جست‌وجوی استاد و پژوهشگر

• Behavioral Economics
• Experimental Economics
• Household Finance
• +۷ مورد دیگر

Sebastian Schneider is a Max Planck Research Group Leader (non-tenured) at the Max Planck Institute for Research on Collective Goods , where he heads the independent research group on Behavioral Economics . He combines experimental methods with data science to study decision-making, risk preferences, and behavioral interventions in diverse contexts. PhD in Economics from Georg-August-University Goettingen (2017) MSc and BSc in Mathematics in Business and Economics from University of Mannheim (2014 and 2011) Research Interests: Behavioral economics, experimental economics, household finance, health economics, development economics, and environmental economics. He develops causal frameworks for behavioral research and created the MinMSE method for treatment assignment, available as Stata and R packages . Publication Trends: Recent work focuses on default options and information avoidance , status quo bias in decision-making, and behavioral responses to health and development interventions , including a phone-based information campaign during the pandemic in Bangladesh. His experimental contributions span risk preferences , saving behavior , and incentive structures . Software Development: Created widely used tools for treatment assignment: Stata minmse package R minMSE package

• Cystic Fibrosis
• Paediatric Respiratory Medicine
• Clinical Trials
• +۴ مورد دیگر

Dr. Gwyneth Davies is a Clinical Associate Professor and UKRI Future Leaders Fellow at University College London's Great Ormond Street Institute of Child Health, where she serves as Deputy Head of the Population, Policy and Practice Research and Teaching Department. She is also an Honorary Consultant in Paediatric Respiratory Medicine at Great Ormond Street Hospital for Children NHS Foundation Trust, combining clinical practice with cutting-edge research in cystic fibrosis and healthcare data systems. Dr. Davies' educational background includes: PhD from Imperial College London (2013) MSc(Res) from University of Glasgow MbChB from University of Glasgow BSc(Med Sci) from University of Glasgow Dr. Davies' primary research focus centers on cystic fibrosis (CF) , with particular emphasis on treatment burden reduction, innovative clinical trial methodologies using routine healthcare data, digital health applications, and monitoring lung disease progression in children and adolescents. Her work bridges the gap between clinical practice and research by leveraging real-world data to improve CF care. She actively contributes to the UK CF Registry Steering Committee and leads several national initiatives including the CF STORM study (a registry-based randomized controlled trial) and serves as National Coordinating Investigator for CFTR modulator trials in children. As chair of the London CF Clinical Trials Accelerator Platform regional network since 2023, she plays a pivotal role in advancing CF research infrastructure. Analysis of Dr. Davies' recent publications reveals a strong focus on optimizing cystic fibrosis care through innovative methodologies. Her work spans clinical trial design, digital health applications, treatment burden measurement, and the use of routinely collected data for research purposes. A notable trend is her increasing emphasis on trial emulation using registry data, demonstrating how observational data can supplement traditional clinical trials. Her research consistently addresses practical challenges in CF management while maintaining rigorous methodological standards. The collaborative nature of her work is evident through extensive multi-center partnerships across the UK and internationally. Among her notable professional achievements: UKRI Future Leaders Fellow (first recipient at GOS Institute of Child Health) Fellow of the Royal College of Paediatrics and Child Health (FRCPCH) Member of the UK Cystic Fibrosis Registry Steering Committee Dr. Davies serves as co-Chief Investigator for the CF STORM study, a national multi-center registry randomized controlled trial, and as National Coordinating Investigator for commercially sponsored CFTR modulator trials in children. Her NIHR Clinical Trials Fellowship with the Pragmatic Clinical Trials Unit at Queen Mary University of London has informed her methodological expertise in trial design. As Deputy Head of Department for Population Policy and Practice, she contributes to academic leadership while maintaining active clinical practice as an Honorary Consultant in Paediatric Respiratory Medicine. Dr. Davies leads a research team focused on CF and healthcare data systems within UCL's Population, Policy and Practice department. Her work connects with the broader UCL Great Ormond Street Institute of Child Health community and collaborates extensively with Great Ormond Street Hospital clinical teams. As chair of the London CF Clinical Trials Accelerator Platform regional network, she coordinates research efforts across multiple institutions to advance CF clinical trials infrastructure.

Rui Pires da Silva Castroمشاهده پروفایل

دانشیار

• Signal Processing
• Learning Theory
• Statistics
• +۱۰ مورد دیگر

Rui Pires da Silva Castro is an Associate Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e), with research spanning signal processing, learning theory, and statistics. He is affiliated with EAISI (Eindhoven Artificial Intelligence Systems Institute) and focuses on non-parametric and high-dimensional statistics, statistical signal/image processing, network inference, and pattern recognition.

• Econometrics
• Time Series Analysis
• Macroeconomics
• +۵ مورد دیگر

Carlos Velasco is a Professor of Fundamentals of Economic Analysis at Universidad Carlos III de Madrid (UC3M) since 2007, with prior academic appointments at the University of Oxford and Universitat Autònoma de Barcelona. He earned his PhD from the London School of Economics, establishing the foundation for his expertise in econometric theory. His academic career includes positions as Junior Lecturer in Statistics at Oxford, Full Professor in Statistics and Econometrics at UC3M, and ICREA Research Professor in Economics at UAB. Professor Velasco's research program centers on dynamic econometric models and economic time series analysis, with significant applications in macroeconomics and finance. His methodological contributions span time series analysis, dynamic modeling, persistence phenomena, predictability frameworks, and causality testing, addressing both theoretical challenges and practical economic questions. Analysis of his publication record reveals consistent innovation in econometric methodology, particularly in handling complex time series properties including noninvertibility, noncausality, and heteroskedasticity. His work demonstrates increasing sophistication in model estimation techniques while maintaining strong connections to substantive economic phenomena like exchange rate dynamics and panel data structures. Professional recognition: Fellow of the Journal of Econometrics (2010) Professor Velasco has supervised 8 doctoral theses and directed multiple research projects, including the MAD-ECO network. His editorial service for leading journals such as Econometric Theory and Journal of Econometrics reflects his standing in the field. He serves as Associate Editor for Econometric Theory, Journal of Time Series Analysis, Journal of Time Series Econometrics, TEST, and the Spanish Economic Review. While specific laboratory facilities aren't detailed, his leadership of the MAD-ECO network indicates active participation in collaborative research frameworks focused on economic modeling, suggesting ongoing contributions to methodological advancements with practical economic applications.

Avi Fellerمشاهده پروفایل

دانشیار

• Program Evaluation
• Quantitative Methods
• Causal Inference
• +۲ مورد دیگر

Avi Feller is an Associate Professor at the Goldman School of Public Policy and holds a joint appointment in Statistics at the University of California, Berkeley. His research operates at the intersection of public policy, data science, and statistics, focusing on developing methodological approaches to improve social policy evaluations and collaborating with governments to design and implement data-driven policies. Education: Ph.D. in Statistics, Harvard University M.Sc. in Applied Statistics, University of Oxford (as Rhodes Scholar) B.A. in Political Science and Applied Mathematics, Yale University Research focuses on quantitative methods for policy evaluation, including causal inference techniques, synthetic control methods, and balancing weights for observational studies. His work spans education, public health, and social policy domains, with particular emphasis on developing robust statistical approaches for real-world policy analysis. Methodological innovations include the Augmented Synthetic Control Method and applications of machine learning to causal inference problems. Publications demonstrate consistent focus on causal inference methodology with applications to policy-relevant domains including education, gun violence prevention, and COVID-19 interventions. Recent work shows increasing emphasis on machine learning integration and complex observational study designs with temporal and spatial components. Awards and Honors: Rhodes Scholarship Prior professional experience includes serving as Special Assistant to the Director at the White House Office of Management and Budget and working at the Center on Budget and Policy Priorities. No information is available regarding current graduate students, research grants, or laboratory affiliations.

Edgar Dobribanمشاهده پروفایل

دانشیار

• Statistics
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

Edgar Dobriban is an Associate Professor of Statistics and Data Science at the University of Pennsylvania's Wharton School, with a secondary appointment in Computer and Information Science. He leads a research group focused on problems at the interface of statistics, machine learning, and AI. Education PhD in Statistics, Stanford University (2017) BA in Mathematics, Princeton University (2012, Summa cum Laude/with Highest Honors) Research Focus His work spans uncertainty quantification, AI safety, robustness, high-dimensional asymptotic statistics, distributed learning, fairness, and COVID-19 testing methodologies. Current projects include developing conformal prediction methods, jailbreaking robustness benchmarks (JailbreakBench), and safety alignment techniques for large language models. Publication Trends Recent papers predominantly address AI safety and reliability, featuring novel methods for uncertainty quantification in language models (calibration, conformal prediction), adversarial robustness (jailbreaking defenses), and distribution shift adaptation. Theoretical foundations blend with practical applications in high-dimensional statistics. Awards and Honors Peter Gavin Hall IMS Early Career Prize (2024) Sloan Research Fellowship (2023) ICSA Outstanding Young Researcher Award (2023) NSF CAREER Award (2021) AFOSR/Army Research Office YIP Awards (2024, 2023) COPSS Emerging Leader Award (2023) Research Leadership He leads the Wharton Statistics and Data Science research group, recruiting PhD students through Statistics & Data Science, CIS, and AMCS programs. Current projects involve collaborations with Penn Medicine and the NSF-Simons Mathematical and Scientific Foundations of Deep Learning initiative. He co-founded the ASA StatsUpAI Special Interest Group and co-organized the Shenzhen Conference on Random Matrix Theory (2023).

Eugene Katsevichمشاهده پروفایل

استادیار

• Genetics
• Genomics
• High-dimensional Statistics
• +۳ مورد دیگر

Eugene Katsevich is an Assistant Professor at the University of Pennsylvania's Wharton Department of Statistics and Data Science. His research spans statistical theory, methodology, and applications in genomics and computational biology. Education: Ph.D. in Statistics (Stanford, 2019), A.B. in Mathematics (Princeton, 2014) His work focuses on: High-dimensional variable selection and multiple testing Conditional independence testing and computational efficiency Applications to genetics and genomics Development of statistical software for biological discovery Recent publications address: Robust differential expression analysis in single-cell CRISPR screens Theoretical advances in false discovery rate control Multi-resolution causal variant localization CRISPRi-based enhancer-gene mapping Scientific awards include: Wharton Undergraduate Teaching Excellence Award (2021) He teaches courses in modern data mining (STAT4710/5710) and statistical methodology (STAT9610), and leads the Katsevich Lab at UPenn, funded by NSF and Analytics at Wharton. His research develops methods like KnockoffZoom and SCEPTRE for genome-wide analysis and single-cell CRISPR data.

Burcu F. Darstمشاهده پروفایل

استادیار

• Genetic Epidemiology
• Metabolomic Epidemiology
• Prostate Cancer
• +۷ مورد دیگر

Burcu F. Darst, PhD is an Assistant Professor at the Public Health Sciences Division of Fred Hutchinson Cancer Center , where she leads research on the genetic and multi-omic basis of prostate cancer risk in diverse populations. As a member of the Translational Data Science Integrated Research Center , her work bridges genetic epidemiology, metabolomics, and health disparities research. Research Interests : Identifying genetic risk factors for prostate cancer using whole-exome sequencing and polygenic risk scores Investigating health disparities in prostate cancer and Alzheimer's disease Developing methods for multi-omics data integration in complex trait analysis Advancing precision medicine through genomic-clinical risk modeling Scientific Contributions include: Prostate Cancer : Discovery of ancestry-specific risk variants and development of clinical risk prediction models Diabetes & Alzheimer's : Identification of cross-ancestry protein biomarkers and metabolomic associations Method Development : Creation of admix-kit for admixed population analysis and LUCID multi-omics framework Scientific Awards : Prostate Cancer Foundation Young Investigator Award AACR NextGen Star Award

Xin Zanمشاهده پروفایل

استادیار

• Weakly Supervised Learning
• Statistical Machine Learning
• Knowledge-Enhanced Deep Learning
• +۶ مورد دیگر

Xin Zan is an Assistant Professor in the Department of Industrial and Systems Engineering at the University of Iowa's College of Engineering, where he joined in Fall 2024. His research develops advanced data-driven analytics for complex systems in healthcare, manufacturing, and service sectors through novel weakly supervised learning methodologies. His educational background includes: PhD in Industrial and Systems Engineering from the University of Florida (2024) MS in Statistics from the University of Florida (2022) BS in Industrial Engineering from Shanghai Jiao Tong University, China (2019) Dr. Zan specializes in statistical and machine learning under weak supervision, knowledge-enhanced deep learning, spatio-temporal modeling, causal inference, and health informatics. His work bridges theoretical advances with practical healthcare applications including sleep disorder diagnostics, infectious disease surveillance, and medical system optimization. He actively develops frameworks for complex system monitoring where labeled data is scarce or imperfect. His publication portfolio reveals a concentrated focus on healthcare AI applications (60% of recent work), particularly sleep apnea monitoring and infectious disease tracking, while extending methodologies to recommender systems and big data streams. Core technical themes include Bayesian modeling, nonparametric statistics, and causal analysis across temporal and spatial dimensions. Dr. Zan is recruiting PhD students with engineering, statistics, or mathematics backgrounds and strong programming skills in Python, R, or Matlab. Prospective students should submit CVs, transcripts, and research statements to xin-zan@uiowa.edu for positions in applied statistics, data science, and AI-driven systems engineering.

• AI
• Robotics
• Sequential Decision Making
• +۵ مورد دیگر

Siddharth Srivastava is an Associate Professor at the School of Computing and Augmented Intelligence , Arizona State University , where he directs the Autonomous Agents and Intelligent Robotics (AAIR) Lab. His research focuses on principled AI systems for generalizable planning , sequential decision-making under uncertainty , and user-driven AI assessment . He previously served as Staff Scientist at United Technologies Research Center and postdoctoral researcher at UC Berkeley . Education Ph.D. in Computer Science, University of Massachusetts-Amherst M.S. in Mathematics, Indian Institute of Technology (IIT) Kanpur His work spans AI planning , robotics , and neuro-symbolic methods , emphasizing safe, reliable AI systems that can learn abstractions , transfer knowledge , and self-assess capabilities . Recent publications focus on user-aligned explanations , integrated task-motion planning , and stochastic reinforcement learning . Scientific recognition includes: NSF CAREER award Top 5% Faculty Award (Fulton Schools of Engineering) Best Paper at ICAPS Outstanding Dissertation (UMass Amherst) Best Final Year Thesis (IIT Kanpur) Chambliss Medal (AAS) He has supervised multiple Ph.D. students including Pulkit Verma , Naman Shah , and Rushang Karia , and developed the JEDAI system for teachable robot planning. His research has been featured in BBC , Motherboard , and IEEE Spectrum .

Mayur Naikمشاهده پروفایل

استاد

• Programming Languages
• Artificial Intelligence
• Neurosymbolic Programming
• +۲ مورد دیگر

Mayur Naik is the Misra Family Professor in the Department of Computer and Information Science at the University of Pennsylvania. His research specializes in programming languages and artificial intelligence, with current focus areas including neurosymbolic programming, trustworthy AI for healthcare, and AI-enhanced development tools. He leads the development of the Scallop neurosymbolic programming language and maintains active collaborations in bioinformatics and clinical research. Education: Ph.D. in Computer Science, Stanford University (2008) M.S. in Computer Science, Purdue University (2003) B.S. from BITS Pilani (1999) Research Focus: His work bridges symbolic AI with neural approaches through neurosymbolic programming, enabling interpretable and domain-aware solutions. Current projects include Dolphin (scalable neurosymbolic learning), IRIS (LLM-assisted static analysis), and applications in computer vision and healthcare. Research emphasizes both theoretical foundations and practical system implementations. Publication Trends (Recent Focus): Recent work demonstrates strong emphasis on neurosymbolic learning frameworks (55%), AI-enabled programming tools (30%), and healthcare applications (15%). Key technical threads include LLM integration with program analysis, GPU acceleration for symbolic reasoning, and weakly-supervised learning paradigms. Awards & Honors: Test of Time Award, Foundations of SW Engineering (2023) Distinguished Paper Awards (PLDI 2019, SIGSOFT 2015, FSE 2015, SIGPLAN 2014) NSF CAREER Award (2013) Lockheed-Martin Teaching Excellence Award (2015) Outstanding Junior Faculty Award, Georgia Tech (2016) Academic Leadership: Advises 8 PhD students with research spanning neurosymbolic systems, program analysis, and AI security. Teaches core courses CIS 5470 (Software Analysis) and CIS 5500 (Database Systems). Research supported by NSF and industry partnerships. Labs & Teams: Leads the neurosymbolic programming research group at UPenn, collaborating with Intel Labs, Google AI, and healthcare institutions. Group develops open-source tools including Scallop compiler and LLM-assisted analysis frameworks.

Yushan Liمشاهده پروفایل

پژوهشگر ارشد

• Control Systems
• Networked Dynamical Systems
• Multi-Robot Systems
• +۳ مورد دیگر

Yushan Li is currently a Postdoc Researcher in the Division of Decision and Control Systems at KTH Royal Institute of Technology, Sweden. He received his Ph.D. in Control Science and Engineering from Shanghai Jiao Tong University (SJTU) in 2024 and a B.E. in Automation from Huazhong University of Science and Technology (HUST) in 2018. His research focuses on cooperation and inference in networked systems, with applications to robotic networks and control security. Education: B.E. in Automation (2018) from HUST Ph.D. in Control Science and Engineering (2024) from SJTU His research interests include networked dynamical systems, multi-robot cooperation, topology inference, and cybersecurity in control systems. He has contributed to adversarial strategies against formation control, secure collaboration algorithms, and topology preservation under attacks. Recent publications highlight his work on network topology inference, secure control mechanisms, and adversarial strategies. His research has been accepted in prestigious journals such as IEEE Transactions on Automatic Control and IEEE Transactions on Robotics , with presentations at international conferences like CDC and IFAC World Congress. Scientific Awards: 2025 SJTU Outstanding Doctoral Dissertation Award 2023 SJTU Academic Star National Scholarships (2023, 2022, 2019) 2023 Best Oral Presentation Award at ZJU Swarm Intelligence Forum Li has also served as a teaching assistant for undergraduate courses on intelligent optimization of network systems and as a reviewer for leading journals including IEEE Transactions on Automatic Control and Automatica . He co-founded the Distributed Control, Optimization, and Security Zhizhen Forum and contributed to organizing major international conferences.

Aaron Schefflerمشاهده پروفایل

استادیار

• Biostatistics
• Functional Data Analysis
• Neuroimaging Statistics
• +۵ مورد دیگر

Aaron Scheffler is an Assistant Professor in the Department of Epidemiology & Biostatistics at the University of California, San Francisco (UCSF) School of Medicine. His research program addresses statistical challenges arising from highly structured biomedical data, particularly in neuroimaging and wearable technologies. As a core statistician for the UCSF Memory and Aging Center, including the Alzheimer's Disease Research Center and ALBA Language Neurobiology laboratory, he collaborates extensively with clinical researchers across multiple disciplines. Dr. Scheffler received his PhD and MS in Biostatistics from the University of California, Los Angeles and his BA in Biochemistry from Columbia University. His methodological expertise spans functional data analysis, disease progression modeling, and multi-modal brain imaging data integration. His research focuses on developing computationally efficient statistical methods for complex biomedical data that maintain information across multiple dimensions while providing interpretable components. His work addresses challenges in disease progression modeling for neurodegenerative disorders, joint analysis of multi-modal brain imaging data, and functional data analysis of electroencephalography (EEG) data. Current projects include Bayesian generalized nonlinear mixed effects models for disease progression, joint modeling of structural and network brain imaging data, and advanced methods for EEG analysis in developmental disorders. Analysis of his recent publications reveals a strong interdisciplinary focus spanning neurology, orthopedics, and neonatology. His work demonstrates expertise in analyzing complex data structures including longitudinal imaging, EEG, and biomechanical measurements. Key methodological themes include Bayesian modeling, functional data analysis, and machine learning applications to biomedical problems. His research consistently bridges advanced statistical methodology with clinically relevant questions. Dr. Scheffler's research is supported by extramural grants from the National Institutes of Health Institute of Neurological Disorders and Stroke and the National Science Foundation (Division of Mathematical Sciences). His recent NIH/NINDS R01 grant titled 'Bayesian Object-Oriented Modeling of Multi-Modal Imaging Data' and NSF DMS grant 'Use of Random Compression Matrices For Scalable Inference in High Dimensional Structured Regressions' demonstrate his leadership in methodological innovation for complex biomedical data. He maintains active collaborations with researchers at UCSF in neurology, orthopedics, and HIV/AIDS research, contributing statistical expertise to diverse clinical investigations. His work with the Memory and Aging Center and ALBA Language Neurobiology laboratory positions him at the forefront of statistical methods development for neurodegenerative disease research.

Anne Juppoمشاهده پروفایل

استاد

• Industrial Pharmacy
• Pharmaceutical Formulations
• Drug Delivery Systems
• +۵ مورد دیگر

Anne Juppo serves as Professor and Principal Investigator in the Division of Pharmaceutical Chemistry and Technology at the University of Helsinki's Faculty of Pharmacy, leading the Helsinki One Health Drug Research Program. Her roles include supervision in the Doctoral Programme in Drug Research and active participation in the Pharmaceutical Design and Discovery group. Her research spans critical pharmaceutical domains with particular emphasis on: Industrial pharmacy and solid-state transformations during manufacturing Age-specific formulation development for pediatric and geriatric populations Tablet compression optimization and quality control systems Crisis management in pharmacy operations during pandemics Analysis of her 15 most recent publications reveals consistent focus on formulation science challenges, with increasing integration of AI methodologies in pharmaceutical manufacturing (2024-2025) and sustained investigation into temperature-sensitive drug stability. Her work bridges fundamental material science with practical clinical applications, particularly in vulnerable populations. Supervision activities include doctoral theses on personalized cancer therapy compounding and pediatric formulation challenges, reflecting her commitment to training next-generation pharmaceutical scientists. Current projects like LifeFactFuture LFF (2024-2026) demonstrate ongoing leadership in translational research with Business Finland funding.

Julien Mairalمشاهده پروفایل

پژوهشگر

• Machine Learning
• Optimization
• Computer Vision
• +۲ مورد دیگر

Julien Mairal is a Senior Research Scientist at Inria Grenoble within the Thoth research team, focusing on machine learning, optimization, and computer vision. His research spans theoretical foundations and practical applications across multiple domains including signal processing, bioinformatics, and neuroimaging. His primary research interests include machine learning , particularly optimization algorithms for large-scale problems, computer vision with applications in image restoration and super-resolution, and kernel methods for structured data. His work demonstrates strong connections between theoretical optimization and practical implementations, as evidenced by his Cyanure toolbox for empirical risk minimization. Analysis of his recent publications reveals a consistent focus on optimization techniques for machine learning, with increasing emphasis on large-scale applications, self-supervised learning, and connections between kernel methods and deep learning architectures. His work shows strong interdisciplinary applications spanning computer vision, neuroscience, and remote sensing. ERC Consolidator Grant (2023) for the Apheleia project ICML Test of Time Award (2019) ERC Starting Grant (2016) for the SOLARIS project Mairal serves extensively in editorial capacities as area chair for major conferences including ICML, NeurIPS, CVPR, and ECCV. He is an associate editor for the Journal of Machine Learning Research (JMLR) and previously served for IEEE PAMI and IJCV. His Cyanure toolbox has become a significant contribution to the field of large-scale optimization in machine learning. His research demonstrates strong connections between theoretical foundations and practical implementations across multiple application domains.