H. Andrew Schwartz is an Assistant Professor at Stony Brook University's Department of Computer Science. His research bridges computational social science, natural language processing, and psychological discovery, focusing on social media language analysis for health and demographic insights. Education : PhD in Computer Science (2011) from the University of Central Florida Past Affiliations : Postdoctoral Research Fellow and Visiting Assistant Professor at University of Pennsylvania's Computer & Information Science department Current Role : Lead Research Scientist for the World Well-Being Project, a multidisciplinary collaboration between computer scientists and psychologists Dr. Schwartz's research utilizes machine learning to analyze large-scale social media data for mental and physical health prediction, automatic lexicon refinement, and measuring human temporal orientation. His work has been recognized by Almetric's top 25 most discussed research papers in 2013. Recent publications examine psychological state detection through NLP, including PTSD analysis, mental health screening algorithms, and LLM adaptation for personality modeling. He develops methodologies like adaptive question-asking systems and self-supervised learning frameworks for behavioral analysis. Key scientific contributions include: Developing the WWBP-SQT-lite system for identifying mental health change moments Creating AlBA (Adaptive Language-Based Assessments) for mental health evaluation Advancing temporal analysis of social media language for health informatics
Shailee Jain is a postdoctoral researcher at the Chang Lab in the Department of Neurosurgery at University of California, San Francisco (UCSF). Previously, she completed her PhD in Computer Science at the Huth Lab, University of Texas at Austin, with collaborations at Google AI Language and Intel Brain-Inspired Computing Lab. Her work bridges artificial neural networks with biological language processing systems. Education : PhD in Computer Science (2023), UT Austin; BSc at NITK Surathkal Shailee's research focuses on Neuro-AI intersections, particularly interpreting neural NLP models to understand brain language processing . Her recent work explores voxel function modeling , context-sensitive speech encoding , and geometric signatures in neuro-AI systems . Key publications include: 2024: Frameworks for generative causal testing in language neuroscience 2023: Natural language fMRI dataset for voxelwise modeling 2022: Self-supervised speech modeling for cortical response prediction Awards & Recognition : 2025: Rookie of the Year Award (CogHear'25) 2024: Glushko Dissertation Prize 2024: SNL Dissertation Award 2023: UT Austin Graduate School Fellowship Active in academic service as Handling Editor for JoCNForum and Co-organizer for BayLI meetings, Shailee mentors through Women in Computer Science and reviews for top-tier venues. She teaches at summer schools like Cajal's NeuroAI program in Lisbon.
Ted Pedersen is a Professor in the Department of Computer Science at the University of Minnesota Duluth, where he is part of the Swenson College of Science and Engineering. He serves as the Study Abroad Advisor for the CS Department and was a member of the UMN AI Task Force from January 2025 to August 2025, where he addressed questions regarding AI use at the University of Minnesota. Dr. Pedersen's research focuses on computational linguistics and natural language processing, with particular expertise in word sense disambiguation, semantic similarity measurement, and biomedical text analysis. His work bridges theoretical computational linguistics with practical applications in healthcare informatics and social media analysis. He has developed several widely-used open-source tools including SenseClusters, WordNet::Similarity, and WordNet::SenseRelate that have become standard resources in the NLP community. His publication record shows a clear evolution from foundational work in semantic similarity and word sense disambiguation toward contemporary applications in social media analysis, bias detection, and multilingual processing. Recent work demonstrates increasing focus on ethical AI considerations, particularly in sentiment analysis, offensive language detection, and news credibility assessment. His research maintains strong connections between theoretical linguistic frameworks and practical computational implementations. Multiple publications in AMIA Annual Symposium Proceedings Extensive work on semantic similarity measures in biomedical contexts Development of open-source NLP tools adopted by the research community Active participation in SemEval shared tasks since 2017 Dr. Pedersen actively mentors students through research opportunities in natural language processing, with current opportunities available for both undergraduate and graduate students. His teaching portfolio includes courses on Computer Ethics, Algorithms, Race and Computing, and advanced Natural Language Processing topics. He has taught consistently since at least 1999, demonstrating a long-standing commitment to computer science education. His laboratory work focuses on developing computational approaches to linguistic problems, with particular emphasis on unsupervised and semi-supervised methods for semantic analysis. Current projects appear to focus on multilingual processing, social media analysis, and ethical considerations in AI systems.
David Louton is a Professor of Finance at Bryant University's College of Business, holding a Ph.D., M.B.A., and B.S. from Michigan State University. Based in BELC Room S259E, he can be contacted at dlouton@bryant.edu. His educational background includes: Ph.D. in Finance from Michigan State University M.B.A. from Michigan State University B.S. from Michigan State University Professor Louton's research bridges investments with computational linguistics and machine learning, focusing on extracting insights from financial text corpora. His interdisciplinary work applies NLP and AI to analyze SEC filings, market behavior, and investment strategies, creating novel methodologies for processing large-scale financial data and enhancing decision-making frameworks in quantitative finance. His publication trajectory reveals a clear evolution toward computational finance, with recent work (2022-2024) emphasizing machine learning applications to ESG disclosures and SEC filings, while earlier research (2014-2017) examined options market microstructure. This progression demonstrates increasing sophistication in leveraging LSTM networks, topic modeling, and meta-analysis to solve complex financial problems. Professor Louton has received significant recognition including: Best Paper Award from Market Technicians Association (2013) Best Paper Award from Academy of Financial Services (2008) Best Paper in Investments from Academy of Financial Services (1998) Best Paper in Investments from Southwestern Finance Association (1994) Information regarding grant funding and student advising details is not provided in available sources, though his research collaborations with scholars like Holowczak, Saraoglu, and Cullinan indicate active team-based scholarship.
Lubomir Ivanov is a Professor of Computer Science and current Chair of the Computer Science Department at Iona University, where he also serves as Game Development Coordinator and primary contact for all undergraduate programs. He joined Iona in 1999 after completing his academic training at Stevens Institute of Technology and has established himself as a versatile educator teaching courses ranging from computer architecture to virtual reality development. Education: Ph.D. in Computer Science, Stevens Institute of Technology M.S. in Computer Science, Stevens Institute of Technology B.S. in Computer Engineering, Stevens Institute of Technology Dr. Ivanov's research demonstrates exceptional interdisciplinary breadth, with dual pillars in Natural Language Processing and Applied Virtual Reality . His NLP work pioneers authorship attribution techniques using linguistic features from phonetic patterns (alliteration, assonance) to semantic dimensions (abstractness/concreteness), particularly for historical texts like Thomas Paine's writings. Simultaneously, his VR research creates impactful educational tools, including anti-bullying simulators with facial-expression recognition and mental health applications for non-suicidal self-injury assistance, reflecting strong collaborations with psychology departments. Analysis of his 15 most recent publications (2016-2024) reveals an evolving trajectory: early work focused on phonetic features for authorship analysis, progressively incorporating semantic and sentiment features, while his VR research matured from basic environments to sophisticated systems integrating real-time facial recognition and user experience optimization. Both strands consistently address real-world problems through computational methods. Scientific Awards: None mentioned in source material. Dr. Ivanov actively mentors students on research projects including the 'Bully' virtual environment and NSSI assistance software, securing collaborative grants with psychology departments. His leadership extends to university governance through the Faculty Senate and previous department chairmanship (2006-2009), alongside curriculum development for game development courses targeting non-CS majors. He directs a cross-disciplinary research ecosystem centered on the Game Development concentration, integrating computer science with education and psychology to create socially impactful technologies through ongoing VR and NLP innovation.
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.
Louisiana State University of AlexandriaUnited States
Rafal Rzepka is an Associate Professor at Hokkaido University's Faculty of Information Science and Technology, where he leads the Language Media Lab and serves as Associate Editor for Information Processing & Management. With a prolific research output of 322 publications and significant citation impact, his work bridges theoretical NLP with practical applications in ethical AI systems. His research interests span multiple interconnected domains in artificial intelligence, with primary focus on Natural Language Processing, Machine Ethics, Common Sense Knowledge acquisition, and Affect Processing. He has pioneered work in Artificial Humor, Metaphor Understanding and Generation, Cyber-bullying Detection, and recently expanded into Speciesism analysis in language models. His approach combines computational linguistics with cognitive science to develop systems that better understand human behavior and values. Analysis of his recent publications reveals a strong trend toward ethical AI applications, with significant work on bias detection (including fame bias and speciesism), cross-cultural ethics, and moral decision-making frameworks. His research increasingly focuses on practical implementations of ethical AI in security contexts, medical diagnostics through speech analysis, and regulatory compliance systems. Rafal Rzepka's work demonstrates consistent innovation in extracting meaningful patterns from language to address complex social and ethical challenges. His research bridges theoretical advances in NLP with real-world applications that improve AI safety, fairness, and human-AI interaction. As an active researcher, he collaborates across multiple institutions and disciplines, with recent work spanning Japanese, English, Polish, French, and Chinese language contexts. His contributions to developing evaluation datasets like JETHICS provide important resources for the broader AI ethics community.
