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Anurag AgarwalView profile

Associate Professor

• Number Theory
• Cryptography
• Algebra
• +7 more

Anurag Agarwal is an Associate Professor in the School of Mathematical Sciences at Rochester Institute of Technology (RIT), College of Science. He holds a BS and MS from the Indian Institute of Technology (India) and a Ph.D. from the State University of New York at Buffalo. Dr. Agarwal's research spans multiple areas of mathematics with a focus on Number Theory , Cryptography , and Algebra . His specific interests include Diophantine equations and approximations, Continued Fractions, Finite Field Theory, Algebraic Number Theory, Abstract Algebra, and Combinatorial Mathematics. His work bridges theoretical mathematics with practical applications, particularly in the field of cryptography and mathematical information retrieval. Dr. Agarwal's publications demonstrate a strong focus on graph theory, geometric properties of mathematical objects, and mathematics education. His research combines theoretical rigor with practical applications, particularly in the areas of combinatorial mathematics and number theory. The interdisciplinary nature of his work connects pure mathematics with computer science applications, especially in cryptography. Dr. Agarwal has been actively involved in developing innovative educational tools, including MathDeck, an online search interface for mathematical formulas that allows users to create, edit and lookup sophisticated math formulas. Created by an interdisciplinary team of faculty and students at RIT, MathDeck aims to make math notation interactive and easily shareable. He teaches a variety of courses including Discrete Mathematics, Linear Algebra, Number Theory, Abstract Algebra, and Cryptography at both undergraduate and graduate levels. His teaching portfolio reflects his expertise across theoretical and applied mathematics, with courses ranging from foundational topics like Project Based Calculus to advanced subjects like Topics in Number Theory & Cryptography.

Alípio JorgeView profile

Associate Professor

• Data Mining
• Machine Learning
• Artificial Intelligence
• +5 more

Alípio Jorge is an Associate Professor at the Department of Computer Science, Faculty of Science, University of Porto. He coordinates LIAAD (Artificial Intelligence and Decision Support Lab), part of INESC TEC since 2007. His research focuses on Data Mining, Machine Learning, and Text Intelligence, with past work in Inductive Logic Programming and Collaborative Data Mining. He has led research projects on web intelligence and taught courses in programming, data mining, and computing. Previously, he co-founded and directed the MSc in Data Analysis and Decision Support Systems at the Faculty of Economics (1996–2009). Education: PhD in Computer Science (University of Porto) MSc in Foundations of Advanced Information Technology (Imperial College) BSc in Applied Maths and Computer Science (University of Porto) Research Interests: Data Mining methodologies and applications Machine Learning for decision support Narrative understanding and NLP challenges LLM capabilities and cross-domain language variety analysis Publications: Recent works explore narrative structures using LLMs, Portuguese language variety identification, and pre-trained model capabilities. His 2025 publications highlight advancements in cross-domain language processing and NLP resource development. Advising & Leadership: Supervised over 8 PhD/MSc theses on topics like sentiment analysis and European Portuguese resources Directed the Master in Computer Science (2010–2013) Co-chaired major conferences (ECML/PKD, Discovery Science) Labs & Teams: LIAAD focuses on AI-driven decision support systems, collaborating with INESC TEC on interdisciplinary projects.

Dorothea BlosteinView profile

Professor

• Computer Science
• Biomechanics
• Graphics Recognition
• +6 more

Roles and Affiliations: Dorothea Blostein is a Professor in the School of Computing at Queen's University, part of the Faculty of Arts and Science. She has held this position since 1988 after earning her Ph.D. (1987) and B.Sc. (1978) from the University of Illinois and an M.Sc. (1980) from Carnegie Mellon University. Research: Her work focuses on the interface between paper and electronic documents, with specialties in graphics recognition, document analysis, and biomedical computing. Key areas include tensegrity structures for biomechanical modeling, adaptive systems, and artificial fascial networks. She also explores music notation recognition and software engineering document classification. Collaborations include projects with NASA on tensegrity robotics and studies on concussion prevention through helmet fitting systems. Teaching: She teaches courses such as CISC 859 (Pattern Recognition) and CISC 324 (Operating Systems). Her pedagogical approach emphasizes hands-on projects and theoretical foundations in AI and algorithms. Labs and Teams: Her research leverages tools like the NASA Tensegrity Robotics Toolkit and ArtiSynth. She collaborates with institutions like the Gordon Lab for embryonic modeling and biomechanical engineers on cytoskeleton simulations.

• Information Retrieval
• Recommender Systems
• Machine Learning
• +6 more

Professor Iadh Ounis is a leading academic in the field of Information Retrieval at the University of Glasgow's School of Computing Science. He heads the 60-strong Information, Data and Analysis (IDA) Section and leads the Terrier Team, known for developing advanced text retrieval techniques. His work focuses on machine learning applications in IR, recommender systems, and big data architectures. Roles: Professor of Information Retrieval, Head of IDA Section, Principal Investigator of Terrier search engine. Education: PhD from University Joseph Fourier (Grenoble, France), MSc from ENSIMAG (Grenoble). Awards: 2022 Tony Kent Strix Award. Research interests include fairness in AI, multi-modal recommendation systems, and large-scale IR architectures. He has published over 200 papers and led major initiatives like TREC Blog/Microblog tracks and NTCIR Math IR task. Active in conference program committees and journal editorial boards. Labs/Teams: Terrier Team, IDA Section. Collaborations include Scottish Informatics & Computer Science Alliance (SICSA) and The Data Lab Innovation Centre.

• Categorical Data Analysis
• Multivariate Statistics
• Psychometrics
• +2 more

Dr. Dave Hessen is an Assistant Professor at the Department of Methodology and Statistics within the Faculty of Social Sciences at Utrecht University. His academic career spans over two decades, with expertise in Categorical Data Analysis , Multivariate Statistics , Psychometrics , and Structural Equation Modeling . He has contributed to Applied Data Science through methodological innovations and cross-disciplinary research. MA (1998, cum laude), PhD (2003), Post-Doctoral Training (2005) in Psychological Methods and Psychometrics at the University of Amsterdam Hessen's research bridges statistical methodology with applications in child development, educational assessment, and social sciences. Notable projects include LITMUS language tests for multilingual children, Distractors in the classroom studying working memory in educational settings, and the Dutch norms for Bayley-III project adapting developmental assessment tools for Dutch populations. His methodological work focuses on log-linear models , multidimensional Rasch models , and factor score reliability . Recent publications emphasize correspondence analysis for text mining, maximum entropy distributions , and random effects models in psychometrics. He has secured funding from Utrecht University (DoY Public Engagement Fund, DoY Invigoration Grant) and government grants for developmental assessment research. Collaborations include Prof. Elma Blom, Prof. Sarah Durston, and international institutions. Contact: djhessen@uu.nl | d.j.hessen@uu.nl | Office: Sjoerd Groenman Building, Padualaan 14, Room C.113, Utrecht

• Natural Language Processing
• Computational Linguistics
• Artificial Intelligence
• +2 more

Daisuke Kawahara is a Professor at Waseda University's Faculty of Science and Engineering and a Visiting Professor at the National Institute of Informatics. He holds a PhD in Informatics from Kyoto University (2005) and has previously served as Associate Professor at Kyoto University and Senior Researcher at NICT. His research spans natural language processing, computational linguistics, and AI infrastructure. Education: Ph.D. in Informatics, Kyoto University (2005) Graduate Studies in Intelligent Informatics, Kyoto University (1999–2002) M.Eng. in Electronic & Communication Engineering, Kyoto University (1997–1999) B.Eng. in Electrical Engineering, Kyoto University (1993–1997) Research Focus: Kawahara specializes in NLP, including syntactic parsing, semantic role labeling, language resource development (e.g., JGLUE benchmark), and multilingual corpus construction. His work integrates machine learning with linguistic theory to improve text understanding systems, error correction tools, and dialogue agents. Publication Trends: His recent articles emphasize Japanese and Chinese NLP, neural network-based parsing, and practical applications like educational tools and pandemic information systems. Common themes include benchmarking, corpus annotation, and cross-lingual adaptation. Awards: 情報処理学会 自然言語処理研究会 優秀研究賞 (2025) 言語処理学会最優秀論文賞 (2024, 2023) 科学技術分野の文部科学大臣表彰 (2017) Multiple Best Paper Awards from NLP conferences (2000–2025) Projects & Advising: He leads JSPS-funded projects like Building General Language Understanding Infrastructure (2021–2025) and Acquisition of Knowledge Frames (2018–2021). No student advisees are listed. Labs & Teams: Collaborates with RIKEN Center for Advanced Intelligence Project and maintains ties to Kyoto University's NLP lab. Focuses on large-scale language modeling and collaborative AI-human intelligence frameworks.

Tamara SumnerView profile

Professor

• Artificial Intelligence
• Digital Libraries
• Natural Language Processing
• +7 more

Tamara Sumner is a Professor at the University of Colorado Boulder with a joint appointment between the Institute of Cognitive Science and the Department of Computer Science. She serves as Director of the Institute of Cognitive Science since 2016 and is currently Interim Dean of University Libraries. Her interdisciplinary research focuses on how computational tools combining cognitive science, machine intelligence, and interactive media can improve teaching practice, learning outcomes, and learner engagement in STEM education. She leads a research-practice partnership involving faculty from CU Boulder, UCAR staff, and Denver Public Schools educators. Sumner's major research projects include: Inquiry Hub: Research-Practice Partnership with Denver Public Schools focused on STEM learning Engineering Experiences: Studying youth motivation in engineering through out-of-school programs TalkBack: Using speech recognition and NLP to support math teachers Chicago City of Learning: Developing equity measurement tools for city-scale learning opportunities Her research has been supported by significant funding including an NSF award of $1.8 million for AI-enhanced tutoring systems. Sumner has published 140 articles with over 80 peer-reviewed publications in human-computer interaction, educational technology, and intelligent systems. She teaches graduate courses including Research Applications Seminar and Topics in Cognitive Science, focusing on cognitive science research methods and user-centered design principles.

Dora DemszkyView profile

Assistant Professor

• Natural Language Processing
• Education Data Science
• Educational Technology
• +6 more

Dora Demszky is an Assistant Professor in Education Data Science at Stanford University's Graduate School of Education, with a courtesy appointment in Computer Science. She leads the EduNLP Lab, where she develops natural language processing tools to support equitable, student-centered instruction through analyzing educational discourse including student-teacher interactions, student group work, and textbooks. PhD in Linguistics from Stanford University (advised by Dan Jurafsky) BA summa cum laude in Linguistics with a minor in Computer Science from Princeton University Co-founder of Tarisznya Alapítvány (Knapsack Foundation), a nonprofit supporting underprivileged children in Hungary Dr. Demszky's research combines natural language processing, linguistics, and practitioner input to develop interpretable and scalable education measures. Her work focuses on creating tools that analyze classroom discourse to identify features of high-quality instruction and provide actionable feedback to educators. She has particular expertise in developing AI-powered systems that help teachers improve questioning quality, analyze textbook representation, measure dialect features, and scaffold curricula to meet diverse student needs. Her approach emphasizes the importance of human connections in teaching and learning, using technology to enhance rather than replace these critical interactions. Analysis of Dr. Demszky's recent publications reveals a strong trend toward practical applications of NLP in real educational settings, with increasing emphasis on randomized controlled trials to validate effectiveness. Her work spans multiple educational contexts from K-12 classrooms to higher education, with growing attention to equity considerations in AI-powered educational tools. Recent publications show expansion into mathematical education, speaker diarization for noisy classrooms, and open-source tools for the broader research community. MathemaTikZ dataset received the inaugural best dataset prize at Learning at Scale NCTE classroom transcript dataset received the best IEDMS Publicly Available Educational Dataset Prize Selected as a Leading Woman in AI at the ASU GSV AIR Show Dr. Demszky has secured significant research funding including grants from the Gates Foundation, NSF RAPID program, and Stanford HAI. Her work with teachers extends beyond research through initiatives like the Practitioner Voices Summit, which brought together 60 teachers from 22 states to inform AI research related to classrooms. She actively collaborates with educators to ensure her tools address real classroom needs while maintaining a focus on socially responsible edtech development. At Stanford, Dr. Demszky leads the EduNLP Lab, which operates at the intersection of education, computer science, and linguistics. The lab follows a three-pronged approach: gathering evidence through data science using NLP models, developing algorithms through co-design with domain experts, and piloting solutions that practitioners can use in real educational settings. Current projects include M-Powering Teachers, which provides automated feedback to educators, and tools for adapting mathematics curricula to support students with diverse learning needs.