جست‌وجوی استاد و پژوهشگر

Armeen Taebمشاهده پروفایل

استادیار

• Selective Inference
• Multiple Testing
• Graphical Models
• +۷ مورد دیگر

Armeen Taeb is an Assistant Professor in the Department of Statistics at the University of Washington . Previously, he was a postdoctoral fellow at ETH Zürich under the ETH Foundations of Data Science, mentored by Peter Bühlmann. He earned his PhD in Electrical Engineering at Caltech under Venkat Chandrasekaran's supervision. Research Interests : His work bridges optimization and statistics , focusing on Graphical and latent-variable modeling Provably optimal causal model learning False positive error control in non-traditional settings Domain adaptation Applications in physical sciences Article Trends : His publications span causal inference (2022-2025), graphical models (2017-2025), convex optimization (2018-2025), and statistical robustness (2020). Recent work (2025) addresses extremal graphical modeling and selective inference challenges. Scientific Awards : ETH Zürich Foundations of Data Science Postdoctoral Fellowship (2019-2021) Caltech Resnick Institute Fellowship (2016-2018) W. P. Carey & Co. Prize for Applied Mathematics (2020) Caltech Graduate Fellowship (2013-2014) Grants : National Science Foundation DMS-2413074 (PI), University of Washington Royalty Research Fund (PI). Service : President of the Institute of Mathematical Statistics New Researcher Group; co-organized IMS New Researchers Conferences (2024-2025).

Lea Petrellaمشاهده پروفایل

استاد

• Quantile regression
• Graphical models
• Random Forest
• +۳ مورد دیگر

Lea Petrella is a Full Professor at the Department of Methods and Models for Economics, Territory, and Finance, Sapienza University of Rome. She teaches courses in Time Series Analysis and Advanced Statistical Methods , focusing on practical applications using R software. Research Interests: Quantile regression, Graphical models, Hidden Markov Models, Risk measures, and Time Series analysis Key Projects: Generalized Dynamic Graphical Models for pandemic impacts, Penalized quantile regression for risk assessment, Multivariate quantile regression frameworks Her recent publications include: 2025: Mid-quantile mixed graphical models for public shootings 2025: Spatial quantile random forests for economic mobility 2024: Expectile hidden Markov models for cryptocurrency returns 2024: Mixed-frequency quantile regressions for risk forecasting She supervises postdocs and PhD students including Maria Saiz, Beatrice Foroni, and Valentina Raponi. Her work spans financial risk modeling, environmental statistics, and biomedical applications. Email: Lea.Petrella@uniroma1.it or lea.petrella@uniroma1.it

Hans Julius Skaugمشاهده پروفایل

استاد

• Biostatistics
• Marine Ecology
• Computational Statistics
• +۴ مورد دیگر

Hans Julius Skaug is a Professor in the Department of Mathematics at the University of Bergen (UiB), Norway, with a distinguished career spanning several decades focused on statistical methodology and its applications to biological and ecological problems. Dr. Skaug's research expertise encompasses several interconnected domains: Biostatistics, particularly applications of statistics and probability to marine ecology Computational statistics, with pioneering work on Automatic Differentiation and Laplace approximation for complex model fitting Artificial Intelligence, where he has recently focused on variational autoencoders and diffusion models Development of innovative methods for line transect surveys and close-kin mark recapture (CKMR) His publication record shows a clear progression from foundational statistical methodology to increasingly sophisticated applications. The 2016 paper with Bravington and Anderson on 'Close-kin mark-recapture' in Statistical Science established him as a leader in population estimation methods. Recent publications (2023-2025) demonstrate continued innovation in refining CKMR techniques, applying TMB to diverse fields like insurance claims analysis, exploring sparse Bayesian learning, and addressing measurement errors in marine mammal surveys. His work consistently bridges theoretical statistical development with practical applications to real-world conservation and management problems. Dr. Skaug served as co-Editor in chief of the Scandinavian Journal of Statistics from 2018 to 2021, demonstrating his significant standing in the international statistical community. He teaches STAT110 Basic course in statistics at UiB during both spring and fall semesters and has conducted specialized workshops on CKMR and TMB at institutions including Dalhousie University in Halifax and the Institute of Marine Research in Bergen. He is actively involved in software development for statistical modeling through the TMB project (https://github.com/kaskr/adcomp) and ADMB (http://admb-project.org/), which implement his theoretical work on combining Automatic Differentiation with statistical modeling. His current research trajectory shows increasing integration of AI techniques with traditional statistical approaches, reflecting his observation that backpropagation in deep learning is fundamentally the same computational technique as Automatic Differentiation, which he has worked with for over 20 years.

Manolis Zampetakisمشاهده پروفایل

استادیار

• Theoretical Machine Learning
• Statistics
• Optimization
• +۴ مورد دیگر

Manolis Zampetakis is an Assistant Professor of Computer Science at Yale University. Previously, he was a postdoctoral researcher in the EECS Department at UC Berkeley working with Michael Jordan. He earned his Ph.D. and M.S. from the EECS Department at MIT, where he was advised by Constantinos Daskalakis. His undergraduate education was completed at the National Technical University of Athens (NTUA). His research interests span Theoretical Machine Learning, Statistics, Optimization, Computational Complexity, Game Theory, Mechanism Design, and Sublinear Algorithms. His work bridges theoretical computer science with practical applications in machine learning and economics. His research consistently appears in top-tier conferences such as STOC, FOCS, COLT, NeurIPS, and EC, with a strong focus on the theoretical foundations of machine learning and game theory. Zampetakis has received numerous prestigious awards including the ACM SIGEcom Doctoral Dissertation Award and the Google Ph.D. Fellowship. His publications from 2023-2025 demonstrate a consistent output of high-quality research across multiple domains, with a notable emphasis on bridging statistical theory with algorithmic approaches. His work shows increasing integration of robust statistics with mechanism design and game-theoretic approaches. ACM SIGEcom Doctoral Dissertation Award Google Ph.D. Fellowship Special Issue at SAGT 2015 Special Issue at WINE 2013 Second Prize in IMC 2012 Top 5 in Panhellenic Physics Competition 2008 Zampetakis actively advises multiple graduate students including Anay Mehrotra, Jane Lee, Vikram Kher, Katerina Mamali, Shuchen Li, and Nikolaos Koumpis. He has served on program committees and organized workshops including the Workshop on Algorithms for Learning and Economics (WALE 2019, WALE 2022). His research has been supported by grants from Google and likely other major funding agencies given his publication record in top venues. His research group focuses on theoretical aspects of machine learning with connections to statistics and game theory. He collaborates extensively with researchers across institutions including MIT, UC Berkeley, and Yale colleagues.

• Latent Variable Models
• Dynamic Latent Variable Models
• Latent Class Methods
• +۸ مورد دیگر

Augustin Kelava is a Professor at the Department of Quantitative Methods, Eberhard Karls University of Tübingen. He has held this position since 2018 and leads the Methods Center as Managing Director. Previously, he was Professor at the Hector Institute for Empirical Educational Research (2013-2018) and Junior Professor at Technical University of Darmstadt (2011-2013). PhD in Psychology (Goethe University Frankfurt, 2009) Diploma in Psychology (Goethe University Frankfurt, 2004) Kelava specializes in latent variable modeling, machine learning in social sciences, and educational research. His work spans dynamic latent class models, Bayesian regularization techniques, and prediction of human behavior using intensive longitudinal data. He contributes to psychometric theory (e.g., item response theory extensions) and applies these methods to diverse fields including sports science and emotion regulation. Editor of "Testtheorie und Fragebogenkonstruktion" (3rd ed., Springer, 2020) Key researcher in the Cluster of Excellence "Machine Learning in Science" Active in methodological conferences (FGME 2017, SEM 2019) Review activities for 20+ journals and foundations including Psychometrika, DFG, and SNSF His recent publications focus on integrating machine learning with psychometrics, addressing identifiability in complex models, and evaluating personality assessment validity for large language models. He collaborates with researchers across psychology, education, and computational fields.

Victor Hugo Lachosمشاهده پروفایل

استاد

• Mixed-effects models
• Censored regression models
• Time series analysis
• +۷ مورد دیگر

Dr. Victor Hugo Lachos is a Professor in the Department of Statistics at the University of Connecticut. His research focuses on advanced statistical methodologies for handling complex data structures, including censored regression models, mixed-effects models, and heavy-tailed distributions. He has contributed extensively to Bayesian inference, EM algorithms, and software development for statistical analysis. Multivariate Student-t and skew-normal distributions Longitudinal and spatial data modeling Regularization techniques for high-dimensional data Software packages for censored data analysis His recent publications emphasize robust modeling of censored and irregularly observed data, with applications in medical research (e.g., HIV longitudinal studies) and environmental modeling (e.g., acid rain analysis). His work integrates theoretical advances in distribution theory with practical computational tools in R packages like ‘StempCens’ and ‘mixsmsn’. These contributions are complemented by methodological innovations in EM algorithm applications, influence diagnostics, and semiparametric regression. Dr. Lachos' research has been applied to diverse fields such as medical data analysis, environmental science, and educational measurement. While no explicit awards or student advisement details are listed, his prolific output in top-tier journals and software development underscores his active academic engagement.

Müjdat Çetinمشاهده پروفایل

استاد

• Signal Processing
• Image Processing
• Data Science
• +۱۲ مورد دیگر

Müjdat Çetin is a Professor of Electrical and Computer Engineering and serves as the Robin and Tim Wentworth Director of the Goergen Institute for Data Science and Director of the New York State Center of Excellence in Data Science at the University of Rochester. He previously held faculty positions at Sabancı University and was a Research Scientist at MIT, with visiting roles at Boston University, Northeastern University, and MIT. Education: PhD in Electrical Engineering, Boston University, 2001 MS in Electrical Engineering, University of Salford, 1995 BS in Electrical Engineering, Boğaziçi University, 1993 His research lies at the intersection of signal processing, machine learning, and data science, with applications in biomedical imaging, radar, and brain-computer interfaces. He develops probabilistic and deep learning models for robust information extraction from noisy and complex data. His work emphasizes computational imaging, sparse representations, and multimodal data fusion. The recent publications reflect a strong trend toward integrating Bayesian methods and deep learning in imaging sciences, particularly in medical image reconstruction, neuroimaging analysis, and radar systems. His group actively explores transformer architectures, federated learning, and model-based deep learning for solving inverse problems in imaging. Scientific Awards and Honors: IEEE Fellow IEEE Signal Processing Society Best Paper Award IET Radar, Sonar and Navigation Premium Award Elsevier Signal Processing Best Paper Award Turkish Academy of Sciences Distinguished Young Scientist Award (GEBİP) ODTÜ Mustafa Parlar Foundation Research Incentive Award TÜBİTAK Career Award Boston University Best Engineering Research Award Professor Cetin has advised numerous PhD and Master’s students and led significant research grants in data science and imaging. He has served as a Senior Area Editor for IEEE Transactions on Image Processing and IEEE Transactions on Computational Imaging, and held editorial roles in several top journals. He has chaired major conferences including ICASSP, ICIP, and IVMSP workshops. He leads a multidisciplinary research group focused on data science and imaging, collaborating with neuroscientists and medical researchers. The team develops novel algorithms for brain-computer interfaces, medical image analysis, and remote sensing systems, often integrating machine learning with physical models of data acquisition.

Lukas Luftمشاهده پروفایل

پژوهشگر

• Robot Localization and Mapping
• Causality and Foundations of Quantum Physics
• Probabilistic Methods
• +۳ مورد دیگر

Lukas Luft is a postdoctoral researcher at the Autonomous Intelligent Systems group within the Department of Computer Science at the University of Freiburg. His work spans robotics and quantum physics, focusing on probabilistic methods for robot localization, multi-robot systems, and causal inference. Post Doc (2020–present) PhD in Computer Science, University of Freiburg (2020) Master and Bachelor in Physics, RWTH Aachen and University of Freiburg His research in Robot Localization and Mapping includes advanced probabilistic techniques like Bayes filters, decentralized algorithms for multi-robot systems, and change detection in environments using full posterior distributions. He also explores Causality and Foundations of Quantum Physics , applying entropic inequalities and information theory to causal discovery and non-locality. The articles highlight his contributions to robotics, particularly in sensor modeling for Lidar, simultaneous localization and mapping (SLAM), and efficient probabilistic methods. In quantum physics, his work addresses causal structures and entropic information, bridging AI with foundational physics. Luft has collaborated with leading researchers, including Prof. Wolfram Burgard and Bernhard Schölkopf, and contributed to key conferences like Robotics: Science and Systems (RSS) and IEEE IROS.

Michael Mumaمشاهده پروفایل

استاد

• Robust Data Science
• Signal Processing
• Machine Learning
• +۵ مورد دیگر

Michael Muma is a Professor in the Department of Electrical Engineering and Information Technology at Technische Universität Darmstadt. His research focuses on robust data science theory and methods applied to signal processing and machine learning in biomedicine and engineering. He leads the ERC Starting Grant ScReeningData project, developing methods for reproducible information discovery in biomedical databases, and is a Principal Investigator in the LOEWE center emergenCITY and BMBF cluster curATime. Prior roles include Independent Junior Research Group Leader (Athene Young Investigator) and Lecturer at TU Darmstadt from 2017 to 2022, and Research Associate (Post-Doc since 2014) from 2009 to 2017. His research interests span robust statistical methods, high-dimensional data analysis, emergency response systems, and biomedical signal processing. Notable projects include FDR-controlled portfolio optimization, ECG delineation algorithms, and radar-based vital sign estimation. Muma has contributed to distributed sensor networks, robust clustering, and sparse regression techniques. His work addresses challenges in multi-source detection, financial data analysis, and genomics through interdisciplinary approaches combining signal processing, machine learning, and robust statistics. Recent publications emphasize scalable solutions for high-dimensional problems, including applications in robotics, cardiology, and financial index tracking.

Pengcheng Liuمشاهده پروفایل

مدرس ارشد

• Robotics
• Bio-inspired Robotics
• Soft Robotics
• +۱۴ مورد دیگر

Pengcheng Liu is an Associate Professor (Senior Lecturer) in Robotics and Applied Control at the Department of Computer Science, University of York, UK. He has previously held academic positions at Cardiff Metropolitan University, University of Lincoln, Bournemouth University, and visiting roles in China. He is a member of IEEE and IFAC, and serves on several IEEE Technical Committees related to robotics and automation. PhD in Robotics and Control MSc in Control Theory and Control Engineering BEng in Measurement Control and Instrumentation His research focuses on the convergence of robotics and biology, particularly in bio-inspired design, soft robotics, human-robot collaboration, rehabilitation, and embodied AI. He explores how biological systems can inform adaptive, resilient, and energy-efficient robotic systems. His work spans modeling animal/human motion, sensory-motor learning, morphological design, and control optimization. The 15 most recent publications reflect a strong interdisciplinary trend combining robotics, control theory, machine learning, and biomedical applications. Key themes include soft robotics, bio-inspired actuators, motion planning, rehabilitation systems, and wireless communication for robotic systems. The research integrates simulation, hardware prototyping, and intelligent control, often using ROS and Gazebo. Global Peer Review Awards — Top 1% in Cross-Field (2019) Global Peer Review Awards — Top 1% in Engineering (2019) Outstanding Associate Editor of 2020, IEEE Access Outstanding Contribution Awards, Elsevier (2017) Dr Liu has extensive experience in securing and managing research grants from EPSRC, Innovate UK, Horizon 2020, Erasmus Mundus, FP7-PEOPLE, HEIF, NHS I4I, and NSFC. He serves as a regular reviewer for around 30 journals and 8 flagship conferences in robotics, AI, and control. He is an Associate Editor for IEEE Access and Academic Editor for PeerJ Computer Science, and has contributed to special issues on computational learning for robotics. He leads research in bio-inspired robotics and advises PhD students in areas such as lifelong learning machine intelligence, soft robotics, motion planning, and cyber-physical systems. He is actively recruiting motivated PhD candidates with backgrounds in engineering, computer science, or applied mathematics.

Annette Kopp-Schneiderمشاهده پروفایل

استاد

• Medical Biometry
• Biostatistics
• Clinical Trials
• +۷ مورد دیگر

Prof. Dr. Annette Kopp-Schneider is Head of Department at the German Cancer Research Center (DKFZ) and affiliated with the Faculty of Medicine at the University of Heidelberg. She holds a professorial rank and leads research in medical biometry with a focus on clinical trial methodology in precision oncology. Diploma in Mathematics, RWTH Aachen, 1984 Dr. rer. nat. in Mathematics, RWTH Aachen, 1987 Habilitation in Medical Biometry, University of Heidelberg, 1997 Her research interests include clinical trials for precision oncology , dose-response modeling , and biologically based mechanistic modeling of cell systems . She develops advanced statistical methods for trial design, particularly in pediatric oncology and settings with small sample sizes. Her work emphasizes rigorous control of type I error when borrowing historical data and optimal experimental designs for drug combinations. The recent publications reflect a strong trend in methodological biostatistics , particularly in Bayesian and frequentist approaches to clinical trial design , external data incorporation , and transparent evaluation in biomedical image analysis . Her contributions span oncology, toxicology, and translational medicine, with a consistent focus on statistical rigor and reproducibility. She has contributed to major initiatives such as the Pediatric Precision Oncology INFORM Registry, advancing personalized treatment for children with cancer. Her methodological work supports fair comparisons in hybrid and single-arm trials and improves the reliability of challenge-based research in medical imaging. Prof. Kopp-Schneider is actively engaged in grant-funded research and mentoring within her department. She leads a team focused on biostatistical innovation in clinical research, particularly in oncology settings where data are sparse and traditional designs face limitations. She is involved in collaborative teams working on biomedical image analysis challenges and pediatric cancer registries. Her lab contributes to open science through toolkits for challenge analysis and transparent reporting guidelines like BIAS (Transparent Reporting of Biomedical Image Analysis Challenges).

Shuchin Aeronمشاهده پروفایل

دانشیار

• Data Science
• Statistical Signal Processing
• Inverse Problems
• +۸ مورد دیگر

Shuchin Aeron is an Associate Professor in the Department of Electrical and Computer Engineering at Tufts School of Engineering, with joint appointments in the Departments of Computer Science and Mathematics. He holds a Ph.D. from Boston University (2009) and completed postdoctoral research at Schlumberger Doll Research, focusing on borehole acoustic signal processing. His research spans statistical signal processing, machine learning, compressed sensing, and information theory, with applications in geophysics, bioengineering, and imaging. Aeron has authored over 175 publications and holds patents in acoustic signal processing. He received the NSF CAREER Award (2016) and is a Senior Member of the IEEE. Educations: Ph.D., Electrical Engineering, Boston University, 2009 M.S., Electrical Engineering, Boston University, 2004 B.Tech., Indian Institute of Technology, 2002 Research Interests: Statistical signal processing (SSP), inverse problems, compressed sensing, information theory, convex optimization Machine learning applications in geophysical signal processing, imaging, and bioengineering His work emphasizes optimal sampling and recovery of multidimensional signals, with contributions to compressed sensing architectures and generative models for particle physics experiments. He leads NSF-funded projects on data science and domain generalization, and collaborates with industry partners like Schlumberger and Mitsubishi Electric Research Labs. Awards: NSF CAREER Award (2016) Mitsubishi Electric Research Lab Research Gift (2015) Grants and Funding: NSF HDR TRIPODS (2019–2023) AFOSR: Enabling Trusted Human-Like Artificial Teammates (2018–2023) NSF: Optimal Sampling and Recovery for Multilinear Signals (2013–2016) Aeron teaches advanced courses in probabilistic systems analysis, information theory, and machine learning. He directs the Tufts Data Science undergraduate and graduate programs, and serves on editorial boards of journals including Frontiers in Signal Processing and IEEE Transactions on Geoscience and Remote Sensing .

Yuheng Buمشاهده پروفایل

استادیار

• Machine Learning
• Information Theory
• Signal Processing
• +۴ مورد دیگر

Yuheng Bu is an Assistant Professor in the Department of Computer Science at the University of California, Santa Barbara (UCSB). Previously, he held positions at the University of Florida and MIT. He earned his Ph.D. from the University of Illinois at Urbana-Champaign (2019) and B.S. from Tsinghua University (2014). His research focuses on foundational machine learning, integrating tools from information theory and signal processing to address challenges in fair learning, uncertainty quantification, and watermarking generative AI. Notable contributions include work on generalization error analysis, adaptive watermarking techniques, and fairness-aware algorithms. Affiliations: UCSB (current), University of Florida (2020-2023), MIT (Postdoc, 2019-2020) Education: Ph.D. Electrical & Computer Engineering, UIUC (2019); B.S. Electronic Engineering, Tsinghua University (2014) Research Interests: Machine Learning Theory, AI Ethics, Information-Theoretic Bounds, Signal Processing, and Applications in Generative AI Security. His work emphasizes rigorous theoretical foundations for trustworthy algorithms with guarantees on generalization, fairness, and robustness. Key Achievements: Developed adaptive watermarking schemes for LLMs, derived exact generalization error characterizations for Gibbs algorithms, and advanced understanding of fairness overfitting. His work has been recognized with awards including the Abraham Wald Prize (2023) and the MLSP Best Student Paper (2022). Grants & Collaborations: Collaborations with institutions like MIT, Turing Institute, and INRIA. Research supported by NSF and industry partnerships. Labs/Teams: Leads the Information-Theoretic Learning Group at UCSB, fostering interdisciplinary research on AI foundations.

• Machine Learning
• Computer Vision
• Medical Image Analysis
• +۶ مورد دیگر

Matthew Blaschko is a Senior Lecturer BOF at KU Leuven, affiliated with the Department of Electrical Engineering (ESAT) within the Faculty of Engineering Sciences. He directs the KU Leuven ELLIS unit and serves as a fellow in the ELLIS Health program, part of the European Laboratory for Learning and Intelligent Systems. As a Core PI in the Flanders AI Research Program, he leads work packages for Decision Support Systems and Medical Imaging. He is also a member of the KU Leuven Institute for Artificial Intelligence and co-leads the working group on Machine Learning and Data Science. Habilitation (HDR) from École Normale Supérieure de Cachan Newton International Fellow at University of Oxford Dr. rer. nat. from Max Planck Institutes Tübingen (awarded by Technische Universität Berlin) M.S. from University of Massachusetts Amherst B.S. from Columbia University Blaschko's research focuses on machine learning, computer vision, and medical image analysis, with particular expertise in uncertainty quantification in deep neural networks and trustworthy AI for healthcare applications. His work bridges theoretical foundations with practical implementations, developing methods for calibration, uncertainty estimation, and efficient model deployment. He has made significant contributions to neural network architectures, loss functions, and evaluation metrics for medical imaging tasks, with applications spanning Alzheimer's disease research, surgical phase recognition, and ophthalmic image analysis. His recent publications reveal a strong emphasis on calibration methods, uncertainty quantification, and medical applications of AI. The research spans diverse areas including Alzheimer's disease analysis, Bayesian optimization, novel view synthesis, knowledge extraction from text, and surgical phase recognition. Many papers focus on improving model reliability and safety for healthcare applications, reflecting his commitment to developing trustworthy AI systems that can be deployed in clinical settings. Best Student Paper Award, ECCV 2008 Université Paris-Saclay STIC Doctoral School Best Scientific Contribution Award Best paper award, CVPR 2008 Best paper award, Benelearn 2014 Blaschko actively mentors numerous PhD and MSc students, with current advisees working on topics ranging from uncertainty in deep neural networks to medical image analysis and AI for healthcare. He leads multiple significant research projects including 'onzekerheid in diepe neurale netwerken' (2025-2029), 'Trustworthy AI for Medical Image Analysis and Computer Vision' (2025), and 'Van metingen naar biomarkers in medische beeldanalyse' (2024-2028). His research is supported by the Flanders AI Research Program and other substantial funding sources. As director of the KU Leuven ELLIS unit and a key member of the KU Leuven Institute for Artificial Intelligence, Blaschko leads a vibrant research team focused on machine learning and data science. His laboratory develops cutting-edge AI technologies with practical applications, particularly in healthcare. Technology from his research has been incorporated into MONA, software for ophthalmic image analysis, demonstrating the real-world impact of his work.

Min Xuمشاهده پروفایل

استادیار

• Machine Learning
• High-dimensional Statistics
• Network Data Analysis
• +۲ مورد دیگر

Min Xu is an Assistant Professor in the Department of Statistics at Rutgers University – New Brunswick. He is affiliated with the School of Arts and Sciences and focuses his research on theoretical and methodological aspects of machine learning and high-dimensional statistics, with applications in network analysis and nonparametric estimation. Education: Ph.D. in Machine Learning, Carnegie Mellon University (2015) B.S. in Electrical Engineering and Computer Science (with minor in Mathematics), UC Berkeley Research Interests: Min Xu’s research lies at the intersection of machine learning , high-dimensional statistics , and network science . He develops computationally scalable methods with strong theoretical guarantees for complex data structures, particularly in nonparametric estimation , network analysis , and large-scale inference . His work addresses fundamental challenges in estimating high-dimensional distributions and understanding the structure of evolving networks, with applications in economics and social sciences. Grants & Funding: NSF Grant DMS-2113671 NSF Grant DMS-2311299 Research Trends: Across his publications, a consistent theme is the development of statistically rigorous methods for high-dimensional and network data. His work spans optimal estimation in stochastic block models, convex M-estimation, and inference on dynamic network structures, with a strong emphasis on theoretical guarantees and practical scalability. Affiliations: Previously, Min Xu served as a departmental postdoctoral researcher in the Statistics Department at the Wharton School, University of Pennsylvania. He is currently based at Hill Center, Rutgers University.