جست‌وجوی استاد و پژوهشگر

Jieqiong Zhaoمشاهده پروفایل

استادیار

• Visual Analytics
• Data Visualization
• Human-Computer Interaction (HCI)
• +۵ مورد دیگر

Jieqiong Zhao is an Assistant Professor in the School of Computer and Cyber Sciences at Augusta University, specializing in visual analytics and human-computer interaction. She holds a Ph.D. in Electrical and Computer Engineering from Purdue University and has postdoctoral experience at Arizona State University's VADER lab. Education: Ph.D. Electrical & Computer Engineering, Purdue University (2020) M.S. Computer Science, Tufts University (2013) B.E. Computer Software Engineering, Zhejiang University (2010) Research: Focuses on visual analytics for decision-making in domains like healthcare, cybersecurity, and environmental science. Key projects include ATVis (adversarial training visualization), FeatureExplorer (hyperspectral data analysis), and MetricsVis (law enforcement performance evaluation). Current interests span trustworthy AI, human-AI collaboration, and uncertainty visualization. Service: Organized the IEEE VIS 2024 panel on the future of visual analytics, serves on IEEE Transactions review boards, and mentors the WiCyS student chapter. Active in conference organizing and review roles. Awards: Received the 2020 VAST Mini-Challenge award for ConstellationBuilder's innovative cybersecurity interface design.

Jiannong Caoمشاهده پروفایل

استاد

• Cloud and edge computing
• Parallel and distributed computing
• Big data analytics
• +۲ مورد دیگر

Jiannong Cao is a Chair Professor and Director of the University Research Facility in Big Data Analytics at the Department of Computing, Hong Kong Polytechnic University. He has held various academic roles since 1990, including Assistant Professor at City University of Hong Kong and Lecturer at Australian universities. PhD in Computer Science, Washington State University (1990) MSc in Computer Science, Washington State University (1986) BSc in Computer Science, Nanjing University (1982) His research focuses on cloud and edge computing , parallel and distributed computing , and mobile computing , with significant contributions to wireless sensor networks (WSN) for structural health monitoring (SHM) and software-defined networking (SDN) for vehicular communications. Recent work includes WiFi-based non-invasive health monitoring systems and multi-user computation partitioning in mobile cloud environments. Dr. Cao’s publications demonstrate trends in WSN optimization , SDN architectures , and cognitive modeling for network embedding , with applications in smart healthcare , transportation systems , and industrial IoT . Ministry of Education Natural Science Award (2018) ACM Distinguished Member (2017) IEEE Fellow (2014) Best Paper Awards at IEEE DSAA, SMARTCOMP, WCNC He has mentored numerous researchers, including Linchuan Xu , Xuefeng Liu , and Weigang Wu , who have authored key publications in top venues like ACM WSDM and IEEE INFOCOM . His professional roles include chairing IEEE committees and serving on grant panels for the Hong Kong Research Grant Council.

Farhad Aslaniمشاهده پروفایل

دانشیار

• Structural Engineering
• Concrete Engineering
• Sustainable Construction Materials
• +۵ مورد دیگر

Dr. Farhad Aslani is an Associate Professor in the Department of Civil, Environmental and Mining Engineering at the University of Western Australia (UWA), serving as Director of the Materials and Structures Innovation Group. He leads the $250M Australian Composites Manufacturing CRC and co-leads UWA's Engineering Materials Research Cluster. His research focuses on innovative construction materials, including self-sensing concrete, 3D-printed composites, and fire-resistant materials. He has secured over $20M in CRC funding and holds leadership roles in national/international conferences. Key awards include the 2024 Concrete Institute WA Rising Star Award and multiple citation milestones. His work aligns with sustainable development goals, emphasizing eco-friendly materials and infrastructure resilience. Education: PhD in Structural Engineering, University of Technology Sydney (2014) Certificates in Leadership, Research Commercialization, Public Policy, and Project Management from Curtin University, Queensland University of Technology, RMIT, and UTS. Research Interests: Smart materials, sustainable concrete technologies, additive manufacturing, blast/fire resistance, and composites for infrastructure. His lab develops self-sensing concretes, lightweight engineered composites, and electromagnetic shielding materials. Grants & Projects: $4.3M ACM CRC project on composite repairs ARC grants for nanocomposite coatings and fire facilities Main Roads WA funding for timber bridge strengthening Expertise: Structural design, composite materials, and industrial collaborations (e.g., Woodside, Holcim). He advises on major projects like the Morley Ellenbrook Rail Line and Forrestfield Airport Link. Awards: 2024 Concrete Institute WA Rising Star Highly Cited Scholar (ScholarGPS, 2024) Most Cited Paper Awards (2018–2020)

E.I. Friedمشاهده پروفایل

دانشیار

• Clinical psychology
• Psychiatry
• Epidemiology
• +۶ مورد دیگر

Dr. Eiko Fried is an Associate Professor at Leiden University's Faculty of Social and Behavioural Sciences, where he works at the intersection of clinical psychology, psychiatry, epidemiology, methodology, and complexity science. His research focuses on improving psychological science through open science practices and innovative measurement approaches. PhD in clinical psychology, Free University of Berlin Postdoctoral training at KU Leuven and University of Amsterdam Promoted to Associate Professor at Leiden University in 2021 Key research areas include: Psychopathology measurement and classification Network analysis in mental health research Ecological momentary assessment (EMA) methodology Open science advocacy and implementation Dynamic systems modeling in psychology Transdiagnostic approaches to mental disorders Recent publications demonstrate expertise in: Symptom network analysis across disorders Improving depression measurement standards Transdiagnostic assessment protocols Mental health data integration challenges Psychological theory construction Methodological innovations in clinical research

Nezir KÖSEمشاهده پروفایل

استاد

• Econometrics
• Macroeconomics
• Financial Economics
• +۷ مورد دیگر

Nezir KÖSE is a Professor and currently serves as the Dean of the Faculty of Economics and Administrative Sciences at Beykent University. He has previously held academic positions at Istanbul Gelişim University and Gazi University, where he advanced from Research Assistant to full Professor. His academic career spans over three decades, with continuous contributions in teaching, research, and administrative leadership. Beykent University – Faculty of Economics and Administrative Sciences (2020–Present) Istanbul Gelişim University – Faculty of Economics, Administrative and Social Sciences (2017–2020) Gazi University – Faculty of Economics and Administrative Sciences (1990–2017) Education: Doctorate, Institute of Social Sciences, Gazi University (1992–1998) Degree, Faculty of Economics and Administrative Sciences, Gazi University (1990–1992) Licence, Faculty of Science, Gazi University (1985–1989) His primary research interests include Econometrics , Macroeconomics , Financial Economics , Time Series Analysis , and Energy and Environmental Economics . He has made significant contributions to the analysis of inflation, exchange rate volatility, foreign direct investment, oil price impacts, and financial stability, with a regional focus on Turkey and emerging markets. His recent publications (2023–2025) reflect a dynamic research agenda involving cryptocurrency markets , climate change economics , machine learning applications , and nonlinear macroeconomic modeling . His work frequently employs advanced econometric techniques such as panel data analysis, VAR/SVAR models, GARCH models, and time-varying parameter estimation. Scientific Awards: No awards mentioned in the provided text. Nezir KÖSE has supervised numerous graduate students, including PhD candidates who have completed theses on topics such as foreign direct investment, financial stability, oil price effects, and inflation uncertainty. He has also contributed to academic grants and collaborative research projects, particularly in energy and financial economics. He teaches core courses including Econometrics I & II , Time Series Analysis , and Nonparametric Statistics , demonstrating a strong commitment to pedagogy. He has authored several textbooks in econometrics and statistics, enhancing educational resources in Turkish academia. There is no indication of lab or team leadership, but his collaborative publications suggest active participation in research groups.

Ralf Steinhauserمشاهده پروفایل

پژوهشگر ارشد

• Behavioural Economics
• Fiscal Policy
• Applied Econometrics
• +۳ مورد دیگر

Dr. Ralf Steinhauser serves as a Senior Research Fellow at the Australian National University's POLIS: The Centre for Social Policy Research and as a Fellow at the Tax and Transfer Policy Institute. His research leverages advanced data analysis and experimental design to inform economic policy across carbon emissions forecasting, consumer behaviour, corporate governance, and taxation domains. He holds a Ph.D. and M.S. from the University of California, Berkeley, and an M.A. and B.A. from Humboldt University Berlin. Steinhauser's research spans Behavioural Economics, Fiscal Policy, Applied Econometrics, Experimental Economics, Environmental and Resource Economics, and Corporate Social Responsibility. His work consistently addresses policy-relevant questions through rigorous econometric analysis of large datasets and experimental methodologies, with particular emphasis on environmental and public finance applications. Analysis of his 2023-2025 publications reveals concentrated focus on green economy collaborations, behavioral public economics frameworks, and testing foundational economic hypotheses. His research demonstrates evolving integration of environmental sustainability with behavioral insights while maintaining methodological rigor in policy evaluation. Dr. Steinhauser actively contributes to collaborative research projects: Cost of road crashes in Australia (2021-2022) The use of nudges as a local government environmental policy instrument (2023-2025) Research services to investigate barriers to and boosters of the total fertility rate (2021) Impact of ACT Government tax reform (2019-2020) Estimation of elasticities for revenue bases (2018-2019) He maintains active affiliations with POLIS and the Tax and Transfer Policy Institute, where he participates in interdisciplinary teams addressing contemporary social policy challenges through evidence-based research approaches.

Reinhard Heckelمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Computational Imaging
• +۵ مورد دیگر

Reinhard Heckel is a Tenured Associate Professor (equivalent to Professor) of Machine Learning at the Department of Computer Engineering, Technical University of Munich (TUM), and Adjunct Faculty in Electrical and Computer Engineering at Rice University. He was previously an Assistant Professor at Rice (2017–2019), a postdoc in the Berkeley Artificial Intelligence Research (BAIR) Lab at UC Berkeley, and a researcher at IBM Research Zurich. Education: PhD, 2014 – ETH Zurich Visiting PhD student – Department of Statistics, Stanford University Research Interests: His work centers on machine learning and information processing with three major thrusts: (1) developing algorithms and theoretical foundations for deep learning, especially for accelerated magnetic resonance imaging ; (2) establishing rigorous mathematical and empirical underpinnings for modern machine-learning systems; and (3) leveraging DNA as a digital information-storage medium , including error-correction coding and system design for DNA-based storage. Across more than 100 peer-reviewed papers since 2017, Heckel’s research exhibits a strong interdisciplinary blend of computational imaging , machine-learning theory , and molecular data storage . Recent 2024–2025 publications show intensive focus on robust MRI reconstruction using diffusion priors, evaluation of bias in large web-text corpora, and state-of-the-art error-correcting codes for DNA storage channels. A forthcoming book, Deep Learning for Computational Imaging (Oxford University Press), consolidates his contributions to the field. Outreach & Media: Keynote and panel talks at DLD, TUM, and major ML conferences Op-eds in Frankfurter Allgemeine on ChatGPT and DNA storage Science features on Netflix, BBC, and German television (Galileo, “Gut zu Wissen”) Research Environment: At TUM he leads a group investigating theoretical and applied aspects of deep learning, compressed sensing, and coding for DNA storage. Open-source repositories on GitHub (e.g., dna_data_storage , supplement_deep_decoder ) provide code and data supplements accompanying his publications.

Scott Michael Olsonمشاهده پروفایل

استاد

• Geotechnical Engineering
• Earthquake Engineering
• Soil Mechanics
• +۵ مورد دیگر

Scott Michael Olson is a Professor in the Department of Civil and Environmental Engineering at the University of Illinois at Urbana-Champaign, with a 0% affiliate appointment in the Department of Geology. He holds roles as Associate Head and Director of Graduate Studies. His academic journey includes a B.S., M.S., and Ph.D. in Civil Engineering from UIUC (1993, 1995, 2001). Prior to academia, he worked in private practice with firms like Woodward-Clyde Consultants and URS Corporation, while also teaching at the University of Missouri-Rolla. Dr. Olson specializes in geotechnical engineering, focusing on geohazard identification, liquefaction engineering, laboratory testing, and paleoseismology. He teaches courses in geotechnical engineering, including CEE 380, CEE 484, and graduate-level topics like rock mechanics. His research emphasizes practical applications in infrastructure resilience, with notable contributions to understanding soil behavior under seismic loads and tailings material dynamics. Dr. Olson has been recognized with prestigious awards, including the Walter L. Huber Prize (2012), NSF CAREER Award (2009), and Arthur Casagrande Award (2004). He actively contributes to professional organizations such as the American Society of Civil Engineers and the Earthquake Engineering Research Institute, and has served on national review panels for NSF and USGS. His consulting work bridges academic research with industry challenges in geotechnical risk mitigation.

Aswin Sankaranarayananمشاهده پروفایل

استاد

• Computational Photography
• 3D Shape Estimation
• Imaging Systems
• +۴ مورد دیگر

Aswin Sankaranarayanan is a Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU) , where he leads the Image Science Lab . His research focuses on computational photography , 3D shape estimation , and novel imaging system design . He earned his Ph.D. in Electrical and Computer Engineering (2009) from the University of Maryland and completed a postdoctoral fellowship at Rice University (2012) . Research Themes: Developing imaging systems that exploit low-dimensional signal models to overcome traditional sensing limitations Co-design of optics and processing algorithms for efficient sensing Application of non-linear signal models to high-dimensional data Advancing compressed sensing and big data processing techniques Scientific Recognition: SIGGRAPH 2023 Best Paper Award (Split-Lohmann Multifocal Displays) CVPR 2019 Best Paper Award (Fermat Paths for NLOS Reconstruction) NSF CAREER Award (2017) Dean’s Early Career Fellowship (2018-2021) Herschel Rich Invention Award (2016) Technical Contributions: His recent publications reveal expertise in non-line-of-sight shape reconstruction , VR/AR display systems , and biomedical imaging . Collaborations span institutions like University College London and University of Toronto.

Yan Maمشاهده پروفایل

استاد

• Missing Data Imputation
• Machine Learning
• Meta-Analysis
• +۱۲ مورد دیگر

Yan Ma serves as Professor and Chair of Biostatistics at the University of Pittsburgh, with additional appointments in Orthopaedic Surgery and Clinical and Translational Science. Previously, he was Professor and Vice Chair at George Washington University Milken Institute of Public Health (2014-2022) and Assistant Professor at Hospital for Special Surgery/Weill Cornell Medical College (2008-2014). His educational background includes: PhD in Statistics, University of Rochester (2008) MA in Statistics, University of Rochester (2004) MS in Mathematics, Syracuse University (2003) BS in Statistics, Beijing Normal University (2001) Ma's research centers on advanced statistical methodologies including missing data imputation, machine learning, meta-analysis, causal inference, and longitudinal methods, applied across orthopedics, anesthesiology, health disparities, and emergency medicine through team science and translational research frameworks. His publication trajectory demonstrates sustained innovation from methodological foundations (2008-2012) to contemporary applications in health disparities and machine learning (2016-2022), consistently addressing complex biomedical challenges through high-impact journals like JAMA and Health Services Research. His scientific recognition includes: ASA's Statistics in Epidemiology Young Investigator Award (2010) Interorganizational Team Science Award (2012) ORISE FDA Research Fellowship (2017) APHA Achievement in Academia Award Ma has secured R01 funding from NIH/AHRQ for missing data methods in health disparities research while serving as Associate Editor for ASA journals and reviewer for NIH/PCORI/VA panels, demonstrating leadership in statistical methodology development and interdisciplinary collaboration. His team-science approach bridges statistical innovation with clinical implementation across orthopedics and anesthesiology, driving evidence-based practice through methodological rigor and cross-disciplinary partnerships.

Bruce E. Hansenمشاهده پروفایل

استاد

• Econometrics
• Time Series Analysis
• Model Selection and Averaging
• +۴ مورد دیگر

Bruce E. Hansen is the Mary Claire Aschenbrener Phipps Distinguished Chair and Trygve Haavelmo Professor of Economics at the University of Wisconsin-Madison, Department of Economics. He maintains an active research program with publications extending through 2025, demonstrating his continued prominence in econometric methodology. His research interests include: Econometric theory and methodology Time series analysis and forecasting Model selection, averaging, and shrinkage techniques Threshold and structural change models Statistical inference for clustered and dependent data Hansen's recent work focuses on innovative approaches to model averaging, standard error estimation for complex data structures, and unit root testing. His publications demonstrate both theoretical rigor and practical applicability to economic data analysis, with particular attention to handling clustered data, serial correlation, and model uncertainty. His influential publications include 'Least Squares Model Averaging' in Econometrica (2007) which introduced Mallows Model Averaging, and 'A Modern Gauss-Markov Theorem' (2022), both representing significant theoretical contributions to econometrics. His two textbooks 'Probability and Statistics for Economists' and 'Econometrics' (Princeton University Press, 2022) reflect his commitment to teaching and disseminating econometric knowledge. Hansen's research has been supported by multiple National Science Foundation grants (SES-9022176, SES-9120576, SBR-9412339, and SBR-9807111), highlighting the significance and quality of his contributions to the field.

• Econometrics
• Applied Econometrics
• Financial Econometrics
• +۶ مورد دیگر

Professor Yongcheol Shin is a faculty member in the Department of Economics at the University of York. His academic background includes a BA (Hanyang University), MA (Hanyang University), and PhD (Michigan State University). He specializes in applied and theoretical econometrics, focusing on financial and macroeconomic modeling. Education: BA (Hanyang University), MA (Hanyang University), PhD (Michigan State University) His research spans econometric theory and applications in finance and macroeconomics. Key areas include nonlinear panel data modeling, cointegrating VAR models, regime-switching models, and statistical hypothesis testing for time series. Recent work addresses interactive effects in panel data and multilevel factor models. Recent publications (2023) focus on panel data analysis, canonical correlation, and unit root testing. These studies reflect his expertise in econometric methodology and its application to financial economics, macroeconomics, and trade dynamics. Scientific awards include: 2018 Maekyung-KAEA Economist Award He leads the ESRC-funded project 'New Cross-Sectionally Dependent Panel Data Methods for the Analysis of Macroeconomic and Financial Networks' (2020-2024), collaborating with researchers like J. Chen and W. Wang.

Hengrui Caiمشاهده پروفایل

استادیار

• Causal Inference
• Reinforcement Learning
• Graphical Models
• +۷ مورد دیگر

Dr. Hengrui Cai is an Assistant Professor of Statistics at the University of California Irvine (UCI), affiliated with the Donald Bren School of Information and Computer Sciences. She holds a Ph.D. in Statistics from North Carolina State University (NCSU) and a B.S. in Statistics from Zhejiang University. Her research focuses on causal inference, reinforcement learning, and graphical models, with applications in precision medicine, healthcare analytics, and epidemiology. She develops interpretable solutions for individualized decision-making, particularly in healthcare settings such as ICU patient treatment optimization and pandemic analysis. Notable achievements include the NSF CDS&E-MSS Award (2024), ICS Research Awards (2023–2024), and recognition for contributions to causal discovery and policy evaluation. Dr. Cai advises graduate and undergraduate students on projects involving causal AI, machine learning, and healthcare data analysis. She teaches courses like 'Causal Machine Learning' and 'Introduction to Probability and Statistics,' emphasizing interdisciplinary approaches to real-world problems. Her work integrates statistical theory with practical applications, exemplified by software tools like ANOCE-CVAE for causal mediation analysis and the Sepsis EHR Benchmark Environment for reinforcement learning. Dr. Cai collaborates widely, contributing to projects such as quantifying the impact of the 2020 Hubei lockdowns on virus spread in China through causal graph analysis.

Maarten Kroesenمشاهده پروفایل

دانشیار

• Travel Behavior
• Sustainable Transport
• Quantitative Research Methods
• +۳ مورد دیگر

Maarten Kroesen is an Associate Professor in the Transport and Logistics group at Delft University of Technology's Faculty of Technology, Policy and Management. His research focuses on travel behavior analysis, sustainable transport, and quantitative methods. He earned his PhD cum laude in 2011 with work on aircraft noise annoyance and has since shifted to mobility patterns and policy analysis. Kroesen teaches courses on statistics, data analysis, and travel behavior research, and has received multiple awards including the Best Teacher of the Year (2016) and Henk Sol Award (2011). His work bridges behavioral theory with policy implications, emphasizing longitudinal panel data methods. Education: PhD (cum laude) in Transport Policy and Management (TU Delft, 2011), MSc in Systems Engineering (TU Delft) Research Interests: Travel behavior dynamics, sustainable mobility transitions, latent class models, accessibility disparities Awards: Over six major accolades, including best thesis and innovation awards Expertise: Policy advising on aviation, transport equity, and behavioral modeling Recent work explores emerging time-use patterns, zero-emission flight impacts, and bidirectional effects between accessibility and travel behavior. He actively contributes to Dutch policy discussions on aviation and transport infrastructure.

• Constitutional Law
• Administrative Law
• Devolution and Territorial Constitution
• +۳ مورد دیگر

Professor Aileen Mcharg is a leading academic in Public Law and Human Rights, holding a professorship at Durham Law School since 2019. Previously, she held chairs at Strathclyde, Glasgow, and Bristol Universities. Her research focuses on UK and Scottish constitutional law, devolution, and administrative law. She co-edits Public Law and serves on committees like the UK Constitutional Monitoring Group and the Scottish Government’s Expert Panel on Environmental Charges. Her research interests emphasize the UK’s territorial constitution, devolution dynamics, and Brexit’s legal implications. Notable contributions include analyses of the Miller Case, the Sewel Convention, and the UK Internal Market Act 2020. She frequently engages in interdisciplinary work, exploring energy law and policy post-Brexit. Recent publications include Constitutional change and territorial consent: The Miller Case and the Sewel Convention (2024) and The Silence of Constitutions (2024). She also contributes to blogs and journals on topics like the 2024 UK General Election’s constitutional implications and Scottish independence pathways. Her esteem indicators include roles as Research Excellence Framework panel member (2020), co-chair of the British-Irish Chapter of the International Society of Public Law (2018), and editorial board positions for Public Law and Journal of Energy and Natural Resources Law . Professor Mcharg advises PhD students and collaborates on grants exploring devolution, Brexit, and constitutional resilience. She is affiliated with Durham’s legal research networks and contributes to interdisciplinary projects on energy policy and constitutional futures.