Neurovascular Oscillations in Stroke Recovery: Linking Microcirculation Dynamics and Electrophysiology

Wednesday, October 28, 2026; 12:15 - 1:15 pm
W306 Millennium Science Complex
Speaker: David Boas from Boston University

Slow hemodynamic oscillations (0.05–0.4 Hz) in cortical vasculature predict stroke recovery. In an awake photothrombotic mouse model, low-frequency oscillations (LFOs) in total hemoglobin rise one week post-stroke and predict four-week behavioral outcomes—independent of neural calcium signals—implicating vascular dynamics, not perfusion, in recovery. LFO power correlates with a lower aperiodic (1/f) exponent, suggesting a shift in excitatory/inhibitory balance linked to parvalbumin interneuron loss. Gamma Entrainment Using Sensory Stimulation (GENUS, 40 Hz) robustly elevates vascular oscillations in healthy mice, and ongoing work tests whether this enhances recovery after stroke.

https://psu.zoom.us/j/94639233394

Hosted by: Rebecca Benson,  rle4@psu.edu

AI in Smart Manufacturing – Can I trust

Wednesday, October 28, 2026; 3:35-4:25 pm
254 Health and Human Development
Speaker: SOUNDAR from KUMARA

Abstract: AI and machine learning are changing manufacturing, but can we trust what these systems recommend? And is explainability where our attention should go? Drawing on his own journey in smart manufacturing, the speaker will take up these questions and show how they led to the DoE’s Genesis Mission project on Neurosymbolic Intelligence (NeSyI), which is currently funded. He will then present his group’s work building NeSyI architectures for two manufacturing problems: assembly line balancing and real-time quality control.

Bio: Soundar Kumara’s research focuses on complex systems and on AI and machine learning in manufacturing and healthcare. Over his career he has advised or co-advised 71 Ph.D. students and 80 M.S. and M.Eng. students. He is a Fellow of IISE, ASME, SME, CIRP, and AAAS. His honors include the David F. Baker Distinguished Research Award from IISE and the Excellence in Research Award from the ASME Computers and Information in Engineering (CIE) Division. The Society of Manufacturing Engineers named him one of the 20 most influential academics in smart manufacturing. At Penn State, he has received several Penn State Engineering Society (PSES) awards from the College of Engineering and the Faculty Scholar Medal. He has also received the Outstanding Industrial Engineer Award from Purdue’s School of Industrial Engineering and the Distinguished Alumnus Award from the Indian Institute of Technology Madras. He is a consultant to the Army Science Board and a member of the Pennsylvania Joint State Government Commission’s Advisory Committee on Artificial Intelligence Impact and Potential in Pennsylvania

Hosted by: Lana Fulton,  lub18@psu.edu

Cortical Spreading Depolarization in Health and Disease

Wednesday, November 4, 2026; 12:15 - 1:15 pm
W306 Millennium Science Complex
Speaker: Ethan Goldberg from The Children's Hospital of Philadelphia

Cortical spreading depolarization (CSD) is an inhibitory electrochemical wave that involves collapse of ionic gradients as it moves slowly across the brain. CSD may be deleterious, associated with secondary brain injury in the context of stroke, TBI, or subarachnoid hemorrhage. Yet, post-ictal CSD may also represent an endogenous anti-seizure mechanism. In otherwise healthy brain, CSD can be temporary and reversible, and may underlie the aura associated with migraine headache. We use mesoscopic imaging of biosensors in mice in vivo and noninvasive monitoring of blood flow/oxygen in human to explore the role of CSD in epilepsy and in the context of electroconvulsive therapy.

https://psu.zoom.us/j/94639233394

Hosted by: Rebecca Benson,  rle4@psu.edu

Ultrasonic Imaging to Reveal the Physics of Earthquake Precursors

Wednesday, November 4, 2026; 3:35-4:25 pm
254 Health and Human Development
Speaker: Jacques Riviere from

Abstract
Earthquakes result from sudden frictional instabilities along tectonic faults. Death toll due to earthquakes can be enormous, especially in regions where proper infrastructure is lacking. Anthropogenic earthquakes near geothermal reservoirs or due to wastewater injection also threaten communities in regions of historically low seismicity. The objective of this research is to discover the mechanisms of precursory changes in rock and fault properties preceding laboratory earthquakes, to improve our understanding of earthquake precursors and ultimately better assess seismic risk. I will first begin this talk by providing an overview of the lab work conducted in this area and describe how it can be used to inform field observations. I will also describe the recent application of machine learning models to predict laboratory earthquakes. Finally, I will present more recent results from laboratory experiments where dense arrays of ultrasonic sensors are utilized (i) to characterize laboratory earthquakes and their foreshocks and (ii) to understand precursory changes to failure on heterogeneous faults. Our recent results show that, under similar conditions, some laboratory earthquakes exhibit simple ruptures while others reveal a rich seismic response, including ruptures of small asperities taking place concomitantly with the main rupture. Together, our laboratory experiments show that datasets with a seemingly simple configuration and simple mechanical response can exhibit both complex precursory changes to failure and complex seismic responses.

 

Biographical 

Jacques Rivière is an associate professor of Engineering Science and Mechanics, as well as Acoustics, at the Pennsylvania State University. Prior to his appointment at Penn State, he was a Marie-Curie postdoctoral fellow at the Université Grenoble Alpes, France (2016-2018), a postdoctoral scholar in the Department of Geosciences at the Pennsylvania State University (2015-2016), and a postdoctoral scholar at Los Alamos National Laboratory (2012-2015). He earns a Ph.D. from Université Pierre et Marie Curie in Paris, France and an M.S. in Mechanics and Acoustics from Université du Maine, Le Mans, France. He is a recipient of the U.S. Department of Energy Office of Science Early Career Award (2022). His research expertise spans across acoustics, ultrasonics and geophysics. It includes the use linear/nonlinear ultrasonics, ultrasonic imaging and acoustic emission for (i) the nondestructive evaluation (NDE) and/or structural health monitoring (SHM) of materials for industrial and civil applications, as well as (ii) the characterization and monitoring of rocks, granular media and fault friction in the context of earthquake physics and related geophysical applications.

Hosted by: Lana Fulton,  lub18@psu.edu

2026 Hankin Distinguished Lecture

Wednesday, November 11, 2026; 4 PM
Robb Hall, Hintz Family Alumni Center
Speaker: Dr. Todd Usher from

Hosted by: Jessica Wilson,  jrw412@psu.edu

Toward Robust and Efficient Reasoning with LLM Agents

Wednesday, November 18, 2026; 254 Health and Human Development Building
3:35-4:25 p.m.
Speaker: Aram Galstyan from

Abstract: Test-time scaling (TTS) aims to improve the reasoning capabilities of large language models (LLMs) and agents by allocating more compute at inference time. However, TTS introduces its own challenges, such as redundant reasoning, error propagation across multiple steps, and increased inference latency. In this talk, I will present our recent work addressing these challenges. First, I will show how selectively pruning redundant tokens from reasoning trajectories can improve accuracy without extra training. Next, I will introduce a framework for structured communication among parallel agents that detects and resolves factual inconsistencies during exploration while preserving diversity in reasoning trajectories. Finally, I will present an approach that uses idle time during tool execution for adaptive speculative planning, improving agent performance without increasing latency. Together, these results show how more effective use of test-time computation can make LLM agents more robust and efficient.

BIO: Aram Galstyan is a recognized leader in AI and machine learning with more than 25 years of experience and 150+ peer-reviewed publications in leading AI, machine learning, and natural language processing venues. He has a strong track record of leading interdisciplinary research teams, setting scientific strategy, and delivering large-scale, high-impact projects. His current research spans large language models, agentic systems, personalization, human-AI collaboration, and trustworthy AI.

As an Amazon Scholar in Amazon AGI Foundations, Aram provides technical leadership across initiatives in large language model safety, efficiency, and personalization. His work includes shaping research roadmaps, fostering collaboration between research and engineering teams, and developing academic partnerships that accelerate strategic AI initiatives.

At the USC Information Sciences Institute, Aram established and led the Machine Intelligence and Data Science group and directed the institute’s AI Division, its largest research unit. He led and mentored an organization of more than 100 researchers, engineers, faculty, postdoctoral scholars, and Ph.D. students. He also initiated and led major interdisciplinary research programs supported by DARPA, IARPA, NSF, and NIH, contributing to more than $60 million in sponsored research funding.

Hosted by: Lana Fulton,  lub18@psu.edu