جست‌وجوی استاد و پژوهشگر

Alexis Palmerمشاهده پروفایل

دانشیار

• Computational Linguistics
• Low-Resource Language Processing
• Language Documentation
• +۴ مورد دیگر

Dr. Alexis Palmer is an Associate Professor in the Department of Linguistics at the University of Colorado. She specializes in computational discourse and semantics, computational linguistics for low-resource languages, and automated detection of offensive language in social media. Previously, she served as an Assistant Professor at the University of North Texas and held postdoctoral positions in Germany. She leads the NSF-funded FOLTA project (From One Language to Another), focusing on cross-linguistic methods for low-resource language processing tools. Her work emphasizes language documentation accessibility and pedagogical material development for endangered languages. Education: PhD in Linguistics from the University of Texas at Austin (2009). Research Interests: Computational methods for endangered languages Low-resource NLP systems Social media discourse analysis Universal Meaning Representation (UMR) frameworks Linguistic documentation infrastructure Notable Achievements: Recipient of NSF CAREER Grant Pioneered the OLEA tool for offensive language analysis Developed the BELT initiative for endangered language technology Grants & Projects: FOLTA Project (NSF-funded) National Decade of Indigenous Languages initiatives Collaborative research on UMR infrastructure Labs/Teams: Directs the Computational Linguistics Lab at CU, collaborating with global partners on language documentation and revitalization efforts.

• Music Theory
• Schenkerian Analysis
• Brahms Studies
• +۳ مورد دیگر

Robert Snarrenberg is an Associate Professor of Music Theory and Composition at Washington University, where he served as Department Chair from 2000-2005. He holds a BM from Covenant College (1979), MM from University of Cincinnati College-Conservatory of Music (1985), and PhD from University of Michigan (1991). His research focuses on Schenkerian analysis, Brahms studies, and computational musicology. Education: Bachelor of Music, Covenant College (1979) Master of Music, University of Cincinnati (1985) PhD in Music Theory, University of Michigan (1991) Snarrenberg's work bridges historical analysis and modern computational methods. His award-winning book Schenker's Interpretive Practice (1997) examines Schenker's analytical rhetoric, supported by a NEH fellowship. Current projects involve Python-based tools like WesterParse for counterpoint analysis. His Brahms studies explore textual-musical relationships in German Lieder, while his Schenker translations clarify foundational theoretical texts. Publications span computational musicology, Schenker interpretation, and Brahms's compositional techniques. Recent work applies machine learning to tonal analysis, reflecting his dual interest in historical and digital methodologies. Awards: Young Scholar Award (Society for Music Theory, 1998) NEH Fellowship (1995) Jacob K. Javits Fellowship (1987–89) Teaching focuses on counterpoint, tonal analysis, and German art song. He advises graduate research on Schenkerian theory and computational musicology. Ongoing projects include translating Schenker's Der Tonwille and developing AI tools for music analysis.

Jieun Leeمشاهده پروفایل

پژوهشگر ارشد

• Health Economics
• Econometrics
• Machine Learning
• +۳ مورد دیگر

Jieun Lee is a Research Fellow in the Hubert Department of Global Health at Emory University. She holds a PhD in Economics from the University of Illinois Urbana-Champaign (UIUC), an MS in Mathematics (Statistics-Applied), MA in Economics from Ewha Womans University (South Korea), and a BA in Economics. Her research focuses on econometric methodologies and health policy, particularly spatial/spillover econometrics, predictive AI, and chronic disease management. She also explores transnational cultural narratives through theater and performance studies, analyzing diaspora identity formation and gender dynamics in Korean contexts. Education milestones include: PhD in Economics, UIUC (2023) MS in Mathematics (Statistics), UIUC (2022) MS in Mathematics, UIUC (2021) MA in Economics, Ewha Womans University (2016) BA in Economics, Ewha Womans University (2013) Her interdisciplinary work bridges quantitative methods with cultural sociology, addressing topics like gender disparities in STEM participation, transnational adoption narratives, and K-pop feminism. Over 15 publications analyze theater performances, diaspora literature, and socio-economic interactions through both econometric and cultural lenses. Advising and grants: No formal advisees listed. Her research is supported by projects at Emory’s Global Health Institute, focusing on spillover effects in health policy evaluation. Labs/Teams: Currently affiliated with Emory’s Spatial Health Analytics Lab and Cultural Studies Research Group.

Hans-Wolfgang Loidlمشاهده پروفایل

دانشیار

• Functional Programming
• Parallel Programming
• Program Analysis
• +۷ مورد دیگر

Hans-Wolfgang Loidl is an Associate Professor at the School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh. His primary research areas include functional programming, parallel computation, program analysis, symbolic computation, and high-performance machine learning with applications in embedded systems, FinTech, and health informatics. He leads the dependable systems group and serves as Senior Programme Director for Computer Science, chairing the Undergraduate Board of Studies and leading the 2025 Academic Review. Member of dependable systems group Coordinated SICSA MultiCore Challenge Hosted 2nd International Summer School on Advances in Programming Languages in 2014 He designs and implements programming languages for easy-to-use parallelism (Glasgow Parallel Haskell, Glasgow Distributed Haskell, mobile Haskell) and focuses on formal guarantees for resource bounds. Recent expansions include computer security (Secrious project EP/T017511/1) via serious games, high-performance machine learning for FinTech (BA grant + industry PhD), and Brain-Computer Interfaces (EPSRC proposal). His research output spans 1999–2025, including 64 peer-reviewed publications and 5 datasets. Notable works include articles on parallel Haskell dialects (PAEAN), NUMA performance analysis, unikernel benchmarking, and playful learning exercises for security education. He offers PhD projects in functional programming, parallel programming, and high-performance machine learning applications. His teaching vision emphasizes strategic focus in software engineering education, with courses on industrial programming, hardware-software interfaces, and parallel/distributed technology.

Rafael Oliveiraمشاهده پروفایل

استادیار

• Computational Complexity
• Algebraic Geometry
• Optimization Algorithms
• +۱ مورد دیگر

Rafael Oliveira is an Assistant Professor at the University of Waterloo's Cheriton School of Computer Science, conducting research from office DC 1313. His work explores fundamental connections between complexity theory, optimization, and geometry. Research spans: Algebraic complexity barriers Geometric combinatorics Efficient tensor algorithms Proof complexity frameworks His publications demonstrate consistent focus on mathematical foundations of computation , with recent advances in polynomial identity testing, interactive proofs, and geometric optimization. Article analysis shows recurring themes in algebraic complexity (40%), combinatorial geometry (30%), and optimization (30%).

Gaël Richardمشاهده پروفایل

استاد

• Audio Signal Processing
• Speech Synthesis
• Source Separation
• +۵ مورد دیگر

Gaël Richard is a Professor at Télécom Paris, part of the Institut polytechnique de Paris. He leads the Signal, Statistics and Learning (S²A) research group within the LTCI laboratory and is the Executive Director of Hi! Paris, a hub for AI and data science. His primary research focuses on audio signal processing, including speech synthesis, source separation, and non-negative matrix factorization techniques. He has contributed significantly to applications in automotive and music industries, and his work has been recognized with the 2020 IMT-Académie des Sciences Grand Prix and a 2022 ERC Advanced Grant for the HI-Audio project. Education and Career: Gaël Richard transitioned from classical music to applied mathematics, earning a PhD in speech synthesis. His academic journey blends technical expertise with creative problem-solving, leading to impactful contributions in machine learning and audio technologies. Research Interests: His work spans machine learning for audio signals, including speech synthesis, sound separation, and music information retrieval. He emphasizes reproducibility, publishing tools like YAAFE and databases widely used in the field. Key Projects: The HI-Audio project explores AI-driven audio processing, while his ERC grant supports advancements in hybrid deep learning for audio. He co-supervises PhD students in areas like intelligibility in noisy environments and EEG-based source separation. Awards and Recognition: In addition to major grants and the IMT-Académie des Sciences prize, his work on source separation and audio indexing has earned international acclaim. He actively contributes to academic conferences and serves on editorial boards. Labs and Teams: He leads the S²A team at LTCI, fostering collaborations between academia and industry partners like Thales and Deezer. His research bridges theoretical advancements with practical applications in audio technology.

• Neuromorphic Engineering
• Spiking Neural Networks
• CMOS Circuit Design
• +۳ مورد دیگر

Hugh Greatorex serves as a Researcher at the University of Groningen within the Faculty of Science and Engineering, affiliated with the Bio-inspired Circuits & Systems research group. His work centers on neuromorphic hardware design, specializing in spiking neural networks and event-based processing systems that prioritize energy efficiency and real-world applicability in robotics and embedded AI. His research spans critical areas in neuromorphic engineering: Neuromorphic Engineering Spiking Neural Networks CMOS Circuit Design Memristive Computing Event-based Vision and Audio Processing Symbolic Computation in Neural Hardware Greatorex integrates circuit design with neural algorithms to solve power efficiency challenges, frequently exploring beyond-CMOS technologies like memristors and multi-timescale neural dynamics for practical implementations. Analysis of his 14 recent publications (2022-2025) reveals a cohesive trajectory toward hybrid neuromorphic processors combining CMOS with emerging devices. Key contributions include the TEXEL and Fused-MemBrain architectures for beyond-CMOS integration, alongside advancements in event-based sensory processing and symbolic operations within spiking hardware. This work consistently bridges neural computation with symbolic AI paradigms while emphasizing scalability and robustness. No scientific awards were documented in available sources. Information regarding student advisement and research grants was not provided in the source materials. As a core member of Groningen's Bio-inspired Circuits & Systems group, Greatorex contributes to internationally collaborative projects focused on biologically inspired electronic systems for intelligent processing, with applications spanning edge AI and neural computation modeling.

John Thangarajahمشاهده پروفایل

استاد

• Artificial Intelligence
• Autonomous Systems
• Knowledge-based Reasoning
• +۳ مورد دیگر

John Thangarajah is a Professor in AI and Director of Research at the Center for Industrial AI Research & Innovation (CIAIRI) at RMIT University, Melbourne. He leads research directions, academic partnerships, and industry collaborations, focusing on applied research with over $4M in government/industry funding. His expertise spans Autonomous Systems, Knowledge-Based Reasoning, and Agent-Based Modelling. He holds awards including the 2020 Vice Chancellor’s Leadership Award for fostering collaboration in Computer Science and the 2017 AAMAS Best Paper Blue Sky Track. Education: Extensive background in CS/IT education, transformed first-year Computer Science curricula to hands-on experiential learning. Research: Specializes in AI-driven smart systems, human-machine teams, and multi-agent systems. Member of IFAAMAS board. Publications: Over 147 peer-reviewed works in AI, including advancements in explainable agents, reinforcement learning, and medical imaging applications. Awards: Recognized for innovation in AI (Telstra 2011), demonstration excellence (2005), and leadership in academia. Grants: Secured significant funding from DST Group and industry partners, emphasizing practical impact. His lab focuses on AI applications in security, resilience, and industrial innovation through CIAIRI and the Security & Resilience Hub.

• Artificial Intelligence
• Robotics
• Knowledge Representation
• +۳ مورد دیگر

Axel Großmann is a former PhD Researcher affiliated with the International Center for Computational Logic (ICCL) at the Technische Universität Dresden . He was part of the Faculty of Computer Science and the Knowledge Representation and Reasoning Research Group . His work focuses on robotics , particularly in areas such as mobile robot localization, control systems, and probabilistic sensor models. He contributed to integrating reactive control, reasoning, and execution monitoring in dynamic environments. Key research interests include knowledge representation , symbolic dynamic programming , and robot-environment interaction . His publications span topics like semi-supervised learning methods, visual sensor models, and robust localization techniques using Hough transforms and probability grids. He collaborated on technical reports and conference papers addressing autonomous navigation and adaptive control systems. While no awards or current students are explicitly mentioned, his involvement in the ICCL and the Knowledge Representation group highlights his contributions to advancing computational logic and robotics applications. His work integrates theoretical foundations (e.g., Fluent Calculus) with practical robotics challenges.

Jordan B. L. Smithمشاهده پروفایل

مدرس

• Music Structure Analysis
• Audio Signal Processing
• Music Information Retrieval
• +۴ مورد دیگر

Jordan B. L. Smith is a Lecturer in Audio Signal Processing at Queen Mary University of London's School of Physical and Chemical Sciences. He teaches digital audio courses in the Queen Mary School Hainan program, requiring annual 2-week trips to China. His research focuses on music structure analysis, music decomposition, and interactive tools for musical creativity. Education: BA in Music and Physics (Harvard College) MA in Music Technology (McGill University) MSc in Operations Research Engineering (University of Southern California) PhD in Computer Science (Queen Mary University of London) Research Interests: Jordan explores nested patterns in musical structure, listener perception of structure, algorithmic music generation, and interfaces for music remixing. His work bridges computer science and musicology, with contributions to tools like Unmixer (loop extraction) and CrossSong (musical logic puzzles). Publications Trends: His work emphasizes music structure segmentation, computational creativity, and human-audio interaction. Recent studies focus on scalable music generation (e.g., SymPAC) and improving structural analysis with semi-supervised methods (e.g., MuSFA). Awards & Collaborations: No explicit awards listed, but collaborations include AIST Japan, IRCAM Paris, and TikTok. Past roles include postdoc at AIST (2014–2017) and industry work at TikTok (2018–2022). Labs & Teams: Collaborates with the Centre for Digital Music (C4DM) in EECS. Engages in interdisciplinary projects combining music theory, AI, and user interaction design.

Raphael Milliereمشاهده پروفایل

مدرس

• Artificial Intelligence
• Philosophy of Cognitive Science
• Deep Learning
• +۷ مورد دیگر

Dr. Raphael Milliere is a Lecturer in Philosophy of Artificial Intelligence at Macquarie University , Australia. Previously, he held the Robert A. Burt Presidential Scholar in Society and Neuroscience at Columbia University. He earned his PhD in Philosophy from the University of Oxford in 2020, focusing on Self in Mind: A Pluralist Account of Self-Consciousness . His research bridges philosophy, cognitive science, and AI, focusing on understanding modern neural networks (e.g., large language models) and their implications for human cognition. Key interests include semantic competence in AI, the grounding problem, AI ethics, and consciousness studies. He explores interdisciplinary questions about machine-human comparisons, compositional representations, and the ethical deployment of AI. Recent research highlights include analyzing transformer architectures, evaluating language model capabilities, and investigating psychedelic-induced states of consciousness. He has co-authored influential papers such as "Beyond the Imitation Game" (2023) and "Philosophy of Cognitive Science in the Age of Deep Learning" (2024). Awards & Fellowships : AI2050 Fellowship (Schmidt Sciences) Australian Research Council (ARC) DECRA Fellowship ARC Discovery Project Principal Investigator Grants & Projects : Leading an ARC-funded project on AI alignment and cognitive modeling Initiated the Altered Xperience Project , a citizen science study on consciousness Labs/Teams : Member of Macquarie's Ethics and Agency Research Centre Collaborates with interdisciplinary teams in AI ethics and neuroscience

• Formal verification
• Model checking
• Binary decision diagrams
• +۷ مورد دیگر

Gianpiero Cabodi is an Associate Professor in the Department of Control and Computer Science (DAUIN) at the Polytechnic of Turin, where he is also a member of the University Internship Commission. He is actively involved in teaching and research within the College of Computer, Film and Mechatronics Engineering. Research Interests: His research spans formal verification, model checking, binary decision diagrams, SAT solvers, embedded system design, and cybersecurity. His work emphasizes formal methods for ensuring correctness and security in hardware and software systems, particularly in safety-critical and automotive applications. Recent Publications: His 2024 publications demonstrate a strong focus on optimizing formal verification techniques. He explores interpolation-based improvements in bounded model checking, hardware model checking algorithms, and the use of binary decision diagrams in interpretable machine learning—highlighting a convergence of formal methods with modern AI interpretability and hardware verification. Teaching and Leadership: He has served as a course instructor for advanced data structures in Python, systems programming, and algorithms. He has also directed several doctoral programs in Computer and Systems Engineering. His leadership extends to research projects such as REBECCA and OSMOSIS, and he has chaired international workshops including FMCAD and DIFTS. Research Projects: REBECCA (2023–2026): Scientific Head of Structure, focusing on reconfigurable platforms for secure AI. OSMOSIS (2008–2010): Scientific Responsible for EU-funded codesign of embedded systems. Multiple commercial research projects on embedded systems in automotive, RFID traceability, cybersecurity, and HMI design (2008–2025). Advising and Grants: While no formal students are listed, he has supervised numerous research projects, often as Scientific Manager or Responsible, funded by both EU programs and industry contracts. His work bridges academic research and industrial applications, particularly in automotive and embedded domains. Labs and Teams: He is a key member of the FM - Formal Methods (DAUIN) research group, which focuses on formal verification techniques for digital systems.

Gabriele Ciravegnaمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Neural Networks
• Explainable AI
• +۳ مورد دیگر

Gabriele Ciravegna is a Postdoctoral Research Fellow at Inria, working within the MAASAI (Models and Algorithms for Artificial Intelligence) research team. He earned his Ph.D. in Computer Engineering with honours from the University of Florence in 2022 and a Master’s degree in Computer Engineering from the Polytechnic of Turin in 2018. Research Interests: Gabriele's work focuses on overcoming the limitations of machine learning and neural networks by integrating domain knowledge into AI models. His research spans Explainable AI, adversarial robustness in multi-label classifiers, constraint-based learning frameworks, active learning strategies, and topological gradient-driven competitive learning algorithms. Presented at prestigious venues: AAAI, IJCAI, IJCNN Active reviewer for AAAI and IEEE TNNLS Contributed to neurocomputing and multi-omics data integration Advising & Collaborations: Collaborated with Professor Marco Gori (University of Florence) and other researchers across institutions. While his primary affiliation is with Inria's MAASAI team, he has also participated in academic seminars at the University of Cambridge.

Tor Jarle Wergelandمشاهده پروفایل

دانشیار

• Visual Arts Education
• Digital Storytelling
• Creative Processes
• +۶ مورد دیگر

Tor Jarle Wergeland is an Associate Professor in the Department of Visual Arts and Drama at the University of Agder, Norway. His academic work bridges visual arts, digital storytelling, and educational innovation, with a strong focus on creative processes in teaching and learning. Research Interests: Visual Arts Education Digital Storytelling Integration of Artificial Intelligence in Art Creative Workshops and Formative Assessment Educational Technology in Art and Craft His recent publications reflect a consistent engagement with digital tools in art education, particularly in exploring how AI can function as a co-creator. He investigates collaborative cultures, assessment practices, and the transformation of traditional artistic methods through digital media. His work often appears in educational anthologies and institutional publications. Scientific Contributions: Active contributor to the ProDig project on digital tools in art and craft education. Regular publications on digital storytelling, formative assessment, and creative digital practices. Emphasis on practical, workshop-based, and student-centered pedagogy. Wergeland has collaborated with colleagues such as Tor Jørund Føreland Pedersen and Cornelia Brodahl, and his work is disseminated through the University of Agder’s Cristin repository. He has no listed scientific awards, and no information is available about students he may have supervised. He maintains an active academic profile with recent outputs extending into 2025.

• Conversational AI
• Natural Language Processing (NLP)
• Human-Robot Interaction (HRI)
• +۵ مورد دیگر

Dr Yanchao Yu is a Lecturer at the School of Computing Engineering and the Built Environment of Edinburgh Napier University. He earned a BSc from Beijing Information Science and Technology University, an MSc in Software Engineering (Distinction), and a PhD in Artificial Intelligence from Heriot-Watt University (UK). PhD in Artificial Intelligence (Heriot-Watt University) MSc in Software Engineering (Distinction) BSc in Computer Science His research focuses on building smart agents and robots that assist humans in daily tasks through natural conversations. Key areas include: Natural Language Understanding (NLU), Generation (NLG), and Dialogue Management (DM) Visually grounded language learning Human-Robot Interaction (HRI) Machine learning for interactive systems Recent publications emphasize context-aware dialogue systems, visual scene understanding, and ethical challenges in HRI. He leads the EPSRC-funded project Learning from Collaborative Storytelling (2025-2028, £439,581) to enhance robot perception through interactive storytelling. Director of Studies for Lewis Watson's PhD on AI-Driven Conversational Storytelling Co-supervisor for multiple PhD projects including medical training robotics and machine unlearning Previously worked at Heriot-Watt's Interaction Lab (postdoc) and industry roles at Voysis (acquired by Apple) and Alana AI.