Professor Donald Robertson is a faculty member at the University of Cambridge , holding the position of Professor of Economics and Director of Graduate Studies and PhD Programme within the Faculty of Economics . He is affiliated with Pembroke College and contributes to econometric research and graduate education. Research Interests : His work focuses on Econometrics , Applied Macroeconomics , and Financial Economics , with methodological expertise in Time Series Analysis , Panel Data Analysis , and Predictive Modeling . His publications address topics like cross-sectional dependence, unit root testing, and instrumental variable estimation. Teaching : He instructs modules such as Introduction to Probability and Statistics , Time Series Methods , and MPhil Prep Course - Statistics . Publications : Recent contributions include work on R² bounds for predictive models, factor residuals in panel data, and fiscal fatigue in debt ratios, reflecting his focus on econometric theory and macroeconomic applications. Contact : Email dr10011@cam.ac.uk or phone +44(0)1223 335270. Office hours by email appointment in Room 70.
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Deborah Balk is a Professor at the Marxe School of Public and International Affairs at Baruch College, part of the City University of New York (CUNY). She also serves as Director of the CUNY Institute for Demographic Research and holds appointments in the CUNY Graduate Center's Sociology and Economics programs, as well as the CUNY School of Public Health's Epidemiology Program. Her expertise lies in spatial demography, integrating earth and social science data to address policy challenges related to urbanization, climate change, and population dynamics. Dr. Balk has led significant roles in climate assessments, including Co-Chair of the New York City Panel on Climate Change’s 4th Assessment (2019–2024) and membership in the U.S. National Climate Assessment’s 6th Health Chapter (2025). She holds a PhD in Demography from UC Berkeley and degrees from the University of Michigan (MPP and AB in International Relations). Her research focuses on urbanization, migration, poverty, health, and environmental interactions, particularly climate adaptation and equity. Notable projects include analyzing population vulnerability in coastal zones and developing spatial demographic tools for global health and policy. Awards include the Andrew Carnegie Fellowship (2016–2018) and the William and Flora Hewlett Foundation Fellowship (1991). Dr. Balk has secured grants exceeding $6 million from NSF, NASA, and others, supporting work on urbanization, climate justice, and demographic data integration. She advises multiple institutions, including the U.S. Census Bureau and National Academy of Sciences. Her teaching spans spatial demography, urban policy, and statistical methods, reflecting her commitment to bridging demographic science and real-world applications.
Fariya Sharmeen is an Associate Professor of Mobility and Urban Planning at KTH Royal Institute of Technology's School of Architecture and the Built Environment (ABE), affiliated with the Digital Futures Faculty. She holds a PhD from Eindhoven University of Technology and has previously served as Assistant Professor at Radboud University, Lecturer at Bangladesh University of Engineering and Technology (BUET), and research fellow at institutions including TU Delft and Imperial College London. Her research focuses on sustainable mobility transitions, social network dynamics in travel behavior, and policy responses to emerging transport technologies like MaaS and cycling innovations. Notable honors include the 2017 Piet Rietveld Award for transport research and a 2013 Royal Geographic Society award for transport geography. Sharmeen advises doctoral and master’s students on topics such as urban transformation and mobility governance. She coordinates courses like Sustainable Mobility (FAG3187) and leads projects like Bicification and ENCom. Her work integrates quantitative methods with policy analysis, addressing challenges in both global north and south contexts.
Andrew Liمشاهده پروفایل
دانشیار
- Statistics
- Optimization
- Machine Learning
- +۳ مورد دیگر
Andrew Li is an Associate Professor of Operations Research at Carnegie Mellon University's Tepper School of Business since 2024, previously serving as Assistant Professor since 2018. His research bridges statistics, optimization, and machine learning with applications to healthcare operations and retail management. Current teaching: Optimization, Business Analytics Capstone, and Topics in Optimization and Statistics PhD from MIT's Operations Research Center (2018), BS in Operations Research/Applied Mathematics from Columbia University (2012) Research Focus: Dr. Li develops data-driven decision frameworks for complex systems. Key areas include: Experience-based learning models with fairness constraints (organ allocation) Anomaly detection in low-rank matrices (retail inventory accuracy) Nanoparticle-based diagnostic systems for CAD and Alzheimer's Nonstationary demand forecasting in supply chains Publication Trends: Recent work combines bandit algorithms with healthcare applications (split liver transplants, CAD detection) and retail operations (inventory accuracy). Theoretical contributions include regret-optimal policies and entrywise anomaly detection guarantees. Scientific Honors: INFORMS Nicholson Award (2018) INFORMS Pierskalla Award (2021) NSF CAREER Award (2023) Professional Leadership: Active in INFORMS and CMU committees including MBA Analytics Curriculum, Thompson Award, and ENAiBLE AI-driven retail collaborative co-founder since 2021.
Christine Eckert is a Professor of Marketing Analytics at the TUM School of Management (Technische Universität München). She holds a doctorate in economics from Goethe University Frankfurt am Main and has previously held academic positions at University of Technology Sydney and EBS University of Business and Law. Education: Mathematics (Johannes Gutenberg University Mainz, Christian-Albrechts University Kiel); Economics (Goethe University Frankfurt am Main) Her research focuses on quantitative modeling of market participants' decisions, spanning consumer financial behavior, strategic innovation decisions, and corporate social responsibility. She also explores methodological advancements in management research, particularly causal inference techniques. Notable contributions include serving as co-editor for Big Data and Business Analytics (Journal of Business Research) and receiving an Australian Research Council Discovery Grant (2019-2021). Her work has been recognized with the Center for Financial Planning's Best Paper Award (2021). Key journals: Journal of the Academy of Marketing Science, Journal of Management, Journal of Marketing Research She contributes to academic governance through roles like Panel Member for New Zealand's Performance Based Research Fund (2018) and advisory board membership with Super Consumers Australia (since 2022).
Yang Luoمشاهده پروفایل
پژوهشگر ارشد
Yang Luo is a Kennedy Trust Senior Research Fellow in Data Science at the University of Oxford's Kennedy Institute of Rheumatology. His research bridges statistical genomics and computational immunology to unravel genetic contributions to immune-mediated traits, with a focus on the major histocompatibility complex (MHC) region. His work leverages large biobank datasets (UK Biobank, Biobank Japan), gene expression resources (GTEx), and proteomic data to decode molecular mechanisms linking genetic variation to disease risk. Specific interests include tuberculosis genetics, multi-ancestry polygenic risk scores, and single-cell eQTL modeling. Recent publications highlight expertise in HLA association studies, evolutionary immunogenetics, and disease-specific cell state dynamics. Key contributions include constructing a global HLA haplotype panel and developing novel statistical methods for admixed population genetics. Scientific Awards: Kennedy Trust Senior Research Fellow in Data Science His lab integrates computational and experimental approaches to translate genetic findings into clinical applications for immune disorders.
Ye Zhishengمشاهده پروفایل
دانشیار
Ye Zhisheng is the Dean’s Chair and Associate Professor in the Department of Industrial Systems Engineering & Management at the National University of Singapore (NUS). His research focuses on reliability engineering, inventory control, emergency response systems, and statistical modeling. He holds a PhD in Industrial and Systems Engineering from NUS, along with a BEng in Material Science and Engineering and a BEco in Economics from Tsinghua University. His work emphasizes practical applications in mission-critical systems, predictive maintenance, and data-driven decision-making. Current research initiatives include optimal maintenance policies for manufacturing systems, degradation analysis of bearings, and federated learning approaches for battery lifecycle prediction. He has pioneered methods for integrating physics-informed neural networks into prognostics and health management (PHM) systems. Key technical contributions span advanced statistical methodologies like sieve estimation for survival data, phase-type distributions modeling, and condition-based maintenance optimization. His interdisciplinary approach bridges operations research, mechanical engineering, and computer science to address complex reliability challenges. Recent projects include resilient consensus-based power grid management and contamination source identification frameworks. Notable collaborations involve developing intelligent cross-domain fault diagnosis systems using transformer networks and advancing the Internet of Federated Things (IoFT) for distributed data analytics. His work has been applied in aerospace, telecommunication infrastructure, and medical emergency response systems.
Jieqiong Zhaoمشاهده پروفایل
استادیار
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.
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 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.
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
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.
- 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.
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.
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.