جستجوی استاد راهنمای دانشگاهی

Alexander P. Frankelمشاهده پروفایل

استاد

• Mechanism Design
• Game Theory
• Contract Theory
• +۲ مورد دیگر

Alexander P. Frankel is the Isidore Brown and Gladys J. Brown Professor of Economics at the University of Chicago Booth School of Business. His research focuses on mechanism design, game theory, and contracting, with applications across various economic domains. Previously, he worked at Yahoo! Research and has published in top economics journals including the American Economic Review and Journal of Political Economy. Education: BS in Mathematics from the University of Chicago BA in Economics from the University of Chicago PhD in Economic Analysis and Policy from Stanford Graduate School of Business Frankel specializes in information economics, mechanism design, and contract theory. His work explores how information structures affect economic outcomes, with applications to delegation, signaling, and strategic communication. He has made significant contributions to understanding how information is designed and used in strategic settings, particularly in areas such as R&D investment, admissions policy, and central banking. Frankel's publication record demonstrates a consistent focus on information design and its applications across diverse contexts. His work spans theoretical developments in signal structures and information hierarchies to practical applications in education policy, corporate decision-making, and monetary policy. The research shows increasing sophistication in modeling information environments and their economic consequences, with recent work addressing contemporary issues like test-optional admissions while maintaining strong theoretical foundations. As a faculty member at Chicago Booth, Frankel teaches Microeconomics (33001) and The Economics of Contracts (33931). His research has received attention in major media outlets including the New York Times, Chicago Tribune, and Freakonomics blog, indicating the broader relevance of his theoretical work to practical economic issues.

Nour Aliمشاهده پروفایل

دانشگاهی

• Software Architecture
• Distributed Systems
• Adaptive Systems
• +۶ مورد دیگر

Dr Nour Ali is a Reader in the Department of Computer Science at Brunel University London , where she co-heads the Brunel Software Engineering Lab and serves as Vice-Dean of Education for the College of Engineering, Design and Physical Sciences. She holds a PhD in Software Engineering from Universidad Politecnica de Valencia, Spain, and a Major in Computer Science from Bir-Zeit University, Palestine. Research Interests: Software architecture for distributed and adaptive systems, integrating techniques like Model-Driven Engineering, Reverse Engineering, and Machine Learning. Teaching: Module leader for Software Project Management and supervisor of undergraduate group projects and final-year projects. Scientific Contributions: Over 70 publications in journals, conferences, and books. Key research areas include microservice architecture recovery, autonomic healthcare systems, and mobile self-adaptive architecture. Scientific Awards: Fellow of the Higher Education Academy (HEA). Membership: Deputy Editor-in-Chief for IET Software, member of multiple conference program committees, and reviewer for EPSRC, NWO, and other funding bodies.

Emilio J. Castillaمشاهده پروفایل

استاد

• Organizational Theory and Behavior
• Economic Sociology
• Human Resources Management
• +۳ مورد دیگر

Emilio J. Castilla is the NTU Professor of Management and Professor of Work and Organization Studies at the MIT Sloan School of Management. He co-directs the MIT Institute for Work and Employment Research and has previously held faculty positions at the Wharton School. Research Focus: Organizational Theory and Behavior Economic Sociology Human Resources Management Workplace Inequality Mechanisms of Meritocracy Social Networks in Employment Scientific Contributions: Identified the "Paradox of Meritocracy" with Stephen Benard Documented systematic racial/gender pay disparities despite formal merit systems Investigated legacy admissions practices and their inequality-reinforcing effects Developed frameworks for longitudinal analysis of employment processes Selected Honors: Richard Beckhard Memorial Prize (2017) Career Trajectory: Joined MIT Sloan in 2005 after Wharton faculty appointment. Serves as Research Fellow at Wharton Financial Institutions Center and Center for Human Resources. His work appears in top journals across sociology, management, and economics.

David A. Plaistedمشاهده پروفایل

استاد پژوهش

• Mechanical theorem proving
• Term rewriting systems
• Logic programming
• +۳ مورد دیگر

David A. Plaisted is a Research Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill. He joined UNC-Chapel Hill as a full professor after serving on the faculty of the Computer Science Department at the University of Illinois at Urbana-Champaign until 1984. His academic career spans several decades with significant contributions to automated reasoning and computational logic. Bachelor's degree in Mathematics from the University of Chicago (1970) Ph.D. in Computer Science from Stanford University (1976) Professor Plaisted's research focuses on mechanical theorem proving, term rewriting systems, logic programming, and algorithms. His work in term-rewriting systems investigates methods of combining them with first-order theorem provers, including techniques for applying efficient permutation group algorithms to equational theorem proving. In mechanical theorem proving, he has developed a sequence of methods including clause linking with semantics and ordered semantic hyper-linking. His research in logic programming includes developing tests to eliminate the occurrence check in Prolog while maintaining semantics. His work spans theoretical foundations to practical applications in program verification and generation. His recent publications demonstrate continued innovation in automated reasoning, particularly in semantic guidance for theorem proving. His work shows a consistent focus on improving the efficiency and effectiveness of automated deduction systems, with recent contributions to SGGS (Semantically-Guided Goal-Sensitive) theorem proving and analysis of the relationship between semantics and unification in proof systems. Professor Plaisted has served on numerous program committees and editorial boards including the Journal of Symbolic Computation, Information Processing Letters, Mathematical Systems Theory, and Fundamenta Informaticae. He is currently on the editorial board of ACM Transactions on Computational Logic and the electronic Journal of Functional and Logic Programming. He has organized significant conferences including serving as co-chair of the Second International Conference on Rewriting Techniques and Applications in 1987. He has spent several sabbaticals at prestigious institutions including SRI in Menlo Park (1982-1983), the Max-Planck Institute and University of Kaiserslautern in Germany (1993-1994), and research visits to groups in Grenoble and Nancy, France (1998).

Kantaro Fujiwaraمشاهده پروفایل

دانشیار

• Computational Neuroscience
• Neural Data Analysis
• Machine Learning
• +۱ مورد دیگر

Kantaro Fujiwara serves as Associate Professor at the Graduate School of Medicine, The University of Tokyo, with concurrent appointments at the International Research Center for Neurointelligence (IRCN) and the Department of Mathematical Informatics, Graduate School of Information Science and Technology. He also manages the Data Science Core infrastructure for IRCN. His academic background includes a Ph.D. in Information Science and Technology from the University of Tokyo (2008), followed by postdoctoral research at the University of Tokyo (JSPS) and University of Cambridge, then assistant professorships at Saitama University and Tokyo University of Science before joining the University of Tokyo faculty. Dr. Fujiwara's research bridges computational neuroscience and neural data analysis through mathematical modeling of neural networks, development of neural data analysis methodologies, and exploration of brain-inspired machine learning. His work extends to biological information processing with specific applications in pancreatic beta cell modeling for diabetes research, establishing connections between theoretical frameworks and experimental neuroscience. His publication record (2017-2023) reveals consistent interdisciplinary contributions applying echo state networks, recurrence analysis, and nonlinear dynamics to neural data classification, physiological signal processing, and disease modeling. These works demonstrate strong integration of computer science, neuroscience, and biomedical engineering methodologies to solve complex neurobiological problems. As Data Science Core Manager at IRCN, he oversees computational infrastructure and software resources that enable advanced neurointelligence research across the University of Tokyo ecosystem, providing critical support for data-intensive neuroscience projects.

Pierre Marquisمشاهده پروفایل

استاد

• Artificial Intelligence
• Knowledge Representation
• Automated Reasoning
• +۶ مورد دیگر

Pierre Marquis is a distinguished Professor of Computer Science at Université d'Artois , affiliated with the Centre de Recherche en Informatique de Lens (CRIL-CNRS, UMR 8188) . Since December 2024, he has served as the vice-president for research and doctoral studies at Université d'Artois. His research focuses on Artificial Intelligence , particularly knowledge representation , automated reasoning , inconsistency handling , and knowledge compilation , with recent emphasis on Explainable AI (XAI) . Research Interests: Marquis's work spans foundational AI topics including abduction , induction , belief revision , and preference modeling . He has pioneered knowledge compilation techniques to optimize AI tasks and developed frameworks for reasoning under inconsistency through paraconsistent logics and argumentation. His EXPEKTATION chair (2020-2026) under France's national AI program drives his current focus on interpretable machine learning models. Scientific Awards: 2025: CNRS Silver Medal 2022: AAIA Fellow 2017: Senior Member of Institut Universitaire de France (IUF) 2009: EurAI (ECCAI) Fellow Doctoral Students: Mentoring Clément Lens (critical patient monitoring systems) and Mehdi Sabiri (data-knowledge integration for AI explanations). Collaborating with students like Louenas Bounia (formal XAI models) and Romain Wallon (pseudo-Boolean constraints). Grants & Projects: Leads the EXPEKTATION research chair (2020-2026) and participates in ANR PING/ACK (2019-2023), ANR THEMIS (2021-2025), CNRS IRP MAKC (2020-2024), and H2020 TAILOR (2020-2024). Previously led PIA4 MAIA (2023-2032) and Pint (2022-2023). Labs & Teams: Active in CRIL-CNRS, contributing to PyXAI (Python XAI library) and d4 (model counting), while mentoring teams on consensus belief merging and dynamic constraint processing .

Jason Bennett Thatcherمشاهده پروفایل

استاد

• Information Systems
• Cybersecurity
• Strategic Alignment
• +۶ مورد دیگر

Dr. Jason Bennett Thatcher is a Professor at Temple University in the Department of Management Information Systems at the Fox School of Business. He holds additional faculty appointments at the Technical University of Munich, Information Technology University-Copenhagen, and Hong Kong Polytechnic University. His research bridges human behavior and information technology, focusing on cybersecurity, strategic alignment, and digital innovation. 20-year track record in top FT50 journals Top 35 active IS researcher by productivity Senior Editor roles at MIS Quarterly , Information Systems Research , and Journal of the AIS Research spans three pathways: strategic IT decisions (firm performance, governance), IT workforce management (job satisfaction, turnover), and post-adoption IT innovation (technostress, IT identity). 2022 publications emphasize digital commerce, social media ethics, and technostress mitigation. Awards include: Clemson's 2008 Undergraduate Teaching Award KPMG Foundation Circle of Compadres Top Associate Editor recognition by Information Systems Research Multiple productivity rankings Teaching spans undergraduate to Ph.D. levels with global mentorship experience. Editorial leadership roles include Senior Editor positions and former editorial board memberships. Productivity metrics highlight 12,000+ Google Scholar citations and consistent publication in FT50 journals since 2002.

Christian Hilbesمشاهده پروفایل

مدرس

• Safety-Critical Systems
• Systems Engineering
• STPA (Systems-Theoretic Process Analysis)
• +۳ مورد دیگر

Christian Hilbes is a Lecturer at the School of Engineering of Zurich University of Applied Sciences (ZHAW). He serves as Deputy Head of the Institute for Applied Mathematics and Physics (IAMP) and co-leads the research focus on Safety-Critical Systems. Active projects include leadership roles in Autonomous Predictive Interlock Systems , Personnel Safety Systems , and Triggering Conditions for Autonomous Cars . He specializes in STPA (Systems-Theoretic Process Analysis) , UML-based modeling , and Dynamic Flowgraph Modeling for safety-critical applications. Email: christian.hilbes@zhaw.ch His research integrates safety analysis with emerging technologies like autonomous systems and nuclear facilities, contributing to publications at MIT STAMP Workshops and in journals like Nuclear Engineering and Design . He actively collaborates on European Spallation Source (ESS) safety frameworks and develops domain-specific languages for STPA.

Andrzej Murawskiمشاهده پروفایل

استاد

• Semantics of Programming Languages
• Automata Theory
• Software Verification
• +۴ مورد دیگر

Andrzej Murawski is a Professor of Computer Science at the University of Oxford and a Tutorial Fellow at Worcester College . His research focuses on the semantics of programming languages and software verification, with applications in automata theory, probabilistic computation, and concurrency. Current affiliations: University of Oxford, SIGLOG Vice-Chair, FoSSaCS Steering Committee Research interests: Game semantics, higher-order recursion, probabilistic systems, differential privacy Recent publications address probabilistic verification, equivalence checking, and game semantics for concurrent systems. He has chaired program committees for conferences such as ESOP and PERR, and his work has earned recognition like the POPL 2025 Distinguished Paper Award . Current students : Benedict Bunting, Haoxuan Yin Past students : Conrad Cotton-Barratt, David Hopkins, Guanyan Li, Dominik Wagner, Fabian Zaiser

Farzaneh Derakhshanمشاهده پروفایل

استادیار

• Type Theory
• Proof Theory
• Programming Languages
• +۷ مورد دیگر

Dr. Farzaneh Derakhshan is an Assistant Professor in the Computer Science Department at Illinois Institute of Technology (Illinois Tech), where she explores logical foundations of concurrency and develops formal methods for program verification. She earned her Ph.D. in Pure and Applied Logic from Carnegie Mellon University in 2021 under Frank Pfenning, followed by a postdoctoral fellowship at CMU with Limin Jia and Stephanie Balzer. Current affiliation: Illinois Tech (since ~2021) Previous affiliation: Carnegie Mellon University (Ph.D. and postdoc) Research focus: Type theory, logical verification, and security for concurrent systems Teaching: Courses on programming languages, type systems, and security Her research addresses fundamental challenges in concurrent programming, including: Developing modal logic frameworks for system verification Designing type systems for intermittent computing Creating behavioral type systems for security guarantees Applying relational logic to GPU security and secure compilation Investigating logical foundations of session-typed processes Formal verification of cyclic process networks Current research trends include: Hybrid dynamic verification for parallel systems Logical approaches to side-channel security Formal methods for cyber-physical systems Crash-resilient computing models Security verification in decentralized applications Noninterference proofs in session-typed concurrency Scientific recognition: NSF SaTC CORE Collaborative Award #2350217 Organizing committee member at Dagstuhl Seminar 26071 Professional leadership: Program committee co-chair for PLACES 2025 Committee roles at LICS 2026, ESOP 2026, ICFP 2025, and ECOOP 2025 Regular reviewer for ACM Transactions journals Laboratory involvement: Co-director of behavioral types research at Illinois Tech Collaboration with Carnegie Mellon's formal verification group Key participant in the FACCT workshop

Steve Zdancewicمشاهده پروفایل

استاد

• Programming Languages
• Type Theory
• Logic
• +۷ مورد دیگر

Steve Zdancewic is the Schlein Family President's Distinguished Professor and Associate Chair in the Department of Computer and Information Science at the University of Pennsylvania's School of Engineering and Applied Science. He is a leading researcher in programming languages, formal methods, and computer security with over two decades of impactful contributions to the field. His research interests span programming languages, type theory, logic, computer security, quantum programming, and formal verification. Zdancewic has made significant contributions to information-flow security, memory safety, program synthesis, and the verification of low-level systems. His work often bridges theoretical foundations with practical applications, particularly through the development of verified systems using Coq and other proof assistants. Analysis of his recent publications reveals a strong focus on formal verification techniques, particularly using Interaction Trees and the Coq proof assistant. His research trajectory shows consistent evolution from foundational work on information-flow security toward increasingly sophisticated verification of complex systems including LLVM, quantum computing, and distributed systems. His work demonstrates a commitment to building practically useful verification tools while maintaining rigorous theoretical foundations. Distinguished Paper Award for Semantics for Noninterference with Interaction Trees (ECOOP 2023) Schlein Family President's Distinguished Professor (2021) Distinguished Paper Award for Interaction Trees (POPL 2020) Christian R. and Mary F. Lindback Foundation Award for Distinguished Teaching (2018) IEEE MICRO top picks (2013) Alfred P. Sloan Fellow (2009-2010) NSF CAREER award (2004) Zdancewic has advised numerous PhD students who have gone on to successful careers in academia and industry. His research has been supported by significant grants from NSF, including the NSF Expedition on the Science of Deep Specification. He is actively involved in multiple major research projects including Vellvm (verified LLVM), DeepSpec, and quantum programming verification. Zdancewic also co-organizes Penn's PL Club programming languages research group with Benjamin Pierce and Stephanie Weirich.

Sneha Dasمشاهده پروفایل

استادیار

• Speech and Language Technology
• Machine Learning
• Data Privacy
• +۵ مورد دیگر

Sneha Das is an Assistant Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), specializing in Speech and Language Technology, Machine Learning, and Privacy-Preserving AI. Her research bridges technical innovation with applications in mental health and physiological signal analysis. Her work focuses on Speech Emotion Recognition , Distributed Speech Processing , and Explainable AI , with recent publications exploring model interpretability, speaker anonymization, and physiological data analysis for emotion detection. She actively supervises PhD students in projects involving AI for mental health and hydroacoustic modeling of fish behavior. Key Research Areas: Speech Emotion Recognition (SER) Privacy and Fairness in Speech Processing Transfer Learning with Physiological Time Series AI Applications in Health and Aquaculture Notable achievements include earning a DSc (Tech) degree for her thesis on robust distributed speech processing. She also contributes to educational activities, including teaching applied statistics and R programming to PhD students.

• Logic Synthesis
• Digital Circuit Design
• Machine Learning
• +۳ مورد دیگر

Andrea Costamagna is a researcher affiliated with the École Polytechnique Fédérale de Lausanne (EPFL), working within the School of Computer and Communication Sciences and the Department of Communication Systems. His research focuses on logic synthesis, digital circuit design, and the intersection of machine learning with hardware implementation. His work includes optimizing digital circuits using techniques like resynthesis, resubstitution, and decomposition, with applications in FPGA design and low-power systems. Recent publications explore symmetry-based synthesis, glitch-aware power minimization, and the use of resistive switching devices in machine learning hardware. His research also extends to quantum physics modeling with deep learning.

Jung-Eun Kimمشاهده پروفایل

استادیار

• Trustworthy AI
• Interpretable AI
• Efficient Deep Learning
• +۷ مورد دیگر

Jung-Eun Kim is an Assistant Professor in the Department of Computer Science at North Carolina State University, where she conducts research at the intersection of artificial intelligence, machine learning, and cyber-physical systems. Her work focuses on creating trustworthy, interpretable, and efficient AI systems, particularly for safety-critical applications. Education: Ph.D. in Computer Science, University of Illinois at Urbana-Champaign (2017) M.S. in Computer Science and Engineering, Seoul National University (2009) B.S. in Computer Science and Engineering, Seoul National University (2007) Dr. Kim's research primarily investigates how to make AI systems more trustworthy, interpretable, and efficient, with particular emphasis on understanding failure modes, safety risks, vulnerabilities, and biases in deep learning models. Her work bridges theoretical understanding with practical applications in safety-critical systems. She explores how efficiency considerations interact with these issues, seeking to fundamentally anatomize neural networks to understand what causes failure modes and how to mitigate them. Her approach has been described as 'like a heart surgeon, we open the heart of a neural network architecture, look into it, interpret it, and cure it.' Her recent publications demonstrate a strong focus on safety alignment in large language models, mitigation of spurious correlations, privacy preservation against membership inference attacks, and sustainable AI development. Her work spans theoretical foundations of trustworthy AI while addressing practical challenges in model deployment, particularly for resource-constrained environments. She has made significant contributions to understanding how model compression techniques like pruning and quantization can inadvertently amplify biases and vulnerabilities. Scientific Awards: ICLR Spotlight, 2025 IBM Faculty award, 2023 CRA Early & Mid Career Mentoring Workshop, 2023 Cloud GPU provided by Lambda, worth $17,280, for course, Spring 2023 NeurIPS Spotlight and nomination for Best Paper Award, 2022 CRA Career Mentoring Workshop, 2022 GPU Grant by NVIDIA Corporation, 2018 The MIT EECS Rising Stars, 2015 The Richard T. Cheng Endowed Fellowship, 2015-2016 Dr. Kim actively mentors PhD students, currently advising Xingli Fang, Varun Mulchandani, Jianwei Li, Rishi Singhal, and Minseon Kim. She has secured significant research funding, including an NSF SaTC (Secure and Trustworthy Cyberspace) grant as Co-PI for 'Partition-Oblivious Real-Time Hierarchical Scheduling' ($281,629.00, 2022-2024). Her research has also been supported by an NVIDIA GPU Grant and cloud resources from Lambda. She serves on program committees for top AI conferences including ICLR, ICML, NeurIPS, AAAI, and IJCAI, and has held roles such as Publicity Chair for IJCAI 2024. Her research group focuses on developing methods to make AI systems more trustworthy, interpretable, and efficient, with particular attention to safety-critical applications. The group investigates how to identify and mitigate failure modes in neural networks while maintaining efficiency, exploring the fundamental relationship between model architecture, safety risks, and computational constraints.

• Computer Systems Security
• Software Supply Chain Security
• Distributed Systems Security
• +۳ مورد دیگر

Santiago Torres-Arias is an Assistant Professor at Purdue University, affiliated with the Elmore Family School of Electrical and Computer Engineering . His research spans computer systems security , software supply chain security , and applied cryptography . Campus: West Lafayette, Indianapolis Office: BHEE 324B Contact: santiagotorres@purdue.edu , +1 765-496-6610 Research Interests: Computer systems security Software supply chain security Distributed systems security Applied cryptography Password storage mechanisms Publication Trends: His recent work focuses on software supply chain security , including code signing , provenance mechanisms , and IoT vulnerabilities . Key projects address Sigstore , DevSecOps , and zero-trust dependencies . Scientific Awards: Advising & Grants: No student details provided No grant information listed