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Ruihong HuangView profile

Associate Professor

• Natural Language Processing (NLP)
• Information Extraction
• Machine Learning
• +6 more

Ruihong Huang is an Associate Professor in the Department of Computer Science and Engineering at Texas A&M University, part of the College of Engineering. She holds a Ph.D. from the University of Utah (2014) and completed a postdoctoral fellowship at Stanford University. Her research focuses on Natural Language Processing (NLP) with emphasis on information extraction, event recognition, media bias analysis, and ethical AI applications. She teaches courses like Information Storage and Retrieval (CSCE 470), Natural Language Processing (CSCE 638), and has advised numerous PhD, Master’s, and undergraduate students. Key research contributions include work on event coreference resolution, discourse analysis, and detecting media bias through event relation graphs. Huang has developed benchmark datasets like UAL-Bench and EMONA, and her work spans applications in disaster management, fake news detection, and moral reasoning in LLMs. She is a recipient of the NSF CAREER Award and serves on program committees for top conferences like ACL and EMNLP. Her academic service includes roles as Area Chair for ACL 2024 and Senior Area Chair for EMNLP 2024. She maintains an active lab group focusing on NLP fundamentals and real-world applications, with projects involving propaganda identification, polarity calibration for opinion summarization, and multimodal dialog act classification. Huang has published over 80 papers in top-tier venues such as NAACL, EMNLP, ACL, and NeurIPS.

Herb LinView profile

Research Professor

• Cybersecurity Policy
• Information Warfare
• Nuclear Security
• +4 more

Dr. Herb Lin is the Hank J. Holland Fellow in Cyber Policy and Security at the Hoover Institution and Senior Research Scholar for Cyber Policy and Security at the Center for International Security and Cooperation (CISAC), both at Stanford University. He also serves as Chief Scientist, Emeritus for the Computer Science and Telecommunications Board of the National Academies, where he worked from 1990 to 2014, and as Adjunct Senior Research Scholar and Senior Fellow in Cybersecurity at Columbia University's Saltzman Institute for War and Peace Studies. Dr. Lin received his doctorate in physics from MIT, providing him with a strong technical foundation that informs his policy work at the intersection of technology and national security. His academic background bridges the gap between deep technical understanding and policy application in the cybersecurity domain. Dr. Lin's research focuses on the policy dimensions of cybersecurity and cyberspace, with particular emphasis on offensive cyber operations as instruments of national policy, information warfare, and influence operations affecting national security. His work addresses critical challenges at the intersection of emerging technologies like artificial intelligence, quantum computing, and their implications for nuclear security, election integrity, and democratic processes. He has been instrumental in analyzing how cyber capabilities are transforming traditional security paradigms and how policy frameworks must adapt to these changes. His extensive publication record reveals a consistent focus on the evolving relationship between cyber capabilities and national security strategy. Lin's work demonstrates increasing attention to the implications of artificial intelligence for warfare, the vulnerabilities created by quantum computing for current cryptographic systems, and the growing threat of cyber-enabled information warfare to democratic institutions. His research trajectory shows a progression from traditional cybersecurity issues toward the broader implications of digital technologies for national and international security. Member of Science and Security Board of Bulletin of the Atomic Scientists Served on President Obama's Commission on Enhancing National Cybersecurity (2016) Regular congressional testimony on cybersecurity matters Contributor to Stanford Emerging Technology Review As a mentor and collaborator, Dr. Lin works extensively with policymakers, military officials, and technology experts to translate complex cybersecurity concepts into actionable policy recommendations. His approach emphasizes practical solutions to emerging threats while recognizing the technical limitations and policy constraints that shape real-world security outcomes. Dr. Lin is associated with multiple research initiatives including Stanford's Center for International Security and Cooperation and the Stanford Emerging Technology Review, which aim to connect technological advances with policy considerations for national security decision-makers.

• Settlement Archaeology
• Hittite Studies
• Ceramics
• +6 more

Elif BAŞTÜRK is an Associate Professor at Kırşehir Ahi Evran University's Faculty of Arts and Sciences, Department of Archaeology, specializing in Settlement Archaeology, Hittite studies, and Early Bronze Age ceramics. With a PhD from Ege University (2014), she has led excavations at Tanır Yassıhöyük since 2022 and participated in major projects across Anatolia. Education: PhD in Protohistory and Near Eastern Archaeology, Ege University (2014) MA in Protohistory and Near Eastern Archaeology, Ege University (2006) BA in Archaeology, Ege University (2003) Research Focus: Her work examines archaeological transitions in Anatolia and the Near East, particularly ceramic traditions, settlement patterns, and Assyrian influence during the Bronze and Iron Ages. She employs statistical and technological methods to analyze material culture. Projects: Notable projects include the Tanır Yassıhöyük excavations (2021–present), Assyrian trade influence studies in Afşin-Elbistan plain, and documentation of Early Bronze Age ceramics in Siirt Başur Höyük. She has also led regional surveys threatened by the Ilısu Dam. Publications: Recent outputs include 2025 studies on Iron Age ceramics and Assyrian mill technology, alongside excavation reports from 2016–2024. Her work bridges field archaeology with material analysis, contributing to understanding cultural dynamics in ancient Anatolia.

Thomas HackerView profile

Professor

• Cloud Computing
• High-Performance Computing
• Operating Systems
• +3 more

Thomas Hacker is a Professor in the Department of Computer and Information Technology at Purdue Polytechnic Institute, Purdue University. His research focuses on cloud computing, high-performance computing, operating systems, computer networking, and cyber infrastructure . He holds a Ph.D. and M.S. in Computer Science & Engineering from the University of Michigan, along with dual B.S. degrees in Computer Science and Physics from Oakland University. Education: PhD (Computer Science & Engineering), University of Michigan (2004) MS (Computer Science & Engineering), University of Michigan (1993) BS (Computer Science, Mathematics Minor), Oakland University (1989) BS (Physics), Oakland University (1989) Dr. Hacker's research spans cloud and grid computing, operating systems, and distributed systems , with applications in earthquake engineering data systems and AI-driven infrastructure analysis. His recent work explores extended layer 2 networking for bare-metal provisioning ( 2023 IEEE Cloud Summit ) and machine-supported bridge inspection using artificial intelligence ( Transportation Research Record, 2023 ). Notable scientific contributions include 15+ publications on topics like cyberinfrastructure for earthquake engineering, container-based virtualization, and data-intensive systems. His work has been recognized with awards such as the NSF CAREER Award (2010) and multiple Purdue Seed for Success Awards . Key Scientific Awards: NSF CAREER Award (2010) Purdue Seed for Success Awards (2008-2013) ASEE Information Systems Division Best Paper Award (2012) College of Technology Outstanding Faculty in Discovery Award (2010) He has held leadership roles at Purdue, including Department Head (2018-2021) and Interim Department Head (2011-2016) . His career spans academic positions at Indiana University, University of Michigan, and industry roles at Storage Technology Corporation.

• Computer Vision
• Deep Learning
• Reinforcement Learning
• +7 more

Dr. Chandranath Adak is an Assistant Professor at the Department of Computer Science and Engineering, Indian Institute of Technology Patna (IIT Patna), and concurrently serves as a Visiting Fellow at the School of Computer Science, University of Technology Sydney (UTS), Australia. He holds a Ph.D. in Analytics from UTS (2019) and previously served as an Assistant Professor at Indian Institute of Information Technology Lucknow (IIITL) and the Centre for Data Science at JIS Institute of Advanced Studies, Kolkata. Education: Ph.D. (Analytics), University of Technology Sydney (2019) M.Tech., Computer Science and Engineering, University of Kalyani (2014) B.Tech., Computer Science and Engineering, West Bengal University of Technology (2012) Research Interests: His work spans Computer Vision, Deep Learning, Reinforcement Learning, Document Image Analysis, and AI-driven solutions for healthcare, forensics, and industrial automation. He has pioneered methods in biomarker detection using electrochemical sensors combined with ML models, handwriting analysis for educational and forensic applications, and anomaly detection in industrial systems. His research bridges theoretical advances with real-world applications, such as medical diagnostics and quality control systems. Publications: His recent work includes innovations in biosensor-based medical diagnostics, handwriting evaluation systems, and transformer networks for historical document analysis. These contributions reflect a focus on interdisciplinary applications of AI across healthcare, cultural heritage preservation, and industrial automation. Awards: Start-up Research Grant, SERB, India (2022) Dr. Kalam Doctoral Scholarship, UTS (2018) IEEE CIS Graduate Student Research Grant (2017) Senior Member, IEEE (2024) Teaching & Supervision: Taught courses at UTS including 'Introduction to Data Analytics' and supervised research in machine learning and computer vision. His mentorship emphasizes hands-on experience with AI tools and real-world problem-solving. Labs & Teams: Engaged in collaborative projects at UTS's CIBCI Centre and Griffith University's IIIS, focusing on computational intelligence and sensor-driven AI systems.

William Yang WangView profile

Professor

• Artificial Intelligence
• Natural Language Processing
• Machine Learning
• +9 more

William Yang Wang serves as the Mellichamp Professor of Artificial Intelligence at the University of California, Santa Barbara (2019-present). He directs the UCSB Center for Responsible Machine Learning, the Mind and Machine Intelligence Initiative, and the UCSB NLP Group. His research focuses on theoretical foundations and practical algorithms for AI, particularly in NLP, LLMs, and neuro-symbolic reasoning. PhD in Computer Science from Carnegie Mellon University Active in AI theory and applications (2016-present) Research interests span multiple AI domains, with special emphasis on NLP and responsible machine learning. He has pioneered datasets like HybridQA, TabFact, and VaTeX, enabling advancements in multi-hop QA, fact verification, and video-language tasks. His work combines statistical relational learning with modern deep learning paradigms. Recent publications center around multimodal reasoning, knowledge graph integration, and responsible AI development. He has received numerous accolades including the IEEE SPS Pierre-Simon Laplace Award (2024) and NSF CAREER Award (2021). Karen Sparck Jones Award (2022) DARPA Young Faculty Award (2018) IBM Faculty Award Mentoring 15+ PhD and postdoc researchers who now hold positions at Microsoft Research, Amazon, Meta GenAI, and academic institutions like Arizona and Rutgers. His lab maintains active collaborations with industry partners through initiatives like ChipAgents.ai, which he founded as CEO.

Antti HonkelaView profile

Professor

• Privacy-preserving machine learning
• Differential Privacy
• Bayesian Machine Learning
• +3 more

Antti Honkela is a Professor of Data Science at the University of Helsinki's Department of Computer Science, within the Faculty of Science. He also serves as the Coordinating Professor for the Privacy-preserving and Secure AI Research Programme at the Finnish Center for Artificial Intelligence (FCAI), and as Deputy Director of the Master's Programme in Data Science. His roles include membership in the Health and Social Data Permit Authority (Findata) and as an Action Editor for Transactions on Machine Learning Research. Honkela's research focuses on privacy-preserving machine learning, differential privacy, Bayesian methods, and their applications in computational biology and healthcare. He leads projects such as the European Lighthouse in Secure and Safe AI (ELSA) and the Data Literacy for Responsible Decision-making initiative. His work emphasizes developing robust frameworks for privacy-aware AI, including differentially private synthetic data and federated learning. Honkela has advised numerous PhD and Master's students, and his contributions span theoretical advancements and practical implementations, such as the D3p Python package for differentially private probabilistic programming. Key contributions include advancements in Bayesian inference from synthetic data, privacy accounting mechanisms, and computational methods for genomic epidemiology. His interdisciplinary approach bridges machine learning, statistics, and healthcare, addressing challenges in data privacy and secure AI deployment.

• Natural Language Processing
• Knowledge Graphs
• Machine Learning
• +5 more

Dr. Sirui Li is a Lecturer at Murdoch University's School of Information Technology within the College of Science, Technology, Engineering and Mathematics. Her research focuses on Artificial Intelligence, Natural Language Processing (NLP), Machine Learning, Knowledge Graphs, Data Analysis, Temporal Data, and Multi-modal Models, with applications in medicine, agriculture, and mining. She collaborates with industry partners like BHP and has published in journals such as Food Chemistry and Knowledge and Information Systems , as well as conferences like ICSME and IJCNN. Education: Bachelor of Advanced Computing (Honours) in Computer Science at Australian National University Master of Computing (Specialising in AI) at ANU Ph.D. in Information Technology (AI) at Murdoch University Research interests include interdisciplinary applications of AI, such as clinical coding privacy solutions, disease spread modeling, and drug repurposing for pandemics. Her work emphasizes practical industry integration, demonstrated through awards like the 2024 EMNLP Best Demo Award and the 2023 Iron Ore Circuit Hackathon innovation prize. Professional roles include IEEE Western Australia Section committee membership, conference chair positions, and peer review for top journals. She actively mentors students pursuing Honours, Master's, or PhD projects in her areas of expertise.

Farnoush Banaei-KashaniView profile

Associate Professor

• Data Management
• Big Data
• Decision Support Systems
• +4 more

Farnoush Banaei-Kashani is an Associate Professor (Tenured) in the Department of Computer Science and Engineering at the University of Colorado Denver. She also holds an Adjunct Associate Professor position in the Department of Mathematical and Statistical Sciences. As the founder and director of the Big Data Management and Mining Lab (BDLab), she leads multiple GAANN Fellowship Programs, including BDSE (Big Data Science and Engineering), DDC (Data-Driven Cybersecurity), and II (Infrastructure Informatics). She directs the 'Data Science in Biomedicine' MS Track and focuses on data-driven decision systems (DDSs), integrating machine learning and big data analytics into healthcare, energy, transportation, and environmental applications. Education: Details not explicitly provided in the text. Her research spans data management cycles for DDSs, addressing challenges like big data volume, velocity, and variety. Key projects include iWatch (crime surveillance), POCM (mobility monitoring), and GeoSIM (urban texture documentation). She teaches courses such as Machine Learning Systems, Big Data Science, and Data Mining. Publications highlight advancements in sea ice classification, federated learning, proteomic networks, and privacy-preserving AI. Her work is funded by NSF, NIH, DOT, and industry partners like Google and IBM. She has advised numerous students and contributes to academic leadership as editor, conference chair (ACM SIGSPATIAL 2018/2019), and program committee member for venues like SIGMOD and KDD.

Peter N. BelhumeurView profile

Professor

• Computer Vision
• Machine Learning
• Species Identification Technology
• +3 more

Peter N. Belhumeur is a Professor in the Department of Computer Science at Columbia University and Director of the Laboratory for the Study of Visual Appearance (VAP LAB). He holds a Sc.B. from Brown University and a Ph.D. from Harvard University, followed by a postdoctoral fellowship at the University of Cambridge. His career includes roles at Yale University before joining Columbia in 2002. Education: Brown University (Sc.B., 1985), Harvard University (Ph.D., 1993) Postdoc: Isaac Newton Institute, University of Cambridge (1994) His research focuses on computer vision and machine learning, with applications in biodiversity and mobile technology. Notable projects include the Leafsnap, Birdsnap, and Dogsnap apps – pioneering species/breed identification tools using machine learning. He has received awards such as the PECASE, Helmholtz Prize, and EO Wilson Biodiversity Technology Pioneer Award. His work bridges academia and industry, demonstrated by collaborations with Dropbox and contributions to consumer-facing AI applications. The VAP LAB explores visual appearance modeling and computational photography.

Jiaqi GongView profile

Associate Professor

• Artificial Intelligence
• Smart and Connected Health
• Cyber-Physical Systems
• +13 more

Dr. Jiaqi Gong serves as Associate Professor in Computer Science and Adjunct Associate Professor in Mechanical Engineering at The University of Alabama's College of Engineering, while directing the Alabama Center for the Advancement of Artificial Intelligence. His academic foundation includes: B.S. in Engineering, China University of Geoscience (2004) Ph.D. in Engineering, Huazhong University of Science and Technology (2010) Dr. Gong's research pioneers human-AI convergence through cyber-physical systems and smart health technologies, developing mobile/wearable platforms to enhance human perceptual, cognitive, and physical capabilities. His work spans artificial intelligence, machine learning, computer vision, and IoT with applications in healthcare, environmental monitoring, and education. The Sensor-Accelerated Intelligent Learning (SAIL) laboratory he founded drives innovation in behavior change interventions, human movement modeling, and educational data mining. Recent publications reveal strong interdisciplinary trends: healthcare AI dominates with medication adherence prediction and surgical classification systems, while environmental applications feature flood-risk communication and drought analysis. His work increasingly integrates generative AI and LLMs across domains, demonstrating methodological innovation in federated learning, knowledge graphs, and explainable storytelling frameworks. Notable recognitions include: Best Student Paper Award, IEEE/ACM Connected Health Conference (2022) Best Student Paper Award, Body Sensor Networks Conference (2019) Data Challenge Win, IEEE Biomedical Health Informatics (2018) Best Paper Award, Body Area Networks Conference (2014) Best Demonstration Award, IEEE Wireless Health Conference (2014) Dr. Gong leads significant funded projects including a $2M CDC/NIOSH grant for first responder safety and $3M NSF funding for hydrologic research. As SAIL laboratory director, he mentors students in developing clinically deployed technologies for multiple sclerosis, dementia, and mental health. Future work focuses on scaling AI applications in chronic disease management and climate resilience through the Alabama AI Center. The SAIL laboratory (founded 2017) operates as a multidisciplinary hub developing wearable/mobile systems for health applications, with active collaborations across medical clinics and engineering departments for real-world deployment of behavior change interventions and movement analysis tools.

• Representation Theory
• Finite Dimensional Algebras
• Algebraic Combinatorics
• +3 more

Kay Jin Lim serves as Senior Lecturer and Director of the MSc (Analytics) program at Nanyang Technological University's School of Physical & Mathematical Sciences, where he contributes significantly to both academic leadership and mathematical research within the Division of Mathematical Sciences. His educational foundation includes a B.Sc. (2005), M.Sc. (2007), and Ph.D. (2009) from the National University of Singapore, followed by doctoral research at the University of Aberdeen under David John Benson's supervision. Lim's research centers on representation theory of finite dimensional algebras, with deep connections to algebraic combinatorics and algebraic geometry. His work explores modular representation theory of symmetric groups, Specht modules, and Lie modules, emphasizing combinatorial structures and geometric interpretations in positive characteristic settings. This specialized focus has established him as a contributor to advanced algebraic frameworks. Analysis of his publication trajectory (2013-2025) reveals consistent advancement in understanding module complexities, rank varieties, and symmetric group representations, with increasing emphasis on interdisciplinary connections between algebraic combinatorics and geometric methods. His collaborative approach with international researchers like Karin Erdmann and David Benson demonstrates engagement with cutting-edge developments in the field. Scientific awards: No awards, fellowships, or major prizes are documented in the available information. Lim maintains an active supervisory role with four doctoral students: Yu Jiang (graduated May 5, 2021), Jialin Wang (graduated March 31, 2024), and current candidates Kua Hao Yan Manzu and Chen Siyuan. His teaching portfolio spans foundational to advanced algebra courses including MH2220 Algebra I, MH3220 Algebra II, and specialized topics in Homological Algebra, reflecting his commitment to mathematical education at multiple levels. He operates within NTU's vibrant mathematical research ecosystem, maintaining significant collaborations with leading algebraists globally while directing the MSc (Analytics) program to integrate theoretical mathematics with practical analytical applications.

Aage Hill-MadsenView profile

Associate Professor

• Translation Studies
• Medical Communication
• Systemic Functional Grammar
• +5 more

Aage Hill-Madsen is an Associate Professor at the Department of Culture and Learning, Aalborg University (Denmark), affiliated with the Communication, Language, and Discourse (CLD) research unit. His work focuses on translation studies, medical communication, and systemic-functional linguistics. He holds a Cand.mag. et ling.merc. and a PhD in English linguistics. Research Interests: Translation theories (especially intralingual translation) Medical terminology simplification and communication Systemic-functional grammar applications Legitimation Code Theory in educational contexts Genre analysis of specialized texts Semiotic approaches to knowledge transmission Recent Publications Trends: His 2024-2025 work emphasizes medical translation between technical and lay audiences, with studies on EPAR summaries, patient information leaflets, and historical East-West medical terminology exchanges. He combines functionalist translation theory with systemic-functional frameworks to analyze textual transformations. Grants & Projects: Participated in the 2024-ongoing project Udvikling af kommunikative kompetencer gennem frivilligt brobygningsarbejde i NGO’en Social Sundhed , exploring communication skills through NGO activities. Academic Engagement: Active peer reviewer for journals like Globe and Across Languages and Cultures , with conference presentations on intralingual translation strategies and medical text accessibility.

Timothy R. TangherliniView profile

Professor

• folklore
• Danish culture
• Danish cinema
• +9 more

Timothy R. Tangherlini serves as the Elizabeth H. and Eugene A. Shurtleff Chair in Undergraduate Education and Professor in the Department of Scandinavian and the School of Information at the University of California, Berkeley. A distinguished folklorist and ethnographer, he has pioneered computational approaches to folklore studies, bridging traditional humanities scholarship with cutting-edge digital methods. His research expertise spans multiple interconnected domains: Digital humanities and computational folkloristics Danish and Scandinavian cultural traditions Network analysis of narrative structures Machine learning applications in cultural analysis Conspiracy theory formation and transmission Korean cultural studies and K-Pop analysis Tangherlini's work focuses on how stories circulate across social networks and how individuals use narratives to negotiate ideology within their social groups. He has been instrumental in developing the field of Culture Analytics, co-directing a three-year program at the NSF's Institute for Pure and Applied Mathematics and leading the NEH's Institute for Advanced Topics in Digital Humanities on Network Analysis for the Humanities. His research combines ethnographic depth with computational sophistication, creating novel methodologies for analyzing large cultural corpora. His scholarly contributions have earned him significant recognition: Fellow of the American Folklore Society Fellow of the Royal Gustav Adolf Academy (one of Sweden's Royal Academies) Elizabeth H. and Eugene A. Shurtleff Chair in Undergraduate Education Tangherlini has secured substantial funding from prestigious organizations including the NEH, NSF, NIH, AFOSR, Mellon Foundation, Nordic Council of Ministers, and Google. His work on conspiracy theories, K-Pop choreography analysis, and historical Danish folklore has garnered media attention, demonstrating the public relevance of his research. With extensive international experience including appointments at the University of Copenhagen, University of Iceland, and Harvard University, he maintains a global scholarly perspective while contributing significantly to undergraduate education at Berkeley.

Farhad Pourkamali AnarakiView profile

Assistant Professor

• Machine Learning
• Data Science
• Computational Mathematics
• +3 more

Farhad Pourkamali Anaraki is an Assistant Professor in the Department of Mathematical and Statistical Sciences at the University of Colorado Denver, part of the College of Liberal Arts and Sciences. His research focuses on Machine Learning, Data Science, and Computational Mathematics, with interdisciplinary applications in engineering, materials science, and uncertainty quantification. He specializes in developing data-driven methodologies for complex systems, including composite materials, seismic response prediction, and additive manufacturing. Key research themes include probabilistic neural networks, adaptive machine learning for sparse data, and computational techniques for engineering challenges. His work integrates advanced algorithms with domain-specific problems, such as optimizing material properties and enhancing predictive models in civil and mechanical engineering contexts. Despite his prolific publication record, no specific scientific awards or grants are explicitly listed in the provided information. He maintains an active profile in teaching and mentoring, though formal advisee details are not documented here.