جست‌وجوی استاد و پژوهشگر

Brian Baughمشاهده پروفایل

دانشیار

• household finance
• corporate finance
• behavioral finance
• +۱ مورد دیگر

Brian Baugh is an Associate Professor in the Finance department at the Jeffrey S. Raikes School of Computer Science and Management , University of Nebraska-Lincoln. He earned his Ph.D. from Fisher College of Business at The Ohio State University and spent four years as a systems engineer at Boeing before transitioning to academia. His research focuses on Household Finance Corporate Finance Behavioral Finance Tax Policy Publications in top journals like the American Economic Review and the Journal of Finance explore themes such as income volatility, consumption smoothing, mental accounting, and tax impacts on industries. His work has been featured in major media outlets including the Wall Street Journal, New York Times, Bloomberg, and NPR. Beyond academia, Brian enjoys outdoor activities like backpacking, biking, and rock climbing, and spends time with his five children.

• Microbial Genetics
• Genomics
• Bioinformatics
• +۴ مورد دیگر

Dr. Madhusudan Choudhary is a Professor in the Department of Biological Sciences at Sam Houston State University. He holds a Ph.D. from McMaster University and completed post-doctoral training at Duke University. His research laboratory focuses on microbial genetics, genomics, and bioinformatics using Rhodobacter sphaeroides as a model organism to study bacterial cell-cycle regulation, metabolic innovations through gene duplication, and the evolution of genomic complexity in prokaryotes. Key investigations include chromosome replication origins, duplicate gene expression patterns, and the functional specialization of multipartite genomes. Dr. Choudhary's research spans diverse areas including bacterial responses to microgravity, CRISPR-Cas systems, heavy metal resistance mechanisms, and nanoparticle interactions. He directs studies on transcriptomics under stress conditions and develops computational methods for genome analysis. His work has significant implications for understanding microbial evolution and environmental adaptation.

Xiaokui Xiaoمشاهده پروفایل

استاد

• Data Management
• Data Analytics
• Data Privacy
• +۵ مورد دیگر

Prof. Xiaokui Xiao is a Professor and Deputy Head (Research & Graduate Matters) at the Department of Computer Science, National University of Singapore (NUS). He previously held an Associate Professor position at Nanyang Technological University (NTU). His academic journey includes a PhD from the Chinese University of Hong Kong (2008) and a postdoctoral stint at Cornell University. His research focuses on data management and analytics, with emphasis on large data algorithms, privacy (e.g., differential privacy), and data mining. Notable contributions include the PrivBayes system for private data release and scalable network embedding techniques. Awarded the 2024 ACM SIGMOD Test-of-Time Award, IEEE Fellowship (2022), and ACM Distinguished Membership (2021), Xiao is also a trustee of the VLDB Endowment and Editor-in-Chief of Proceedings of the VLDB Endowment . He has advised over 30 PhD students and researchers, many of whom hold academic or industry leadership roles. His work bridges theoretical foundations with practical systems, addressing challenges in privacy, scalability, and machine learning integration.

• Statistical Inference
• Machine Learning
• Bioinformatics
• +۳ مورد دیگر

Lucy Gao is an Assistant Professor in the Department of Statistics at the University of British Columbia (UBC). Her research focuses on statistical inference, machine learning, and bioinformatics, with particular emphasis on clustering methods, high-dimensional data analysis, and optimal design theory. She holds a faculty position within UBC’s Faculty of Science and is affiliated with the Vancouver Campus. Her work spans theoretical developments in statistical methodologies and applications to biological data, such as single-cell RNA sequencing analysis. She has contributed to areas like selective inference for hierarchical clustering and algorithmic optimization for complex systems. Her email is lucy.gao@stat.ubc.ca, and she maintains a website at https://www.lucylgao.com/ . Recent publications highlight her expertise in data thinning techniques, latent variable modeling, and multiview network analysis. She has explored topics ranging from negative binomial count splitting in genomics to bilevel optimization algorithms. Her research bridges statistical theory and practical computational challenges in modern data science. No scientific awards or advising records are explicitly listed in the provided materials. Her academic contributions are primarily through peer-reviewed articles and methodological innovations.

Laurens Deboمشاهده پروفایل

استاد

• Operations Management
• Supply Chain Management
• Operations Strategy
• +۵ مورد دیگر

Laurens Debo is the C.V. Starr Professor of Operations Management and Area Chair in the Operations and Management Science Department at Tuck School of Business, Dartmouth College. He holds a PhD (2002) and MSc (2000) from INSEAD, France, and dual MSc degrees in Operations Management (1995) and Electrical Engineering (1994) from Université Catholique de Louvain, Belgium. Prior to Tuck, he taught at Carnegie Mellon University and the University of Chicago Booth School of Business. Research Interests: Debo specializes in service operations, strategic consumer behavior, and discretionary service management. His work explores how consumer decisions shape service demand and how providers optimize service delivery in contexts where value increases with service duration. Recent focus areas include queueing systems, tipping behavior, and pandemic-era retail operations. Awards & Editorial Roles: He received the MSOM Meritorious Service Award (2008, 2010) and served as associate editor for Management Science , Manufacturing & Service Operations Management , and Operations Research . He currently edits Production and Operations Management and advises the IIE Transactions . Grants & Impact: NSF-funded research has informed his work on discretionary service design and retail safety protocols during pandemics. His tipping behavior studies have been featured in WSJ , Harvard Business Review , and Marketplace . Labs & Collaborations: Collaborates with global institutions like MIT, Stanford, and the Chinese Academy of Sciences on behavioral operations projects. Active in presenting at leading business schools including Wharton, Kellogg, and INSEAD.

Ralf Schlüterمشاهده پروفایل

مدرس

• Automatic Speech Recognition
• Machine Learning
• Pattern Recognition
• +۴ مورد دیگر

Ralf Schlüter is a senior researcher and Lecturer (Privatdozent) at RWTH Aachen University, where he leads the Automatic Speech Recognition Group within the Department of Computer Science. His academic roles include serving as Academic Director and leading research initiatives at Lehrstuhl Informatik 6: Human Language Technology and Pattern Recognition. He holds a Dipl. in Physics (1995), a Dr. rer. nat. in Computer Science (2000), and a Habilitation in Computer Science (2019), all from RWTH Aachen University. His research focuses on advanced speech recognition technologies, including end-to-end architectures, acoustic modeling, discriminative training, and machine learning applications. He has led numerous national and international projects, such as EU-funded initiatives like BABEL, BOLT, and GALE. Schlüter has published extensively in top-tier conferences like Interspeech and ICASSP, contributing to advancements in neural network frameworks, feature extraction, and error analysis. He teaches courses on speech recognition, pattern recognition, and signal processing, and serves as a Subject Editor for Speech Communication . His work also involves developing the RWTH ASR toolkit, emphasizing scalable and efficient speech recognition systems. Schlüter’s contributions to speech technology have earned recognition, including the ISCA Best Student Paper Award (2019).

Magued Iskanderمشاهده پروفایل

استاد

• Geotechnical Engineering
• Sustainable Construction Materials
• Foundation Engineering
• +۶ مورد دیگر

Magued Iskander is the Department Chair and Professor in the Civil and Urban Engineering Department at the NYU Tandon School of Engineering. With over 25 years of expertise, he focuses on geotechnical engineering, including foundation design, soil-structure interaction, and sustainable materials. His research emphasizes transparent soil modeling, high-strain rate soil behavior, and offshore geotechnology. He leads projects on machine learning applications for geotechnical analysis, pile capacity prediction, and UXO (unexploded ordnance) penetration studies. Research Interests: Transparent soil modeling for soil-structure interaction Sustainable piling using recycled polymers Geotechnical instrumentation and monitoring Offshore/marine foundation design High-strain rate soil mechanics Penetrating dynamics in granular media Publications highlight advancements in machine learning for geotechnical data analysis, projectile penetration mechanics, and tunneling-induced ground settlements. His work bridges experimental, computational, and AI-driven approaches to solve complex geotechnical challenges. He advises NYU Tandon’s Concrete Canoe and Steel Bridge teams, fostering student engagement in competitive engineering projects.

Kerstin Bunteمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• XAI (Explainable Artificial Intelligence)
• +۷ مورد دیگر

Kerstin Bunte is a Professor of Machine Learning for interdisciplinary data analysis at the University of Groningen, affiliated with the Faculty of Science and Engineering and the Bernoulli Institute's Intelligent Systems Group. She holds an Honorary Fellowship at the University of Birmingham and leads the Intelligent Systems Group. Her research focuses on interpretable machine learning, interdisciplinary applications (e.g., astrophysics and biomedical data), and visualization techniques. Research Interests: - Machine Learning - Artificial Intelligence - Explainable AI (XAI) - Interpretable Models - Dimensionality Reduction - Data Visualization - Astrophysical Data Analysis - Medical Imaging Awards & Grants: - DSSC XS funding (2023) - NWO VIDI grant (2020) - Rosalind Franklin Fellowship (2016–present) Advising & Students: Supervised PhD students include Elisa Oostwal, Janis Norden, Matteo Marcantoni, and Petra Awad. Research spans topics like tumor segmentation in medical imaging, astrophysical structure detection, and autonomous navigation systems. Labs & Collaborations: Leads the Intelligent Systems Group, collaborating with institutions like the University of Birmingham and the University of Warwick. Work involves interdisciplinary projects combining machine learning with astronomy, biomedical sciences, and robotics.

Andrew B. Nobelمشاهده پروفایل

استاد

• Statistical Genomics
• Machine Learning
• Network Analysis
• +۳ مورد دیگر

Andrew B. Nobel is the Robert Paul Ziff Distinguished Professor of Statistics and Operations Research and Professor of Biostatistics at the University of North Carolina at Chapel Hill (UNC). He holds affiliations with the Lineberger Comprehensive Cancer Center and the Computational Genomics Program. His research focuses on statistical genomics, machine learning, network analysis, and inference from dynamical systems. Nobel has collaborated extensively with researchers in Biology, Computer Science, Genetics, and Mathematics. Dr. Nobel earned his PhD in Electrical Engineering from Stanford University (1992), an MS from Stanford (1988), a Certificate of Study in Mathematics from Cambridge University (1986), and a BS in Electrical Engineering from Cornell University (1985). He has taught courses ranging from undergraduate discrete mathematics to advanced graduate-level theoretical statistics and machine learning. His research interests include developing methodologies for analyzing complex biological and network data, with applications to cancer genomics, systems biology, and medical informatics. Recent work emphasizes optimal transport methods for network analysis and statistical approaches for genomic data integration. Awards: Elected Fellow of the Institute of Mathematical Statistics (2008) National Science Foundation CAREER Grant (1995) Beckman Institute Fellow (1992–1995) Churchill Scholar (1985–1986) Dr. Nobel serves on the editorial board of the Journal of the Royal Statistical Society, Series B and has previously contributed to the Annals of Statistics and IEEE Transactions on Information Theory . His work bridges theoretical statistical foundations with practical applications in public health and biomedical research.

Luca Manneschiمشاهده پروفایل

مدرس

• Machine Learning
• Neuromorphic Computing
• Biological Networks
• +۳ مورد دیگر

Dr Luca Manneschi is a Lecturer in Machine Learning at the School of Computer Science, University of Sheffield, with an IBM Liaison role. He holds a PhD in Physics from the University of Sheffield (2021) and completed a PostDoc there before his current position since March 2022. His research focuses on designing learning algorithms inspired by biological networks for physically defined systems, emphasizing neuromorphic computing, reservoir computing, and stochastic environments. Education: Bachelor's in Physics: University of Padua (Italy) Master's in Physics: Sapienza University of Rome (Italy) PhD in Physics: University of Sheffield (2021) Research Interests: Dr. Manneschi explores algorithms for physically defined networks, leveraging biological network principles to enhance computation in dynamic environments. His work bridges machine learning with neuromorphic hardware, emphasizing adaptability and energy efficiency. Key areas include reservoir computing, magnetic metamaterials, and multi-timescale learning strategies. Grants & Funding: "Real-time Reservoir Computing on Prosthetic Devices" (2023–2025, Royal Society, PI) "MARCH: Magnetic Architectures for Reservoir Computing Hardware" (2021–2025, EPSRC, Co-PI) "CausalXRL: Causal Explanations in Reinforcement Learning" (2021–2024, EPSRC, Co-PI) "ActiveAI" (2019–2024, EPSRC, Co-PI) Lab/Team Affiliation: Machine Learning Research Group, School of Computer Science.

Xinge Jessie Jengمشاهده پروفایل

دانشیار

• High-Dimensional Inference
• Machine Learning
• Weak Signal Discovery
• +۳ مورد دیگر

Xinge Jessie Jeng is an Associate Professor of Statistics at North Carolina State University (NC State), Department of Statistics. She serves as Director of Masters Programs and holds a Ph.D. in Statistics from Purdue University (2009). Her research focuses on high-dimensional inference, machine learning, weak signal discovery, and statistical genomics, with applications in genetics and bioinformatics. Her work develops methodologies for analyzing large-scale genomic data, including false negative control in high-dimensional settings, transfer learning for polygenic risk prediction, and eQTL mapping. She has contributed R packages such as AFNC and FastLORS for statistical analysis. Her GitHub repository demonstrates active software development in statistical genomics and machine learning. Recent research emphasizes adaptive methods for weak signal detection under dependence, spatially adaptive variable screening in neuroimaging, and efficient SNP ranking in genetic studies. She collaborates across disciplines to bridge statistical theory with real-world genomic and biomedical applications.

Daniel Mosseمشاهده پروفایل

استاد

• Computer Architecture
• Computer Systems
• Cyber-Physical Systems
• +۸ مورد دیگر

Dr. Daniel Mosse is a Professor in the Department of Computer Science at the University of Pittsburgh. He holds a PhD from the University of Maryland, College Park, and has been a faculty member since 1993. His research focuses on distributed systems, real-time systems, power management, security, and fault tolerance, bridging operating systems and networking research. He has held leadership roles, including Chair of the Department of Computer Science since 2009 and Visiting Professorships in Brazil. His work spans grants totaling millions, addressing energy-efficient computing, secure infrastructure, and embedded systems. Education: PhD (1993), MS (1990), and BS (1985) in Mathematics and Computer Science. Research interests include distributed real-time systems, multicore systems, embedded systems, and wireless networks. Notable awards include the 2007 Provost's Innovation in Education Award and the 2006 Bellet Teaching Award. Key grants include NSF-funded projects on energy-efficient data centers, secure emergency management systems, and power-aware scheduling. He advises numerous students and teaches courses on operating systems and distributed systems. His contributions to real-time systems, fault tolerance, and energy management have been recognized through over 150 publications and international collaborations.

• Non-parametric statistics
• High-dimensional statistics
• Bioinformatics
• +۴ مورد دیگر

Professor Jian Zhang is a Professor of Statistics at the University of Kent's School of Mathematics, Statistics and Actuarial Science. His research focuses on non-parametric and high-dimensional statistics, bioinformatics, computational biology, statistical genetics, neuroimaging methods, and Bayesian modeling. He has advised students including Jie Li and Tong Wang. His work spans theoretical advancements and applied methodologies across diverse fields such as genomics, neuroimaging, and biomedical data analysis. Publications highlight contributions to Bayesian inference, neuroimaging techniques, and statistical genetics. Notable collaborations include studies on mixture models for genetic association analysis and beamforming methods for functional connectivity. He holds an ORCID iD and is based at Canterbury Campus, University of Kent.

Jin Zhuمشاهده پروفایل

پژوهشگر

• Reinforcement Learning
• Machine Learning
• Statistical Software Development
• +۳ مورد دیگر

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

Serra Favilaمشاهده پروفایل

استادیار

• Cognitive Neuroscience
• Higher-Level Cognition
• Neuroscience
• +۳ مورد دیگر

Serra Favila is an Assistant Professor in the Department of Cognitive and Psychological Sciences at Brown University, affiliated with the Carney Institute for Brain Science. She holds a BA in Human Biology from Stanford University and a PhD in Psychology from New York University, followed by postdoctoral training at Columbia University. Her research focuses on the neural mechanisms underlying episodic memory, employing methods such as functional neuroimaging, intracranial recordings, behavioral measurements, eye-tracking, and computational modeling. She teaches CLPS 1480I: Memory, Space, and the Hippocampus. Dr. Favila's work investigates how humans form flexible yet durable memories, emphasizing the interaction between environmental cues, cognitive goals, and neural processes. Her lab explores memory’s selectivity and susceptibility to interference, with implications for understanding how memory shapes future behavior. Key topics include hippocampal contributions to spatial and temporal memory, neural remapping, and memory-guided navigation. Her articles span themes like hippocampal mechanisms for resolving memory competition, visual cortex contributions to recall success, and neural underpinnings of spatial perception. She maintains an active lab website detailing ongoing projects and collaborates across disciplines to advance cognitive neuroscience. Office hours are held Mondays from 4-5pm in Metcalf Research 245.