Maria Gouskova is a Professor of Linguistics at the Department of Linguistics, New York University (NYU). She is affiliated with the College of Arts and Science and holds editorial roles as an Associate Editor of Language and board member of NLLT and Phonology . Her research focuses on phonology, morphology, and lexicon, with a particular emphasis on morphophonological interactions, sublexicons, and phonotactic constraints. She earned her Ph.D. in Linguistics from the University of Massachusetts, Amherst (2003) and a B.A. in English Linguistics and German Language/Literature from Eastern Michigan University (1998). Her work bridges theoretical phonology and experimental methods, addressing questions such as how phonological patterns interact with morphology, the role of sublexical phonotactics in grammatical processes, and the learnability of complex segmental inventories. Recent research includes studies on Russian diminutive affixes, gradient phonological constraints, and the phonological properties of compounds. Her publications span topics like allomorphy, lexical phonology, and the typology of morpheme structure constraints. She frequently collaborates on projects investigating the interplay between syntax, phonology, and morphology, as seen in studies of Russian prepositions and compound stress patterns. Her contributions to phonological theory include advancing models of sublexicon theory and nonlocal constraint induction.
Lourdes Agapito is a Professor of 3D Vision at the Department of Computer Science, University College London (UCL), within the Faculty of Engineering Sciences. She leads research in Non-Rigid Structure from Motion (NR-SFM) and 3D reconstruction from monocular video sequences. Her work addresses dynamic scenes, deformable objects, and articulated structures, with applications in robotics and computer vision. She holds an ERC Starting Grant (2008–2014) and led the EU Horizon 2020-funded Second Hands project (2014–2019), collaborating with institutions like EPFL and KIT to develop robots with 3D visual perception for maintenance tasks. Her research group focuses on dense optical flow estimation, video registration, and deformable tracking. Agapito’s research interests include monocular 3D reconstruction, non-rigid motion analysis, and neural approaches to 3D modeling. She has supervised multiple PhD students and postdocs, including notable researchers such as Ravi Garg and Marco Paladini (Sullivan Prize recipient). Her contributions to conferences include roles as Program Chair for CVPR 2016 and CVPR 2017, and she has authored influential papers on topics like Video-Popup (ECCV 2014) and Modal Space (CVPR 2017). Current projects involve advancing neural parametric models and real-time 3D reconstruction techniques. Awards include the ERC Starting Grant and recognition for her team’s work in non-rigid reconstruction. She actively mentors students and collaborates on grants, with recent openings for postdocs and PhD candidates in 3D vision and robotics.
Olga Saukh is an Associate Professor at the Institute of Technical Informatics, Graz University of Technology (TU Graz), and a Faculty member at the Complexity Science Hub Vienna (CSH). She leads the Embedded Learning and Sensing Systems research group, which operates across both institutions, focusing on the design and deployment of efficient AI-based systems on edge and mobile platforms. Her work bridges deep learning and embedded systems, with applications in environmental monitoring, precision agriculture, and digital health. Ph.D. in Computer Science, University of Bonn (2009) Habilitation in Embedded Systems, TU Graz (2020) Postdoctoral Training, ETH Zurich (2010–2016) B.Sc. in Applied Mathematics, Taras Shevchenko National University of Kyiv (2002) M.Sc. in Applied Computer Science, University of Freiburg (2004) Her research centers on efficient machine learning, particularly model optimization, neural network pruning, and contrastive learning for resource-constrained devices. She is deeply engaged in solving real-world challenges in IoT, sensor networks, and cyber-physical systems. Her work emphasizes data privacy, sustainability, and practical deployment of AI at the edge. The 15 most recent publications highlight a strong trend in efficient deep learning, including model compression, pruning, and transfer learning, applied to diverse domains such as environmental sensing (air quality, pollution tracking), digital agriculture (cattle farming), and embedded AI (sensor calibration, on-demand sensing). Her work frequently appears in top-tier venues like NeurIPS, ICLR, and IEEE/ACM IPSN, reflecting her leadership at the intersection of machine learning and embedded systems. Scientific awards include: CONET Ph.D. Academic Award (2010) Multiple Best Paper Awards at IEEE PerCom, ACM/IEEE IPSN, IEEE ICPADS, IEEE SECON, and UrbCom Spotlight and Oral presentations at ICML and CoLLAs workshops Ph.D. scholarship from IPVS, University of Stuttgart (2004–2005) Prizes in Ukrainian national mathematics competitions (1996–1998) Olga Saukh actively serves on program committees of leading international conferences in machine learning and embedded systems. She has advised multiple students and leads a collaborative research group spanning TU Graz and CSH Vienna. Her group develops practical AI systems for real-world deployment, with a focus on sustainability and privacy. She co-organizes the public EfficientML reading group and has secured recognition through numerous grants and awards. Her future work continues to explore the theoretical and practical challenges of deploying efficient, trustworthy AI in mobile and embedded environments. Her research group, Embedded Learning and Sensing Systems, operates jointly between TU Graz and CSH Vienna, fostering interdisciplinary collaboration across institutions. The team develops AI solutions for edge computing, sensor networks, and cyber-physical systems, with a strong emphasis on environmental sustainability and data privacy. Members work on joint challenges using advanced collaboration tools, reflecting the distributed nature of modern academic research.
Shai Ben-David is a Professor and University Research Chair at the Department of Computer Science, University of Waterloo. He is affiliated with the Cheriton School of Computer Science and can be reached at shai@uwaterloo.ca . His office is located in DC 2643. Education: Ph.D., Hebrew University, Jerusalem, Israel (1987) M.Sc., Hebrew University Jerusalem, Israel (1979) B.Sc., Hebrew University Jerusalem, Israel (1978) Research interests focus on foundational aspects of machine learning theory, including unsupervised learning (clustering), domain adaptation, fairness, interpretability, and alternative approaches to worst-case computational complexity. He also explores logic applications in computer science theory. His research trends emphasize theoretical challenges in machine learning, particularly clustering, fairness in representations, and the interplay between computational feasibility and learnability. He investigates how unlabeled data and sample compression techniques impact learning robustness and efficiency. No scientific awards are listed. His advising record shows no formal advisees listed here. Grants and funding details are not provided in the text. He has contributed to organizing events like the Dagstuhl Seminar on Foundations of Unsupervised Learning (2017) and co-edited MFCS 2016 proceedings. His work addresses both theoretical questions and practical gaps in ML implementation.
Bo Han is an Associate Professor in the Department of Computer Science at Hong Kong Baptist University's Faculty of Science, where he leads the Trustworthy Machine Learning and Reasoning (TMLR) Group. He also holds a visiting scientist position at the RIKEN Center for Advanced Intelligence Project (RIKEN AIP) in Japan. His research focuses on developing trustworthy and efficient machine learning systems, particularly under imperfect data conditions such as noisy labels, out-of-distribution data, and weak supervision. Bo Han's research interests span Machine Learning , Deep Learning , Foundation Models , Causal Representation Learning , Weakly and Self-supervised Learning , Robustness and Security in Machine Learning , Federated Learning , and AI for Science . His work aims to build intelligent systems that can reliably learn and reason from complex, imperfect real-world data. His recent publications reveal a strong trend toward trustworthy foundation models , robust reasoning with large language models , out-of-distribution detection , privacy-preserving learning , and causal robustness . His research integrates theoretical foundations with practical applications, often published in top-tier venues like NeurIPS, ICML, ICLR, and TPAMI. Notable Awards and Honors: Outstanding Paper Award, NeurIPS Most Influential Paper, NeurIPS IEEE AI's 10 to Watch Award IJCAI Early Career Spotlight INNS Aharon Katzir Young Investigator Award Dean's Award for Outstanding Achievement RGC Early CAREER Scheme Bo Han has been actively involved in the academic community, serving as a Senior Area Chair and Area Chair for NeurIPS, ICML, and ICLR, and as an Associate Editor for IEEE TPAMI, MLJ, and JAIR. He has advised numerous PhD and research students and leads a globally distributed research group. His work is supported by major grants from RGC, NSFC, GDST, RIKEN, and industry partners including Microsoft, Alibaba, Tencent, and Baidu. He also leads research initiatives in Trustworthy Machine Learning , including projects on federated learning, model unlearning, privacy-preserving AI, and robust foundation models, often in collaboration with industry and international institutions.
Stephen Robert Hanneke is an Assistant Professor in the Department of Computer Science at Purdue University, specializing in theoretical machine learning and statistical learning theory. His work focuses on reducing the number of training examples required for learning, with contributions to supervised, semi-supervised, active, and transfer learning. He joined Purdue in Fall 2021 after roles including Research Assistant Professor at the Toyota Technological Institute at Chicago (2018–2021), Visiting Lecturer at Princeton University (2018), and Visiting Assistant Professor at Carnegie Mellon University (2009–2012). Education: B.S. in Computer Science from the University of Illinois at Urbana-Champaign (2005), Ph.D. in Machine Learning from Carnegie Mellon University (2009). Research interests include statistical learning theory, machine learning foundations, algorithms, and quantum computing. Notable contributions explore the theoretical underpinnings of active learning, adversarial robustness, and universal learning frameworks. His research bridges disciplines like probability theory, philosophy of science, and algorithmic information theory. Key awards include the Best Paper Award at ALT 2021 for 'Stable Sample Compression Schemes' and runner-up for COLT 2021. He has also received the COLT 2020 Best Paper Award and an Honorable Mention for the ICML 2017 Test of Time Award. His work on 'A Bound on the Label Complexity of Agnostic Active Learning' (ICML 2007) received further recognition in 2017. Teaching includes courses on machine learning theory and data mining at Purdue, Princeton, and Carnegie Mellon. He has organized workshops like the ALT 2019 'When Smaller Sample Sizes Suffice for Learning' and chaired the program committee for ALT 2017. His research outputs span over 100 publications in top venues like COLT, NeurIPS, and JMLR, focusing on foundational questions in learning theory and algorithmic efficiency.
Richard M. Stern is a Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU), holding courtesy appointments in the Language Technologies Institute and Department of Computer Science, and serving as an Artist Lecturer in the School of Music since 2007. His interdisciplinary work bridges engineering and music technology through the School of Music's programs. Education: Ph.D. in Electrical Engineering from Massachusetts Institute of Technology (MIT), 1976 Professor Stern's research spans sound, speech, hearing, and music, with core emphases on robust speech processing in variable acoustic environments, music information retrieval, automated accompaniment, and foundational contributions to binaural perception theory. His work integrates psychoacoustic principles with machine learning to address challenges in speech recognition and human-robot interaction. Recent publications (2022-2025) reveal intensified focus on deep learning for speech enhancement in reverberant/noisy conditions, human-robot interaction scenarios, and music tagging—highlighting innovations in beamforming, source separation, and temporal modulation modeling. Awards and Honors: Fellow of the IEEE Fellow of the Acoustical Society of America Fellow of the International Speech Communication Association (ISCA) ISCA Distinguished Lecturer Allen Newell Award for Research Excellence (1992) Lutron Award for Teaching Excellence (2018) Professor Stern has advised numerous graduate students in speech and audio research, though specific names are unlisted in source materials. His grant portfolio includes significant National Science Foundation and industry-funded projects in speech technology, with leadership roles in initiatives like Interspeech 2006. He actively collaborates with CMU's Language Technologies Institute and Music and Technology program. He maintains strong ties to CMU's interdisciplinary ecosystem through the Language Technologies Institute and School of Music's Music and Technology program, contributing to research that merges acoustic engineering with musical applications.
Julie Anne Legate is Professor and Chair of Linguistics at the University of Pennsylvania's Department of Linguistics. She earned her PhD from the Massachusetts Institute of Technology in 2002. Legate co-directs the Penn Syntax Lab and served as Editor-in-Chief of Natural Language & Linguistic Theory for 10 years. Her research focuses on syntactic theory, morphosyntax, and syntax-semantics interfaces, with specialization in endangered/understudied languages. Key interests include passives, impersonals, causatives, null subjects, case theory, and locality domains. She maintains secondary research interests in language acquisition. Legate's publications demonstrate extensive work on voice systems (especially passives), ergativity, case theory, recursion, and cross-linguistic analysis of languages including Acehnese, Warlpiri, and Lithuanian. Her research frequently combines theoretical innovation with empirical fieldwork. Awards include the LSA’s Best Paper in Language Award (2020) for On Passives of Passives . She has advised multiple PhD students including Faruk Akkuş (2021), Milena Šereikaitė (2020), Ava Irani (2019), Helen Jeoung (2018), and Einar Freyr Sigurðsson (2017). Current projects include: syntactic islands (with Charles Yang), Mandarin bridge verbs (with Jiayi Lu and Charles Yang), null subjects in Brazilian Portuguese (with Gesoel Mendes), and passive agent binding.
Soosan Beheshti is a Professor and Program Director in the Department of Electrical, Computer, and Biomedical Engineering at Toronto Metropolitan University. She holds a B.S. from Isfahan University of Technology and M.S./Ph.D. from MIT. Her research focuses on signal processing, statistical learning, and information theory, with applications in biomedical systems, data denoising, and system modeling. She has received awards such as the Dean's Teaching Award (2010) and the EECS Carlton E. Tucker Award (1998). Education: B.S., Electrical Engineering, Isfahan University of Technology (1996) M.S. & Ph.D., Electrical Engineering, MIT (2002) Research Interests: Statistical Signal Processing Information Theory Data Denoising & Compression System Modeling & Control Machine Learning Applications Awards: Dean's Teaching Award (2010) Gold Paper Award (PacRim 2009) Best Paper Award (Remote Sensing 2008) MIT Teaching Excellence Award (1998) Teaching: Courses include Signals and Systems, Control Systems, and Statistical Inference. She has supervised numerous graduate students and postdocs in her Signal and Information Processing (SIP) Lab. Labs/Teams: Director of the SIP Lab, conducting research in signal processing, information theory, and biomedical applications. Collaborates with industry partners like Myant Inc. and Huawei Technologies.
Jeffrey Heinz is a Professor at Stony Brook University, with a joint appointment in the Department of Linguistics and the Institute for Advanced Computational Science. He holds a Ph.D. from UCLA (2007) and previously served on the faculty at the University of Delaware from 2007–2017. His research bridges theoretical linguistics, computational learning theory, and formal language models, focusing on phonology, linguistic typology, and grammatical inference. He has contributed to influential works on computational phonology and edited volumes on topics like phonological stress and learning theory. Key academic achievements include the 2017 Linguistic Society of America Early Career Award for contributions to computational inference in language. His work emphasizes the intersection of formal models and empirical linguistics, with applications to reduplication, phonological processes, and machine learning benchmarks like MLRegTest. Heinz has co-authored a book on grammatical inference and guest-edited special issues in Machine Learning and Phonology . His research also extends to interdisciplinary applications, such as modeling human-robot interaction and pediatric motor rehabilitation through grammatical inference techniques.
Jonas Kuhn is a professor at the Institute for Natural Language Processing (IMS) at University of Stuttgart. He is working at the interface between language and computers, combining linguistics and computer science. Kuhn's research interests span a wide range of computational linguistics topics including: Language models and spatial reasoning Analysis of large language models (LLMs) through linguistic theories Political text analysis and discourse networks Computational approaches to literature and cultural studies Retrieval-augmented language modeling Semantic change detection Dependency parsing and syntactic analysis His recent publications (2023-2025) focus on the intersection of neural language processing with fields as diverse as spatial reasoning, literary analysis, and political discourse. This reflects his interdisciplinary approach that bridges fundamental language research with practical technology development. As a faculty member at one of Germany's largest computational linguistics centers, Kuhn contributes to both fundamental research and technological development in language processing systems.
Ruth Urner is an Associate Professor in the Department of Electrical Engineering and Computer Science at York University's Lassonde School of Engineering. She holds a PhD in Computer Science from the University of Waterloo (2013) and completed postdoctoral research at Max Planck Institute for Intelligent Systems (Germany), Carnegie Mellon University, and Georgia Tech. She was a Simons-Berkeley Fellow at the Simons Institute in 2017. Research Focus: Dr. Urner develops mathematical foundations for machine learning paradigms including semi-supervised/active learning, transfer learning, and adversarial robustness. Her current work addresses societal impacts of ML through interpretability and fairness frameworks. She leads projects on strategic classification, robust PAC learning, and calibrated model evaluation. Awards & Leadership: Simons-Berkeley Fellowship (2017) Best Paper Award at NIPS 2015 Workshop on Transfer Learning Organizer: Women in Machine Learning Theory workshops (COLT/ALT) Program Committee: NeurIPS, ICML, COLT, ICLR, AISTATS Teaching & Advising: She teaches Machine Learning Theory, Computational Logic, and Introduction to ML at York University. Current student advisees include Master's candidate Alireza Torabian. She has lectured at international summer schools including Hausdorff School on Algorithmic Data Analysis (Germany) and SMILES Summer School (Russia). Affiliations: Faculty affiliate at Vector Institute (Toronto) and collaborator with Max Planck Institute systems. Her lab investigates theoretical guarantees for learning algorithms under distribution shifts and adversarial conditions.
Dr. Zhi Chen is a Lecturer in Computing at the School of Mathematics, Physics and Computing, University of Southern Queensland, specializing in Artificial Intelligence and Machine Learning with applications spanning digital agriculture and healthcare systems. Education: Master of Information Technology (MIT), University of Queensland, 2018 PhD, University of Queensland, 2023 Research Focus: His work centers on zero-shot learning, domain adaptation, and multimodal systems, addressing core challenges in computer vision and deep learning. Current projects integrate AI with agricultural risk modeling and medical diagnostics, emphasizing real-world deployment of robust algorithms under data-scarce conditions. Publication Trends: Recent output (2022-2025) shows concentrated expertise in source-free domain adaptation and generalized zero-shot learning, with significant contributions to plant disease recognition (via mobile multimodal systems) and diabetes subgroup analysis. His work consistently appears in premier venues including AAAI, CVPR, and ACM MM, demonstrating methodological innovation applied to critical domains like climate-resilient agriculture and precision medicine. Supervision: Currently serves as Associate Supervisor for a doctoral candidate developing parametric insurance models for oyster farms to mitigate climate-related risks from king tides and extreme weather events. Awards: No scientific awards were documented in the provided materials.
Moïse Blanchard is an Assistant Professor and Tennenbaum Early Career Professor at the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology, having joined in August 2025. Previously, he was a Postdoctoral Fellow at Columbia University Data Science Institute. His academic journey includes a Ph.D. in Operations Research from MIT (2024), and M.Sc. and B.Sc. degrees in Applied Mathematics from École Polytechnique. Blanchard's research focuses on the intersection of machine learning theory, statistics, and optimization. His work addresses fundamental questions in universal learning, online algorithms, and convex optimization under memory constraints. His research program explores learnability under minimal assumptions, query complexity/memory tradeoffs, and decision-making in adversarial environments. This work has significant implications for theoretical computer science, operations research, and statistical learning theory. His publications reveal a strong emphasis on foundational aspects of learning theory and optimization. Recent work demonstrates expertise in universal learning frameworks, memory-constrained optimization, and probabilistic analysis of combinatorial problems. His research often bridges theoretical computer science with practical optimization challenges, particularly in contexts where traditional i.i.d. assumptions don't hold. Columbia DSI postdoctoral fellowship, 2024 INFORMS Transportation Science & Logistics (TSL) best student paper award, 2023 Air Force Office of Scientific Research Grant (AFOSR), with Prof. Patrick Jaillet, 2023 COLT 2022 Best student paper runner-up Bronze medal, Alibaba Global Mathematics Competition, 2022 Blanchard has received significant research funding including an Air Force Office of Scientific Research Grant. His work has been recognized with multiple prestigious awards, including the INFORMS TSL best student paper award for his research on the k-Traveling Salesman Problem. His extensive publication record in top venues demonstrates a strong research trajectory with impactful contributions to theoretical machine learning and optimization.
Richard Futrell is an Associate Professor at the University of California, Irvine (UCI), affiliated with the Department of Language Science. He leads the Language Processing Group, focusing on computational models of human and machine language processing. His work bridges information theory, Bayesian cognitive modeling, and natural language processing (NLP) interpretability. University of California, Irvine Department of Language Science Language Processing Group leader His research examines how linguistic structures emerge from cognitive and communicative pressures. Key areas include dependency locality, surprisal theory in sentence processing, and efficiency-driven language evolution. He investigates how memory constraints, predictability, and information density shape syntactic and morphological patterns across languages. Recent publications analyze code-switching efficiency, syntactic priming, ERP component modeling, and agent-based language contact simulations. His work frequently employs Bayesian modeling, neural network analysis, and cross-linguistic corpora to uncover universal principles in language processing. ACL Best Paper Award (2024) Best Paper Award for Computational Modeling of Language (2023) Marr Prize for Best Student Paper (2017) He has developed datasets like SPACER for error repair analysis and contributed to phonotactic learning frameworks. His collaborations span cognitive scientists, computational linguists, and neuroscientists, advancing understanding of language production, comprehension, and structural optimization.
