جستجوی استاد راهنمای دانشگاهی

• Housing Market
• Real Estate Economics
• Tax Policy
• +۵ مورد دیگر

Are Oust is a Professor of Financial Economics at the Norwegian University of Science and Technology (NTNU School of Economics) and a Professor II at the Norwegian School of Economics. He specializes in housing market dynamics, real estate economics, and tax policy. His research focuses on housing bubbles, property valuation, and the impact of regulation on real estate markets. Affiliations: NTNU School of Economics (Professor) Norwegian School of Economics (Professor II) Deputy Head of Research at NTNU School of Economics (2021–present) Deputy Director of NTNU Center for Housing and Environmental Economics (2016–present) Education: PhD in Economics, NTNU (2013) Master’s in Accounting and Auditing, Economics, and Business Administration from NHH (Norwegian School of Economics) Bachelor of Science in Economics, NTNU Research Interests: Dr. Oust’s work emphasizes automated valuation models, housing market regulation, energy labeling in real estate, and the interplay between taxation and home ownership. His research has been published in journals such as Quantitative Finance , Journal of Real Estate Research , and Energy Policy . Key Contributions: His studies on housing bubbles, rental market dynamics, and the application of AI in real estate valuation have shaped policy debates. Recent work explores the role of adverse selection in iBuyer models and the predictive power of dwelling conditions in automated valuations. Grants & Leadership: He advises on real estate policy and serves on multiple boards, including the NTNU Center for Housing and Environmental Economics, and private real estate firms like Strinda Eiendom AS. His teaching focuses on personal finance, investment strategies, and tax planning.

Scot M. Millerمشاهده پروفایل

دانشیار

• Atmospheric Science
• Greenhouse Gases
• Air Pollution
• +۱۱ مورد دیگر

Scot M. Miller is an Associate Professor in the Department of Environmental Health and Engineering at the Whiting School of Engineering, Johns Hopkins University. He leads the Greenhouse Gas Research Group, focusing on quantifying emissions of greenhouse gases and air pollutants using satellite, aircraft, and tower observations. His research spans global scales, from Arctic ecosystems to urban and industrial sources in the U.S. and China. His research interests include atmospheric science, greenhouse gas emissions, inverse modeling, big data analytics, and climate policy. He integrates tools from statistics, high-performance computing, and satellite remote sensing to improve emission estimates and inform environmental regulations. Recent publications highlight trends in methane, ethane, and sulfuryl fluoride emissions, carbon cycle dynamics, and innovative methods for analyzing massive satellite datasets. His work increasingly leverages OCO-2 and OCO-3 satellite data to study carbon dioxide and methane fluxes across diverse ecosystems. Scientific awards include the NSF CAREER Award, JHU Catalyst Award, and the Carnegie Distinguished Postdoctoral Fellowship. He was also recognized with Harvard’s Certificate of Excellence in Teaching. Miller advises multiple PhD students, including Mingyang Zhang, Dylan Gaeta, and Leyang Feng, and has secured grants from NASA, NSF, NOAA, and JHU. He is a member of the NASA OCO Science Team and collaborates with institutions such as Northern Arizona University, Carnegie Institution, and NOAA. His lab is involved in urban environmental monitoring in Baltimore and interdisciplinary climate policy research.

• Machine Learning in Energy Systems
• Renewable Energy
• Energy Forecasting
• +۱۲ مورد دیگر

Prof. Erdem Günay is a full Professor in the Department of Energy Systems Engineering at Istanbul Bilgi University, where he has been serving since 2013, rising through the academic ranks. He holds a Ph.D. in Chemical Engineering from Bogazici University, where he also completed his B.S. and M.S. degrees, and conducted postdoctoral research. His academic journey reflects a deep commitment to energy systems and sustainable technologies. B.S. in Chemical Engineering, Bogazici University, 2002 M.S. in Chemical Engineering, Bogazici University, 2005 Ph.D. in Chemical Engineering, Bogazici University, 2012 Postdoctoral Research Associate, Catalyst Design and Reaction Engineering Laboratory, Bogazici University, 2013 Prof. Günay’s research centers on the integration of machine learning and artificial intelligence with energy systems engineering. His work spans renewable energy (solar, wind, bioenergy), hydrogen production, CO₂ utilization, fuel cells, and energy demand forecasting. He employs advanced data mining, neural networks, and explainable AI to model, simulate, and optimize complex energy processes, contributing significantly to sustainable energy solutions. His interdisciplinary approach bridges chemical engineering, environmental science, and computational modeling. His recent publications demonstrate a strong trend in applying machine learning to sustainable bioenergy, catalysis, and environmental management. From optimizing biochar production to forecasting global temperature anomalies and enhancing microbial fuel cells, his research leverages AI to address pressing energy and environmental challenges. The articles reflect a consistent focus on sustainability, efficiency, and innovation in energy technologies, with a growing emphasis on explainability and real-world applicability of AI models. Prof. Günay has not been mentioned to have received any specific scientific awards, but his extensive publication record in high-impact journals indicates strong recognition in his field. He has advised several Master’s students, including Muaaz Jnani, Duru Akalın, and co-advised Ahmet Coşgun and Meltem Baysal. He actively supervises senior design projects in areas such as biogas production, biodiesel from shea butter, pyrolysis, and solar desalination, fostering hands-on learning and innovation among students. While no external grants are explicitly mentioned, his research output suggests active involvement in funded projects. His teaching portfolio includes core courses such as Thermodynamics, Fluid Mechanics, Fuels and Combustion, and Energy Systems Modeling and Simulation. Although specific lab or research team names are not provided, his frequent collaborations with researchers like Ramazan Yıldırım, N. Alper Tapan, and Ahmet Coşgun suggest active participation in a research group focused on AI-driven energy and catalysis research at Istanbul Bilgi University.

Bryce Frederick John Kellyمشاهده پروفایل

دانشیار

• Greenhouse Gas Emissions
• Hydrogeology
• Isotope Chemistry
• +۶ مورد دیگر

Associate Professor Bryce Frederick John Kelly is an academic at the University of New South Wales (UNSW), affiliated with the School of Biological, Earth and Environmental Sciences. His research focuses on greenhouse gas emissions, hydrogeology, and groundwater management, with a specialization in methane and carbon dioxide isotopic analysis. He leads the Greenhouse Gas Measurement Laboratory, which analyzes gas isotopes to trace emissions from coal seam gas (CSG), agriculture, and urban environments. Education: BSc (Hons) in Environmental Geology (UNSW, 1989); PhD in Environmental Geophysics (UNSW, 1995). Research Interests: Measuring methane emissions from CSG, coal mining, and agriculture Soil carbon sequestration and groundwater sustainability Isotope geochemistry for source attribution of greenhouse gases Satellite and airborne greenhouse gas monitoring Impact of CSG development on aquifers and ecosystems Key Projects: Leading the United Nations Environment Programme Methane Science Studies team, quantifying emissions in the Surat Basin. Co-supervising 60+ students. Collaborating with ANSTO on soil carbon and groundwater modeling. Awards: 2016 Cotton Seed Distributor Researcher of the Year finalist, 2011 Eureka Prize finalist for water research, and multiple industry awards for hydrogeological innovation. Labs/Teams: Connected Water Initiative, Centre for Ecosystem Science, Earth and Sustainability Science Research Centre (ESSRC). Active in policy outreach through The Conversation and Australian Geographic.

François Brémondمشاهده پروفایل

پژوهشگر

• Computer Vision
• Cognitive Vision
• Video Understanding
• +۲۲ مورد دیگر

François Brémond is a Research Director (DR1) at INRIA Sophia Antipolis, where he leads the STARS research team, which he founded on January 1, 2012. He was previously head of the PULSAR team starting September 2009. He is also a co-founder of the CoBTeK team at Nice University in collaboration with Nice Hospital, focusing on behavioral disorders in elderly patients with dementia. His research is centered on dynamic scene interpretation using video and sensor data, with applications in surveillance, healthcare, transportation, and ambient intelligence. Research Interests: Computer Vision: video processing, object detection and tracking, motion analysis, pattern recognition Cognitive Vision: video understanding, scene understanding, event recognition, behavior analysis, multi-sensor fusion, multimedia interpretation Machine Learning: deep learning architectures, self-attention, knowledge distillation, contrastive learning, self-learning, lifelong learning, knowledge-based systems, spatio-temporal reasoning Autonomous Systems: real-time systems, system evaluation, parameter tuning, system design, 3D visualization His work bridges low-level pixel data with high-level semantic behavior modeling, enabling systems to detect and interpret complex human and vehicle activities in real-world environments. Applications include crowd monitoring, fraud detection, airport operations, homecare for the elderly, and biological monitoring. He has authored or co-authored over 200 scientific papers and has (co-)supervised 18 PhD theses. He has participated in 12 European projects (e.g., FP6, FP7), 12 French national projects (ANR, DGE), and numerous industrial collaborations with companies such as Thales, SNCF, RATP, STMicroelectronics, and Alstom. He also serves as an expert reviewer for ANR and the European Commission. Scientific Leadership and Technology Transfer: Co-founder of Keeneo (acquired by Digital Barriers), Ekinnox, and Neosensys — startups in intelligent video monitoring and business intelligence Reviewer for top-tier journals (PAMI, CVIU, AIJ) and conferences (CVPR, ICCV, AVSS) Contributor to the ARDA workshops on video event ontology He has taught numerical classification at Nice University and video understanding at a Master’s level engineering school. His research program emphasizes generic, scalable systems for behavior modeling and long-term activity mining. Research Projects: Stress ID dataset (ECG and video for stress detection) Toyota Smarthome (Activities of Daily Living) SafEE2 (Homecare for elderly with autonomy loss) Praxis dataset (RGB-D upper-body gestures) GER'HOME, CARETAKER, RATP Project, ETISEO, AVITRACK, CASSIOPEE, ADVISOR, PASSWORDS

• Materials Science and Engineering
• Metallurgy
• Severe Plastic Deformation (SPD)
• +۱۸ مورد دیگر

Tatyana Konkova is a Senior Lecturer in the Department of Design, Manufacturing and Engineering Management at the University of Strathclyde, Faculty of Engineering, Glasgow, UK. She is actively engaged in research, teaching, and professional leadership in the field of Materials Science and Engineering, with a focus on metallurgy and advanced manufacturing techniques. Education: Doctor of Science, Mechanisms of cryogenic plastic deformation and features of microstructure formation in technically pure copper, Institute for Metals Superplasticity Problems, Russian Academy of Sciences (awarded 2011) Master of Business Administration (MBA), Strathclyde Business School (awarded 2023) PG Certificate in Learning and Teaching in Higher Education, University of Strathclyde (awarded 2021) MSc (Hons) in Materials Science and Engineering, Ufa State Aviation Technical University (awarded 2005) BSc in Engineering, Ufa State Aviation Technical University (awarded 2004) Research Interests: Her research spans Severe Plastic Deformation (SPD), cryogenic deformation, additive manufacturing, microstructure evolution, and advanced characterization using EBSD, TEM, and SEM. She focuses on materials such as titanium alloys, copper, and nickel-based superalloys, aiming to bridge fundamental science with industrial applications. Her work emphasizes grain boundary engineering, abnormal grain growth, and deformation-induced boundaries. Publication Trends: Recent publications highlight her growing interdisciplinary work combining additive manufacturing with electric machine design, as well as continued deep microstructural investigations in aerospace and microelectronic materials. Her research integrates data-driven optimization and advanced characterization to improve material performance and manufacturing efficiency. Scientific Awards and Honors: Fellow of the Institute of Materials, Minerals and Mining (FIMMM) Chartered Engineer (CEng) by the Engineering Council Member of the Institution of Mechanical Engineers (MIMechE) Fellow of the Higher Education Academy (FHEA) PG Certificate in Learning and Teaching in Higher Education Advising and Grants: She has supervised undergraduate, postgraduate, and PhD students and serves as Principal Investigator on multiple research projects, including EPSRC-funded CDT in AI-enabled Digital High-Value Manufacturing and AFRC projects on titanium alloy forgeability. She has secured funding from national and international sources, including the Russian Foundation for Fundamental Research. Her leadership in industrial collaboration and knowledge exchange is evident through her roles in Catapult projects and industrial group supervision. Labs and Teams: She has led the Materials Characterisation Theme at AFRC and represented the center in Cross-Catapult forums on additive manufacturing. She is part of the Horizon Europe Working Group with the University of Waterloo and actively collaborates with national and international research teams.

Qiang Zhuمشاهده پروفایل

استاد

• Data Science
• Data Management
• Machine Learning
• +۳ مورد دیگر

Qiang Zhu is a Professor in the Department of Computer and Information Science at the University of Michigan-Dearborn, holding the William E. Stirton Professorship (2017–2024). He founded the Data Science/Management Research Laboratory and is affiliated with the Michigan Institute for Data Science (MIDAS). His research spans data science, data management, and machine learning. Ph.D., University of Waterloo M.S., McMaster University M.Eng., Southeast University B.S., Southeast University Research focuses on advanced data indexing, query optimization, and AI-driven data management, with applications in genomics, network systems, and education. His work integrates machine learning with database systems for scalable solutions. Recent publications include topics in federated learning fairness, digital twin middleware, project-based CS education, and genome data indexing. Scientific contributions recognized through awards like the Wilkes Award (2008), ACM Distinguished Scientist (2013), and Springer Nature Editor of Distinction (2025). 2013–2018: Department Chair NSF, IBM, and Ford grants Over 250 conference committee roles He directs the Data Science/Management Research Lab, focusing on collaborative projects in genome analytics and smart computing infrastructures.

Shamik Senguptaمشاهده پروفایل

استاد

• Wireless Networking & Mobile Computing
• Unmanned Autonomous Systems
• Cognitive Radio
• +۲۸ مورد دیگر

Shamik Sengupta is the Ralph E. and Rose A. Hoeper Professor at the University of Nevada, Reno (UNR) , where he serves as Professor in the Department of Computer Science & Engineering and Executive Director of the Cybersecurity Center . He holds a PhD in Computer Science from the University of Central Florida (2007) and a BE in Computer Science from Jadavpur University (2002). IEEE Senior Member Director, UNR Cybersecurity Center NSF CAREER Award Recipient

Kathleen R. McKeownمشاهده پروفایل

استاد

• Natural Language Processing
• Text Summarization
• Natural Language Generation
• +۴ مورد دیگر

Kathleen R. McKeown is the Henry and Gertrude Rothschild Professor of Computer Science at Columbia University and the Founding Director of Columbia's Data Science Institute (2012-2017). She has been a faculty member since 1982 and served as Department Chair (1998-2003) and Vice Dean for Research in the School of Engineering and Applied Science. Her research focuses on natural language processing , text summarization , natural language generation , and social media analysis . Current projects include neural methods for extractive/abstractive summarization, electricity usage message generation via reinforcement learning, and social media sentiment analysis in low-resource languages like Uyghur. She leads the Columbia NLP Group and developed the long-running Newsblaster system (2001-present) for automated news tracking and multi-document summarization. Key scientific awards include NSF Presidential Young Investigator (1985) NSF Faculty Award for Women (1991) AAAI Fellow (1994) ACM Fellow (2003) ACL Founding Fellow (2012) Columbia Great Teacher Award (2010) Anita Borg Woman of Vision Award (2010) She has held leadership roles in major academic organizations: President of the Association for Computational Linguistics (1992), Vice President (1991), Secretary-Treasurer (1995-1997), and board member of the Computing Research Association with secretary role.

Koushik Senمشاهده پروفایل

استاد

• Software Engineering
• Programming Languages
• Formal Methods

Koushik Sen is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. His research focuses on developing software tools and methodologies to enhance programmer productivity and software quality, with expertise in Software Engineering, Programming Languages, and Formal Methods. Education: B.Tech from Indian Institute of Technology, Kanpur M.S. and Ph.D. in Computer Science from University of Illinois at Urbana-Champaign Research Focus: Professor Sen pioneers automated testing techniques including concolic testing and DART (Directed Automated Random Testing). His work bridges formal methods with practical software development, emphasizing bug detection, program synthesis, and AI-driven software analysis tools. Recent innovations include machine learning approaches for code recommendation and fuzzing. Publication Trends: His recent publications (2019-2023) demonstrate strong emphasis on fuzzing techniques, program synthesis, and AI/ML applications in software engineering. Notable domains include smart contract security, automated testing, and developer tooling, with frequent collaborations in top-tier conferences. Awards and Honors: NSF CAREER Award (2008) Sloan Foundation Fellowship (2011) IFIP TC2 Manfred Paul Award (2010) Okawa Foundation Research Grant (2015) Multiple ACM SIGSOFT Distinguished Paper Awards UIUC Distinguished Alumni Educator Award (2014) Leadership: Active program committee member for premier conferences (PLDI, ICSE, ISSTA) and keynote speaker. His research is supported by NSF, Okawa Foundation, and Sloan Foundation.

Shuting Wangمشاهده پروفایل

استادیار

• Big Data in Marketing
• Data Science/Analytics
• IT Strategy
• +۱۰ مورد دیگر

Shuting Wang is an Assistant Professor in the Paul H. Chook Department of Information Systems and Statistics at Baruch College's Zicklin School of Business. With a PhD in Management Information Systems from Temple University and prior degrees in Operations Management and Logistics Management, her research focuses on social media risks, fake news detection, and digital health platforms. PhD: Temple University (Management Information Systems) MSc: Shanghai University of Finance and Economics (Operations Management) BSc: Zhongnan University of Economics and Law (Logistics Management) Her expertise spans Big Data Marketing , Social Media Analytics , Technology Strategy , and Digital Health . Current research examines: Fake news mitigation through multimedia elements Telemedicine accessibility via SMS technology Social media's impact on business reputation FinTech startup success factors Psychological impacts of digital appearance Cybersecurity effects on startup funding Key Scientific Awards : Research Cluster Interdisciplinary Incentive Grants (2025) Eugene M. Lang Fellowship (2021) Presidential Fellowship at Temple University (2014) Full Tuition Scholarship at Shanghai University of Finance and Economics (2009) Active in teaching roles including Data Mining for Business Analytics and Database Management, she has received grants from the National Science Foundation and CUNY Central Office. Her work has been featured in outlets like NPR and Campaign US.

• Graph Representation Learning
• Deep Learning
• Reinforcement Learning
• +۷ مورد دیگر

Jian Tang is an Assistant Professor at HEC Montreal and the Montreal Institute for Learning Algorithms (MILA), as well as an Associate Professor at the Department of Computer Science and Operations Research (DIRO) at Université de Montréal. He is also affiliated with IVADO (Institut de valorisation des données) as a member. His research spans multiple institutions including collaborations with leading biology labs worldwide and access to extensive computational resources through industry partners. Ph.D. in Computer Science, Peking University (2009-2014) Visiting Ph.D. student, University of Michigan (2011.10-2013.8) B.S. in Mathematics, Beijing Normal University (2005-2009) Professor Tang's research focuses on the intersection of deep learning and graph theory, with particular emphasis on geometric deep learning, knowledge graph reasoning, and applications in drug discovery. His work bridges symbolic and neural approaches to create robust reasoning systems that can handle complex structured data. He has pioneered techniques in graph representation learning that have significantly advanced the field of molecular property prediction and protein design. His publication record shows a clear trajectory toward applying geometric deep learning to biological problems, with a growing emphasis on protein design, molecular conformation generation, and multi-omics analysis. Recent work demonstrates sophisticated integration of 3D geometry with deep learning architectures to model complex biomolecular interactions. Canada CIFAR Artificial Intelligence Chairs (CCAI Chair) Tencent AI Lab Rhino-Bird Gift Fund Amazon Faculty Research Award Microsoft-Mila collaboration grant National Research Council Canada (NRC) Collaborative Research and Development Grant Professor Tang actively mentors doctoral and master's students, with six recent graduates working on cutting-edge topics including graph neural networks for reasoning, protein design, and molecular representation learning. His research is supported by substantial funding from industry partners including Microsoft, Amazon, and Tencent, as well as government agencies like NRC. He collaborates extensively with biology labs worldwide, applying AI to solve real-world biomedical challenges. He leads a research group focused on geometric deep learning for drug discovery, with active projects in protein design using geometric-aware models and large language models for multi-omics analysis. The group has access to thousands of GPUs through industry collaborations, enabling large-scale experiments in molecular simulation and generative modeling.

• Financial Econometrics
• Statistics
• Empirical Asset Pricing
• +۵ مورد دیگر

Dacheng Xiu is the Joseph Sondheimer Professor of Econometrics and Statistics at the Booth School of Business , University of Chicago, and an Affiliated Faculty in the Department of Statistics. He serves as a Research Associate at the National Bureau of Economic Research and holds editorial roles at journals like Journal of Business & Economic Statistics and Journal of Financial Econometrics . PhD and MA in Applied Mathematics from Princeton University BS in Mathematics from University of Science and Technology of China His research focuses on statistical methodologies for financial data , including risk measurement , portfolio management , and empirical asset pricing using high-frequency data and machine learning . Recent work analyzes text data and large language models for economic forecasting. Editorial leadership includes Co-Editor and Associate Editor roles at top journals like Journal of Finance and Annals of Statistics . His lab ( Risk Lab ) specializes in systemic risk assessment through transaction-level data analysis. 2024 Dimensional Fund Advisors Prize 2023 GSU-RFS FinTech Conference Best Paper Award 2022 Society for Financial Econometrics Fellow 2018 Swiss Finance Institute Outstanding Paper Award

Steven O. Kimbroughمشاهده پروفایل

استاد

• Artificial Intelligence
• Computational Rationality
• Decision Support Systems
• +۶ مورد دیگر

Steven O. Kimbrough is a Professor of Operations, Information and Decisions at the Wharton School, University of Pennsylvania. His research spans artificial intelligence, computational rationality, and strategic optimization with applications to political science, economics, and service innovation. He teaches courses like Agents, Games, and Evolution and Thinking With Models , focusing on experimental approaches to bounded rationality and uncertainty in decision-making. Primary Email: kimbrough@wharton.upenn.edu Office: 3730 Walnut Street, 565 Jon M. Huntsman Hall, Philadelphia, PA 19104 His research interests include: Artificial intelligence and metaheuristics for constrained optimization Evolutionary computation in electoral redistricting Agent-based modeling of market dynamics Logic modeling for normative reasoning Text mining applications in event analysis Publications demonstrate expertise in computational economics, political modeling, and service analytics. Recent work focuses on: Empirical validation of electoral compactness Strategic learning in oligopolies Multi-objective matching algorithms Feasible-infeasible solution spaces Service network optimization Teaching emphasizes: Game-theoretic approaches to strategic behavior Modeling life-cycle for energy sustainability Computational experiments in social science

Mingfeng Wangمشاهده پروفایل

مدرس ارشد

• Continuum Robotics
• Legged Robotics
• Soft Robotics
• +۵ مورد دیگر

Dr. Mingfeng Wang is a Senior Lecturer in Robotics and Autonomous Systems at Brunel University London, affiliated with the Department of Mechanical and Aerospace Engineering within the College of Engineering, Design and Physical Sciences. His research focuses on specialized robotic systems including continuum, legged, soft, precision farming, and miniaturized robots. Chartered Engineer (CEng) with Engineering Council UK Fellow of the Higher Education Academy (FHEA) Member of IEEE, IEEE-RAS, IMechE, and IFToMM Editorial roles: Associate Editor of International Journal of Advanced Robotic Systems (JCR-Q3); Associate Editor of Frontiers in Robotics and AI (JCR-Q2); Editor of Information Processing in Agriculture (JCR-Q1), Biomimetic Intelligence and Robotics (JCR-Q1), and STEM Education Research expertise includes: Continuum Robotics : Design of extra-slender continuum robots (diameter-to-length ratio Legged Robotics : Parallel mechanism-based biped and hexapod robots for extreme environments Miniaturized Robotics : Active locomotion and drug delivery in capsule endoscopes Soft Robotics : Compliant end-effectors and bio-inspired designs Precision Farming : Laser weeding systems and agricultural automation Key scientific awards: BRIEF award (2022) TAROS Best Paper Post Nomination (2022) IFToMM Asian-MMS Best Paper Award (2014) Recent publications focus on: Cochlear implant surgery robotics Passive compliance in train fluid servicing Snake-biomimetic sealing surfaces Parallel kinematic manipulators Capsule endoscope image enhancement Professional services include conference organization (TAROS 2023/2024 Steering Committee; TAROS 2024 Programme Chair) and journal refereeing for IEEE-ASME Transactions on Mechatronics and Scientific Reports.