News & EventsDepartment Events & Announcements
Events
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Mar20
EVENT DETAILS
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The CS Department welcomes Prof. Maneesh Agrawala, Forest Baskett Professor of CS & Director of the Brown Institute for Media Innovation at Stanford University.
Agrawala will present a talk entitled "Making and Breaking Video" on Wednesday, March 20 at 12:00 PM in MUDD Hall 3rd Floor Seminar Room.
Abstract: Video has become a primary medium for communicating ideas, making sense of information and telling stories. It is central to making, establishing and conveying human cultures throughout the world. Whether it is a soap opera, broadcast news, cinematic film, or even viral YouTube clips, the most watched video is engaging and usually involves careful attention to production.
Technological advances have made it easy to capture video using our mobile devices. But the raw media rarely tells a compelling story. In the first part of this talk I'll present recent projects that aim to significantly reduce the effort required to edit and produce high-quality video. Our tools are designed to let storytellers specify high-level objectives and let the computer handle lower-level details in putting together the raw materials. In the second part of the talk I’ll present our work on building tools for analyzing large collections of video. I’ll show how we can extract trends and patterns from a collection of 10 years of 24-7 cable TV news coverage, and from a set of over 500 cinematic films covering the last 100 years.
Bio: Prof. Maneesh Agrawala is the Forest Baskett Professor of Computer Science and Director of the Brown Institute for Media Innovation at Stanford University. He works on computer graphics, human computer interaction and visualization. His focus is on investigating how cognitive design principles can be used to improve the effectiveness of audio/visual media. The goals of this work are to discover the design principles and then instantiate them in both interactive and automated design tools. He received an Okawa Foundation Research Grant (2006), an Alfred P. Sloan Foundation Fellowship (2007), an NSF CAREER Award (2007), a SIGGRAPH Significant New Researcher Award (2008), a MacArthur Foundation Fellowship (2009), and an Allen Distinguished Investigator Award (2014).
Hosted by CS Prof. Jessica Hullman
TIME Wednesday, March 20, 2019 at 12:00 PM - 1:00 PM
LOCATION 3rd Floor Seminar Room, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Brianna Mello brianna.mello@northwestern.edu EMAIL
CALENDAR Electrical Engineering & Computer Science
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Apr29
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Monday / CS Seminar
April 29th / 12:00 PM
Hybrid / Mudd 3514Speaker
Kaize Ding, Northwestern UniversityTalk Title
Data-Efficient Graph Learning
Abstract
The world around us -- and our understanding of it -- is rich in relational structure: from atoms and their interactions to objects and entities in our environments. Graphs, with nodes representing entities and edges representing relationships between entities, serve as a common language to model complex, relational, and heterogeneous systems. Despite the success of recent deep graph learning, the efficacy of existing efforts heavily depends on the ideal data quality of the observed graphs and the sufficiency of the supervision signals provided by the human-annotated labels, leading to the fact that those carefully designed models easily fail in resource-constrained scenarios.In this talk, I will present my recent research contributions centered around data-efficient learning for relational and heterogeneous graph-structured data. First, I will introduce what data-efficient graph learning is and my contributions to different research problems under its umbrella, including graph few-shot learning, graph weakly-supervised learning, and graph self-supervised learning. Based on my work, I will elucidate how to push forward the performance boundary of graph learning models especially graph neural networks with low-cost human supervision signals. I will also touch upon the real-world applications of data-efficient graph learning to different domains and finally conclude my talk with a brief overview of my future research agenda.
Biography
Kaize Ding is an Assistant Professor in the Department of Statistics and Data Science at Northwestern University. Before joining Northwestern, he obtained his Ph.D. degree in Computer Science at Arizona State University in 2023 under the supervision of Prof. Huan Liu. His research interests are generally in data mining, machine learning, and natural language processing, with a particular focus on graph machine learning, data-efficient learning, and reliable AI. His work has been published in top-tier conferences and journals (e.g., AAAI, EMNLP, IJCAI, KDD, NeurIPS, TheWebConf, and TNNLS), and has been recognized with several prestigious awards and honors, including the AAAI New Faculty Highlights, SDM Best Posters Award, Best Paper Award at the Trustworthy Learning on Graphs workshop, etc.Research Area/Interests
Data Mining; Machine Learning; Natural Language ProcessingZoom: https://northwestern.zoom.us/j/99555833103?pwd=TEtaakVGL2xUYytDa2Nvbk14Z3VhUT09
TIME Monday, April 29, 2024 at 12:00 PM - 1:00 PM
LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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May1
EVENT DETAILS
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May 1st / 12:00 PM
Hybrid / Mudd 3514Speaker
Tong Zhang, University of Illinois Urbana-ChampaignTalk Title
TBAAbstract
TBABiography
TBAResearch Area/Interests
TBAZoom: TBA
TIME Wednesday, May 1, 2024 at 12:00 PM - 1:00 PM
LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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May8
EVENT DETAILS
lessWednesday / CS Distinguished Lecture
May 8th / 12:00 PM
Hybrid / Mudd 3514Speaker
Monica Lam, Stanford UniversityTalk Title
TBAAbstract
TBABiography
TBAResearch Area/Interests
TBAZoom
TBATIME Wednesday, May 8, 2024 at 12:00 PM - 1:00 PM
LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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May8
EVENT DETAILSmore info
lessThe Center for Scientific Studies in the Arts is a collaboration between the Art Institute of Chicago and materials science-related departments at Northwestern University to pursue objects-based and objects-inspired scientific research. Materials research benefits ongoing work in conservation, archaeology, art history, and curatorial scholarship.
Learn how the Center uses materials research to care for art objects in sustainable, innovative ways with Maria Kokkori, Senior Scientist in the Center for Scientific Studies in the Arts. She will be in conversation with Corey Byrnes, Northwestern Associate Professor of Chinese Culture and co-founder/co-director of the Environmental Humanities Workshop in Kaplan Humanities Center.
This event is presented by the McCormick School of Engineering and Applied Science in conjunction with exhibition Actions for the Earth: Art, Care & Ecology.
TIME Wednesday, May 8, 2024 at 6:00 PM - 7:30 PM
LOCATION Block Museum of Art, Mary and Leigh map it
CONTACT Block Museum of Art block-museum@northwestern.edu EMAIL
CALENDAR Block Museum of Art
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May10
EVENT DETAILS
lessFriday / CS Distinguished Lecture
May 10th / 12:00 PM
Hybrid / Mudd 3514Speaker
Shafi Goldwasser; Simons Institute for the Theory of Computing, University of California BerkeleyTalk Title
Trust, Backdoor Vulnerabilities and Possible MitigationsAbstract
Cryptographic tools and models enable to trust the use of technology platforms controlled by worst case computationally bounded adversaries. In this talk I will use cryptographic modeling and tools to view trust dilemmas in various phases of the machine learning pipelines. We will touch on privacy in the training stage, verification of properties of
machine learning models, and the possibility of achieving robustness in presence of backdoors.Biography
Shafi Goldwasser is Director of the Simons Institute for the Theory of Computing, and Professor of Electrical Engineering and Computer Science at the University of California Berkeley. Goldwasser is also the RSA Professor (post tenure) of Electrical Engineering and Computer Science at MIT and Professor Emeritus of Computer Science and Applied Mathematics at the Weizmann Institute of Science, Israel. Goldwasser holds a B.S. Applied Mathematics from Carnegie Mellon University (1979), and M.S. and Ph.D. in Computer Science from the University of California Berkeley (1984).
Goldwasser's contributions include the introduction of probabilistic encryption, interactive zero knowledge protocols, elliptic curve primality testings, hardness of approximation proofs for combinatorial problems, combinatorial property testing, and pseudo deterministic algorithms.
Goldwasser was the recipient of the ACM Turing Award in 2012, the Gödel Prize in 1993 and in 2001, the ACM Grace Murray Hopper Award in 1996, the RSA Award in Mathematics in 1998, the ACM Athena Award for Women in Computer Science in 2008, the Benjamin Franklin Medal in 2010, the IEEE Emanuel R. Piore Award in 2011, the Simons Foundation Investigator Award in 2012, and the BBVA Foundation Frontiers of Knowledge Award in 2018. Goldwasser is a member of the NAS, NAE, AAAS, the Russian Academy of Science, the Israeli Academy of Science, the London Royal Mathematical Society and a Foreign Member of the Royal Society. Goldwasser holds honorary degrees from Ben Gurion University, Bar Ilan University, Carnegie Mellon University, Haifa University, University of Oxford, and the University of Waterloo, and has received the UC Berkeley Distinguished Alumnus Award and the Barnard College Medal of Distinction.
Research Area/Interests
Cryptography, Complexity, Probabilistic AlgorithmsZoom: TBA
TIME Friday, May 10, 2024 at 12:00 PM - 1:00 PM
LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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May30
EVENT DETAILS
lessTBA
TIME Thursday, May 30, 2024 at 9:00 AM - 11:00 AM
LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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May30
EVENT DETAILS
lessTBA
TIME Thursday, May 30, 2024 at 3:00 PM - 5:00 PM
LOCATION TBA, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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Jun10
EVENT DETAILSmore info
lessMcCormick School of Engineering PhD Hooding and Master’s Degree Recognition Ceremony
TIME Monday, June 10, 2024 at 9:00 AM - 11:00 AM
LOCATION Welsh-Ryan Arena
CONTACT Amy Pokrass amy.pokrass@northwestern.edu EMAIL
CALENDAR McCormick School of Engineering and Applied Science
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Jun10
TIME Monday, June 10, 2024 at 2:00 PM - 4:00 PM
LOCATION Welsh-Ryan Arena
CONTACT Amy Pokrass amy.pokrass@northwestern.edu EMAIL
CALENDAR McCormick School of Engineering and Applied Science
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Aug14
EVENT DETAILSmore info
lessInnovation in Quantum Pedagogy, Application, and its Relation to Culture (IQ-PARC), funded by the Department of Defense — National Defense Education Program, is extending an invitation to underrepresented students nationwide (including military-connected students) to participate in a summer school focused on quantum technologies, to be held at Northwestern University. Join us for presentations by faculty and industry partners introducing quantum technologies, cutting-edge research areas, and related job opportunities particularly in the areas of national security.
This effort aims to foster an inclusive environment that encourages participation from all corners of the academic community. Successful applicants will receive up to $1,000 USD to cover travel and lodging expenses in Evanston, IL.
Priority application deadline: May 15, 2024.
Decisions will be sent by June 15, 2024.
TIME Wednesday, August 14, 2024
LOCATION Ford Motor Company Engineering Design Center map it
CONTACT Dongyang Li lidongyang@northwestern.edu EMAIL
CALENDAR McCormick School of Engineering and Applied Science