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The NC State CS AI Seminar Series, hosted by the Department of Computer Science at NC State University, brings together researchers and practitioners from academia, industry, and national laboratories to share recent advances, emerging directions, and open challenges in artificial intelligence and machine learning.
Fridays · 11 AM–12 PM ET · Online and/or In Person
Stay connected for upcoming speaker announcements, seminar updates, and talk highlights.
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Faculty Organizers: Xiaorui Liu · Dongkuan (DK) Xu · Kaixiong Zhou · Munindar Singh
Date: 08/28/2026, Friday
Time: 11 AM - 12 PM (EST)
Location: Online Only
Zoom: https://ncsu.zoom.us/j/99696402288?pwd=9JbJbC8PsJEpfXnIgOHb1I2rL1GReM.1
Title: AI agents to accelerate scientific discoveries
Speaker: James Zou (Stanford University)
Abstract: AI agents—large language models equipped with tools and reasoning capabilities—are emerging as powerful research enablers. This talk will explore how agentic AI can accelerate scientific discoveries. I’ll first introduce the Virtual Lab—a collaborative team of AI scientist agents conducting in silico research meetings to tackle open-ended research projects. As an example application, the Virtual Lab designed new nanobody binders to recent Covid variants that we experimentally validated. Then I will introduce the Virtual Biotech, a platform where tens of thousands of AI agents work together to advance drug discovery and development.
Biography: James Zou is an associate professor of Biomedical Data Science, CS and EE at Stanford University. He works on developing cutting-edge AI for biomedical applications. His group developed many widely used innovations including EchoNet AI (FDA cleared for assessing cardiac function), Gradio (used by over a million developers), and SyntheMol (NY Times 2024 Good Tech). He has received the Overton Prize, Sloan Fellowship, NSF CAREER Award, two Chan-Zuckerberg Investigator Awards, a Top Ten Clinical Achievement Award, best paper awards at ICML and other AI conferences, and faculty awards from Google, Amazon, Adobe and Apple.
Web: https://www.james-zou.com/
Date: 09/25/2026, Friday
Time: 11 AM - 12 PM (EST)
Location: Online Only
Zoom: TBD
Title: TBD
Speaker: Prasanna Balaprakash (PrimaLabs)
Abstract: TBD
Biography: Visionary AI executive and Co-founder of PrimaLabs with a distinguished tenure as Director of AI Programs at Oak Ridge National Laboratory, where Prasanna spearheaded a research portfolio influencing over $200M in funding and secured strategic partnerships with industry titans including NVIDIA, Microsoft, and AMD. Two times Gordon Bell Prize finalists with deep technical authority in exascale computing, Prasanna successfully led the development of the world’s first non-industry trillionparameter AI training capability and the 113-billion parameter ORBIT foundation model. His background uniquely bridges the gap between deep-tech innovation and strategic commercialization, combining elite R&D leadership with the operational acumen to drive the next generation of secure, scalable, and high-ROI enterprise AI solutions.
Web: https://pbalapra.github.io/
Date: 10/09/2026, Friday
Time: 11 AM - 12 PM (EST)
Location: Online Only
Zoom: TBD
Title: TBD
Speaker: Paul Liu (North Carolina State University)
Abstract: TBD
Biography: Paul Liu is a Professor in the Department of Marine, Earth and Atmospheric Sciences at North Carolina State University and Director of the College of Sciences’ AI Hub for Science. His research spans artificial intelligence for science and geoscience, with a particular focus on generative AI and large language models, AI agents, retrieval-augmented generation, and AI-enabled modeling of complex Earth and ocean systems. He has co-developed several domain-focused AI platforms, including OceanAI, OceanGPT, and DeltaGPT, and has led graduate courses and hands-on training programs on generative AI and LLMs for scientific research. His broader scientific work focuses on river–delta–coastal systems, sediment transport, and large-scale environmental data modeling.
Web: https://meas.sciences.ncsu.edu/people/jpliu/
Date: 10/23/2026, Friday
Time: 11 AM - 12 PM (EST)
Location: Online Only
Zoom: TBD
Title: TBD
Speaker: Paris Perdikaris (University of Pennsylvania)
Abstract: TBD
Biography: Paris's research interests span a range of topics at the interface of computational science and machine learning. Current efforts are focused on the development of foundation models for accelerating physical simulations, physics-informed machine learning, neural operators, and uncertainty quantification. By bridging the gap between data-driven approaches and scientific knowledge, his group’s work is paving the way for more accurate, efficient, and interpretable simulations across fields like Earth system modeling, fluid dynamics, and materials science. Paris's methods not only aim to accelerate scientific discovery but also to address real-world challenges in engineering design and the optimization of complex physical systems.
Web: https://ai4science.seas.upenn.edu/
Date: 11/06/2026, Friday
Time: 11 AM - 12 PM (EST)
Location: Online Only
Zoom: TBD
Title: TBD
Speaker: Ian T. Foster (Argonne National Laboratory & University of Chicago)
Abstract: TBD
Biography: Ian Foster is the Director of Argonne’s Data Science and Learning Division, Argonne Senior Scientist and Distinguished Fellow and the Arthur Holly Compton Distinguished Service Professor of Computer Science at the University of Chicago. He was the Director of Argonne’s Computation Institute from 2006 to 2016. He is an elected Fellow of the American Association for the Advancement of Science, the Association for Computing Machinery, and British Computer Society. Foster’s research contributions span high-performance computing, distributed systems, and data-driven discovery. He has published hundreds of scientific papers and eight books on these and other topics. Methods and software developed under his leadership underpin many large national and international cyberinfrastructures. Foster received a BSc (Hons I) degree from the University of Canterbury, New Zealand, and a PhD from Imperial College, United Kingdom, both in computer science. His awards include the Global Information Infrastructure (GII) Next Generation award, the British Computer Society’s Lovelace Medal, R&D Magazine’s Innovator of the Year, the IEEE Tsutomu Kanai award, and honorary doctorates from the University of Canterbury, New Zealand and CINVESTAV, Mexico.
Web: https://www.anl.gov/profile/ian-t-foster
Date: 11/20/2026, Friday
Time: 11 AM - 12 PM (EST)
Location: Online Only
Zoom: TBD
Title: TBD
Speaker: George Em Karniadakis (Brown University)
Abstract: TBD
Biography: George Karniadakis is from Crete. He is an elected member of the National Academy of Engineering, National Academy of Arts and Sciences, and a Vannevar Bush Faculty Fellow. He received his S.M. and Ph.D. from Massachusetts Institute of Technology (1984/87). He was appointed Lecturer in the Department of Mechanical Engineering at MIT and subsequently he joined the Center for Turbulence Research at Stanford / Nasa Ames. He joined Princeton University as Assistant Professor in the Department of Mechanical and Aerospace Engineering and as Associate Faculty in the Program of Applied and Computational Mathematics. He was a Visiting Professor at Caltech in 1993 in the Aeronautics Department and joined Brown University as Associate Professor of Applied Mathematics in the Center for Fluid Mechanics in 1994. After becoming a full professor in 1996, he continued to be a Visiting Professor and Senior Lecturer of Ocean/Mechanical Engineering at MIT. He is an AAAS Fellow (2018-), Fellow of the Society for Industrial and Applied Mathematics (SIAM, 2010-), Fellow of the American Physical Society (APS, 2004-), Fellow of the American Society of Mechanical Engineers (ASME, 2003-) and Associate Fellow of the American Institute of Aeronautics and Astronautics (AIAA, 2006-). He received the William Benter Prize (2026), SES G.I. Taylor medal (2014), the SIAM/ACM Prize on Computational Science & Engineering (2021), the Alexander von Humboldt award in 2017, the SIAM Ralf E Kleinman award (2015), the J. Tinsley Oden Medal (2013), and the CFD award (2007) by the US Association in Computational Mechanics.
Web: https://engineering.brown.edu/people/george-e-karniadakis
Date: 04/30/2026, Thursday
Time: 3 - 4 PM (EST)
Location: Online Only
Zoom: https://ncsu.zoom.us/j/95437928324?pwd=wzLmLrAfDsqW3dl13iD8i90Fa5biEV.1&jst=2
Title: Pushing the Frontier of (Small) Language Models
Speaker: Mojan Javaheripi (Reflection AI)
Abstract: In this talk, I will explore key research contributions in efficient deep learning, with a focus on training smaller yet highly capable language models. I will discuss approaches such as curating high-quality datasets and designing effective training curricula. The talk will cover different stages of training—including pre-training, mid-training, and agentic reasoning—and highlight techniques for pushing the boundary of performance via transfer from larger and/or more powerful union of models. I will conclude by outlining promising future research directions aligned with these ideas.
Biography: Mojan Javaheripi leads the midtraining and synthetic data pillar at Reflection AI. Prior to joining Reflection, she was a Principal researcher and technical advisor to the CTO at Microsoft, as well as a resident researcher at OpenAI. Her research enhances open-source LLMs through new data sources, training regimens, and model architectures. She received her PhD from the University of California San Diego and her dissertation focused on efficient deep learning training and inference, adversarial robustness, and privacy-preserving deep learning.
Web: https://sites.google.com/view/mojan-javaheripi/home
Date: 04/10/2026, Friday
Time: 11:00 AM - 12:00 PM (EST)
Location: Online Only
Zoom: https://ncsu.zoom.us/j/96516238257?pwd=gqI0a1F8ZB1fYn3tIAgKozVLF96YSV.1&jst=2
Title: Learning Foundation Operators and Diffusion Models over Function Spaces
Speaker: Lu Lu (Department of Statistics and Data Science at Yale University)
Abstract: As an emerging paradigm in scientific machine learning (SciML), deep neural operators pioneered by us can learn nonlinear operators of complex dynamic systems via neural networks. In this talk, I will present the vanilla deep operator network (DeepONet) and several extensions of DeepONet, such as DeepONet with Fourier decoder layers and geometry-dependent/manifold operator learning. I will demonstrate their effectiveness on diverse multiphysics and multiscale 3D problems, such as geological carbon sequestration, full waveform inversion, and topology optimization. I will present the first operator learning method that requires only one PDE solution, i.e., one-shot learning, by introducing a new concept of local solution operator based on the principle of locality of PDEs. I will also present the first systematic study of federated SciML for approximating functions and solving PDEs with data heterogeneity. Moreover, I will present our recent work on diffusion models, including FunDiff as a novel framework of diffusion models over function spaces for physics-informed generative modeling and solving forward and inverse PDE problems, and RED-DiffEq as regularization by denoising diffusion models for solving inverse PDE problems.
Biography: Lu Lu is an Assistant Professor in the Department of Statistics and Data Science at Yale University. Prior to joining Yale, he was an Assistant Professor in the Department of Chemical and Biomolecular Engineering at the University of Pennsylvania from 2021 to 2023, and an Applied Mathematics Instructor in the Department of Mathematics at the Massachusetts Institute of Technology from 2020 to 2021. He obtained his Ph.D. degree in Applied Mathematics at Brown University in 2020, master's degrees in Engineering, Applied Mathematics, and Computer Science at Brown University, and bachelor's degrees in Mechanical Engineering, Economics, and Computer Science at Tsinghua University in 2013. His current research interest lies in scientific machine learning and artificial intelligence for science, including theory, algorithms, software, and its applications to engineering, physical, and biological problems. His broad research interests focus on multiscale modeling and high performance computing for physical and biological systems. He has received the Department of Energy Early Career Award, MIT Technology Review Innovators under 35 Asia Pacific, Mathematics Young Investigator Award from MDPI, and Joukowsky Family Foundation Outstanding Dissertation Award of Brown University.
Date: 04/03/2026, Friday
Time: 11:00 AM - 12:00 PM (EST)
Location: Online Only
Zoom: https://ncsu.zoom.us/j/97799769424?pwd=N1f6LOplPtbUgqE8v1kW7zJ8JA0u97.1
Title: Discovering and Controlling Safety Risks in Foundation Models: A Probabilistic Perspective
Speaker: Ruqi Zhang (Computer Science Department at Purdue University)
Abstract: As foundation models, including large language models and multimodal models, are increasingly deployed in complex and high-stakes settings, ensuring their safety has become more important than ever. In this talk, I present a probabilistic perspective on AI safety: safety risks are treated as structured distributions to be discovered and controlled, rather than isolated failures to be patched. I first introduce probabilistic red-teaming methods that characterize distributions of failures, revealing systematic safety risks that standard evaluation often misses. I then describe probabilistic defense methods that control model behavior during deployment by adaptively steering generation toward constraint-aligned distributions. By unifying failure discovery and behavior control under a probabilistic perspective, this talk highlights a distributional approach for understanding and managing safety risks in foundation models.
Biography: Ruqi Zhang is an Assistant Professor in the Department of Computer Science at Purdue University. Her research focuses on probabilistic machine learning, generative modeling, and trustworthy AI. Prior to joining Purdue, she was a postdoctoral researcher at the Institute for Foundations of Machine Learning (IFML) at the University of Texas at Austin. She received her Ph.D. from Cornell University. Dr. Zhang has been a key organizer of the Symposium on Probabilistic Machine Learning. She has served as an Area Chair and Editor for ML conferences and journals, including ICML, NeurIPS, ICLR, AISTATS, UAI, and TMLR. Her contributions have been recognized with several honors, including AAAI New Faculty Highlights, Amazon Research Award, Spotlight Rising Star in Data Science, Seed for Success Acorn Award, and Ross-Lynn Research Scholar.
Date: 11/14/2025, Friday
Time: 11:00 AM - 12:00 PM (EST)
Location: Online Only
Zoom: https://ncsu.zoom.us/j/91683735738
Title: Accelerating Biomolecular Design with Generative AI
Speaker: Wengong Jin (Computer Science Department at Northeastern University)
Abstract: The discovery of biomolecules with desired properties is critical to advances in drug discovery and synthetic biology. This problem is challenging due to the combinatorial search space of biomolecules. In this talk, I will present how generative AI can be used to accelerate the discovery process across small molecules, proteins, and RNAs. First, I will present a generative deep learning approach for de novo antibiotic design, where AI methods successfully discovered two lead compounds with in vivo bactericidal efficacy against multidrug-resistant bacteria in mice models. Second, I will present an energy-based modeling approach for protein design named BindEnergyCraft, which provides a principled way for calculating the likelihood of 3D structures and substantially improves the in silico binder success rate of current state-of-the-art binder design methods. Lastly, I will present a diffusion model-based approach for designing RNA translational control elements, using internal ribosome entry sites (IRESs) as a model system. Validated in human cells, we find that AI-generated IRESs circumvent natural sequence constraints and improve IRES activity by nearly 10 fold. In summary, our in silico and experimental results highlight the potential of generative AI for accelerating biomolecular design.
Biography: Wengong Jin is an assistant professor at Khoury College of Computer Sciences at Northeastern University and a visiting research scientist in the Eric and Wendy Schmidt Center at Broad Institute. His research focuses on geometric and generative AI models for drug discovery and synthetic biology. His work has been published in journals including ICML, NeurIPS, ICLR, Nature, Science, Cell, and PNAS, and covered by such outlets as the Guardian, BBC News, CBS Boston, and the Financial Times. He is the recipient of the Google Research Scholar Award, BroadIgnite Award, Dimitris N. Chorafas Prize, and MIT EECS Outstanding Thesis Award.
Date: 10/17/2025, Friday
Time: 11:00 AM - 12:00 PM (EST)
Location: EB2-3-Bridge-3001B-Lactation Room
Zoom: https://ncsu.zoom.us/j/91683735738
Title: Behavior-Aware Data Valuation for LLMs at Scale
Speaker: Zhaozhuo Xu (Computer Science Department at Stevens Institute of Technology)
Abstract: Large Language Models (LLMs) depend on massive datasets whose quality and influence remain largely opaque. Data valuation offers principled methods to quantify how training data contributes to model performance and behavior. Yet, scaling classical approaches such as influence functions to trillion-token corpora continues to be a major challenge. This talk introduces recent advances that address this gap, including the linearized influence kernel, a new and efficient metric that extends to LLMs with billion-scale parameters. We will also highlight system-level frameworks such as RapidIn and present empirical findings of LLM training, including the slowly change phenomenon, which enables forward-looking valuation of future training data. By combining principled algorithms, system optimizations, and case studies, the talk aims to bridge the gap between theory and practice.
Biography: Zhaozhuo Xu is an Assistant Professor in the Department of Computer Science at Stevens Institute of Technology. He received his Ph.D. from Rice University and an M.S. from Stanford University. His research develops randomized algorithms to enhance the efficiency of AI systems on commodity hardware. Dr. Xu’s work has appeared in leading venues such as NeurIPS, ICML, ICLR, OSDI, and ACL, as well as in journals including Nature NPJ AI. His innovations in scalable AI have been integrated into widely used libraries like Hugging Face. He serves as an Associate Editor for Neurocomputing and as an Area Chair for major conferences, including NeurIPS, ICLR, ICML, ACL, EMNLP, NAACL, and COLING. He is a recipient of the AAAI New Faculty Highlights (2025), the NSF CRII Award (2025), and the Stevens Bridging Award.
Date: 10/03/2025, Friday
Time: 11:00 AM - 12:00 PM (EST)
Location: EB II, Conference Room 3211
Zoom: https://ncsu.zoom.us/j/91683735738
Title: Synergizing Sparse Sequence, Experimental, and AI-Predicted Structures for Protein-Nucleic Acid Interaction Predictions
Speaker: Xingcheng Lin (Physics Department at NC State University)
Abstract: Sequence-specific nucleic acid recognition underlies essential processes in gene regulation, yet experiment-independent methods for simultaneous predictions of genomic DNA recognition sites and their binding affinity remain limited. Our group developed data-driven methods and simulation tools to predict and elucidate protein-nucleic acid interactions and their contributions in reshaping chromatin structures. Specifically, we introduce the Interpretable protein-DNA Energy Associative (IDEA) model, an interpretable residue-level biophysical model capable of predicting binding sites and affinities of DNA-binding proteins without relying on experimental binding data. By integrating the structures and sequences of known protein-DNA complexes into an optimized energy model, IDEA enables a direct interpretation of the physicochemical interactions among individual amino acids and nucleotides. Using transcription factors as examples, we demonstrate that IDEA accurately predicts genomic DNA recognition sites and their binding strengths. Additionally, IDEA is incorporated into a coarse-grained simulation framework that quantitatively captures the absolute protein-DNA binding free energies. Collectively, IDEA provides an integrated computational platform that alleviates experimental costs and biases in the assessment of DNA recognition and can be utilized for mechanistic studies of various DNA recognition processes. Finally, I will present our recent progress in extending this framework to predict protein-single-stranded nucleic acid interactions and to design therapeutic aptamers.
Biography: Xingcheng Lin is an assistant professor in the Physics Department at North Carolina State University, starting in August 2023. He is also affiliated with the Bioinformatics Cluster of the Chancellor’s Faculty Excellence Program. Dr. Lin earned his Ph.D. in Biological Physics from the Center for Theoretical Biological Physics and the Physics Department at Rice University. During his graduate studies, he employed both atomistic and coarse-grained simulations to investigate the molecular mechanisms behind the invasion of influenza viruses. He also developed simulation-based tools to refine folded protein structures and to simulate intrinsically disordered proteins. Following his doctorate, Dr. Lin conducted postdoctoral research in the Chemistry Department at the Massachusetts Institute of Technology (MIT), where he broadened his research focus to include the chromatin system. The Lin group focuses on integrating top-down data-driven approaches with bottom-up biophysical simulations to predict protein-nucleic acid interactions and understand their implications for genome regulation.
Date: 9/19/2025, Friday
Time: 11:00 AM - 12:00 PM (EST)
Location: Online Only
Zoom: https://ncsu.zoom.us/j/91683735738
Title: Breaking Barriers: Advancing Long Context LLMs
Speaker: Zirui Liu (University of Minnesota)
Abstract: LLMs have demonstrated impressive conversational abilities. However, scaling them to handle longer contexts, such as extracting information from lengthy articles—a critical task in healthcare, law, and finance applications—presents significant challenges. The two main obstacles are: first, LLMs struggle to process input lengths beyond what they encountered during pre-training; second, even when information is accurately extracted from extended contexts, deploying LLMs in real-world scenarios is limited by hardware capacity. I will discuss recent advances in serving long context LLMs at scale. To address the first challenge, I’ll present our work on extending LLM context length 10X by coarsening the positional encoding. For the second challenge, I will highlight our recent success in 2-bit KV Cache quantization. Lastly, I will briefly discuss the reproducibility issue of reasoning evaluation.
Biography: Zirui Ray Liu is an Assistant Professor of Computer Science at University of Minnesota. His interests lie in the broad area of Machine Learning and Data Mining. He regularly published papers in top venues such as, NeurIPS, ICML, ICLR, and MLSys. His work has been integrated into widely used NLP tools like Llama.cpp and Huggingface Transformers, and was highlighted at Google I/O sessions. Website: https://zirui-ray-liu.github.io/
Date: 9/5/2025, Friday
Time: 11:00 AM - 12:00 PM (EST)
Location: EB II, Conference Room 3211
Zoom: https://ncsu.zoom.us/j/91683735738
Title: Toward Real-Time Ultrasound Computed Tomography: Bridging Wave Physics and Data-Driven Learning
Speaker: Youzuo Lin (University of North Carolina at Chapel Hill & Los Alamos National Laboratory)
Abstract: Ultrasound Computed Tomography (USCT), also known as Full Waveform Inversion (FWI), reconstructs the mechanical properties of biological tissues by modeling the full propagation of ultrasound waves. This modality shows great promise for advanced applications such as breast, neuro, and prostate imaging, yet its clinical adoption has been limited by the trade-off between accuracy and computational efficiency. Physics-based reconstruction methods achieve high-resolution, quantitative maps of tissue properties but are computationally demanding and sensitive to model uncertainties. Data-driven approaches, particularly deep learning, have recently offered accelerated solutions but often lack robustness and generalizability. In this work, we present hybrid USCT strategies that bridge wave physics and machine learning. By embedding physical principles into self-supervised learning frameworks, our methods substantially reduce computational cost while maintaining reconstruction fidelity. We demonstrate their efficacy in challenging prostate imaging scenarios, highlighting their potential to advance USCT toward real-time clinical translation.
Biography: Youzuo Lin is an Associate Professor in the School of Data Science and Society at the University of North Carolina at Chapel Hill. Previously, he served as a Senior Scientist at Los Alamos National Laboratory. He earned his Ph.D. in Applied and Computational Mathematics from Arizona State University in 2010. Youzuo’s research focuses on scientific machine learning methods and their applications, particularly in computational wave imaging, ultrasound tomography, geophysical inversion, and UAV image analysis. He has published over 100 articles in leading journals and conference proceedings and is a co-inventor on several U.S. patents related to ultrasound imaging techniques.
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