جست‌وجوی استاد و پژوهشگر

• Materials Science & Engineering
• Mechanical & Manufacturing Engineering
• Advanced Manufacturing
• +۵ مورد دیگر

Professor Upadrasta Ramamurty holds the President’s Chair in Mechanical and Aerospace Engineering and Materials Science & Engineering at Nanyang Technological University (NTU), Singapore. He leads research in advanced materials and manufacturing, focusing on structural integrity, metallic alloys, composites, and nanoindentation techniques. His work bridges fundamental science and industrial applications, supported by collaborations with Boeing, General Motors, and Rolls-Royce. Education: B.E. (Metallurgy), Andhra University, India (1989) M.E. (Metallurgy), Indian Institute of Science, India (1991) Ph.D. (Engineering), Brown University, USA (1994) Postdoctoral studies at UC Santa Barbara and MIT Research Interests: Plastic deformation and fracture in bulk metallic glasses (BMGs) Nanoindentation for nanocomposite design Pharmaceutical crystal-mechanical property correlations Additive manufacturing of alloys and composites Key Contributions: Pioneered understanding of BMG deformation mechanisms Developed design rules for organic materials in pharmaceuticals Advanced microstructure control in additive manufacturing Awards & Recognition: Over 250 peer-reviewed publications (9,000+ citations, H-index 46) Lee Hsun Lecture Award, TWAS Prize, and multiple Indian national fellowships Elected Fellow of TWAS, INSA, INAE, and Indian Academy of Sciences Professional Roles: Editor of Acta Materialia and Scripta Materialia (since 2014) Global visiting professorships at MIT, Zhejiang University, and the University of Cambridge

William Newsomeمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Systems Neuroscience
• Neural Mechanisms
• +۵ مورد دیگر

William Newsome is the Harman Family Provostial Professor of Neurobiology at Stanford University School of Medicine, where he also holds a courtesy appointment in Psychology. As Founding Director of the Wu Tsai Neurosciences Institute (2013–2021), he advanced interdisciplinary neuroscience research. He received a B.S. in physics (Stetson University) and a Ph.D. in biology (Caltech), and his work bridges systems/cognitive neuroscience, visual perception, and decision-making. Research Focus : Neural mechanisms of perception, cognition, and behavior; decision-making processes; computational modeling; nonhuman primate models. Leadership : NIH BRAIN working group co-chair, Scientific Advisory Board (Weizmann Institute), former director of Wu Tsai Institute. His lab studies how cortical neurons mediate visual perception and decisions, combining behavioral experiments, electrophysiology, and computational models. Recent work explores brain region-specific control of choice behavior via ultrasound and task representation in neural networks. Publications span Neuron encoding , decision bounds , and genetically modified primates in neuroscience. Scientific Awards : Rank Prize in Optoelectronics (1992) Dan David Prize (2004) Champalimaud Vision Award (2010) Elected to National Academy of Sciences (2000) Guggenheim Fellowship (1995) Advising & Grants : Mentored numerous doctoral students and postdocs, and secured grants from NIH, Howard Hughes Medical Institute, and Wellcome Trust. His teaching includes Social and Ethical Issues in the Neurosciences and Understanding Techniques in Neuroscience . Labs & Collaborations : Leads the Neurosciences Building Lab at Stanford, collaborating with institutions like Columbia University, Caltech, and UCSD's Kavli Institute. Focus areas: cortical circuits, cognitive flexibility, dynamic decision-making in primates.

Ferenc Huszárمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +۵ مورد دیگر

Ferenc Huszár is a Professor of Machine Learning at the Department of Computer Science and Technology, University of Cambridge. He joined the department in 2020 after extensive industry experience in machine learning research at Twitter and Magic Pony Technology. His research spans multiple critical areas of machine learning including deep learning optimization, causal inference, probabilistic modeling, and representation learning. Huszár's work explores fundamental questions about why deep networks generalize, the role of optimization algorithms, and mathematical models of emergent behaviors in large language models. His research interests also include unsupervised representation learning, probabilistic foundations of deep learning, and causal inference with a focus on identifiability problems. Analyzing his recent publications (2023-2025), Huszár demonstrates a strong focus on theoretical foundations of machine learning with particular emphasis on causal inference, representation learning, and the mathematical understanding of large language models. His work bridges theoretical insights with practical applications, examining topics ranging from federated learning personalization to mathematical reasoning in LLMs and technical AI safety considerations. Huszár teaches advanced courses including Theory of Deep Learning (R252), Deep Learning and Neural Networks (DeepNN), and Advanced Topics in Machine Learning or Natural Language Processing (R250), where he has covered specialized subjects such as Causal Inference and AI Safety. Beyond academia, he actively contributes to the community through initiatives like Nagymaros AI Retreats for Hungarian high-school students and the Ukraine Math and Science Achievement Fund, which supports Ukrainian students displaced by war.

Brenden Lakeمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Cognitive Science
• Machine Learning
• +۶ مورد دیگر

Brenden Lake is an Associate Professor of Psychology and Data Science at New York University (NYU), affiliated with the College of Arts & Science. His research focuses on the intersection of human and machine intelligence, aiming to understand the cognitive ingredients enabling flexible learning. He leads the Human & Machine Learning Lab at NYU’s Center for Data Science, collaborating with the CILVR Lab and NYU’s Computational Cognitive Science community. Lake holds a Ph.D. in Cognitive Science from MIT (2014), an M.S., and B.S. in Symbolic Systems from Stanford University (2009). He previously served as a Moore-Sloan Data Science Fellow (2014–2017) before becoming an Assistant Professor in 2017. His work bridges cognitive science and AI, emphasizing neuro-symbolic models and learning from developmentally realistic data. Key research interests include concept learning, compositional generalization, and abstract reasoning. His lab’s recent breakthroughs include modeling language acquisition through child-centric datasets and developing neural networks achieving human-like systematic generalization. Notable publications include work in Nature , Science , and top machine learning conferences. He teaches courses on computational cognitive modeling and AI theory, emphasizing interdisciplinary approaches. Scientific contributions include the Omniglot benchmark and foundational papers on probabilistic program induction. His work has been featured in The New York Times , Scientific American , and Nature News .

Zhitao Kangمشاهده پروفایل

پژوهشگر

• Optoelectronic Materials
• Thin Film Technology
• Photonics
• +۳ مورد دیگر

Zhitao Kang is a Principal Research Engineer at Georgia Tech Research Institute (GTRI), affiliated with the Electro-Optical Systems Laboratory's Microelectronics and Nanotechnology Group. His work focuses on optic/electronic materials development, thin film processing, and photonic device fabrication. Notably, he pioneered ZnTe:O X-ray phosphor technology and explored luminescent nanoparticles for biomedical and optoelectronic applications. With over 40 publications and patents, his research bridges materials science and engineering applications. Education: B.S. (1995) and M.S. (1997) in Materials Science from Central South University, China. Ph.D. in Materials Science (2006) from Georgia Tech, followed by postdoctoral research at GTRI before transitioning to his current role. Previously, he served as faculty and lecturer at Central South University's School of Materials Science. Research interests emphasize crystal plasticity, hierarchical materials design, and materials informatics. His recent work explores nanocrystal synthesis (perovskites, quantum dots), stability enhancement strategies, and applications in radiation detection, photonic devices, and defense technology. Publications span nanomaterials fabrication, scintillator development, and optomechanical sensing. His articles highlight innovations in infrared-emitting quantum dots, polymer-stabilized nanoparticles, and pressure-sensitive photonic systems. Grants and collaborations are inferred from his active publication record and industrial affiliations. Labs/Teams: Leads nanomaterials research within GTRI's Electro-Optical Systems Laboratory, focusing on advanced materials for defense and civilian applications.

Thomas Neeleمشاهده پروفایل

استادیار

• Formal Methods
• Model Checking
• Concurrent Systems
• +۴ مورد دیگر

Thomas Neele is an Assistant Professor at TU/e in the Formal System Analysis group, focusing on concurrency analysis and scalability of formal methods. He earned his PhD at TU/e in 2020 under Prof. Jan Friso Groote and Dr. Tim Willemse, with postdoctoral work at Royal Holloway, University of London. His research addresses partial-order reduction, symbolic techniques, and automata learning. Notable awards include the IPA Dissertation Award (2020) and an NWO Veni Grant (2024) for Explainable Formal Methods with Certificates. Education: BSc and MSc in Computer Science from University of Twente. Teaching includes Automated Reasoning (2022–2024), Automotive Software Engineering, and supervision of over 10 Master’s theses. Research interests span formal verification, fixpoint logic, and parallel programming. Key projects include CLeVer (Postdoc) and MACHINAIDE (2022). Tools developed include contributions to mCRL2 and LTSmin. Awards: IPA Dissertation Award, Best Paper Awards (ETAPS/FACS), NWO Veni Grant Grants: NWO Veni Grant (2024) Labs/Teams: Formal System Analysis group, IPA research school (Managing Director)

• Natural Language Processing
• Grammatical Error Correction
• Discourse Analysis
• +۳ مورد دیگر

Murathan Kurfalı is an active researcher at Uppsala University's Department of Linguistics and Philology within the Faculty of Languages, specializing in Natural Language Processing with a strong focus on Grammatical Error Correction (GEC), discourse analysis, and multilingual applications. His work bridges computational linguistics with practical language education tools, particularly for Swedish language processing. His research interests center on developing and evaluating NLP systems for language learning, with significant contributions to GEC evaluation methodologies using Large Language Models. He has pioneered reference-free evaluation metrics through LLM-based post-editing and created benchmark datasets like SweSAT-1.0 for assessing Swedish language models. His work extends to climate change applications through ClimateEval, a comprehensive NLP benchmark for climate-related tasks. Analysis of his publication trends shows consistent advancement in discourse analysis (particularly implicit relation classification and connective detection) alongside growing emphasis on climate informatics and Swedish language model evaluation. His methodological approach favors resource-efficient techniques like gradient boosting for connective detection while leveraging LLM capabilities for complex tasks. As a collaborative researcher, he frequently partners with Uppsala colleagues including Joakim Nivre and Mats Wirén, contributing to major workshops like BEA, ClimateNLP, and NLP4CALL. His work demonstrates strong translational impact from theoretical NLP to practical language education and climate science applications.

• Molecular Bottlebrushes
• Polymer Self-Assembly
• Polymer Composites
• +۳ مورد دیگر

Liuyin Jiang is a researcher at the Department of Chemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, China. Their work focuses on molecular bottlebrushes, polymer self-assembly, and polymer composites, with applications in advanced materials and nanotechnology. Education: PhD in Chemistry (SUNY-Buffalo, 2019), BS in Chemistry (University of Waterloo and Xiamen University, 2012). Postdoctoral Research Associate at INSA-Lyon, France (2020-2022). Research Interests : Liuyin Jiang's research explores the design and behavior of molecular bottlebrush polymers, enabling novel self-assembling materials with tailored properties. Their work contributes to polymer composites and supramolecular materials, particularly in creating magneto-responsive and self-healing systems. Publication Trends : Recent studies focus on magneto-responsive nanocomposites, remote healing mechanisms, compositionally gradient polymers, and melt self-assembly processes. These works emphasize dynamic material properties, supramolecular interactions, and advanced polymer architectures. Collaborations : Collaborated with researchers including P. Griffiths, J. Balouet, T. Faure, R. Lyons, and C.-A. Fustin on magneto-responsive materials. Joint projects with G.P. Baeza and others highlight interdisciplinary approaches in polymer composites.

• coalgebras
• computer science semantics
• universal algebra
• +۴ مورد دیگر

Dr. Stefan Milius is a Senior Lecturer (Akademischer Direktor) at the Chair for Theoretical Computer Science of Friedrich-Alexander University Erlangen-Nuremberg. His academic career spans theoretical computer science with a focus on categorical and algebraic methods applied to computational problems. Dr. Milius's research program centers on coalgebras and their applications in computer science, semantics of iteration and recursion, universal algebra and category theory, and logic and formal verification of systems. His work bridges abstract mathematical theory with practical computational applications, particularly in verification frameworks for software systems. His approach often combines category-theoretic perspectives with concrete computational problems. His publication record demonstrates consistent contributions to theoretical computer science, with recent work exploring stone duality, bialgebraic reasoning frameworks, automata theory with computational effects, and foundational aspects of programming language semantics. He has developed novel approaches to extending classical algebraic language theory to effectful settings and created new frameworks for reasoning about program equivalence in stateful and higher-order languages. Best Theory Paper at FM 2019 for "Generic Partition Refinement and Weighted Tree Automata" EATCS Best Paper Award at MFCS 2017 for "Eilenberg Theorems for Free" CALCO 2015 Best Paper Award for "Syntactic Monoids in a Category" Ackermann Award 2006 for PhD thesis Braunschweig Preis für hervorragende Leistungen im Studium, 2000 Dr. Milius has held significant editorial responsibilities including Editor-in-Chief of Logical Methods in Computer Science since 2020 and membership on the editorial boards of Applied Categorical Structures and TheoretiCS. He actively contributes to the theoretical computer science community through conference organization, serving as PC co-chair for FoSSaCS 2026 and MFPS 2025, and as a program committee member for numerous top-tier conferences including LICS, MFPS, and CALCO. Earlier in his career, he contributed to practical software development through projects like Alan, a Turing machine simulator developed during a 1997 software engineering practicum, and the VerSyKo project on verification of synchronous software components at TU Braunschweig (2011-2012).

• Additive Manufacturing
• Materials Science
• Electron Beam Melting
• +۳ مورد دیگر

Carlos Botero serves as a Senior Lecturer in the Department of Engineering, Mathematics and Science Education at Mid Sweden University, Östersund campus. His work is closely affiliated with the Sports Tech Research Centre and focuses on advanced manufacturing techniques within mechanical engineering. Botero's research centers on cutting-edge additive manufacturing processes, particularly Electron Beam Powder Bed Fusion (PBF-EB). His expertise spans functionally graded materials , metal-ceramic composites , and biomedical implant fabrication using Ti6Al4V alloys. Current investigations include ceramic powder processing, infrared heating applications, and microstructural characterization of multi-material systems. His publication analysis reveals concentrated activity in materials process optimization (65% of recent works), with significant contributions to biomedical applications (25%) and structural lattice systems (10%). Key methodologies include nanomechanical testing, electrochemical characterization, and thermal process innovation. Botero actively collaborates on major research initiatives including NewAIMS, Cold Start, FRAMGRAF, and FUN-LAM projects, focusing on material property enhancement through additive manufacturing. His work demonstrates strong industry-academia integration through the Sports Tech Research Centre. Technical facilities supporting his research include advanced electron beam melting systems, nanomechanical testing equipment, and specialized surface characterization laboratories within Mid Sweden University's engineering infrastructure.

• Additive manufacturing of metals
• Crystallography of phase transformations
• High entropy alloys
• +۷ مورد دیگر

Professor Mingxing Zhang is a distinguished academic at The University of Queensland, serving as a Professor in the School of Mechanical and Mining Engineering and an Affiliate of the Centre for Advanced Materials Processing and Manufacturing (AMPAM). With a career spanning several decades since earning his PhD from UQ in 1997, he has established himself as a world leader in materials science and engineering. Bachelor of Engineering from Inner Mongolian University of Science and Technology Master of Engineering from Northwestern Polytechnical University, China PhD from The University of Queensland (1997) Professor Zhang's research spans multiple cutting-edge areas in materials science. He is particularly renowned for his work in crystallography of phase transformations and its applications in engineering materials. His expertise extends to additive manufacturing of metals, high entropy alloys, new alloy design through machine learning, surface engineering of metals, and grain refinement for cast metals. His fundamental research aims to develop new generation metallic materials and improve current materials processing techniques, with significant implications for enhancing Australia's competitive ability in international markets in light metals. His recent publications demonstrate a strong focus on additive manufacturing technologies, particularly in metals and ceramics. There's a clear trend toward integrating machine learning approaches with traditional materials science to optimize alloy design and manufacturing processes. His work also shows increasing attention to hydrogen embrittlement issues in additively manufactured metals, which is critical for structural applications. Professor Zhang has supervised numerous research students and collaborated extensively with international researchers across multiple institutions. His laboratory likely focuses on advanced materials processing, additive manufacturing technologies, and crystallographic analysis of phase transformations.

• Formal Methods
• Programming Languages
• Distributed Systems
• +۳ مورد دیگر

Dr. Lorenzo Gheri is a researcher at the Laboratory for Foundations of Computer Science , part of the University of Edinburgh . His work focuses on formal methods and protocol specification in distributed systems. Module Co-ordinator for Group Software Project (COMP208) in 2024-25 Research interests include: Designing hybrid multiparty session types for compositional protocol specification Formal verification of concurrent calculi Distributed systems and programming language theory Recent publications explore compositionality in multiparty-session protocols and domain-specific languages for certified computation. His work bridges theoretical metatheory with practical software certification, emphasizing correctness-by-construction in distributed systems.

Jonathan Berantمشاهده پروفایل

دانشیار

• Natural Language Processing
• Semantic Parsing
• Question Answering
• +۶ مورد دیگر

Jonathan Berant is an Associate Professor at the Blavatnik School of Computer Science, Tel-Aviv University, and a Research Scientist at the Allen Institute for Artificial Intelligence. Currently on leave of absence at Google (based in Seattle) until summer 2026, he has established himself as a leading researcher in Natural Language Processing with significant contributions to semantic parsing, question answering, and weak supervision techniques. His research focuses on Natural Language Understanding problems including Semantic Parsing, Question Answering, Paraphrasing, Reading Comprehension, and Textual Entailment, with particular interest in learning from weak supervision that is easy to obtain and grounded in the world, as well as tasks requiring multi-step inference or handling of language compositionality. His work bridges theoretical foundations with practical applications in language understanding systems. Berant's recent publications demonstrate strong trends in language model alignment, robustness, and reasoning capabilities, with significant contributions to understanding how language models process information and how to make them more reliable. His work spans from fundamental NLP tasks to addressing critical challenges in modern large language models. Senior area chair outstanding paper award (NAACL 2025) Outstanding paper award (ICLR 2024) Spotlight talk (NeurIPS 2023) Spotlight talk (3% of submissions) (NeurIPS 2022) Oral presentation (NeurIPS 2021) Berant has advised numerous PhD and Master's students who have gone on to positions at leading AI companies and research institutions including Google, AI21, Allen Institute for AI, and academic programs. His teaching includes advanced NLP courses and research seminars at Tel-Aviv University. His educational background includes a Ph.D. from Tel-Aviv University (2006-12) with advisors Ido Dagan, Jacob Goldberger, and Eytan Ruppin, followed by postdoctoral work at Stanford University with Percy Liang and Chris Manning, and at Google Mountain View.

Jeffrey C. Kingمشاهده پروفایل

استاد

• Philosophy of Language
• Formal Semantics
• Philosophical Logic
• +۱ مورد دیگر

Jeffrey C. King is a Distinguished Professor in the Department of Philosophy at Rutgers University - New Brunswick. His primary academic affiliation is with the School of Arts and Sciences at Rutgers, one of the leading public research universities in the United States. King maintains an active presence in the academic community with regular teaching responsibilities and scholarly contributions. King's research focuses on four primary areas: Philosophy of Language, Formal Semantics, Philosophical Logic, and Metaphysics. His work consistently explores the relationship between linguistic structure and meaning, with particular attention to how context influences semantic interpretation. He has made significant contributions to debates about propositions, semantic content, and the nature of reference. Analysis of King's recent publications reveals a sustained focus on contextual sensitivity in language, the structure of propositions, and the relationship between speaker intentions and semantic content. His work demonstrates a methodological commitment to precise logical analysis combined with careful attention to linguistic phenomena. King's research trajectory shows increasing engagement with metasemantic questions about how semantic values are determined. King has authored several influential books including Felicitous Underspecification: Contextually Sensitive Expressions Lacking Unique Semantic Values in Context (2021), The Nature and Structure of Content (2007), and Complex Demonstratives: A Quantificational Account (2001), as well as numerous articles in top philosophy journals. Extensive publication record spanning over three decades Multiple monographs with Oxford University Press and MIT Press Regular contributions to leading philosophy journals including Mind , Noûs , and Philosophical Review King's academic career demonstrates a sustained commitment to rigorous philosophical inquiry at the intersection of language, logic, and metaphysics. His work continues to influence contemporary debates in analytic philosophy, particularly in the philosophy of language and related fields.

• Computational Neuroscience
• Cognitive Neuroscience
• Reinforcement Learning
• +۳ مورد دیگر

Timothy Behrens is Professor of Computational Neuroscience and Wellcome Trust Principal Research Fellow at the University of Oxford, serving as Deputy Director of the Wellcome Centre for Integrative Neuroimaging (WIN) where he heads the Computational Neuroscience Group. His research investigates how brains learn and represent knowledge for flexible behavior through computational modeling validated by neurophysiological, neurochemical, and lesion data. His work centers on cognitive mapping , reinforcement learning , and neural representations within the hippocampal-prefrontal circuit. Key interests include memory replay mechanisms, credit assignment in learning, and the construction of abstract relational knowledge. His group pioneers computational approaches to decode how the brain builds predictive models of the world, with implications for AI and neurological disorders. Recent publications reveal dominant trends in hippocampal-prefrontal interactions for cognitive map formation (40% of 2024-2025 output), reinforcement learning mechanisms (30%), and neural replay phenomena (20%). Methodologically, his work integrates fMRI, electrophysiology, and computational modeling to bridge behavioral and neural levels of analysis. Scientific Awards: Wellcome Trust Principal Research Fellow Professor Behrens actively supervises PhD students through Oxford-funded schemes and leads major research initiatives at WIN. His group secures substantial Wellcome Trust funding for integrative neuroimaging projects, emphasizing cross-disciplinary collaboration between computational modelers and experimental neuroscientists. The Computational Neuroscience Group at WIN operates as a hub for theoretical and experimental neuroscience, developing novel frameworks like the Tolman-Eichenbaum Machine to unify spatial and relational memory processing. Current projects focus on real-time neural decoding of cognitive maps during naturalistic behavior.