جست‌وجوی استاد و پژوهشگر

Damian Arellanesمشاهده پروفایل

مدرس

• Theoretical Computer Science
• Algebraic Composition
• Models of High-Level Computation
• +۵ مورد دیگر

Dr. Damian Arellanes is a Lecturer (Assistant Professor) in Computer Science at Lancaster University, UK, affiliated with the Software Engineering Group and Lancaster Centre for Intelligent, Robotic and Autonomous Systems (LIRA). He holds a PhD from The University of Manchester (2020) and a Postgraduate Certificate in Academic Practice from Lancaster University (2023). His research focuses on theoretical foundations of algebraic composition for high-level computation models, including emergent/self-organising systems and software composition. He has contributed to areas such as category theory, control-flow separation, and compositional programming for IoT systems. Education: PhD in Computer Science, University of Manchester (2020) Postgraduate Certificate in Academic Practice, Lancaster University (2023) MSc in Computer Science, supported by CONACYT (2012–2014) BEng in Computer Engineering, supported by PRONABES (2009–2012) Research Interests: Damian’s work emphasizes algebraic semantics, compositional models for software, and theoretical computer science principles. He explores how abstract mathematical frameworks (e.g., category theory) can formalize computational systems and enable scalable IoT solutions. Publications: Damian has published extensively on algebraic composition, IoT systems, and formal methods. Key themes include compositional programming, self-organizing software, and scalable service architectures. Awards: Official Nominator for the VinFuture Prize (2024) Honourable Mention for Most Outstanding Mexican Student in STEM (2021) Nick Sanders Kickstarter Fund (2019) Outstanding Doctoral Paper Award (2019) Best MSc Thesis in AI (2015) Advising & Grants: Damian supervises PhD students, such as Mina Yavari, and actively reviews for journals like IEEE TSC and conferences like TASE. He has secured scholarships and fellowships from CONACYT and the Mexican government. Labs/Teams: Member of LIRA’s Fundamentals Section and the Software Engineering Group at Lancaster University.

• Decision Making
• Information Sampling
• Machine Learning Applications
• +۴ مورد دیگر

Gaël Georges Marcel Le Mens is a Full Professor at Pompeu Fabra University (UPF), holding a position in the Department of Economics and Business. He is also affiliated with the Barcelona School of Economics and serves as academic co-director of the Executive Master in Business Administration (EMBA) at the UPF Barcelona School of Management. His academic journey includes teaching roles at INSEAD, London Business School, ESADE, and the University of Lugano, alongside positions at the universities of Southern Denmark and New York. Education: Doctor in Business Administration, Stanford Graduate School of Business MSc in Management Science and Engineering, Stanford University Diploma in Engineering, Supélec Bachelor of Economics, University of Paris XI His research focuses on decision-making processes, information sampling, machine learning applications in semantics, and organizational behavior. Key themes include cognitive heuristics, social media impact on political expression, and the interplay between popularity and evaluation dynamics. He has explored how feedback mechanisms shape political communication and developed methodologies to compare human and machine conceptual judgments using models like BERT. His publications span journals such as PNAS , Psychological Review , and Industrial and Corporate Change , reflecting his interdisciplinary approach. Though no explicit awards are noted, his prolific output highlights sustained academic impact. He has advised multiple institutions on curriculum design and executive education, leveraging his cross-university teaching experience. Le Mens is affiliated with the Barcelona School of Management’s research teams and contributes to initiatives bridging artificial intelligence and social sciences. His work often addresses practical challenges in organizational decision-making and digital communication strategies.

Jeffrey Sanford Russellمشاهده پروفایل

استاد

• Decision Theory
• Ethics
• Formal Epistemology
• +۴ مورد دیگر

Jeffrey Sanford Russell is a Professor of Philosophy at the University of Southern California's School of Philosophy, where he also serves as Director of Graduate Studies. He holds a PhD from New York University (2011). His research focuses on decision theory, ethics, formal epistemology, metaphysics, and the philosophy of religion and mathematics. Russell teaches courses on topics like probability, rational choice, and the limits of logic, emphasizing interdisciplinary approaches blending philosophy with mathematics, psychology, and economics. His work explores foundational questions in ethics, such as infinite value aggregation paradoxes, and decision theory, including stochastic dominance and self-locating evidence in cosmology. He has contributed to debates on multiverse hypotheses, moral hedging, and the structure of possible worlds. Russell’s publications appear in top journals like Noûs , Philosophical Review , and Philosophy and Phenomenological Research . His recent research addresses moral and epistemic challenges in infinite scenarios, such as infinite ethics and fanaticism in decision-making. He also investigates the intersection of metaphysics and formal logic, including composition principles and qualitative grounds for non-qualitative facts. Russell’s work bridges abstract philosophical inquiry with practical implications, such as ethical dilemmas in collective action and the epistemology of divine hiddenness.

Eetu Mäkeläمشاهده پروفایل

استاد

• Digital Humanities
• Computational History
• Text Reuse Analysis
• +۳ مورد دیگر

Eetu Mäkelä is a Professor in Digital Humanities at the University of Helsinki, affiliated with the Helsinki Institute for Social Sciences and Humanities (HSSH). He holds a Doctor of Science in Computer Science and a Title of Docent in Computer Science. He supervises doctoral programs in History and Cultural Heritage, and Theology and Religious Studies. His research focuses on computational methods applied to humanities, including text reuse analysis, historical discourse studies, and digital cultural heritage. He leads the Helsinki Computational History Group (COMHIS) and participates in projects like CASCADE (computational semantic change analysis) and HPC-HD (high-performance computing for historical discourse analysis). Key research areas include computational analysis of historical texts, formulaic language in oral poetry, and linked open data applications in cultural heritage. Notable projects involve developing digital infrastructures for Finnic oral poetry and analyzing WWII data through linked open systems. Awards include the Best Paper Award at DHN 2020 and the Open Data Prize for the WarSampo system. Projects: CASCADE (EU Horizon Europe), HPC-HD (Academy of Finland), COMHIS (ongoing since 2018) Supervision: Doctoral programs in History, Cultural Heritage, and Theology Labs/Teams: Helsinki Computational History Group (COMHIS), leader of digital humanities initiatives

Grant Weddellمشاهده پروفایل

دانشیار

• Database Technology
• Real-Time Applications
• Schema Management
• +۵ مورد دیگر

Grant Weddell is an Associate Professor in the David R. Cheriton School of Computer Science at the University of Waterloo. His research focuses on database technology for real-time applications, including large-scale schema management, information clustering, dependency theory, and query optimization for heterogeneous data sources. He teaches courses such as CS338 (Introductory Databases), CS348 (Advanced Databases), CS446 (Software Engineering), and CS848 (Advanced Database Systems). His research interests emphasize the interplay between description logics and database systems, particularly in optimizing query processing and managing complex schemas. Recent work explores path agreements, functional dependencies, and ontology-mediated querying to enhance data integration and schema management efficiency. Teaching responsibilities include foundational database courses (CS338/348), software engineering (CS446), and advanced topics in information integration (CS848). No scientific awards are explicitly listed, though his contributions to database theory and optimization are extensive.

Parisa Kordjamshidiمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۷ مورد دیگر

Parisa Kordjamshidi is an Associate Professor of Computer Science and Engineering at Michigan State University (MSU), leading the Heterogeneous Learning and Reasoning (HLR) Lab. Her research focuses on Neuro-Symbolic AI, spatial language understanding, and structured learning, with notable contributions to frameworks like Saul for declarative programming. She joined MSU in 2019 after roles at Tulane University and the Florida Institute for Human and Machine Cognition. Education: Ph.D. in Computer Science from KU Leuven (2013), postdoctoral research at UIUC's Cognitive Computation Group, and work in the KnowEng project. Research Interests: Artificial Intelligence, Machine Learning, Natural Language Processing, Neuro-Symbolic systems, spatial semantics extraction, structured output learning, and multimodal reasoning. Key projects include NSF CAREER awards for spatial language understanding and ONR grants for integrating domain knowledge into AI. Awards: NSF CAREER (2019), Amazon Faculty Research Award (2022), Fulbright Scholar (2025), and Rising Stars at MIT EECS (2015). Grants: Active projects on Neuro-Symbolic compositional generalization (ONR), spatial language learning (NSF), and collaborations with the Department of Media and Information for health misinformation management. Professional Activities: Editorial roles at JAIR, TACL, and Frontiers journals; service on program committees for ACL, EMNLP, and AAAI; organization of workshops like Spatial Language Understanding (SpLU) and CLeaR. Lab and Software: HLR Lab develops Saul (declarative learning-based programming framework) and tools for spatial role labeling. Her team emphasizes mentoring, with structured weekly meetings, reading groups, and conference participation for students.

• Type Theory
• Dependent Types
• Functional Programming
• +۱۱ مورد دیگر

Andreas Abel is a Senior Lecturer in the Division of Computing Science at the Department of Computer Science and Engineering, Chalmers University of Technology and the University of Gothenburg. He has previously served as an Assistant Professor at Ludwig-Maximilians-Universität (LMU) Munich and has been a visiting researcher at INRIA in Paris. His primary affiliations are with Chalmers and the University of Gothenburg, where he conducts research and teaches in programming languages and type theory. Chalmers University of Technology, Department of Computer Science and Engineering, Senior Lecturer University of Gothenburg, Division of Computing Science LMU Munich, Assistant Professor (former) INRIA Paris, Visiting Researcher Abel’s research lies at the intersection of type theory, functional programming, and formal verification. He is particularly known for his work on dependent types, normalization by evaluation, and the development of the Agda proof assistant. His interests include constructive logic, logical frameworks, modal and linear typing, program verification, and compiler construction. He leads the Modal Dependent Type Theory project funded by the Swedish Research Council (Vetenskapsrådet) and has contributed to several other major research initiatives in programming language theory. His recent publications reflect a strong focus on foundational aspects of type systems, including cubical type theory, decidability of conversion, and formalization of algebraic completeness. These works appear in top-tier venues such as ICFP, LICS, POPL, and TYPES, showcasing both theoretical depth and practical implementation in Agda. Distinguished Paper Award, ICFP 2019 Editor, Theoretical Pearls column, Journal of Functional Programming Member, IFIP WG 1.3 on Foundations of System Specification Abel actively supervises students and contributes to the research community through program committee memberships for major conferences including LICS, ICFP, and CPP. He is a senior developer of Agda and the maintainer of the BNFC (Backus-Naur Form Compiler) tool. His work bridges theoretical computer science with practical software development for formal methods. He is involved in several research groups and projects, including the Programming Logic Group at Chalmers and the international EUTYPES network. His role as principal investigator and core contributor in multiple funded projects highlights his leadership in the field of programming language foundations.

Bernadette Bucherمشاهده پروفایل

استادیار

• Embodied AI
• Vision-Language Grounding
• Mobile Manipulation
• +۳ مورد دیگر

Bernadette Bucher is an Assistant Professor in the Robotics Department (primary) and Computer Science and Engineering Department at the University of Michigan. Her research focuses on embodied AI, vision-language grounding, and mobile manipulation, with an emphasis on interpretable visual representations and uncertainty estimation for robotics tasks. She previously worked at Boston Dynamics AI Institute, NVIDIA Research, and Lockheed Martin Corporation. Her academic background includes a PhD in Computer Science from the University of Pennsylvania (GRASP Lab) under advisors Kostas Daniilidis and Nikolai Matni, alongside MA degrees in Mathematics and Economics from the University of Alabama (2014). Research interests include robotics, computer vision, and machine learning intersections, particularly autonomous mobile manipulation. Her work emphasizes uncertainty-aware systems and deployable learning-based methods. Notable achievements include the Best Paper in Cognitive Robotics at ICRA 2024. Her research spans projects like EVORA for off-road autonomy and ASHiTA for hierarchical task analysis. She has contributed to open-source projects like RoboNet and actively publishes in top conferences (CVPR, CoRL, ICRA). Key projects: EVORA, ASHiTA, Vision-Language Frontier Maps (VLFM) Grants and funding: Honda Research Institute (Curious Minded Machines project) Labs/Teams: Active participation in robotics labs at University of Michigan and prior collaborations with industry partners

Zhu-Tian Chenمشاهده پروفایل

استادیار

• Data Visualization
• Human-Computer Interaction
• Augmented Reality
• +۷ مورد دیگر

Zhu-Tian Chen is an Assistant Professor in the Department of Computer Science and Engineering at the University of Minnesota, Twin Cities, where he leads research in data visualization, human-computer interaction, and augmented reality. Prior to this, he held postdoctoral positions at Harvard University and UC San Diego, working with leading researchers in visual computing and interactive design. Ph.D. in Computer Science, Hong Kong University of Science and Technology B.Eng. in Software Engineering, South China University of Technology His research focuses on augmenting human intelligence through hybrid human-AI systems, particularly in everyday and outdoor environments. He specializes in designing intelligent AR interfaces, embedded visualizations, and language-oriented interactions for applications in sports analytics, education, and data analysis. His work integrates human-centered design with applied machine learning to create intuitive and effective visualization tools. The recent trend in his publications shows a strong emphasis on intelligent AR systems for dynamic scenes, LLM-based code generation interfaces, and real-time augmentation of sports videos using natural language and gaze-based interactions. His work frequently appears in top-tier venues such as IEEE VIS, ACM CHI, and UIST. Best Paper Award, ACM CHI'23 Best Short Paper Honorable Mention, EuroVis'23 Best Paper Honorable Mention, IEEE VIS'22 (twice) Certificate of Distinction and Excellence in Teaching, Harvard University Hong Kong Ph.D. Fellowship Dr. Chen actively mentors undergraduate, master’s, and PhD students, as well as visiting scholars and interns, and is building a new research lab focused on visualization for intelligent AR systems. He has served on program committees for major conferences including ACM CHI, IEEE VIS, and EuroVis, and has been invited to speak at institutions such as Apple, JP Morgan, and multiple universities worldwide. He also contributes to the academic community through grant reviewing for NSF and the Department of Energy. He leads research projects in intelligent AR systems for sports, language-oriented interactions with LLMs, and immersive data visualization, often in collaboration with institutions like Harvard, UC San Diego, and HKUST. His lab welcomes students and collaborators interested in visualization, HCI, and applied AI.

Hassan Sajjadمشاهده پروفایل

دانشیار

• Natural Language Processing
• Deep Learning
• Safe and Trustworthy AI
• +۷ مورد دیگر

Hassan Sajjad is an Associate Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. He is also the Director of HyperMatrix, a research group focused on AI and deep learning. His work centers on Natural Language Processing, Safe and Trustworthy AI, interpretability, and robustness of language models. PhD - University of Stuttgart, Germany (2012) Masters - National University of Computer and Emerging Sciences, Pakistan (2007) BSc - National University of Computer and Emerging Sciences, Pakistan (2005) Hassan Sajjad's research focuses on making AI systems more interpretable, robust, and safe. He investigates how deep learning models, particularly transformers, encode linguistic and conceptual knowledge. His work spans language generation , model editing , interpretability , and generalization . He develops tools like NeuroX and NxPlain to analyze neuron-level behavior in NLP models. His research also extends to crisis informatics and multilingual NLP, especially Arabic and Urdu. The recent publications show a strong trend in analyzing and interpreting deep NLP models, with a focus on neuron interpretation , latent concept discovery , and model robustness . His work appears in top-tier venues like NeurIPS, ICLR, ACL, and EMNLP, indicating high impact in the AI and NLP communities. There is a clear emphasis on developing practical tools and frameworks for model analysis. No formal scientific awards are mentioned in the provided text. Hassan Sajjad mentors students and has fellowship opportunities available. While specific grants are not listed, his extensive publication record and leadership roles suggest active research funding. He advises students in AI, NLP, and deep learning, and collaborates widely across institutions. He leads the HyperMatrix research group at Dalhousie University, which focuses on AI, deep learning, and NLP. The group develops tools for model interpretation and works on safe and trustworthy AI. Collaborations extend to institutions like MBZUAI, NYU Abu Dhabi, and various international research centers.

Riccardo Tommasiniمشاهده پروفایل

دانشیار

• Stream Processing
• Knowledge Graphs
• Query Languages
• +۳ مورد دیگر

Riccardo Tommasini is an Associate Professor at INSA Lyon , a leading engineering institution in France. He leads the Stream Processing and Knowledge Graphs research within the DB Team at LIRIS laboratory under Professor Angela Bonifati. His academic journey began with a PhD in Computer Science from Politecnico di Milano under Emanuele Della Valle, with a dissertation titled Velocity on the Web to be published as a Springer book. Research Interests : Advancing stream processing for real-time data systems Extending knowledge graphs with dynamic data Designing graph databases for big data applications Creating query languages for heterogeneous data environments Building data engineering pipelines with Apache Airflow Enabling big graph processing in distributed settings Key Contributions : Developed Zodiac framework for Datalog reasoning under rule amendments (ICDE 2025) Co-authored foundational Streaming Linked Data book with Springer (2023) Created RSP4J API for RDF stream processing (ESWC 2021) Designed challenge-based learning curriculum for Data Engineering courses Scientific Recognition : Received ANR JCJC grant for POLYFLOW project (2024) Awarded Best Resource at ESWC 2021 Managed industrial collaborations with Neo4j, InfluxData, and Confluent Advising & Teaching : Supervises Mohamed Ragab (PhD candidate at University of Tartu) Course Leadership : Foundational Data Engineering course at INSA Lyon and University of Tartu Structured around Apache Airflow , Docker, and graph databases

• Machine Learning
• Explainable AI (XAI)
• Deep Learning
• +۸ مورد دیگر

Haoyi Xiong is an active academic researcher in artificial intelligence, machine learning, and data science, with extensive publications in top-tier journals and conferences including IEEE TPAMI, NeurIPS, ICML, KDD, and AAAI. His work spans explainable AI, graph neural networks, diffusion models, remote sensing, and large language models. Research Interests: Explainable AI (XAI) and model interpretability Graph Neural Networks and contrastive learning Diffusion models and generative AI Medical and remote sensing image analysis Large language models and autonomous agents Learning to rank and web search His recent publications (2023–2025) show a strong trend toward self-supervised learning , model robustness , and integration of LLMs with structured data and knowledge graphs . He frequently collaborates with researchers from major tech and academic institutions. Scientific Awards: No explicit awards mentioned in the provided text. Advising and Grants: While no direct mention of students or grants, his role as a senior author on numerous papers suggests he advises graduate students and likely leads funded research projects in machine learning and AI. His work on frameworks like COLTR , GS2P , and MUSCLE indicates leadership in developing scalable AI systems. Labs and Teams: Though not explicitly stated, his frequent collaboration with Jiang Bian, Dejing Dou, and Dawei Yin suggests affiliation with a well-established AI research lab or industry-academia partnership focused on data mining, intelligent systems, and large-scale learning.

Martín Hötzel Escardóمشاهده پروفایل

استاد

• Homotopy Type Theory
• Univalent Mathematics
• Martin-Löf Type Theory
• +۱۲ مورد دیگر

Martín Hötzel Escardó is a Professor of Theoretical Computer Science in the School of Computer Science at the University of Birmingham, UK. He has been a faculty member since 2000, following previous academic positions at Imperial College London, the University of Edinburgh, and the University of St Andrews. His research bridges theoretical computer science and pure mathematics, with a strong emphasis on foundational aspects of computation. His educational background includes a BSc and MSc from Universidade Federal do Rio Grande Sul (Brazil) and a PhD from Imperial College London (1997) under Michael B. Smyth. Escardó's research interests center on topology in computation , constructive mathematics , dependent and univalent type theory (including Homotopy Type Theory and Cubical Type Theory), domain theory , locale theory , and exact real-number computation . His work explores deep connections between logic, topology, and programming, often using functional languages like Haskell and Agda to formalize and experiment with theoretical ideas. He is particularly known for his discoveries on exhaustively searchable infinite sets and the topological nature of computability. The trend in his recent publications reflects a sustained focus on univalent foundations, constructive domain and order theory, game semantics with dependent types, and the logical structure of type universes. His work consistently advances the formalization and understanding of higher-type computation and constructive mathematics within modern type theories. He has no listed scientific awards in the provided text, but his influence is evident through his extensive publication record and software developments like TypeTopology. Escardó has advised several students and collaborators, though specific names are not listed. He has been involved in significant research projects, particularly in the formalization of mathematics in type theory and the semantics of programming languages. His work often involves developing Agda libraries to formalize new mathematical results constructively. He leads and contributes to a vibrant research group in theoretical computer science at Birmingham, with a focus on logic, semantics, and type theory. His public research blog, lecture notes, and open-source Agda code (e.g., TypeTopology, HoTT-UF-in-Agda) serve as important resources for the community.

Mohammed J. Zakiمشاهده پروفایل

استاد

• Data Mining
• Machine Learning
• Graph-structured Data Analysis
• +۶ مورد دیگر

Mohammed J. Zaki is a Professor and Department Head at the Department of Computer Science , Rensselaer Polytechnic Institute . He co-directs the NSF IUCRC Center for Research Towards Advancing Financial Technologies (CRAFT) and has authored the textbook Data Mining and Machine Learning (2nd Ed, Cambridge University Press, 2020). Research Focus : Novel data mining techniques for graph-structured and textual data with applications in bioinformatics, personal health, and financial analytics. Leadership : Co-chair of BIOKDD workshops, Associate Editor for multiple journals, and Program Co-chair for SIGKDD, SDM, ICDM, and other top conferences. Scientific Awards include: EDBT'24 Test of Time Award NSF CAREER Award DOE Early Career Award HP Innovation Research Award Google Faculty Research Award Fellowships: IEEE, ACM, AAAS, SIAM Recent Publications highlight advancements in: Knowledge Graph Completion with Directed Attention Multi-Sense Embeddings for Language Models Financial Trend Prediction via Graph Neural Networks Health-Guided Recipe Recommendation Systems Triplet Interaction in Molecular Graph Learning Temporal Personal Health Data Analysis

Ji-young Shinمشاهده پروفایل

استادیار

• Language assessment
• Corpus linguistics
• Educational technology
• +۴ مورد دیگر

Ji-young Shin serves as an Assistant Professor in the Teaching Stream within Education Studies at the University of Toronto Mississauga's Department of Language Studies. Her position integrates teaching, research, and practical application in language education with emphasis on assessment innovation and technology integration. Her academic credentials include: PhD in English / Second Language Studies & ESL from Purdue University, USA MS in Educational Psychology & Research Methodology from Purdue University, USA MA in Teaching English to Speakers of Other Languages (TESOL) from Hankuk University of Foreign Studies, South Korea BA in English Education (Japanese minor) from Hankuk University of Foreign Studies, South Korea Shin's research program focuses on language assessment, corpus linguistics, and technology-mediated pedagogy. She employs mixed methods with quantitative emphasis, utilizing corpus-informed computational analysis and latent-trait modeling complemented by qualitative discourse analysis. Her work addresses critical gaps in second language acquisition, educational measurement, and digital learning environments, particularly regarding AI applications and test validation across diverse language contexts. Her publication trajectory from 2017-2025 demonstrates consistent innovation in language assessment methodologies and technology integration. Key themes include AI-powered chatbots for L2 Korean instruction, online vocabulary testing for Spanish, and corpus-based writing pedagogy. Her work shows increasing focus on artificial intelligence applications and cross-linguistic validation studies, reflecting evolving priorities in language education research. Shin has secured competitive research funding including: Global Classrooms Grant (2025–2026) for VR-enhanced intercultural competence Black, Indigenous, and/or Racialized Scholar Grant (2025–2026) for cross-border AI language learning IRCC Engage3 Project (2022–2024) as Co-PI on VR/AI educational advancement Advancement Fund Plus (2022) for generative AI in teacher education British Council Award (2019) for technology-mediated assessment development She teaches advanced courses including 'The Future of Ed Tech: Active Learning Classrooms and Artificial Intelligence' (EDS285) and supervises community-based experiential learning (EDS388). Her grant portfolio indicates active supervision of research assistants and collaboration with international teams focused on AI implementation and assessment validation. Current projects explore generative AI for language learning across diverse contexts and virtual reality applications in teacher education. While specific laboratory infrastructure isn't documented, her collaborative publications with researchers across Korea, Spain, and the United States suggest participation in international research networks focused on language assessment technology and corpus applications.