AI Innovation Institute

Professor Nanpeng Yu from UC Riverside present Machine Learning and Big Data Analytics in Power Distribution Systems.

Abstract: The electric utility industry is being swamped by petabytes of data coming from various sources such as smart meters, phasor measurement units, SCADA systems, geographical information systems and customer management systems. The primary and secondary value embedded in the complex and heterogeneous data sets from power distribution systems is immense. However, algorithms and applications for unlocking the potential of big data in power systems are at an early stage of development. This talk discusses the recent advancement of machine learning algorithms and big data analytics methods in power distribution systems. In particular, we will explain how to develop hybrid algorithms, which synergistically combine the merits of state-of-the-art machine learning algorithms and physical model-based methods. We will take a deep dive into the following applications: network topology identification, electricity theft detection, estimation of behind-the-meter solar generation and data-driven distribution system controls.

Bio: Dr. Nanpeng Yu received his B.S. in Electrical Engineering from Tsinghua University, Beijing, China, in 2006. Dr. Yu received his M.S. degrees in Electrical Engineering and Economics and Ph.D. degree from Iowa State University in 2010. Before joining University of California, Riverside, Dr. Yu was a senior power system planner and project manager at Southern California Edison from Jan, 2011 to July 2014.

Currently, he is an Associate Professor in the Department of Electrical and Computer Engineering at the University of California, Riverside, CA. Dr. Yu is the recipient of the Regents Faculty Fellowship and Regents Faculty Development award from University of California. He received multiple best paper awards from IEEE Power and Energy Society General Meeting, IEEE Power and Energy Society Grand International Conference and Exposition Asia and the Second International Conference on Green Communications, Computing and Technologies.

Dr. Yu is the director of Smart City Innovation Laboratory at UC Riverside. He currently serves as the vice chair of the distribution system operation and planning subcommittee of IEEE Power and Energy Society and the co-chair for IEEE Big Data Applications in Power Distribution Networks Task Force. Dr. Yu currently serves as the associate editor for IEEE Transactions on Smart Grid and International Transactions on Electrical Energy Systems.

Abstract: Visual generation is a fundamental problem in computer vision and graphics, with applications ranging from 3D capture to image and video synthesis. Despite rapid progress in neural rendering and generative models, efficiency remains a key bottleneck: high-quality 3D reconstruction often relies on dense multi-view supervision; high-fidelity 3D synthesis requires costly optimization, training, and rendering; and modern image and video generators require substantial computation as the number of tokens grows rapidly for high-resolution generation. This dissertation focuses on efficient visual generation by improving sample efficiency in 3D reconstruction, representation efficiency in 3D generation, and computational efficiency in image and video synthesis. First, we improve the sample efficiency of neural implicit surface reconstruction. We integrate multi-view stereo probability volumes as a geometric regularizer, enabling high-quality sparse-view reconstruction from as few as three input images. Next, we introduce an explicit 3D representation for 3D generation, built from multi-view depth and RGB images with 3D Gaussian features. This design allows the model to use 2D generative priors while enforcing multi-view consistency through epipolar attention. We then address the computational bottleneck in image and video synthesis with importance-based token merging. Our method uses importance signals available during generation to preserve critical information while merging redundant tokens. Finally, we enable efficient mixed-resolution diffusion transformers via phase-aligned attention. This approach stabilizes attention under mixed-resolution token grids and unlocks high-fidelity image and video generation at reduced cost. Taken together, these contributions reduce the data requirements, representational overhead, and computational demands, thereby providing a foundation for high-quality, scalable, and efficient visual generation.

Speaker: Haoyu Wu

Location: NCS 120

The Future Histories Studio will host Young Maeng, an artist and professor at California State University, Fresno, for a talk exploring the intersection of artificial intelligence (AI) and traditional painting, examining how two seemingly disparate fields can converge to create new artistic expressions.

The lecture is part of the Future History Studio series at Stony Brook University, a platform dedicated to examining the evolving relationship between technology, art, and society.

Young will discuss her innovative approach to expanded painting, an integration of AI-generated images and traditional techniques such as Korean ink and acrylic painting. Through this fusion, she visualizes complex philosophical and ethical questions about the coexistence of humans, nature, and AI companion robots. The lecture will highlight the broader implications of AI in the art world, touching on how AI technologies challenge conventional notions of creativity and human-centric perspectives in art.

Speaker Bio:

Young Maeng is an artist and professor at California State University, Fresno, whose work explores the intersection of artificial intelligence (AI) and traditional painting techniques such as Korean ink and acrylic.

Maeng's innovative approach to expanded painting blends AI technology with traditional methods to visualize complex philosophical and ethical questions surrounding the coexistence of humans, nature, and AI companion robots.

Location: Future Histories Studio
Register here: https://www.eventbrite.ca/e/ai-and-painting-tickets-1021050809457?aff=oddtdtcreator

Developing research questions and effective search strategies can be challenging, and the growing use of generative AI adds a new layer of complexity to the research process. While these tools can help you find, evaluate, and work with the health sciences literature, knowing when and how to use them effectively is not always straightforward. This workshop will explore how generative AI can complement traditional research methods while navigating important considerations around accuracy, transparency, privacy, and reliability.

Location: Health Sciences Center, 2-154

Place:  https://stonybrook.zoom.us/j/99167126152?pwd=TFpEYzM0aFhiOFJxSFJEb1JSS3YyQT09  

Time: 3 PM EST - Dec, 16th, 2020 

Abstract: 

Shadows provide useful cues to analyze visual scenes but also hamper many computer vision algorithms such as image segmentation, object detection, or tracking. For those reasons, shadow detection and shadow removal have been well-studied in computer vision.

Early work on shadow detection and removal focused on physical illumination models of shadows. These methods can express, identify, and remove shadows in a physically plausible manner. However, these models are often hard to optimize and are slow during inference due to their reliance on hand-designed image features. Recently, deep-learning approaches have achieved breakthroughs in performance for both shadow detection and removal. They learn to extract useful features through training while being extremely efficient during inference. However, these models are data-dependent, opaque, and ignore the physical aspects of shadows. Thus they often lack generalization and produce inconsistent results.

We propose incorporating physical illumination constraints of shadows into deep-learning models. These constraints force the networks to more closely follow the physics of shadows, enabling them to systematically and realistically modify shadows in images. For shadow detection, we present a novel Generative Adversarial Network (GAN) based model where the generator learns to generate images with realistic attenuated shadows that can be used to train a shadow detector. For shadow removal, we propose a method that uses deep-networks to estimate the unknown parameters of a shadow image formation model that removes shadows. The system outputs high-quality shadow-free images with little or no image artifacts and achieves state-of-the-art performance in shadow removal when trained on a fully-supervised setting. Moreover, the system is easy to train and constrain since the shadow removal mapping is strictly defined by the simplified illumination model with interpretable parameters. Thus, it can be trained even with a much weaker form of supervision signal. In particular, we show that we can use two sets of patches, shadow and shadow-free, to train our shadow decomposition framework via an adversarial system. These patches are cropped from the shadow images themselves.
Therefore, this is the first deep-learning method for shadow removal that can be trained without any shadow-free images, providing an alternative solution to the paired data dependency issue. The advantage of this training scheme is even more pronounced when tested on a novel domain such as video shadow removal where the method can be fine-tuned on a testing video with only the shadow masks generated by a pre-trained shadow detector and further improves shadow removal results.

CSE 600 Seminar Series | Fall 2025

Speaker: Prof. R Sekar

Prof. Eugene A. Feinberg, from the Department of Applied Mathematics and Statistics, presents, Recent Developments in Markov Decision Processes Relevant to AI on April 4 at 4p. The talk discusses recent developments in Markov Decision Processes potentially relevant to artificial intelligence. These developments include complexity estimations for exact and approximate algorithms, decision making with incomplete information and multiple criteria, and continuity properties of optimal values and expectations. Dr. Eugene A. Feinberg is currently Distinguished Professor in the Department of Applied Mathematics and Statistics at Stony Brook University. He is an expert on applied probability, stochastic models of operations research, Markov decision processes, and on industrial applications of operations research and statistics. He has published more than 150 papers and edited the Handbook of Markov Decision Processes. His research has been supported by NSF, DOE, DOD, NYSTAR (New York State Office of Science, Technology, and Academic Research), NYSERDA (New York State Energy Research and Development Authority) and by industry. He is a Fellow of INFORMS (The Institute for Operations Research and Management Sciences) and has received several awards including 2012 IEEE Charles Hirsh Award for developing and implementing smart grid technologies, 2012 IBM Faculty Award, and 2000 Industrial Associates Award from Northrop Grumman. Dr. Feinberg is an Associate Editor for Mathematics of Operations Research and for Applied Mathematics Letters. He is an Area Editor for Operations Research Letters. Refreshments will be provided

CSE 600 Seminar Series | Fall 2025

Abstract: Imagine machines that can see the invisible: drones locating wildfire survivors, cameras predicting building failures, and smartphones detecting skin tumors. These applications lie beyond today's vision systems, which focus only on human-visible information. In this talk, I argue that a wealth of scene information is hidden in light properties invisible to the human eye, such as the travel time of photons and polarization of light waves. I will present how co- designing camera hardware, graphics models, and learning algorithms unlocks these invisible properties to create superhuman vision systems. I will present three superhuman vision capabilities: seeing around blind corners, turning objects into cameras, and extracting internal stress fields. By analyzing faint light reflections on diffuse walls and shiny objects, we create virtual cameras that reveal scenes hidden from the line of sight - enabling autonomous systems to navigate safely. Using the polarization of light, we recover mechanical stress fields hidden inside objects - opening new possibilities for non-destructive material characterization. These capabilities point toward a future where machines can see the invisible: around us, beneath our bodies, and beyond our scientific understanding.

Bio:
Akshat Dave is an Assistant Professor in the Department of Computer Science at Stony Brook
University, USA. His research lies at the intersection of applied optics, computer vision, and
machine learning. His work has been recognized by Rice University's Best Thesis Award, Optica Best Paper Prize, SIGGRAPH Asia Doctoral Consortium, and fellowships by Qualcomm, Texas Instruments, and INK Global Foundation. Prior to Stony Brook, he was a Postdoctoral Associate at MIT Media Lab. He holds a Ph.D. from Rice University and a Masters and a Bachelors from Indian Institute of Technology Madras.