Find Academic Supervisors

Jin ZhuView profile

Researcher

• Reinforcement Learning
• Machine Learning
• Statistical Software Development
• +3 more

Jin Zhu is a Researcher in the Department of Statistics at the London School of Economics and Political Science (LSE), working with Prof. Chengchun Shi on reinforcement learning and machine learning. His research focuses on developing algorithms with statistical and computational guarantees, alongside statistical software design to enhance algorithmic applications. Prior to LSE, he earned his PhD in Statistics at Sun Yat-Sen University under Dr. Xueqin Wang and Dr. Na You. Key expertise includes reinforcement learning, machine learning, and computational statistics. His work addresses challenges in off-policy evaluation, robustness in RL, and sparsity-constrained optimization. He has contributed to open-source tools like skscope and abess for efficient statistical computation. Research interests also span causal inference, high-dimensional data analysis, and algorithmic design for complex systems. Notable contributions include methodologies for genetic factor identification, spatial experimental design, and nonparametric statistical inference. Jin’s research bridges theoretical advancements with practical software implementations to address real-world computational and statistical challenges.

Yu ChengView profile

Professor

• Dynamic Treatment Regimes
• SMART (Sequential Multiple Assignment Randomized Trials)
• Causal Inference
• +6 more

Yu Cheng is a Professor and Chair of the Department of Statistics at the University of Pittsburgh, affiliated with the Dietrich School. She holds secondary appointments in Biostatistics and Psychiatry. Her research focuses on dynamic treatment regimes, SMART trials, causal inference, and multiple endpoints analysis. She leads a PCORI-funded method grant on SMART studies and co-investigates projects on cardiovascular disease and maternal/child health outcomes. Her awards include ASA Fellow (2024) and Pittsburgh Chapter Statistician of the Year (2020). Dr. Cheng earned her PhD in Statistics from the University of Wisconsin-Madison (2006), following MS (National University of Singapore, 2001) and BS (University of Science and Technology of China, 1999) degrees. She has held leadership roles including ASA LiDS Section Treasurer (2021–2023), Department Chair (2014–present), and Director of Graduate Studies (2016). Her research integrates methodological innovation with clinical collaboration, addressing topics like HIV, smoking cessation, lupus, depression, and cystic fibrosis. Her 150+ publications span journals like Biometrics , Journal of the American Statistical Association , and Biostatistics . Teaching includes courses in Survival Analysis, Probability, and Statistical Consulting. She actively mentors students through supervised consulting programs.

Mingli ChenView profile

Associate Professor

• Econometrics
• Machine Learning
• Time Series Econometrics
• +4 more

Mingli Chen is an Associate Professor of Economics at the University of Warwick’s Department of Economics. She holds affiliations including Turing Fellow at the Alan Turing Institute, External Fellow at the Centre for Panel Data Analysis (University of York), and Warwick-China Coordinator. Her research focuses on econometrics, machine learning, time series analysis, financial econometrics, and empirical industrial organization. She has served as an Associate Editor for the Journal of Econometrics since 2024 and organized workshops on data science and network analysis. Education: Ph.D. in Economics from Boston University (2015), B.A. in Information and Computing Science from Shanghai University (2009). She has held visiting positions at Stanford University, UC Berkeley, and the Federal Reserve Bank of Boston. Research Interests include high-dimensional econometrics, panel data models, social networks, quantile regression, and the integration of AI with econometrics. Key publications cover topics like quantile graphical models for systemic risk, latent panel quantile regression in asset pricing, and sparse β-models for network analysis. Awards include the International Partnerships Fund (2023), Turing PDRA Award (2020), and Co-Winner of the LABOUR Prize (2017). She advises Ph.D. students at Warwick and Cambridge, with placements at leading institutions like the University of Tokyo. Grants include leadership in UK-China partnerships and the Turing Institute. Teaching focuses on advanced econometrics at the Ph.D. level, including causal inference and machine learning. She co-organizes workshops and serves on conference committees, emphasizing data science and policy applications.

Andrea RotnitzkyView profile

Professor

• Causal Inference
• Epidemiological Methods
• Statistical Theory
• +3 more

Andrea Rotnitzky is a Professor and Chair of Biostatistics at the University of Washington. Born in Argentina with Jewish heritage, she values both Argentine culture and Jewish traditions. She joined UW in 2022 after a global recruitment, attracted by its collaborative research environment and partnerships with institutions like the Fred Hutchinson Cancer Center. Her work focuses on causal inference, missing data analysis, and semiparametric methods to address public health challenges. Rotnitzky holds a Licenciate in Mathematics from the Universidad de Buenos Aires (1982) and a PhD in Statistics from UC Berkeley (1988). Her research emphasizes developing statistically efficient tools to estimate intervention effects from observational or imperfect experimental data. Key areas include causal graphical models, longitudinal data analysis, and survival analysis. Her recent work highlights include articles on efficient adjustment sets in causal inference, debiased regression methods, and applications to post-COVID-19 symptom studies. Awards include IMS Fellowship (2025) and a $1M Rousseeuw Prize (2023) for statistical contributions. She prioritizes mentoring students and translating statistical innovations into real-world health solutions.

• Network Analysis
• Random Graph Theory
• High-Dimensional Statistics
• +5 more

Can M. Le is an Associate Professor in the Department of Statistics at the University of California, Davis, within the College of Letters and Science. His research lies at the intersection of statistics, network science, and high-dimensional data analysis, with a focus on theoretical and applied aspects of network modeling and inference. Ph.D. in Statistics, University of Michigan, Ann Arbor His research interests include network analysis, random graph theory, community detection, high-dimensional statistical inference, and regularization of network data. He develops methods for analyzing noisy, complex network structures and has contributed significantly to spectral methods and low-rank approximations in network science. His work bridges theoretical statistics with practical applications in social and biological networks. The recent publications demonstrate a strong trend in modeling and inference for network-linked data, with emphasis on robustness, adaptivity, and concentration properties of random graphs. His work combines deep probabilistic analysis with statistical methodology, particularly in community detection and network estimation under noise and heterogeneity. His research is supported by the National Science Foundation (NSF) grant DMS-2015134, indicating active funding and ongoing contributions to the field. While no formal list of advisees is provided, his collaborative work with leading statisticians such as Elizaveta Levina and Roman Vershynin suggests an active research group and mentoring role. He has no listed scientific awards in the provided text. However, his consistent publication record in top journals (JASA, JRSSB, Annals of Statistics, JMLR) underscores his scholarly impact. Dr. Le's work is closely tied to theoretical and applied statistical research on networks, likely involving a research lab or team focused on network data science, though specific lab names or team structures are not mentioned in the text.

Davide La VecchiaView profile

Professor

• Time series analysis
• Robust and semiparametric inference
• Financial econometrics
• +3 more

Davide La Vecchia is a Full Professor at the Research Institute for Statistics and Information Science (RISIS) at the University of Geneva, Switzerland. He holds dual PhDs in Statistics from Bocconi University (2007) and Economics from Università della Svizzera italiana (2011). His academic journey includes roles as an Assistant Professor at the University of St. Gallen and Monash University, and he has held visiting positions at Princeton University, the University of Copenhagen, and CREST (Paris). His expertise spans time series analysis, robust and semiparametric inference, financial econometrics, and spatial statistics. Key research contributions include work on saddlepoint approximations, optimal transportation methods, and latent variable models. Davide has led multiple grants, including from the Swiss National Science Foundation and the Australian Research Council. He serves as a referee for top-tier journals in statistics and econometrics. Teaching focuses on advanced statistical methods, including probability theory, time series analysis, and multivariate inference. His recent work emphasizes high-dimensional data modeling, spatio-temporal factor models, and applications to commodities trading networks. He has been honored with editorial roles and speaker invitations at global conferences, reflecting his leadership in statistical methodology.

Qin LuView profile

Assistant Professor

• Signal Processing
• Machine Learning
• Communications
• +3 more

Qin Lu is an Assistant Professor at the University of Georgia's School of Electrical & Computer Engineering. Her research focuses on robust and adaptive Bayesian methods for decision-making using sequential/networked data, including online Gaussian processes, Bayesian optimization, reinforcement learning, and spatio-temporal inference over graphs. Applications span autonomous systems and mobile edge computing. Education details are not explicitly listed in the provided text. Her work emphasizes scalability and uncertainty quantification, with contributions to edge computing resource management, active learning strategies, and multi-agent systems in IoT. Recent publications highlight advancements in Gaussian process dynamical modeling, adaptive Bayesian optimization, and ensemble learning techniques for online systems. No scientific awards are mentioned in the provided materials. Advising and grants details are not provided, though her research indicates collaborations in autonomous systems, underwater acoustics, and sensor networks. She is affiliated with the Boyd GSRC building and reachable at Qin.Lu@uga.edu.

Pau Batlle FranchView profile

Research Fellow

• Statistics
• Applied Mathematics
• Uncertainty Quantification
• +4 more

Pau Batlle Franch is a Research Fellow in the Computing and Mathematical Sciences Department at California Institute of Technology (Caltech), working with Professor Houman Owhadi. He holds a PhD from Caltech (June 2025) and was a research affiliate at NASA Jet Propulsion Laboratory (JPL). His research focuses on the intersection of statistics and applied mathematics, including frequentist confidence intervals in inverse problems, game-theoretical uncertainty quantification, and Gaussian processes. He has applied his work to domains like remote sensing, biology, earthquake prediction, and telecommunications engineering. Education : PhD in Computing and Mathematical Sciences (Caltech, 2025); Double undergraduate degree in Mathematics and Engineering Physics from Universitat Politècnica de Catalunya (CFIS program); Research visitor at NYU's Center for Data Science. His research interests include optimization-based statistical methods, Gaussian process frameworks for scientific computing, and uncertainty quantification in physical systems. Notable contributions include resolving the Burrus conjecture and developing computational hypergraph discovery techniques applied to NASA JPL projects. His work bridges theory and application, addressing challenges in ill-posed inverse problems and robust statistical inference. Recent activities include presenting at SIAM conferences and workshops on inverse problems in Earth science. His Gaussian process methods have been published in journals like PNAS and SIMODS, with applications ranging from PDE solving to RNA classification. Collaborations include JPL and the Groningen seismic study. Grants & Collaborations : Ongoing work with NASA JPL on lunar rover control and computational graph discovery; Seismic modeling in the Groningen gas field with epistemic/aleatoric uncertainty frameworks. He maintains an active GitHub profile showcasing projects in machine learning and scientific computing, including repositories like DarwinProjectAnalytics and emb4class .

Suryanarayana SankagiriView profile

Researcher

• Discrete Choice Models
• Recommendation Systems
• Blockchain Security
• +4 more

Suryanarayana Sankagiri is a postdoctoral researcher at the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland, affiliated with the Information and Network Dynamics (INDY1) group under Professor Matthias Grossglauser. Previously, he earned his Ph.D. in Electrical & Computer Engineering (2018-2022) from the University of Illinois at Urbana-Champaign , where he was supervised by Bruce Hajek and participated in the Coordinated Science Lab . He also holds an M.S. in Electrical & Computer Engineering from the University of Illinois (2016-2018) and a B.Tech. in Electrical Engineering from the Indian Institute of Technology Bombay (2012-2016). Education : Ph.D., Electrical & Computer Engineering, University of Illinois (2018-2022) M.S., Electrical & Computer Engineering, University of Illinois (2016-2018) B.Tech., Electrical Engineering, IIT Bombay (2012-2016) Suryanarayana's research focuses on discrete choice models and their application to recommendation systems , with a particular emphasis on learning from choice data and developing novel models for human decision-making. His broader interests include blockchain security under adverse network conditions, network dynamics , probabilistic modeling , and algorithm design . Recent work explores nonconvex matrix factorization and contextual dueling bandits for recommendation systems. His publications span theoretical and applied domains, including high-impact venues like ICML , Stochastic Systems , and IEEE Transactions on Networking . Themes include blockchain efficiency , hidden community detection in preferential attachment graphs, and temporal analysis of Indian classical music. Current projects involve refining recommendation systems through sparse comparison data and designing protocols for resilient blockchain networks. Scientific Awards : zkCapital Paper of the Week (2021) Rambus Fellowship (2021) Mavis Future Faculty Fellowship (2019) List of Teachers Ranked as Excellent (2019) Nomination for IIT Bombay Undergraduate Colloquium (2016) Best Poster Award, IIT Bombay Undergraduate Research Symposium (2013) Suryanarayana has advised no students listed in the provided materials. His work has been supported by fellowships such as the Mavis Future Faculty Fellowship and Rambus Fellowship . He contributes to the INDY1 group at EPFL, which investigates information and network dynamics through interdisciplinary approaches combining probability , network theory , and algorithmic design .

André BorrmannView profile

Professor

• Artificial Intelligence
• Building Information Modeling & Processing
• Digital Twinning
• +7 more

Prof. Dr.-Ing. André Borrmann is an academic leader at the Technical University of Munich (TUM) , where he has headed the Chair of Computing in Civil and Building Engineering since 2011 (formerly Computational Modeling and Simulation). He serves as Director of the TUM Georg Nemetschek Institute - AI for the Built World since 2025 and Spokesperson for the Leonhard Obermeyer Center since 2013. Research Interests Artificial Intelligence in Civil Engineering Digital Twinning Building Information Modeling (BIM) Pedestrian Dynamics Knowledge Representation Construction Simulation His work focuses on AI application across the built environment lifecycle - from generative design to maintenance prediction - with significant contributions to BIM standardization and buildingSMART International IFC extensions. He co-authored the German Ministry of Transport BIM Roadmap and led the BIM4INFRA2020 project. Awards include the 2024 Konrad Zuse Medal and multiple best paper awards at international conferences.

Catherine FaronView profile

Professor

• Artificial Intelligence
• Knowledge Management
• Semantic Web
• +8 more

Catherine Faron is a Full Professor at Université Côte d'Azur , affiliated with the I3S laboratory and Inria center . She serves as vice-head of the Wimmics joint research team and leads the Artificial Intelligence and Data Engineering (IAID) program at Polytech Nice Sophia engineer school. Habilitation à diriger les recherches (HDR) in Computer Science, UCA (2017) PhD in Computer Science, Univ. Paris 6 (1997) Her research focuses on Artificial Intelligence , particularly in Knowledge Representation and Reasoning (KRR) and Semantic Web technologies. She develops hybrid intelligent systems combining KRR with machine learning for knowledge extraction, integration, and exploitation across education, health, and digital humanities. Recent publications highlight her work on: 2025 : Knowledge graphs for historical zoological data 2024 : Semantic annotation frameworks in agronomy 2023 : Agricultural data mapping and medical record enrichment 2022 : Visual exploration of big linked data Scientific recognitions include: 2023: Best Paper Award, ESWC 2017: Scientific Excellence Award, UCA 2016: Best Demo Award, ISWC 2015: Best Paper Award, IC 2008: Best PhD Paper Award, ECPPM She has supervised 19 PhD/Master's students and leads/has led projects like D2KAB , DEKALOG , and ZOOMATHIA , with partnerships across academic and industrial institutions.

• Adversarial Machine Learning
• Cybersecurity
• Data Mining
• +5 more

Bowei Xi is an Associate Professor at the Department of Statistics at Purdue University. He holds a Ph.D. in Statistics from the University of Michigan (2004) and has made significant contributions to adversarial machine learning, cybersecurity, and metabolomics. Education: Ph.D. in Statistics, University of Michigan (2004) His research focuses on: Adversarial Machine Learning – Developing defenses against attacks on AI systems Cybersecurity – Integrating statistical methods with network security Metabolomics – Applying statistical analysis to biological data Big Data – Creating scalable analytical frameworks Differential Privacy – Balancing data utility with privacy preservation Recent publications highlight: Advancements in causal inference under privacy constraints Novel cyber deception strategies for battlefield IoT Applications of graph neural networks with dropout techniques Statistical defense mechanisms against adversarial examples Awards include: Faculty of 1000 Evaluation (2011) US Patents #7272707 and #7490234 for application server optimization He teaches STAT 514: Design of Experiments with a focus on hands-on learning and statistical software (SAS) integration. His office hours and contact details are publicly listed for student engagement.

Noemi CorsiniView profile

Research Fellow

• Statistical Modeling
• Clustering
• Latent Variable Models
• +4 more

Noemi Corsini is a Research Associate in Statistics at the Institute of Criminology, University of Cambridge. She holds a PhD in Statistical Sciences (2021-2024) from the University of Padova, Italy, and completed her bachelor's and master's degrees in Statistical Sciences at the University of Bologna, Italy. Her research focuses on statistical modeling, particularly in clustering complex data structures , density-based clustering , latent variable models , and Bayesian inference . She develops methodologies for identifying overlapping clusters in high-dimensional network data and has contributed to advancements in computational statistics for multivariate count data. Key trends in her publications include: Extending Bayesian stochastic block models for overlapping cluster detection in biographical networks (2024) Addressing overdispersion in multivariate count data (2022) Investigating tourism participation patterns through statistical analysis (2023) Creating meta-frontier efficiency models for the insurance industry (2024) Advancing modal clustering techniques for categorical/mixed data (2023) Her technical expertise spans R, Python, and STATA, with fluent Italian and English communication skills.

Bhaswar B. BhattacharyaView profile

Associate Professor

• nonparametric statistics
• distribution-free inference
• networks analysis
• +4 more

Bhaswar B. Bhattacharya is an Associate Professor of Statistics and Data Science at The Wharton School of the University of Pennsylvania, with a secondary appointment in the Department of Mathematics. His research spans several interconnected areas at the intersection of statistics, probability, and computational geometry. Dr. Bhattacharya received his Ph.D. in Statistics from Stanford University in 2016 under the supervision of Persi Diaconis. Prior to that, he earned both his Bachelor of Statistics (2009) and Master of Statistics (2011) from the Indian Statistical Institute in Kolkata. His research interests focus on three main pillars: nonparametric statistics (including distribution-free inference, nearest-neighbor methods, and inference on networks), combinatorial probability (covering counting problems in random graphs, random colorings, and graph limit theories), and discrete and computational geometry (including facility location problems, Voronoi games, and geometric Ramsey problems). His work often bridges theoretical developments with practical applications in network analysis, statistical learning, and geometric optimization. Recent publications demonstrate a strong trajectory in developing distribution-free methods for network analysis, with significant contributions to understanding fluctuations in graphon-based random graphs and developing optimal tests for inhomogeneous random graph models. His work also shows increasing focus on higher-order network structures through hypergraph models and applications to real-world problems like vaccination site optimization. NSF Career Award (2021-2026) Alfred P. Sloan Research Fellowship (2021) Probability Dissertation Award, Stanford University (2016) Sabyasachi Roy Memorial Gold Medal for best master's thesis, Indian Statistical Institute (2009-2011) Dr. Bhattacharya teaches advanced courses in mathematical statistics at both undergraduate and graduate levels at Wharton. His research program involves collaborations across multiple institutions and disciplines, with recent work applying statistical methods to public health challenges such as optimizing vaccination site locations. He maintains active research collaborations with colleagues in statistics, computer science, and applied mathematics departments.

Goran GlavašView profile

Professor

• Computational Semantics
• Multilingual Representation Learning
• Low-Resource NLP
• +2 more

Goran Glavaš is a Professor at the University of Würzburg's Faculty of Mathematics & Computer Science, holding the Chair for Natural Language Processing (Computer Science XII) and affiliated with the Center for Artificial Intelligence and Data Science (CAIDAS). His research focuses on computational semantics, multilingual/low-resource representation learning, and democratizing language technologies through fairness and sustainability. Former Assistant Professor at University of Mannheim (2017-2021) Interim Associate Professor at LMU Munich (2021-2022) Doctorate in 2014 at University of Zagreb under Jan Šnajder Recent research trends emphasize cross-lingual learning, multilingual knowledge integration, and ethical AI frameworks. His group contributes to robust multilingual models, vision-language systems, and sustainable NLP applications in social sciences. Outstanding Paper Award at ACL 2024 (IRCoder) Outstanding Paper Award at EACL 2024 (Kardeş-NLU) Extensive publications in EMNLP, ACL, NAACL, EACL, and TACL Advises a team of researchers at the University of Würzburg's NLP Chair, including Benedikt Ebing, Gregor Geigle, and Fabian David Schmidt. Leads the WüNLP group within CAIDAS, focusing on democratizing language technologies.