Inside Our ProgramProgram Events
Events
-
Apr29
EVENT DETAILS
less
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)
-
May1
EVENT DETAILS
lessIt looks like AI is here to stay. Not only that, its impact on society will rival or exceed that of the world wide web. However, we are likely just scratching the surface and AI is still in its infancy. The expected growth is immense and there is an active arms race in both hardware and algorithms. Nvidia dominates hardware today, largely because we are still in the training era, where time-to-accuracy at any cost rules. In this era, algorithmic innovations offer the biggest benefits and require a robust programmable platform (i.e., Nvidia GPUs) upon which to innovate. However, there are signs the inference era is imminent, where the rules are different. The projected scales of AI inference deployments will demand highly efficient hardware. With AI touching all layers of the computing stack today, we can leverage algorithm-hardware co-design to uncover interesting opportunities and trends for efficiency via customization and acceleration. This talk presents an overview of a NSF-funded project we had proposed to develop a cloudless universal translator, which we thought eventually possible given the state of ML technology at the time. Throughout
the project, we designed three custom AI SoCs--targeting automatic speech recognition (FlexASR), natural language processing (EdgeBERT), and on-device learning/fine-tuning (CAMEL). These designs highlight ways to maximize energy efficiency given the probabilistic nature of machine learning algorithms. Finally, the talk closes with some thoughts on
chiplets and carbon.TIME Wednesday, May 1, 2024 at 11:00 AM - 12:00 PM
LOCATION L440, Technological Institute map it
CONTACT Catherine Healey catherine.healey@northwestern.edu EMAIL
CALENDAR Department of Electrical and Computer Engineering (ECE)
-
May1
EVENT DETAILS
lessWednesday / CS Distinguished Lecture
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)
-
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)
-
May9
EVENT DETAILSmore info
lessWant to join the decision-makers? Register for Northwestern University's Master of Science in Information Technology (MSIT) Program Information Session and learn how you can enhance your knowledge of IT and gain the business management skills you need to direct effective IT strategy.
TIME Thursday, May 9, 2024 at 6:00 PM - 7:00 PM
CONTACT Svetlana Korzeniowski msit@northwestern.edu EMAIL
CALENDAR MS in Information Technology (MSIT) Program
-
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)
-
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)
-
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)
