جستجوی استاد راهنمای دانشگاهی

• Judicial Decision-Making
• Prosecutorial Decision-Making
• Criminal Justice
• +۳ مورد دیگر

Rachel Bowman is a Faculty Associate at the NCIAS School of Social and Behavioral Sciences at Arizona State University (ASU). She is a doctoral candidate in the School of Criminology and Criminal Justice, focusing on judicial and prosecutorial decision-making, particularly the impact of race, ethnicity, and gender on criminal court outcomes. Her work bridges legal processes and social equity, addressing systemic biases in sentencing and prosecution. Education includes an M.S. in Justice, Law, and Criminology from American University and a B.S. in Psychology from Eastern Mennonite University. Her research emphasizes prosecutor role orientations, pre-arrest screening efficiency, and rehabilitation programs for offenders. Recent publications explore prosecutor decision models, gender disparities in court outcomes, and the role of race in sentencing. She has contributed to policy discussions on socially just prosecution practices and wrongful conviction prevention. Teaching includes courses like Introduction to Psychology (PSY 101) and Criminology (CRJ 309). Her work aligns with interdisciplinary efforts to reform criminal justice systems through empirical research and policy advocacy.

Zoey Zhiyu Chenمشاهده پروفایل

استادیار

• Artificial Intelligence
• Natural Language Processing
• Healthcare Technology
• +۳ مورد دیگر

Zoey Zhiyu Chen is an Assistant Professor in the Department of Computer Science at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. Her research focuses on advancing artificial intelligence, particularly in natural language processing (NLP), healthcare technology, financial technology, and dialogue systems. She explores the application of large language models (LLMs) in critical domains such as mental health therapy, cognitive behavior therapy (CBT), and finance, emphasizing robustness and ethical considerations. Key research areas include hypothesis discovery, rule learning, patient simulation for mental health training, and improving medical predictions through multimodal data analysis. Her work also addresses challenges in retrieval-augmented models, agentic search optimization, and zero-shot dialogue state tracking. Chen’s contributions span theoretical advancements in AI reasoning and practical applications in societal domains like law and healthcare. Her articles highlight trends in leveraging LLMs for clinical support, financial analysis, and improving human-AI interaction through preference learning and function calling mechanisms. She advocates for responsible AI development, as seen in studies on energy efficiency benchmarks (Hulk) and domain-specific robustness (RobustFin).

Jeanne Bovetمشاهده پروفایل

مدرس ارشد

• Evolutionary Psychology
• Human Behavior
• Facial and Body Perception
• +۴ مورد دیگر

Dr. Jeanne Bovet is a Senior Lecturer in the Department of Psychology at Northumbria University since June 2020. She holds a PhD in Biological Sciences from Université de Montpellier (2014), was a Research Fellow at the Institute for Advanced Study in Toulouse, and previously served as a Lecturer at Stony Brook University. Her work spans psychology, biology, and economics to explore evolutionary influences on human behavior. Education: PhD, Biological Sciences, Université de Montpellier Her research focuses on the evolutionary origins of facial/body perception, mate preference homogamy, and developmental influences on economic behavior. She employs interdisciplinary methods including eye-tracking, geometric morphometrics, and fieldwork in China. Key themes include parent-offspring mate conflict, historical beauty standards, and hormonal predictors of decision-making. Key article trends reveal expertise in evolutionary psychology , facial analysis , and cultural variation , with subtopics ranging from testosterone exposure effects to Waist-to-Hip Ratio theories and cross-cultural kissing behavior .

Thinh Nguyenمشاهده پروفایل

استاد

• Stochastic Processes
• Signal Processing
• Video Coding
• +۳ مورد دیگر

Thinh Nguyen is a Professor in the School of Electrical Engineering and Computer Science at Oregon State University . His research spans theories and applications of stochastic processes, with focus areas in signal processing, video coding, networking, communication systems, and quantum information theory. He co-authored multiple award-winning papers and received the NSF CAREER Award (2009) and Engelbrecht Young Faculty Award (2007) . Education : Ph.D. in Electrical Engineering and Computer Science from UC Berkeley (2003), B.S. in Computer Engineering from University of Washington (1995) Research Focus : Information representation, processing, and transmission with applications in multimedia streaming optimization Key Contributions : Network coding theories for efficient multimedia transmission His recent publications address topics in machine learning optimization, fluid dynamics in medical systems, and microfabrication techniques. Outside academia, he maintains strong musical interests as a classical violinist and emphasizes family values. Scientific Awards NSF CAREER Award (2009) Engelbrecht Young Faculty Award (2007) Currently advises graduate students in information theory and signal processing while leading research at the intersection of electrical engineering and computer science.

• Statistical Signal Processing
• Information Theory
• Machine Learning
• +۶ مورد دیگر

Yu Xiang is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Utah since 2018. He holds a B.E. (highest distinction) from Xidian University (2008), a Ph.D. from UC San Diego (2015), and completed a postdoc at Harvard University (2015-2018). His research focuses on statistical signal processing, information theory, and machine learning, with applications in healthcare, IoT, and computational biology. Key areas include distributed testing, trustworthy AI under distribution shifts, and non-stationary time series analysis. Education: Ph.D., Electrical and Computer Engineering, UC San Diego (2015) B.E., Telecommunications Engineering, Xidian University (2008) His work emphasizes causal inference, invariant representation learning, and communication-efficient distributed systems. Recent studies explore Byzantine-robust testing, covariate shift adaptation, and causal discovery in mental health contexts. He has contributed to theoretical bounds in machine learning and signal processing. Grants & Labs: Active in federally funded projects on distributed inference and causal AI. Leads the Invariant Learning Lab at the University of Utah, focusing on interdisciplinary applications of statistical theory.

• Computational Neuroscience
• Motor Control
• Machine Learning
• +۳ مورد دیگر

Elizabeth Thomas is an Associate Professor at the University of Burgundy, affiliated with the Institut Marey. She holds an HDR (Habilitation à Diriger les Recherches) in Neuroscience and a Ph.D. in Chemistry from the University of Texas, Austin, along with a Bachelor's in Chemistry (summa cum laude) from St. John’s University, New York. Her research focuses on computational neuroscience, motor control, and embodiment of cognition. She employs machine learning techniques to analyze electrophysiological recordings in the motor cortex and investigates how motor control strategies adapt across aging and neurological conditions. Key themes include neural network dynamics, neuromuscular coordination, and the intersection of movement and cognitive processes. Her teaching includes courses such as Introduction to Neuroscience, Neuroscience for Rehabilitation, and MATLAB programming for engineers. Over 15 years, she has published extensively on motor control mechanisms, temporal processing, and computational models of neural activity. Notable recent work includes studies on neural clustering in primate motor cortices and machine learning applications in movement analysis. Dr. Thomas collaborates on projects related to assistive technologies and aging motor systems. Her work bridges basic neuroscience with clinical applications, aiming to improve rehabilitation strategies through computational insights.

Gregory Valiantمشاهده پروفایل

دانشیار

• Algorithms
• Machine Learning
• Statistics
• +۳ مورد دیگر

Gregory Valiant is an Associate Professor of Computer Science at Stanford University , specializing in Algorithms, Machine Learning, Statistics, and Information Theory. He holds a PhD from UC Berkeley and a BA in Mathematics from Harvard University. His research focuses on designing efficient algorithms for inferring information from limited data, addressing challenges in computation, memory, communication, and data quality. He advises multiple PhD students and collaborates with institutions like Microsoft Research New England. Educations: PhD, Computer Science, UC Berkeley (2012) BA, Mathematics, Harvard University (2006) Research Interests: Gregory’s work centers on the interplay between algorithms and statistical inference, particularly in high-dimensional settings. His lab explores topics like distribution learning, sample amplification, and adversarial robustness. Recent projects include developing algorithms for trace reconstruction, matrix completion, and transformer-based in-context learning. His methodologies often combine theoretical rigor with practical applications in machine learning and data science. Key Article Trends: His publications span theoretical foundations (e.g., sample amplification, convex optimization complexity) and applied machine learning (e.g., transformer capabilities, adversarial testing). Notable work includes contributions to distribution testing, statistical estimation under constraints, and algorithmic lower bounds. Advising: Current advisees include Annie Marsden, Steven Cao, and Chirag Pabbaraju. Former students have pursued roles at institutions like Harvard, Berkeley, and companies like Google, Waymo, and Facebook.

Venugopal V. Veeravalliمشاهده پروفایل

استاد

• Statistical Inference
• Machine Learning
• Information Theory
• +۹ مورد دیگر

Venugopal V. Veeravalli is the Henry Magnuski Professor of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign (UIUC), affiliated with the Grainger College of Engineering, Coordinated Science Laboratory (CSL), Department of Statistics, and Discovery Partners Institute (DPI). He holds a Ph.D. from UIUC (1992) and has been on the faculty since 2000, previously at Cornell University. His research spans statistical inference, machine learning, information theory, and their applications in wireless communications, sensor networks, and cyberphysical systems. He has authored/co-authored influential books like Statistical Inference for Engineers and Data Scientists and Interference Management in Wireless Networks , and serves as Editor-in-Chief of the IEEE Transactions on Information Theory. Notable awards include the IEEE Fellow (2006), PECASE (1998), and twice the Abraham Wald Prize in Sequential Analysis (2016, 2023). He advises students on topics like robust learning, distributed algorithms, and adaptive sensing. Current research focuses on sequential change detection, non-stationary environments, and IoT resilience. Education: B.Tech. (IIT Bombay, 1985), M.S. (Carnegie Mellon, 1987), Ph.D. (UIUC, 1992) Professional Roles: Editor-in-Chief (IEEE Transactions on Information Theory), NSF Program Director (2003–2005), Fulbright-Nokia Chair (2023) Research Interests: His work bridges theoretical foundations and practical applications, including quickest change detection, robust machine learning, and distributed systems. Recent projects involve anomaly detection in power grids, pandemic monitoring via wastewater data, and adaptive algorithms for wireless networks. Grants & Collaborations: Active in NSF-funded projects on robust learning and IoT resilience. Collaborates internationally, including with Aalto University (Finland) under the Fulbright-Nokia Chair. Teaching: Teaches courses on probability, optimization, communication theory, and data science. Recognized with the Michael Tien Excellence in Teaching Award (1999).

Soummya Karمشاهده پروفایل

استاد

• Decision-Making in Large-Scale Networked Systems
• Stochastic Systems
• Multi-Agent Systems
• +۷ مورد دیگر

Soummya Kar is the Buhl Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU). He holds a Ph.D. (2010) and B.Tech (2005) in Electrical Engineering from CMU and IIT Kharagpur, respectively, and has been a Postdoctoral Research Associate at Princeton University (2010-2011). Research Interests: Decision-making in large-scale networked systems, stochastic systems, multi-agent control, and data science with applications in cyber-physical systems and smart energy grids. Scientific Awards: IEEE Fellow (2022) Dean’s Early Career Fellowship (2016) Wilton E. Scott Institute for Energy Innovation - Energy Fellow Recent Article Trends: His work focuses on distributed optimization, secure inference in sensor networks, smart grid resilience, and coded computing for fault tolerance, with keywords spanning Computer Science , Control Theory , and Energy Systems . Students & Team: He advises current and former Ph.D. students including Brian Swenson (Penn State), Javad Mohammadi (UT Austin), and Sergio Pequito (TU Delft), alongside postdoctoral researchers like Panayiotis Moutis. His research involves collaborations with labs such as CyLab Security and Privacy Institute and the Wilton E. Scott Institute for Energy Innovation.

Yury Polyanskiyمشاهده پروفایل

استاد

• Information Theory
• Machine Learning
• Statistics
• +۳ مورد دیگر

Yury Polyanskiy is a Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT), affiliated with the Laboratory for Information and Decision Systems (LIDS), the Institute for Data, Systems, and Society (IDSS), and the MIT Statistics and Data Science Center. He holds a Ph.D. from Princeton University (2010) and an M.S. from the Moscow Institute of Physics and Technology (2005). His research focuses on information theory, machine learning, statistical inference, error-correcting codes, and wireless communication. He has contributed to fundamental limits of communication systems, finite-blocklength analysis, and applications of information theory to learning and signal processing. Notable awards include the 2020 IEEE Information Theory Society James Massey Award, the 2013 NSF CAREER Award, and the 2011 IEEE Information Theory Society Paper Award. His work spans theoretical advancements and practical applications, including the development of the SPECTRE toolbox for short-packet communication. He is also co-authoring a textbook on information theory. Recent research highlights include studies on quantization techniques for machine learning (e.g., NestQuant), transformer-based empirical Bayes methods, and novel approaches to massive random access in wireless networks (e.g., unsourced multiple access). His contributions bridge information theory and modern data science, addressing challenges in high-dimensional data representation, neural network dynamics, and efficient communication architectures.

• Data Management
• Database Systems
• Data Discovery
• +۶ مورد دیگر

Raul Castro Fernandez is a prominent researcher in data management and database systems, with a focus on data discovery, integration, and marketplaces. He has collaborated extensively with leading institutions and researchers, contributing to projects like Data Station and Nexus for secure data sharing. His work bridges theoretical innovation with practical implementations in cloud optimization, differential privacy, and LLM-driven data tools. Key Contributions : Data market frameworks, LLM applications in databases, differential privacy platforms Collaborators : Yue Gong, Samuel Madden, Michael Stonebraker, Eugene Wu, Kyle Chard Research Themes Fernandez explores automated metadata management for data catalogs, spatiotemporal data sharing with privacy guarantees, and LLM-based data discovery . His work on stateful stream processing (e.g., SABER system) and cost optimization in cloud analytics shows technical depth. Recent Trends 2023-2025 publications highlight his pivot toward LLM applications in data management, including tabular data representation and hypothesis assessment tools. He also investigates sustainability in HPC through carbon credit systems.

Tilo Schwalgerمشاهده پروفایل

استادیار

• Computational Neuroscience
• Theoretical Neuroscience
• Multiscale Modeling
• +۴ مورد دیگر

Tilo Schwalger is an Assistant Professor in Computational Neuroscience and Data Assimilation at the Institut für Mathematik , Technische Universität Berlin . He leads a junior research group affiliated with the Bernstein Network Computational Neuroscience (BCCN) and the Einstein Center Digital Future (ECDF) . His work bridges experimental neuroscience with theoretical models, focusing on multiscale modeling of brain activity. PhD in Theoretical Physics (2013, Max Planck Institute Dresden & Humboldt-Universität Berlin) Diploma in Physics (2006, Humboldt-Universität Berlin) DAAD-funded studies at Lomonosov Moscow State University (2003-2004) His research spans the mathematical modeling of neural activity across scales, from microscopic spiking neurons to mesoscopic neuronal populations and macroscopic brain dynamics . Using tools from statistical physics , stochastic processes , and nonlinear dynamics , he develops frameworks like mean-field theories , coarse-graining techniques , and stochastic delay differential equations to analyze neural variability, synchronization, and information processing. Key applications include understanding cortical circuits , hippocampal replay , and associative memory mechanisms . Recent publications highlight his focus on multiscale modeling , including finite-size networks , stochastic neural dynamics , and short-term synaptic plasticity effects . His work enables rapid simulations of large-scale brain activity through low-dimensional firing-rate models consistent with microscopic spiking neuron dynamics. He has held postdoctoral positions at the University of Pittsburgh , Columbia University , and the École Polytechnique Fédérale de Lausanne (EPFL) , and actively collaborates with experimental and theoretical labs globally.