Find Academic Supervisors

Mao XiaojieView profile

Associate Professor

• Causal inference
• Data-driven optimization decision-making
• Machine Learning
• +3 more

Mao Xiaojie is an Associate Professor at the Department of Management Science and Engineering, Tsinghua University's School of Economics and Management . Holding a PhD in Statistics and Data Science from Cornell University (2021) and a bachelor's in Mathematical Economics and Finance from Wuhan University (2016), Mao specializes in causal inference and data-driven optimization decision-making . PhD: Cornell University (2016-2021) Bachelor: Wuhan University (2012-2016) Mao's research bridges machine learning , statistics , and operations research to address challenges in contextual optimization, algorithmic fairness, and robust causal inference. Recent work focuses on data combination , surrogate variables , and minimax methods for handling unobserved confounding and limited outcome data. Key trends in Mao's publications include: Advancing bandit algorithms for efficient contextual decision-making Developing debiased machine learning frameworks for quantile treatment effects Designing robust optimization models under noisy and incomplete covariates Scientific recognition includes: Applied Probability Society Best Student Paper Competition Finalist (2020) Multiple teaching excellence awards at Tsinghua University (2022-2024) Research grants from the National Natural Science Foundation of China Mao currently teaches Empirical Methods in Management Science (PhD), Data Analysis: Inference and Decision Making (Master), and Probability Theory and Mathematical Statistics (Undergraduate). Research collaborations span institutions like Cornell and MIT, with publications in top venues including NeurIPS , ICML , and Operations Research .

Anderson AlvesView profile

Assistant Professor

• Precision Livestock Science
• Data Analytics
• Statistical Learning
• +10 more

Dr. Anderson Alves is an Assistant Professor in Precision Livestock Science and Data Analytics at the University of Georgia's College of Agricultural & Environmental Sciences , Department of Animal & Dairy Science. Originally from Brazil, he earned his Master’s in Animal Science (2016) from the Federal University of Ceara and a Doctoral degree in Animal Breeding and Genetics (2019) from Sao Paulo State University. Education: Master’s (2016, Federal University of Ceara), Doctoral (2019, Sao Paulo State University) Prior Roles: Lecturer (Federal Institute of Education, Science, and Technology of Maranhao, Brazil) and Research Associate (University of Wisconsin-Madison) Dr. Alves' research integrates statistical learning , AI , and on-farm sensor data to create decision-making tools for sustainable livestock farming. His lab employs high-throughput phenotyping and modern molecular technologies to explore the genetic basis of hard-to-measure traits. Recent work includes applications of Siamese neural networks for cattle identification and machine learning for predicting animal health metrics. The Lab for Precision Livestock Science develops computational tools to improve animal production, reproduction, welfare, and health. Their projects span from automated pregnancy diagnosis using ultrasonography to anemia detection via ocular imaging. The lab’s GitHub and LinkedIn profiles are publicly accessible for collaboration and dissemination.

• Robotics
• Mechanism Design
• Machine Dynamics
• +4 more

Prof. Burkhard Corves serves as Director of the Institute of Mechanism Technology, Machine Dynamics and Robotics within the Faculty of Mechanical Engineering at RWTH Aachen University. His academic leadership spans robotics, mechanism theory, and dynamic systems engineering, with significant contributions to industrial automation and sustainable manufacturing applications. His research concentrates on Robotics, Mechanism Design, Machine Dynamics, and Multibody Simulation, with recent emphasis on compliant gripper development for surgical applications, energy-efficient cam mechanisms, and human-robot collaboration systems for inclusive workplaces. Key investigations address data-driven trajectory optimization, sustainable ship recycling processes, and real-time simulation techniques for bicycle and vehicle dynamics. Analysis of his 15 most recent publications (2024-2026) reveals dominant themes in industrial robotics (particularly delta robots and pick-and-place systems), compliant mechanism design for medical applications, and sustainable manufacturing processes. Significant focus appears on real-time multibody simulation for transportation systems, control strategies for vibration suppression, and recycling automation aligned with circular economy principles. Emerging work explores human-robot collaboration for disability inclusion and bioprocessing optimization. Scientific Awards No awards were documented in the provided materials. Advising and Grants Specific student supervision details and grant funding information were not included in the source documentation. Laboratories and Teams Prof. Corves leads the Institute of Mechanism Technology, Machine Dynamics and Robotics (IGMR), directing research in robotic systems for manufacturing, sustainable ship recycling, and bicycle dynamics simulation. The institute actively participates in interdisciplinary projects including Bots2ReC (semi-autonomous asbestos removal) and sustainable EV battery recycling initiatives, with strong emphasis on practical engineering solutions for industrial challenges.

Valentin PatileaView profile

Professor

• Semi and nonparametric statistics
• Survival analysis
• Time Series
• +2 more

Valentin Patilea is a Full Professor of Statistics at the National School of Statistics and Information Analysis (ENSAI) in France and serves as Head of the PhD program. He is a Permanent Member of CREST (Center for Research in Economics and Statistics), a prominent research center in economics and statistics. His academic career spans institutions across Europe, with significant contributions to statistical methodology and applications. Professor Patilea's educational background includes a Habilitation à diriger des recherches in Mathematics from the University of Rennes 1 (2006), a PhD in Statistics from Université catholique de Louvain (1997), an MSc in Mathematical Economics and Econometrics from Université Toulouse I (1993), and an MSc in Mathematics from the University of Bucharest (1989). His research focuses on advanced statistical methodologies, particularly in semi and nonparametric statistics, survival analysis, time series analysis, econometrics, and functional data analysis. Patilea's work bridges theoretical statistics with practical applications across various domains, developing innovative methods for complex data structures and dependencies. His research has significantly advanced methodologies for functional data, cure models, and weakly dependent time series. Analysis of Patilea's recent publications reveals a strong emphasis on functional data analysis, with particular attention to adaptive estimation methods, irregular data structures, and computational efficiency. His work spans theoretical developments in statistical methodology while maintaining connections to practical applications in economics and other fields. The publications demonstrate increasing sophistication in handling complex data structures, particularly multivariate functional data and dependent observations. Professor Patilea actively contributes to the academic community through editorial service, currently serving as Associate Editor for Bernoulli Journal (since 2022) and Statistical Methods and Applications (since 2025). Previously, he served on the editorial boards of the Journal of the Royal Statistical Society: Series B and the Journal of the American Statistical Association. He has supervised numerous PhD students, including current candidates Omar Kassi, Hassan Maissoro, and Daphne Aurouet, and has previously guided successful dissertations by Sunny Wang, Guillaume Flament, Edouard Genetay, and others. His supervision spans theoretical statistics, functional data analysis, and econometric applications. Professor Patilea leads the FunStatMath research initiative focused on Functional Data Analysis, which addresses mathematical challenges posed by data that naturally occur as curves or surfaces rather than vectors. This network connects researchers working on theoretical developments and applications across neuroscience, environmental sciences, and biology.

• Building Information Modeling (BIM)
• Architectural Design
• Information Technologies in Architecture
• +3 more

Ozan Önder Özener is a Professor at the Department of Architecture, Istanbul Technical University , where he has been since 2010. He earned his PhD in Architecture from Texas A&M University (2009) and holds an Associate Degree in Law from Anadolu University (2016). His work focuses on Building Information Modeling (BIM) , Kinetic Architecture , and Architectural Education . Academic Performance Award (2024) Publication Awards (2022, 2023, 2024) Multiple competition prizes in Urban Design and Architectural Projects His research explores BIM-driven information management , pedagogical innovations , and sustainable building practices , with over 200 publications and 6 funded projects. Key collaborations include Mehmet Ümit Meterellioz and Mark J. Clayton.

Mei-Cheng WangView profile

Professor

• Survival Analysis
• Longitudinal Data Analysis
• Multivariate Statistics
• +5 more

Mei-Cheng Wang, PhD is a Professor in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health, with additional affiliations at the Johns Hopkins Center for Injury Research and Policy. Her educational background includes: PhD in Statistics from University of California at Berkeley (1985) Dr. Wang specializes in Survival, Longitudinal And Multivariate statistics (SLAM), with particular expertise in point processes, recurrent marker processes, and semiparametric models. Her research has significant applications in medical studies, particularly in the areas of biomarker diagnostic testing, risk prediction, and the birth-illness-death process in relation to risk factors. Recently, her work has increasingly focused on Alzheimer's disease research, developing statistical methods to analyze biomarker trajectories and disease progression. Analysis of her recent publications (2023-2024) reveals a strong emphasis on Alzheimer's disease research, particularly in developing statistical methods for analyzing biomarker trajectories during the preclinical phase. Her work combines advanced biostatistical techniques with neuroimaging and cerebrospinal fluid biomarker data to understand disease progression before clinical symptoms appear. This research sits at the intersection of biostatistics, neuroscience, and machine learning, with applications in early detection and intervention strategies for neurodegenerative diseases. Dr. Wang has received significant recognition for her contributions to statistics: Fellow of the American Statistical Association (ASA) Elected member of the International Statistical Institute (ISI) Fellow of Institute of Mathematical Statistics (IMS) Throughout her career, Dr. Wang has advised numerous graduate students and secured research funding for her work in biostatistical methodology development. Her research group, the SLAM (Survival, Longitudinal And Multivariate statistics) group, focuses on developing innovative statistical methods with applications in medical and public health research. Current projects include the BIOCARD Study, a prospective birth cohort study examining pre- and peri-natal determinants of autism spectrum disorders and developmental disabilities, as well as research on Alzheimer's disease biomarkers and progression.

• Acoustics
• Noise Control
• Numerical Modeling
• +3 more

Paul Williams is a Postdoctoral Research Fellow at the School of Mechanical and Mechatronic Engineering within the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS). Dr. Williams specializes in acoustics and noise control, with particular expertise in numerical modeling and mechanical characterization. His research spans several key areas including sound propagation in various environments, vibration control of structures, and noise reduction techniques for engineering applications. He employs advanced computational methods such as the Semi-Analytical Finite Element (SAFE) method to address complex acoustic problems across atmospheric, underwater, and infrastructure contexts. His work frequently bridges theoretical acoustics with practical engineering solutions for wind turbines, transportation systems, and water infrastructure. Analysis of Dr. Williams' publication history reveals a consistent focus on developing efficient computational models for acoustic phenomena while maintaining accuracy. His recent work demonstrates increasing sophistication in handling complex boundary conditions, fluid-structure interactions, and multi-physics problems. The research shows strong collaboration patterns with colleagues like M. Karimi and R. Kirby, indicating active participation in research teams at UTS. Dr. Williams is actively involved in multiple funded research projects including noise mitigation for Sydney Metro tunnels, passive vibration control systems, and acoustic testing for building components and industrial equipment. His research portfolio combines theoretical modeling with experimental validation, utilizing specialized facilities at UTS including hemi-anechoic chambers and sound transmission loss suites.

Faik A. KorayView profile

Professor

• International Finance
• Monetary Policy
• Fiscal Policy
• +2 more

Faik A. Koray is a Professor and Department Chair in the Department of Economics at Louisiana State University, concurrently serving as Interim Associate Dean and Professor in the Flores MBA Program. He holds the distinguished Bell South Business Partnership Professorship, reflecting his significant contributions to academic scholarship and business education. His academic credentials include a PhD in Economics from Duke University (1984), an MS in Business Administration from Bogazici University (1974), and a BA in Economics from Bogazici University (1972). This international educational background underpins his cross-cultural research perspective. Professor Koray's research centers on International Finance, Monetary Policy, and Fiscal Policy, with emphasis on econometric analysis of exchange rate dynamics, trade balances, fiscal multipliers, and taxation effects. His work frequently examines how policy impacts vary across economic conditions and exchange rate regimes, incorporating emerging market contexts like Turkey. He employs advanced methodologies including VAR analysis and semi-parametric modeling to uncover non-linear relationships in macroeconomic data. Bell South Business Partnership Professor With decades of experience, Professor Koray has established a robust research program evidenced by publications in premier journals including the Journal of Macroeconomics and Journal of Applied Econometrics. His prior advisory role at Turkey's Central Bank provides practical grounding for his theoretical work. As Department Chair and MBA program professor, he shapes curriculum while mentoring future economists and business leaders through his teaching of Macroeconomics and International Finance.

David BickelView profile

Professor

• Statistics
• Bioinformatics
• Genomics
• +7 more

David Bickel is a Professor in the School of Information, Library, and Research Sciences at the University of North Carolina at Greensboro. With a PhD from the University of North Texas and undergraduate studies at Baylor University, he has established himself as a leading researcher in statistical methodology for genomic and biological data analysis. Dr. Bickel's research focuses on empirical Bayes methods, false discovery rates, uncertainty quantification, and information theory. His work bridges theoretical statistics with practical applications in genomics, molecular evolution, metabolomics, and proteomics. He has developed several influential software packages including LFDRenrich, LFDRhat, and LFDREmpiricalBayes that implement his methodological contributions. His publications reveal a consistent focus on improving statistical inference in high-dimensional biological data, with particular attention to proper calibration of statistical evidence and uncertainty quantification. His work helps researchers navigate the challenges of multiple testing that are ubiquitous in modern genomics. Dr. Bickel has authored three major books: Phylogenetic Trees and Molecular Evolution: A Hands-on Introduction with Uncertainty Quantification , Genomics Data Analysis: False Discovery Rates and Empirical Bayes Methods , and a book on Bayes-frequentist statistics that explores integrating these complementary approaches. His research has practical impact across biological sciences, providing methodological tools that help scientists extract reliable insights from complex data while properly accounting for statistical uncertainty. His software implementations make advanced statistical methods accessible to domain scientists who may not have deep statistical expertise. Dr. Bickel maintains an active research program as evidenced by recent publications and software updates, continuing to contribute to the evolving field of statistical genomics and bioinformatics.

• Statistical Network Analysis
• Non-parametric Statistics
• Causal Inference
• +7 more

Dr. Alexander Fuchs-Kreiß is a Junior Professor for Statistics at the Institute of Mathematics, Faculty of Mathematics and Computer Science at Leipzig University. He joined the university in April 2022 as a member of the Stochastics research group. He is also a scientific member of the International Max Planck Research School for Mathematics in the Sciences and affiliated with the HKMetrics Network. His research focuses on the statistical analysis of network data, particularly semi- and non-parametric methods for dynamic interaction networks. He has developed methodologies for relational event models using counting processes like Hawkes processes, and has contributed to causal inference, high-dimensional statistics, and quantile regression. His work often bridges multiple statistical areas, creating innovative approaches to complex data structures. His recent publications show a strong trend toward developing statistical methods for network data with applications in economics, epidemiology, and social sciences. His research combines theoretical developments with practical implementations, as evidenced by the R packages accompanying his publications. As an educator, he teaches Mathematical Statistics, Statistical Network Analysis, and Causal Inference at Leipzig University. He supervises PhD students working on network models and advises diploma theses in mathematics and business mathematics. Dr. Fuchs-Kreiß maintains active research collaborations with institutions including Heidelberg University, UC Davis, KU Leuven, and LSE. He co-organizes academic seminars and participates in international conferences on statistical learning and probability.

• Financial Accounting
• Sustainability Accounting
• Green Accounting
• +4 more

Alexios Kythreiotis serves as Associate Professor of Financial Accounting at the European University of Cyprus within the School of Business Administration and Department of Accounting, Economics and Finance. He has held continuous faculty positions since 2012, including a term as Department President (2017–2022). His academic credentials include: Doctoral Degree in Financial Accounting (2008–2012, Athens University of Economics and Business) on 'Qualitative characteristics in Accounting Disclosures' MBA (2005–2006, Cardiff University) B.Sc. Accounting and Finance (2001–2005, Athens University of Economics and Business) Diploma in E-learning Design (2013, UOC Barcelona) Professor Kythreiotis specializes in sustainability accounting and financial fraud detection , investigating how green policies like the European Green Deal impact corporate reporting, and developing frameworks for fraud prevention through cross-cultural analysis. His methodological expertise spans bibliometrics, meta-analysis, and risk modeling. Recent publications (2023–2025) reveal a pronounced shift toward EU sustainability policy impacts and green finance instruments , with 70% of recent work analyzing environmental disclosures, green bonds, and circular economy accounting. Concurrently, he maintains rigorous scholarship in core accounting theory through studies on financial statement value relevance and fraud detection. His recognition includes: State Scholarship for Doctoral Studies (State Scholarships Foundation of Greece, 2009–2012) As Principal Investigator and Academic Coordinator, he directs €1.2M+ in research funding including the EIT HEI Initiative project B-KINETIC (2025–2027) on entrepreneurial training and Erasmus+ projects advancing circular economy accounting. He also serves on the editorial committee of an academic journal for the European Investment Bank.