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• Dec

  8

  ECE Distinguished Seminar Series - Memristive Devices for Neuromorphic Computing with J. Joshua Yang

  Department of Electrical and Computer Engineering

  2:00 PM

  EVENT DETAILSmore info

  Hosted by Professor Pedram Khalili

  CMOS technology has been the mainstream hardware technology for the development of ubiquitous information technology so far. In the era of ‘big data’ and ‘Internet of Things’ nowadays, the traditional computing architecture based on CMOS hardware has become increasingly inefficient to support Artificial Intelligence (AI) and Machine Learning (ML), which necessitates some emerging technologies, such as memristive technology. Memristive devices(1) have become a promising candidate to enable bio-inspired computing with much improved efficiency and throughput(2,3). Such computing can be implemented on a Resistive Neural Network(4) with memristive synapses(5) and neurons(6) or a Capacitive Neural Network(7,8) with memcapacitive synapses and neurons. I will first briefly introduce the promises and challenges of memristive materials and devices for those applications and then discuss examples with different levels of bio-inspiration: first, deep learning accelerators(9) with supervised online learning(10); second, neuromorphic computing for pattern classification with unsupervised learning(6); last, other computing applications, such as reinforcement learning(11) for decision making, artificial nociceptors for robotics(12), provable key destruction(13) and true random number generators(14) for cybersecurity.

  1 Nature Nanotechnology 8, 13 (2013)

  2 Nature materials 18, 309 (2019)

  3 Nature Reviews Materials 5, 173 (2020)

  4 Nature Machine Intelligence 1, 434 (2019)

  5 Nature Materials 16, 101 (2017)

  6 Nature Electronics 1, 137 (2018)

  7 Nature Communications 9, 3208 (2018)

  8 Nature Machine Intelligence 1, 49 (2019)

  9 Nature Electronics 1, 52 (2018)

  10 Nature communications 9, 2385 (2018)

  11 Nature Electronics 2, 115 (2019)

  12 Nature communications 9, 417 (2018)

  13 Nature Electronics 1, 548 (2018)

  14 Nature communications 8, 882 (2017)

  J. Joshua Yang is a professor of the Department of Electrical and Computer Engineering at the University of Southern California. He was a professor of the ECE department at the University of Massachusetts Amherst between 2015 and 2020. He spent about 8 years at HP Labs between 2007 and 2015, leading the emerging devices team for memory and computing. His current research interest is Post-CMOS hardware for neuromorphic computing, machine learning and artificial intelligence, where he published several pioneering papers and holds 118 granted and about 60 pending US Patents. He is the Founder Chair of the IEEE Neuromorphic Computing Technical Committee, a recipient of the Powell Faculty Research Award and a recipient of UMass distinguished faculty lecturer and UMass Chancellor's Medal, the highest honor of UMass. He was named as a Spotlight Scholar of UMass Amherst in 2017.He is a Clarivate™ Highly Cited Researcher in the field of Cross-Field and serves on the Advisory Boards of a number of prime international journals and conferences.

  **This will be a virtual seminar, please use the Zoom link provided in this listing to attend the event.

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  TIME Wednesday, December 8, 2021 at 2:00 PM - 3:30 PM

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  CONTACT ECE Admin Team    eceadmin@northwestern.edu EMAIL

  CALENDAR Department of Electrical and Computer Engineering

• Apr

  29

  CS Seminar: Data-Efficient Graph Learning (Kaize Ding)

  Department of Computer Science (CS)

  12:00 PM 3514, Mudd Hall ( formerly Seeley G. Mudd Library)

  EVENT DETAILS

  

  Monday / CS Seminar
  April 29th / 12:00 PM
  Hybrid / Mudd 3514

  Speaker
  Kaize Ding, Northwestern University

  Talk 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 Processing

  Zoom: https://northwestern.zoom.us/j/99555833103?pwd=TEtaakVGL2xUYytDa2Nvbk14Z3VhUT09

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  TIME Monday, April 29, 2024 at 12:00 PM - 1:00 PM

  LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library)    map it

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  CONTACT Wynante R Charles    wynante.charles@northwestern.edu EMAIL

  CALENDAR Department of Computer Science (CS)

• May

  1

  CS Distinguished Lecture: Tong Zhang

  Department of Computer Science (CS)

  12:00 PM 3514, Mudd Hall ( formerly Seeley G. Mudd Library)

  EVENT DETAILS

  Wednesday / CS Distinguished Lecture
  May 1st / 12:00 PM
  Hybrid / Mudd 3514

  Speaker
  Tong Zhang, University of Illinois Urbana-Champaign

  Talk Title
  TBA

  Abstract
  TBA

  Biography
  TBA

  Research Area/Interests
  TBA

  Zoom: TBA

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  TIME Wednesday, May 1, 2024 at 12:00 PM - 1:00 PM

  LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library)    map it

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  CONTACT Wynante R Charles    wynante.charles@northwestern.edu EMAIL

  CALENDAR Department of Computer Science (CS)

• May

  8

  CS Distinguished Lecture: Monica Lam

  Department of Computer Science (CS)

  12:00 PM 3514, Mudd Hall ( formerly Seeley G. Mudd Library)

  EVENT DETAILS

  Wednesday / CS Distinguished Lecture
  May 8th / 12:00 PM
  Hybrid / Mudd 3514

  Speaker
  Monica Lam, Stanford University

  Talk Title
  TBA

  Abstract
  TBA

  Biography
  TBA

  Research Area/Interests
  TBA

  Zoom
  TBA

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  TIME Wednesday, May 8, 2024 at 12:00 PM - 1:00 PM

  LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library)    map it

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  CONTACT Wynante R Charles    wynante.charles@northwestern.edu EMAIL

  CALENDAR Department of Computer Science (CS)

• May

  9

  MS in Information Technology (MSIT) Online Information Session

  MS in Information Technology (MSIT) Program

  6:00 PM

  EVENT DETAILSmore info

  Want 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.

  Register Now

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  TIME Thursday, May 9, 2024 at 6:00 PM - 7:00 PM

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  CONTACT Svetlana Korzeniowski    msit@northwestern.edu EMAIL

  CALENDAR MS in Information Technology (MSIT) Program

• May

  10

  CS Distinguished Lecture: Trust, Backdoor Vulnerabilities and Possible Mitigations (Shafi Goldwasser)

  Department of Computer Science (CS)

  12:00 PM 3514, Mudd Hall ( formerly Seeley G. Mudd Library)

  EVENT DETAILS

  Friday / CS Distinguished Lecture
  May 10th / 12:00 PM
  Hybrid / Mudd 3514

  Speaker
  Shafi Goldwasser; Simons Institute for the Theory of Computing, University of California Berkeley

  Talk Title
  Trust, Backdoor Vulnerabilities and Possible Mitigations

  Abstract
  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 Algorithms

  Zoom: TBA

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  TIME Friday, May 10, 2024 at 12:00 PM - 1:00 PM

  LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library)    map it

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  CONTACT Wynante R Charles    wynante.charles@northwestern.edu EMAIL

  CALENDAR Department of Computer Science (CS)

• May

  30

  TGI Bagel Thursday

  Department of Computer Science (CS)

  9:00 AM 3514, Mudd Hall ( formerly Seeley G. Mudd Library)

  EVENT DETAILS

  TBA

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  TIME Thursday, May 30, 2024 at 9:00 AM - 11:00 AM

  LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library)    map it

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  CONTACT Wynante R Charles    wynante.charles@northwestern.edu EMAIL

  CALENDAR Department of Computer Science (CS)

• May

  30

  End of Year Awards

  Department of Computer Science (CS)

  3:00 PM TBA, Mudd Hall ( formerly Seeley G. Mudd Library)

  EVENT DETAILS

  TBA

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  TIME Thursday, May 30, 2024 at 3:00 PM - 5:00 PM

  LOCATION TBA, Mudd Hall ( formerly Seeley G. Mudd Library)    map it

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  CONTACT Wynante R Charles    wynante.charles@northwestern.edu EMAIL

  CALENDAR Department of Computer Science (CS)