جستجوی استاد راهنمای دانشگاهی

برای مسیر پژوهشی‌تان، استاد راهنما، استاد دانشگاه و پژوهشگر مناسب را پیدا کنید و با آن‌ها ارتباط بگیرید.

نمایش ۷۳۶-۷۵۰ از ۱۱٬۳۱۲ استاد راهنما

اطلاعات پروفایل‌ها از صفحات عمومی دانشگاه‌ها گرفته شده و ممکن است به‌روز نباشد.

Roland N. Horneمشاهده پروفایل

استاد

Stanford UniversityUnited States

Roland N. Horne is the Thomas Davies Barrow Professor of Earth Sciences at Stanford University and Senior Fellow at the Precourt Institute for Energy. He holds positions in the Department of Energy Science & Engineering and is an Affiliate at the Stanford Woods Institute for the Environment. With degrees from the University of Auckland (BE, PhD, DSc), Horne has established himself as a leading expert in geothermal reservoir engineering and energy production optimization. His research focuses on inverse problems in reservoir modeling, including tracer analysis of fractures, computer-aided well test analysis, production schedule optimization, and automated history matching. Horne has made significant contributions to understanding geothermal reservoir engineering and multiphase flow of boiling fluids through porous materials and fractures. The analysis of his recent publications (2023-2025) reveals a strong emphasis on enhanced geothermal systems (EGS), with particular focus on flexible operations, economic modeling, and advanced characterization techniques. His work increasingly incorporates machine learning approaches for reservoir analysis and has expanded into microbial tracing methods for interwell connectivity assessment. There's also significant attention to US geothermal resource potential and integration into the broader energy transition. Honorary Member of the Society of Petroleum Engineers Member of the US National Academy of Engineering Multiple SPE Distinguished Lecturer appointments (1998, 2009, 2020) John Franklin Carl Award recipient Five Best Paper awards from Geothermal Resources Council Patricius Medal from German Geothermal Society Core Values Award from Women in Geothermal (2023) Horne has supervised 60 PhD and 135 MS students throughout his career. His current teaching includes undergraduate and graduate courses in Fundamentals of Energy Processes, Geothermal Reservoir Engineering, Mass and Energy Transport in Porous Media, and Well Test Analysis. He previously served as President of the International Geothermal Association (2010-2013) and Technical Program Chair for multiple World Geothermal Congress events. Horne maintains active research collaborations worldwide, including with the University of Tokyo (where he was a Fellow of the School of Engineering in 2016) and China University of Petroleum. His current research group focuses on advancing EGS technologies and developing more accurate reservoir characterization methods for geothermal applications.

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Danielle Liمشاهده پروفایل

استاد

Massachusetts Institute of TechnologyUnited States

Danielle Li is the David Sarnoff Professor of Management of Technology and a Professor at the MIT Sloan School of Management, specializing in the Technological Innovation, Entrepreneurship, and Strategic Management academic group. She is also a Faculty Research Fellow at the National Bureau of Economic Research (NBER). Her academic journey includes an AB in mathematics and the history of science from Harvard College and a PhD in economics from MIT. Prior to joining MIT, she taught at Harvard Business School and the Kellogg School of Management. AB in Mathematics and History of Science, Harvard College PhD in Economics, MIT Professor Li's research focuses on the economics of innovation and labor economics, with particular emphasis on how organizations evaluate ideas, projects, and people. She investigates the intersection of technology and workplace dynamics, especially how AI impacts worker productivity, the nature of work, and career trajectories in AI-intensive environments. Her work examines how businesses implement AI tools and the resulting effects on workforce composition and skill requirements. Her publication portfolio reveals a consistent focus on innovation economics, labor market dynamics, and the organizational implications of technology. Recent work increasingly centers on AI's workplace impact, with her 2025 Quarterly Journal of Economics paper 'Generative AI at Work' demonstrating how AI assistance increases worker productivity by 15% on average, with differential effects across experience levels. Her research combines rigorous economic analysis with practical business implications, spanning pharmaceutical innovation, hiring practices, promotion decisions, and gender gaps in the workplace. Best Paper Prize: 2017 FIRCG Conference Best Paper Prize: 2018 CEPR Management, Organizations, and Entrepreneurship Conference Best Paper Prize: 2017 Red Rock Conference Best Paper Prize: 2018 LBS Summer Finance Symposium Best Paper Prize: 2019 American Economic Journal: Applied Economics Professor Li's research has been supported by significant grants and has influenced both academic discourse and business practice. Her work on AI in the workplace has informed executive education programs at MIT Sloan, including 'Making AI Work: Machine Intelligence for Business and Society' and 'Artificial Intelligence' courses. She actively engages with media and business leaders to translate research findings into practical insights, frequently appearing in the New York Times, Wall Street Journal, and Economist. Her research on gender promotion gaps and hiring practices has particular relevance for organizational human resource policies. Professor Li is deeply embedded in MIT's AI research ecosystem, collaborating with colleagues across Sloan and CSAIL. She contributes to MIT's AI Expert Spotlight series, focusing on how businesses should implement AI responsibly and effectively. Her work bridges economic theory with practical business applications, particularly in understanding how AI transforms work processes and organizational structures.

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Kannan Srinivasanمشاهده پروفایل

استاد

Carnegie Mellon UniversityQatar

Kannan Srinivasan is the H.J. Heinz II Professor of Management, Marketing and Business Technology at Carnegie Mellon University's Tepper School of Business, a position he has held since 1999. Prior to joining CMU, he taught at the business schools of the University of Chicago and Stanford University. His academic career spans over three decades with significant contributions to marketing science and data analytics. His educational background includes: Ph.D. in Management from University of California Los Angeles (1986) MBA in Marketing/Finance from Xavier School of Management, Jamshedpur, India (1980) BA in Engineering from University of Madras, Chennai, India (1978) Srinivasan's research focuses on advanced data analytics models applied to marketing problems, with particular expertise in internet-generated large-scale data analysis. His work bridges the gap between theoretical marketing models and practical business applications, especially in the areas of algorithmic pricing, consumer behavior analysis, and AI-driven marketing strategies. He has pioneered research in dynamic pricing systems, location-aware marketing technologies, and the economic implications of AI in consumer markets. Analysis of his recent publications reveals a strong trend toward examining the intersection of artificial intelligence, consumer welfare, and market dynamics. His work increasingly focuses on ethical implications of AI in marketing, algorithmic bias, and the socioeconomic impacts of digital platforms across various sectors including real estate, social media, and e-commerce. His scientific achievements include: Elected Fellow of the Informs Society of Marketing Science (2013) for lifetime contribution to the field Served as President of the Informs Society of Marketing Science Holds multiple patents related to time and location aware dynamic push content, dynamic pricing, and online advertising Srinivasan has advised numerous doctoral students whose careers have led them to faculty positions at top institutions including Duke, Harvard, Columbia, Yale, University of Chicago, Wharton, University of Michigan, and Indian Institute of Management Bangalore. He has extensive consulting experience with large firms and startups, translating academic research into practical business applications. His professional service includes editorial roles at prestigious journals including Management Science, Marketing Science, and Quantitative Marketing and Economics, as well as significant committee service within CMU including the Elliott D. Smith Award Committee and various Dean's Advisory committees. His research is organized around several key initiatives focused on applying advanced analytics to solve complex marketing problems, with particular emphasis on developing interpretable AI models that balance business objectives with consumer welfare considerations.

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Michael Bellمشاهده پروفایل

استاد

University of SydneyAustralia

Professor Michael Bell is the Foundation Professor of Ports and Maritime Logistics at the University of Sydney Business School's Institute of Transport and Logistics Studies. He holds a BA from Cambridge University, MSc and PhD from Leeds University, and has held roles including Director of Imperial College London's PORTeC and academic positions at Newcastle University and Karlsruhe Technical University. His research focuses on ports, transport networks, sustainability, and intelligent transport systems. He has authored over 150 publications, including seminal works like Transportation Network Analysis . Current projects include circular economy diversification for ports and autonomous delivery systems. Awards include fellowship in multiple transport societies. Education: BA (Economics) Cambridge (1975), MSc (Transportation) Leeds (1976), PhD (Freight Distribution) Leeds (1981). Postdoctoral research at Karlsruhe Technical University (1982–1984). Research Interests: Ports logistics, transport network resilience, urban logistics, cybersecurity in supply chains, and sustainable transport policy. Publications span 40+ years, emphasizing empirical and theoretical advancements in maritime systems, network modelling, and policy analysis. Recent work explores autonomous systems integration and port diversification strategies. Grants include a 2023 iMOVE CRC project on land use trip surveys and a 2022 ARC Discovery Project on automated transport decisions. Media appearances address global trade disruptions (e.g., Suez Canal blockage), autonomous vehicles, and port sustainability. Led PORTeC (Imperial College) and co-founded the Institute of Transport and Logistics Studies at Sydney. Active in policy advisory roles for government agencies and industry bodies.

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Lynn Carol Millerمشاهده پروفایل

استاد

University of Southern CaliforniaUnited States

Lynn Carol Miller is a Professor of Communication at the University of Southern California’s Annenberg School for Communication and Journalism. Her research focuses on leveraging virtual environments, AI agents, and computational models to address health-related social behaviors, particularly in HIV/AIDS prevention and mental health. Funded by NIH, CDC, and DARPA (over $20M), her work integrates neuroscience, behavioral science, and technology. She pioneered interventions like SOLVE (Socially Optimized Learning in Virtual Environments) and Systematic Representative Design. Education: PhD in Personality Psychology from University of Texas at Austin. Key areas include health communication, gaming for behavior change, and computational modeling of social processes. She has supervised 17 doctoral students and collaborators across universities globally. Research emphasizes scalable interventions using fMRI-compatible tools and virtual reality. Notable contributions include reducing shame in HIV prevention games and analyzing neural correlates of risk-taking behaviors. Awards include the Early Career Award (2003) and ICA’s Outstanding Contribution to Communication Science (2020). Labs/Teams: Active in multidisciplinary teams at USC and collaborating institutions, focusing on virtual environment design, AI-driven interventions, and neurobehavioral studies. Current projects explore AI for public health and inclusive avatar representations in social VR.

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Tom Bootمشاهده پروفایل

دانشیار

University of GroningenNetherlands

Dr. Tom Boot is an Associate Professor at the Department of Economics, Econometrics & Finance at the University of Groningen. He holds a PhD in Econometrics from Erasmus University Rotterdam (2017) and an MSc in Econometrics from the same institution (2012), along with an MSc in Physics from the University of Groningen (2010). His research focuses on econometric theory applied to macroeconomic forecasting, high-dimensional data analysis, and causal inference. He has been recognized with the Veni grant (2021–2024) for his work on forecasting methodologies. Boot’s research interests include improving forecast accuracy through methods like subspace projections, structural break modeling, and privacy-aware marketing analytics. His recent work explores privacy-utility trade-offs in data-driven marketing and unbiased estimation techniques for clustered errors. He has supervised PhD students including Jhordano Aguilar Loyo and Gilian Ponte, whose theses addressed panel data heterogeneity and differential privacy applications. Boot is also a program director for the MSc Econometrics, Operations Research, and Actuarial Studies (since 2024). His contributions to econometrics span over a dozen peer-reviewed publications, with a focus on advanced statistical techniques for economic forecasting and policy analysis. Collaborations include work with institutions like Harvard/MIT and the organization of workshops on causal inference and machine learning.

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Kenichi Shimizuمشاهده پروفایل

استادیار

University of AlbertaCanada

Kenichi Shimizu is an Assistant Professor in Econometrics (tenure-track) at the Department of Economics, University of Alberta. He holds a PhD in Economics from Brown University (2021) and previously worked at the Adam Smith Business School, University of Glasgow. His research focuses on Bayesian econometrics, quantitative marketing, industrial organization, and time-series analysis. He teaches courses such as Introductory Econometrics (ECON 399) and Applied Econometrics (ECON 599). Education: PhD in Economics from Brown University (2021). Professional affiliations include roles at the University of Alberta and University of Glasgow. His work emphasizes methodological advancements in econometrics with applications to marketing and industrial organization. Research trends in his publications highlight Bayesian methodologies for dynamic modeling, structural breaks, and high-dimensional data. Key topics include semiparametric estimation, sparse models, and policy evaluation frameworks. Grants: Recipient of SSHRC Insight Development Grant (2024-2026) for research on Bayesian econometric methods in industrial organization and marketing. Active presenter at major conferences including the NBER-NSF Seminar, Canadian Economic Association meetings, and the World Congress of the Econometric Society. Teaching responsibilities include undergraduate and graduate econometrics courses with emphasis on applied regression methods and model specification.

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Raja Jurdakمشاهده پروفایل

استاد

Queensland University of TechnologyAustralia

Professor Raja Jurdak is a leading academic in distributed systems and applied data sciences at Queensland University of Technology (QUT), where he directs the Trusted Networks Lab. He holds dual roles as Professor of Distributed Systems and Chair in Applied Data Sciences, alongside leadership in the Centre for Data Science. His research focuses on dynamic network modeling, blockchain-based trust frameworks, and IoT applications, with particular emphasis on cybersecurity, energy efficiency, and mobility-driven diffusion processes. Jurdak formerly led CSIRO's Distributed Sensing Systems Group and maintains a visiting scientist role there. Education: PhD in Information and Computer Science, University of California, Irvine MS in Computer Networks and Distributed Computing, University of California, Irvine BE in Computer and Communications Engineering, American University of Beirut Research Interests: Network science, blockchain technology, IoT security, sustainable energy systems, and data-driven decision-making. His work bridges theoretical advancements with practical applications in smart grids, health surveillance, and urban mobility. Awards: Finalist for the 2019 Eureka Prize, multiple CSIRO accolades, and IEEE Senior Member status. His research has received industry recognition for interdisciplinary innovation, including the DiNeMo project's real-time disease surveillance system. Advisory & Grants: Leads high-impact projects funded by government and industry partnerships. Supervises PhD candidates in areas like decentralized data processing and privacy-preserving AI. Holds editorial roles at journals such as Ad Hoc Networks and PLoS ONE . Labs & Teams: Directs the Trusted Networks Lab at QUT, fostering collaborations with institutions like Oxford University and MIT. His work emphasizes cross-disciplinary teams to address global challenges in cybersecurity and sustainable systems.

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Vikrant Vazeمشاهده پروفایل

دانشیار

Dartmouth CollegeUnited States

Vikrant Vaze is the Stata Family Career Development Associate Professor and Executive Director of the Master of Engineering Management Program at Dartmouth's Thayer School of Engineering. He leads research in transportation systems, aviation optimization, and healthcare analytics, developing data-driven solutions for complex logistics challenges. His work integrates game theory, statistical modeling, and large-scale optimization. Research spans sustainable urban mobility, airline disruption recovery, multimodal pricing alliances, and healthcare operations. Articles consistently focus on optimization algorithms for real-world transportation and healthcare systems, with recent emphasis on electric aerial mobility and pandemic-responsive logistics. Major Awards: INFORMS Aviation Applications Best Paper (2024, 2023) AGIFORS Best Innovation Award (2024) NSF CAREER Award (2018) President of India Gold Medal As founding co-director of the Operations Research Group, he collaborates with industry partners like Multivariate Systems to translate academic research into deployable solutions.

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Parinaz Naghizadehمشاهده پروفایل

استادیار

University of California, San DiegoUnited States

Parinaz Naghizadeh is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of California San Diego (UCSD), affiliated with the Design Lab. She holds a PhD from the University of Michigan and has prior roles at Ohio State University and postdoctoral positions at Purdue and Princeton. Her research focuses on network economics, game theory, AI ethics, optimization, and cybersecurity. She received the NSF CAREER Award (2022), Rising Stars in EECS (2017), and Barbour Scholarship (2014). Education: PhD in Electrical Engineering (University of Michigan), M.Sc. in Mathematics and Electrical Engineering (University of Michigan), B.Sc. in Electrical Engineering (Sharif University of Technology, Iran). Research Interests: She develops mathematical models to analyze decision-making in complex networks, with emphasis on AI ethics, multi-agent systems, and cybersecurity. Recent work explores biases in AI, strategic classification, and incentive mechanisms for security investments. Article Trends: Her recent publications (2023-2025) address strategic classification challenges, multiplex network equilibria, federated learning fairness, and robust control in cyber-physical systems. Themes include ethical AI, game-theoretic security design, and optimization under uncertainty. Awards: NSF CAREER Award (2022), Rising Stars in EECS (2017), Barbour Scholarship (2014) Advising & Grants: No student advisees listed, but active in securing research grants (e.g., NSF CAREER). Works with interdisciplinary teams in UCSD's Design Lab. Labs/Teams: Affiliated with UCSD's Design Lab, focusing on innovative engineering solutions for societal challenges.

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Linda Newnesمشاهده پروفایل

استاد

University of BathUnited Kingdom

Professor Linda Newnes is a faculty member in the Department of Mechanical Engineering at the University of Bath, leading the Made Smarter Innovation: Centre for People-Led Digitalisation (£5M) and the TRansdisciplinary ENgineering Design (TREND) research group (£1.8M). Her work focuses on transdisciplinary engineering, whole life value analysis, and sustainable manufacturing. She directs The Foundry: Centre for Digital, Manufacturing & Design, emphasizing people-centric digitalization and cross-sector collaboration. Her research integrates natural/social sciences and industry stakeholders to address challenges in aerospace, defense, and energy sectors. Notable projects include models for whole life value (cradle-to-cradle) and tools for transdisciplinary working. She actively promotes Equality, Diversity & Inclusion (ED&I), leading the University’s Aurora programme and Athena SWAN submissions. Recent publications explore digital skill premiums, transdisciplinary frameworks, and AR deployment challenges. She advocates Industry 5.0 principles, emphasizing human-centric innovation and resilience in socio-technical systems. Current grants focus on net-zero transitions and cellular agriculture manufacturing. Her advising spans doctoral students in transdisciplinary engineering, Industry 5.0, and future manufacturing. She collaborates with industry partners like Airbus and Innovate UK to advance cost estimation, decision support tools, and lifecycle analysis.

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Liu Yongمشاهده پروفایل

استادیار

Texas A&M UniversityUnited States

Yong Liu is an Assistant Professor in the Department of Agricultural Economics at Texas A&M University. His research focuses on applied econometrics, Bayesian methodologies, machine learning, and agricultural policy with emphasis on crop insurance and risk management. He holds a Ph.D. in Agricultural Economics from the University of Guelph (2016), preceded by dual M.S. degrees in Economics and Agricultural Economics from Yunnan University and the University of Guelph, respectively, and a B.S. in Chemistry from Yunnan University. His research explores econometric techniques for agricultural policy evaluation, including causal inference methods and behavioral economics applications. Recent work analyzes climate change impacts on crop yields, the efficacy of insurance subsidies, and the integration of weather data into risk assessment models. Liu’s publications demonstrate a strong focus on improving crop insurance mechanisms through statistical innovations like Bayesian model averaging and spatial-temporal forecasting. His academic contributions span theoretical advancements in density estimation and practical applications in agricultural finance. While no specific awards or grants are listed, his active publication record indicates sustained research engagement in agricultural economics and quantitative methods.

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Umut Cetinمشاهده پروفایل

استاد

London School of Economics and Political Science (LSE)United Kingdom

Umut Cetin is a Professor of Statistics at the Department of Statistics, London School of Economics and Political Science (LSE). He holds a PhD in Applied Mathematics from Cornell University and has been affiliated with LSE since 2004, progressing through roles as Assistant Professor (2004–2010), Associate Professor (2010–2017), and full Professor since 2017. His research focuses on stochastic analysis, market microstructure, and mathematical finance, with an emphasis on equilibrium models under asymmetric information and liquidity risk. He contributes to editorial boards of journals like Market Microstructure and Liquidity and Frontiers of Mathematical Finance . Key research areas include stochastic filtering, Markov processes, and applications to financial economics. Notable achievements include the 2008 Europlace Institute of Finance Best Paper Award for work on insider trading models. He advises PhD students and teaches advanced courses such as Stochastic Processes and Markov Processes and Their Applications . Professional roles include Director of the LSE PhD Programme, member of the Steering Committee for the London Graduate School of Mathematical Finance, and external examiner for City University’s MSc Financial Economics. His recent work addresses power laws in market microstructure, equilibrium models with legal risk, and numerical methods for killed diffusions.

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David Egglestonمشاهده پروفایل

استاد

North Carolina State UniversityUnited States

Dr. David Eggleston is an Alumni Distinguished Undergraduate Professor and Director of the Center for Marine Sciences and Technology (CMAST) at NC State University. He leads the Marine Ecology and Conservation program, focusing on fisheries ecology, habitat restoration, and deep-sea biology. His research integrates field observations, modeling, and molecular tools to address ecological theory and sustainable resource management. Eggleston holds affiliations with the Department of Marine, Earth & Atmospheric Sciences and serves on national/international advisory boards. His academic roles include teaching courses like Introduction to Oceanography and Marine Benthic Ecology . Eggleston’s research spans tropical to temperate marine systems, emphasizing interdisciplinary approaches linking biology, physics, and economics. Awards include NSF’s Early Career Award and recognition in teaching/outreach. Current students focus on topics like oyster reef acoustics, coral reef soundscapes, and blue crab habitat dynamics. Eggleston’s lab emphasizes graduate mentorship, fostering future leaders in academia and resource management. Outreach initiatives include Ecological Synergism and educational tools like The Blue Crab in NC . Collaborative projects involve NOAA, The Nature Conservancy, and international marine organizations.

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Bill Howeمشاهده پروفایل

دانشیار

University of WashingtonUnited States

Bill Howe is an Associate Professor at the University of Washington's Information School, with adjunct appointments in Computer Science & Engineering and Electrical Engineering. He serves as Founding Program Director and Faculty Chair of the UW Data Science Masters Degree, Founding Associate Director and Senior Data Science Fellow at the UW eScience Institute, Director of the Urbanalytics Lab, and Co-Founding Director of the Center for Responsible AI Systems and Experiences. He also co-founded Urban@UW and created the first Data Science MOOC through Coursera. His research focuses on making data science accessible in public sector applications with emphasis on equity, privacy, and compliance. Current interests include: Algorithmic fairness in urban and social contexts Privacy-preserving synthetic data generation Machine learning for heterogeneous data Database systems and high-performance computing Human-computer interaction for data systems Responsible AI development and deployment Publication analysis reveals strong focus on responsible data science, with recent work emphasizing differential privacy, COVID-19 data equity, urban mobility fairness, and relational data systems. Earlier foundational work established contributions to scientific workflow systems and data pricing models. Awards and Honors: Runner-up Best Paper Award (VLDB 2023) Best Paper Award (SIGMOD 2019) Best Paper Award (VIs 2019) Best Paper Award (InfoVis 2018) He leads the Urbanalytics Lab and advises multiple students including An Yan (fairness in urban mobility), Sean Yang (machine learning embeddings), and Dominik Moritz (visualization systems). His projects span EZLearn for automatic claim validation, privacy-preserving synthetic data, and Myria middleware for polystores.

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