جست‌وجوی استاد و پژوهشگر

Amir Shayan Ahmadianمشاهده پروفایل

پژوهشگر

• Software Engineering
• Privacy-Preserving Technologies
• Data Spaces
• +۴ مورد دیگر

Dr. Amir Shayan Ahmadian is a Researcher at the Institute of Software Engineering within Department 4 at the University of Koblenz-Landau. His primary role involves software engineering research and teaching responsibilities. He has been actively involved in academic activities since at least 2015, with notable contributions to privacy-preserving technologies, model-based analysis, and industrial data ecosystems. His work emphasizes ethical AI, algorithmic fairness, and privacy-by-design frameworks. Research Interests: His core research focuses on privacy engineering , data space architectures , algorithmic fairness , and model-driven software development . He explores practical solutions for integrating privacy into system design, developing methodologies for detecting biases in AI, and creating scalable frameworks for secure industrial data sharing. His work often bridges theoretical computer science with real-world applications in healthcare, logistics, and manufacturing. Teaching Highlights: He has taught Web Engineering (2015-2019) and supervised seminars on Engineering Responsible Information Systems (2018). These courses emphasize ethical technology development and responsible innovation. Publications Overview: Ahmadian's recent work (2018-2024) reflects a strong focus on privacy-preserving systems and fairness in AI. Key themes include health data spaces, model-based verification for discrimination analysis, and industrial 4.0 platforms. His publications often propose novel architectures, methodologies, and tools to address emerging challenges in privacy, security, and algorithmic ethics. Labs/Teams: He contributes to the development of the VisiOn Privacy Platform , a tool for visual privacy management, and collaborates on projects like MBFair (model-based fairness verification). His work integrates closely with interdisciplinary teams focusing on healthcare IT, industrial automation, and cybersecurity.

Mounim El Yacoubiمشاهده پروفایل

استاد

• Biometrics
• Machine Learning
• Healthcare Technology
• +۳ مورد دیگر

Professor Mounim El Yacoubi is affiliated with Telecom SudParis, where he holds a Professorship in the ARMEDIA research group. His work spans biometrics, healthcare AI, computer vision, and transportation systems. He has published extensively on topics like vein recognition, Alzheimer's detection via handwriting analysis, and demand-responsive transit optimization. His research integrates machine learning, deep learning, and generative adversarial networks (GANs) to address challenges in biometric security, agricultural monitoring, and medical diagnostics. Key contributions include: Developing vein recognition systems (finger/palm) resilient to adversarial attacks. Pioneering applications of handwriting and voice analysis for early Alzheimer's diagnosis. Advancing models for personalized diabetes management through glucose prediction. Optimizing urban transit accessibility using demand-responsive systems. Contributing to historical Arabic document processing via deep learning. His work frequently appears in top-tier journals like IEEE Transactions and Pattern Recognition , and he has co-edited books on pattern recognition and AI. He holds patents in population estimation and trajectory reconstruction using mobile data.

Zhaodan Kongمشاهده پروفایل

دانشیار

• Control Theory
• Machine Learning
• Formal Methods
• +۴ مورد دیگر

Zhaodan Kong is an Associate Professor in the Department of Mechanical and Aerospace Engineering at the University of California, Davis. He leads the Cyber-Human-Physical Systems Lab , focusing on interdisciplinary research at the intersection of control theory, machine learning, and formal methods. His work applies these techniques to autonomous systems, human-autonomy teaming, cyber-physical systems, and neural engineering. His research emphasizes interpretable and safe systems, with applications in robotics, healthcare, and environmental monitoring. Research interests include control theory, machine learning, formal methods, and their applications to autonomous systems. Recent efforts explore energy-efficient UAV trajectory optimization, human-AI collaboration frameworks, and fault diagnosis in cyber-physical systems. His lab develops frameworks for cohort explanation in machine learning models and studies trust dynamics in human-autonomy interactions. Publications span topics such as UAV mission planning, neural dynamics modeling, and interpretable AI. His work bridges theoretical foundations with practical applications, aiming to advance autonomous systems' reliability and human-system integration. Office hours are held during Winter 2025, indicating active academic engagement.

Massimiliano Todiscoمشاهده پروفایل

استاد

• Audio and Speech Technologies
• Voice Biometrics
• Deepfake Detection
• +۷ مورد دیگر

Massimiliano Todisco is a Professor at EURECOM, specifically within the Digital Security Department, where he leads research in audio and speech technologies. His work focuses on enhancing security and privacy in voice-based systems through advanced machine learning techniques. His research interests include voice biometric authentication, deepfake detection, and privacy-preserving technologies. He develops explainable deep neural network architectures and applies adversarial learning and generative AI to improve system robustness and transparency. These efforts are critical in combating synthetic media threats and securing identity verification systems. The recent publications and research activities indicate a strong focus on biometric security, adversarial attacks on voice systems, and the application of AI in digital forensics. His work bridges theoretical innovation with practical deployment in secure environments. Speaker, LIAvignon 2025 Workshop on adversarial attacks in voice biometrics Invited lecturer, AI-DLDA 2025 International Summer School on voice deepfake detection Contributor, BIOSIG 2025 – International Conference on Biometrics He is currently supervising PhD candidates and expanding his research team, with open positions advertised in voice biometrics, deepfake detection, and privacy preservation. While specific grants are not listed, his active recruitment and speaking engagements suggest ongoing funded research projects. Massimiliano Todisco is part of the research team at EURECOM's Digital Security Department, contributing to cutting-edge projects in secure speech technologies and AI-driven biometric systems. The lab actively collaborates on international workshops and conferences, fostering a dynamic research environment.

Birhanu Esheteمشاهده پروفایل

دانشیار

• Trustworthy Machine Learning
• Cybersecurity
• Privacy
• +۶ مورد دیگر

Birhanu Eshete is an Associate Professor at the University of Michigan-Dearborn in the Department of Computer and Information Science within the College of Engineering and Computer Science. He leads the Data-Driven Security & Privacy Lab (DSPLab) and serves as a faculty affiliate at the Michigan Institute for Data Science (MIDAS) . His academic journey includes a Ph.D. from the University of Trento , M.Sc. and B.Sc. from Addis Ababa University . Research Focus : Trustworthy Machine Learning (security, privacy, transparency, fairness), Cyber Threat Intelligence, and Cybercrime Analysis. Teaching : Courses in Trustworthy AI, Data Security & Privacy, and Compiler Design. Awards : 2023 NSF CAREER Award (#2238084) 2021 NSF Research Grant (#2103596) 2020 U.S. Patent (#0059481) 2018 Distinguished Paper at USENIX Security His recent publications emphasize adversarial machine learning , privacy-preserving techniques , and cybersecurity innovation . Former students hold positions at Google , Amazon , and Tesla , while current advisees include Ph.D. candidates and Master's students focusing on cutting-edge security and ML research.

Will Crichtonمشاهده پروفایل

استادیار

• Programming Languages
• Human-Computer Interaction
• Type Systems
• +۴ مورد دیگر

Will Crichton is an Assistant Professor of Computer Science at Brown University. His research bridges programming language theory and cognitive psychology to improve developer experience, particularly in Rust and type systems. He has developed tools like Flowistry (Rust IDE plugin) and Argus (trait error debugger) used by over 100,000 developers. Education: PhD from Stanford University (2022), advised by Pat Hanrahan and Maneesh Agrawala Prior Roles: Postdoctoral researcher with Shriram Krishnamurthi at Brown University His research focuses on: Type Systems: Creating usable type-level programming frameworks like Tyrade Rust Ecosystem: Ownership models, trait error debugging, and learning tools Human Factors: Applying cognitive psychology to programming language design Key contributions include: Interactive visualizations for Rust (AquaScope) Scientific programming workshops and educational materials Foundational work on security type systems and session types in Rust

Claire Tomlinمشاهده پروفایل

استاد

• Hybrid Systems
• Control Theory
• Unmanned Aerial Vehicles (UAVs)
• +۵ مورد دیگر

Claire Tomlin is a Professor at the University of California, Berkeley, holding the Charles A. Desoer Chair in Engineering. She works at the intersection of hybrid systems, control theory, and robotics, with applications to air traffic management, biological cell networks, and autonomous systems. Ph.D. (EECS) UC Berkeley (1998) M.Sc. (Electrical Engineering) Imperial College, London (1993) B.A.Sc. (Electrical Engineering) University of Waterloo (1992) Research Interests: Her work focuses on hybrid systems (combining continuous/discrete dynamics), decentralized optimization , and human-automation systems . Key applications include UAV control , air traffic automation , and biological modeling (e.g., HER2+ cancer, Drosophila development). Selected Articles (2024-2025) span topics from dynamic programming for autonomous farms to deep learning-based safety filters , multi-agent reinforcement learning , and perception uncertainty analysis . Recent trends emphasize certifiable safety in AI-driven systems and Hamilton-Jacobi reachability for high-dimensional problems. Scientific Honors: MacArthur Fellow (2006) IEEE Fellow (2010) National Academy of Engineering Member (2019) American Academy of Arts and Sciences Member (2019) IEEE Transportation Technologies Award (2017) Advising & Collaborations: She has advised 21 Ph.D. students and 5 postdoctoral researchers. Her group collaborates with institutions like OHSU, LBNL, and Stanford. She leads the VeHICaL project on verified human-robot interfaces. Labs & Teams: Affiliated with the Berkeley Artificial Intelligence Research Lab (BAIR), VeHICaL, and the Berkeley Center for New Media (BCNM). Her work integrates with the UC Berkeley robotics ecosystem, including the SWARM Lab and FORCES.

Markus Krötzschمشاهده پروفایل

استاد

• Knowledge Graphs
• Semantic Web
• Ontologies
• +۶ مورد دیگر

Markus Krötzsch is a Professor for Knowledge-Based Systems at the Institute for Theoretical Computer Science, Faculty of Computer Science, Technische Universität Dresden. He is also affiliated with TUD's Center for Advancing Electronics (cfaed) and directs the School of Embedded Composite AI (SECAI). His work bridges theoretical research and practical applications in knowledge representation and semantic technologies. Current Projects: Wikidata, Semantic MediaWiki, Nemo (Datalog Engine), ELK Reasoner Areas: Intelligent Knowledge Management, Ontology Languages, Rule-Based Reasoning His research focuses on knowledge graphs , semantic web technologies , and rule-based systems for data integration and reasoning. He contributes to standards like OWL 2 and develops tools for collaborative data management and large-scale knowledge processing. Recent publications highlight advancements in Datalog reasoning (2024-2025), with applications in knowledge graph querying, ontology languages, and computational logic. Keywords include semantic technologies, knowledge representation, and scalable rule engines. He supervises students working on topics like reasoning complexity , chase algorithms , and rule-based data access . His activities include editorial roles (Area Editor of Transactions on Graph Data and Knowledge ) and leadership in academic organizations like the Open Semantic Data Association.

Tomáš Kliegrمشاهده پروفایل

استاد

• Explainable machine learning
• Cognitive biases in machine learning
• Natural language processing
• +۴ مورد دیگر

Tomáš Kliegr is a Professor at the Faculty of Informatics and Statistics of Prague University of Economics and Business, leading the Data Science & Explainable AI (DSXAI) research team. He specializes in explainable machine learning, cognitive biases in AI, natural language processing for entity recognition and knowledge graphs, and preference learning. 2025: Professor of Applied Informatics at Prague University of Economics and Business 2019: Associate Professor (docent) in Applied Informatics at Prague University of Economics and Business 2017: PhD in Research in Electronic Engineering from Queen Mary University London, recognized in the Czech Republic as Ph.D. in Artificial Intelligence and Biocybernetics 2012: PhD in Applied Informatics at Prague University of Economics and Business His research focuses on explainable and interpretable machine learning, particularly rule-based models, cognitive biases in AI, natural language processing, and knowledge graphs. He has contributed to software tools like arc, action-rules, and pyIDS, and leads the EasyMiner.eu project. His articles between 2018 and 2025 span topics like counterfactual explanations, text clustering visualization, SARS-CoV-2 diagnostic modeling, and rule-based ML optimization. He co-organizes the RuleML Webinars and the XI-ML workshop, and was a program committee member for RuleML+RR 2020. Best paper award at RuleML Challenge 2019 He has advised PhD students including Lukas Sykora and Stanislav Vojíř, and collaborates with institutions like the Czech Academy of Sciences and Lawrence Berkeley National Laboratory. His work addresses the intersection of machine learning interpretability and human cognitive factors, with applications in public health and semantic web technologies.

Jonas Theinerمشاهده پروفایل

پژوهشگر

• Sports Analytics
• Computer Vision
• Deep Learning
• +۳ مورد دیگر

Jonas Theiner is a Researcher at the Technische Informationsbibliothek (TIB) – German National Library of Science and Technology , affiliated with the Data Science & Digital Libraries division. His work bridges Sports Analytics , Computer Vision , and Deep Learning , focusing on camera calibration for sports field registration and enriching player position data with visual information. Previously, he worked on large-scale visual place recognition from photos. Research Trends : His publications since 2019 demonstrate expertise in: Camera calibration for soccer field registration Monocular depth estimation analysis Visual geolocation of photos Multimodal sports data integration Football formation classification Scientific Recognition : Best Paper Award at ACM International Conference on Multimedia Retrieval (ICMR 2020)

• software verification
• automated reasoning
• code synthesis
• +۳ مورد دیگر

Ruzica Piskac is a Professor of Computer Science at Yale University's School of Engineering & Applied Science, where she leads the Rigorous Software Engineering (ROSE) group. She joined Yale's Department of Computer Science in 2013 after serving as an Independent Research Group Leader at the Max Planck Institute for Software Systems in Germany. Her educational background includes a PhD from the Swiss Federal Institute of Technology (EPFL) in 2011, where her dissertation won the Patrick Denantes Prize. Her advisor was Viktor Kuncak, and her thesis focused on Decision Procedures for Program Synthesis and Verification . Piskac's research centers on improving software reliability and trustworthiness through formal techniques. Her primary interests span software verification, security and applied cryptography, automated reasoning, and code synthesis. She has developed innovative approaches in symbolic execution for Haskell, privacy-preserving formal methods, functional reactive synthesis, and verification for configuration files. Her work bridges theoretical foundations with practical applications in security, programming languages, and AI systems. Her recent publications (2022-2024) demonstrate a strong focus on applying formal methods to emerging challenges in AI, legal reasoning, quantum computing security, and zero-knowledge proofs. There's a clear trend toward interdisciplinary work connecting formal verification with legal accountability, large language models, and security applications. Multiple Amazon Research Awards (2021-2023) for work on formalizing legal reasoning and automated reasoning Yale University's Ackerman Award for Teaching and Mentoring Facebook Communications and Networking Award Microsoft Research Award for the Software Engineering Innovation Foundation Donna L. Dubinsky Associate Professor of Computer Science (2019) Patrick Denantes Prize for her PhD dissertation Piskac has graduated five PhD students, four of whom now hold assistant professor positions in computer science. Her research has been supported by multiple Amazon Research Awards and other funding sources. She has served as Program Chair of the 37th International Conference on Computer Aided Verification and on the Steering Committee of the Formal Methods in Computer-Aided Design conference. Her ROSE group at Yale conducts cutting-edge research in formal methods with applications across software engineering, security, and AI systems.

Arie van Deursenمشاهده پروفایل

استاد

• Software Engineering
• Software Testing
• Trustworthy AI
• +۵ مورد دیگر

Arie van Deursen is a Professor in Software Engineering at Delft University of Technology, where he is affiliated with the Department of Software Technology within the Faculty of Electrical Engineering, Mathematics, and Computer Science (EEMCS). He previously served as head of department from 2016-2023 and currently chairs the Software Engineering Research Group (SERG). He is also the scientific co-director of AI4SE, a five-year collaboration between JetBrains and TU Delft investigating novel uses of artificial intelligence in software engineering. His research interests span software engineering, software testing, trustworthy AI, language models for code, software architecture, human aspects of software engineering, and digitalization in the financial and public sector. His work often bridges academic research with practical industry applications, as evidenced by his co-founding of two companies: The Software Improvement Group (2000) and PerfectXL (2010/2013). Recent publications show a strong focus on large language models in software engineering contexts, with multiple 2024-2025 papers examining code generation quality, multilingual code comments, contamination-free code datasets, and license infringement concerns in LLM training data. His research demonstrates significant engagement with the challenges and opportunities presented by AI in software development. AIWare 2024 ACM SIGSOFT Distinguished Paper Award ASE 2024 ACM SIGSOFT Distinguished Paper Award CAIN 2023 Distinguished Paper Award CSCW 2015 ACM SIGCHI Best Paper Award EASE 2016 Best Paper Award Professor van Deursen has (co-)supervised 50 PhD students and 100 MSc students throughout his career. He serves on the Advisory Council on IT Assessment (AcICT) of the Dutch government (since 2022) and the Advisory Board of ING Bank Netherlands (since 2024). He was elected as a fellow of the Netherlands Academy of Engineering (NAE) in 2023. He leads the AI4SE lab, which hosts 10 PhD students, dozens of MSc and BSc students, and collaborates with many TU Delft faculty members and JetBrains specialists. The lab focuses on investigating novel uses of artificial intelligence in software engineering, with particular attention to practical applications and industry relevance.

• Logic Programming
• Abstract Interpretation
• Program Analysis
• +۷ مورد دیگر

J. M. Howe is a Professor in the Department of Computer Science at City, University of London, with an active research career spanning over 25 years from 1997 to the present. With an ORCID identifier 0000-0001-8013-6941, Howe has established himself as a prominent researcher in formal methods, logic programming, and the intersection of symbolic and neural approaches to artificial intelligence. Howe's research interests center on abstract interpretation, program analysis, constraint solving, and more recently, neural-symbolic AI. His early work focused on logic programming and abstract domains, particularly systems of two variables per inequality (TVPI), which has remained a consistent thread throughout his career. In recent years, he has expanded into explainable AI, rule extraction from neural networks, and the development of the Neural Multi-Space (NeMuS) framework for integrating symbolic and neural approaches. His research has practical applications in program verification, security (particularly cross-site scripting detection), and agricultural technology. Analysis of Howe's publication trajectory reveals a consistent focus on foundational programming language theory that gradually evolved toward machine learning applications while maintaining strong theoretical underpinnings. His work shows increasing collaboration with researchers in neural networks while preserving his expertise in formal methods, creating a distinctive research niche at the intersection of symbolic and connectionist AI approaches. Howe has made significant contributions to the understanding of widening operators in abstract interpretation, constraint solving techniques, and methods for extracting interpretable rules from black-box neural networks. His research demonstrates both theoretical depth and practical applicability across multiple domains including software verification, security analysis, and computer vision applications. His collaborative work spans multiple institutions, with frequent collaborations with researchers like A. King, M. Brain, F. Mereani, and E. Robbins. This pattern of collaboration demonstrates his integration within both the formal methods and machine learning research communities.

Roee Shragaمشاهده پروفایل

استادیار

• Data Integration
• Data Discovery
• Human-in-the-loop Machine Learning
• +۷ مورد دیگر

Roee Shraga is an Assistant Professor in the Computer Science department and Data Science program at Worcester Polytechnic Institute (WPI). He previously held a Postdoctoral Research Associate position at the Data Lab within Khoury College of Computer Sciences at Northeastern University in Boston, MA. Ph.D. in Data Science, Technion – Israel Institute of Technology (2020) M.E. in Data Science, Technion (2017) B.Sc. in Industrial Engineering and Management (Information Management Engineering), Technion (2015) Shraga’s research focuses on Data Integration , Data Discovery , Human-in-the-loop Machine Learning , Information Retrieval , and Large Language Models (LLMs) . His work bridges data management, machine learning, and human-computer interaction to improve data quality in AI systems. Recent publications include advancements in LLM-driven data integration , semantic table union search , and contrastive entity resolution . Key contributions include the Explain-Da-V framework for semantic data versioning and FlexER for intent-aware entity resolution. Scientific recognition includes: iiWAS 2024 best paper award TaDA@VLDB24 best paper award Council for Higher Education (VATAT) scholarship Leonard & Diane Sherman Interdisciplinary Fellowship (2017) Daniel Excellence Scholarship (2019) Miriam & Aaron Gutwirth Memorial Fellowship (2020) He leads NSF-funded research on Human-in-the-loop AI (CRII grant) and co-organizes the HILDA workshop. His teams have won multiple Outstanding MQP Awards for undergraduate research.

• Artificial Intelligence
• Human-Computer Interaction
• User Modeling
• +۵ مورد دیگر

Cristina Conati is a Professor at the University of British Columbia, Canada, with an extensive research portfolio spanning artificial intelligence, human-computer interaction, and educational technology. Her work focuses on developing intelligent systems that adapt to user characteristics and behaviors, particularly through the analysis of eye-tracking data and other implicit user signals. Her research interests include user-adaptive visualizations, intelligent tutoring systems, explainable AI, and eye-tracking analysis. She investigates how AI can be used to personalize user experiences, particularly in educational contexts, by modeling user cognitive abilities, emotional states, and learning behaviors. Her work often bridges theoretical AI research with practical applications in education and decision support. Analysis of her recent publications (2023-2025) shows a strong focus on explainable AI in educational contexts, adaptive visualization techniques, and the use of eye-tracking to understand and support user interactions. Her research demonstrates a consistent trajectory toward making AI systems more transparent, adaptive, and effective for diverse users across various domains including education, healthcare, and decision-making. Extensive work on user modeling through eye-tracking data Development of adaptive visualization techniques Research on explainable AI for educational applications Studies on how individual differences affect user-system interaction Her research has significant implications for designing more effective educational technologies, decision support systems, and user interfaces that can adapt to individual user needs and characteristics. She has been instrumental in advancing the field of user-adaptive interfaces through both theoretical contributions and practical implementations.