Machine Learning based Voltage Monitoring in  Real-time Unobservable Distribution Systems

Due to increasing penetration of solar photovoltaic generation and electric vehicle charging loads, there is a genuine need to closely monitor the voltage over the entire length of the distribution feeder. Smart meters, present only at the terminal nodes of the feeder, cannot fulfill this need; they also have high reporting delays.  Distribution phasor measurement units have the necessary speed, but it is cost- prohibitive to place them in bulk. Thus, monitoring voltages in real-time unobservable distribution systems is challenging. This talk will describe how the use of machine learning can help overcome this challenge by performing high-speed voltage estimation while accounting for the physical attributes and operational characteristics of modern distribution systems. To ensure trust in the machine learning-based approach, formal guarantees of performance will also be provided.

Anamitra Pal is an Associate Professor in the School of Electrical, Computer, and Energy Engineering at Arizona State University (ASU). His research interests include data analytics with a special emphasis on time-synchronized measurements, artificial intelligence applications in power systems, renewable generation integration studies, and critical infrastructure resilience. Dr. Pal has received the 2018 Young CRITIS Award for his contributions to the field of critical infrastructure protection, the 2019 Outstanding Young Professional Award from the IEEE Phoenix Section, the National Science Foundation CAREER Award in 2022, and the 2023 Centennial Professorship Award from ASU.

Host: Rajasekhar Anguluri

Every year, UMBC honors a faculty member with the Presidential Research Professor Award. CSEE professor Tülay Adali was selected this year as UMBC's Presidential Research Professor for 2024-2027.

Professor Adali received the Ph.D. in electrical engineering from North Carolina State University, Raleigh, NC, in 1992 and joined the UMBC’s faculty the same year.  In 2015, she was named a Distinguished University Professor in recognition of her outstanding contributions to statistical signal processing and machine learning and excellence in teaching and mentoring the next generation of engineers and scholars who continue to advance the field of signal processing and machine learning.

Dr. Adali's commitment to her professional community is evident in her diverse roles and responsibilities. She currently serves as Editor-in-Chief of the prestigious IEEE Signal Processing Magazine and has held significant positions such as the Chair of the IEEE Brain Technical Community and the IEEE Signal Processing Society Vice President for Technical Directions from 2019 to 2022. Her professional involvement includes the organization of many conferences and workshops, including the IEEE International Conference on Acoustics, Speech, and Signal Processing, where she has served in various capacities.

Professor Adali is a Fellow of the IEEE, AIMBE, and AAIA, a Fulbright Scholar, and an IEEE SPS Distinguished Lecturer. She has received the SPS Meritorious Service Award, Humboldt Research Award, IEEE SPS Best Paper Award, the SPIE Unsupervised Learning and ICA Pioneer Award, the University System of Maryland Regents' Award for Research, and the NSF CAREER Award.

Her current research interests are in the areas of statistical signal processing, machine learning, and applications in medical image analysis and fusion.

Dr. Adali leads the Machine Learning for Signal Processing laboratory, and primarily with support from the NSF and the NIH, she and her research associates, research students, and network of collaborators develop theory and tools for processing signals that arise in today's growing array of applications and pose challenges for traditional signal processing techniques with a focus on medical image analysis and fusion.

His demonstration will cover key features that differentiate the business version from Copilot Pro, focusing on use cases in business and enterprise environments to integrate with the Microsoft 365 suite. Dhruv will share some tips to enhance productivity, streamline workflows, and foster collaboration across teams.

Host: Thu Nguyen

Earth artificial intelligence (AI) has become a research frontier by leveraging AI techniques to understand the complex Earth system and help various Earth applications. Challenges for Earth AI include a large volume of available data, spatial-temporal high-dimensionality, incompatible data from multiple sources, data-driven causal understanding of the Earth system. This talk will present two related Earth AI studies. The first study proposes a Time-Series Causal Neural Network (TS-CausalNN) - a deep learning technique to discover contemporaneous and lagged causal relations simultaneously from non-stationary and non-linear Earth observation time series data. The second one studies how to leverage deep domain adaptation techniques and multiple satellite data to improve cloud remote sensing retrieval. Both studies use real-world Earth data to evaluate their advantages over state-of-art approaches.

Dr. Jianwu Wang is an Associate Professor in UMBC's Department of Information Systems. He leads the Big Data Analytics Lab (BDAL) and co-leads the NSF HDR Institute for Harnessing Data and Model Revolution in the Polar Regions (iHARP). He is also an affiliate faculty in CSEE and the Joint Center for Earth Systems Technology (JCET). 

AI4ALL Ignite is a free virtual AI career accelerator that offers opportunities for Black, Hispanic/Latinx, Indigenous and/or women or non-binary undergraduate students to cultivate long-term AI industry connections and mentorship by working with AI professionals on a hands-on AI technical portfolio project. Students will present their work at a virtual student symposium, understand the technology through a responsible AI lens, and participate in career readiness training. The goal is to empower participating students with the in-demand skills needed to interview for AI technical internships. Apply by June 17 for early consideration. The year-long program begins in September 2024 and continues into the spring semester. For more information, visit the AI4ALL Ignite website.

The learned behaviors of AI and robot agents should align with the intentions of their human designers. Toward this goal, people must be able to easily specify, inspect, and model agent behaviors. For specifications, we will consider expert-written reward functions for reinforcement learning (RL) and non-expert preferences for reinforcement learning from human feedback (RLHF). I will show evidence that experts are bad at writing reward functions: even in a trivial setting, experts write specifications that are overfit to a particular RL algorithm, and they often write erroneous specifications for agents that fail to encode their true intent. Next, I will show that the common approach to learning a reward function from non-experts in RLHF uses an inductive bias that fails to encode how humans express preferences, and that our proposed bias better encodes human preferences both theoretically and empirically. For inspection, humans must be able to assess the behaviors an agent learns from a given specification. I will discuss a method to find settings that exhibit particular behaviors, like out-of-distribution failures. Lastly, cognitive science theories attempt to show how people build conceptual models that explain agent behaviors. I will show evidence that some of these theories are used in research to support humans, but that we can still build better curricula for modeling. Collectively, my research provides evidence that—even with the best of intentions— current human-AI systems often fail to induce alignment; my research proposes promising directions for how to build better aligned human-AI systems.

Serena Booth received her PhD at MIT CSAIL in 2023. Serena studies how people write specifications for AI systems and how people assess whether AI systems are successful in learning from specifications. While at MIT, Serena served as an inaugural Social and Ethical Responsible Computing Scholar, teaching AI Ethics and developing MIT’s AI ethics curriculum that is also released on MIT OpenCourseWare. Serena is a graduate of Harvard College (2016), after which she worked as an Associate Product Manager at Google to help scale Google’s ARCore augmented reality product to 100 million devices. Serena currently works in the U.S. Senate as a AAAS AI Policy Fellow, where she is working on AI policy questions for the Senate Banking, Housing, and Urban Affairs Committee. Her research has been supported by an MIT Presidential Fellowship and by an NSF GRFP. She is a Rising Star in EECS and an HRI Pioneer.

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UMBC Center for AI

In an era where Generative Artificial Intelligence (GAI) is increasingly becoming a part of our daily professional and personal lives, understanding how to effectively interact with these systems is crucial. This workshop is designed to equip participants with practical skills and insights for engaging with GAI interfaces, specifically focusing on optimizing the quality of results, integrating GAI outputs into user responses, and adhering to ethical citation practices.  This workshop is being hosted by the Graduate Program in Geographic Information Systems. Friends of the program are welcome to join virtually.

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UMBC Center for AI

Join UMBC Information Systems PhD student Rachael Kang as she shares her research journey of how she came to study AI, starting from her master's thesis on the utility of machine learning in predicting suicide risk to her current research interests of integrating a large language model into patient portals to increase patient health literacy. Rachael will also discuss how she has introduced ChatGPT as a learning tool for the students she TAs in Information Systems 303, the activities she has conducted with the students to demonstrate the strengths and weaknesses of ChatGPT, and the types of conversations she has with students about what is proper and improper use of ChatGPT.

Get more information and join the event here.

Here is a video of the session.

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UMBC Center for AI

Rigorous measurement in text-to-image systems (and AI more broadly?)

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UMBC Center for AI