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Events

• Jan

  26

  CS Colloquium - Nan Tang "From Absolute Good Data to Relative Good Data: Data Preparation for Data-centric AI"

  Department of Computer Science

  12:00 PM

  EVENT DETAILS

  Abstract
  

  A well-known principle in data science (and AI) is garbage-in-garbage-out, which means that if you feed bad data to your analytical tools, you will get bad results. The only way to get good results is to use good data. Moreover, Data science (or AI) = Code (or algorithms, models) + Data. On one hand, because 99% of the research efforts are on the code part, many analytical tools and AI models have become commodities. On the other hand, real-world data is getting bigger and messier, which causes the dilemma for practitioners that it is harder and harder to get good data. The mission of data democratization, which allows anyone to have access to good data, is more important than ever.

  In this talk, I will first introduce what is data preparation and how to prepare data for absolute good data, where absolute means the ground truth. The need for absolute good data is evident, because it can be used to support many data science tasks such as statistical analysis, data visualization, and data mining. However, the pursuit of the ground truth is impossible without enough general and domain knowledge. I will overview two main directions that I have been pushing to make it possible, with human intelligence and with artificial intelligence.

  Despite all the efforts of preparing absolute good data, it is not enough for AI applications, where they typically have unseen test data. In this case, what we need is relative good data. I will introduce four main directions to prepare relative good data for data-centric AI: (1) not enough train data, (2) not aligned train and test data, (3) not ideal data preparation pipelines, and (4) not consistent data and labels in the train data. In particular, I will present my recent research on model charging for discovering relative good data in the wild for (1) handling not enough train data, and model patching for (2) handling not aligned train and test data due to noise shift. I will give concrete use cases (3&4) for the other two cases and discuss possible ways to address them.

  Biography

  Dr. Nan Tang is a senior scientist at Qatar Center for Artificial Intelligence, Qatar Computing Research Institute (QCRI), Qatar. His research interests center around preparing good data for successful data science. Prior to joining QCRI in Dec 2011, He was a Research Fellow at LFCS (Laboratory for Foundations of Computer Science) at the University of Edinburgh, Edinburgh, UK (2010--2011). He was a scientific staff member with the CWI (Dutch National Research Center for Mathematics and Computer Science), Amsterdam, Netherlands (2008--2010). He got his PhD. degree from The Chinese University of Hong Kong, China (2007). He holds a visiting position at MIT, US (07/2017-08/2017) and a visiting position at University of Waterloo, Canada (03/2007-08/2007). He has been a PC member of many flagship conferences in data science, such as SIGMOD, VLDB, ICDE, KDD, VIS, and CHI. He has received the VLDB 2010 best paper award, SIGMOD 2020 reproducibility award, VLDB 2021 distinguished reviewer award, and other four best paper nominations in VLDB and ICDE.

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  TIME Wednesday, January 26, 2022 at 12:00 PM - 1:00 PM

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  CONTACT Pamela Villalovoz    pmv@northwestern.edu EMAIL

  CALENDAR Department of Computer Science

• 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

  8

  Extending Care: A Conversation about Conservation and Futurity

  Block Museum of Art

  6:00 PM Block Museum of Art, Mary and Leigh

  EVENT DETAILSmore info

  The 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.

  Register Now

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  TIME Wednesday, May 8, 2024 at 6:00 PM - 7:30 PM

  LOCATION Block Museum of Art, Mary and Leigh    map it

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  CONTACT Block Museum of Art    block-museum@northwestern.edu EMAIL

  CALENDAR Block Museum of Art

• 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)

• Jun

  10

  Northwestern Engineering PhD Hooding and Master's Degree Recognition Ceremony

  McCormick School of Engineering and Applied Science

  9:00 AM Welsh-Ryan Arena

  EVENT DETAILSmore info

  McCormick School of Engineering PhD Hooding and Master’s Degree Recognition Ceremony

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  TIME Monday, June 10, 2024 at 9:00 AM - 11:00 AM

  LOCATION Welsh-Ryan Arena   

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  CONTACT Amy Pokrass    amy.pokrass@northwestern.edu EMAIL

  CALENDAR McCormick School of Engineering and Applied Science

• Jun

  10

  Northwestern Engineering Undergraduate Convocation

  McCormick School of Engineering and Applied Science

  2:00 PM Welsh-Ryan Arena

  EVENT DETAILSmore info

  McCormick School of Engineering Undergraduate Convocation

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  TIME Monday, June 10, 2024 at 2:00 PM - 4:00 PM

  LOCATION Welsh-Ryan Arena   

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  CONTACT Amy Pokrass    amy.pokrass@northwestern.edu EMAIL

  CALENDAR McCormick School of Engineering and Applied Science

• Aug

  14

  Undergraduate Quantum Summer School

  McCormick School of Engineering and Applied Science

  All Day Ford Motor Company Engineering Design Center

  EVENT DETAILSmore info

  Innovation 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.

  Register Now

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  TIME Wednesday, August 14, 2024

  LOCATION Ford Motor Company Engineering Design Center    map it

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  CONTACT Dongyang Li    lidongyang@northwestern.edu EMAIL

  CALENDAR McCormick School of Engineering and Applied Science