AI Seminar: Video Architecture Search - Michael Ryoo
Abstract: Video understanding is a challenging problem. Because a video contains spatio-temporal data, its feature representation is required to abstract both appearance and motion information. This is not only essential for automated understanding of the semantic content of videos, such as Web-video classification or sport activity recognition, but is also crucial for robot perception and learning. Previously, convolutional neural networks (CNNs) for videos were normally built by manually extending known 2D architectures such as Inception and ResNet to 3D or by carefully designing two-stream CNN architectures that fuse together both appearance and motion information. However, designing an optimal video architecture to best take advantage of spatio-temporal information in videos still remains an open problem. In this talk, we discuss recent progress in neural architecture search for videos, obtaining more optimal network architectures for video understanding.
Artificial Intelligence is rapidly reshaping research, education, and industry--but its growth carries important environmental implications. From the energy demands of large-scale computing to AI's potential to advance climate modeling, conservation, and sustainable design, the relationship between AI and the environment is both challenging and promising. This interdisciplinary panel explores AI's ecological footprint, its role in environmental solutions, and how universities can pursue innovation while upholding sustainability commitments.
Panelists:
Dana Golden -- PhD student in Economics, Stony Brook University.
Dr. Sharon Pochron -- Associate Professor in Sustainability Studies Program, School of Marine and Atmospheric Sciences, Stony Brook University.
Dr. Jordanna Sprayberry -- Associate Professor, Ecology & Evolution, Director of Undergraduate Biology, Stony Brook University.
Dr. Lav Varshney -- Director of the Artificial Intelligence Innovation Institute (AI3) and inaugural Della Pietra Infinity Chair, Stony Brook University.
Register here.
Panelists:
Dana Golden -- PhD student in Economics, Stony Brook University.
Dr. Sharon Pochron -- Associate Professor in Sustainability Studies Program, School of Marine and Atmospheric Sciences, Stony Brook University.
Dr. Jordanna Sprayberry -- Associate Professor, Ecology & Evolution, Director of Undergraduate Biology, Stony Brook University.
Dr. Lav Varshney -- Director of the Artificial Intelligence Innovation Institute (AI3) and inaugural Della Pietra Infinity Chair, Stony Brook University.
Register here.
The Department of AI and Society (AIS) at the University at Buffalo is hosting a two-day AI and Society Workshop focused on building AI systems by society, for society. This workshop brings together researchers and community organizers to explore how AI systems can be developed through meaningful collaboration across disciplines.
Topics include:
- Labor and AI
- Public services and AI
- Community-centered AI systems
- Intersections of humanities, social sciences, arts, and computing
The vision of UB's Department of AI and Society is to create a future where AI systems are built by society, for society. AIS centers community engagement at every stage of AI development through collaboration across disciplines and sectors. AIS was established with a $5 million grant from SUNY, and this workshop is made possible through that support.
Who Should Attend?
- Researchers
- Students
- Community organizers
- Practitioners interested in AI's societal impact
CSE 600 Talk: Haibin Ling - Computer Vision Research and Applications
Abstract: Having been intensively studied over half a decade, computer vision has evolved as a broad research area and become mature in many applications. In this talk, we will summarize our work in computer vision in both core vision topics and application-oriented ones. In particular, for core vision problems, we will report studies on visual tracking, visual matching and visual detection; for applications, we will describe our work on medical image analysis, intelligent transportation, smart projector systems and preliminary work on material property prediction.
Bio: Haibin Ling received the BS and MS degrees from Peking University in 1997 and 2000, respectively, and the PhD degree from the University of Maryland, College Park, in 2006. From 2000 to 2001, he was an assistant researcher at Microsoft Research Asia. From 2006 to 2007, he worked as a postdoctoral scientist at the University of California Los Angeles. In 2007, he joined Siemens Corporate Research as a research scientist. From 2008 to 2019, he worked as a faculty member of Temple University. In fall 2019, he joined the Department of Computer Science of Stony Brook University where he is currently a SUNY Empire Innovation Professor. His research interests include computer vision, augmented reality, medical image analysis, and human computer interaction. He received the Best Student Paper Award at the ACM UIST in 2003, and the NSF CAREER Award in 2014. He serves as Associate Editor for several journals including IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), Pattern Recognition (PR), and Computer Vision and Image Understanding (CVIU). He has served or will serve as Area Chair for CVPR 2014, 2016, 2019 and 2020.
Abstract: Having been intensively studied over half a decade, computer vision has evolved as a broad research area and become mature in many applications. In this talk, we will summarize our work in computer vision in both core vision topics and application-oriented ones. In particular, for core vision problems, we will report studies on visual tracking, visual matching and visual detection; for applications, we will describe our work on medical image analysis, intelligent transportation, smart projector systems and preliminary work on material property prediction.
Bio: Haibin Ling received the BS and MS degrees from Peking University in 1997 and 2000, respectively, and the PhD degree from the University of Maryland, College Park, in 2006. From 2000 to 2001, he was an assistant researcher at Microsoft Research Asia. From 2006 to 2007, he worked as a postdoctoral scientist at the University of California Los Angeles. In 2007, he joined Siemens Corporate Research as a research scientist. From 2008 to 2019, he worked as a faculty member of Temple University. In fall 2019, he joined the Department of Computer Science of Stony Brook University where he is currently a SUNY Empire Innovation Professor. His research interests include computer vision, augmented reality, medical image analysis, and human computer interaction. He received the Best Student Paper Award at the ACM UIST in 2003, and the NSF CAREER Award in 2014. He serves as Associate Editor for several journals including IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), Pattern Recognition (PR), and Computer Vision and Image Understanding (CVIU). He has served or will serve as Area Chair for CVPR 2014, 2016, 2019 and 2020.
This event brings together people with interests in Computer Vision theory and techniques and examines current research issues in the field.
Each seminar consists of multiple short talks (around 15 minutes) by several students.
Join Zoom Meeting:
https://stonybrook.zoom.us/j/93547152068?pwd=WVpoRVgzelBXeloxdXVEakNSb2M5UT09
Meeting ID: 935 4715 2068 | Passcode: 481832
Each seminar consists of multiple short talks (around 15 minutes) by several students.
Join Zoom Meeting:
https://stonybrook.zoom.us/j/93547152068?pwd=WVpoRVgzelBXeloxdXVEakNSb2M5UT09
Meeting ID: 935 4715 2068 | Passcode: 481832
Abstract:
What is the nature of linguistic knowledge, and how is it acquired from limited data? In recent years, the program of subregular linguistics has identified formal language classes expressive enough to account for most phenomena in natural language but also sufficiently limited to be efficiently learned from positive data. An advantage to these formal learning algorithms is that they come with mathematically proven guarantees about their performance, and it is easy to reason about how and why they behave the way they do.
In this talk, I discuss the Multi Tier-based 2-Strictly Local Inference Algorithm (MT2SLIA), which probably learns the syntactically relevant class of 2-Factor Muti Tier-based Strictly Local (2FMSTL) tree languages. This algorithm efficiently learns from a polynomially-sized sample of positive data by identifying missing substructures and generalizing these as constraints over tiers in a principled manner.
I will introduce a working prototype implementation of this algorithm and demonstrate its behavior on a curated sample of natural language data to show how it can learn relevant syntactic patterns.
Bio:
Logan Swanson is a third year PhD student in the Department of Linguistics at Stony Brook University. He is advised by Dr. Jefferey Heinz and Dr. Thomas Graf. His interests include learning theory, computational syntax, and language change. His current research focuses on understanding the learning-theoretic elements of natural language by designing, implementing, and testing learning algorithms for linguistically relevant formal language classes.
*Please note: this seminar will be held in person (IACS Seminar Room w/ food provided) and online.
Join Zoom Meeting
https://stonybrook.zoom.us/j/95707958315?pwd=6ITUJ0ffCXjRJb4wpt0KMDTApfSLZ0.1
Meeting ID: 957 0795 8315
Passcode: 920473
What is the nature of linguistic knowledge, and how is it acquired from limited data? In recent years, the program of subregular linguistics has identified formal language classes expressive enough to account for most phenomena in natural language but also sufficiently limited to be efficiently learned from positive data. An advantage to these formal learning algorithms is that they come with mathematically proven guarantees about their performance, and it is easy to reason about how and why they behave the way they do.
In this talk, I discuss the Multi Tier-based 2-Strictly Local Inference Algorithm (MT2SLIA), which probably learns the syntactically relevant class of 2-Factor Muti Tier-based Strictly Local (2FMSTL) tree languages. This algorithm efficiently learns from a polynomially-sized sample of positive data by identifying missing substructures and generalizing these as constraints over tiers in a principled manner.
I will introduce a working prototype implementation of this algorithm and demonstrate its behavior on a curated sample of natural language data to show how it can learn relevant syntactic patterns.
Bio:
Logan Swanson is a third year PhD student in the Department of Linguistics at Stony Brook University. He is advised by Dr. Jefferey Heinz and Dr. Thomas Graf. His interests include learning theory, computational syntax, and language change. His current research focuses on understanding the learning-theoretic elements of natural language by designing, implementing, and testing learning algorithms for linguistically relevant formal language classes.
*Please note: this seminar will be held in person (IACS Seminar Room w/ food provided) and online.
Join Zoom Meeting
https://stonybrook.zoom.us/j/95707958315?pwd=6ITUJ0ffCXjRJb4wpt0KMDTApfSLZ0.1
Meeting ID: 957 0795 8315
Passcode: 920473
Chat with Sociology faculty as they share their paths to StonyBrook-what inspired their careers, what led them to teaching,and the experiences that shaped their academic journey.
Dr. Yongjun Zhang
Assistant Professor of Sociology, Departments of Sociology and AAAS
Join this opportunity to talk to Yongjun Zhang about his new interest in the following responsible usage of AI in addressing climate and health issues. Lunch will be served.
Location: SBS Level 4- Sociology Reading Room
Zoom Link: https://github.com/giorgianb/spdhackspring2021/blob/main/bit.ly/spdhack2021
ΣΦΔ Hack Spring 2021 is ΣΦΔ's first annual machine learning hackathon. ΣΦΔ Hack Spring 2021 aims to introduce Stony Brook students to the rich and challenging field of machine learning, and develop the skills necessary to build sophisticated machine learning models on their own.
More info here: https://github.com/giorgianb/spdhackspring2021/blob/main/README.md
ΣΦΔ Hack Spring 2021 is ΣΦΔ's first annual machine learning hackathon. ΣΦΔ Hack Spring 2021 aims to introduce Stony Brook students to the rich and challenging field of machine learning, and develop the skills necessary to build sophisticated machine learning models on their own.
More info here: https://github.com/giorgianb/spdhackspring2021/blob/main/README.md
The event will take place on Zoom and will feature two distinguished guest speakers: SBU alumnus, Velchamy Sankarlingam, president of Product and Engineering at Zoom, and Simeon Ananou, vice president for Information Technology and CIO at Stony Brook University. The discussion will be moderated by Haresh Gurnani, dean of the College of Business at Stony Brook University.
Exploring AI's Impact on Communication and Connection
Artificial Intelligence (AI) has rapidly evolved, becoming an integral part of various industries, including education and business. This event aims to delve into how AI is reshaping the way we learn and work, particularly in enhancing communication and fostering human connections. Velchamy Sankarlingam, an SBU alumnus and a key figure at Zoom, will share his insights on how AI-driven tools are revolutionizing virtual communication platforms, making interactions more seamless and effective.
Simeon Ananou, with his extensive experience in information technology, will provide a perspective on how AI is being integrated into educational institutions to improve learning outcomes and administrative efficiency. His role at Stony Brook University places him at the forefront of implementing innovative technologies that benefit both students and staff.
A Conversation Led by Expertise
Dean Haresh Gurnani, known for his leadership and expertise in business education, will guide the conversation, ensuring that the discussion remains focused on the practical implications of AI. He will explore how AI is not only boosting productivity but also enriching overall experiences in the workplace and educational settings. The event will include an interactive Q&A session, allowing attendees to engage directly with the speakers and gain deeper insights into the topics discussed.
As AI continues to develop, events like this are crucial for understanding its impact and potential. Stony Brook University's College of Business is committed to providing platforms for such important discussions, fostering an environment where innovation and education intersect.
This event is open to all. Please visit https://www.givecampus.com/schools/StonyBrookUniversity/events/artificial-intelligence-reshaping-learning-and-work to register.