Find Academic Supervisors

• Artificial Intelligence
• Machine Learning
• Data Science
• +2 more

Dongjie Wang is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Kansas, School of Engineering. His research focuses on artificial intelligence, machine learning, and data science applications in bioinformatics, transportation planning, and data-centric AI. Wang's research interests span several key areas: Development of reinforcement learning frameworks for biological pathway analysis and gene selection Generative AI applications in urban transportation systems Multi-modal representation learning for cross-domain retrieval Neuro-symbolic approaches for feature engineering His recent publications demonstrate a consistent focus on developing novel AI methodologies for real-world applications, particularly in transportation systems optimization, biological data analysis, and tabular data processing. The articles collectively show strong emphasis on reinforcement learning architectures, graph neural networks, and multimodal integration techniques. Wang advises graduate students in computer science and leads research in data-centric AI applications. His laboratory focuses on developing scalable machine learning solutions for complex systems.

Chaewon BaekView profile

Assistant Professor

• Macroeconomics
• Macro-labor
• Macroeconometrics

Chaewon Baek is an Assistant Professor of Economics at Tufts University, affiliated with the Department of Economics within the School of Arts and Sciences. His research focuses on macroeconomics, macro-labor dynamics, and macroeconometrics. He holds a Ph.D. from the University of California, Berkeley (2021), and degrees from Seoul National University (M.A. 2015, B.A. 2013). His work examines business cycles, monetary policy impacts, and labor market mechanisms. Recent studies include analyzing unemployment effects of pandemic-related stay-at-home orders using high-frequency data and advancing econometric techniques for impulse response analyses. Teaching responsibilities include macroeconomic theory, econometrics, and doctoral supervision. No awards or grants are explicitly listed in available materials.

Zheng Chris CaoView profile

Lecturer

• Tourism Economics
• Tourism Demand Modelling and Forecasting
• Globalisation
• +3 more

Zheng Chris Cao is a Lecturer in Economics at Aston Business School, Aston University, since 2018. His research focuses on tourism economics, globalisation, and applied econometrics, particularly examining tourism market interdependencies and policy impacts. He has published widely in top-tier journals and contributed invited book chapters, with expertise in macroeconometric and spatial methods. Prior roles include Research Fellow at the University of Surrey (2015–2018) and Research Assistant at the Hong Kong Polytechnic University (2009–2011). He holds a PhD from the University of Surrey, an MSc from the Hong Kong University of Science and Technology, and a BEcon from Shanghai Jiao Tong University. His research interests span tourism demand modelling, economic development, and social network analysis. He serves on editorial boards for the Annals of Tourism Research and Journal of Sustainable Tourism , and is a member of key academic bodies like the International Association of Tourism Economics and American Economic Association. Teaching includes courses on macroeconomics and the global economic environment. Awards include Fellow of the Higher Education Academy (FHEA) and Chartered Membership (CMBE) from the Chartered Association of Business Schools.

• Econometrics
• Financial Economics
• Causal Inference
• +5 more

Alessio Sancetta is a Professor of Economics at the Department of Economics, Royal Holloway, University of London, and Chair of the Department. He co-founded and co-directs the Centre for Robust Inference in a Digital Economy (RIDE). His research focuses on econometrics, causal inference, financial economics, and machine learning applications in finance. He earned his PhD in Econometrics from the University of Cambridge in 2002. Education: PhD in Econometrics, University of Cambridge (2002). Professional experience includes roles as a University Lecturer at the University of Cambridge and a trader in investment banks and proprietary trading firms, specializing in algorithmic trading strategies. Research interests span high-dimensional estimation, market microstructure, asset pricing, and forecasting methods. He has co-authored influential papers on topics like price drift before macroeconomic news and empirical asset pricing with functional factors. His work has been featured in major financial media such as Bloomberg and the Financial Times. He previously directed the MSc Finance program and co-directed the joint MSc Computational Finance with the Computer Science Department. Current projects include research funded by the Leverhulme Trust on information content in high-frequency trading and market manipulation in cryptocurrencies. His academic contributions include over 35 peer-reviewed articles in journals like the Journal of Econometrics, Journal of Financial Econometrics, and IEEE Transactions on Information Theory.

Loukia MeligkotsidouView profile

Associate Professor

• Bayesian Inference
• Computational Statistics
• Hidden Markov Models
• +4 more

Loukia Meligkotsidou is an Associate Professor of Statistics at the Department of Mathematics of the National and Kapodistrian University of Athens. She holds a BSc in Statistics from Athens University of Economics and Business (2002) and a PhD in Statistics from Lancaster University (2005), focusing on filtering methods for population genetics, phylogenetics, and mixture models. Her research interests span Bayesian inference, computational statistics, hidden Markov models, biostatistics, and econometrics. She teaches undergraduate courses on Bayesian Inference and postgraduate courses on Time Series analysis. Her work emphasizes methodological advancements in statistical modeling, particularly in longitudinal data analysis, competing risks, and model uncertainty. Recent publications address issues like shared parameter models for longitudinal data, Bayesian unit root testing, and quantile regression approaches in financial forecasting. She is a Fellow of the Royal Statistical Society and active in professional societies like ISBA and IMS. Her research integrates theoretical contributions with applied problems in economics, finance, and biology. Notable contributions include frameworks for handling informative dropout mechanisms and developing efficient Bayesian computational methods for complex models. Her work bridges statistical methodology and real-world applications, particularly in econometrics and biostatistics.

Zongwu CaiView profile

Professor

• Econometrics
• Quantile Regression
• Macroeconomic Policy Analysis
• +5 more

Zongwu Cai is the Charles Oswald Professor of Econometrics at the University of Kansas . His research focuses on econometric methodologies for policy evaluation, financial networks, income inequality, and climate change impacts. He holds a Ph.D. in Statistics from the University of California and prior degrees from institutions in China. Key research themes include quantile regression, panel data models, and machine learning applications in economics. Recent work addresses topics like trade war effects, AI-driven labor market shifts, and climate change's economic consequences. His contributions bridge theoretical econometrics with applied policy analysis. He has published extensively on topics such as predictive regression testing, risk assessment models, and structural break analysis. His methodological innovations include functional-coefficient VAR models and time-varying risk aversion frameworks.

Lingjie LiuView profile

Assistant Professor

• Computer Graphics
• Computer Vision
• Artificial Intelligence
• +7 more

Lingjie Liu is the Aravind K. Joshi Assistant Professor in the Department of Computer and Information Science at the University of Pennsylvania, leading the Penn Computer Graphics Lab and co-chairing the GRASP Lab. Previously, he was a Lise Meitner Postdoctoral Fellow at the Max Planck Institute for Informatics, and earned his Ph.D. from the University of Hong Kong (2019). His research focuses on neural scene representations, neural rendering, human performance modeling, and 3D reconstruction, blending computer graphics, vision, and AI. He is actively recruiting PhD students in areas like neural representations, generative models, and LLM-guided graphics. Education: Ph.D. in Computer Science (University of Hong Kong, 2019); BEng in Computer Science (Huazhong University of Science and Technology, 2014). Research highlights include NeuS/NeuS2 for 3D reconstruction, DragGAN for interactive image manipulation, and Holodeck for text-driven 3D environments. He has received awards such as the IVA Best Paper Award (2021) and holds editorial roles at IEEE TVCG and program committees for SIGGRAPH/ICCV. Professional activities include organizing workshops on neural fields, generative models, and robotics. His lab emphasizes 3D/4D reconstruction, generative models, and physics-aware systems. Current students include Chen Wang (Meshy AI Fellow), Yiming Huang, and Qiao Feng. His work spans over 50 publications in top venues like SIGGRAPH, CVPR, and NeurIPS.

• Computer Vision
• Artificial Intelligence
• Machine Learning
• +4 more

Abhinav Shrivastava is an Associate Professor in the Department of Computer Science at the University of Maryland, College Park, with a joint appointment in the Institute for Advanced Computer Studies (UMIACS). His research focuses on AI, particularly computer vision, machine learning, and robotics, with applications in graphics, natural language processing, and cognitive neuroscience. He holds a Ph.D. from Carnegie Mellon University (2017) and an M.S. from the same institution (2011). Research Interests: His work emphasizes temporal understanding, visual perception, and open-world learning. He explores how machines can analyze temporal phenomena like human actions in videos and develop robust models for dynamic environments. Key Projects: Recipient of the NSF CAREER Award (2023) to advance computers' ability to understand temporal phenomena. His research includes developing computational frameworks for video understanding, object discovery, and neural representations for videos. Awards: NSF CAREER (2023), Best Paper Award (WACV 2020), Microsoft Research PhD Fellowship (2014-16), and recognition in media outlets like Wired and Forbes for contributions to AI and computer vision. Labs/Teams: Leads research in the Vision & Learning Center and contributes to the Institute for Trustworthy AI in Law & Society. Active in organizing workshops on open-world vision and novelty handling in AI systems.

• Mathematical Statistics
• Non-Parametric Statistics
• Bayesian Non-Parametrics
• +5 more

Paulo Serra is an Assistant Professor at the Mathematics Department of the Faculty of Science at Vrije Universiteit Amsterdam. He holds additional roles including Board Member of the MSc Internship Board for Business Analytics, Examination Board Mathematics and Business Analytics, and the Mathematical Statistics section of VVSOR. Previously, he served as Assistant Professor at Eindhoven University of Technology and held postdoctoral positions at the University of Amsterdam, University of Goettingen, and UNINOVA in Portugal. Education: PhD in Mathematical Statistics (Eindhoven University of Technology, 2009–2013) MSc in Mathematical Sciences (Utrecht University, 2006–2008) Licenciatura in Applied Mathematics (New University of Lisbon, 2000–2005) Research Interests: Focus on non-parametric mathematical statistics, including Bayesian non-parametrics, spline estimators, statistical tracking of time-varying parameters, quantile regression, and Markov processes. His work bridges theoretical foundations with practical applications in algorithm design and implementation, particularly in biomedical and engineering domains. Teaching: Teaches courses in Stochastics, Statistics for Business Analytics, and Stochastic Processes for Finance at VU Amsterdam, as well as the Mastermath Bayesian Statistics course. Provides introductory materials on Probability and Statistics using Python and Jupyter Notebook. Supervision: Currently co-supervises three PhD students, one MSc student, two BSc students, and one MSc internship. Offers thesis projects in areas aligned with his research expertise. Key Themes in Publications: Recent work emphasizes medical applications (e.g., perioperative patient deterioration prediction) and methodological advancements in robust estimation, nonparametric Bayesian techniques, and adaptive algorithms. Earlier contributions include fuzzy logic systems for spacecraft thermal monitoring and network dimension estimation in inhomogeneous graphs.

Kyle A CaudleView profile

Professor

• Forecasting
• Time Series Analysis
• Multilinear Algebra
• +3 more

Kyle A. Caudle is a Professor of Mathematics at the South Dakota School of Mines and Technology, holding a Ph.D. from George Mason University, an M.S. from Salve Regina University, and a B.A. from Western State College. His research spans forecasting, tensor analysis, and anomaly detection, with applications in engineering and data science. Caudle develops computational tools for time series forecasting, tensor decomposition, and graph representation learning. He created software packages like Flow Field, rTensor2, and LTAR, published on CRAN. His interdisciplinary projects include collaborations with NIST and Naval Surface Warfare Centers on surface ship maintenance and anomaly detection. Publications emphasize multilinear algebra, temporal forecasting, and machine learning. Recent work advances tensor factorization for high-dimensional data, hierarchical graph networks, and deep generative models. Awards include the 2015 Peter Holmes Prize for innovative statistics teaching and accreditation as a Professional Statistician (ASA, 2013). He mentors graduate students and co-developed the Ph.D. in Data Science.

Randy C HooverView profile

Professor

• Machine Learning
• Tensor Analysis
• Time-Series Forecasting
• +2 more

Randy C. Hoover is a Professor and Assistant Department Head in the Computer Science and Engineering department at South Dakota Mines. He directs the Mines Machine Learning and Intelligent Systems Lab (MMLIS-L), focusing on multilinear systems theory, forecasting methods, and subspace learning. His research bridges machine learning, tensor analysis, and dynamical systems. Research expertise includes high-dimensional data modeling, time-series forecasting, and pattern recognition, with applications spanning computer vision, cybersecurity, and fluid dynamics. His work frequently employs tensor decomposition and advanced statistical methods to solve complex multidimensional problems. Articles demonstrate consistent focus on tensor-based machine learning innovations, with recent emphasis on time-series forecasting and anomaly detection. Earlier works establish foundations in multilinear algebra applications for pattern recognition. Teaching covers signal processing, embedded systems, and dynamic systems. Education includes a BS and MS from Idaho State University and a PhD from Colorado State University.

Robert LundeView profile

Assistant Professor

• Statistical Network Analysis
• Time Series
• Resampling Methods
• +2 more

Robert Lunde is an Assistant Professor in the Department of Mathematics and Statistics at Washington University in St. Louis. He holds a PhD in Statistics and Data Science from Carnegie Mellon University and completed postdoctoral research at the University of Michigan and University of Texas. Lunde's research focuses on statistical inference for complex data structures including networks and time series. His expertise encompasses resampling methods, distribution-free inference, and high-dimensional statistics. Specific interests include conformal prediction for network data, validity of jackknife methods for graphs, and bootstrap techniques for streaming algorithms. His recent publications explore subsampling sparse graphons, bootstrap error analysis for Oja's algorithm, and theoretical foundations for network-assisted regression. Lunde teaches courses in mathematical statistics and probability theory, employing his research expertise in statistical learning and inference methods.

Simon RudkinView profile

Senior Lecturer

• Topological Data Analysis
• Data Science
• Social Sciences
• +5 more

Simon Rudkin is a Senior Lecturer in Data Science and Social Statistics at the University of Manchester, affiliated with the Institute for Data Science and AI. He holds a PhD in Economics from the same institution (2008). His research focuses on Topological Data Analysis (TDA), particularly applying the Ball Mapper algorithm to social sciences and humanities. Key areas include regional productivity, health impacts of supermarkets, Brexit's economic topology, and digital economy policy. He welcomes PhD applications in TDA and statistical methodologies. Research interests span TDA visualization and dynamics, multi-level modeling, and quantile regression. His work explores trajectories of regional growth, migration effects on economic cohesion, and voting behavior analysis. Recent studies include cultural gravity's role in growth redistribution and cryptocurrency market topology. Education: Doctor of Philosophy, Economics, University of Manchester (2008) Expertise: TDA, regional policy, social sciences, data science Affiliations: Institute for Data Science and AI, University of Manchester Articles highlight TDA applications in financial markets, electoral geography, and health policy. His methodologies emphasize explainability and interpretability, aligning with sustainable development goals in education and regional policy.

• Statistics
• Econometrics
• Applied Mathematics
• +4 more

Professor Guy Nason is the Chair in Statistics at Imperial College London's Department of Mathematics, part of the Faculty of Natural Sciences. He leads the NeST Programme Grant, a UKRI-funded initiative involving nine investigators across six universities, focusing on network stochastic processes and time series. He also serves as co-Chair of the Home Office Science Advisory Council, advising the UK Government on scientific matters. His research spans time series analysis, network time series (e.g., GNAR models), multiscale methods, and applications in official statistics, energy, and epidemiology. His work emphasizes practical methodologies, with over 40 collaborators and significant industry partnerships (e.g., EDF Paris, Shell). He develops R packages like 'GNAR' and 'NetTree' for statistical analysis and forecasting. His contributions include advancing locally stationary time series modeling and network-based forecasting techniques, particularly for high-dimensional and missing data scenarios. Key roles: Director of NeST Programme Grant, Home Office Science Advisory Council co-Chair, Head of Statistics Section at Imperial Research themes: Network time series, multiscale wavelet methods, official statistics Software contributions: 18+ R packages including 'wavethresh', 'GNAR', and 'NetTree' His research addresses challenges in forecasting, variance stabilization, and network-driven data analysis, with applications to public health (e.g., Mumps and COVID-19 modeling), economic indicators, and political dynamics (e.g., Early Day Motion analysis in UK Parliament).

• Dynamic Network Analysis
• Bayesian Statistics
• Latent Variable Models
• +6 more

Francesco Sanna Passino is an Assistant Professor in Statistics at Imperial College London's Department of Mathematics, within the Faculty of Natural Sciences. His research focuses on statistical analysis of dynamic networks, Bayesian methods, latent variable models, and applications in cybersecurity, social networks, and transportation systems. He holds a PhD in Statistics from Imperial College London (2021), an MSc in Statistics from Imperial College London (2017), and dual BSc degrees in Statistics from the University of Glasgow (2016) and the University of Bologna (2016). Research interests span dynamic network analysis, event-time data, latent factor models, and model-based clustering. Notable applications include statistical cybersecurity (e.g., attack pattern detection), social network dynamics, and infrastructure systems like bike-sharing. His work integrates advanced statistical techniques such as latent variable models, point processes, and spectral embedding. Publications emphasize methodological innovations in network time series, synthetic data generation, and graph-based processes. Collaborations include Imperial's Artificial Intelligence Network. No awards are explicitly listed, but his research aligns with cutting-edge topics like AI-driven financial trend detection and privacy-preserving synthetic data. He advises no listed students and has no documented grants in the provided texts. His work is housed in the Huxley Building on the South Kensington Campus. For further details, visit his Imperial webpage or Google Scholar profile .