جست‌وجوی استاد و پژوهشگر

Nianwen Xueمشاهده پروفایل

استاد

• Machine Learning
• Natural Language Processing
• Chinese language processing
• +۴ مورد دیگر

Nianwen Xue is a Professor in the Computer Science Department and Language & Linguistics Program at Brandeis University, leading the Chinese Language Processing Group. He holds affiliations with the Michtom School of Computer Science, Benjamin and Mae Volen National Center for Complex Systems, and serves on editorial boards of Computational Linguistics and Lingua Sinica. His research focuses on computational linguistics, machine learning, and cross-linguistic meaning representations. He previously held roles at the University of Colorado and University of Pennsylvania. Education: Ph.D. in Linguistics from University of Delaware, M.A. and B.A. in Linguistics from Nankai University. Research projects include the Chinese Treebank, OntoNotes, and initiatives funded by NSF, DARPA, and IARPA. Teaching includes courses on Natural Language Systems and Computational Corpus Linguistics. He has organized major NLP conferences like CoNLL Shared Tasks and DMR workshops. Research interests emphasize semantic parsing, discourse analysis, and computational social science, with over 100 publications in top venues like ACL, EMNLP, and Computational Linguistics. His work bridges theoretical linguistics and applied NLP, advancing cross-lingual resources and formal meaning representations.

• Computational Social Science
• Natural Language Processing
• Human-Computer Interaction
• +۳ مورد دیگر

Cristian Danescu-Niculescu-Mizil is an Associate Professor in the Department of Computer Science at Cornell University's College of Engineering, where he leads research at the intersection of computational social science and human-computer interaction. His work examines how language shapes and reflects social dynamics in digital environments, with applications for platform design and community health. His research focuses on computational social science , natural language processing , and human-computer interaction , particularly analyzing online discourse evolution, social influence mechanisms, and community dynamics. Key methodologies include large-scale data analysis, machine learning, and longitudinal modeling of language behavior across platforms like Reddit, Twitter, and Wikipedia. Analysis of his recent publications reveals strong trends in cross-platform misinformation tracking , AI-driven content moderation , and linguistic adaptation in support communities . His work consistently bridges technical NLP advances with sociological insights, emphasizing ethical implications and real-world interventions for healthier online ecosystems. Scientific recognition includes: NSF CAREER Award for foundational work in computational social science Multiple industry research awards from Google and Facebook ACM SIGCHI Best Paper Award for community health research He actively mentors doctoral students in computational social science, with several advisees now faculty at top institutions. His research is supported by NSF grants focused on social dynamics modeling and NLP for community health, often collaborating with social scientists and industry partners. Current projects investigate AI moderation systems and longitudinal language evolution in online communities through the Digital Discourse Lab .

Anja Feldmannمشاهده پروفایل

استاد

• Internet Measurement
• Network Performance Debugging
• Software-Defined Networking
• +۷ مورد دیگر

Anja Feldmann is Director at the Max Planck Institute for Informatics in Saarbrücken and Professor of Internet Network Architectures at Technische Universität Berlin (since 2006). Previously she held a full professorship at Technische Universität München (2002–2006) and conducted research at AT&T Labs Research , Saarland University , and Carnegie Mellon University , where she earned her Ph.D. in 1995. Education Ph.D. in Computer Science, Carnegie Mellon University, 1995 M.Sc. in Computer Science, Carnegie Mellon University, 1991 Diplom in Computer Science, Universität Paderborn, 1990 Research Interests Anja Feldmann’s research centers on measurement-driven understanding of the Internet. She tackles challenges such as software-defined networking , cloud-network interactions , performance debugging , and traffic characterization . A growing focus is the privacy and security of networked systems, evidenced by recent studies on online tracking, DNS security, and disinformation ecosystems. Her group designs scalable measurement platforms that combine passive and active monitoring , programmable data planes , and machine-learning analytics to dissect phenomena ranging from terabit-scale traffic to covert tracking on illegal streaming sites. Recent Publication Themes The 2021-2025 publications reveal a methodological evolution toward large-scale, longitudinal measurement . Topics include: Impact of global events (COVID-19, CrowdStrike outage) on Internet traffic Cross-country tracking ecosystems and privacy leaks DNS root and routing plane stability and security ML-driven real-time monitoring at terabit speeds Disinformation campaigns on encrypted messaging platforms Scientific Awards Gottfried Wilhelm Leibniz Prize (2011) – Germany’s highest research honor Berliner Wissenschaftspreis (2011) Elected Member of the German National Academy of Sciences Leopoldina (2009) Advising & Grants While individual student names are not listed, Prof. Feldmann leads a vibrant team at MPI-INF’s Internet Architecture department. She has supervised numerous doctoral candidates and post-doctoral researchers whose work is reflected in the co-authored papers. Funding sources include the German Research Foundation (DFG) via the Leibniz Prize and EU Horizon projects, although explicit grant numbers are not provided in the source material. Labs & Teams She heads the Internet Architecture department at MPI-INF, located at the Saarland Informatics Campus . The department operates state-of-the-art measurement infrastructure—including programmable switches, honeynets, and global vantage points—to support empirical network science.

Mark Kramerمشاهده پروفایل

استاد

• Neuroscience
• Computational Neuroscience
• Mathematical Modeling of Neural Activity
• +۵ مورد دیگر

Mark Kramer is a Professor in the Department of Mathematics & Statistics at Boston University. He belongs to the Applied Mathematics research group, focusing on mathematical, statistical, and machine learning approaches to characterize brain activity. His work bridges data-driven neuroscience with computational methods, exploring topics like biophysical models of neurons, field models of neural populations in epilepsy, and theoretical questions about brain rhythms. His research interests include: Biophysical modeling of single-neuron dynamics Neural population activity in pathological states Machine learning for detecting abnormal brain rhythms Analysis of cross-frequency coupling and coherence Kramer has developed educational resources like Case Studies in Neural Data Analysis using both MATLAB and Python. These materials teach practical data analysis techniques for spike trains and field data, emphasizing hands-on implementation over theoretical mathematics. He has received funding from NIH and NSF for computational neuroscience projects. His recent publications focus on epilepsy research, sleep spindle analysis, and neural signal processing. The work spans from developing statistical frameworks to understanding network dynamics in seizure termination and exploring phase consistency in neural data. Notably, his coherence studies revealed non-intuitive coupling patterns between brain regions, demonstrating that low-amplitude rhythms can be more informative than dominant ones.

• Machine Learning
• Data Science
• Social Sciences
• +۷ مورد دیگر

Prof. Dr. Markus Strohmaier holds the Chair of Data Science in Economics and Social Sciences at the University of Mannheim's Business School. His research focuses on applying machine learning and data science to understand socio-economic systems and human behavior, leveraging text, relational, and emerging data types. He teaches graduate courses and supervises theses in these areas. Research interests include algorithmic fairness, behavioral analysis via computational methods, and the societal implications of AI. Notable work addresses demographic representativeness in large language models, bias mitigation in rankings, and gender gaps in blockchain adoption. His team is located at L15, 3rd floor – Room 307 in Mannheim, adjacent to the central station. Collaborations span interdisciplinary topics like social media analysis, network dynamics, and computational social science. Key contributions include developing benchmarks for neural network similarity (Resi) and frameworks for measuring algorithmic fairness perceptions (FairCeptron). Research often bridges technical innovation with societal impact, addressing ethical challenges in AI and data-driven decision-making.

Johan Svenssonمشاهده پروفایل

دانشیار

• Biostatistics
• Oncology
• Surgical Outcomes
• +۳ مورد دیگر

Johan Svensson is an Associate Professor at Umeå School of Business, Economics and Statistics (USBE) at Umeå University, specializing in statistical methodology applied to medical research. His work bridges biostatistics with clinical outcomes, focusing on oncology, surgical outcomes, and public health epidemiology. Collaborations span multiple disciplines including cancer research, vascular medicine, and surgical innovation. His research interests emphasize the application of statistical models to clinical problems, such as prognostic biomarker identification in breast and bladder cancers, surgical technique optimization for rectal cancer, and cardiovascular effects of sleep disorders. He has published widely in journals like Sleep Medicine , Clinical Breast Cancer , and Pancreatology . Recent studies include analyzing postoperative fatality windows in colorectal cancer surgery and the role of collagen types in tumor proliferation. His work often employs randomized controlled trials (RCTs) and multicenter cohort studies to evaluate surgical techniques and treatment efficacy. Notably, Svensson has contributed to understanding the impact of stoma use in rectal cancer management and thromboembolism risks in bladder cancer patients. His interdisciplinary approach integrates statistical rigor with clinical practice to improve patient outcomes.

Maozhen Liمشاهده پروفایل

استاد

• High Performance Computing
• Big Data Analytics
• Artificial Intelligence
• +۱۳ مورد دیگر

Maozhen Li is a Professor in the Department of Electronic and Electrical Engineering at Brunel University of London , within the College of Engineering, Design and Physical Sciences . He serves as the Vice-Dean of the NCUT Transnational Education (TNE) programme, overseeing a joint school with North China University of Technology. He has been at Brunel since 2002, progressing from Lecturer to Professor in 2013. Education: PhD, Institute of Software, Chinese Academy of Sciences (1997) Postdoctoral Research, School of Computer Science and Informatics, Cardiff University (1999–2002) His primary research interests lie in high performance computing, big data analytics, and artificial intelligence, with applications in smart grids, smart manufacturing, and cybersecurity. He focuses on developing interpretable, robust, and lightweight AI models, including work in causal AI, parallel machine learning, and edge computing. His research integrates advanced techniques such as deep learning, reinforcement learning, and blockchain for real-world system optimization. An analysis of his recent publications reveals a strong and consistent research trajectory in AI-driven solutions for environmental monitoring (e.g., PM2.5 prediction), industrial defect detection, IoT security, and intelligent transportation. His work frequently combines deep learning with graph-based modeling and federated or reinforcement learning, emphasizing scalability, efficiency, and robustness in distributed and edge environments. Scientific Awards and Recognition: Fellow of the Institution of Engineering and Technology (IET) Fellow of the British Computer Society (BCS) Shortlisted for the Computing UK BIG DATA EXCELLENCE AWARDS 2018 in the category of Most Innovative Big Data Solution Maozhen Li has successfully supervised 25 PhD students and examined over 30 PhD theses externally. He has secured significant research funding from EPSRC, the European Union (Horizon 2020), Innovate UK, and the Royal Society , with projects including Z-BRE4K, IoRL, and TDX-ASSIST. He serves as an Associate Editor for journals such as the Journal of Cloud Computing and the International Journal of Grid and High Performance Computing . Research Groups and Teams: He is affiliated with the Intelligent Engineering Frameworks (IEF) research group at Brunel, contributing to collaborative efforts in AI, IoT, and smart systems. His leadership in transnational education also fosters international research collaboration between Brunel and Chinese institutions.

Kurt Keutzerمشاهده پروفایل

استاد

• Artificial Intelligence
• Deep Learning
• Computer Vision
• +۶ مورد دیگر

Kurt Keutzer is a Professor in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley, and a key member of the Berkeley AI Research Lab (BAIR). He holds a Ph.D. in Computer Science from Indiana University (1984) and was previously Chief Technical Officer at Synopsys, Inc. His research focuses on systems issues in deep learning, particularly for computer vision, speech recognition, NLP, and finance. He has published over 250 refereed articles and six books, and is a highly cited author in hardware and design automation. Keutzer has received multiple IEEE Fellowships, DAC awards, and best paper accolades at conferences like Embedded Vision Workshop and ICPP. Educations: 1984, PhD, Computer Science, Indiana University Kurt Keutzer's research interests span Artificial Intelligence , Computer Architecture , and Scientific Computing , with a focus on computational efficiency in AI systems. His work explores hardware-aware neural architecture search, domain adaptation, and quantization techniques to optimize models from edge to cloud. Recent publications highlight advancements in vision transformers , LLM inference efficiency , and autonomous driving . He also contributes to multimodal AI and self-supervised learning frameworks. Scientific Awards: Institute of Electrical & Electronics Engineers (IEEE) Fellow (1996) DAC's Most Influential Paper Award (2023) Top Ten Cited Author and Paper at DAC Best Paper Awards at Embedded Vision Workshop and ICPP Kurt Keutzer has advised numerous Ph.D. and Master’s students, including Forrest Iandola (co-founder of DeepScale), Sheng Shen, and Michael Murphy. His research teams have pioneered hardware-efficient deep learning solutions like SqueezeNet and FireCaffe. Current projects include optimizing large language models (LLMs) for edge deployment and advancing 3D reconstruction for autonomous vehicles. He is also involved in diffusion models , sparse attention mechanisms , and multi-agent coordination for complex tasks.

Heather B. Cunninghamمشاهده پروفایل

دانشیار

• Teacher preparation for marginalized students
• Urban education equity
• Culturally responsive teaching
• +۵ مورد دیگر

Dr. Heather B. Cunningham is an Associate Professor of Education in the College of Arts & Sciences at Chatham University , Pittsburgh, PA. With 13 years of K-12 teaching experience across Washington D.C., Pittsburgh, Honduras, and Malawi, she specializes in preparing educators to address systemic inequities. Ph.D. in Instruction and Learning, University of Pittsburgh (2015) M.A. in International Training and Education, American University (1997) B.A. in Social Sciences, Allegheny College (1993) Her research focuses on: Reimagining classroom management through equity and justice lenses Integrating global sustainability frameworks with social justice Culturally responsive teaching in urban contexts International service-learning applications Restorative discipline practices Recent publications demonstrate interdisciplinary approaches combining: Environmental education and social justice (2021) Restorative discipline models (2020) International student teaching methods (2019) Equity-focused classroom management (2018) Poverty-informed teaching strategies (2017) Dr. Cunningham teaches undergraduate and graduate teacher education courses, emphasizing the intersection of: Teacher cultural identity Effective instruction Family-community partnerships Social justice advocacy

Afra Alishahiمشاهده پروفایل

استاد

• Computational Linguistics
• Language Acquisition
• Neural Models of Language
• +۵ مورد دیگر

Afra Alishahi is a Full Professor at Tilburg University's Department of Cognitive Science and Artificial Intelligence within the Tilburg School of Humanities and Digital Sciences. Her research focuses on computational models of human language acquisition and grounded language learning, leveraging neural models to explore how language processing and acquisition occur. She has held roles including Assistant Professor at Tilburg University (since 2011) and Postdoctoral Fellow at Saarland University (2008-2011). Her work bridges computational linguistics, cognitive science, and artificial intelligence, with contributions to understanding language learning mechanisms through models that integrate visual, auditory, and linguistic data. Education: PhD (university unspecified), with prior academic roles in Iran and Germany. Awards: CoNLL 2017 Best Paper Award, 2023 Outstanding Paper Award, NWO Aspasia Grant (2015), and NWO Natural Artificial Intelligence Grant (2015). Her research has been supported by grants such as the Dutch National Research Agenda-funded project on interpreting deep learning models for text and sound. Research Interests: Grounded language learning, interaction effects in language acquisition, and neural model interpretability. Key areas include multi-modal learning (e.g., linking speech to visual scenes), computational modeling of child language learning, and probing neural networks for linguistic knowledge. She co-organized workshops like BlackboxNLP (2018-2020) and has authored over 60 publications, including influential works on phonology encoding in neural models and gender disambiguation in machine translation. Teaching: Courses include Cognitive Models of Language Learning , Computational Linguistics , and Language, Cognition & Computation . She advises master's theses and leads projects in data science and AI. Lab/Team: Leads research on computational modeling, collaboration with interdisciplinary teams (e.g., with Grzegorz Chrupała, Afsaneh Fazly), and involvement in initiatives like the Interpreting Deep Learning Models for Text and Sound project.

Sal Hagenمشاهده پروفایل

پژوهشگر

• Digital Humanities
• Social Media Analysis
• Online Communities
• +۵ مورد دیگر

Sal Hagen is a postdoctoral researcher at the Institute for Logic, Language and Computation (ILLC) at the University of Amsterdam, working within the Natural Language Processing & Digital Humanities group. His research focuses on the intersection of computational methods and cultural analysis, particularly examining online communities, political discourse, and meme culture. His educational background includes a Research Master's in Media Studies from the University of Amsterdam, for which he received the prestigious 2018 Internet Thesis Prize in the Internet & Social Sciences or Humanities category for his work "Here I Am, Praying to an Egyptian Frog: Exploring Political Fluidity on 4chan/pol/". He also won the Audience Award for his presentation at the ceremony. Hagen's research interests center on understanding the dynamics of online communities, with particular expertise in 4chan and related platforms. His work combines qualitative and quantitative approaches to analyze political discourse, far-right movements, and the evolution of memes across digital spaces. He examines how anonymity, platform architecture, and cultural context shape online interactions and political expression. His publication record demonstrates a consistent focus on tracing political movements through digital footprints, with particular attention to the interplay between platform-specific cultures and broader political trends. Hagen's work often bridges computational social science with cultural studies, creating methodologies that capture both the quantitative patterns and qualitative meanings of online discourse. Scientific Awards: 2018 Internet Thesis Prize (Internet & Social Sciences or Humanities category) Audience Award for presentation at Internet Thesis Prize ceremony Hagen has secured significant research funding, including an NWO PhD grant for humanities research running from 2019-2024. He has been involved in multiple research projects including the CAT4SMR project (2024), OILab (2017-present), and the ERC-funded ODYCCEUS project (2018-2019). As part of OILab (Online Intelligence Lab), Hagen contributes to research on online political subcultures, while his work on the CAT4SMR project focuses on stabilizing and developing tools for social media data collection and analysis. He is also the developer of the 4CAT Capture and Analysis Toolkit, which provides transparent and traceable methods for social media research.

• Machine Learning
• Domain Adaptation
• Transfer Learning
• +۳ مورد دیگر

Dr. Sinno Jialin Pan is a leading researcher in machine learning and artificial intelligence at Nanyang Technological University, Singapore. His work focuses on domain adaptation, sentiment analysis, and efficient neural network optimization. Key research areas: Machine Learning, Domain Adaptation, Reinforcement Learning, Sentiment Analysis Recent publications demonstrate expertise in time-series classification (2025) using hierarchical domain adaptation, LLM efficiency (2025) through expert pruning, and graph generation (2024) via spectral diffusion. His work spans both theoretical advancements and practical applications in neural architecture optimization and adversarial learning. Scientific contributions include: 2025: Virtual-label hierarchical domain adaptation 2024: Spectral diffusion for graph generation 2024: Multilingual jailbreak analysis in LLMs Current trends show increasing focus on large language model optimization and robust neural architectures , with applications in fault diagnosis, recommender systems, and misinformation detection.

João Magalhãesمشاهده پروفایل

استاد

• Vision and Language Understanding
• Multimodal Information Understanding
• Multimodal Conversational AI
• +۵ مورد دیگر

João Magalhães is a Full Professor in the Department of Computer Science at the Faculty of Science and Technology, Universidade NOVA de Lisboa, Portugal. He serves as Group Coordinator of the Multimodal Systems Group at the NOVA Laboratory for Informatics and Computer Science and leads the NOVASearch research group at FCT/UNL. His research focuses on vision and language information understanding, with particular emphasis on multimodal information understanding, multimodal conversational AI, multimedia search and summarization, temporal and memory models, and social media information quality. His work spans both theoretical foundations and practical applications across web, social media, and clinical domains. Analysis of his recent publications reveals a strong trajectory in multimodal conversational AI systems, with increasing sophistication in handling both voice and visual inputs. His research has evolved from foundational work in cross-modal embeddings to advanced large language models for dual-goal conversational settings, demonstrating consistent innovation in the field of multimodal understanding. 1st prize winner of the second Alexa TaskBot Challenge (2023) Award-winning solution in the Alexa TaskBot Challenge (2022) Best paper award at the Portuguese NLP conference (PROPOR) (2020) Best paper nominations at ACM conferences (2018) Professor Magalhães has advised numerous graduate students through the NOVASearch group and has secured substantial research funding through projects including Amazon Alexa TaskBot Challenge (2021-2023), iFetch (2020-2023), SmartyFlow (2017-2020), COGNITUS (2016-2019), GoLocal (2016-2020), QSearch (2012-2015), ImTV (2010-2013), and CS4SE (2010-2013). He actively serves the research community as ACM Multimedia 2026 Program Committee Chair and has held leadership roles in numerous conferences including ACM Multimedia 2022 General Chair and ECIR2020 General Chair. He leads the Multimodal Systems Group within the NOVA Laboratory for Informatics and Computer Science, where his team develops cutting-edge solutions for multimodal understanding with applications in conversational AI, multimedia search, and social media analysis.

Ole-Christoffer Granmoمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Tsetlin Machines
• +۷ مورد دیگر

Professor Ole-Christoffer Granmo is a distinguished academic at the University of Agder, Norway, where he serves as Professor in the Department of Information and Communication Technology. He is the Founding Director of the Centre for Artificial Intelligence Research (CAIR) at the University of Agder, leading cutting-edge research in artificial intelligence and machine learning. Dr. Granmo obtained his master's degree in 1999 and his PhD in 2004, both from the University of Oslo. His academic journey has been marked by significant contributions to the field of AI, most notably the creation of the Tsetlin machine in 2018, for which he received the AI research paper of the decade award from the Norwegian Artificial Intelligence Consortium (NORA) in 2022. Professor Granmo's research primarily focuses on logical and causal world modeling across multiple modalities including images, sound, and natural language. His work spans logical auto-encoding, convolution, regression, transformer architectures, and reinforcement learning, all with the overarching goal of creating ultra-low-power artificial general intelligence through transparent logical learning and reasoning. His publications reveal a strong emphasis on interpretable AI systems, hardware implementations, and applications across diverse domains including cybersecurity, healthcare, social media analysis, and bioinformatics. AI Research Paper of the Decade (2022) - Norwegian Artificial Intelligence Consortium (NORA) Eight paper awards in machine learning Professor Granmo has coordinated over seven research projects and mentored 55+ master's students and nine PhD students. His leadership extends to co-founding the Norwegian Artificial Intelligence Consortium (NORA) and establishing two companies: Anzyz Technologies AS and Tsense Intelligent Healthcare AS. As an advisor at Literal Labs, he actively bridges academic research with practical industry applications, demonstrating his commitment to translating theoretical innovations into real-world solutions that address complex challenges across multiple sectors.

• Child Development
• Parenting Practices
• Cross-Cultural Psychology
• +۵ مورد دیگر

Monika Abels is an Associate Professor at UiT The Arctic University of Norway , affiliated with the Child development research group. Her work focuses on cross-cultural child development, infant socialization, emotion regulation, and the impact of digital technology on parental responsiveness. Research Interests : Child development, cross-cultural parenting, emotion understanding, hunter-gatherer societies, and fieldwork ethics. Labs/Teams : Member of the Barns utvikling (Child Development) research group at UiT. Recent Research Trends : Her publications highlight comparative studies across Norway, Tanzania, India, and sub-Saharan Africa, examining infant sleep, parental phone use, triadic interactions, and cultural differences in hidden emotion understanding. Collaborative work with African communities (San, Hadza) explores regulatory and ethical fieldwork challenges. Email : m.abels@uit.no