جست‌وجوی استاد و پژوهشگر

Yasutaka Kameiمشاهده پروفایل

استاد

• Empirical Software Engineering
• Software Reliability
• Software Testing and Debugging
• +۷ مورد دیگر

Yasutaka Kamei is a Full Professor at the Graduate School and Faculty of Information Science and Electrical Engineering , Kyushu University since 2024. He holds a Doctorate in Information Science from Nara Institute of Science and Technology and has been a InaRIS Fellow since 2023. His career spans roles as Associate Professor (2015-2023), Visiting Researcher at Queen's University (2015-2017), and Assistant Professor (2011-2015). Research Fields: Specializing in Empirical Software Engineering , he investigates software reliability, defect prediction, code review practices, and mining software repositories. His work on fuzz testing , technical debt , and automated program repair has produced influential frameworks like TraceJIT and PAFL . He pioneered studies on developer behavior in GitHub and federated learning for cross-project defect prediction. Scientific Trends: Recent publications focus on LLM applications in mutation testing , visual bug reporting , and CI/CD inefficiencies . His team explores developer-centric challenges in modern practices, including token-based micro commits and fuzzing build failures . Awards & Grants: InaRIS Fellowship (2023): 10-year, 100M yen grant for human-machine interaction in software development IPSJ/ACM Early Career Award (2013) Leadership: He chairs PC for MSR2018 , co-organizes NII Shonan Seminars , and serves on committees for ICSE , ASE , and SANER conferences. His editorial roles include Empirical Software Engineering and Automated Software Engineering journals.

Aaron Courvilleمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Deep Learning
• +۶ مورد دیگر

Aaron Courville is a Full Professor in the Department of Computer Science and Operations Research at the University of Montreal, and a Canada Research Chair in Learning Representations. He holds a PhD in Robotics from Carnegie Mellon University and degrees from the University of Toronto. His research focuses on deep learning models, probabilistic methods, and applications in vision and natural language processing. He co-leads the LISA lab and is Scientific Director at Mila, Quebec's AI institute. Education: PhD in Robotics, Carnegie Mellon University (2006) MSc in Electrical Engineering, University of Toronto BSc in Applied Sciences, University of Toronto Research Interests: Developing deep learning architectures, probabilistic models, and reinforcement learning techniques. Applications include computer vision, NLP, and generative models. His work emphasizes systematic generalization and scalable methods. Grants & Awards: Canada Research Chair (2022–2029) CIFAR Fellowship (Learning in Machines & Brains) NSERC Discovery Grants Mitacs Acceleration Funds Students & Collaborations: Supervised over 40 graduate students, many contributing to foundational AI work (e.g., Ian Goodfellow, inventor of GANs). Leads projects on generative models and reinforcement learning efficiency. Affiliations: Mila, IVADO, and member of CIFAR's AI program. Active in organizing conferences like ICLR and teaching at MIT/online.

• Visualization
• Geometric Modeling
• Topological Analysis
• +۵ مورد دیگر

Joshua A. Levine is an Assistant Professor in the Department of Computer Science at the University of Arizona, where he has been since 2016. Previously, he held positions at Clemson University’s School of Computing (2012–2016) and the University of Utah’s Scientific Computing and Imaging Institute (2009–2012). He earned his Ph.D. in Computer Science from The Ohio State University, along with BS and MS degrees in Computer Engineering and Mathematics from Case Western Reserve University. Education: Ph.D., Computer Science, The Ohio State University (2009) M.S., Computer Science, Case Western Reserve University (2005) B.S., Computer Engineering and Mathematics, Case Western Reserve University (2003) Research Interests: His work focuses on visualization , geometric modeling , topological analysis , and computational topology . He develops tools like the Topology ToolKit for analyzing complex datasets and explores applications in mesh generation, vector fields, and performance analysis. His methods bridge theory with practical systems for scientific and engineering domains. Key Contributions: His publications span top-tier venues like IEEE VIS, SIGGRAPH, and Computer Graphics Forum , emphasizing scalable visualization techniques and algorithms for high-performance computing. Notable work includes Neural Volume Compression and Particle Merging-and-Splitting . Awards: DOE Early Career Research Program (2018) Best Paper Honorable Mention (IEEE VIS SciVis 2017) Multiple Best Paper Awards at visualization and meshing conferences (2010–2017) Labs & Collaborations: Leads visualization research at the University of Arizona, contributing to the Topology ToolKit (TTK), an open-source platform for topological data analysis. Engages in interdisciplinary projects with civil engineering and natural language processing communities.

Rose Yuمشاهده پروفایل

دانشیار

• Machine Learning
• Spatiotemporal Data Analysis
• AI for Scientific Discovery
• +۵ مورد دیگر

Rose Yu is an Associate Professor in the Department of Computer Science and Engineering at the University of California, San Diego. She serves as a primary faculty member with the AI Group and is affiliated with the Halıcıoğlu Data Science Institute. Previously, she was a faculty member at Northeastern University where she taught advanced machine learning courses. Her research focuses on machine learning for large-scale spatiotemporal data, with particular emphasis on AI for scientific discovery. She develops physics-guided deep learning models that integrate physical principles with neural networks, with applications spanning climate science, healthcare, and dynamical systems. Her work on symmetry in neural networks has led to significant advances in model generalization and optimization. Dr. Yu's recent publications demonstrate a strong trend toward grounding large language models with physical laws, developing symmetry-aware learning frameworks, and applying AI to climate science. Her work bridges theoretical advances in machine learning with practical applications in scientific domains, particularly in spatiotemporal forecasting and scientific discovery. Major Awards and Honors: Presidential Early Career Award for Scientists and Engineers (PECASE) DARPA Young Faculty Award NSF CAREER Award MIT Technology Review Innovators Under 35 Hellman Fellowship Multiple faculty awards from major tech companies (Google, Amazon, Meta, etc.) Dr. Yu actively mentors PhD students, postdocs, and undergraduate researchers, with many of her former students now holding positions at leading institutions and companies. She serves as program chair for major conferences including ICLR 2025 and has received substantial research funding from agencies including DARPA, NSF, DOE, and CDC. Her research group develops tools for scientific machine learning, with particular focus on climate informatics, symmetry-aware learning, and physics-integrated AI systems.

Piotr Szwedمشاهده پروفایل

مدرس

• Data Mining
• Software Engineering
• Computational Intelligence
• +۵ مورد دیگر

Dr inż. Piotr Szwed is a Lecturer in the Department of Applied Computer Science at AGH University of Science and Technology, Kraków. He is affiliated with the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering (EAIiIB), where he teaches courses ranging from imperative and object-oriented programming to data exploration and computational intelligence. Research Interests: Data mining and knowledge discovery Fuzzy cognitive maps and their applications in transportation, risk assessment, and security Software engineering methodologies, including agile and ontology-driven development Intelligent transportation systems (ITS) and real-time traffic management Natural language processing for Polish texts, stylometry, and authorship attribution Ontology engineering, enterprise architecture verification, and model checking His work often bridges theoretical computer science with practical applications in urban mobility, safety systems, and enterprise software. Scientific Contributions: Pioneered the integration of fuzzy cognitive maps with evolutionary algorithms to predict traffic flows. Developed rule-based and machine-learning approaches to determine speed limits from geospatial data. Contributed to the INSIGMA intelligent transportation system aimed at enhancing urban mobility in Polish cities. Created formal verification techniques for ArchiMate business processes using NuSMV model checking. Advanced stylometric analysis for Polish texts, introducing part-of-speech features for authorship attribution. Teaching & Academic Service: Regularly teaches Programming (C, C++, Java), Software Engineering, Data Exploration, and Computational Intelligence. Supervises engineering and master theses in applied computer science. Maintains a public wiki with course materials, lab exercises, and consultation schedules for students. Utilizes Git-based repositories to streamline code submission and continuous assessment in laboratory classes. Laboratory & Facilities: He is located in building C-2, room 403, at AGH’s main campus, al. Mickiewicza 30, 30-059 Kraków. The lab supports courses that emphasize practical software development, version control, and collaborative project work.

Artur Tarassowمشاهده پروفایل

استاد

• Macroeconometrics
• Machine Learning
• Business Forecasting
• +۷ مورد دیگر

Artur Tarassow is a Professor of Economics at the Brandenburg University of Technology , specializing in empirical economic research . His work bridges macroeconometric methodologies with cutting-edge applications in machine learning and forecasting . Teaches courses in Applied Macroeconometrics, Time Series Analysis, Machine Learning, and Business Forecasting Focuses on developing open-source tools like Gretl for econometric analysis Research interests span macroeconomic modeling , asymmetric cointegration , LLM applications in coding , and structural vacancy analysis in real estate markets. His projects involve collaborations with institutions such as the University of Melbourne and TH Brandenburg. Recent publications emphasize forecast evaluation , financial constraints , and AI-driven software development . He actively supervises bachelor's and master's theses with a focus on empirical rigor and practical relevance. Contact: artur.tarassow@th-brandenburg.de

• Bayesian Statistics
• Category Theory
• Compiler Construction
• +۴ مورد دیگر

Matthijs Vákár is an Assistant Professor at the Department of Information and Computing Sciences , Utrecht University , focusing on the software foundations of machine learning . He actively develops the Stan language , a state-of-the-art probabilistic programming language. Areas of Expertise: Bayesian Statistics, Category Theory, Compiler Construction, Machine Learning, Programming Languages. Research Themes: Probabilistic programming languages, differential programming languages, separation of modeling from algorithmic tasks, automatic differentiation in ML-family languages. His research bridges theoretical computer science and practical machine learning, emphasizing logical relations, monoidal structures, and efficient compiler designs for differentiable programming. His recent publications explore correctness proofs, category-theoretic models, and optimization techniques in automatic differentiation. Scientific Awards: ERC Starting Grant NWO Veni Fellowship Marie Sklodowska-Curie Fellowship Academic Journey: PhD from the University of Oxford (dependent types and computational effects), MSc in Pure Mathematics (University of Cambridge), and BScs in Mathematics and Physics (Utrecht University). Prior roles include postdoctoral research at Columbia University and the University of Oxford. Industrial Experience: Build systems at Microsoft Research, NLP at Linguamatics, and compiler design for Stan.

• Artificial Intelligence in Healthcare
• Natural Language Processing
• Large Language Models
• +۶ مورد دیگر

Dr. Jie Yang is an Assistant Professor of Medicine at Harvard Medical School and lead investigator at Brigham and Women's Hospital (BWH) . He serves as affiliate faculty at the Broad Institute of MIT and Harvard, Harvard Data Science Initiative, and Kempner Institute of Harvard University. Harvard Medical School, Boston - Postdoc (2020) Singapore University of Technology and Design - Ph.D. (2018) Chinese Academy of Sciences - M.S. (2014) Chongqing University - B.S. in Electronics (2011) and Physics (2011) His research focuses on medical informatics , clinical NLP , and large language models for healthcare applications. He develops AI solutions for analyzing electronic health records (EHRs) to improve clinical decision-making and real-world evidence generation. 2025 NIH R01 grant ($1.6M) for LLMs in drug safety analysis 2024 Fellow of American Medical Informatics Association 2022 Yunfan Award at World AI Conference He has developed popular NLP tools including NCRF++, LatticeLSTM, and YEDDA (5,000+ GitHub stars). His publications span clinical AI applications, social media health analysis, and medical text processing. Contact: jyang66@bwh.harvard.edu

David Liben-Nowellمشاهده پروفایل

استاد

• Computational Social Sciences
• Social Networks
• Theoretical Computer Science
• +۵ مورد دیگر

David Liben-Nowell is a Professor in the Department of Computer Science at Carleton College, with research spanning computational social sciences, algorithmic modeling, and interdisciplinary applications of theoretical computer science. He contributes to ACM Technical Symposia on Computer Science Education (SIGCSE) and maintains active roles in academic leadership as an Associate Provost since 2022. AISTATS'25: Machine Learning & Social Choice SIGCSE'24: Student Enrollment Systems SIGCSE'22: Intro CS Pedagogy His research explores social network structures , spoken-word recognition , and computational biology through algorithmic frameworks, with applications in peer-to-peer systems and game theory. Recent work on AISTATS'25 addresses probabilistic ranking models, while SIGCSE publications focus on educational algorithms.

Marco Cuturiمشاهده پروفایل

استاد

• Machine Learning
• Optimal Transport
• Statistics
• +۳ مورد دیگر

Marco Cuturi is a Professor of Statistics at ENSAE (École Nationale de la Statistique et de l'Administration Économique), part of Institut Polytechnique de Paris, where he has been affiliated since 2016, working part-time since 2018. He is also a CREST Permanent Member and a Research Scientist at Apple ML Research in Paris, having previously worked at Google Brain from 2018 to 2022. His academic journey includes positions as an Associate Professor at Kyoto University's Graduate School of Informatics (2010-2016), a lecturer at Princeton University's ORFE department (2009-2010), and postdoctoral research at the Institute of Statistical Mathematics in Tokyo (2005-2007). Dr. Cuturi earned his Ph.D. from École des Mines de Paris in 2005 and holds a master's degree from ENS Cachan, having graduated from ENSAE in 2001. His research centers on the application of optimal transport theory to machine learning and statistics, with significant contributions to computational optimal transport, kernel methods, and time series analysis. His influential work includes the development of the Sinkhorn distance for fast computation of optimal transport, soft-DTW for time series alignment, and various applications of optimal transport in machine learning. His research program explores how geometric structures can be leveraged in statistical learning, with recent work focusing on entropic optimal transport solvers, Gromov-Wasserstein distances, and applications to single-cell biology and multimodal learning. Dr. Cuturi's publications reveal a consistent trajectory of developing theoretically grounded yet computationally efficient methods that bridge optimal transport theory with practical machine learning applications across diverse domains including computer vision, natural language processing, and computational biology. Among his professional recognitions is a Notable Paper Award at AISTATS 2020 for his work on regularization in optimal transport. He serves as an Associate Editor for JMLR and has held senior area chair positions at NeurIPS and ICML. Dr. Cuturi has organized major machine learning events including the Machine Learning Summer School in Kyoto (2012, 2015) and co-organized workshops on optimal transport at major conferences. Dr. Cuturi has supervised numerous doctoral students including Nina Vesseron, Théo Uscidda, Charlotte Bunne, and Othmane Sebbouh, with research spanning optimal transport applications in generative modeling, representation learning, and biological data analysis. His current research group at ENSAE and Apple continues to push the boundaries of optimal transport methodology and its applications to cutting-edge machine learning problems.

Jack Dempseyمشاهده پروفایل

مدرس

• Reading Comprehension
• Educational Psychology
• Eye-Tracking Research
• +۵ مورد دیگر

Jack Dempsey is a Lecturer in the Department of Educational Psychology at the University of Illinois at Urbana-Champaign's College of Education . He also serves as the Director of Research at Cascade Reading, where he works full-time. Dempsey earned his PhD in Educational Psychology and focuses on understanding text comprehension mechanisms to advance educational technologies globally. Expertise: Eye-tracking research, behavioral task analysis, machine learning in education Courses: EPSY 401 (Child Language and Education), EPSY 201 (Educational Psychology) His research investigates how humans comprehend grammatical structures and discourse through eye-tracking and behavioral tasks , employing advanced modeling techniques. Recent work explores: Grammatical comprehension in multilingual contexts Eye movement patterns during programming code reading Syntactic adaptation and discourse-sentence interactivity Contextual biases in language processing Dempsey emphasizes communicating scientific findings to broad audiences and has contributed to both educational technology development and psycholinguistic research.

Vincent Chevalمشاهده پروفایل

دانشیار

• Formal Verification
• Security Protocols
• Protocol Design
• +۳ مورد دیگر

Vincent Cheval is an Associate Professor at the University of Oxford , Department of Computer Science, and a Tutorial Fellow at Balliol College . His research focuses on the formal analysis and design of cryptographic protocols, utilizing automated verification in symbolic models and developing state-of-the-art tools like ProVerif and DeepSec. Academic Rank: Associate Professor of Computer Science University: University of Oxford College: Balliol College Vincent’s work spans formal verification , security protocols , protocol design , and automated reasoning . He explores privacy-type properties (anonymity, unlinkability) through behavioral equivalences and has expanded to broader security aspects like accountability and liveness since 2018. His research applies to protocols such as TLS, blockchain systems, electronic voting, and RFID. Recent Articles highlight verification of transparency protocols, polynomial recursive program analysis, and hash function weaknesses. Awards include Distinguished Paper Awards at IEEE CSF 2023 and IEEE S&P 2022, and recognition in the ACM Computing Reviews' Notable Articles list for 2016. Distinguished Paper Awards: USENIX Security 2023 IEEE Symposium on Security and Privacy 2022 ACM Recognition: Notable Article 2016 Vincent actively supervises PhD students and collaborates on tool development for cryptographic protocol verification. He has held positions at Inria (France), University of Birmingham (UK), and University of Kent, with a PhD from ENS-Cachan.

• Statistical Science
• Data Science
• Machine Learning
• +۷ مورد دیگر

Nick Whiteley is a Professor in the School of Mathematics at the University of Bristol and serves as Director of the Institute for Statistical Science. He also held a Heilbronn Chair in Data Science and previously directed the EPSRC Centre for Doctoral Training in Computational Statistics and Data Science. His research spans statistical science and data science with interdisciplinary applications. Education : Ph.D. (University of Cambridge, 2009), M.Eng. (University of Oxford, 2003) Whiteley's research focuses on sequential Monte Carlo methods , Bayesian inference , hidden Markov models , and nonlinear filtering . His work connects statistical theory with practical applications in epidemic modeling, graph representation, and temporal data analysis. His recent publications emphasize scalable algorithms for high-dimensional problems and theoretical foundations of approximation methods. Key article trends include epidemic modeling , dynamic network analysis , stochastic processes , and machine learning theory . Sub-fields span from computational statistics to geometric methods in AI. Whiteley has received prestigious recognition through the Heilbronn Chair and Brunel Research Fellowship.

• Algorithms
• Concurrency
• Concurrent algorithms
• +۱۲ مورد دیگر

Paola Quaglia is an Associate Professor at the Department of Information Engineering and Computer Science, University of Trento. Her primary teaching activities include courses on Concurrency and Linguaggi formali e compilatori (Formal Languages and Compilers), focusing on shared-memory concurrency and compiler design methodologies applicable to formal/natural language analysis. Quaglia's research expertise spans Algorithms, Concurrency, Process Calculi, Formal Languages, Systems Biology, and Stochastic Processes . Her work bridges computational modeling with biological systems, emphasizing formal verification and concurrent programming paradigms. Notable publication trends include contributions to stochastic process algebra , BlenX/Beta-binders for biological modeling, and model-checking techniques for concurrent systems. She has extensively explored the intersection of programming language theory and computational biology.

Owolabi Legunsenمشاهده پروفایل

استادیار

• Software Engineering
• Software Testing
• Runtime Verification
• +۳ مورد دیگر

Owolabi Legunsen is an Assistant Professor of Computer Science at Cornell University, specializing in software engineering with a focus on software testing and runtime verification. He is part of Cornell's Software Engineering Group and has made significant contributions to automated testing frameworks and tools. Ph.D. in Computer Science, University of Illinois at Urbana-Champaign Master's in Computer Science, University of Texas at Dallas B.Sc. in Computer Engineering, Obafemi Awolowo University His research explores software testing for cloud systems, probabilistic programming, and regression test selection. He develops tools like TraceMOP and pytest-inline to enhance testing precision and automation. NSF CAREER Award recipient Intel Rising Star Faculty Award Three ACM SIGSOFT Distinguished Paper Awards Legunsen actively mentors graduate students and collaborates on projects addressing runtime verification overheads, cloud configuration testing, and test flakiness detection. He leads grants focused on improving software reliability through evolution-aware techniques.