جست‌وجوی استاد و پژوهشگر

Federico Olmedoمشاهده پروفایل

پژوهشگر

• Program Semantics
• Formal Verification
• Probabilistic Programming
• +۲ مورد دیگر

Federico Olmedo is a post-doctoral researcher at RWTH Aachen University's Software Modeling and Verification Group (MOVES), led by Professor Joost-Pieter Katoen. His research focuses on program semantics and verification, probabilistic programs, security, and interactive theorem proving. He is affiliated with the Faculty of Computer Science and Mathematics within the university. While no personal academic awards are explicitly mentioned, the MOVES group has achieved notable recognitions, such as Kevin Batz receiving the ETAPS Dissertation Award in 2025 for his work on automated deductive verification. Federico’s research contributes to advancements in formal methods and probabilistic systems analysis. He is located at Room 4230, Ahornstraße 55, 52074 Aachen, and can be reached via federico.olmedo@cs.rwth-aachen.de. His work integrates theoretical foundations with practical verification techniques, emphasizing interdisciplinary approaches to software reliability.

• Software Testing
• Compiler Testing
• Software Engineering
• +۳ مورد دیگر

Yongqiang Tian is a Lecturer in the Department of Software Systems & Cybersecurity at Monash University. His research focuses on software testing, compiler testing, and software engineering. He holds a Ph.D. in Computer Science from the University of Waterloo (Canada) and another Ph.D. in Computer Science from the Hong Kong University of Science and Technology, alongside a BEng in Information Engineering from City University of Hong Kong. His research interests emphasize compiler testing methodologies, program reduction techniques, and deep learning system reliability. Recent work includes leveraging large language models (LLMs) for enhancing compiler testing and program analysis. He has explored probabilistic delta debugging, program reduction algorithms, and evaluating DNN model behaviors under compression. Key contributions include frameworks like T-Rec for language-agnostic program reduction, Lampr for LLM-aided optimization, and tools such as EvalDNN for deep learning model evaluation. His articles highlight trends in compiler bug analysis, testing deep learning libraries, and improving fault localization through algorithmic innovations. No scientific awards are explicitly mentioned. Advising and grants details are not provided in the text. His work often intersects with program analysis, compiler validation, and applying AI techniques to software engineering challenges.

Tim Hunterمشاهده پروفایل

استاد

• minimalist grammars
• mild context-sensitivity
• syntax-semantics interface
• +۴ مورد دیگر

Professor Hunter researches formal syntax, computational linguistics, and cognitive modeling of language. Key areas include minimalist grammars, mild context-sensitivity, probabilistic models of language processing and acquisition, and the syntax of ellipsis. Recent work explores computational approaches to syntactic variation, noise-tolerant learning models, and integrating grammatical theories into cognitive architectures. Teaches computational linguistics and syntax at undergraduate and graduate levels. Education includes PhD from University of Maryland (2010) focusing on syntactic effects of conjunctivist semantics.

Carlotta Paveseمشاهده پروفایل

دانشیار

• Philosophy of Mind
• Cognitive Science
• Philosophy of Language
• +۴ مورد دیگر

Carlotta Pavese is an Associate Professor of Philosophy at Cornell University’s Sage School of Philosophy and will assume the Fulford Clarendon Professorship at Oxford University in 2025. She specializes in philosophy of mind, cognitive science, and epistemology, with secondary interests in philosophy of language and formal semantics. Pavese holds editorial roles at Philosophical Review , Ergo , and Analysis Reviews . Prior to Cornell, she taught at Duke University (2014–2018) and NYU (2013–2014), and earned her Ph.D. in Philosophy from Rutgers University (2013). Her research integrates epistemological and cognitive science perspectives, exploring topics like skill theory, practical knowledge, and the nature of intelligence. Notable contributions include The Practical Mind (Cambridge, 2026) and Routledge Handbook of Philosophy of Skill and Expertise (2020). Pavese has received prestigious awards such as the Andrew Mellon Assistant Professorship and Marie Sklodowska Curie Fellowship. Her work spans theoretical philosophy and empirical engagement, with recent focus on agentive control, epistemic luck, and the semantics of argumentation. She has held visiting positions at UCLA, LMU Munich, and the University of Turin. Outside academia, Pavese emphasizes her working-class origins in Italy and family legacy in carpentry, reflecting on intergenerational resilience and education access. Current projects include investigating skill-based intelligence ("Intelligence Socialism") and bridging Aristotelian technē with scientific knowledge frameworks. Pavese’s interdisciplinary approach bridges analytic philosophy with cognitive science methodologies.

Stanley KOKمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• E-Commerce
• +۶ مورد دیگر

Stanley KOK is an Assistant Professor at the Department of Information Systems & Analytics, School of Computing, National University of Singapore (NUS). He also serves as the Associate Director of the NUS AI Lab. He holds a Ph.D. in Computer Science from the University of Washington and a combined A.B./S.cB. in Computer Science & Economics from Brown University. His research focuses on artificial intelligence, machine learning, and their applications in e-commerce, fintech, healthcare, and social networks. He has led projects on AI-driven product categorization and interpretable healthcare models. Education: Ph.D., Computer Science, University of Washington, 2010 Combined A.B./S.cB., Computer Science & Economics, Brown University, 1999 Research Interests: AI/ML applications in e-commerce and healthcare Machine Translation for Product Categorization Interpretable Machine Learning Models Social Network Analysis Recent work emphasizes scalable AI solutions for product taxonomy (e.g., translating product descriptions to categories) and healthcare predictive systems. His articles span topics like graph neural networks for credit scoring and accountable relational triple extraction. Awards include teaching excellence and research grants from NUS and industry partners. Advising and Grants: Guided over 20 students to academic and industry roles Recipient of National Innovation Challenge grant (2019) and Rakuten collaborations Labs/Teams: Leads the SGEMS lab, focusing on energetic and motivated student-driven AI research. Collaborates with NUS AI Lab and industry partners like Rakuten.

• Applied Machine Learning
• Large Language Models
• Generative ML
• +۴ مورد دیگر

Dr. Andrei Kucharavy is an Assistant Professor at HES-SO Valais-Wallis in the Faculty of Economics and Services, where he teaches Business Economics courses including Fundamentals of Algorithms and Computational Thinking. He co-founded the GenLearning Center, an applied research entity focused on safety and deployment challenges of Generative AI technologies. His research centers on machine learning safety, privacy, and distribution from cybersecurity and human factors perspectives. PhD from Paris Sorbonne University (research at Johns Hopkins University and Stowers Institute for Medical Research) Engineer degree from Ecole Polytechnique Former postdoctoral researcher at EPFL's Distributed Computing Lab Dr. Kucharavy's research spans Applied Machine Learning, Large Language Models, and Cybersecurity with particular expertise in generative ML since 2018 and its cybersecurity applications since 2020. His work bridges theoretical machine learning with practical security implementations in Swiss defense contexts. His publications reveal strong focus on LLM vulnerabilities, privacy leakage detection, and developing bibliometric methods resilient to AI-generated content. His recent publications demonstrate expertise in LLM security vulnerabilities, code generation risks, and developing frameworks for technology forecasting using iterative self-prompting. The research shows consistent progression from foundational LLM understanding to specialized security applications, particularly in code vulnerability injection through dataset poisoning and malicious fine-tuning. Distinguished Cyber-Defence (CYD) Postdoctoral Fellowship recipient Dr. Kucharavy leads multiple Cyber-Defence Campus-funded projects totaling over CHF 430,000, including work on characterizing LLM attacks in code generation, developing vulnerability characterization frameworks, and secure privacy leakage detection protocols. His research team includes Percia David Dimitri and Vallez Cyril, with collaborations extending to Cyber-Defence Campus experts like Dr. Ljiljana Dolamic. He actively develops applied solutions through the GenLearning Center, focusing on on-premises deployment of generative models for healthcare applications and knowledge transfer tools that maintain data privacy while enhancing educational outcomes. His work directly informs Swiss defense technology standards for LLM-based solutions in cyber-physical systems.

Zeliha Dilsun Kaynarمشاهده پروفایل

عضو هیئت علمی

• Distributed Systems
• Security
• Formal Verification
• +۳ مورد دیگر

Zeliha Dilsun Kaynar is an Associate Teaching Professor at the School of Computer Science, Carnegie Mellon University (CMU). She holds a Ph.D. (2002, University of Edinburgh) and B.Sc. (1996, Middle East Technical University) in Computer Science, and a Doçent degree from Turkey's higher education council. Prior to CMU, she worked at CMU CyLab and MIT's Theory of Distributed Systems Group (2001–2006). Her teaching focuses on principles of programming, programming languages, formal modeling, and verification. Research interests include distributed systems, security, formal verification of protocols, and accountability via causation. Notable work includes the Theory of Timed I/O Automata (2006/2010) and NSF-funded projects on blameworthy programs and accountability determination. Collaborations span cybersecurity, education technology (e.g., Open Learning Initiative), and healthcare sustainability. She advises undergraduate CS students and co-authored over 50 peer-reviewed papers across journals like Journal of Computer Security and conferences such as CSF and Oakland.

Dimitri Bertsekasمشاهده پروفایل

استاد

• Optimization
• Game Theory
• Systems
• +۳ مورد دیگر

Dimitri Bertsekas is the Jerry Mcafee (1940) Professor in Engineering at the Massachusetts Institute of Technology. His research focuses on optimization, game theory, systems, networking, control, and autonomy. He works within the Laboratory for Information and Systems Decisions. His recent publications demonstrate a strong emphasis on reinforcement learning, dynamic programming, and algorithmic solutions for complex systems. Work spans applications in robotics, transportation optimization, computer vision, game AI, and knowledge systems. Common themes include multi-agent coordination, real-time decision-making under uncertainty, and scalable computational methods. Bertsekas contributes to both theoretical frameworks and practical implementations, with innovations in auction algorithms, rollout methods, and model predictive control integration.

• Machine Learning
• Natural Language Processing
• Natural Language Understanding
• +۳ مورد دیگر

Abulhair Saparov is an Assistant Professor of Computer Science at Purdue University , where he focuses on statistical machine learning applications in natural language processing (NLP), reasoning, and symbolic/neuro-symbolic systems. Prior to Purdue, he was a postdoctoral researcher at the Center for Data Science at New York University under Professor He He. He holds a Ph.D. and M.S. in Machine Learning from Carnegie Mellon University (advised by Professor Tom Mitchell) and a B.S.E. in Computer Science from Princeton University with certificates in Applied Mathematics and Neuroscience. His research emphasizes improving the generalizability of ML models through reasoning over knowledge and symbolic representations. He has developed algorithms for abductive theory formation, generative probabilistic models of grammar for semantic parsing, and frameworks for neuro-symbolic integration. Techniques used include Bayesian nonparametrics, approximate posterior inference, and combinatorial optimization. Active on GitHub , he maintains repositories like learning_to_search (transformer scaling analysis), prontoqa (synthetic QA dataset for LLM reasoning), and PWL (probabilistic abduction for theory induction). No formal awards or student advisees are listed in available texts.

Ankur Parikhمشاهده پروفایل

استادیار مدعو

• Natural Language Processing
• Machine Learning
• Spectral Learning
• +۳ مورد دیگر

Ankur Parikh is a staff research scientist at Google NYC and an adjunct assistant professor at New York University. His research focuses on natural language processing, machine learning, and spectral learning methods with applications to graphical models and biological networks. He holds a PhD from Carnegie Mellon University’s Machine Learning Department (2014), advised by Eric Xing, and an undergraduate degree in Computer Science and Applied Math from Princeton University. His work bridges theoretical advancements in machine learning with practical applications, including decomposable attention models for NLP, kernel graphical models, and network analysis of biological systems. Notable contributions include the TREEGL algorithm for gene network inference and spectral methods for latent structure discovery. He has been recognized with best paper awards at EMNLP and ISMB conferences. Education: PhD, Machine Learning, Carnegie Mellon University (2014) BS, Computer Science & Applied Math, Princeton University Parikh has taught advanced courses on spectral learning and probabilistic graphical models, including guest lectures at CMU and tutorials at Microsoft Research. His research is supported by an NSF Graduate Fellowship and has led to open-source implementations like the PLRE language modeling toolkit. Current affiliations include NYU’s machine learning community and Google’s NYC research team, with ongoing collaborations in NLP, computational biology, and statistical network analysis.

Kasra Khosoussiمشاهده پروفایل

مدرس ارشد

• Robot Perception
• Robotic Autonomy
• Mathematical Foundations
• +۳ مورد دیگر

Dr. Kasra Khosoussi is a Senior Lecturer (equivalent to US Associate Professor) at the University of Queensland's School of Electrical Engineering and Computer Science. He leads research on mathematical and algorithmic foundations of robot perception and autonomy, with prior roles including Robotic Mapping Team Leader at CSIRO and Research Scientist at MIT. His research focuses on: Robotic perception systems including SLAM and multi-sensor fusion Autonomous navigation in extreme environments Distributed optimization for multi-robot collaboration Probabilistic modeling for robotic decision-making Recent publications demonstrate strong emphasis on resilient SLAM systems, energy-aware robotics, multi-agent coordination, and real-time perception algorithms for challenging environments like subterranean spaces and marine contexts. Awards include: Hilti SLAM Challenge Winner (ICRA 2022) IEEE Transactions on Robotics Best Paper Honorable Mention (2021) ICRA Best Paper Finalist (2018) Professional activities include Associate Editorship for IEEE Robotics and Automation Letters and committee roles at leading robotics conferences including ICRA and RSS.

Paolo Perroneمشاهده پروفایل

پژوهشگر

• Category Theory
• Probability and Information Theory
• Complexity and Networks
• +۲ مورد دیگر

Paolo Perrone is a Researcher in the Department of Computer Science at the University of Oxford. His research focuses on Category Theory , Probability and Information Theory , Complexity and Networks , and Theoretical Computer Science . He holds a PhD in Mathematics from the Max Planck Institute for Mathematics in the Sciences and University of Leipzig (2018), after completing a Master’s (2013) and Bachelor’s (2011) in Physics from the University of Milan and University of Milan-Bicocca. His work bridges foundational mathematical structures with applications in probability, including contributions to categorical probability , Markov categories , and information geometry . He actively participates in academic events, such as the Applied Category Theory conferences, and collaborates with leading researchers like Tobias Fritz and Sam Staton. His book Starting Category Theory (2024) provides an accessible introduction to the field. Teaching roles include courses on Category Theory and Probability at Oxford, alongside tutorials in mathematics and theoretical computer science. Recent research highlights include advancements in empirical measures, martingale convergence, and entropy theory within categorical frameworks.

• Philosophy of Science
• Formal Epistemology
• Philosophy of Probability
• +۳ مورد دیگر

Dr. Michael Nielsen is a Senior Lecturer in Mathematical Philosophy at the University of Sydney's Department of Philosophy. His work bridges formal epistemology, philosophy of probability, decision theory, and philosophy of science. He explores foundational issues in probabilistic reasoning, rational decision-making, and the ethical implications of algorithms. Key research themes include accuracy arguments for probabilism, Bayesian coherence principles, and the mathematical structures underpinning rational belief revision. His teaching encompasses philosophy of science, formal epistemology, decision theory, and logic. Notable contributions include analyzing algorithmic fairness in AI systems, resolving paradoxes in conditional probability, and advancing theories of epistemic convergence. Recent work examines how non-philosophers intuitively handle probabilistic reasoning and the philosophical foundations of disagreement dynamics in Bayesian models. Publications span Philosophy and Technology , Mind , Analysis , and Philosophical Studies , with a focus on rigorous formal methods applied to longstanding philosophical problems. His research often intersects with statistical theory, computer science, and social epistemology, reflecting a commitment to interdisciplinary rigor.

Patrik Haslumمشاهده پروفایل

پژوهشگر

• AI Planning
• Plan Quality Optimization
• Optimization for Decision Support
• +۵ مورد دیگر

Roles and Affiliations : Patrik Haslum is a part-time Researcher in the AI group at the Research School of Computer Science (ANU College of Engineering and Computer Science), Australian National University. He is seconded to Data61's Decision Sciences program. Previously, he was at NICTA and Linköpings Universitet. Education : PhD in Computer Science from Linköpings Universitet (Sweden), with research on admissible heuristics for automated planning. Research Interests : Focuses on AI planning, particularly plan quality optimization and heuristic search. Specializes in optimal planning, domain analysis, and applying planning techniques to problems like genome edit distance, diagnosis, and robotics. His work bridges automated planning with Petri nets and discrete event systems. Advising & Students : Supervised four PhD students including Jing Cui (2018), Franc Ivankovic (2018), Fazlul Hasan Siddqui (2015), and Debdeep Banerjee (2015). Offers student projects in areas like story planning, optimal troubleshooting, and policy validation for MDPs. Labs & Contributions : Leads research in the AI group at ANU, contributing to tools like the INVAL plan validator and PDDL domain encodings. Active in international planning competitions and workshops.

• Computer Vision
• 3D Reconstruction
• Neural Rendering
• +۵ مورد دیگر

Dr. Miaomiao Liu is a Research Fellow at the School of Computing, The Australian National University. Her research focuses on computer vision, 3D reconstruction, and neural rendering, with applications in robotics, autonomous systems, and renewable energy forecasting. She leads multiple projects including Next-Generation Aviation Safety Net Air Traffic Management, Machine Vision Techniques for Solar Power Forecasting, and 3D Vision Geometric Optimization in Deep Learning. Her work integrates cutting-edge techniques such as neural radiance fields, depth estimation, and self-supervised learning to address challenges in dynamic scene reconstruction, motion forecasting, and image deblurring. She has pioneered methods for mining supervision signals in dynamic regions and developing language-driven deblurring networks. Key Projects: Aviation safety systems, solar irradiance prediction, and geometric optimization in deep learning Research Themes: 3D scene understanding, human motion prediction, and neural rendering Technical Expertise: Neural networks, multi-view stereo, and physics-based modeling Dr. Liu's research has been published in top-tier venues like CVPR and IEEE conferences, with over 2,450 citations. While not explicitly listed as part of a lab, her work demonstrates strong collaboration with industry partners such as CSIRO and aerospace stakeholders. She actively supervises research students in areas like 3D vision and energy systems.