Find Academic Supervisors

• Linguistic Theory
• Syntax
• Morphology
• +4 more

Susana Béjar is an Associate Professor in the Department of Linguistics at the University of Toronto, Faculty of Arts and Science. Her research focuses on syntax, morphology, and linguistic theory, particularly exploring topics like DP recursion, phi-features, copular clauses, and language acquisition. She holds a PhD, MA, and BA from the University of Toronto. Her work examines syntactic structures, formal feature theory, and the acquisition of complex NPs in child language. Béjar has collaborated on numerous projects, including studies on Inuttitut corpus development and split ergativity in Kurdish varieties. She actively participates in research networks and has presented at conferences such as NWAV and LSA. Research Interests: Syntax, Morphology, Linguistic Theory, Language Acquisition, Phi-Features, Copular Clauses, DP Recursion. Recent Work Trends: Her publications emphasize syntactic recursion, feature-driven operations, and cross-linguistic agreement patterns. Notable contributions include analyses of copular clauses and the role of phi-features in syntactic dependencies. Grants: Over 30 grants secured from SSHRC and other agencies, including projects on case agreement in Laki, possessive extraction in English, and corpus development for Inuttitut. Labs/Teams: Collaborates with the Department of Linguistics research groups at U of T and participates in international research networks on syntax and language acquisition.

Xin XingView profile

Assistant Professor

• Medical Imaging
• Machine Learning
• Neuroscience
• +3 more

Xin Xing is an Assistant Professor at the University of Nebraska at Omaha, College of Information Science & Technology, Department of Computer Science. Their research bridges machine learning, neuroscience, and biomedical applications. Education: Ph.D. in Computer Science (2023), University of Kentucky M.S. in Information Technology (2016), University of Stuttgart B.S. in Communications Engineering (2011), Shandong University Research Interests: Medical Imaging AI: Developing models for Alzheimer's disease diagnosis using 3D PET/MRI and transformers (e.g., ADViT, CAT-XPLAIN) Gut-Brain Axis: Investigating microbiome impacts on neurodegeneration, particularly in APOE4 carriers Computer Vision: Innovating diffusion models, self-supervised learning, and attention mechanisms for biomedical and geospatial applications Publication Trends: Recent work focuses on neuroimaging biomarkers for Alzheimer's, microbiome interventions, and scalable vision-language architectures. They integrate cutting-edge ML techniques with clinical data analysis. Collaborations: Engaged in interdisciplinary projects spanning genetics, nutrition, and traffic safety. Their work appears in journals like Communications Biology and explores real-world applications such as roadway hazard detection via satellite imagery.

Geoffrey PullumView profile

Professor

• Theoretical Linguistics
• English Grammar
• Philosophy of Cognitive Sciences
• +3 more

Geoffrey Pullum is Professor Emeritus of General Linguistics at the University of Edinburgh's School of Philosophy, Psychology, and Language Sciences. He previously held the position of Head of Linguistics and English Language at Edinburgh (2008–present) and was a Professor of Linguistics at UC Santa Cruz (1983–2007). His academic journey includes a B.A. (First Class Honours) in Language from the University of York (1968–1972) and a Ph.D. in General Linguistics from University College London (1974–1976). Research interests span theoretical linguistics, English grammar formalization, syntax, and the philosophy of cognitive sciences. He co-authored the seminal The Cambridge Grammar of the English Language (2002) and wrote A Student's Introduction to English Grammar (2005). His work challenges prescriptive grammar myths and emphasizes empirical rigor in linguistic analysis. Awards include Fellowships from the American Academy of Arts and Sciences (2003), Linguistic Society of America (2007), and British Academy (2009). He served as Associate Editor of Language (1983–1985) and held visiting positions at institutions like Stanford, Brown, and Harvard. Grants include NSF-funded research at the Center for Advanced Study in the Behavioral Sciences (1990–1991) and leadership in the Jacob R. Javits Fellowship Program (1992–1993). Active in academic service, he chairs editorial boards for journals like Linguistic Analysis and the Oxford International Encyclopaedia of Linguistics .

Johanna BasnakView profile

Tutor

• Linguistics
• Phonology
• Language Acquisition
• +5 more

Johanna Basnak is a PhD student and Tutor in the Department of Linguistics and English Language at the University of Edinburgh's School of Philosophy, Psychology and Language Sciences (PPLS), actively contributing to teaching and research since 2022. Her doctoral work examines consonant similarity effects on lexical learning under supervisor Mits Ota. Her academic credentials include: PhD in Linguistics (University of Edinburgh, 2022-present) MSc in Linguistics (University of Edinburgh, 2020-2021; Dissertation: Preference/learnability mismatch in sound repetition) MA in French Literature (Université Paris IV-Sorbonne, 2012-2014) French Teaching Degree (Instituto en Lenguas Vivas Juan Ramón Fernández, 2007-2011) Basnak's research centers on developmental phonology and lexical acquisition, investigating how phonological typology interacts with cognitive learning biases. Her work employs experimental methodologies to analyze segmental repetitions in word forms, revealing critical insights into universal constraints on language learning and phonemic inventory organization. This bridges theoretical linguistics with cognitive science through empirical cross-linguistic analysis. Her recent publications demonstrate sustained focus on typological alignment in form-learning biases, with a 2023 journal article establishing segmental repetition as a learnability advantage and multiple 2022 conference presentations exploring methodological extensions across phonological domains. Key recognitions include: Scottish Graduate School of Social Science studentship (2022-2026) Bourse Master Ile-de-France (2012-2014) She serves as Editorial Assistant for Language & History journal and co-supervises MSc students, including Yitong Shi (2023-2024). Her PhD research is fully funded by the Scottish Graduate School of Social Science studentship, enabling comprehensive experimental work on phonological learning mechanisms.

• Chinese Language Education
• Second Language Acquisition
• Pedagogical Practice
• +3 more

Yanyin Zhang is a Senior Lecturer at the School of Culture, History & Language, Australian National University. Her research focuses on second language acquisition of Chinese, pedagogical practices, and international student language challenges. She holds a PhD and has published extensively on L2 Chinese syntax, teaching methodologies, and comparative language education. Key research areas include: Chinese as a second language syntax, pedagogical practice reflection, and curriculum development. Her work emphasizes the interplay between input structures and learning outcomes, particularly in existential constructions and grammatical morphemes. Notable publication: Frontiers of L2 Chinese Language Education: A Global Perspective (2021) Research Projects: "Teachable and learnable? From input to learning outcome in L2 Chinese acquisition" (2020-2023) "Second Language Acquisition and Processability: Theory and Practice" (2017-2018) Her recent work explores future directions in L2 pedagogy, family case studies in morphology acquisition, and the application of processability theory in Chinese instruction.

Borja HerceView profile

Researcher

• Morphology
• Diachrony
• Paradigmatic Relations
• +7 more

Borja Herce is a Postdoctoral Researcher at the University of Zurich's Department of Comparative Linguistics, affiliated with the Distributional Linguistics Lab. His research focuses on morphology and diachrony, particularly paradigmatic relations in inflectional systems, morphomes, and inflection classes. He employs qualitative and quantitative methods across global languages to study complex morphological structures. University: University of Zurich Department: Department of Comparative Linguistics Herce's work spans phonological, morphological, and typological analysis. Key projects include comparative studies of Romance languages (Spanish, Catalan, Romanian) and Otomanguean languages (Central Pame), examining phenomena like stem alternations, morphomic templates, and positional splits in agreement paradigms. He has developed computational resources like VeLeSpa (Peninsular Spanish verbs) and VeLeRo (Romanian verbs), enabling detailed morphological predictability analysis. His publications highlight the interplay of naturalness gradients, semantic similarity, and token frequency in shaping morphological systems. Notable Research Contributions Identified diachronic origins of Central Pame's free variation patterns via historical corpus analysis Quantified morphomic productivity in Romance innovations (15% adherence to templates) Linked vowel chain shifts in Central Pame to phonetic fronting among young female speakers Proposed predictive models for morphological complexity metrics as phylogenetic signals Challenged traditional dichotomies between morphemes and morphomes through distributional semantics

Dmitry VetrovView profile

Professor

• Bayesian Deep Learning
• Diffusion Models
• Loss Landscape Analysis
• +5 more

Dmitry Vetrov serves as a Professor of Computer Science at Constructor University in Bremen, where he founded and leads the Bayesian Methods Research Group. His academic foundation includes graduation from Moscow State University in 2003 and completion of his PhD in 2006, establishing a career centered on advancing probabilistic machine learning methodologies. His educational trajectory features: Undergraduate studies at Moscow State University (2003) Doctoral degree (PhD, 2006) Vetrov's research program critically bridges Bayesian statistics with deep learning architectures, with his group pioneering efficient diffusion model algorithms, loss landscape characterization in neural networks, scalable stochastic optimization tools, tensor decomposition applications for large-scale ML systems, and enhanced conditional text generation frameworks. This work manifests practical implementations across generative AI domains while maintaining theoretical rigor in probabilistic modeling. Analysis of his 2024-2025 publications reveals a concentrated research thrust toward diffusion model innovation, spanning text generation (token embedding smoothing, language model encoding properties), image synthesis (hair transfer, gesture generation), and scientific applications (protein modeling, genetic fine-mapping). Key thematic threads include sampler acceleration, theoretical property analysis of diffusion processes, and robust evaluation frameworks for generative systems. No scientific awards were documented in the source materials. Mentorship outcomes demonstrate significant impact, with three recent PhD students securing research positions at DeepMind. While specific grant details remain undisclosed, the group's prolific output across NeurIPS, ICML, and CVPR indicates sustained research funding. The Bayesian Methods Research Group operates as an integrated innovation hub within Constructor University's academic ecosystem. The research collective he directs maintains active development of Bayesian-deep learning fusion techniques, with current projects emphasizing diffusion model efficiency, theoretical foundations of optimization landscapes, and cross-domain applications in computational biology and multimodal generation.

Alexander RakhlinView profile

Professor

• Machine Learning
• Statistics
• Optimization
• +3 more

Alexander Rakhlin is a Professor in the Department of Brain & Cognitive Sciences at MIT, with affiliations at the MIT Statistics and Data Science Center and the Institute for Data, Systems, and Society (IDSS). His research bridges statistics, machine learning, and optimization, focusing on online prediction, reinforcement learning, and decision-making under uncertainty. He holds a PhD from MIT (2006) and previously served as an associate professor at the University of Pennsylvania (Wharton School) before joining MIT in 2023. Research interests include reinforcement learning, neural networks, overparametrized models, and the theoretical foundations of machine learning. His work addresses challenges in sequential decision-making, robust algorithms, and the statistical complexity of modern models like large language systems. Notable contributions include the Decision-Estimation Coefficient framework for RL analysis and insights into benign overfitting in overparametrized settings. Recent publications emphasize algorithmic advances in online learning, privacy-aware contextual bandits, and structural watermarking for language models. His theoretical work often unifies principles from information theory, optimization, and statistical learning. Lab affiliations include the Laboratory for Information and Decision Systems (LIDS), where he collaborates on interdisciplinary projects. No specific awards or grants are explicitly listed in the provided texts.

Balder ten CateView profile

Associate Professor

• Logic in computer science
• Data management
• Knowledge representation
• +5 more

Balder ten Cate is an Associate Professor at the University of Amsterdam, where he leads the Theoretical Computer Science research unit within the Institute for Logic, Language and Computation (ILLC). His academic journey includes previous positions at INRIA, UC Santa Cruz, Stanford, LogicBlox, and Google. He maintains active collaborations across institutions including the University of Bergen (co-advising PhD students) and Tsinghua University. Primary Affiliation: Theoretical Computer Science (TCS) Secondary Affiliation: Mathematical & Computational Logic (MCL) Office: Room L6.38, LAB42, Science Park 900, Amsterdam Dr. ten Cate's research spans diverse applications of logic in computer science and AI, with particular emphasis on data management, knowledge representation, and machine learning. His work bridges theoretical foundations with practical applications, focusing on how logical frameworks can enhance data systems and AI capabilities. He has developed significant contributions in finite model theory, database theory, and computational learning theory, with recent work exploring connections between universal algebra and learning theory. His publication record shows a strong trajectory in theoretical computer science, with recent papers focusing on query algorithms, interpolation in logical fragments, and the learnability of database queries. The research demonstrates consistent contributions at top venues including PODS, ICDT, IJCAI, and ACM Transactions journals. His work on 'Extremal Fitting Problems for Conjunctive Queries' received the ACM PODS 2023 Best Paper Award, while 'SAT-Based PAC Learning of Description Logic Concepts' earned an IJCAI 2023 Distinguished Paper Award. 2024 ETAPS Best Paper Nomination 2023 ACM SIGMOD Research Highlights Award 2023 Alberto Mendelzon Test-of-Time Award ICDT 2012 Best Paper Award 2006 EACSL Ackermann Prize for best dissertation Dr. ten Cate actively supervises PhD and MSc students, with recent theses covering topics like modal formula characterization, conjunctive queries, and temporal logic. He serves on numerous program committees including PODS 2025 and ICALP 2024, and has organized workshops on learning and logic. His current MSCA European Re-Integration Fellowship 'LLAMA: Logic and Learning: an Algebra and Finite Model Theory Approach' (2021-2025) demonstrates ongoing research leadership. He also contributes to academic service through committee memberships including the ASL Committee on Education and the editorial board of the Journal of Logic, Language and Information.

Farrell AckermanView profile

Professor

• Lexical Semantics
• Morphology
• Syntax
• +7 more

Farrell Ackerman is a Professor in the Department of Linguistics at UC San Diego and serves as Director of the Human Development Program. His research focuses on lexical semantics, morphology, and syntax, with a particular emphasis on cross-linguistic typology within the Uralic family and the revitalization of Word & Paradigm models. His work on morphology as a complex adaptive system integrates insights from ecological developmental biology, utilizing information-theoretic measures to analyze cross-linguistic paradigm organization. He has conducted extensive fieldwork on the underdocumented Moro language in Sudan, supported by an NSF grant (BCS-0745973), collaborating with Sharon Rose and students. Recent research trends include entropy-based morphological analysis, correspondence-based mapping theory, and experimental approaches to morphological learnability. Key publications span topics like Finnish nominal inflection, Mandarin resultative compounds, and the low entropy conjecture in morphological systems. Awarded grants and collaborative workshops, including the 1st Language as a Complex Adaptive System Workshop, highlight his interdisciplinary approach combining linguistics with computational and biological models.

Nada AminView profile

Assistant Professor

• Programming Languages
• Artificial Intelligence
• Generative Programming
• +12 more

Nada Amin is an Assistant Professor of Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS). She previously served as a University Lecturer in Programming Languages at the University of Cambridge and was a key contributor to the Scala programming language at EPFL during her PhD. Her research at the Metareflection Lab focuses on neurosymbolic systems that integrate programming languages (PL) and artificial intelligence (AI) to enable correct-by-construction software in domains like program synthesis and precision medicine. Her work emphasizes three pillars: Safer systems via formal verification and type theory; Faster development through generative programming and multi-stage interpreters; Easier access by bridging neural (learnable) and symbolic (interpretable) representations. She has contributed to the POPL, ICFP, PLDI, and OOPSLA conferences, with recent work on Dafny proof assistants and Persimmon's polymorphism techniques. Scientific awards include: Fellow of Jesus College (Cambridge, 2017-2019); Michigan Cambridge Research Initiative Grant (2018); Teaching Assistant Team Award (EPFL, 2015); 6.170 Letter of Commendation (MIT, 2005); ArsDigita Prize Finalist (1999). She has advised committees at ICFP, PLDI, POPL, and SPLASH , and taught courses like Neurosymbolic Programming (2025) and Advanced Semantics of Programming Languages (2022). Her lab explores language design through projects like Persimmon , LURK , and DafnyBench .

Ali GurbuzView profile

Assistant Professor

• Machine Learning
• Signal Processing
• Remote Sensing
• +5 more

Ali Gurbuz is an Assistant Professor in the Department of Electrical and Computer Engineering at Mississippi State University's College of Engineering, specializing in smart sensing systems and machine learning applications. His research integrates signal processing with autonomous systems for environmental monitoring and medical imaging. His educational background includes a Bachelor's degree from Bilkent University (Turkey), and Master's/Doctoral degrees in Electrical and Computer Engineering from Georgia Institute of Technology. Since joining MSU in 2018 after a position at the University of Alabama, he has established himself as a leading researcher in sensing technologies. Gurbuz's research focuses on developing intelligent front-end sensing systems that optimize data acquisition using machine learning, addressing critical bottlenecks in processing capabilities for applications ranging from autonomous vehicles to precision agriculture. His work emphasizes efficient data collection through radar, lidar, and camera systems, with particular attention to soil moisture estimation and medical imaging applications. His publication portfolio demonstrates strong trends in UAS-based remote sensing, RF interference mitigation, and deep learning for signal processing. Key research areas include GNSS reflectometry for soil moisture mapping, radar-based sign language recognition, and seafloor gas seep detection using sonar data. NSF CAREER Award recipient (2021) for $500,000 to advance smart sensing systems research Gurbuz co-directs the Information Processing and Sensing (IMPRESS) research group at MSU, collaborating extensively with the Center for Advanced Vehicular Systems and Geosystems Research Institute. His work bridges theoretical signal processing with practical implementations in agricultural monitoring, environmental sensing, and medical applications, with several projects demonstrating hardware-software co-design approaches for next-generation sensing systems.

Makoto KanazawaView profile

Professor

• Theoretical Computer Science
• Formal Language Theory
• Algorithmic Learning Theory
• +10 more

Professor Makoto Kanazawa is a faculty member at Hosei University's Department of Advanced Sciences within the Faculty of Science and Engineering. He holds a Ph.D. in Linguistics from Stanford University (1994). Previously, he served as an Associate Professor at institutions including the National Institute of Informatics (2004–2018) and the University of Tokyo (2000–2004). His research focuses on theoretical computer science, formal language theory, and computational linguistics. Key areas include multiple context-free grammars (MCFGs), abstract categorial grammars (ACGs), and algorithmic learning theory. He has organized workshops like the Lambda Calculus and Formal Grammar series, advancing interdisciplinary research at the intersection of logic, linguistics, and computation. His recent publications address grammar learning from positive data, formal language hierarchies, and parsing algorithms. He has contributed to foundational work on MCFGs, ACGs, and their applications in natural language processing. Kanazawa has led collaborative research projects at the National Institute of Informatics, emphasizing the typed lambda calculus as a framework for linguistic models.

Dogus ÖksüzView profile

Research Fellow

• Psycholinguistics
• Second Language Acquisition
• First Language Acquisition
• +3 more

Dr. Dogus Öksüz serves as a Research Fellow in the Department of Theoretical and Applied Linguistics at the Faculty of Modern and Medieval Languages and Linguistics, University of Cambridge. He is currently engaged in the Leverhulme Trust funded project "Linguistic Typology and Learnability in Second Language" under Professors Dora Alexopoulou and Ianthi Tsimpli. His academic credentials include: PhD in Linguistics, Lancaster University Dr. Öksüz investigates the interplay between language and cognition, specializing in first and second language acquisition and processing mechanisms. His methodological approach combines corpus analysis with psycholinguistic experimentation to examine multiword expressions (collocations, idioms), frequency effects, individual differences in learners, implicit learning systems, and typological influences on language processing. Central to his work is understanding how linguistic distance between native and target languages impacts second language acquisition. His publication in Language Learning (2021) demonstrates his focus on quantitative analysis of collocational processing, revealing how word frequency, collocational frequency, and association strength differentially affect L1 and L2 comprehension. This research exemplifies his broader trajectory in empirical psycholinguistics, emphasizing cross-linguistic comparisons and cognitive load factors in language learning. Currently supported by the Leverhulme Trust grant for the Linguistic Typology project, Dr. Öksüz operates within Cambridge's Department of Theoretical and Applied Linguistics research ecosystem. No advisory roles or additional funding sources are documented in the provided materials. His work occurs within the Leverhulme project team alongside Professors Alexopoulou and Tsimpli, leveraging Cambridge's language processing laboratories and corpus resources to investigate typological constraints on second language learnability through controlled experimentation and large-scale data analysis.

Lukas Paul Achatius GalkeView profile

Assistant Professor

• Machine Learning
• Natural Language Processing
• Deep Learning
• +10 more

Lukas Paul Achatius Galke is an Assistant Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), where he conducts research in machine learning, natural language processing, and cognitive modeling. He is affiliated with the IMADA research group and holds an ORCID identifier (0000-0001-6124-1092), indicating active academic engagement. His research interests lie at the intersection of deep learning and linguistic cognition, with a focus on emergent communication, language modeling, and graph neural networks. Key areas include compositional language structure, quantization-aware training, and the generalization abilities of large language models across languages. His work combines computational methods with insights from cognitive science and neuroscience. The recent publications (2024–2025) highlight a strong trend in analyzing how neural networks mimic human language learning, assess scientific convergence through bibliographic data, and optimize model efficiency. His work spans both theoretical and applied domains, including automated analysis of research fields and cognitive modeling of language acquisition. Lukas collaborates with a diverse group of researchers and co-authors, indicating active supervision and mentorship. While no formal awards are listed, his publications in high-impact journals like Nature Communications and IEEE Transactions on Engineering Management reflect significant scholarly contributions. He is involved in research teams focusing on data science and AI, particularly within the IMADA group. His work on graph-structured metadata and neural network behavior suggests ongoing projects in scientific intelligence and cognitive AI systems.