جست‌وجوی استاد و پژوهشگر

• Computational Linguistics
• Syntax
• Semantics
• +۷ مورد دیگر

Dr. Fermin Moscoso del Prado Martin is an Associate Professor of Computational Linguistics at the Department of Computer Science and Technology, University of Cambridge. He is based in the William Gates Building and can be contacted via fm611@cam.ac.uk. His research focuses on the computational and cognitive foundations of language, integrating formal linguistic theory with experimental and computational methods. Key research interests include syntactic and semantic processing, language evolution, and the application of machine learning to linguistic analysis. He explores topics such as grammatical diversity metrics, causal modeling of linguistic changes, and the interplay between syntax and semantics in real-time language processing. His work bridges theoretical linguistics with empirical methods, employing tools from information theory, dynamical systems modeling, and artificial intelligence. His recent publications highlight contributions to understanding language universals, computational models of lexical decision tasks, and the use of ontologies for explaining black-box AI systems. Notable studies include investigations into bidialectal homophone effects in Japanese, the fractal structure of behavioral sequences, and the efficiency of linguistic structures. Though no awards or grants are explicitly listed, his extensive publication record indicates active engagement in interdisciplinary research at the intersection of computer science, linguistics, and cognitive science. His work often involves collaborations across linguistic typology, computational modeling, and experimental psychology.

Jill Adlerمشاهده پروفایل

استاد

• Mathematics Education
• Teacher Professional Development
• Multilingual Education
• +۶ مورد دیگر

Jill Adler is a Professor of Mathematics Education at the University of the Witwatersrand in South Africa and an Honorary Research Fellow at the Department of Education, Oxford University . Her career spans large-scale teacher development projects, including the Wits Maths Connect Secondary (WMCS) Project and the Nuffield-funded “Developing Language-Responsive Mathematics Classrooms” . She served as President of the International Commission on Mathematical Instruction (ICMI) from 2017-2020 and held the SARChI Mathematics Education Chair (2010-2019). Education: PhD in Mathematics Education. Awards: 2015 ICMI Hans Freudenthal Medal 2012 ASSAf Gold Medal for Science in the Service of Society 2015 Svend Pedersen Lecture Award 2003 Vice Chancellor’s Research Award 2003 Vice Chancellor’s Academic Citizenship Team Award Research Interests focus on mathematics teacher development in multilingual and resource-constrained settings. She has pioneered frameworks like Mathematical Discourse in Instruction (MDI) and Mediating Primary Mathematics (MPM) to analyze classroom practices, emphasizing coherence in explanations, representation transformations, and language-responsive pedagogy. Her work bridges curriculum reform , pedagogical theory , and teacher identity formation . Publications highlight trends in language-responsive teaching , variation theory , and teacher professional development . Key themes include exemplification strategies , mathematical discourse , and policy implementation challenges in diverse educational systems.

Mark Johnsonمشاهده پروفایل

استاد

• Natural Language Processing
• Computational Linguistics
• Machine Learning
• +۵ مورد دیگر

Mark Johnson is a Professor at Macquarie University's School of Computing, affiliated with the Centre for Language Sciences (CLaS). His research focuses on computational linguistics, natural language processing, and machine learning, with contributions to parsing algorithms, child language acquisition models, and generative AI systems. He leads projects on generative models of life stories, logistics optimization algorithms, and visual dialogue systems using deep neural networks. Research interests include natural language understanding, data augmentation techniques, and model robustness. Notable projects involve developing frameworks for synthetic training data, OOD detection, and integrating named entity recognition with context tags. His work emphasizes practical applications in dialog systems, document analysis, and language model optimization. Collaborations span cross-linguistic studies and AI-driven visualization systems. Key achievements include over 200 research outputs, including influential articles on child language acquisition models and OOD detection. He oversees projects funded by MQRC and industry partnerships, focusing on advancing NLP and AI methodologies. His lab, CLaS, fosters interdisciplinary research in language sciences and computational methods.

• Software Testing
• Formal Methods
• Specification-Based Testing
• +۳ مورد دیگر

Dr. Kirill Bogdanov is a Lecturer in the Department of Computer Science at the University of Sheffield, UK. He completed his PhD in 2000 on specification-based software testing using X-machines and Statecharts. Before his current role, he worked as a Research Associate on the MOTIVE project, focusing on testing object-oriented systems. Education: PhD in Computer Science, University of Sheffield (2000) Research focused on applying X-machine testing methods to Statecharts specifications Research Interests: Specification-based testing methodologies X-machine formalisms and their applications Automated model inference from software code Passive inference of software models from logs Formal methods for safety-critical systems Grants & Projects: PI on EPSRC grants: REGI (2009-2012), StaMInA (2009-2012), and Automated Abstraction (2005-2008) Developed tools like the Statechum project for model inference Labs/Teams: Member of the Verification and Testing Research Group and Foundations of Computation research group. Awards: No specific awards listed, but contributions include pioneering work in formal testing methods.

• Explainable AI
• Interpretable Machine Learning
• Deep Learning
• +۱۳ مورد دیگر

Wojciech Samek is a Professor in the Department of Electrical Engineering and Computer Science at the Technical University of Berlin and Head of the AI Department at Fraunhofer Heinrich Hertz Institute (HHI), Germany. He holds a joint appointment, bridging academic research and industrial application in artificial intelligence. He is a Fellow at BIFOLD and ELLIS Unit Berlin, and a Principal Investigator in several DFG projects including DeSBi and BIOQIC. PhD (Dr. rer. nat.) with distinction, Technical University of Berlin, 2014 Studies in Computer Science, Humboldt University, Heriot-Watt University, University of Edinburgh His research centers on Explainable AI (XAI) , Trustworthy Deep Learning , and Efficient AI . He pioneered Layer-wise Relevance Propagation (LRP), a foundational method for interpreting deep neural networks. His work spans model interpretation, robustness against adversarial attacks, neural network compression, federated learning, and applications in healthcare, communications, and multimedia. The recent articles highlight a strong trend toward extending explainability beyond deep models , with research on concept-level explanations, unsupervised learning interpretability, and XAI-driven model improvement. There is a consistent focus on robustness, privacy, and efficiency in distributed learning settings, particularly for federated and edge AI. Applications in medical AI and neuroscience are prominent, emphasizing safety and interpretability in high-stakes domains. Best Paper Award, Pattern Recognition (2020) Digital Signal Processing Best Paper Prize (2022) Fellow, BIFOLD - Berlin Institute for the Foundation of Learning and Data Member, Germany's Platform for Artificial Intelligence Senior Editor, IEEE TNNLS; Associate Editor, Pattern Recognition, Digital Signal Processing, PLoS ONE Area Chair, NeurIPS, ICML, NAACL Program Chair, IEEE MLSP 2023 Contributor to ISO/IEC MPEG-17 NNC standard Prof. Samek advises a large group of PhD students and leads a vibrant research team at Fraunhofer HHI and TU Berlin. His group has secured significant funding through DFG, BIFOLD, and industrial collaborations. He has co-authored over 200 peer-reviewed papers, many of which are ESI Hot or Highly Cited. He is deeply involved in organizing workshops and tutorials on XAI, federated learning, and neural compression at top venues like NeurIPS, ICML, CVPR, and IEEE conferences. His lab develops open-source tools such as the LRP Toolbox , Keras Explanation Toolbox , DeepCABAC , and Quantus , promoting reproducibility and adoption of interpretable and efficient AI methods. The team is actively working on next-generation AI that is not only accurate but also transparent, robust, and trustworthy.

Ravi Tandonمشاهده پروفایل

استاد

• Information Theory
• Machine Learning
• Security and Data Privacy
• +۱۲ مورد دیگر

Ravi Tandon is a Professor in the Department of Electrical and Computer Engineering at the University of Arizona, where he holds a Craig M. Berge Faculty Fellowship and a courtesy appointment in Applied Mathematics. He joined the university in 2015 after serving as a Research Assistant Professor at Virginia Tech. Education: PhD in Electrical and Computer Engineering, University of Maryland, College Park, 2010 B.Tech in Electrical Engineering, Indian Institute of Technology (IIT) Kanpur, 2004 Research Interests: His research spans information theory and its applications to wireless networks, machine learning, security, and privacy. He focuses on trustworthy machine learning, privacy-preserving AI, federated learning, secure communications, and private information retrieval. His work aims to develop foundational theories and practical systems for secure, fair, and efficient AI and communication systems. Recent Research Trends: His most recent publications (2023–2025) emphasize trustworthy AI, including fairness-accuracy tradeoffs, robustness, uncertainty quantification, and privacy in generative models and causal inference. He also continues to advance information-theoretic foundations of secure communications and distributed learning. Scientific Awards: NSF CAREER Award (2017) Keysight Early Career Professor Award (2018) Best Paper Award, IEEE GLOBECOM 2011 Advising and Grants: Dr. Tandon actively supervises PhD and MS students, with former students placed at Apple, Google, Meta, NXP, and other leading tech firms. He has led significant research projects, including an NSF SaTC grant on differential privacy in graph mining. His editorial service includes roles at IEEE Transactions on Information Theory, IEEE Transactions on Wireless Communications, and IEEE Transactions on Communications. Labs and Teams: He leads a research group focused on information theory, machine learning, and privacy, collaborating with researchers in computer science, applied mathematics, and industry. His work bridges theoretical foundations with real-world AI and communication system design.

Julie Wallمشاهده پروفایل

استاد

• Artificial Intelligence
• Advanced Computing
• Deep Learning
• +۱۲ مورد دیگر

Professor Julie Wall is a faculty member at the University of West London, serving as Professor of AI and Advanced Computing in the School of Computing and Engineering. She is actively engaged in research, teaching, and professional service, including her role as an expert at the British Standards Institution (BSI) in the domain of artificial intelligence. Research Interests: Julie Wall's research is centered on the design and application of intelligent systems for processing and modeling temporal data, particularly in speech and language. She leverages neural network architectures—ranging from biologically inspired models to computationally efficient deep learning systems—to analyze diverse data types such as audio, video, images, tabular data, and 3D features. Her work extends to developing production-grade deep learning and natural language understanding systems for immersive environments like virtual and augmented reality. Publications and Research Trends: Her body of work, comprising over 50 high-quality papers and multiple patents, reflects a strong trajectory in AI systems that integrate multimodal data with temporal dynamics. The research spans core areas of machine learning, natural language processing, and applied AI, with increasing focus on real-world deployment, efficiency, and intelligent interaction in extended reality platforms. Scientific Awards: US Patent UK Patent Advising and Grants: Julie Wall supervises research students across disciplines including forensic science and artificial intelligence. She contributes extensively to academic programs, teaching courses such as BSc and MSc in Computer Science, Artificial Intelligence, Data Science, and specialized AI programs in cybercrime and criminal justice. While specific grant details are not listed, her patent holdings and publication volume suggest sustained research funding and project leadership. Labs and Teams: As a leading researcher in AI and advanced computing, she is likely involved in or leads research groups focused on intelligent systems, deep learning, and multimodal AI at the University of West London, though specific lab names are not mentioned in the text.

• Biomedical Natural Language Processing
• Large Language Models in Healthcare
• Ophthalmology AI Applications
• +۳ مورد دیگر

Xuguang Ai, MS, is a researcher at Yale School of Medicine specializing in biomedical natural language processing and large language models for healthcare applications. Their work focuses on applying AI to medical domains, particularly ophthalmology, with numerous publications in 2024-2025. Research interests center on biomedical natural language processing , large language model evaluation in clinical contexts , and specialty-specific AI applications . Their work bridges the gap between artificial intelligence and practical healthcare needs, developing systems that can understand and process medical text with clinical accuracy. Their publication record shows a strong emphasis on benchmarking methodologies for medical AI systems, with multiple studies evaluating LLM performance across thousands of clinical questions. Recent work includes developing ophthalmology-specific language models and improving medical information extraction systems. Award recognition has not been specified in available materials, but their high-impact publications in venues like Nature Communications demonstrate significant scholarly contribution to the field. Research activities include leading projects on medical question answering systems, clinical decision support tools, and specialized language models for healthcare domains. The work integrates systems immunology, engineering, and AI to monitor, predict, and improve human health as part of Yale's broader research initiatives.

Yu Yinمشاهده پروفایل

استادیار

• Computer Vision
• Generative Models
• 3D Modeling and Animation
• +۷ مورد دیگر

Yu Yin is an Assistant Professor in the Department of Computer and Data Sciences at Case Western Reserve University (CWRU), joined in Fall 2023. Previously, they earned a Ph.D. (2023) and M.S. (2018) in Computer Engineering and Electrical & Computer Engineering from Northeastern University, and a B.E. (2016) in Electrical and Information Engineering from Wuhan University of Technology. Their research focuses on visual synthesis, 3D modeling/animation, and applications like face restoration, deepfake detection, and generative models. They teach courses including Deep Generative Models (CSDS 570) and Computer Vision (CSDS 465). Key achievements include a 2024 Teaching Award, NeurIPS 2024 Oral paper, and organizing workshops like ICCV-AMFG 2023. Active in program committees for CVPR, NeurIPS, and ICLR. Over 20+ publications in top venues (CVPR, AAAI, ACM MM) and 2 patents. Open to advising motivated students in machine learning and computer vision.

Hao Zhouمشاهده پروفایل

دانشیار

• Generative AI
• Molecular Modeling
• Protein Engineering
• +۳ مورد دیگر

Hao Zhou is a Research Associate Professor at the Institute for AI Industry Research (AIR), Tsinghua University. His research focuses on developing Generative AI models for discrete symbols such as text, small molecules, and proteins. Previously, he worked as a Research Scientist/Manager at Bytedance AI Lab. Currently, he leads the Generative Symbolic Intelligence group at Tsinghua AIR and co-leads the SIA Lab, a joint laboratory with ByteDance for LLM research. His research interests span molecular representation learning, generative models for scientific discovery, and applications in drug design and protein engineering. Recent work includes advancements in 3D molecular modeling, protein language models, and non-autoregressive sequence generation. These efforts aim to bridge AI techniques with scientific domains like chemistry and biology. Recent publications highlight contributions to areas such as auto-encoder-based molecular representation (Mol-AE), multi-scale protein modeling (ESM All-Atom), and structure-based drug design (MolCRAFT). These papers reflect a focus on improving generative models' accuracy and applicability to real-world scientific challenges. Best Short Paper of INLG (2022) Ranked #1 on WMT EN-DE translation (2021) CCF NLPCC Distinguished Young Scientist (2021) Best Paper Award of ACL (2021) CAAI Doctoral Dissertation Award (2019) He advises several students, including Qiying Yu, Yuxuan Song, and Danqing Wang, and oversees grants related to AI in drug discovery and molecular modeling. His labs are actively hiring postdocs and research assistants, and he encourages student interns to join collaborative projects. Key initiatives include the SIA Lab’s work on LLM research and the Generative Symbolic Intelligence group’s exploration of AI-driven scientific breakthroughs. These efforts emphasize interdisciplinary approaches to tackle complex challenges in AI and life sciences.

Phalangchok Wanphetمشاهده پروفایل

مدرس

• Language and Communication
• Discourse Analysis
• Language Didactics
• +۴ مورد دیگر

Phalangchok Wanphet is a Lecturer at Nord University, specializing in applied linguistics and discourse analysis. He holds a PhD from the University of Wisconsin-Milwaukee (2011), and master’s degrees from Georgia State University (2004) and King Mongkut's University of Technology (2001). His research focuses on classroom discourse, bilingual communication, gesture studies, and digital pedagogy. He has published extensively on language teaching methods, interactional sociolinguistics, and the application of conversation analysis in education. Education: PhD in Linguistics, University of Wisconsin-Milwaukee, 2011 MA in Linguistics, Georgia State University, 2004 MA in Education, King Mongkut's University of Technology, 2001 BEd in Education, Phetchabun Rajabhat University, 1997 Research interests include language didactics, discourse analysis, pragmatics, and qualitative research methods. He explores topics such as bilingual children’s communication, gesture in interaction, and the role of technology in teacher training. Recent work examines classroom discourse dynamics and the sociocultural implications of language learning in immigrant communities. Publications span academic journals in applied linguistics and communication studies, with a focus on empirical studies of language use in educational and digital contexts. His 2023 paper critiques English didactics in Norwegian higher education, while earlier work analyzed conversational dominance through interruption patterns and nonverbal communication strategies in EFL classrooms. No scientific awards explicitly listed, but holds several professional certificates from Harvard University, Columbia University, and other institutions in leadership, diversity, and AI in education. Teaching responsibilities include courses on English linguistics, language classroom discourse, and TESOL assessment methods.

• Causal Inference
• Data Management
• Machine Learning
• +۳ مورد دیگر

Babak Salimi is an Assistant Professor at the Halicioglu Data Science Institute (HDSI) at the University of California San Diego. Previously, he was a postdoctoral research associate at the University of Washington's Department of Computer Science and Engineering, working with Prof. Dan Suciu. He holds a Ph.D. in Computer Science from Carleton University, advised by Prof. Leopoldo Bertossi. His research focuses on integrating causal inference, theoretical data management, and machine learning to build decision-support systems that enhance interpretability for diverse users. Key areas include causal relational learning, fairness in data-driven decisions, and robust offline evaluation frameworks. He has pioneered work on causal DAG summarization, adversarial data corruption testing, and model-agnostic data valuation systems like KAIROS. Salimi's contributions have been recognized with prestigious awards including the ACM SIGMOD Research Highlight Award and Best Paper Award, and the VLDB Best Demonstration Paper Award. His work bridges foundational data science principles with societal impact, emphasizing ethical AI practices and algorithmic accountability. Education: Ph.D., School of Computer Science, Carleton University Research Lab: HDSI's Causal Data Science Group Key Projects: Development of CausaLens, Zorro, and DEMA

• Computer Animation
• Evolutionary Algorithms
• Computational Science
• +۳ مورد دیگر

Ari Sarafopoulos is a Senior Lecturer at Bournemouth University, specializing in computer animation and evolutionary computation. His work bridges computational methods with artistic applications, focusing on iterated function systems (IFS) and material simulation. Key research areas include evolutionary algorithms, fractal modeling, and computational design. Recent publications address material point methods for elastoplastic simulation and procedural shape generation. He has presented work at major conferences including ACM SIGGRAPH and IEEE Congress on Evolutionary Computation.

Mutian Heمشاهده پروفایل

عضو هیئت علمی

• Spoken Language Understanding
• Speech Synthesis
• Natural Language Processing
• +۱۲ مورد دیگر

Mutian He is a PhD candidate and Doctoral Assistant at the École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, affiliated with the Idiap Research Institute and the School of Engineering. He is pursuing his doctoral studies in Electrical Engineering under the supervision of Phil Garner. He holds a B.E. from Beihang University (BUAA) and an MPhil from the Hong Kong University of Science and Technology (HKUST). B.E., Beihang University (BUAA), 2019 MPhil, Hong Kong University of Science and Technology, 2022 PhD Candidate, École Polytechnique Fédérale de Lausanne (EPFL), ongoing His research focuses on spoken language understanding, speech synthesis, and the intersection of speech and language processing with machine learning. He explores efficient model architectures, pretraining strategies, multilingual and low-resource modeling, and the use of large language models in speech tasks. His work spans both theoretical and applied aspects, including distillation to linear-complexity models, robust TTS, and commonsense reasoning via conceptualization. His recent publications at top venues such as ICLR, EMNLP, Interspeech, and KDD demonstrate a strong trend towards efficient and scalable models for speech and language, with increasing emphasis on multilingualism, knowledge transfer, and real-world deployment in low-resource settings. He has also contributed to open-source implementations and community tools like Speech Rankings. Joint Fine-tuning and Conversion of Pretrained Speech and Language Models towards Linear Complexity (ICLR 2025) Acquiring and Modelling Abstract Commonsense Knowledge via Conceptualization (AIJ 2024) The Interpreter Understands Your Meaning: End-to-end Spoken Language Understanding Aided by Speech Translation (Findings of EMNLP 2023) Can ChatGPT Detect Intent? Evaluating Large Language Models for Spoken Language Understanding (Interspeech 2023) Multilingual Byte2Speech Models for Scalable Low-resource Speech Synthesis (2022) Mutian He has served as a teaching assistant for courses including Introduction to Natural Language Processing at HKUST and Introduction to Speech Processing at Idiap. He has also worked on speech synthesis at Microsoft, focusing on robustness and multilingual conditions. He is actively involved in research advising under Phil Garner and has collaborated with multiple researchers across institutions. He is affiliated with the LIDIAP (Laboratory of Intelligent Data Analysis and Pattern Recognition) at EPFL, where he contributes to research in deep learning for speech and language. His lab work involves developing novel neural architectures, conducting experiments on multilingual datasets, and open-sourcing code to promote reproducibility.

Wei Huمشاهده پروفایل

استاد

• Artificial Intelligence
• Knowledge Graphs
• Semantic Web Technologies
• +۵ مورد دیگر

Wei Hu is a full Professor and Ph.D. supervisor in the Department of Computer Science and Technology at Nanjing University, China. He earned his Ph.D. and B.S. from Southeast University in 2009 and 2005, respectively, and joined Nanjing University faculty in 2009. He has held visiting positions at Stanford University (2014-2015), University of Texas at Arlington (2016-2017), and University of Toronto (2017) as a visiting scholar/professor. Research Interests focus on Knowledge Graphs : Representation learning, foundation models, and error detection Databases : Entity alignment, crowdsourcing, and blockchain integration Digital Medicine : Collaboration with Nanjing University's National Institute of Health Data Science Publication Trends show expertise in knowledge graph reasoning, federated learning, and biomedical applications. Recent works include in-context learning for graph reasoning and blockchain-based data fusion systems. Scientific Awards include Huawei 2025 Challenges Spark Award ASE 2024 Distinguished Paper Award CHIP 2021 Best Paper Award CCKS 2018 Best English Paper Award Nanjing University Study Abroad Program Awardee (2013) IBM China Excellent Student (2008) Advising involves leading the Knowledge Fusion Group at Nanjing University, mentoring 18 current and former Ph.D. and Master's students. Professional services include editorial roles at Transactions on Graph Data and Knowledge and Big Data Research , plus committee memberships in CCF, CIPSC, and JSCS.