جست‌وجوی استاد و پژوهشگر

Oiwi Parker Jonesمشاهده پروفایل

پژوهشگر ارشد

• Neural Engineering
• Machine Learning
• Robotics
• +۳ مورد دیگر

Dr. Oiwi Parker Jones is a Hugh Price Fellow in Computer Science at Jesus College, University of Oxford, and Principal Investigator leading the Parker Jones Neural Processing Lab (PNPL) at the Oxford Robotics Institute. Their research focuses on neural speech prosthetics, combining machine learning with neuroscience to develop technologies for speech restoration. They hold an honorary fellowship in the Nuffield Department of Clinical Neurosciences and teach in Engineering Science, Computer Science, and Neuroscience, including roles as a stipendiary lecturer in neuroscience and medical teaching at St Peter’s and Oriel Colleges. Jones’ work spans robotics, neural decoding, and endangered language preservation, with a particular emphasis on Hawaiian linguistics. Their lab develops large-scale machine learning methods for neural data analysis and collaborates on interdisciplinary projects involving clinical neuroimaging and AI ethics. Jones is also engaged in cultural preservation efforts through computational linguistics and has published widely on language contact, phonology, and indigenous protocols in technology. Education: Doctoral research in NLP and machine learning at Oxford Neuroscience training at UCL and Oxford Research Interests: Neural prosthetics, speech decoding, small-data machine learning, robotics, and endangered language preservation. Current projects include non-invasive brain-to-text systems, clinical fMRI applications, and computational tools for Hawaiian linguistic analysis. Teaching Contributions: Undergraduate lectures on generative deep learning and robotics, postgraduate supervision in DPhil projects across Engineering, Computer Science, and Neuroscience. Known for interdisciplinary teaching methods integrating neuroscience and AI. Awards & Recognition: No explicit awards listed, but recognized for innovative interdisciplinary research and leadership in neural engineering. Labs & Teams: Leads PNPL, collaborates with Oxford Robotics Institute and Applied Artificial Intelligence Lab. Active in global networks for clinical neuroimaging and indigenous AI ethics.

• Natural Language Processing
• Machine Learning
• Large Language Models
• +۳ مورد دیگر

Hao Peng is an Assistant Professor at the Siebel School of Computing and Data Science, part of the Grainger College of Engineering at the University of Illinois Urbana-Champaign. He holds a B.S. from Peking University (2016) and a Ph.D. from the University of Washington’s Paul G. Allen School of Computer Science & Engineering (2022). His research focuses on Natural Language Processing (NLP) , Machine Learning , Large Language Models (LLMs) , and AI for Science , with particular emphasis on improving LLM efficiency, factuality, and interdisciplinary applications. Recent courses include 'CS 598 PEN - Efficiency in NLP' and 'CS 598 PEN - LLM Post-pretraining.' Hao’s work spans advancing LLM generalization capabilities, mitigating hallucinations, and addressing hardware constraints. In 2024, he co-authored an award-winning paper on 'LM-Infinite,' enabling zero-shot extreme length generalization in LLMs. He collaborates internationally, including with the Hebrew University of Jerusalem under a joint research grant since 2019. He also contributes to Argonne National Laboratory’s AI initiatives through invited lectures. Education : B.S., School of Electronics Engineering and Computer Science, Peking University, 2016 Ph.D., Paul G. Allen School of Computer Science & Engineering, University of Washington, 2022 Key Collaborations : Interdisciplinary research with Hebrew University of Jerusalem (2025) Joint seed grant program with HUJI since 2019 His advising includes graduate student Chi Han, who contributed to the NAACL award-winning work. Grants and seed funding focus on accelerating economic development through tech innovation. No specific lab affiliations are explicitly mentioned, but his research aligns with Argonne’s AI Distinguished Lecture series and open-source platforms like OpenDevin/OpenHands.

Brygg Ullmerمشاهده پروفایل

استاد

• Tangible User Interfaces
• Human-Computer Interaction
• Computational Genomics
• +۷ مورد دیگر

Brygg Ullmer is a Professor at Clemson University's School of Computing and Chair of the Human-Centered Computing Division. He leads the Tangible Visualization group, focusing on Tangible User Interfaces (TUIs) Computational Genomics Interactive Computational STEAM Rapid Physical/Electronic Prototyping Computationally-Mediated Art and Design His work bridges physical and digital domains, with applications in K-12 education, high-performance computing, and culturally-rooted design. Research trends from his 15 most recent articles show a focus on Generative AI for cyberphysical systems Shape-changing interfaces Token+Constraint interaction models Genomics data visualization Hybrid tangible-gestural interfaces Multi-display collaboration These span both theoretical and applied work in computer science, biology, and design. Ullmer has held significant roles including Postdoctoral work at Zuse Institute Berlin Associate Professor at Louisiana State University (CCT & Computer Science) Visiting Lecturer at Hong Kong Polytechnic University Contributions to IBM Systems Journal and special editions of Springer's Personal and Ubiquitous Computing He has also co-edited journal special issues and served on conference committees. His scientific contributions include co-invented U.S. patents (6164541, 6263507, 6259441) related to Invisible hyperlinking Audiovisual data browsing Digital video time-shifting These patents reflect early innovations in tangible interface technology that have influenced modern interactive systems.

Allison Sullivanمشاهده پروفایل

استادیار

• Software Engineering
• Formal Methods
• Computer Science
• +۳ مورد دیگر

Allison Sullivan is an Assistant Professor of Computer Science at The University of Texas at Arlington (UTA), affiliated with the Software Engineering Research Center (SERC) and the College of Engineering. She holds a PhD in Software Engineering from The University of Texas at Austin (2017) and previously served as an Assistant Professor at North Carolina A&T State University (2018-2020). Her research focuses on software reliability through automated engineering techniques and formal methods, supported by NSF and DoD grants. Education: PhD in Software Engineering (UT Austin, 2017), MS (UT Austin, 2014), BS (UT Dallas, 2012). Research interests include automated test generation, mutation testing, program synthesis, and formal verification of autonomous systems. Key areas span Model-Based Testing, First-Order Logic, and SAT/SMT solvers. Publications reflect her work in Alloy-based tools (e.g., AUnit, MuAlloy), mutation testing frameworks, and educational outreach. Notable awards include NSF CAREER (2024), UTA Rising Star (2024), and Outstanding Early Career Faculty (2025). She advises multiple graduate and undergraduate students, teaches courses in algorithms, software testing, and formal methods, and serves on program committees for conferences like ASE, MODELS, and ISSRE. Her broader impacts include K-12 outreach, curriculum development, and mentoring through initiatives like Google Faculty In Residence and AMIE Design Challenge. Labs/Teams: Leads the SCOPE lab at UTA, focusing on program correctness verification.

• Human-Centered Artificial Intelligence
• Human-AI Interaction
• Explainable Artificial Intelligence
• +۳ مورد دیگر

Andrea Esposito is a Research Fellow at the University of Bari Aldo Moro, affiliated with the IVU Laboratory (Interaction, Visualization, Usability & UX). He holds a Ph.D. in Computer Science (Artificial Intelligence) from the same institution, with visiting research stints at Ludwig-Maximilians Universität (LMU) Munich and Eusoft S.r.l. His work focuses on Human-Centered Artificial Intelligence, emphasizing explainability, human-AI collaboration, and ethical frameworks. He contributed to projects like the ISO 25000-certified 'eGLU-Box PA' and the 'SERENE' platform for UX evaluation. Education: B.Sc. in Computer Science and Digital Communication (University of Bari Aldo Moro), M.Sc. in Computer Science (Artificial Intelligence) (same university). Research interests include Human-Computer Interaction, XAI, and AI applications in healthcare. He collaborates with the European Humane-AI network and is active in ACM SIGCHI, SIGAI, and related organizations. Teaching roles include assisting in Human-Computer Interaction courses, focusing on UCD sprints. Labs/Teams: Core member of IVU Lab, contributing to interdisciplinary AI research.

• Civil Engineering
• Materials Science
• Structural Health Monitoring
• +۳ مورد دیگر

Yi Bao is an Associate Professor at Stevens Institute of Technology, affiliated with the Department of Civil, Environmental and Ocean Engineering within the Charles V. Schaefer, Jr. School of Engineering and Science. His research group focuses on developing advanced sensors, materials, and machine intelligence algorithms for intelligent, resilient, and sustainable civil, energy, and environmental systems. Key areas include ultra-high-performance concrete (UHPC), fiber optic sensors, machine learning for infrastructure monitoring, and sustainable material design. Recent work emphasizes AI-driven material optimization, automated structural health monitoring, and environmental impact assessments. His publications span topics such as UHPC crack reduction, microplastic characterization via AI, and high-speed railway safety. He leads studies integrating robotics and computer vision for infrastructure inspection, and his group explores low-carbon concrete solutions and digital twin technologies for bridge decks. Collaborations highlight interdisciplinary approaches to address challenges in smart infrastructure, renewable energy systems, and material sustainability. Notably, his team has developed knowledge graph-guided design frameworks for UHPC and ECC materials, leveraging machine learning to predict properties and discover physicochemical reactions. Current projects include real-time corrosion monitoring of pipelines and fatigue life assessment of orthotropic steel decks using deep learning.

Shaowei Wangمشاهده پروفایل

استادیار

• Software Engineering
• Data Mining
• Recommendation Systems
• +۵ مورد دیگر

Shaowei Wang is an Assistant Professor in the Department of Computer Science at the University of Manitoba's Faculty of Science. His research focuses on software engineering and data mining, aiming to develop algorithms that leverage big software data (e.g., code repositories, developer social media) for efficient and effective software development. Key interests include recommendation systems, data-driven software engineering, software debugging, program comprehension, and secure software development. He leads the Mamba Lab, which explores these areas through empirical studies and tool development. His work spans topics such as large language model (LLM) applications in code analysis, vulnerability detection, graph fairness, and automated evaluation frameworks for API-oriented code generation. Recent studies address challenges like input order bias in LLMs, silent vulnerability fixes, and fair graph learning. He has contributed to over 50 peer-reviewed publications, emphasizing practical and theoretical advancements in software engineering and data science. Teaching interests include software engineering, data-driven software engineering, and data mining. His research has been recognized through collaborations with industry and academic partners, though no specific awards are listed. Advising and grant details are not explicitly mentioned in available texts.

Kyumin Leeمشاهده پروفایل

دانشیار

• Social Computing
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Kyumin Lee is an Associate Professor in the Computer Science Department at Worcester Polytechnic Institute (WPI), with affiliations to both the Data Science Program and Artificial Intelligence Program. He maintains his office in Unity Hall 363 and leads the Infolab research group at WPI. Dr. Lee earned his BS from Kyonggi University in 2005, MS from Sungkyunkwan University in 2007, and PhD from Texas A&M University in 2013. Prior to joining WPI, he served as an assistant professor at Utah State University from 2013 to 2017. His primary research interests focus on social computing, artificial intelligence, machine learning, natural language processing, and information retrieval within large-scale systems like the Web and social media. A significant portion of his work addresses threats to these systems, developing methods to mitigate negative behaviors such as misinformation and hate speech. He also explores positive applications of these technologies, including recommender systems and natural language understanding. Notably, his work on AI for social good, particularly disrupting wildlife trafficking networks, has received substantial recognition including a $2 million grant from the National Science Foundation and the Paul G. Allen Family Foundation. Analysis of his recent publications (2023-2025) reveals a strong focus on recommendation systems, graph neural networks, large language models, and applications for social good. His work demonstrates a consistent trajectory toward addressing both theoretical challenges in AI and practical societal problems, with particular emphasis on context-aware systems, contrastive learning approaches, and multi-modal representations. Google Faculty Research Award (2013) NSF CAREER Award (2016) ACM CIKM Test of Time Award (2020) ACM SIGIR Test of Time Award - Honorable Mention (2022) Air Force Research Lab Summer Faculty Fellow (2022) WPI Recognition of Faculty Achievement (2018, 2025) Nominee for Outstanding Academic Advisor of the Year (2019, 2024, 2025) Dr. Lee has demonstrated exceptional commitment to student mentorship, evidenced by multiple nominations for Outstanding Academic Advisor of the Year. His research has been supported by prestigious grants including the NSF CAREER Award and a $2 million grant for wildlife trafficking research. He actively serves the academic community through program committee memberships for top conferences including SIGIR, WSDM, ACL, and AAAI. As the leader of Infolab at WPI, Dr. Lee directs research efforts focused on social computing, AI, and information retrieval. His lab has produced significant work on detecting misinformation, hate speech, and wildlife trafficking activities on social media platforms, while also developing next-generation recommendation systems and natural language understanding techniques.

Nurit Kirshenbaumمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Tangible User Interfaces
• Data Visualization
• +۴ مورد دیگر

Nurit Kirshenbaum is an Assistant Professor in the Information and Computer Sciences department at the University of Hawaii. She is affiliated with the Laboratory for Advanced Visualizations and Applications (LAVA) and the Hawaii Creativity and Technology (HiCat) lab. Her work focuses on Human-Computer Interaction , Tangible User Interfaces , and Data Visualization . Education: PhD in Computer Science, University of Hawaii (2016-2021) M.Sc. in Computer Science, University of Hawaii (2014-2016) M.Sc. in Interactive Media, Quinnipiac University (2012-2014) B.Sc. in Electrical Engineering, Technion - Israel Institute of Technology (1997-2001) Research Interests: Physical Visualization : Encoding data into physical objects (e.g., terrain models with projected data) SAGE3 : Reimagining collaboration tools for hybrid meetings with AI integration PEPA : Bendable interactive cards exploring novel interaction gestures Real-Time Drone Visualization : Streaming environmental data for microbiome research Awards : Best Software Innovation Award (UIST 2015) for ShowFlow , later renamed Set&Motion Advising and Labs: LAVA Lab: Focused on scalable displays and collaborative sense-making HiCat Lab: Exploring creativity-technology intersections Current Teaching: ICS-664: Advanced Human-Computer Interaction ICS/DATA/ACM-484: Data Visualization

Thomas Naselarisمشاهده پروفایل

دانشیار

• Neuroscience
• Cognitive Neuroscience
• Neuroimaging
• +۳ مورد دیگر

Thomas Naselaris is an Associate Professor at the University of Minnesota, specializing in cognitive neuroscience and neuroimaging. His research focuses on decoding brain activity using advanced fMRI techniques and machine learning models, particularly in understanding visual perception, mental imagery, and memory. He leads the development of benchmark datasets like the Natural Scenes Dataset (NSD) and NSD-Imagery, which have become foundational for studying human visual cortex dynamics. His work integrates computational methods with neuroimaging to uncover how the brain represents and processes complex visual information. Notable contributions include reconstructing seen images from fMRI data and analyzing neural representations across different brain regions. He collaborates on large-scale studies involving ultra-high-field MRI and interdisciplinary approaches combining electrophysiology (iEEG) with fMRI. Key research themes include the role of generative models in visual processing, signal-to-noise dynamics in neural responses, and systems consolidation in memory. His grants include collaborative research proposals funded by CRCNS, focusing on evaluating machine learning architectures using benchmark datasets. Ongoing projects aim to bridge cognitive neuroscience with artificial intelligence, emphasizing interpretable models and scalable brain mapping techniques.

Ian Wassellمشاهده پروفایل

مدرس ارشد

• Wireless Communications
• Sensor Networks
• Cooperative and Relay Networks
• +۷ مورد دیگر

Dr. Ian Wassell is a University Senior Lecturer at the University of Cambridge's Department of Computer Science and Technology. He holds a PhD from the University of Southampton (1990) and BSc/BEng degrees from the University of Loughborough (1983). His research focuses on wireless communications, sensor networks, and electromagnetic propagation modeling. He has authored over 200 publications since 1999 and supervised 23 PhD and 6 MPhil students. Currently, he leads wireless communications research in the Digital Technology Group and is a Fellow of Churchill College. His research interests include broadband fixed wireless access (FWA), cooperative networks, MIMO systems, compressive sensing, and AI-driven radio propagation models. He teaches Digital Electronics and Hardware Practical Classes at the undergraduate level. Key contributions include developing data-driven propagation models, deep learning applications in wireless networks, and optimizing heterogeneous radio access networks. Dr. Wassell's work integrates theoretical advancements with practical systems, addressing challenges in high-speed rail communications, indoor/outdoor detection, and 6G IoT opportunities. His research bridges signal processing, machine learning, and wireless network design, emphasizing real-world deployment in infrastructure like tunnels and vehicles.

Harish Tayyar Madabushiمشاهده پروفایل

مدرس

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

Dr. Harish Tayyar Madabushi is a Lecturer in the Department of Computer Science at the University of Bath, affiliated with the Bath Institute for the Augmented Human and the Artificial Intelligence and Machine Learning group. His research focuses on Large Language Models (LLMs), their mechanisms, and applications in bias mitigation, speech-to-text systems, and construction grammar integration. He previously held an Honorary Research Fellow position at the University of Birmingham (2021–2024). Madabushi's work bridges computational linguistics and AI ethics, addressing challenges like regional dialect adaptation in public services and healthcare applications. He leads the Wyser project, funded by Innovate UK, aiming to reduce bias in Automatic Speech Recognition (ASR) systems. His research has been featured in foundational discussions at the UK AI Safety Summit and contributes to UN SDGs related to innovation and sustainable development. He supervises doctoral students in areas such as neuro-symbolic AI and explainable NLP, offering projects funded by ART-AI. His publications span conferences like ACL, COLING, and LREC-COLING, with over 45 peer-reviewed outputs. He actively collaborates internationally, addressing multilingual, code-switched, and specialized lexical learning challenges in LLMs.

• Privacy
• Cryptography
• Information Security
• +۶ مورد دیگر

Hassan Asghar is a Senior Lecturer in Computer Science at Macquarie University's School of Computing and a Research Scientist at CSIRO since 2014. His research focuses on privacy, cryptography, and information security, including quantifying privacy risks, developing secure cryptographic protocols, and analyzing authentication systems. He has contributed to areas such as differential privacy, secure multiparty computation, and malware analysis. His work integrates mathematics (number theory, probability, and combinatorics) with computer science disciplines like machine learning and algorithms. Notable projects include Privacy Preserving Data Sensing Algorithms (2018–2019) and the Macquarie University Cyber Security Hub (2016–2023). He has authored over 40 research outputs, including studies on adversarial attacks in AI, malware obfuscation, and privacy-preserving data release. Awards: ACM AsiaCCS 2019 Best Paper Award; 2023 Faculty of Science and Engineering Inter-School Collaboration Award. Key Research Themes: Privacy quantification, cryptographic protocol design, biometric authentication, and adversarial machine learning. Collaborations: Extensive international collaborations in cybersecurity and privacy research. Asghar’s contributions span technical publications, policy submissions (e.g., NSW Cybersecurity Inquiry), and practical tools like ConvoCache for chatbot efficiency. He leads the Future Communications Research Centre at Macquarie and actively engages in interdisciplinary cybersecurity initiatives.

Simon Coltonمشاهده پروفایل

استاد

• Computational Creativity
• Artificial Intelligence
• Game Design
• +۴ مورد دیگر

Simon Colton is a Professor of Computational Creativity, Games, and Artificial Intelligence at Queen Mary University of London, affiliated with the School of Electronic Engineering and Computer Science. His research focuses on AI-driven creativity in domains like music, game design, and visual arts, emphasizing generative techniques and philosophical aspects of creative systems. He leads projects in automated composition, procedural content generation, and interdisciplinary AI applications. Education: Extensive background in AI and computational creativity, with a focus on generative systems. Research Interests: Computational Creativity, Generative AI, Music Generation, Game Design Automation, and Neuro-Symbolic Systems. His work explores how AI can autonomously create artistic content, including music, games, and visual art, with notable contributions to frameworks like ANGELINA for game design and The Painting Fool for automated art. Recent projects include AI-driven music composition and tools for creative support (e.g., Gamika, Danesh). Key grants include the £3M EPSRC-funded IGGI2 Centre for Doctoral Training, focusing on Intelligent Games and Game Intelligence. His lab, the Centre for Multimodal AI, advances research in cross-modal AI creativity.

Nyamsuren Enkhboldمشاهده پروفایل

مدرس

• Artificial Intelligence
• Cognitive Science
• Software Engineering
• +۵ مورد دیگر

Dr. Nyamsuren Enkhbold is a Lecturer in Computer Science (Software Engineering) at the School of Computer Science and Information Technology, University College Cork. He holds a PhD from the University of Groningen (Cognitive Modeling), an MSc from KAIST (Computer Science), and a BSc from Huree University (Computer Science). His research focuses on intelligent software development at the intersection of AI, Cognitive Science, and GIS, with notable contributions to geo-analytical question-answering systems and EU-funded projects like RAGE (serious games) and QuAnGIS (GIS workflow automation). Current interests include GenAI applications via fine-tuning and prompt engineering. He teaches CS3062 - Computing in Society CS6403 - Case Studies in Computing Entrepreneurship and previously contributed to courses at Utrecht University and KAIST. His work has been supported by a School of Computer Science Starter Fund (€10k). Research highlights include developing reusable AI components for serious games and advancing GIS systems that automatically generate workflows from user queries. His publications span cognitive modeling, spatial semantics, and NLP-driven geo-analytical tools.