جست‌وجوی استاد و پژوهشگر

• Programming Languages
• Software Engineering
• Formal Methods
• +۳ مورد دیگر

L. Thomas van Binsbergen is a Professor in the Department of Computer Science & Computer Engineering at the University of Wisconsin-La Crosse, affiliated with the College of Science & Health. His work focuses on language design, formal methods, and policy-based systems. His research explores Executable formal specifications of programming languages Modular meta-language frameworks (e.g., iCoLa+, eFLINT) Data-dependent grammars for network protocols Purpose-based access control derived from GDPR Functional parsing algorithms (GLL, Happy-GLL) Policy enforcement in distributed systems Recent publications (2020-2025) demonstrate trends in language parametric design, security policy formalization, and exploratory programming environments. Notable collaborations include Damian Frölich, Tim Müller, and Tom M. van Engers. Van Binsbergen earned his PhD from Royal Holloway, University of London (2019) and contributes to conferences like GPCE, SLE, and workshops on programming language theory and data security.

Wei Luمشاهده پروفایل

استادیار

• Digital Marketing
• Empirical Industrial Organization
• Platform Economics
• +۳ مورد دیگر

Wei Lu is an Assistant Professor at the Zicklin School of Business, Baruch College, City University of New York (CUNY), specializing in the Allen G. Aaronson Department of Marketing & International Business. His academic work bridges digital marketing, empirical industrial organization, and platform economics. Education: PhD in Marketing, University of Toronto MEcon in Economic Theory and Econometrics, Toulouse School of Economics BBus in Business, Victoria University (Melbourne) BEc in Economics, Central University of Finance and Economics (Beijing) Research Interests: Digital marketing strategies Platform economics and competition Consumer behavior in digital environments Empirical analysis of industrial organization AI-driven marketing insights Microtransaction dynamics Article Trends: His recent work explores large language model alignment, judicial polarization, and platform economics. Google Scholar articles (2023-2025) focus on digital product diffusion, gaming economics, and rating biases in online transaction platforms.

• Graph Representation Learning
• Deep Learning
• Reinforcement Learning
• +۷ مورد دیگر

Jian Tang is an Assistant Professor at HEC Montreal and the Montreal Institute for Learning Algorithms (MILA), as well as an Associate Professor at the Department of Computer Science and Operations Research (DIRO) at Université de Montréal. He is also affiliated with IVADO (Institut de valorisation des données) as a member. His research spans multiple institutions including collaborations with leading biology labs worldwide and access to extensive computational resources through industry partners. Ph.D. in Computer Science, Peking University (2009-2014) Visiting Ph.D. student, University of Michigan (2011.10-2013.8) B.S. in Mathematics, Beijing Normal University (2005-2009) Professor Tang's research focuses on the intersection of deep learning and graph theory, with particular emphasis on geometric deep learning, knowledge graph reasoning, and applications in drug discovery. His work bridges symbolic and neural approaches to create robust reasoning systems that can handle complex structured data. He has pioneered techniques in graph representation learning that have significantly advanced the field of molecular property prediction and protein design. His publication record shows a clear trajectory toward applying geometric deep learning to biological problems, with a growing emphasis on protein design, molecular conformation generation, and multi-omics analysis. Recent work demonstrates sophisticated integration of 3D geometry with deep learning architectures to model complex biomolecular interactions. Canada CIFAR Artificial Intelligence Chairs (CCAI Chair) Tencent AI Lab Rhino-Bird Gift Fund Amazon Faculty Research Award Microsoft-Mila collaboration grant National Research Council Canada (NRC) Collaborative Research and Development Grant Professor Tang actively mentors doctoral and master's students, with six recent graduates working on cutting-edge topics including graph neural networks for reasoning, protein design, and molecular representation learning. His research is supported by substantial funding from industry partners including Microsoft, Amazon, and Tencent, as well as government agencies like NRC. He collaborates extensively with biology labs worldwide, applying AI to solve real-world biomedical challenges. He leads a research group focused on geometric deep learning for drug discovery, with active projects in protein design using geometric-aware models and large language models for multi-omics analysis. The group has access to thousands of GPUs through industry collaborations, enabling large-scale experiments in molecular simulation and generative modeling.

Luise Müllerمشاهده پروفایل

پژوهشگر ارشد

• Political Philosophy
• Ethics of Emerging Technologies
• Animal Justice
• +۴ مورد دیگر

Dr. Luise Müller is a Research Fellow at the Institute of Philosophy, Freie Universität Berlin, where she specializes in political philosophy and ethics of emerging technologies. She previously held interim professorships at Humboldt-Universität zu Berlin and Universität Hamburg. Her academic journey includes a PhD in Political Theory from Freie Universität Berlin (2016), an MA in Legal and Political Theory from University College London (2012), and a BA in Political Science from Freie Universität Berlin (2010). Her research examines relational egalitarianism, social asymmetries in democratic systems, and normative implications of emerging technologies. Müller investigates how technological advancements interact with social power structures and moral equality frameworks. Current work focuses on AI's role in exacerbating or alleviating social inequalities and developing ethical frameworks for human-AI interaction. Publications from 2018-2023 demonstrate consistent focus on political legitimacy, human rights foundations, and justice theories, with recent expansion into technology ethics. Her 2024 monograph 'The Right to Punish' establishes a theoretical framework for international criminal justice authority. Müller maintains active research affiliations with the Center for Social Implications of AI (SOCAI) at Universität Würzburg and ANU's Machine Intelligence and Normative Theory Lab. She co-edits the peer-reviewed Zeitschrift für philosophische Literatur and participates in interdisciplinary projects including DIGI-PPR at EKFZ Digital Health.

Christopher Bishopمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Probabilistic Models
• +۶ مورد دیگر

Christopher Bishop is a Microsoft Technical Fellow and Director of Microsoft Research AI for Science, concurrently serving as Honorary Professor of Computer Science at the University of Edinburgh and Fellow of Darwin College, Cambridge. His distinguished career spans theoretical physics, neural computing, and leadership in AI research. Fellow of the Royal Academy of Engineering (2004) Fellow of the Royal Society of Edinburgh (2007) Fellow of the Royal Society (2017) Founding member of UK AI Council Member of Prime Minister's Council for Science and Technology (2019) Delivered Royal Institution Christmas Lectures (2008) His research focuses on probabilistic models and machine learning, with significant contributions to AI for scientific discovery. Bishop pioneered the concept of the fifth paradigm of scientific discovery , where AI transforms traditional research methodologies across natural sciences. His work bridges theoretical computer science with practical applications in fusion energy, materials science, and computational biology. Analysis of his recent publications reveals a strategic shift toward AI-driven scientific infrastructure , with emphasis on machine learning foundations that endure technological evolution. His 2024 textbook became Springer Nature's top-selling publication, demonstrating exceptional impact in both academic and industrial contexts. Deep Learning: Foundations and Concepts (2024) Pattern Recognition and Machine Learning (2006) Neural Networks for Pattern Recognition (1995) Bishop leads Microsoft's global AI for Science initiative, establishing research teams in Berlin and coordinating interdisciplinary projects that apply machine learning to climate science, fusion energy, and molecular biology. His leadership in the Prime Minister's Council shapes national AI strategy while maintaining active engagement in public science communication through lectures and media appearances.

Steven O. Kimbroughمشاهده پروفایل

استاد

• Artificial Intelligence
• Computational Rationality
• Decision Support Systems
• +۶ مورد دیگر

Steven O. Kimbrough is a Professor of Operations, Information and Decisions at the Wharton School, University of Pennsylvania. His research spans artificial intelligence, computational rationality, and strategic optimization with applications to political science, economics, and service innovation. He teaches courses like Agents, Games, and Evolution and Thinking With Models , focusing on experimental approaches to bounded rationality and uncertainty in decision-making. Primary Email: kimbrough@wharton.upenn.edu Office: 3730 Walnut Street, 565 Jon M. Huntsman Hall, Philadelphia, PA 19104 His research interests include: Artificial intelligence and metaheuristics for constrained optimization Evolutionary computation in electoral redistricting Agent-based modeling of market dynamics Logic modeling for normative reasoning Text mining applications in event analysis Publications demonstrate expertise in computational economics, political modeling, and service analytics. Recent work focuses on: Empirical validation of electoral compactness Strategic learning in oligopolies Multi-objective matching algorithms Feasible-infeasible solution spaces Service network optimization Teaching emphasizes: Game-theoretic approaches to strategic behavior Modeling life-cycle for energy sustainability Computational experiments in social science

Arul Mishraمشاهده پروفایل

استاد

• Consumer Behavior
• Judgment and Decision-Making
• Risk Perception
• +۴ مورد دیگر

Arul Mishra is a David Eccles Professor of Marketing at the University of Utah , affiliated with the David Eccles School of Business . Her academic career spans roles such as Faculty Fellow and Emerging Scholar , focusing on consumer behavior and decision-making processes. Ph.D. in Marketing , University of Iowa MBA in Marketing Bachelor in Mathematics and Physics Her research explores consumer decision-making , behavioral promotions , risk perception , and financial decision-making , utilizing methodologies like predictive analytics and machine learning . Recent publications investigate topics such as clustering algorithms , temporal decision-making , and spatial bias . Arul Mishra has received prestigious awards including the David Eccles Professor title and the Outstanding Reviewer Award from the Journal of Consumer Research . Her work has been featured in top-tier journals like Marketing Science and Psychological Science , with popular mentions in Scientific American and The Wall Street Journal .

• Semantic Web
• Knowledge Representation
• Artificial Intelligence
• +۶ مورد دیگر

Deborah L. McGuinness is the Tetherless World Senior Constellation Chair and Professor of Computer, Cognitive, and Web Sciences at Rensselaer Polytechnic Institute (RPI). She serves as founding director of the Web Science Research Center, focusing on next-generation ontology-enabled research infrastructure for interdisciplinary applications. Leaders in Semantic Web and AI research Bridging AI with eScience and healthcare Developer of semantic frameworks for data-intensive resource efforts Her research spans artificial intelligence, knowledge representation, clinical decision support systems, and ontology engineering. She investigates how semantic technologies can enhance AI explainability, data interoperability, and interdisciplinary science. Her recent publications highlight trends in AI explainability frameworks Knowledge graph construction Clinical decision support Commonsense reasoning benchmarks Health informatics Data harmonization Scientific awards include Fellow of the American Association for the Advancement of Science (AAAS) Robert Engelmore Award from the Association for the Advancement of Artificial Intelligence (AAAI) She leads large data-intensive research efforts and has contributed to projects involving blockchain integration with semantic web standards, FAIR data principles, and incentivized research data sharing mechanisms.

Bradley Hayesمشاهده پروفایل

دانشیار

• Human-Robot Interaction
• Explainable AI (XAI)
• Collaborative Robotics
• +۵ مورد دیگر

Bradley Hayes is an Associate Professor of Computer Science and Director of the Collaborative AI and Robotics (CAIRO) Laboratory at the University of Colorado Boulder. His work focuses on enabling autonomous agents and robots to collaborate safely and effectively with humans through techniques in human-robot interaction, explainable AI, and learning from demonstration. PhD in Computer Science, Yale University (2015) Postdoctoral Associate, MIT Interactive Robotics Group Research interests span collaborative robotics, dependable AI systems, and imitation learning for human-robot teaming, with applications in manufacturing, healthcare, and autonomous vehicles. His lab develops methods for task planning, motion prediction, and trust calibration in human-machine partnerships. Recent publications emphasize explainable sequential decision-making, neuromorphic learning architectures, and augmented reality interfaces for robotic training. Awards include Sustainability Recognition (2025) for motion planning efficiency Best Student Paper Runner-up (AAMAS 2022) Best Technical Paper Runner-up (HRI 2019) As lab director, he has mentored 12 PhD/Master students to completion, including Matthew Luebbers, Aaquib Tabrez, and Christine Chang. Funding sources include NSF grants and industry partnerships like Circadence, where he serves as Chief Technology Officer.

Xiaoyang Wangمشاهده پروفایل

مدرس ارشد

• Graph Processing
• Graph Neural Networks
• Spatial Data Processing
• +۱۰ مورد دیگر

Xiaoyang Wang is a Senior Lecturer in the School of Computer Science and Engineering (CSE) at the University of New South Wales (UNSW). He holds a Bachelor's and Master's degree in Computer Science from Northeastern University, China, and earned his PhD from CSE UNSW. Dr. Wang's research focuses on database systems with a special emphasis on query processing and data mining on large-scale graph, spatial, and streaming data. His expertise extends to data-driven machine learning, smart contract analysis on blockchain, and FinTech with financial network analysis. His work spans Graph Processing, Graph Neural Networks, Spatial Data Processing, AI for Databases (AI4DB), Database for AI (DB4AI), and FinTech applications. His publication record shows significant contributions to the field with 7 book chapters, 56 journal articles, 61 conference papers, 7 edited conference proceedings, and 4 conference abstracts. Recent publications (2022-2025) demonstrate his strong research trajectory in advanced graph processing techniques, neural network applications, and innovative database approaches. Key themes include hierarchical contrastive learning, robust attack frameworks, temporal graph processing, influence maximization, knowledge graph-enhanced reasoning, and rumor mitigation. Dr. Wang actively recruits PhD students interested in pursuing research in related fields and encourages current undergraduate and master's students at UNSW to contact him about research opportunities. He maintains an active research agenda with practical implications for industries dealing with large-scale network data, financial technology applications, and data-intensive systems. He can be reached at xiaoyang.wang1@unsw.edu.au and is located in Engineering building K17-501D at UNSW.

Haobin Niمشاهده پروفایل

پژوهشگر ارشد

• Programming Languages
• Software Engineering
• Formal Verification
• +۵ مورد دیگر

Haobin Ni is a Postdoctoral Scholar at the University of Washington in the Programming Language and Software Engineering (PLSE) group, advised by Professor Zachary Tatlock. His research bridges theoretical formal methods with practical systems development in programming languages and security. Education: Ph.D. in Computer Science, Cornell University (2024). Dissertation: "Formal Modeling Languages for High-assurance Domain-specific Systems." Advisors: Greg Morrisett and Robbert van Renesse. Ni's research focuses on language design, program analysis, and compiler optimization with emphasis on formal verification of distributed systems, concurrent programs, and parsers. He pioneers secure smart contract languages using information flow control type systems and develops novel protocols for distributed systems. His work consistently targets high-assurance systems where correctness and security are critical, spanning blockchain, binary parsing, and state machine replication. Analysis of his 11 publications (2019-2024) reveals three dominant research thrusts: compositional security frameworks for smart contracts (especially against reentrancy attacks), provably correct implementations of critical infrastructure like ASN.1 parsers, and modular abstractions for distributed ledger technologies. His methodology combines deep theoretical formalization with practical implementation, resulting in tools and protocols adopted in real-world systems. Scientific awards: Best Paper Award, IEEE Symposium on Security and Privacy (2021) ICPC World Finals Gold Medal (2016) ICPC World Finals Silver Medal (2014) Ni actively mentors through competitive programming: he coached Cornell's ICPC team (2018-2024), leading them to World Finals qualifications in 2019 and 2023, and volunteered for high school programming contests. Currently, he leads the PLSE Programming Languages Reading Group (PLRG) at the University of Washington, fostering community engagement in PL research. His work shows strong industry collaboration, particularly with Microsoft Research on blockchain and security projects. As a core member of UW's PLSE group, Ni contributes to one of academia's leading programming languages research teams. His current leadership of the PLRG demonstrates active community building, while his technical work on Charlotte and ASN1★ positions him at the forefront of secure distributed systems research.

Aws Albarghouthiمشاهده پروفایل

پژوهشگر

• Program Synthesis
• Formal Verification
• Quantum Computing
• +۵ مورد دیگر

Aws Albarghouthi is affiliated with the University of Wisconsin-Madison, USA. He is an active researcher with significant contributions to program synthesis, formal verification, and machine learning. Key roles: Author, Session Chair, Committee Member in conferences like PLDI, POPL, VMCAI, SPLASH, and ICFP. Research spans quantum computing, differential privacy, and static analysis. Research Trends include: Quantum Circuit Compilation and Optimization Probabilistic Verification of Fairness and Privacy Synthesis of Datalog and MapReduce Programs Neural-Augmented Static Analysis Bias Detection in Data Security Robustness in Machine Learning

Anna Rogersمشاهده پروفایل

دانشیار

• Natural Language Processing (NLP)
• Large Language Models (LLMs)
• Model Interpretability
• +۴ مورد دیگر

Anna Rogers is a tenured Associate Professor at the IT University of Copenhagen , where she leads research in the Data Science Section . Her work bridges NLP , large language models (LLMs) , and sociotechnical impacts of AI , with a focus on interpretability, robustness, and ethical frameworks. She serves as co-editor-in-chief of ACL Rolling Review and is a Villum Young Investigator and ELLIS fellow . Education: PhD in Computational Linguistics (University of Tokyo), Postdocs in Machine Learning for NLP (University of Massachusetts, Lowell) and Social Data Science (University of Copenhagen) Her research explores how LLMs can be designed for transparency and fairness, with recent grants like the Villum Synergy and Inge Lehmann supporting interdisciplinary collaborations. She organizes workshops (e.g., Dagstuhl seminar 24052) and co-leads projects like CAISA (National Centre for Artificial Intelligence in Society). Key themes include: Interpretability in NLP Robustness against synthetic content Data governance and ethical use Peer review systems and academic publishing Her lab has hosted researchers such as Max Müller-Eberstein (funded by DFF) and is actively recruiting for projects on generalization benchmarks and data attribution. She emphasizes collaboration across academia, industry, and policy, shaping Denmark's data science future through roles like the Danish Data Science Academy committee. Recent articles highlight trends in corpus analysis , temporal annotation , and LLM governance .

• Machine Learning
• Cybersecurity
• IoT
• +۳ مورد دیگر

Pedro Miguel Sanchez Sanchez is a researcher affiliated with the University of Murcia , specializing in Machine Learning , Cybersecurity , and IoT . He earned his doctorate in 2024 with the thesis Identical IoT device identification via hardware performance fingerprinting and Machine Learning , supervised by Dr. Alberto Huertas Celdrán and Dr. Gregorio Martínez Pérez. Research Interests Pedro's work focuses on applying Machine Learning and Federated Learning to solve critical challenges in Cybersecurity and IoT environments. His research includes: Developing decentralized federated learning frameworks (e.g., Flighter, ProFe) for secure and efficient model training. Designing malware detection systems using system call data and large language models . Enhancing IoT device authentication via hardware fingerprinting techniques. Exploring moving target defense strategies to counter zero-day attacks on IoT networks. Building knowledge graphs for cyber defense applications. Recent Publications Pedro's 2025–2024 publications demonstrate a strong focus on decentralized federated learning , malware mitigation , and hardware-based security . Key trends include: Advancements in zero-shot learning for multilingual tasks on edge devices. Security frameworks (e.g., Cyberforce, Sentinel) for military reconnaissance and industrial IoT . Behavioral analysis techniques for ransomware detection and continuous authentication . Robustness studies on federated learning architectures under adversarial conditions. Creation of benchmarks like LwHBench for hardware performance evaluation. Collaborations He has collaborated with researchers in the Intelligent Systems and Telematics group, contributing to projects in 5G security , crowdsensing platforms , and trusted execution environments .

Yves Le Traonمشاهده پروفایل

استاد

• Machine learning systems
• Software testing
• Software security
• +۸ مورد دیگر

Dr. Yves Le Traon is a Full Professor of Computer Science at the University of Luxembourg, where he serves as Vice-Director of the Interdisciplinary Centre for Security, Reliability and Trust (SnT). He leads the 25-member SerVal research group (SEcurity, Reasoning and VALidation), focusing on software testing, security, and data-intensive systems. Previously, he chaired the CSC Research Unit (2013-2016) and pioneered model-driven engineering at INRIA. PhD and engineering degree in Computer Science from Institut National Polytechnique, Grenoble (1997) Former Associate Professor at University of Rennes (1998-2004) His research spans three main areas: innovative software testing and repair , Android security through static analysis and machine learning , and robust machine learning system design . Collaborations include industry leaders like PayPal, CREOS, and Cebi in fintech, smartgrid, and industry 4.0 domains. Awarded IEEE Fellow (2022) and Facebook Testing & Verification Research Award (2019) , he chairs editorial boards for STVR, SoSym, and IEEE Transactions on Reliability. His team has produced 20+ PhD graduates including Li Li (Monash University), Donia El Kateb (European Investment Bank), and Alexandre Bartel (SnT Research Associate). Commercial impact includes co-founding Datathings for runtime AI decision systems.