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: How do humans learn the sound patterns of their language? Despite a variety of methods and advances in phonotactic learning, there is still a paucity of computational research, methods and data for languages with tones. In this talk, I will explore this question specifically in light of tone languages, where pitch plays a crucial role in distinguishing words' meaning. I provide an implementation of the Bottom-Up Factor Inference Algorithm over Autosegmental Representations (BUFIA-AR), which learns the rules governing possible tone patterns. Using a dataset of Hausa, a West African tone language, the algorithm successfully identifies patterns that are not permitted in the language. These results (i) confirm long-standing linguistic generalizations, (ii) make more specific predictions about exceptional cases, and (iii) reveal previously unnoticed patterns. The results show how mathematical models of sound structure can be brought into dialogue with both linguistic theory and computational learning, highlighting the broader potential of formal approaches to capture human linguistic knowledge.

Bio: Han Li is a fifth-year Ph.D. student in Linguistics department, specializing in computational linguistics under the supervision of Professor Jeff Heinz. Her research focuses on how sound patterns in language can be formally represented and computationally learned, bridging theoretical linguistics and computer science.

Location: Institute for Advanced Computational Science, Seminar Room

Zoom Meeting: https://stonybrook.zoom.us/j/94043459206?pwd=3ra47h8HghOFRfobRBjZaDMyTwialr.1
Meeting ID: 940 4345 9206
Passcode: 332717

Abstract:

In recent years, the landscape of artificial intelligence (AI) has been reshaped by the rapid emergence of Foundation Models (FMs). These versatile models have garnered widespread attention for their remarkable ability to transcend the boundaries of traditional, bespoke AI solutions and to generalize to a large set of downstream tasks. In this presentation we will describe the development of geospatial FMs with earth observation and weather data and discuss initial results of such models. We will also show how such foundation models can be a new and exciting tool for assisting with and accelerating scientific discovery.

Speaker:

Hendrik Hamann
Distinguished Researcher
IBM T.J. Watson Research Center

Abstract: The recent expansion of online sport wagering and igaming has led to higher rates of problem gambling, particularly among emerging adults and other population subgroups. The Center for Gambling Studies (CGS) at the Rutgers University, School of Social Work, is using big data analysis, machine learning and GIS mapping to identify geographic locations with populations most at risk to guide the development of targeted interventions. This presentation will review the GIS StoryMap for the State of New Jersey, including a blueprint for the highest risk target service areas in the state. It will also present findings from a machine learning model that identifies the key risk factors for high-intensity online casino bettors. Implications for prevention, treatment and policy initiatives will be discussed.

Bio: Lia Nower, J.D., Ph.D., is a Distinguished Professor, Associate Dean for Research, and Director of the Center for Gambling Studies at Rutgers University. A clinician and attorney, her research focuses on big data analysis and machine learning models for online gambling and sports wagering; gambling and video gaming among emerging adults; policy initiatives around harm reduction and responsible gambling, and etiology and treatment of problem gambling. Dr. Nower serves as a senior editor for Addiction. She has received both the Research (2019) and the Lifetime Research Award (2022) from the National Council on Problem Gambling and the Board of Trustees Award for Research (2022) from Rutgers University.

Join Zoom Meeting: https://stonybrook.zoom.us/j/95617197636?pwd=KytzZ2pVRG9SZGpKZUtpNXJISjNjZz09
Meeting ID: 956 1719 7636 Passcode: 924293
Join librarian Christine Fena for an interactive workshop that invites you to explore AI tools firsthand, not just as users, but as critical investigators. Through playful experimentation and collaborative discovery, you'll uncover inherent biases, probe algorithmic flaws, and gain a deeper understanding of AI's limitations and societal impacts.

Register for the Zoom workshop here.

Jerome Liang, PhD 

Professor of Radiology, Biomedical Engineering, Electric and Computer Engineering, and Computer Science 

Co-Director of Research 

Department of Radiology 


Artificial intelligence, machine learning and computer-aided diagnosis in cancer Imaging 

February 11, 2021 

12:00pm - 1:00pm 

Virtual Seminar - Zoom 

https://stonybrook.zoom.us/j/98155629970?pwd=YzRvcnJnTlNTT1E5ak1oZEJvWTZHQT09 

Meeting ID: 981 5562 9970 

Passcode: 950410 

Host: 

Wei Zhao, PhD 

Professor of Radiology and Biomedical Engineering 

Educational Objectives  

Upon completion, participants should be able to:  

(1) Learn different medical image representations of cancer attributes, such as heterogeneity, high tendency to grow, etc.  

(2) Learn how computer (machine) can be trained (or programmed) to recognize the image representations.  

(3) Learn how artificial intelligence can drive the machine learning to maximize the performance of computer-aided diagnosis (CADx).  

Disclosure Statement  

In compliance with the ACCME Standards for Commercial Support, everyone who is in a position to control the content of an educational activity provided by the School of Medicine is expected to disclose to the audience any relevant financial relationships with any commercial interest that relates to the content of his/her presentation.  

 

The speaker, Jerome Liang, PhD, the planners; and the CME provider have no relevant financial relationship with a commercial interest (defined as any entity producing, marketing, re-selling, or distributing health care goods or services consumed by, or used on, patients), that relates to the content that will be discussed in the educational activity.  

 

CONTINUING MEDICAL EDUCATION CREDITS  

The School of Medicine, State University of New York at Stony Brook, is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.  

 

The School of Medicine, State University of New York at Stony Brook designates this live activity for a maximum of 1.0 AMA PRA Category 1 Credits™. Physicians should only claim credit commensurate with the extent of their participation in the activity.  

 

Should you be logging in Zoom by using your tablet or mobile device, please be sure to add your Full Name and/or Email for CME credit. 

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