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Speaker Series

The Research Computing Center (RCC) at the University of Chicago hosts a quarterly speaker series featuring a variety of perspectives on high-end computing, AI, technology, and more.

Upcoming Speakers:

  • Details coming soon!

Past Speakers:

  • February 8, 2024, 1 PM – 2 PM CDT, Zar Room President and CEO Brenda Darden Wilkerson
    The Power of One

    Brenda Darden Wilkerson is the President and CEO of, an organization that connects, inspires, and strives for greater equality for women technologists in business, academia, and government. She founded the original Computer Science for All program, building computer science classes into the curriculum for every student in the Chicago Public Schools, and serving as the inspiration for the Obama administration’s national CS For All initiatives.

    In her impactful speech, "The Power of One," Brenda Darden Wilkerson passionately explores the transformative potential inherent in every woman and non-binary individual in the tech industry. With a poignant emphasis on the concept of The Power of One, Wilkerson illuminates the profound impact that each person can wield independently, addressing society's most pressing challenges and catalyzing meaningful change. Drawing inspiration from the narratives of "reluctant warriors," unsung heroines of recent history who embarked on journeys of innovation without seeking fame, she underscores the significance of individual courage and determination. Through insightful advice and compelling examples, Wilkerson galvanizes her audience to embrace boldness and bravery, echoing the spirit of countless women and non-binary pioneers. Her call to action extends beyond the confines of the tech realm, urging everyone to contribute to the betterment of society at large, echoing the ethos that has shaped the path for progress set by those who came before.

  • November 10, 2023, 11 AM – 12 PM CDT, Regenstein 122
    State Representative Abdelnasser Rashid
    Governing in the Age of AI: Artificial Intelligence & State Regulation

    State Representative Abdelnasser Rashid was elected in 2022 to represent the southwest side of Chicago and the west & southwest suburbs. In August 2023, he was appointed co-chair of the state’s new artificial intelligence (AI) task force which was created earlier this year.

    In this informative talk, Rep. Rashid will give a general overview of how Illinois and other states are grappling with the various questions raised by the proliferation of AI.

  • August 23, 2022, 11 AM – 12 PM CDT, via Zoom
    Salman Habib, Director of Computational Science Division and Distinguished Fellow at Argonne National Lab.
    The Exascale Scientific Computing Landscape: Challenges and Opportunities.

    Computing has become an essential enabling and empowering component across all of science. In particular, large-scale computing is playing an ever-increasing role in theory and modeling and in data collection and analysis, as well as accounting for the increasing influence of machine learning. Early exascale systems are harbingers of a future computing roadmap that promises major leaps in capability, but one that poses significant challenges that will need to be faced by the scientific community. In this talk I will survey some of the history, current trends, and indications of near-future directions in scientific computing, focusing on a subset of examples that lie at the interface of high-performance computing, large data sets, and machine learning.

  • February 21, 2022, 2 PM – 3 PM CST, via Zoom
    Shashi Shekhar, McKnight Distinguished University Professor, University of Minnesota
    What is special about Geo-AI and Spatial Data Science?

    Rapid expansion in spatial big data (e.g., trajectories, remote-sensing) is fueling growth of Geo-AI for making previously unimaginable maps, answering trail-blazing geo-content based queries, discovering groundbreaking spatial patterns, etc. Applications span from apps for navigation, ride-sharing, and delivery to monitoring global crops, climate change, diseases, and smart cities to understanding cellular or urban patterns of life.

    However, one-size-fit-all machine learning performs poorly (e.g., salt-n-pepper noise, inaccuracy) due to spatial autocorrelation and variability, which violate the common i.i.d. assumption (i.e. data samples are generated independently and from identical distribution). Furthermore, high cost of spurious patterns requires guardrails such as noise tolerance, and modeling of spatial concepts (e.g., polygons) and implicit relationships (e.g., distance, inside). In addition, methods discretizing continuous space face the modifiable areal unit problem (e.g., gerrrymandering).

    Thus, the talk suggests spatial data science approaches and describes methods for spatial classification and prediction (e.g., spatial auto-regression, spatial decision trees, spatial variability aware neural networks) along with techniques for discovering patterns such as noise-robust hotspots (e.g., SaTScan, linear, arbitrary shapes), interactions (e.g., co-locations, tele-connections ), spatial outliers, and their spatio-temporal counterparts (e.g., cascade , mixed-drove co-occurrence ). It concludes by calling for inclusion of spatial perspectives in data science courses and curricula.