Invited Speakers

Re-thinking Education in the Face of AI

Time: TBD

Location: TBD

William Swartout

Chief Science Officer, USC Institute for Creative TechnologiesCo-Director, USC Center for Generative AI and SocietyResearch Professor, Computer Science

Abstract: When ChatGPT was released in the fall of 2022, the education community panicked. Suddenly there existed a highly capable AI that was facile with language. Teachers feared students would use Gen AI to cheat and write their essays for them, and the press and internet were rife with articles proclaiming the Death of the Term Paper. Banning AI was problematic since preventing students from using AI while in school would not prepare them for the world into which they would graduate, and the detectors that purported to tell if text was written by an AI or a human had significant false positive and false negative error rates. Working with faculty from the USC undergraduate writing program we developed a writing tool called ABE that takes a different approach. In ABE, we use generative AI to help students brainstorm about a topic, and then when they are finished with their essays (which they write themselves) we use generative AI again, not as a writer, but as a reader, to read their essays and offer critiques, answering questions such as: Does the essay have a good hook? Is there adequate support for the claims? Are there other points of view that should be considered, but weren't? Surveys have shown that students have received ABE very positively and have found it helpful in their writing.
Stepping back a bit, I believe Gen AI is going to force us to reconsider how we teach across a very broad spectrum of intellectual domains. While each domain presents its own challenges, I believe that our experience with ABE is an exemplar of how Gen AI can be integrated into instructional design to actually improve students' critical thinking skills rather than detract from them.
Bio: William Swartout is chief science officer at the USC Institute for Creative Technologies, providing overall direction to the institute’s research programs. He is also co-Director of the Center for Generative AI and Society and a research professor in the Computer Science Department at the USC Viterbi School of Engineering.
Swartout has been involved in cutting edge research and development of artificial intelligence systems throughout his career. In 2009, Swartout received the Robert Engelmore Award from the Association for the Advancement of Artificial Intelligence (AAAI) for seminal contributions to knowledge-based systems and explanation, groundbreaking research on virtual human technologies and their applications, and outstanding service to the artificial intelligence community. Swartout is a Fellow of the AAAI, has served on their Board of Councilors and is past chair of the Special Interest Group on Artificial Intelligence (SIGART) of the Association for Computing Machinery (ACM).
He has served as a member of the Air Force Scientific Advisory Board, the Board on Army Science and Technology of the National Academies and the JFCOM Transformation Advisory Group. Prior to helping found the ICT in 1999, Swartout was the Director of the Intelligent Systems Division at the USC Information Sciences Institute.
His particular research interests include virtual humans, natural language processing, particularly explanation and text generation, knowledge acquisition, knowledge representation, and intelligent computer based education. He received his Ph.D. and M.S. in computer science from MIT and his bachelor’s degree from Stanford University.

Explainable Neural Text Classification

Time: TBD

Location: TBD

Diana Inkpen

ProfessorSchool of Electrical Engineering and Computer Science,University of Ottawa, Canada

Abstract: Advances in Large Language Models (LLMs) allow us to develop highly accurate neural text classifiers. One of their major disadvantages is their lack of explainability, due to their black box nature. I am looking into neural text classifiers that are explainable, in order to open their black box architecture, at least partially. Explainability can come at the level of the classification model or at the level of the decision made for each new test data. The explanations need to look into what was learnt from the training data (unless there is no training or minimal training) and also into the pre-trained model (LLM) that was used as a basis for the classifier. To explain the individual decisions for each test data, one step is to calculate feature importance with methods such as LIME, SHAP, or Integrated Gradients. More useful full-text explanations can be generated via customized prompting, or via joint leaning of classes and explanations during training. I will show results for two case studies: applications to legal text mining and to mental health text mining. The evaluation of the generated explanations is done via automatic measures, as well as with human judges, in order to see if they find the explanations relevant and useful.
Bio: Diana Inkpen is a Professor at the University of Ottawa, in the School of Electrical Engineering and Computer Science. She received her Ph.D. in Computer Science from the University of Toronto, Canada, and her M.Sc. and B.Eng. in Computer Science and Engineering from the Technical University of Cluj-Napoca, Romania. Her research is in applications of Natural Language Processing and Deep Learning. She is the editor-in-chief of the Computational Intelligence journal and the associate editor for the Natural Language Engineering journal. She published a book on Natural Language Processing for Social Media (Morgan and Claypool Publishers, Synthesis Lectures on Human Language Technologies, the third edition appeared in 2020), 11 book chapters, more than 45 journal articles, and more than 150 conference papers. She has received many research grants, the majority of which include intensive industrial collaborations.

AI Systems That Think, Team, and Fight: A New Paradigm for Defense

Time: TBD

Location: TBD

Svitlana Volkova

Chief of AI, Office of Science and TechnologyAptima, Inc.

Abstract: As AI systems become increasingly capable, the Department of War faces a critical challenge: how do we develop, rigorously evaluate, and safely deploy multi-agent AI frontier systems across domains ranging from multimodal knowledge discovery to cognitive warfare? This talk presents lessons learned from building compound AI architectures that orchestrate large language models, vision-language models, and specialized agents through retrieval-augmented generation and agentic AI workflows. I will demonstrate how these systems enable cross-disciplinary knowledge synthesis for biosecurity, cognitive warfare planning and execution, and operator-AI team optimization in wargaming and readiness applications. Finally, I will present our emerging capabilities in multi-domain wargaming, where cognitively inspired AI agents execute doctrine-based maneuvers across air, space, cyber, and information domains.
Evaluating these systems requires moving beyond traditional AI benchmarks. I will present our multi-dimensional ecosystem combining quantitative measures, qualitative SME assessments scaled through simulated domain expert agents, and causal investigations using structure learning algorithms to understand "why" behaviors emerge and "how" interventions affect mission outcomes. For safety evaluation, we examine human-agent-environment interactions holistically addressing alignment failures, emergent capabilities under distributional shift, and systemic risks from multi-agent coordination through counterfactual "what-if" analysis and continuous monitoring. The era of scientifically grounded operationally validated human-AI team optimization has begun, and this talk charts the path forward for defense applications.

Bio: Dr. Svitlana Volkova is Chief of AI at Aptima, Inc., where she sets the company's AI vision and leads a portfolio of advanced research programs in compound frontier AI systems, human-AI teaming, and AI Test and Evaluation for national defense. A recognized thought leader in AI for national security, she has shaped the technical direction of multi-million-dollar federal research initiatives with a focus on transitioning AI technologies to operational use. Her pioneering work spans multimodal frontier models, agentic AI architectures, human digital twins, and causal AI/ML—with a focus on decision advantage, readiness, and cognitive warfare applications. Dr. Volkova has authored 100+ publications with 4,900+ citations, delivered keynotes and invited talks at premier venues spanning AI research (AAAI, ACL, EMNLP), defense (I/ITSEC, MODSIM, INFOPAC), academia (Stanford, CMU), and industry (Google Research, Amazon), and served as a trusted advisor to government leadership on AI strategy. Prior to Aptima, she led AI research initiatives at Pacific Northwest National Laboratory and conducted research at Microsoft Research. She holds a PhD in Computer Science from Johns Hopkins University.

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