جست‌وجوی استاد و پژوهشگر

Thomas Hainمشاهده پروفایل

استاد

• Machine Learning
• Speech Technology
• Natural Language Processing
• +۳ مورد دیگر

Thomas Hain is a Professor of Speech and Audio Technology at the University of Sheffield's School of Computer Science. He is also the Director of the CDT in Speech and Language Technologies and the Liveperson Centre for Speech and Language Technology. His roles include heading the Machine Intelligence for Natural Interfaces group and membership in the Speech and Hearing Research Group and Machine Learning Research Group. Hain is a Fellow of ISCA and AAIA, reflecting his contributions to the field. He holds a Dipl.-Ing in Electrical/Communication Engineering from the University of Technology, Vienna, and a PhD from the University of Cambridge. His research focuses on machine learning applied to speech, audio, and language technologies, including agent modeling and blockchain through his co-founding of fetch.ai. His work spans speech recognition, enhancement, and emotion analysis, with recent publications emphasizing self-supervised learning, robust ASR, and perceptual metrics. Hain has led several projects, including the AMI/AMIDA systems for meeting transcription and webASR for cloud-based speech technology. His awards include fellowships for advancing speech technology, and he actively contributes to open-source toolkits like EmoBox.

Aris Xanthosمشاهده پروفایل

استاد

• Computational Linguistics
• Natural Language Processing
• Morphology
• +۳ مورد دیگر

Aris Xanthos is a Professor (MER 1) in the Section of Language and Information Sciences at the University of Lausanne (UNIL) since 2012. Specializing in computational linguistics and mathematical methods for language sciences, he collaborates with institutions such as the Universities of Zurich, Vienna, and Antwerp. His research focuses on developing innovative computational tools for linguistic analysis, including projects like Textable, an interactive text analysis software. Additionally, he founded and directs a startup specializing in automatic language processing technologies. Xanthos earned his PhD in 2007 with a thesis on morphological learning, conducted in part at the University of Chicago under John Goldsmith. His teaching includes textual computing for arts students at UNIL. Research themes span lexical diversity metrics, computational morphology, and interdisciplinary applications of NLP in fields like child language acquisition and game studies. His work bridges formal linguistics with digital humanities, emphasizing methodological rigor and computational innovation. Publications highlight contributions to emoji ambiguity analysis, poem generation algorithms, and historical corpus development. His startup demonstrates practical applications of academic research, reflecting a commitment to both theoretical and applied linguistic inquiry.

• Political Science
• Local Governance
• Regional Reforms
• +۳ مورد دیگر

Yngve Flo is a Professor at the Department of Archaeology, History, Cultural Studies and Religion, University of Bergen. His work centers on Norwegian political history, local governance, and regional reforms. He has extensively researched the administrative and political implications of municipal and regional restructuring, particularly the 2017 reforms in Western Norway. Flo’s analyses often combine historical perspectives with contemporary political issues, emphasizing democratic representation and institutional legitimacy. His research spans topics like regional identity, electoral behavior, and the interplay between central and local governance. Notable contributions include studies on the 'Vestland' region and the impact of forced municipal mergers. Flo frequently engages with media, offering expert commentary on political developments, such as the 2022 Kinn municipal dissolution referendum and regional reversals. Flo has authored books and articles on governance, democracy, and historical administration, including Regjeringen: Historien, makten og hverdagen (2024). He received the Formidlingspris award in 2022 for his dissemination of academic research to public audiences. His work bridges academic scholarship and practical policy analysis, informing debates on Norway’s evolving local political landscape.

Julian Togeliusمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Player Modelling
• Procedural Content Generation
• +۳ مورد دیگر

Julian Togelius is Associate Professor of Computer Science and Engineering at NYU Tandon School of Engineering, where he co-directs the Game Innovation Lab. His research focuses on artificial intelligence for games, including procedural content generation, player modeling, and believable agent behavior using evolutionary computation and neural networks. Research domains include: AI-driven game design automation Evolutionary content generation General video game AI Large language models for game creation Recent innovations include GAVEL (game generation via evolution/LLMs), PCGRL+ (scalable RL level generators), and Neural MMO competitions. He established benchmark frameworks for procedural content generation and organizes international AI competitions. As Editor-in-Chief of IEEE Transactions on Games, he oversees academic publishing in game AI. His research received coverage in WIRED, IEEE Spectrum, and New Scientist. He teaches courses on AI and game development.

Grigory Vilkovمشاهده پروفایل

استاد

• Financial Economics
• Options Markets
• Volatility Modeling
• +۷ مورد دیگر

Prof. Grigory Vilkov is a Professor of Finance at Frankfurt School of Finance & Management. His research focuses on options markets, volatility dynamics, climate finance, and machine learning applications in finance. He develops quantitative tools like the qmoms Python package for analyzing volatility surfaces. Key research areas include gamma risk, 0DTE options trading, and climate-related financial risk. He collaborates on projects like the BIS Bulletin (2024) and studies firm-level climate exposure. His work bridges theoretical finance with practical applications in portfolio construction and risk management. Research interests span options-implied moments, factor dispersions, and narrative-driven asset pricing using NLP. He has published in top journals like Review of Financial Studies and maintains active data repositories on OSF. His current projects explore ML/DL for portfolio optimization and automatic feature discovery in financial data.

Carlos Ernesto Guestrinمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Explainability of AI
• +۴ مورد دیگر

Carlos Ernesto Guestrin is the Fortinet Founders Professor and Senior Fellow at the Stanford Institute for Human-Centered AI (HAI), and a Professor in Stanford University’s Computer Science Department. He serves as Director of the Stanford AI Lab (SAIL), Chief Scientist at Visual Layer and Virtue AI, and is a Member of the National Academy of Engineering. His research focuses on machine learning methods, explainability, fairness, and ethics in AI, alongside scalable machine learning systems. Prior roles include Amazon Professor of Machine Learning at the University of Washington, Finmeccanica Associate Professor at Carnegie Mellon University, and Senior Director of Machine Learning at Apple. He co-founded Turi, Inc. (now part of Apple), which developed tools for building intelligent applications, and released open-source projects like XGBoost, LIME, Apache TVM, MXNet, and GraphLab. His work has earned prestigious awards, including the IJCAI Computers and Thought Award, PECASE, and recognition as one of Popular Science’s ‘Brilliant 10.’ His research addresses both technical advancements and societal impacts, emphasizing transparency and ethical AI systems. Current projects explore generative AI, model explainability, and systems for scalable machine learning.

Sergio Rodriguez Mendezمشاهده پروفایل

پژوهشگر ارشد

• Knowledge Graphs
• Ontology Engineering
• Linked Data & Semantic Web
• +۷ مورد دیگر

Sergio Rodriguez Mendez is a Research Fellow in Knowledge Graph Engineering at the School of Computing, Australian National University. His work focuses on advancing ontology engineering, linked data, semantic web technologies, and their applications in domains like astronomy, brain-computer interfaces, and IoT. He is a member of the Australian Government Linked Data Working Group (AGLDWG) and the W3C Knowledge Graph Construction Community Group, and holds a senior role at the Software Innovation Institute (SII). His research interests include Knowledge Graphs, Ontology Engineering, Linked Data, Data Science, Machine Learning, Natural Language Processing, Brain-Computer Interfaces, IoT, Cloud/Fog/Edge Computing, and Software Engineering. He has contributed to frameworks like pathfinder for astronomical literature review and Doc-KG for document-to-KG conversion. Recent work emphasizes integrating large language models (LLMs) with knowledge graphs, such as AstroLLaVA for astronomical data unification and hybrid frameworks for entity linking. His publications span conferences like WWW, JCDL, and the ACM/IEEE Joint Conferences. He actively supervises research students and is involved in initiatives like the ASKG project for enriching scholarly knowledge graphs. His work addresses challenges in semantic data representation, automated query processing, and syntactic complexity reduction, with a focus on domain-specific applications in science and cultural heritage.

• Human-Computer Interaction
• Virtual Reality/Augmented Reality
• Digital Twins
• +۳ مورد دیگر

Mukesh BARANGE is an Associate Professor at CESI, affiliated with the 'Engineering and Numerical Tools' research team. His work focuses on Human-Computer Interaction, Virtual/Augmented Reality, and Multimodal Systems. He leads the NEXUS research project (2023-2026) and supervises Tristan LANNUZEL's PhD on collective intelligence in immersive environments. Education : Doctorate in Human-Computer Interaction, University of Western Brittany (2015) Master of Science and Research, IMT Atlantique Bretagne (2010) Bachelor of Engineering Associate Member of Institution of Electronics and Telecommunication Engineers (AMIETE, 2005) Research Interests : BARANGE explores adaptive empathic agent behavior, multimodal interaction analysis, and collaborative virtual environments. His work bridges machine learning, social robotics, and educational technologies to enhance human-machine collaboration and immersive experiences. Selected Achievements : RoboBreizh Team: RoboCup@Home SSPL Champion 2022 Nominated for Best Paper Award at ACM IVA 2022 for empathic agent research Over 20 peer-reviewed publications since 2010 Teaching: He instructs computer science and engineering courses for Engineers (3rd–5th year), Masters, and Bachelor students at CESI. Labs/Teams : Part of CESI LINEACT's research group, contributing to interdisciplinary projects in digital twins and human-agent interaction.

Laureano Moro-Velazquezمشاهده پروفایل

استادیار

• Speech Processing
• Machine Learning
• Voice Pathology Detection
• +۶ مورد دیگر

Laureano Moro-Velazquez is an Assistant Professor at Johns Hopkins University with appointments in the Department of Electrical and Computer Engineering and the Center for Language and Speech Processing (CLSP) . His work bridges signal processing , machine learning , and medical applications , focusing on neurodegenerative disease diagnosis and speech enhancement for under-resourced languages. PhD in Systems and Services Engineering for the Information Society (2018, Universidad Politécnica de Madrid) Master in Telecommunications Engineering (2006, Universidad Politécnica de Madrid) BSc in Sound and Image Engineering (2003, Technical University of Madrid) Research spans Voice pathology detection for Parkinson's and Alzheimer's Speech synthesis and enhancement using AI Cognitive assessment through handwriting and speech Domain adaptation in speaker verification Recent publications focus on multimodal biomarkers , handwriting analysis , and explainable AI for neurological disorders. Notable trends include cross-lingual speech processing and biometric fairness in speech recognition. Scientific contributions include Spanish Ministry of Economy and Competitiveness Mobility Grant (2017) Johns Hopkins University Teaching as Research Fellowship (2020) Teaching roles include Machine Learning for Medical Applications (undergraduate/graduate) and Artificial Intelligence in Medicine Reading Group . Collaborations involve Johns Hopkins School of Medicine (Neurology, Critical Care) and CLSP teams.

• Natural Language Processing
• Intelligent Computer-Assisted Language Learning
• Automatic Essay Scoring
• +۷ مورد دیگر

Rob Reynolds is an Associate Research Professor in the College of Humanities at Brigham Young University (BYU), where he is affiliated with the Office of Digital Humanities. He holds a PhD in Russian Language Technology from UiT The Arctic University of Norway and specializes in Natural Language Processing (NLP) for language education. His education includes: PhD in Russian Language Technology, UiT The Arctic University of Norway (2016) MA in Russian Linguistics, The Ohio State University (2011) BS in Russian, Psychology, and Philosophy, Brigham Young University (2008) Reynolds' research focuses on the intersection of computational methods and language learning. His primary interests include intelligent computer-assisted language learning (ICALL), automatic assessment of L2 essays, text readability rating, and using eye-tracking data to evaluate reading proficiency. His work bridges theoretical linguistics—particularly morphological theory and the structure of Russian—with practical applications in educational technology. His teaching interests align closely with his research, covering Natural Language Processing, Russian language, and general linguistics. As a part-time faculty member, he contributes to both pedagogical innovation and digital humanities initiatives at BYU. He is an active member of several professional organizations: AATSEEL (American Association of Teachers of Slavic and East European Languages), 2017–Present CALICO (Computer Assisted Language Instruction Consortium), 2017–Present IALLT (International Association for Language Learning Technology), 2017–Present Reynolds also holds certifications in workplace diversity and suicide prevention, reflecting his commitment to inclusive and supportive academic environments. He has been recognized through professional development credentials such as the Equality Utah Diversity in the Workplace certification (2021–Present) and the QPR Gatekeeper Certificate (2022–2024). He advises on digital humanities projects and contributes to research grants related to language technology, though specific grant details and student advisees are not listed. His lab or team affiliation centers on the Office of Digital Humanities at BYU, where he collaborates on NLP-driven educational tools.

• Speech Recognition
• Natural Language Processing
• Machine Learning
• +۳ مورد دیگر

Mittul Singh is a Research Fellow at the Department of Information and Communications Engineering at Aalto University , specializing in Speech Recognition and Low-Resource Language Processing . Their work focuses on improving automatic speech recognition (ASR) systems for underrepresented populations, including children and speakers of resource-constrained languages. They are actively involved in developing AI-powered educational tools for language learning and advancing cross-lingual transfer learning techniques. Current Position: Research Fellow, Aalto University Research Areas: Speech Recognition, Low-Resource NLP, Machine Learning, Educational Technology Email: mittul.singh@aalto.fi

• agent-based and multi-agent systems
• fuzzy logic
• web intelligence
• +۷ مورد دیگر

Vincenzo Loia is a Full Professor of Computer Science and Rector of the University of Salerno since November 2019. He earned his degree (1985) and PhD (1989) in Computer Science from the University of Salerno and Université Paris 6, respectively. His research spans agent-based systems , fuzzy logic , ambient intelligence , and smart grids . Recent work focuses on information disorder , LLM vulnerabilities , and formal concept analysis , with 15 most-recent publications emphasizing 2024–2025 trends in AI ethics , cybersecurity , and explainable models . Loia serves as Editor-in-Chief of Ambient Intelligence and Humanized Computing and Soft Computing (Springer), while holding leadership roles in IEEE Computational Intelligence Society committees. His academic career at University of Salerno includes 1989–2000 as researcher and 2000–2004 as associate professor , with over 200 publications and 40+ IEEE conference committee memberships.

Arsha Nagraniمشاهده پروفایل

پژوهشگر

• Machine Learning
• Multi-modal Learning
• Computer Vision
• +۵ مورد دیگر

Arsha Nagrani is a senior research scientist at Google AI Research , focusing on machine learning for video understanding. She earned her PhD at the University of Oxford under Andrew Zisserman with a Google PhD Fellowship , and completed her undergraduate studies at the University of Cambridge with mentorship from Roberto Cipolla and Richard Turner. Research Interests : Self-supervised and multi-modal machine learning Video recognition using sound and text Computer vision for wildlife conservation Cross-modal self-supervision in biometric matching Transformer-based architectures for temporal modeling Zero-shot learning with frozen models Scientific Awards : ELLIS PhD Award Google PhD Fellowship ICASSP 2021 Outstanding Paper Award INTERSPEECH 2017 Best Student Paper Award Service Contributions : Area Chair for CVPR23, ICCV23 Reviewer for top conferences (CVPR, ECCV, ICCV, BMVC, NeurIPS, ICML, AAAI) Organized workshops: Sight and Sound Workshop @ CVPR [2020-2022] VoxSRC Challenges @ INTERSPEECH Video Understanding Pentathlon @ CVPR 2020 Women in Computer Vision (WiCV) Workshops

Mario Baldiمشاهده پروفایل

دانشیار

• Programmable data planes
• Software-defined networking
• Network security
• +۵ مورد دیگر

Mario Baldi is an Associate Professor (on leave) of Information Processing Systems at the Department of Control and Computer Engineering , Politecnico di Torino , and concurrently a Fellow in the Office of the CTO, Adaptive and Embedded Computing Group, at AMD, San Jose, CA. His career blends deep academic research with extensive industry R&D leadership. Education M.Sc. (Summa Cum Laude) in Electrical Engineering, Politecnico di Torino, 1993 Ph.D. in Computer and System Engineering, Politecnico di Torino, 1998 Research Focus Baldi’s research spans programmable data planes, software-defined networking, big-data analytics for network management, network security, high-performance switching architectures, optical networking, QoS mechanisms, and multimedia/voice-over-IP systems . He is especially known for pioneering work on P4 -based programmable networks and SmartNIC architectures. Publication & Patent Impact Across 150+ refereed papers and 35+ US patents (plus European filings), his recent output concentrates on machine-learning-driven network data-plane functions , disaggregated stateful network services , and cloud-grade DPUs . The 2021–2024 articles emphasize real-time inference directly in the network data plane and modular SDN programming. Awards & Honors Best Paper Award, IEEE ICC 2007 Best Paper Award, IEEE ISCC 2008 Best Paper Award, IEEE GreenComm 2009 Best Paper Award, ACM/IEEE WI/IAT 2014 Grants & Projects Baldi has served as Principal Investigator or Scientific Coordinator on numerous EU Framework and Italian national projects (PRIN, FAR), leading consortia on energy-efficient packet networks, trusted software execution, wireless mesh architectures, and streaming media delivery. Teaching & Mentoring He has taught graduate courses on Enterprise Network Technologies, Computer Network Technologies and Services, Networks/Cloud/Application Security at Politecnico di Torino since 2003. He has also supervised PhD collegi for the Computer and Systems Engineering doctoral program (cycles 19–23) and held visiting/adjunct positions across four continents. Labs & Teams Previously headed the NetGroup (Computer Networks Group) at Politecnico di Torino (2001–2007) and co-chairs the p4.org Architecture Workgroup , driving open standards for programmable networking.

• Natural Language Processing
• Machine Learning
• Computational Linguistics
• +۳ مورد دیگر

Benjamin Daniel Nyberg Shultz is an Instructor at the Department of Computer Science (DIKU), University of Copenhagen. His work aligns with the Natural Language Processing (NLP) section , focusing on methods for automated text processing, understanding, and generation using statistical models and machine learning. Research Focus : Core NLP tasks like parsing, cross-lingual processing, and transfer learning; applications in automatic fact-checking, machine translation, and visually grounded language learning. Teaching : Contributes to courses in Computer Science, Machine Learning, and Data Science, including advanced topics in NLP and deep learning. Collaboration : Part of the SCIENCE AI Centre, exploring intersections between NLP and computer vision in multi-modal learning.