder. He has mentored numerous researchers and maintains an active research group at Augsburg University, including postdocs and collaborators working on symplectic geometry problems. His team includes researchers such as Dr. Filip Broćić, Zhen Gao, Dr. Hanna Häußler, Emilia Konrad, Shuaipeng Liu, Dominik Meidert, Dr. Airi Takeuchi, Dr. Evgeny Volkov, Milan Zerbin, and PD Dr. Lei Zhao. Prof. Cieliebak has also organized numerous workshops on symplectic geometry, including the annual "Symplectic Field Theory" workshop series.

• Energy systems optimization
• Conceptual process design
• Life cycle assessment
• +۷ مورد دیگر

André Bardow is a Full Professor at the Department of Mechanical and Process Engineering, ETH Zürich. His research focuses on energy systems optimization, life cycle assessment, computer-aided molecular design, and CO2 capture/utilization. Professor (ETH Zürich, 2020–present) Head of Institute of Technical Thermodynamics (RWTH Aachen University, 2010–2020) Visiting Professor (University of California, Santa Barbara, 2015/16) Part-time Director (Forschungszentrum Jülich, 2017–2022) Associate Professor (TU Delft, 2007–2010) Research Interests: His work spans energy and process systems engineering, with emphasis on sustainable technologies. Key areas include: Computer-aided molecular and process design Machine learning for chemical engineering Carbon capture and utilization (CCU) Life cycle assessment (LCA) of industrial processes Thermo-economic modeling of energy systems Multiphase equilibrium analysis Publication Trends: Recent articles focus on integrating machine learning with process design, optimizing CO2 capture in steel production, and advancing electrochemical cooling technologies. Subfields include sustainable plastics, ORC working fluids, and solvent mixture design. Scientific Awards: Fellow of the Royal Chemical Society Recent Innovative Contribution Award (EFCE, 2019) PSE Model-Based Innovation Prize (2018) Covestro Science Award (first recipient) Arnold-Eucken-Award (VDI-GVC) Highly Cited Researcher (Clarivate, 2024) Advising and Grants: Professor Bardow mentors students in process optimization and leads projects like Systemic expansion of territorial CIRCULAR Ecosystems for end-of-life FOAM (Grant 101036854, EC).

Heng Jiمشاهده پروفایل

استاد

• Natural Language Processing
• Data Mining
• Social Science
• +۲ مورد دیگر

Heng Ji is a Professor at the Siebel School of Computing and Data Science , affiliated with the Department of Computer Science , Electrical and Computer Engineering Department , and multiple research labs including the Coordinated Science Laboratory and Carl R. Woese Institute for Genomic Biology at the University of Illinois Urbana-Champaign. She serves as an Amazon Scholar and Founding Director of the Amazon-Illinois Center on AI for Interactive Conversational Experiences (AICE) and CapitalOne-Illinois Center on AI Safety and Knowledge Systems (ASKS) . B.A. and M.A. in Computational Linguistics from Tsinghua University M.S. and Ph.D. in Computer Science from New York University Her research bridges Natural Language Processing with Vision-Language Models , Knowledge-Enhanced LLMs , and AI for Science (e.g., chemical language modeling). She leads major multi-institutional projects such as DARPA ECOLE MIRACLE , KAIROS RESIN , and DEFT Tinker Bell , while advising governments (U.S. Air Force Data Analytics Expert Panel) and industry (Amazon, Google, IBM). Her work on multimodal reasoning, agent-based systems, and chemical language models (e.g., mCLM ) has been supported by NSF, DARPA, and corporate partners. Recent publications (2025) focus on LLM agents , vision-language integration , and scientific knowledge acquisition . Awards include NSF CAREER , IEEE Intelligent Systems' AI's 10 to Watch , and multiple Outstanding Paper Awards at ACL/NAACL. She advises students like Chi Han (ACL/NAACL awardee) and post-docs Xiusi Chen and Yuji Zhang , and leads the BLENDER Lab , which develops frameworks like WiNELL (Wikipedia updating) and ProteinZero (protein generation). She has also served as NAACL Secretary and Program Co-Chair for ACL-IJCNLP2022. Outstanding Paper Award at ACL2024 Two Outstanding Paper Awards at NAACL2024 Young Scientist by World Laureates Association (2023-2024) AI's 10 to Watch by IEEE (2013) NSF CAREER (2009) Google/IBM/Bosch Research Awards

Minsu Kimمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Deep Learning
• Combinatorial Optimization
• +۷ مورد دیگر

Minsu Kim is a CIFAR AI Safety Post-doc Fellow at KAIST and Mila, collaborating with Prof. Yoshua Bengio, Prof. Sungjin Ahn, and Prof. Sungsoo Ahn. His work bridges System 2 Deep Learning, Bayesian posterior inference, and combinatorial optimization. Ph.D., Industrial Engineering, KAIST (2025) M.S., Electrical Engineering, KAIST (2022) B.S., Mathematics and Computer Science (Dual Degree), KAIST (2020) Kim's research focuses on enabling AI systems to measure uncertainty, represent causality, and perform sequential reasoning for safety-guaranteed planning. His methodology integrates GFlowNets and diffusion models with off-policy amortized inference, targeting applications in scientific discovery , hardware design optimization , and large language model alignment . Recent work explores Bayesian posterior inference through GFlowNets, aiming to unify deep learning with probabilistic reasoning. His 15 most recent publications (2025–2024) reveal a trend of combining combinatorial optimization with generative models for tasks like molecular graph discovery, vehicle routing, and neural architecture search. He also investigates diffusion samplers for Bayesian inverse problems and symmetry-based neural methods (Sym-NCO) to enhance sample efficiency. Jang Yeong Sil Fellowship (2025) KAIST Presidential Best Ph.D. Thesis Award (2025) Qualcomm Innovation Fellowship (2023) DesignCon Best Paper Awards (2021–2022) Kim's collaborations span KAIST's Industrial Engineering department and Mila's AI research groups. He actively contributes to academic peer review for top conferences (NeurIPS, ICML, ICLR) and journals (IEEE TNNLS, TPAMI), emphasizing the intersection of AI safety , uncertainty quantification , and systemic reasoning .

• Climate Change Economics
• Carbon Footprints
• Air Pollution and Health
• +۴ مورد دیگر

Dr. Zhifu Mi is a Professor of Climate Change Economics at the Bartlett School of Sustainable Construction , University College London. His research focuses on carbon footprint analysis, climate change economics, air pollution and health, sharing economy sustainability, and input-output modeling. He has published extensively in top journals like Nature Sustainability , Lancet , and Science Advances . Co-Editor-in-Chief, Structural Change and Economic Dynamics (2020–present) Executive Editor, Journal of Cleaner Production (2019–present) President, Chinese Economic Association (UK/Europe) Council Member, International Input-Output Association Fellow of the Royal Geographical Society His research explores: Carbon Footprints: Economic development and emission convergence in Chinese households Climate-Health Linkages: Air pollution impacts, ultra-low emission policies Sharing Economy: Sustainable societies through shared micro-mobility Policy Analysis: Synergies between environmental regulations Recent publications analyze decarbonization employment gaps, polycentric urban structures, and health-environment policy intersections. Awards include the World Sustainability Award (2018), multiple Clarivate Highly Cited Researcher honors, and UCL's Outstanding Research Supervision Award (2022). He teaches modules on construction economics and input-output analysis.