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John GoldsmithView profile

Professor

• Computational Linguistics
• Artificial Intelligence
• Phonology
• +3 more

Dr. John Goldsmith is a Professor in the Department of Computer Science at the University of Chicago, specializing in computational linguistics and artificial intelligence. His research focuses on understanding symbolic representation in natural language and genomic sequences through software development, notably the open-source Linguistica project, which automates morphological analysis in diverse languages like Somali and Swahili. He co-authored *Battle in the Mind Fields* (2019), exploring the epistemology and sociology of linguistic theory development. Goldsmith's work bridges computational models with phonological theory, including autosegmental phonology and dynamic computational networks. His contributions span morphological algorithms, unsupervised learning techniques, and the historical analysis of linguistic thought. His research interests include phonology, computational morphology, and formal language theory, with a focus on integrating big data and visualization tools. Goldsmith’s academic career spans decades, reflected in his mentorship through *The Chicago Guide to Your Academic Career* (2010), which advises scholars from graduate studies through tenure. His teaching emphasizes modern pedagogical methods, such as applying graphs and networks in linguistics education. Goldsmith’s awards and grants are not explicitly listed, but his extensive publication record and leadership in computational linguistics highlight his disciplinary impact. He collaborates across departments, linking computer science with linguistics and cognitive science. His current projects explore interdisciplinary approaches to language structure and the evolution of linguistic theories, maintaining a lab focused on computational linguistics innovations.

Elias IosifView profile

Assistant Professor

• Blockchain
• Machine Learning
• Natural Language Processing
• +5 more

Dr. Elias Iosif is an Assistant Professor in the Department of Digital Innovation at the School of Business, University of Nicosia. He specializes in blockchain technologies, machine learning, and natural language processing. His research focuses on integrating blockchain with AI, semantic models, and spoken dialogue systems. He holds a PhD in Network-Based Distributional Semantic Models from the Technical University of Crete (2013). His career includes postdoctoral roles at the National Technical University of Athens (2014–2017) and research positions at institutions like AT&T Labs and the Athena Research Center. Notable contributions include blockchain maturity models, fraud detection frameworks, and AI applications in pharmaceuticals. Research highlights include over 80 peer-reviewed publications, with recent work emphasizing blockchain scalability, NFTs, and cross-chain interoperability. He co-authored the Handbook of Blockchain Technology (2025), exploring cryptocurrency regulation and tokenization landscapes.

Hitomi YanakaView profile

Associate Professor

• Natural Language Processing
• Artificial Intelligence
• Computational Linguistics
• +3 more

Hitomi Yanaka is an Associate Professor (tenured) at the University of Tokyo and Team Leader of the Explainable AI Team at RIKEN. Her research focuses on Natural Language Processing (NLP), Artificial Intelligence, and formal semantics, with a specialization in logical inference systems and compositional semantics. She holds a Ph.D. in Engineering from the University of Tokyo and has been recognized with prestigious awards including the Young Scientists Award from the Ministry of Education and Forbes JAPAN's Women In Tech TOP30 in 2024. Her academic roles include leadership in the "覚醒" project and affiliations with ACL, JSAI, and ANLP. Yanaka's work bridges theoretical linguistics and computational methods, contributing to semantic analysis, bias detection in LLMs, and multimodal reasoning. She teaches courses on computational linguistics and logic at the University of Tokyo, and her research spans over 150 publications in top-tier conferences like ACL, NLP, and Cognitive Science Society. Award highlights include the 2024 文部科学大臣表彰 (Ministry of Education Award), 2023船井研究奨励賞, and multiple best paper awards. Her lab, Yanaka Laboratory, focuses on explainable AI, ethical AI, and advancing NLP through logical frameworks. Current projects include analyzing social biases in Japanese LLMs and developing neuro-symbolic systems for multimodal tasks.

• Artificial Intelligence
• Robotics
• Autonomous Systems
• +6 more

Sebastian B Thrun is an Adjunct Professor in the Department of Computer Science at Stanford University. His research focuses on artificial intelligence, robotics, autonomous systems, and medical imaging. He holds positions at Stanford and has affiliations with Udacity, Inc., contributing to innovations in autonomous driving systems, drone technology, and AI-driven healthcare solutions. His work often bridges theoretical advancements with practical applications in transportation and medicine. **Education**: Details not explicitly provided in the text. Research Interests include developing algorithms for autonomous vehicles, deep learning applications in healthcare (e.g., skin cancer detection), and improving medical imaging techniques. He explores Bayesian methods, decision trees, and clustering algorithms to enhance AI systems' efficiency and adaptability. His articles reflect a focus on autonomous systems (e.g., multicopter design, vehicle navigation), healthcare AI (skin cancer classification), and algorithm optimization. Notable trends include leveraging AI for real-world challenges in transportation and medical diagnostics. Scientific Awards**: None explicitly mentioned in the provided text. Advising & Grants**: No student advisees or grant details listed here. His contributions extend to industry partnerships, such as those in autonomous vehicle development and online education platforms like Udacity. Labs/Teams**: Likely associated with Stanford’s AI Lab and Udacity initiatives, though specific lab affiliations are not detailed in the text.

William SchulerView profile

Professor

• Computational Linguistics
• Psycholinguistics
• Natural Language Processing
• +5 more

William Schuler is a Professor in the Department of Linguistics at The Ohio State University and directs the Computational Cognitive Modeling Lab . He is affiliated with the Center for Cognitive Sciences and has been recognized with the prestigious PECASE award for his interdisciplinary research bridging computational linguistics, psycholinguistics, and cognitive neuroscience. Research Interests : His work focuses on computational models of human language processing, surprisal estimation, memory-constrained parsing, and cognitive modeling of linguistic complexity. He investigates how transformer architectures, probabilistic grammars, and attention mechanisms align with human reading behaviors and neural responses. Scientific Contributions : Leveraging transformer models and attention patterns for cognitive processing analysis Developing depth-bounded grammar induction techniques Exploring the role of memory and recency in language comprehension Advancing semantic models via vectorial representations Integrating NLP with neuroscience for brain response prediction Recent Article Trends : His publications from 2022-2025 emphasize transformer-based surprisal modeling, memory-aware language processing, and cross-disciplinary applications in medical education. Key themes include reconciling large language models with psycholinguistic data, optimizing grammar induction algorithms, and analyzing neural correlates of linguistic complexity. Scientific Awards : Presidential Early Career Award for Scientists and Engineers (PECASE) Teaching & Advising : He teaches advanced courses in computational linguistics, machine learning, and linguistic meaning. His advisees include researchers like Byung-Doh Oh and Christian Clark, who contribute to language modeling, grammar induction, and cognitive neuroscience applications.

• Robotics
• Artificial Intelligence
• Machine Learning
• +6 more

Sebastian Thrun is a Professor at Stanford University, USA, with a prolific research career spanning robotics, artificial intelligence, and machine learning. His work has significantly impacted autonomous vehicle technology, computer vision, and medical AI applications. Thrun's research interests focus on robotics, particularly simultaneous localization and mapping (SLAM), autonomous driving systems, and probabilistic state estimation techniques. His work extends to deep learning applications in medical imaging, notably achieving dermatologist-level skin cancer classification. He has pioneered approaches in meta-learning, vector quantization, and efficient clustering algorithms using multi-armed bandits. His recent publications demonstrate a strong trend toward improving efficiency in machine learning algorithms, particularly in areas like decision trees, vector quantization, and nearest neighbor search. Thrun has also made significant contributions to medical AI, applying deep learning to skin cancer detection and molecular property prediction. Max Planck Research Award (2011) Thrun has advised numerous PhD students who have become prominent researchers in robotics and AI, including David Stavens, Anna Petrovskaya, and Jesse Levinson. His research has been supported by significant grants, particularly for autonomous vehicle development, including Stanford's entry in the DARPA Urban Challenge (Junior). His work bridges theoretical advances with practical applications across multiple domains. Thrun has led research teams focused on autonomous driving systems, 3D reconstruction, and medical AI applications. His work on the DARPA Urban Challenge demonstrated advanced capabilities in urban autonomous navigation, while his more recent research explores the intersection of deep learning and efficiency optimization in machine learning algorithms.

Gašper BegušView profile

Associate Professor

• Computational Linguistics
• Artificial Intelligence
• Machine Learning
• +13 more

Gašper Beguš is an Associate Professor in the Department of Linguistics at the University of California, Berkeley, where he integrates linguistics, artificial intelligence, and cognitive science. He leads the Berkeley Speech & Computation Lab and serves as the Linguistics Lead for Project CETI, focusing on decoding sperm whale communication. He is also the College Principal of Bowles Hall, UC Berkeley’s oldest residential college. Research Interests: His work spans computational linguistics, phonetics, phonology, deep learning, and cross-species communication. He develops interpretable AI models—such as 'artificial babies'—that learn speech from raw audio, enabling comparisons between human and machine cognition. His research explores the origins of language, AI interpretability, and the legal and societal implications of understanding animal communication. Publication Trends: His recent publications focus on using generative adversarial networks and convolutional neural networks to model phonological and phonetic learning, interpret neural network layers, and decode animal communication. Themes include unsupervised learning, speech encoding, rule emergence, and cross-modal comparisons between artificial and biological systems. Scientific Awards: (No specific awards listed in source materials) Advising and Grants: While specific students and grants are not listed, Beguš leads a major research lab and a high-profile interdisciplinary project (Project CETI), indicating active mentorship and substantial grant funding. He collaborates with institutions and researchers globally and engages in public outreach through media and invited talks. Labs and Teams: He directs the Berkeley Speech & Computation Lab , which develops models of language learning and AI interpretability. He is a key leader in Project CETI , an international team using AI to decode sperm whale codas. He also collaborates with InterpretAI to translate AI transparency into industry applications.

Nicolas AudebertView profile

Researcher

• Computer Vision
• Machine Learning
• Representation Learning
• +5 more

Nicolas Audebert is a Computer Vision and Machine Learning researcher working as a junior research director at the French National Institute of Geographic and Forest Information (IGN) in the LASTIG laboratory, STRUDEL team. He is currently on leave from his position as Associate Professor of Computer Science at the Conservatoire national des arts et métiers (Cnam) where he was part of the Vertigo team. His research spans computer vision, machine learning, and Earth Observation with applications in remote sensing and video games. Dr. Audebert earned his PhD in Computer Science from ONERA and IRISA in 2018, followed by an MEng in Computer Science from Supélec and an MSc in Human-Computer Interaction from Université Paris-Sud in 2015. In May 2025, he successfully defended his habilitation à diriger des recherches (HDR) titled "Learning representations from observations". His research focuses on representation learning , where he develops methods to create abstract representations of raw data that allow computers to manipulate high-level concepts numerically. In Earth Observation , he processes and makes sense of large volumes of satellite data for land cover mapping, change detection, and image interpretation. His work in machine learning for games explores how to use reinforcement learning to generate diverse and challenging AI in video games. Additional research interests include generative models, multimodal learning, and domain adaptation techniques. His recent publications demonstrate expertise in cross-sensor learning, super-resolution of satellite imagery, diffusion models for Earth Observation, and robust image retrieval systems. His work bridges theoretical advances in deep learning with practical applications in geospatial analysis, with a particular focus on developing methods that work across different sensor types and environmental conditions. Outstanding Reviewer for ECCV 2024 Outstanding Reviewer for BMVC 2021 Outstanding Reviewer for ICCV 2021 Best Benchmarking Contribution Award at GEOBIA 2016 2nd best student paper award at JURSE 2017 Google Research Scholar Program gift Dr. Audebert currently advises four PhD students: Maxime Merizette (semantic segmentation of 3D point clouds), Georges Le Bellier (domain adaptation for Earth Observation), Léo Géré (generative models for music), and Aimi Okabayashi (super-resolution of satellite image time series). He has successfully supervised two PhD students to completion: Perla Doubinsky (controlling generative models) and Elias Ramzi (robust image retrieval), whose thesis won the AFRIF PhD award 2024. He has mentored numerous MSc students on diverse topics including flood detection, procedural generation of video game levels, and deep learning for communication systems. He leads the MAGE project (2022-2026), funded by the Agence Nationale de la Recherche, which investigates using procedural generation and modern rendering engines to create labeled synthetic data for Earth Observation models, particularly for disaster mapping applications. He also leads the SESURE project (2021-2023) focused on super-resolution of Sentinel-2 time series, and previously led the RL-Games project (2020-2022) exploring reinforcement learning applications for video games.

Milan StrakaView profile

Professor

• Machine Learning
• Artificial Neural Networks
• Deep Learning
• +12 more

Milan Straka is a professor at the Institute of Formal and Applied Linguistics (ÚFAL) within the Faculty of Mathematics and Physics at Charles University, Prague. His research focuses on machine learning, artificial neural networks, deep learning, and structured prediction with applications to natural language processing (NLP) tasks including POS tagging, dependency parsing, named entity recognition, and optical music recognition. University: Charles University School: Faculty of Mathematics and Physics Department: Institute of Formal and Applied Linguistics Straka has developed several NLP tools like UDPipe and NameTag, contributing to multilingual coreference resolution, Czech grammar error correction, and historical language processing pipelines. His recent work involves contextualized embeddings, semantic parsing, and large-scale dataset creation for Czech NLP tasks. He collaborates on European Language Grid initiatives and has participated in multiple shared tasks (CRAC, MRP, W-NUT). His publications span topics from algorithm design (functional data structures) to modern transformer-based language models (RoBERTa) with emphasis on Czech language resources.

Friedrich NeubarthView profile

Lecturer

• Speech Synthesis
• Computational Linguistics
• Syntax-Semantics Interfaces
• +6 more

Friedrich Neubarth is a lecturer at the University of Vienna, affiliated with the Department of German Studies and the Department of Philosophy. His work bridges theoretical linguistics and computational approaches to language processing. Research Focus: Speech synthesis for dialectal varieties, computational modeling of morphology/syntax, phonological representation primitives, and prosodic correlates of information structure. Key Projects: VSDS (Viennese Dialect Synthesis), MLT4MLV (Machine Learning for Language Varieties), LEGO Audio & Braille Building Instructions, and MAGNIFICENT (Multifaceted News Analysis). Academic Contributions: 15-year publication record (2008-2023) spanning speech technology, dialectal machine translation, crossmodal word learning, and formal syntactic theory. Notable collaborations with Brigitte Krenn, Harald Trost, and Michael Pucher. Teaching: Delivers courses in linguistics and grammar at the University of Vienna, including exercise courses and lecture+exercise combinations since at least 2003. Technical Expertise: Development of spoken dialogue systems incorporating Viennese dialect synthesis, creation of multimodal language learning frameworks, and construction of annotated linguistic corpora for research.

• Explainable AI
• Human-Like Learning
• Unsupervised Representation Learning
• +8 more

Siddharth Narayanaswamy is an Associate Professor (Reader) in Explainable AI at the University of Edinburgh's School of Informatics. He holds a part-time Senior Research Fellow role at The Alan Turing Institute and is a Visiting Fellow at the University of Oxford’s Department of Engineering Science. His research bridges machine learning, computer vision, natural language processing, cognitive science, robotics, and neuroscience, focusing on unsupervised learning of structured representations and human-AI interaction. Education: PhD in Electrical and Computer Engineering from Purdue University. Previously, he was a Senior Researcher at the University of Oxford and a Postdoctoral Scholar at Stanford University’s Computation and Cognition Lab. Research Interests: Explainable AI, Human-Like Learning, Unsupervised Representation Learning, Probabilistic Programming, and applications to global health decisions via Simulation-Based Inference. Key projects include DGPose (human body analysis) and neuro-symbolic generative models. Publications highlight contributions to generative models, variational autoencoders, and interpretable AI systems. He collaborates with the Torr Vision Group and ELLIS Society. Supervision: Open to motivated PhD/MSc students exploring structured representations and probabilistic inference in AI. Labs/Teams: Member of the Torr Vision Group and ELLIS Scholar Network. Active in interdisciplinary collaborations across robotics, cognitive science, and healthcare AI.

Aris XanthosView profile

Professor

• Computational Linguistics
• Natural Language Processing
• Morphology
• +3 more

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.

Taylor Berg-KirkpatrickView profile

Associate Professor

• Natural Language Processing
• Machine Learning
• Unsupervised Learning
• +4 more

Taylor Berg-Kirkpatrick is an Associate Professor in the Department of Computer Science and Engineering at the University of California, San Diego. They lead the BergLab, focusing on natural language processing (NLP), machine learning, and unsupervised methods for analyzing diverse data types, including historical documents, music, and early modern books. Their work bridges computational techniques with interdisciplinary domains such as music information retrieval, paleography, and cultural heritage analysis. Taylor has been awarded an NSF CAREER grant for research on language evolution via probabilistic models. The BergLab develops tools like Klavier (music transcription), Ocular (historical document recognition), and Puck (constituency parsing). Taylor's research emphasizes deciphering hidden structures in human data, from handwritten texts to musical patterns. Their teaching includes courses on algorithms for NLP at CMU and UC Berkeley. Taylor advises numerous PhD students, many of whom have contributed to impactful projects such as compositor attribution in Shakespeare's works, music separation algorithms, and climate text understanding benchmarks like ClimaBench. Their work is featured in top venues like ACL, NeurIPS, and ICML, with applications spanning from improving AI safety to enhancing cultural preservation efforts.

• Digital Humanities
• Chinese History
• Optical Character Recognition (OCR)
• +4 more

Dr. Donald Sturgeon is an Assistant Professor in the Department of Computer Science at Durham University. He is also a member of the Institute of Medieval and Early Modern Studies. His work focuses on applying computational methods to the study of premodern Chinese texts, including digital archiving, optical character recognition (OCR), and text reuse analysis. He leads initiatives like the Chinese Text Project, a dynamic digital library for premodern Chinese literature and philosophy. Sturgeon has been invited as a keynote speaker at major institutions including Peking University, Chinese University of Hong Kong, and Heidelberg University for his contributions to digital humanities. Research Interests His research bridges computer science and humanities through: Development of linked open data frameworks for historical records OCR solutions for premodern Chinese scripts Unsupervised text reuse identification in classical texts Crowdsourcing methodologies for scholarly knowledge creation Publications Overview His work spans technical advancements in digital humanities, including foundational papers on premodern text digitization, OCR challenges in East Asian scripts, and computational approaches to classical Chinese literature analysis. His most cited contributions address large-scale text preservation and intertextuality in early Chinese philosophical corpora. Professional Contributions Sturgeon's invited lectures (2022-2023) highlight his international recognition in digital methods for East Asian studies. He actively collaborates with institutions across Asia and Europe on projects combining machine learning with historical text analysis. His grants and advisory roles are focused on expanding open-access digital resources for premodern studies.

• Natural Language Processing
• Machine Learning
• Bioinformatics
• +3 more

Adam Teichert is an Assistant Professor of Software Engineering at Snow College, specializing in Natural Language Processing (NLP) and Machine Learning. He holds a Ph.D. in Computer Science from Johns Hopkins University (2022) and has extensive research experience in interdisciplinary fields including bioinformatics and computational linguistics. His teaching spans courses in algorithms, data structures, ethics in software processes, and programming fundamentals. Education Background: Ph.D., Computer Science, Johns Hopkins University (2022) M.S., Computer Science, Johns Hopkins University (2016) M.S., Computing, University of Utah (2010) B.S., Computer Science, Brigham Young University (2008), Cum Laude with University Honors Research Interests: Machine learning applications in NLP Semantic parsing and role labeling Optimization of accuracy-speed tradeoffs in algorithms Development of open-source tools for bioinformatics and phylogenetic analysis Interdisciplinary education in computing and language Awards and Honors: Phi Kappa Phi Honor Society University Honors (BYU) Cum Laude distinction in B.S. program Teaching and Mentorship: Teichert has taught multiple courses at Snow College since 2022, emphasizing practical skills in algorithms and ethical software development. His mentorship includes research projects in NLP and bioinformatics during his graduate years. Labs and Collaborations: Active in the Center of Language and Speech Processing at Johns Hopkins and contributed to open-source projects like PsodaScript for phylogenetic search.