جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Min Peng is a Professor at Wuhan University's School of Computer Science. His research focuses on artificial intelligence, machine learning, natural language processing, and knowledge graphs. He has collaborated extensively with institutions like Hefei University of Technology and the University of Chinese Academy of Sciences. His work bridges theoretical advancements in AI with practical applications in finance, social media analysis, and network optimization. Recent contributions include neural-symbolic reasoning frameworks, contrastive learning for knowledge graphs, and financial benchmarking with large language models. Research interests emphasize scalable machine learning models for complex reasoning tasks, explainable AI, and domain-specific applications in finance and social networks. Over 100 publications span venues like WWW, ACL, and NeurIPS, highlighting interdisciplinary impact. Notable projects include SymAgent (neural-symbolic agent frameworks), PIXIU (financial LLM benchmark), and DTC (commonsense machine comprehension). Key technical trends include integrating large language models with structured data, temporal knowledge graph reasoning, and transfer learning across domains. His work often addresses real-world challenges in data efficiency, interpretability, and cross-domain scalability. Current efforts explore financial LLMs, agent-based reasoning systems, and multimodal applications. While no specific grants or awards are listed in the provided data, his prolific publication record indicates sustained research excellence. Collaboration networks include teams in computer science, electrical engineering, and finance disciplines.

Shunichi Ishiharaمشاهده پروفایل

استاد

• Forensic Voice/Text Comparison
• Computational Linguistics
• Intonational Modelling
• +۶ مورد دیگر

Shunichi Ishihara is a Professor at the School of Culture, History & Language, The Australian National University, where he leads research in forensic linguistics and computational linguistics. His work focuses on forensic text and voice comparison, authorship attribution, and Japanese linguistic studies. He holds qualifications including a PhD (ANU), MSc (Macquarie), MA (ANU), and BEd (Shizuoka). Research Interests: Forensic Voice/Text Comparison Computational Linguistics Intonational Modelling Japanese Language Processing Stylometric Analysis Research Trends: Recent work emphasizes likelihood ratio-based systems for authorship verification, fusion of acoustic and text features for forensic analysis, and applications of deep learning in text evidence evaluation. His studies often explore cross-lingual comparisons (e.g., Japanese, English, Vietnamese) and system validation methodologies. Grants & Projects: "Likelihood project on author recognition" (2024-2026) "Big Australian Speech Corpus" (2010-2015) Multiple forensic voice/text comparison initiatives Labs & Teams: Director of the Speech and Language Lab, collaborating on speech corpus development and forensic linguistic systems.

Seth Kaplanمشاهده پروفایل

استاد

• Industrial/Organizational Psychology
• Employee Well-being
• Emotions at Work
• +۴ مورد دیگر

Seth Kaplan is a Professor in the Department of Psychology at George Mason University. His research focuses on employee well-being, team effectiveness, virtual work, and occupational health through projects like NSF-funded emotion regulation interventions and Army Research Institute collaborations on affective forecasting. He directs the KA-Lab, studying team resilience, metaperceptions, and statistical methodologies. Recent publications analyze job boredom, cognitive reappraisal interventions, and personality measurement innovations. His book Crisis-Ready Teams (2024) synthesizes data from high-risk environments. Courses taught include Occupational Health, Organizational Change, Psychometrics, and Multivariate Statistics. Grants: National Science Foundation (Co-PI): Just-in-Time Adaptive Interventions for Emotion Regulation Army Research Institute (PI): Affective Forecasting Errors Recent Presentations: Personality and generative AI use at work (SIOP 2025) Work situation identification via NLP (SIOP 2025) Helicopter helping in teams (SIOP 2025)

Dinh Phungمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Generative AI
• +۸ مورد دیگر

Professor Dinh Phung is the Head of the Department of Data Science & AI at Monash University. His research focuses on machine learning, deep learning, generative AI, and robust AI systems. He has authored over 250 publications, with applications in NLP, computer vision, digital health, and cybersecurity. Phung holds a PhD and BSc(Hons) in Computer Science from Curtin University. He leads major projects like 'Can Machines Unlearn?' and 'Trustworthy Generative AI', funded by the Australian Research Council and the Department of Defence. Education: Doctor of Philosophy, Computer Science, Curtin University (2005) Bachelor of Science (Honours), Computer Science, Curtin University (2001) Research Interests: Machine learning, deep learning, and generative models Optimal transport and Bayesian methods Robust and trustworthy AI Applications in digital health, cybersecurity, and autism research Key Projects (2023–2029): Can Machines Unlearn? (2025–2029): Safety in AI Trustworthy Generative AI (2024–2026): Foundation models Robust Machine Learning via Optimal Transport (2023–2025) Awards and Grants: Australian Research Council grants for AI safety and robustness Department of Defence funding for robust learning systems Collaborations: Global partnerships in AI ethics, cybersecurity, and healthcare. Active advisory roles, including with the Victorian Parliamentary Library.

Spencer Caplanمشاهده پروفایل

استادیار

• Computational Linguistics
• Psycholinguistics
• Language Acquisition
• +۱۳ مورد دیگر

Spencer Caplan is an Assistant Professor in the Department of Linguistics at the CUNY Graduate Center. He directs the Psycholinguistics Lab, contributes to the Cognitive Science affiliation, and is the interim director of the master's program in computational linguistics through Fall 2025. Research Interests: His work integrates linguistics, computation, and cognition, focusing on language acquisition and processing. He develops computational and algorithmic models of language learning and mental representation, while also employing experimental methods such as eye-tracking, parsing, categorization, and perceptual learning. His research applies quantitative, corpus, and statistical techniques to address questions in theoretical linguistics. Secondary interests include Chinese languages, cognitive psychology (attention, memory, learning), formal language theory, NLP, phonology, and software engineering. Publication Trends: His recent publications appear in high-impact journals like PLOS One , Psychological Science , Cognition , PNAS , and Glossa . The work spans computational modeling of language learning, experimental investigations of linguistic computation, and corpus-based syntactic analysis, reflecting a strong interdisciplinary approach combining theoretical insight with empirical and quantitative rigor. Scientific Affiliations and Mentors: Ph.D. Advisor Team: Charles Yang, John Trueswell, Mitch Marcus Undergraduate Mentor: Eugene Charniak Collaborators: Deniz Beser, Kajsa Djärv, Neal Fox, Doug Guilbeault, Alon Hafri, Jordan Kodner, Eric Mandelbaum, Griffin Pion, Jake Quilty-Dunn, Katie Schuler, Elliot Schwartz, Hongzhi Xu, Chen Yu Advising and Grants: While specific advising and grant records are not detailed in the text, his leadership roles (lab director, interim program director) and active publication record suggest significant involvement in mentoring students and securing research support. His GitHub repositories indicate ongoing, computationally intensive research projects. Labs and Teams: He leads the Psycholinguistics Lab at CUNY Graduate Center and has been affiliated with the Language Learning Lab at the University of Pennsylvania and the Developmental Intelligence Lab at the University of Texas at Austin.

• Speech Technology
• Machine Learning
• Signal Processing
• +۳ مورد دیگر

Shashi Kumar is a doctoral student in the Doctoral Program in Electrical Engineering (EDDEE) at École Polytechnique Fédérale de Lausanne (EPFL) , affiliated with the School of Engineering (STI) and the IDIAP Research Institute (LIDIAP) . He holds the role of Doctoral Assistant at LIDIAP, contributing to research in speech technology and machine learning. His work focuses on advancing automatic speech recognition (ASR), optimal transport frameworks, and variational autoencoders for speech enhancement and signal processing. Research interests include speech recognition systems , multimodal task unification , far-field speech processing , and machine learning applications in signal processing and computer vision. His publications highlight contributions to SLAM-ASR performance analysis, joint speaker change detection, and PCB defect classification using image segmentation techniques. Shashi's research is anchored at the IDIAP Research Institute , where he collaborates on projects involving deep learning, audio signal processing, and speech technology. While no awards or grants are explicitly listed, his work reflects active engagement in international challenges like the Interspeech DiCOVA competition.

• Computational Linguistics
• Machine Translation
• Text Simplification
• +۸ مورد دیگر

Sergiu Nisioi is an Associate Professor at the Faculty of Mathematics and Computer Science, University of Bucharest, with expertise in computational linguistics, machine translation, and text simplification. He bridges cognitive science with NLP through eye-tracking and EEG research, while also exploring sound art and digital autonomy via initiatives like HYPHA.ro. Current projects include PN-IV-P2-2.1-TE-2023-2007 (text complexity/readability), Legal Document Processing , and Europarl Dialectal Corpora Research spans computational psycholinguistics , LSTM-based translation models , and algorithmic composition for sound art His work integrates interdisciplinary methodologies, combining EEG signal processing for architecture data with the University of Architecture, and DSP for ecological projects at chlorophylla.live.

Kalina Bontchevaمشاهده پروفایل

پژوهشگر

• NLP for social media
• semantic search
• GATE infrastructure
• +۸ مورد دیگر

Kalina Bontcheva is a Senior Researcher in the Natural Language Processing Group within the Department of Computer Science at the University of Sheffield. She holds an EPSRC Career Acceleration Fellowship (working part-time since October 2015) focused on personalized summarization of social media content. Her research spans multiple EU-funded projects including PHEME (computing veracity of social media), TrendMiner, DecarboNet, and uComp, with significant contributions to the GATE (General Architecture for Text Engineering) open-source NLP infrastructure since 1999. Dr. Bontcheva's research interests focus on the intersection of natural language processing and social media analysis. Her work encompasses NLP for social media, semantic search, information extraction from social platforms, crowdsourcing of NLP corpora, collaborative text annotation, semantic technologies, and text mining and analytics. She has particular expertise in developing methods for personalized, abstractive multi-document summarization across different social media platforms, addressing the challenges of noisy, jargon-filled and dynamic content. Her interdisciplinary approach combines machine learning, semantic technologies, and social dimension analysis to create systems that adapt to individual users' information seeking goals. Analysis of her recent publications reveals a strong focus on social media processing challenges, with emphasis on Twitter analysis, temporal expression recognition, and handling noisy text. Her work consistently addresses the unique characteristics of social media content and develops specialized techniques for information extraction, sentiment analysis, and user geolocation within these platforms. The GATE framework serves as the foundation for much of her tool development, demonstrating her commitment to creating reusable, open-source NLP infrastructure. Her most significant award is the EPSRC Career Acceleration Fellowship, which supports her work on personalized social media summarization. This prestigious fellowship includes a substantial budget of £560k and involves collaborations with industry partners including The Press Association, British Telecom, and Fizzback. Dr. Bontcheva has led numerous major research projects throughout her career. She was Principal Investigator on three EU-funded projects (MUSING, TAO, and ServiceFinder) between 2006-2009, coordinating the TAO consortium with seven partner institutions. She currently leads the PHEME EU project and serves as PI for TrendMiner and DecarboNet European projects, while also contributing as Co-I on the uComp project. Her project portfolio demonstrates consistent success in securing competitive research funding across multiple domains within NLP and semantic technologies. She works within the Natural Language Processing Group at the University of Sheffield, which has been central to the development of the GATE infrastructure. Her work connects with various initiatives including the GATE Cloud platform and the TextVRE project for e-humanities textual studies. She has established collaborations with organizations including the Press Association, British Telecom, Oxford Internet Institute, and Sheffield's Department of Journalism to ensure her research addresses real-world needs across different user communities.

Dimitris Papanikolaouمشاهده پروفایل

استاد

• Asset Pricing
• Macroeconomics
• Innovation
• +۲ مورد دیگر

Dimitris Papanikolaou serves as the John L. & Helen Kellogg Professor of Finance at Northwestern University's Kellogg School of Management and is a Research Associate of the National Bureau of Economic Research (NBER). His research critically examines the nexus between technological innovation and financial markets, with recent contributions spanning intangible capital measurement, innovation's impact on labor dynamics, executive markets, and financial frictions. Education: PhD in Financial Economics, Massachusetts Institute of Technology (2007) MS in Finance and Economics, London School of Economics (2001) BA in Economics and Finance, University of Piraeus (2000) Papanikolaou's research program centers on Asset Pricing , Macroeconomics , Innovation , Economic Growth , and Intangible Capital . He pioneers methodologies for quantifying innovation through textual analysis of patents while investigating how technological advances reshape financial markets and labor outcomes. His work reveals critical insights into human capital displacement, the economic value of intangibles, and the causal mechanisms linking financial constraints to innovation efficiency, employing rigorous empirical designs including natural experiments and administrative datasets. Analysis of his recent publications shows a pronounced interdisciplinary trajectory, merging finance, labor economics, and innovation studies through cutting-edge techniques like NLP and big data analytics. Key thematic clusters include AI's labor market effects (examining task exposure and wage dynamics), creative destruction measurement (using text-based innovation displacement metrics), and intangible capital's macroeconomic role (exploring firm scope, concentration, and growth implications). These works consistently establish causal relationships through exogenous variation in innovation shocks and patent data. Scientific recognition includes: Kellogg Research Mentorship Award Best Paper Award, London Business School Finance Symposium (2018) Best Paper Award, Red Rock Finance Conference (2017) Crowell Memorial Prize (second place), Panagora Asset Management Amundi Smith Breeden Prize, American Finance Association (2014 and 2013) Roger F. Murray Prize (second place), The Q Group (2011) As Co-Editor of the Journal of Financial Economics and former editorial board member for Management Science, the Journal of Finance, and the Review of Financial Studies, Papanikolaou shapes scholarly discourse in finance. His mentorship excellence—recognized by the Kellogg Research Mentorship Award—demonstrates commitment to developing next-generation scholars, though specific grant details remain undisclosed in available materials. Collaborative research networks with leading economists like Kogan, Schmidt, and Eisfeldt drive his high-impact publications. Papanikolaou's research ecosystem involves extensive collaboration with interdisciplinary teams leveraging computational methods for innovation measurement. Current projects focus on AI-labor market interactions and intangible capital dynamics, often utilizing proprietary patent datasets and administrative worker records to generate policy-relevant insights about technological disruption and market efficiency.

• Bioinformatics and Computational Biology
• Machine Learning
• Data Science
• +۳ مورد دیگر

Fardina Alam is a Lecturer in the Department of Computer Science at the University of Maryland, College Park. She holds a Ph.D. from George Mason University (2023), where she specialized in Structural Bioinformatics and Machine Learning. Her academic rank reflects UMD's Professional Track Faculty, equivalent to an Assistant Professor of Teaching. Dr. Alam's research focuses on deep learning applications in protein structure prediction, generative AI, and ethical data practices. She has contributed to projects funded by an NSF FET Grant (#1900061) and published in journals like Biomolecules. Awards include the 2023 Outstanding Dissertation Award and 2022 Editor's Choice Article recognition. Education: Ph.D. (Computer Science, George Mason, 2023); M.S. (Computer Science, George Mason, 2019); B.S. (Computer Science and Engineering, Military Institute of Science and Technology, Bangladesh, 2013). Her teaching emphasizes data science ethics and interdisciplinary applications, including a new course for UMD's Data Science minor. Professional roles include Associate Guest Editor at Bioinformatics Advances (2024), Faculty Advisor to the Bangladeshi Graduate Student Association, and Program Co-Chair for the ACM-BCB Computational Structural Bioinformatics Workshop (2023). Research Interests: Structural Bioinformatics, Generative AI, Responsible AI Ethics, Deep Learning, Machine Learning, and Data Science. Her work bridges computational biology and AI, with a focus on equitable data practices and protein structure analysis. Recent projects address challenges in generating physically-realistic protein structures and improving question-answering systems through equitable data strategies. Awards: Recipient of the 2023 Outstanding Dissertation Award (George Mason University), 2023 Best Paper Award, and 2022 Biomolecules Editor's Choice Article. These accolades highlight contributions to protein structure prediction and bioinformatics methodology. Advising & Grants: NSF FET Grant #1900061 supported her computational biology research. She advises the Nobanno graduate student group and chairs workshops in her field. Her teaching and research aim to promote inclusivity, particularly for underrepresented groups in STEM. Labs/Teams: Active contributor to the Computational Biology Lab at George Mason University and collaborates with UMD's interdisciplinary teams on data ethics and AI applications. Her work aligns with UMD's vision for data-driven education and innovation.

• Computational Linguistics
• Natural Language Processing
• Artificial Intelligence
• +۵ مورد دیگر

Massimo Poesio is a Professor of Computational Linguistics at Queen Mary University of London and a full Professor of Natural Language Understanding at Utrecht University. He leads the ARCIDUCA project (EPSRC-funded) on conversational agents and the Dealing with Meaning Variation project (NWO-funded) exploring NLP applications. His research focuses on anaphora resolution, games-with-a-purpose, and applying NLP to combat deception and misinformation. He is a Fellow of the Alan Turing Institute and supervises students in PhD programs like IGGI and Health Data in Practice. Education: Previous affiliations include University of Essex and University of Trento (founded CLIC Lab). Research Projects: DALI (ERC), SENSEI (EU), AnaWiki (EPSRC), and LiveMemories (Provincia di Trento). His work combines formal semantics with empirical methods, including brain data and corpus analysis. He has developed tools like Phrase Detectives and BART for anaphora resolution, and co-founded the Lingotowns platform for language learning.

Marco Giacaloneمشاهده پروفایل

استاد پژوهشی

• Digitalisation of Legal Processes
• Private International Law
• Online Dispute Resolution (ODR)
• +۴ مورد دیگر

Marco Giacalone is a Research Professor at the Private and Economic Law Department (PREC) of the Vrije Universiteit Brussel (VUB), where he also serves as Co-Director of the Research Group on Digitalisation and Access to Justice (DIKE). He holds adjunct professorships at VUB (2018-2019) and has been a postdoctoral researcher at the Brussels Research Institute on Development, Governance, and Empowerment. His work focuses on the intersection of digital technologies and legal processes, particularly in Private International Law, ODR, and ADR. **Education**: PhD in Law (Doctor Europaeus, 2016) from VUB and University of Naples Federico II, specializing in Dispute Resolution and emerging IT realities. **Research Interests**: Digital transformation of legal systems, blockchain applications in justice, AI-driven conflict resolution, cross-border dispute mechanisms, and equitable algorithmic systems. His projects include EU-funded initiatives like CREA3 (Equitative Algorithms) and IDEA (Access to Justice tools). **Grants & Projects**: Over 10 projects funded by the EU, including EU683 (Equitative Algorithms), EU681 (IDEA), and EU676 (Digitalising Small Claims). Active in consortiums like IPSU 2022 (Digital Ius Knowledge Empowerment) and OZR4193 (VUB-UNIPARTHENOPE PhD collaboration). **Awards**: 2019 Best Paper Award and Marie Skłodowska-Curie Fellowship (2020). Extensive speaking engagements on topics like AI in judiciary, blockchain legal frameworks, and digital justice. **Labs/Teams**: Leads the DIKE Research Group and collaborates with the Brussels Research Institute. Involved in developing tools like Prodigit (digital tax justice) and CREA’s cloud-based decision support systems.

Grzegorz Chrupałaمشاهده پروفایل

دانشیار

• Computational Linguistics
• Artificial Intelligence
• Cognitive Science
• +۳ مورد دیگر

Grzegorz Chrupała is an Associate Professor at the Department of Cognitive Science and Artificial Intelligence , Tilburg University, where he leads research in computational approaches to multimodal communication. Previously, he was a postdoctoral researcher at Saarland University's Spoken Language Systems group and earned his PhD from Dublin City University's School of Computing. His research bridges biological and artificial computation , focusing on enabling machines to learn language from multimodal data (speech, gestures, visual-auditory stimuli) as children do naturally. This involves developing and interpreting deep learning architectures, analyzing emergent representations, and advancing speech technology for under-resourced languages. Key themes include Visually grounded speech modeling Feature attribution and model interpretability Human-inspired learning paradigms BlackboxNLP workshop leadership His recent publications examine speech model reliability , lexical tone encoding , and contextual dependencies in NLP systems. He mentors a team of PhD candidates and alumni working on topics like user-centric interpretability, bioacoustics, and disentangled speech representations. He also serves on the board of the Dutch Open Speech Technology Foundation, chairs Interspeech 2025 tutorials, and contributes as an Action Editor for TACL.

Marten van Schijndelمشاهده پروفایل

استادیار

• Computational Linguistics
• Psycholinguistics
• Neural Network Language Models
• +۳ مورد دیگر

Marten van Schijndel is an Assistant Professor of Computational Linguistics at Cornell University, affiliated with the Cognitive Science Program and the Department of Linguistics within the College of Arts and Sciences. His research focuses on incremental language processing, comparing human linguistic behavior with computational models like neural networks. He organizes the Computational Psycholinguistic Discussions (C.Psyd) and collaborates with the Cornell Computational Linguistics Lab (CLab) and Cornell NLP Group. Van Schijndel’s work bridges computational modeling and psycholinguistics, probing how humans and models process language incrementally, particularly through methodologies like fMRI and behavioral experiments. His research interests include computational modeling of language processing, neural network representations, and the interplay between syntax, semantics, and discourse. Notably, he investigates ungrounded learning in neural models, exploring what linguistic aspects emerge from statistical patterns alone. Recent contributions include studies on semantic change quantification, phonotactic effects in context, and discourse predictability in Hindi word order. Van Schijndel teaches courses such as Computational Linguistics II and leads seminars on natural language processing. He actively advises students like John R. Starr, Ashlyn Winship, and Zander Lynch, whose work spans experimental paradigms for semantic spaces, implicit arguments in sentence processing, and semantic role localization. His recent activities include co-organizing workshops on event representation (PEER 2025) and presenting at the University of Rochester on event construction from language. Media highlights include research on tracking anti-immigrant hate speech and semantic evolution in French texts.

• Natural Language Processing
• Computational Linguistics
• Language Models
• +۳ مورد دیگر

Kentaro Inui is a distinguished researcher at Tohoku University , specializing in Natural Language Processing , Computational Linguistics , and Machine Learning . His work focuses on advancing language model behavior through rigorous empirical analysis, including mechanisms for detokenization , entity identification , and numerical reasoning . Inui has pioneered methods to rectify spurious beliefs in LLMs via unlearning techniques and explored the dynamics of reasoning strategies in neural models. His research addresses chat translation quality through metrics like MQM-Chat and investigates repetition neurons responsible for text generation patterns. Inui also contributes to argumentation analysis with annotation frameworks like LPAttack and develops resources such as COPA-SSE for commonsense reasoning. His work on universal graph-based relation extraction and cross-stitching architectures has established new benchmarks in NLP task performance. Inui's publications span top-tier conferences including ACL , EMNLP , and LREC , often involving collaborations with researchers like Benjamin Heinzerling and Jun Suzuki. His methodological innovations in semi-structured explanation generation , position embedding (e.g., SHAPE), and zero pronoun resolution demonstrate his focus on both theoretical and practical NLP challenges. While no direct awards or student mentorship data appear in the provided corpus, his extensive publication record (over 20 papers between 2021-2025) underscores significant contributions to NLP education tools , knowledge base integration , and dialogue system consistency . Current projects like ReCall mechanisms and numerical property encoding directions highlight his ongoing impact on model interpretability and reasoning accuracy.