جست‌وجوی استاد و پژوهشگر

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Xiaodan Zhu is an Associate Professor at Queen's University's Department of Electrical and Computer Engineering, affiliated with the Vector Institute for Artificial Intelligence and Ingenuity Labs Research Institute. He leads the Text Analytics and Machine Learning Lab (TAML), focusing on NLP, machine learning, and AI applications in areas like sentiment analysis, legal text, and healthcare. His Ph.D. (2010, University of Toronto) and M.Eng. (2000, Tsinghua University) form the foundation of his research career. Research Interests: Natural Language Processing (NLP), Machine Learning, AI ethics, legal AI solutions, and healthcare informatics. Notable contributions include the Tree-LSTM architecture, adversarial attacks on model calibration, and counterfactual recognition systems. His work has been published in top venues like ACL, NAACL, and EMNLP. Awards: Adam Kilgarriff *SEM Best Paper Award (2015/2024), JP Morgan Faculty Research Award (2022/2023), NSERC Discovery Accelerator Supplement. Key roles include Co-Editor-in-Chief for ACL Rolling Review, Associate Editor for IEEE/ACM TASLP, and organizing SemEval tasks. Professional Affiliations: Cross-joint appointment at the Scotiabank Centre for Customer Analytics. Supervised over 20 students, including recipients of Vector Institute Scholarships and NSERC awards. Active in legal AI initiatives with Queen's Law School and financial text analysis collaborations with industry partners.

• Formal Methods
• Program Analysis
• Verification
• +۹ مورد دیگر

Florian Zuleger is an Associate Professor at TU Wien's Department of Formal Methods in Systems Engineering (E192-04). He has a 100% research focus and serves as Curriculum Coordinator for the Master’s program in Verification and Automated Reasoning. His work emphasizes automated methods for termination analysis, resource-bound estimation, verification of programs with dynamic data structures, parameterized systems, and automated feedback for introductory programming tasks. He has led projects funded by the European Commission (2024–2027), Austrian Science Fund (FWF), Amazon Research Awards, and Vienna Science and Technology Fund. Research Interests: Automated Program Verification Formal Methods for Concurrent Systems Separation Logic and Decision Procedures Resource and Complexity Analysis Parameterized Model Checking Inductive Logic in Verification Grants & Advising: Zuleger’s recent grants explore verification of safety-critical applications and automated cost analysis. He has advised numerous students on topics ranging from verified data structures to fault-tolerant algorithms. His projects include collaboration with Amazon, the European Commission, and FWF. Labs/Teams: Involved with the LogiCS Research Group, focusing on logical methods in computer science, and contributes to tool development (e.g., ATLAS, SpecBMC, SL-COMP competitions).

Corina Cirsteaمشاهده پروفایل

دانشیار

• Coalgebra
• Formal Verification
• Logic in Computer Science
• +۳ مورد دیگر

Dr. Corina Cirstea is an Associate Professor in the Agents, Interaction and Complexity research group at the School of Electronics and Computer Science, University of Southampton. Her research focuses on coalgebra, formal verification, logic, and their applications to computer science. She leads the COVER project (funded by the Leverhulme Trust), advancing quantitative verification through coalgebraic foundations. Dr. Cirstea holds a DPhil in Computation from the University of Oxford and a Junior Research Fellowship at St. John’s College, Oxford (1999–2003). Her research interests include coalgebraic logic, probabilistic systems, and automated verification. She has supervised PhD students in areas like formal methods and multiagent systems. Dr. Cirstea actively participates in academic leadership roles, including co-chairing IFIP TC1 WG 1.3 and editorial board membership at Compositionality . Her work bridges theoretical foundations with practical applications in system verification and agent-based systems. Education: DPhil in Computation (University of Oxford, 2000) Projects: COVER (Leverhulme Trust), EPSRC-funded modular verification work, Royal Society-funded temporal logics research Awards: Junior Research Fellowship (St. John’s College, Oxford)

Corina Mitrohinمشاهده پروفایل

پژوهشگر

• Verification of Hybrid Systems
• Safety Verification
• Stability Verification
• +۲ مورد دیگر

Corina Mitrohin is a Researcher at the Chair of Software Engineering, University of Freiburg. She completed her PhD between June 2007 and June 2012, focusing on verification techniques for hybrid systems. Her research interests include safety and stability verification of hybrid systems, with involvement in the Transregional Collaborative Research Center AVACS (Subproject H4). Education: PhD in Computer Science (University of Freiburg, 2012), Master's thesis on requirements engineering for conflict detection (2007), and Bachelor's in coding theory (2000) from Al. I. Cuza University, Iasi. Teaching: Taught the Seminar Hybrid Systems during Fall 2011/2012. Active in academic projects and publications on formal verification methodologies for complex systems.

• AI Ethics
• Network Analysis
• Machine Learning
• +۳ مورد دیگر

Dr. Doina Bucur is a prominent academic in the Electrical Engineering, Mathematics and Computer Science (EEMCS) faculty at the University of Twente. Her work bridges interdisciplinary research in AI ethics, network science, and computational sustainability. She leads the DECIDE project, which focuses on transparent AI decision-making systems impacting public services, healthcare, and insurance sectors. Her research spans machine learning applications in ecological networks, fault detection in sensor systems, and multi-objective optimization in social networks. She has pioneered methods for analyzing microbial community structures (e.g., gFlora), automated fault tree learning, and bias reduction in AI systems. Grants & Collaborations: Recipient of NWA ORC funding (2024) for the DECIDE initiative. Collaborates with over 50 researchers across disciplines including computer science, philosophy, law, and psychology. Active in developing evolutionary algorithms for network vulnerability analysis and multi-objective optimization. Labs & Teams: Core member of the Twente AI Ethics Lab and the Network Science Research Group. Leads interdisciplinary teams addressing real-world challenges through computational methods.

Xinyu Fengمشاهده پروفایل

استاد

• Programming Languages
• Formal Methods
• Program Verification
• +۴ مورد دیگر

Xinyu Feng is a Professor at the Department of Computer Science and Technology, Nanjing University, with research spanning programming languages, formal methods, and system software verification. His work focuses on developing rigorous frameworks for certifying low-level system programs, particularly those involving concurrency, preemptive threads, and hardware interrupts. Nanjing University, China Huawei (collaboration) Yale University, PhD in Computer Science (2007) Research Interests: His research integrates Separation Logic and Rely-Guarantee Reasoning to create modular verification techniques for concurrent systems. Key areas include: Formal verification of OS kernels and hypervisors Concurrency control in preemptive and interrupt-driven environments Parameterized memory models for relaxed concurrency semantics Certified compilation and proof-carrying code Optimistic concurrency algorithms and non-blocking data structures Modular verification of synchronization primitives Article Trends: His publications emphasize modular and compositional methods for concurrent program verification, with a strong focus on practical applications in system software. Recurring themes include integrating formal reasoning with real-world challenges like hardware interrupts, preemptive scheduling, and SDN programming. Scientific Awards: PLDI'19 Distinguished Paper Award Professional Leadership: Xinyu Feng has served on program committees for leading conferences (POPL, PLDI, ICFP, CAV) since 2005 and chairs sessions at events like PLDI and POPL. He actively contributes to advancing formal verification standards in the field.

M.J.N.V. Prasadمشاهده پروفایل

استاد

• Mechanical behaviour of materials
• Indentation
• Fatigue
• +۱۳ مورد دیگر

M.J.N.V. Prasad is a Professor in the Department of Metallurgical Engineering and Materials Science at the Indian Institute of Technology Bombay (IIT Bombay), where he has been serving since October 2012. He currently holds the Jindal Stainless Chair Professor position (May 2024–present) and has progressed from Assistant Professor (2012–2018) to Associate Professor (2018–2023) before attaining full Professorship in August 2023. Education: Ph.D. in Materials Engineering, Indian Institute of Science, Bangalore (2010) M.E. in Metallurgy, Indian Institute of Science, Bangalore (2004) B.Tech. in Metallurgical Engineering, National Institute of Technology, Warangal (2002) Diploma in Metallurgical Engineering, MRAGR Govt. Polytechnic, Vizianagaram (1997) His research focuses on the mechanical behaviour of materials , including indentation, fatigue, creep, and superplasticity. He also investigates thermo-mechanical processing of ferrous and non-ferrous alloys, electrodeposition and alloy development , material joining via diffusion bonding, severe plastic deformation techniques like HPT and FSP, and protective coatings with emphasis on oxidation behaviour. His work bridges fundamental material science with industrial applications, particularly in aerospace and high-temperature environments. The analysis of his recent publications reveals a strong emphasis on surface engineering, alloy development, and advanced joining techniques. His research integrates microstructural characterization with mechanical performance evaluation, particularly in high-performance alloys and steels. Key themes include the development of wear-resistant coatings, optimization of low-density steels, and understanding interfacial phenomena in dissimilar material joints. Scientific Awards: Excellence in Teaching (2023), IIT Bombay JSPS Invitational Fellowship (FY2022), Japan Society for the Promotion of Science Excellence in Teaching (2017), IIT Bombay Excellence in Teaching (2015), IIT Bombay INAE-Young Engineer Award (2013), Indian National Academy of Engineering Prof. Prasad has been actively involved in research funding and project leadership. He has secured grants from SERB-GE under IMPRINT-2c, Tata Steel, and DMRL, supporting projects on high-temperature Ni-based alloys, Zn-Mn nanocomposite coatings, and powder metallurgy superalloys for aero engines. While no students are listed, his projects suggest mentorship of researchers. His post-doctoral experience was at Brown University, USA (2011–2012). He is associated with advanced research facilities at IIT Bombay, though specific lab names are not mentioned. His collaborations span industrial partners and international institutions, including Nagoya University in Japan under the JSPS fellowship.

Razvan Marinescuمشاهده پروفایل

استادیار

• Machine Learning
• Generative Models
• Bayesian Inference
• +۱۰ مورد دیگر

Razvan Marinescu is an Assistant Professor in the Department of Computer Science and Engineering at the University of California Santa Cruz, where he leads a research lab focused on machine learning for healthcare, biomolecular systems, and neuroimaging. He is also the co-founder and CTO of GiwoTech Inc., a drug screening startup. Education: PostDoc, MIT CSAIL, Polina Golland’s Lab PhD in Computer Science, 2019, Center for Medical Image Computing, University College London MEng in Computer Science, 2014, Imperial College London BSc in Computer Science, 2013, Imperial College London Razvan's research spans machine learning, Bayesian inference, generative models, and their applications in medicine and molecular biology. His work includes modeling neurodegenerative diseases like Alzheimer’s and Posterior Cortical Atrophy, developing differentiable simulators for MRI, and advancing generative AI for medical image reconstruction and drug discovery. He has a strong interest in the mathematical foundations of statistical inference in machine learning. His recent publications demonstrate a strong trend toward using deep generative models, diffusion models, and Bayesian frameworks for solving inverse problems in medical imaging and simulating biological systems. Key themes include super-resolution MRI, microstructure reconstruction, and applying Malliavin calculus to diffusion models. Scientific Awards: Best Paper Award, NeurIPS Deep Generative Models and Downstream Applications Workshop (2021) Razvan advises a large group of PhD and master’s students and has secured significant research visibility through community challenges like TADPOLE. He has also contributed to open-source tools such as BrainPainter and actively collaborates with institutions worldwide. His lab receives support through academic grants and entrepreneurial funding via his startup. Labs and Projects: He leads a research group at UC Santa Cruz focusing on projects including AI for Material Science, Simulating a Virtual Cell, ML Compositionality, Image Reconstruction, Generative Modeling, ML Benchmarks, Disease Progression Modeling, Differentiable Simulators, ML for Molecular Dynamics, and Medical Visualization.

Pawel Sobocinskiمشاهده پروفایل

استاد

• Applied Category Theory
• Compositional Systems
• Theoretical Computer Science
• +۱۰ مورد دیگر

Paweł Sobociński is a Professor of Trustworthy Software Technologies at the Department of Software Science, School of Information Technologies, Tallinn University of Technology (TalTech), where he also leads the Laboratory for Compositional Systems and Methods. He is a principal investigator in several major research projects including the Estonian Research Council’s PRG1210 (ALICE), the EU-funded CHESS cybersecurity hub, and the EXAI Centre of Excellence in AI. His academic background includes faculty positions at the University of Southampton and research roles at the University of Cambridge and institutions in France and Italy. His research lies at the intersection of computer science and mathematics, with a focus on applied category theory. He investigates compositional modeling of systems, where the behavior of complex systems emerges from the structured interaction of their components. His work employs string diagrams, process algebras, Petri nets, and categorical semantics to develop formal methods for verifying and reasoning about concurrent and cyber-physical systems. He is particularly known for his contributions to graphical linear algebra and diagrammatic reasoning. The recent publications highlight a strong trend in diagrammatic methods, especially string diagrams, for expressing logic, algebra, and system behavior. These works span from foundational categorical structures to applications in electrical circuits, concurrency, and formal verification, demonstrating a unifying thread of compositional reasoning. His leadership in organizing conferences like LICS 2024 and ICALP 2024 underscores his central role in the theoretical computer science community. His scientific recognition includes numerous invited talks and tutorials at major international venues such as CONCUR, QPL, MFPS, and ETAPS, as well as leadership roles in academic organizations. He is an Associate Editor for journals including Compositionality , Mathematical Structures in Computer Science , and Logical Methods in Computer Science , and has served on the steering committee of ETAPS. Sobociński has supervised multiple PhD students and postdoctoral researchers, including Owen Stephens, Fabio Zanasi, Mario Román, and Elena di Lavore. He is also the director of the PhD programme in ICT at TalTech and has received research grants from the Estonian Research Council, the European Commission, and the Estonian Ministry of Education and Research, supporting his work in trustworthy software, AI, and cybersecurity. He leads the Laboratory for Compositional Systems and Methods at TalTech, a research group focused on developing and applying compositional techniques in software science, with applications in AI, security, and formal verification. The lab fosters international collaboration and is active in organizing workshops and summer schools.

Rolf Hennickerمشاهده پروفایل

دانشیار

• Formal Methods of Software Development
• Component-based Software Development
• Object-oriented Software Engineering
• +۲ مورد دیگر

Prof. Dr. Rolf Hennicker is an Associate Professor and Academic Director at the Department of Computer Science, Ludwig-Maximilians-Universität München (LMU Munich). He leads the Software and Computational Systems Lab, focusing on formal methods, component-based software engineering, and environmental simulation systems. His research emphasizes system specifications, concurrency, and dynamic logics for verifying complex systems. He actively supervises academic activities, including lectures, seminars, and diploma theses. As project leader of GLOWA-Danube and RAJA , he develops decision support systems and authorization frameworks. Collaborations include the École Normale Supérieure de Cachan (France) on ensemble modeling and verification. His work spans theoretical contributions to formal methods and practical applications in distributed systems, environmental modeling, and autonomous computing. Recent projects involve ensemble architectures and Helena frameworks for autonomic cloud systems.

Martha Lewisمشاهده پروفایل

استادیار

• Compositional Semantics
• Quantum Natural Language Processing
• Conceptual Spaces Theory
• +۳ مورد دیگر

Martha Lewis serves as Assistant Professor of Neurosymbolic AI at the Institute of Logic, Language, and Computation (ILLC) within the Faculty of Science at the University of Amsterdam. Her primary affiliation is with the Natural Language Processing & Digital Humanities research group. Previously, she held positions as a Lecturer at Bristol University's School of Engineering Mathematics and Technology, completed postdoctoral research at the ILLC (funded by a Veni fellowship), and worked with the Quantum Group at the University of Oxford's Department of Computer Science. Her academic background includes a PhD from the Bristol Centre for Complexity Sciences, an MSc in Evolutionary and Adaptive Systems from the University of Sussex, and undergraduate studies at Balliol College, Oxford. Dr. Lewis employs interdisciplinary methodologies spanning computational linguistics, conceptual spaces theory, quantum theory, and category theory to investigate human concept composition. Her research focuses on three interconnected domains: compositional approaches to language and meaning (examining how humans generate novel concept combinations), quantum-theoretic applications in language modeling (using density matrices for hyponymy, entailment, and lexical ambiguity), and evolutionary approaches to language change. Her work bridges theoretical frameworks with practical NLP applications, particularly in metaphor processing and neurosymbolic integration. Her research has attracted significant funding including a €250,000 Veni Postdoctoral Fellowship and multiple international collaboration grants. She has organized major interdisciplinary workshops including the Semantic Spaces series co-located with IWCS and ESSLLI. RIS Collaboration Grant (£5,000) Veni Postdoctoral Fellowship (€250,000) KNAW Conference Fund (€6,000) Lorentz Center Workshop grant (€25,000) EPSRC Doctoral Training Account Scholarship Dr. Lewis actively supervises 11 current graduate students across PhD and MSc programs in Data Science, AI, Logic, and Psychology at the University of Amsterdam, with several past students producing conference publications from their thesis work. She has developed novel educational initiatives including the "Conceptual Spaces for AI" undergraduate module and has served as Unit Director for Bristol's MSc Introduction to Artificial Intelligence course serving 200 students. Her academic service includes editorial roles for Compositionality Journal and Cognitive Processing, along with extensive conference reviewing for top NLP and AI venues. She leads research within the NLP&DH group at ILLC, maintaining strong collaborations with the Quantum Group at Oxford and Edinburgh's NLP researchers. Her methodological approach integrates conceptual spaces with categorical compositional distributional semantics, creating bridges between symbolic reasoning and statistical language models.

• Game Theory
• Multi-Agent Reinforcement Learning
• Cooperative Multi-Agent Systems
• +۳ مورد دیگر

Karl Tuyls is a Professor of Computer Science at the University of Liverpool's School of Electrical Engineering, Electronics and Computer Science, with additional research affiliation at DeepMind. His work bridges theoretical game theory and practical multi-agent reinforcement learning systems. His research focuses on cooperative multi-agent systems, where he pioneers value decomposition techniques and emergent communication protocols. Key contributions include frameworks for handling team reward structures, analyzing inequity aversion in social dilemmas, and developing differentiable game mechanics that separate potential and Hamiltonian game dynamics. Analysis of his 2017-2019 publications reveals dominant trends in cooperative multi-agent reinforcement learning, particularly value decomposition architectures and emergent communication systems. These works span artificial intelligence, game theory, and behavioral economics, with specific focus on fair reward allocation, relational inductive biases in deep learning, and open-source framework development for experimental validation. Professor Tuyls leads collaborative research between the University of Liverpool and DeepMind, directing projects on multi-agent coordination and social dilemma modeling through frameworks like OpenSpiel that enable reproducible experimentation in complex game-theoretic scenarios.

Sanjeev Aroraمشاهده پروفایل

استاد

• Theoretical Computer Science
• Computational Complexity
• Algorithm Design
• +۵ مورد دیگر

Sanjeev Arora is the Charles C. Fitzmorris Professor of Computer Science at Princeton University , where he has been since 1994. He earned a B.S. in Math with Computer Science from MIT in 1990 and a Ph.D. in Computer Science from UC Berkeley in 1994. Education MIT (B.S. 1990) UC Berkeley (Ph.D. 1994) His research spans theoretical computer science , computational complexity , and machine learning theory . Recent work focuses on mathematical frameworks for AI, including skill-based training, model interpretability, and synthetic data pipelines. Key article trends include automated theorem proving , LLM instruction tuning , topic modeling unlearning , and visual reasoning evaluation . Awards span the ACM-EATCS Gödel Prize , Simons Investigator Award , and ACM Infosys Foundation Award . He advises 18 Ph.D. students and directs the Princeton Language and Intelligence center.

Ludmila Pöppelمشاهده پروفایل

استاد

• Discourse Analysis
• Lexical Semantics
• Lexicography
• +۴ مورد دیگر

Ludmila Pöppel is a Professor at Stockholm University , affiliated with the Department of Slavic and Baltic Languages, Finnish, Dutch and German . Her research focuses on discourse analysis , lexical semantics , phraseology , and corpus linguistics , often intersecting with political discourse and construction grammar . Research Themes: Analyzing near-synonyms in Russian (e.g., беспорядки vs. волнения), combinatorial properties of words, and semantic shifts in political terminology like революция (revolution) vs. переворот (coup). Key Projects: Leadership in the TELICORE collaboration between Stockholm University, Higher School of Economics (Russia), and Ilia State University (Georgia), and contributions to the Russian-Swedish dictionary database for discursive units. Publications: Her work spans corpus-driven studies of Russian constructions (e.g., дело в том, что, то-то и N), their cross-linguistic parallels, and the pragmatic functions of focus particles. She emphasizes empirical data from Sketch Engine and the Russian National Corpus to identify language-specific features. Advising: Supervises doctoral students and numerous undergraduate and master's theses, integrating her research on political discourse into teaching at all levels, including courses on corpus linguistics and lexical semantics . Labs/Networks: Member of the Language and Power Network , analyzing how language shapes political narratives in polarized climates.

Mayur Naikمشاهده پروفایل

استاد

• Programming Languages
• Artificial Intelligence
• Neurosymbolic Programming
• +۵ مورد دیگر

Mayur Naik is the Misra Family Professor in the Department of Computer and Information Science at the University of Pennsylvania's School of Engineering and Applied Science. He holds office in Room 642B, Amy Gutmann Hall and maintains an active research program focused on the intersection of programming languages and artificial intelligence. Before joining UPenn, he was faculty at Georgia Institute of Technology and a researcher at Intel Labs, Berkeley. Naik received his PhD in Computer Science from Stanford University in 2008 under Alex Aiken, a Masters from Purdue University in 2003 under Jens Palsberg, and a Bachelors from BITS Pilani in 1999. He grew up in Goa, India. His primary research interests center around neurosymbolic programming, which combines symbolic reasoning with machine learning to create more accurate, interpretable, and domain-aware AI systems. His group develops language design, learning algorithms, and compiler optimizations in this space, with their most mature effort being the Scallop neurosymbolic programming language and compiler toolchain. He also conducts research in trustworthy AI for healthcare applications and AI-enabled programming tools that improve programmer productivity. Analysis of his recent publications shows a strong trend toward neurosymbolic programming frameworks (Scallop, TorchQL), LLM-assisted program analysis (IRIS), and applications of these techniques to security, healthcare, and computer vision. His work consistently bridges theoretical foundations with practical implementations, often releasing open-source systems. Misra Family Professor (endowed chair, effective July 2024) Multiple distinguished paper awards (PLDI 2019, FSE 2015, PLDI 2014) Test-of-Time Paper Awards (FSE 2013, FSE 2012, EuroSys 2011) His student Elizabeth Dinella won the 2025 ACM SIGSOFT Outstanding Dissertation award Naik has advised numerous PhD students who have gone on to faculty positions at top institutions including Peking University, University of Toronto, Ashoka University, Bryn Mawr College, and Johns Hopkins University. His research is supported by grants from NSF, Google, Amazon, and other industry partners. His lab maintains active collaborations with clinicians and bioinformatics researchers to apply neurosymbolic programming to healthcare problems. His research group, which includes current PhD students and postdocs, develops practical open-source systems and applies them to diverse domains including computer vision, cybersecurity, medicine, and bioinformatics. The group maintains strong industry connections with Google, Microsoft, Amazon, and other tech companies.