جست‌وجوی استاد و پژوهشگر

• Multiple Testing Procedures
• Conformal Prediction
• False Discovery Rate
• +۴ مورد دیگر

Wenge Guo is an Associate Professor in the Department of Mathematical Sciences at New Jersey Institute of Technology (NJIT). His research focuses on statistical methodology, particularly in multiple testing procedures, conformal prediction, false discovery rate control, and ordinal classification. He has contributed to advancements in methodologies for clinical trials, machine learning applications, and algorithmic statistical inference. His research interests span areas such as familywise error rate control, distribution-free prediction methods, and adaptive testing strategies. Recent work emphasizes conformal prediction frameworks for ordinal and multi-label classification, as well as two-stage risk control in ranked retrieval systems. Guo’s expertise also extends to applications in biomedical research and information retrieval systems. Key Research Areas: Multiple Testing, Conformal Prediction, Ordinal Classification, Clinical Trials, Statistical Methodology Key Contributions: Development of the MHTdiscrete package, hierarchical testing procedures for non-inferiority trials, and adaptive FWER/FDR controls under block dependence. Notable projects include a National Science Foundation-funded study on large-scale multiple testing problems (2013–2016). His work bridges theoretical statistics with practical applications in machine learning and biomedical research.

• Statistics
• Time Series Analysis
• Biostatistics
• +۵ مورد دیگر

Dr. Jean-Pierre Stockis is affiliated with the Department of Statistics within the College of Mathematics at Rheinland-Pfälzische Technische Universität Kaiserslautern. His work focuses on statistical methodologies applied to interdisciplinary domains including biology, chemistry, medicine, economics, and civil engineering. Research Interests: Applications of statistics, time series analysis, biostatistics, chaos theory, and computational statistical modeling. His publications highlight collaborations in structural engineering, organic synthesis, and nutritional biochemistry. Teaching: He contributes to courses such as GM3 Mathematik/Biostatistik, Financial Statistics, and Statistik II für Wirtschaftswissenschaftler, accessible via university platforms like KIS and OpenOLAT.

Yuliang Xiuمشاهده پروفایل

استادیار

• Computer Vision
• Computer Graphics
• 3D Human Digitization
• +۷ مورد دیگر

Yuliang Xiu is a tenure-track Assistant Professor at Westlake University's AI department, leading the 远兮实验室 (endless.do) as PI. His research focuses on democratizing human-centric digitization through advancements in computer vision, graphics, and machine learning. Previously, he completed his Ph.D. at Max Planck Institute for Intelligent Systems under Prof. Michael J. Black, funded by the CLIPE Marie Sklodowska-Curie fellowship. His work bridges vision and graphics to achieve scalable, photorealistic 3D human digitization. Education: Ph.D., Max Planck Institute for Intelligent Systems (2025, advised by Michael J. Black & Dimitrios Tzionas) M.Sc., Shanghai Jiao Tong University (2019, advised by Cewu Lu) B.Eng., Shandong University (2016, advised by Lu Wang) Research Interests: His work emphasizes human-centric digitization , including 3D clothed human reconstruction, generative AI for garments, and training-free methods. His lab explores foundational models for scalable avatar creation, aiming to make human digitization accessible to all. Recent projects include Easi3R (dynamic motion estimation), ETCH (clothed body fitting), and ECON (explicit-implicit hybrid modeling). He advocates for generalizable, photorealistic systems that align with real-world constraints. Publications Trends: Over 15+ peer-reviewed papers, with 2025 highlights including ICCV and SIGGRAPH contributions. His work is characterized by innovations in: 3D reconstruction from single images/videos Implicit/explicit representation hybrids LLM-driven garment editing Efficient finetuning via butterfly factorization Awards & Recognition: 2025 China3DV Rising Star Award Best in Show (SIGGRAPH RTL 2020) CVPR Highlight 2023 Organized ECCV 2024 workshop on Foundation Models for 3D Humans Advising & Mentorship: Successfully mentored 3 master students into top PhD programs (TUM, MBZUAI, HKU). Actively hiring postdocs, PhDs, and researchers for lab expansion. Labs & Teams: Leads the 远兮实验室 (endless.do), focusing on democratizing human digitization through open-source tools and foundational research. Key contributions include the ECON, TADA, and TeCH frameworks.

• Computer Science
• Distributed Systems
• Blockchain Technology
• +۴ مورد دیگر

Pezhman Nasirifard is a researcher at the Technical University of Munich, specializing in distributed systems and blockchain technology. He earned his PhD from the same institution in 2023, focusing on reducing coordination in permissioned blockchains using Conflict-free Replicated Data Types (CRDTs). His work bridges computer science and energy systems, addressing challenges in blockchain scalability, distributed ledgers, and grid inference through crowdsourced data. Key contributions include the OrderlessChain framework, which eliminates global transaction ordering in blockchains, and the FabricCRDT approach for permissioned blockchain systems. He has also explored energy grid modeling using drone imagery and crowdsourcing, as well as serverless computing architectures. His publications span venues like Middleware, e-Energy, and IEEE Transactions, highlighting interdisciplinary research in distributed systems, blockchain applications, and energy infrastructure. Collaborations with co-authors like Hans-Arno Jacobsen and Ruben Mayer emphasize his engagement with cutting-edge technologies and distributed computing paradigms.

Christopher Quinnمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Data Science
• +۳ مورد دیگر

Christopher Quinn is an Assistant Professor at Iowa State University, specializing in Artificial Intelligence, Machine Learning, and Data Science. He holds a B.S. in Engineering Physics from Cornell University (2008), an M.S. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign (2010), and a Ph.D. in Electrical and Computer Engineering from UIUC (2014). His research focuses on optimization algorithms, neural networks, and information theory, with applications in neuroscience and distributed systems. He has contributed to advancements in submodular optimization, bandit algorithms, and federated learning. Quinn’s recent work explores topics such as combinatorial bandits, stochastic processes, and causal inference in neural data analysis. His publications highlight expertise in machine learning theory, including studies on gradient methods for online optimization, redundancy analysis in code-trained models, and unsupervised change-point detection in time series. He has also investigated neural mechanisms underlying visual experience, such as theta oscillation synchronization in the visual cortex of Fmr1 KO mice. Quinn’s work often bridges theoretical foundations with practical applications, such as federated learning frameworks and robust directed tree approximations of stochastic networks. Christopher Quinn’s research has been supported by grants including the NSF CIF: Small grant for sequential decision-making under uncertainty (2022). He actively publishes in top-tier venues and collaborates on interdisciplinary projects at the intersection of AI and neuroscience. His address is 113 Atanasoff Hall, Ames, Iowa 50010, and he can be reached at cjquinn@iastate.edu.

Hannes Freyمشاهده پروفایل

استاد

• Wireless Networked Systems
• Mobile Robotics
• Sensor Networks
• +۲ مورد دیگر

Hannes Frey is Professor and Head of the Computer Networks Research Group at the University of Koblenz and Landau. His research focuses on the controllability of complex dynamically networked systems including sensor networks, mobile autonomous robot teams, and connected vehicles. The group investigates fundamental theoretical questions and system-level practical issues, with emphasis on transferring theoretical results into practice through prototype implementations. Research interests include: Connectivity and percolation in wireless networked systems Local topology control under graph structure assumptions Cooperative distributed control for wirelessly networked mobile robots Reliable ultra-low latency communication for autonomous platforms Software Defined Networking for IoT systems The group maintains the UniKoRN laboratory for practical research in wireless communication and mobile distributed robotics, supporting research internships and student projects.

Ryan Tibshiraniمشاهده پروفایل

استاد

• High-dimensional statistics
• Nonparametric Estimation
• Distribution-free Inference
• +۵ مورد دیگر

Ryan Tibshirani is a Professor jointly appointed in the Departments of Statistics and Machine Learning at Carnegie Mellon University (CMU). He is also a Principal Investigator in the Delphi group at CMU and an Amazon Scholar in AWS AI Labs under Alex Smola's group. Prior to joining CMU in 2011, he earned his B.S. in Mathematics (2003-2007) and Ph.D. in Statistics (2007-2011) from Stanford University, advised by Jonathan Taylor. Education: B.S. in Mathematics, Stanford University (2003-2007) Ph.D. in Statistics, Stanford University (2007-2011), advised by Jonathan Taylor Research Interests: Ryan's work bridges theoretical and applied statistics, with a focus on high-dimensional statistics, nonparametric methods, distribution-free inference, convex optimization, and numerical algorithms. His applied research emphasizes epidemiological tracking and forecasting. He also explores machine learning and computational statistics. Advising & Collaborations: Current advisees include Addison Hu and Pratik Patil (Ph.D. candidates in Statistics and Machine Learning). Past advisees have transitioned to roles at institutions like Stanford, Cambridge, Argo AI, and Google. Collaborations span academic institutions and industry (e.g., AWS AI Labs). Labs & Groups: Ryan leads research in the Delphi group (epidemiological modeling) and contributes to AWS AI Labs. His work integrates interdisciplinary teams across CMU and external partners.

Bikramjit Banerjeeمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Distributed Artificial Intelligence
• +۳ مورد دیگر

Dr. Bikramjit Banerjee is a Professor of Computer Science at the School of Computing Sciences & Computer Engineering, University of Southern Mississippi. He holds a PhD from Tulane University (2006) and an MS from the University of Tulsa (2000). His research focuses on distributed artificial intelligence, multi-agent systems, and reinforcement learning applications in robotics and autonomous systems. He has served on editorial boards of journals like Autonomous Agents and Multi-Agent Systems (2014–2023) and organized workshops such as the 2006 ICML Workshop on Structural Knowledge Transfer. His professional affiliations include the Association for the Advancement of Artificial Intelligence (AAAI). Education Background: PhD in Computer Science, Tulane University, 2006 MS in Computer Science, University of Tulsa, 2000 Bachelor’s in Computer Science & Engineering, Jadavpur University (India), 1998 Research Interests: Dr. Banerjee’s work centers on advancing reinforcement learning techniques for multi-agent systems, with applications in swarm robotics, multi-robot coordination, and decision-making under uncertainty. His recent publications emphasize theoretical bounds in reinforcement learning (e.g., PALO bounds) and practical challenges like occlusion in inverse reinforcement learning (I2RL framework). These studies bridge foundational AI theory with real-world robotic deployments. Academic Contributions: He has contributed to IEEE Intelligent Systems as a guest editor (2017) and served on NSF panels. His teaching spans courses like Artificial Intelligence, Robotics, and Advanced Algorithms. Though no explicit grants are listed, his NSF panel roles suggest involvement in funding evaluation. No student advisees are listed in the provided texts. Labs/Teams: His research aligns with the university’s robotics and AI initiatives, though specific lab names are not mentioned in the data.

Il-Min Kimمشاهده پروفایل

استاد

• Wireless Artificial Intelligence
• Machine Learning
• Deep Learning
• +۹ مورد دیگر

Professor Il-Min Kim is a Full Professor in the Department of Electrical and Computer Engineering at Queen's University (Smith Engineering). He leads the Wireless Artificial Intelligence Laboratory (WAI Lab), focusing on AI-driven wireless systems, IoT/IoE/IIoT, federated learning, edge computing, and 6G/V2X communications. His work integrates machine learning, signal processing, and cybersecurity with wireless infrastructure. Education: B.S. (Yonsei University, 1996), M.S./Ph.D. (KAIST, 2001). Postdoctoral research at MIT (2001-2002) and Harvard (2002-2003) preceded his faculty role at Queen's since 2003. Promoted to Associate Professor (2009) and Full Professor (2014). Research emphasizes Wireless AI , including on-device AI, federated learning, geoscience AI (Geo-AI), and secure communication protocols. Key areas: AI for 6G/V2X, energy-efficient edge computing, and robust spectrum sensing. His lab develops frameworks for distributed systems, compressive sensing, and nonlinear energy harvesting. Notable contributions include hybrid replay mechanisms for incremental learning, diffusion models for industrial IoT, and deep learning-based channel estimation. His work addresses challenges in data security, model robustness, and real-time inference under resource constraints.

Chinmoy Bhattacharjeeمشاهده پروفایل

استاد

• Stochastic Geometry
• Distributional Approximations
• Stein's Method
• +۴ مورد دیگر

Prof. Chinmoy Bhattacharjee is a Professor of Mathematical Stochastics at the University of Hamburg's Department of Mathematics, part of the Faculty of Mathematics, Computer Science and Natural Sciences. His research focuses on stochastic geometry, distributional approximations via Stein's method, stochastic analysis, random graphs, continuum percolation, and nonparametric inference. He holds a PhD from the University of Southern California and previously held postdoctoral positions at the University of Luxembourg and University of Bern. His work is supported by the German Research Foundation (DFG) through Project No. 531540467. Education: PhD in Applied Mathematics, University of Southern California (2013–2018) M.Stat. and B.Stat. (Hons.), Indian Statistical Institute, Kolkata (2008–2013) Teaching: Recent courses include Stochastic Processes, Probability and Statistics, and seminars on percolation theory and probabilistic methods. Research Highlights: Prof. Bhattacharjee's work bridges stochastic geometry and Stein's method, with applications in spatial random structures, non-Gaussian limit theory, and noise sensitivity in percolation models. Key contributions include Gaussian and Dickman approximation techniques, spectral analysis of Poisson functionals, and statistical applications in random forests. Upcoming Events: Co-organizing session on Spatial Stochastics at German Probability and Statistics Days (2025) Invited talks at Aarhus University and workshops in Germany/Croatia (2025)

Yasser Roudiمشاهده پروفایل

استاد

• Theoretical Neuroscience
• Neural Networks
• Information Processing
• +۳ مورد دیگر

Professor Yasser Roudi holds the position of Professor of Disordered Systems at King’s College London’s Department of Mathematics, within the Faculty of Natural, Mathematical & Engineering Sciences. He earned his PhD from SISSA (International School for Advanced Study) in 2005 and a physics degree from Sharif University of Technology in Tehran. Prior to joining King’s, he worked at prestigious institutions like the Kavli Institute for Systems Neuroscience in Norway and NORDITA. His research focuses on theoretical neuroscience, neural networks, and the physics of disordered systems, employing mathematical tools to model information processing in brains and machines. He has been recognized with awards including the Eric Kandel Young Neuroscientist Prize (2015) and membership in the Royal Norwegian Society of Sciences and Letters. His work explores topics such as grid cells’ toroidal topology, restricted Boltzmann machines, and maximum entropy models for cortical populations. Recent publications highlight advancements in understanding neural oscillations, stochastic processes, and reinforcement learning algorithms. Roudi leads the Disordered Systems research group at King’s, contributing to the study of complex systems and statistical mechanics. Education: PhD, SISSA (2005); BSc, Sharif University of Technology (Physics). Affiliations: King’s College London, Kavli Institute for Systems Neuroscience, NORDITA, UCL, and the Institute for Advanced Study. Roudi’s research bridges theoretical physics and neuroscience, addressing fundamental questions about neural information processing and system dynamics. His awards underscore his impactful contributions to the field, while his interdisciplinary approach continues to push boundaries in understanding complex biological and artificial systems.

Fabrizio Leisenمشاهده پروفایل

استاد

• Bayesian Inference
• Bayesian Nonparametric Methods
• Objective Bayesian Analysis
• +۳ مورد دیگر

Fabrizio Leisen is a Professor of Statistics at King’s College London, Department of Mathematics, within the Faculty of Natural, Mathematical & Engineering Sciences. Previously, he held positions at the University of Nottingham (Professor), University of Kent (Reader), and institutions in Spain and Italy. He earned a PhD in Mathematics from the Università di Modena e Reggio Emilia, specializing in Probability. His research focuses on Bayesian inference, nonparametric methods, objective Bayesian analysis, and foundational statistical theory, with notable contributions to predictive constructions, knockoff procedures, and stochastic processes. He serves as an Associate Editor for Bayesian Analysis , Statistics and Probability Letters , and Statistical Methods and Applications . Leisen’s work bridges theoretical and applied statistics, emphasizing Bayesian nonparametric priors for complex data structures and model selection. His recent articles address survival analysis, prior elicitation without subjective inputs, and algorithmic advancements in Bayesian computation. He collaborates globally, including co-supervising a PhD student at the Università di Bologna. His teaching includes Computational Statistics and Probability and Statistics Skills sessions. Research interests span Bayesian foundations, model selection via knockoffs, stochastic processes, and interdisciplinary applications like genomic and biostatistical analysis. His lab is affiliated with King’s Statistics group, focusing on MCMC methods, Bayesian nonparametrics, and experimental design.

• Biostatistics
• Clinical Trials
• Pediatric Oncology
• +۴ مورد دیگر

Willem H Collier is a Clinical Assistant Professor in the Department of Population and Public Health Sciences at the University of Southern California. His work focuses on the design, conduct, and analysis of pediatric cancer clinical trials through the Children’s Oncology Group, with an emphasis on supportive care, survivorship, and cellular therapy. Additionally, he specializes in statistical methods for surrogate endpoint evaluation, particularly Bayesian hierarchical modeling for meta-regression, in collaboration with the Chronic Kidney Disease Epidemiology Collaboration. Department: Population and Public Health Sciences Specialties: Biostatistics, Pediatric Oncology, Surrogate Endpoint Analysis Affiliations: University of Southern California, Children’s Oncology Group, Chronic Kidney Disease Epidemiology Collaboration Research Interests include developing advanced biostatistical techniques for surrogate endpoints in heterogeneous clinical trials. His work bridges methodological innovation with real-world applications in pediatric oncology and chronic kidney disease, focusing on improving trial efficiency and patient outcomes. He also investigates healthcare utilization patterns in nonelderly adults and postsurgical complications in plastic surgery contexts. Scientific Awards and honors are not explicitly mentioned in the provided text. Advising details are unavailable, but his collaborations with major clinical research groups indicate significant mentorship and team-based contributions to trial design and analysis.

Federico Maggioمشاهده پروفایل

پژوهشگر ارشد

• Political Economy
• Development Economics
• Labor Economics

Federico Maggio is a Postdoctoral Researcher at the Department of Economics, University of Bologna, with a Ph.D. in Economics from the Free University of Bozen (2024). His work bridges political institutions, migration dynamics, and development economics. Education: Ph.D. in Economics (Free University of Bozen, 2024) Research focuses on Political Economy , Development Economics , and Labor Economics , particularly examining migration patterns, institutional legitimacy, and violence-driven displacement. Recent publications analyze self-selection among Venezuelan migrants and the causal effects of police violence on migration. His work leverages microeconomic data, causal inference methods, and institutional frameworks. Scientific awards: None explicitly mentioned. Collaborations include D. Rohner, A. Saia, C. Caporali, F. Campo, G. Barone, M. Cervellati, T. Nannicini, M. G. Onorato, and P. Schilling. Media coverage by Euronews, Outreach, Dirilispostasi, and ILKE TV.

• Robust Methods
• Statistical Genetics
• Multivariate Analysis
• +۷ مورد دیگر

Asuman Turkmen is a Professor of Statistics at The Ohio State University, affiliated with the Translational Data Analytics (TDA) initiative and based at the Newark Campus. She holds a PhD from Auburn University (2008) and specializes in robust statistical methods for multivariate analysis and statistical genetics. Her research focuses on outlier detection, rare variant associations in complex diseases, and addressing missing heritability through approaches like structural variations, epigenetics, and gene-environment interactions. Her professional service includes roles such as Newark Campus Ombudsman (2022–present), judge for student research forums, and leadership in university committees (e.g., JEDI Committee, Undergraduate Committee). She co-directs the TDAI Data Science for Women Summer Camp and mentors in doctoral programs. Notably, she received the Ohio State University Newark Scholarly Achievement Award in 2015. Dr. Turkmen’s recent work emphasizes Bayesian methods for population stratification correction, X-linked variant effects, and robust statistical techniques. Her publications span Genetic Epidemiology , Annals of Human Genetics , and other top journals, reflecting her contributions to both theoretical and applied statistical genetics.