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Events
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Nov20
EVENT DETAILSmore info
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Title: The Lifecycle of Data Science: A Framework for Advancing Computational and Data-enabled Research
Abstract
There is no crisis, but also no time for complacency” said the chair of the National Academies of Sciences, Engineering, and Medicine (NASEM) committee on "Reproducibility and Replicability in Science" in May 2019 (https://vimeo.com/335923468). Questions regarding reproducibility have arisen regarding the transparency of computational methods and discovery, in part due to the leveraging of data and compute resources for scientific and engineering advancements, now pervasive in a staggeringly broad range of academic disciplines and activities. In this talk I present the reproducibility definitions that emerged in our NASEM committee deliberations and discuss an abstract framework for conceptualizing and advancing data science as a discipline, called the Lifecycle of Data Science (forthcoming in CACM). This framework integrates the disparate components of data-enabled discovery, from hardware provisioning to applications to dissemination standards for verification and re-use to ethics, and thereby brings into contextual focus salient issues such as computational reproducibility, standards and policy, and curricular development. I then present the "Knowledge Integrator," an effort to conceptualize and enable the dissemination of reproducible research results based on the Lifecycle of Data Science and community engagement.
Bio
Victoria Stodden joined the School of Information Sciences as an associate professor in Fall 2014. She is a leading figure in the area of reproducibility in computational science, exploring how can we better ensure the reliability and usefulness of scientific results in the face of increasingly sophisticated computational approaches to research. Her work addresses a wide range of topics, including standards of openness for data and code sharing, legal and policy barriers to disseminating reproducible research, robustness in replicated findings, cyberinfrastructure to enable reproducibility, and scientific publishing practices. Stodden co-chairs the NSF Advisory Committee for CyberInfrastructure and is a member of the NSF Directorate for Computer and Information Science and Engineering (CISE) Advisory Committee. She also serves on the National Academies Committee on Responsible Science: Ensuring the Integrity of the Research Process.
Previously an assistant professor of statistics at Columbia University, Stodden taught courses in data science, reproducible research, and statistical theory and was affiliated with the Institute for Data Sciences and Engineering. She co-edited two books released in 2014—Privacy, Big Data, and the Public Good: Frameworks for Engagement published by Cambridge University Press and Implementing Reproducible Research published by Taylor & Francis. Stodden earned both her PhD in statistics and her law degree from Stanford University. She also holds a master’s degree in economics from the University of British Columbia and a bachelor’s degree in economics from the University of Ottawa.
TIME Wednesday, November 20, 2019 at 12:00 PM - 1:00 PM
LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Brianna White brianna.mello@northwestern.edu EMAIL
CALENDAR Department of Computer Science
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Apr16
EVENT DETAILS
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Jill Wilson, Charles Deering McCormick Distinguished Professor of Instruction and Assistant Chair, Industrial Engineering and Management Sciences
Marita Labedz Poll, senior academic advisor, Industrial Engineering and Management Sciences
TIME Friday, April 16, 2021 at 12:00 PM - 12:30 PM
CONTACT Northwestern Engineering Events northwestern-engineering-events@northwestern.edu EMAIL
CALENDAR McCormick School of Engineering and Applied Science
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Apr16
EVENT DETAILS
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Despite notable successes on several carefully controlled benchmarks, current machine learning (ML) systems are remarkably brittle, raising serious concerns about their deployment in safety-critical applications like self-driving cars and predictive healthcare. In this talk, I discuss fundamental obstacles to building robust ML systems and develop principled approaches that form the foundations of robust ML. I will focus on two settings where standard ML models degrade substantially: adversarial attacks on test inputs, and presence of spurious correlations like image backgrounds. I will demonstrate the need to question common assumptions in ML, particularly about the role of training data. On the one hand, I will describe how and why naively using more data can surprisingly hurt performance in these robustness settings. On the other hand, I will show that unlabeled data, when harnessed in the right fashion, is extremely beneficial and enables state-of-the-art robustness. In closing, I will discuss how to build on the foundations of robust ML and achieve wide-ranging robustness in various domains including natural language processing and vision.
Biography
Aditi Raghunathan is a fifth year PhD student at Stanford University advised by Percy Liang. She is interested in building robust machine learning systems with guarantees for trustworthy real-world deployment. Her research in robustness has been recognized by a Google PhD Fellowship in Machine Learning and the Open Philanthropy AI Fellowship. Among other honors, she is also the recipient of the Anita Borg Memorial Scholarship and the Stanford School of Engineering Fellowship.
TIME Friday, April 16, 2021 at 12:00 PM - 1:00 PM
CONTACT Pamela Villalovoz pmv@northwestern.edu EMAIL
CALENDAR Department of Computer Science
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Apr16
EVENT DETAILS
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We give a 2-pass semi-streaming algorithm to find the exact global minimum cut of an unweighted, undirected graph. This result is adapted from a paper by Aviad Rubinstein, Tselil Schramm, and S. Matthew Weinberg called Computing Exact Minimum Cuts Without Knowing the Graph (ITCS 2018).
Biography
Shreya Mogulothu is a junior at West Windsor-Plainsboro High School North. She has been a participant in the Program in Algorithmic and Combinatorial Thinking (PACT) since the summer of 2019. She loves working on problems, especially collaboratively. In her free time, she writes problems with her friends and blogs about things she finds curious.TIME Friday, April 16, 2021 at 2:00 PM - 3:00 PM
CONTACT Pamela Villalovoz pmv@northwestern.edu EMAIL
CALENDAR Department of Computer Science
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Apr21
EVENT DETAILS
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Mental health problems such as anxiety disorders and depression have grown in the last decades, with a huge increase in the rate of growth for people under age 26 since the early years of smartphone adoption. Depression is on track to be the global #1 disease burden by 2030. To date, doctors do not understand what causes or precipitates depression, or how to forecast it; nor is there a reliable treatment, although most people will get better. Mobile and wearable devices are improving in their ability to model continuous physiology and activity/sleep/social data for detecting subtly changing patterns related to changing health, and providing early AI-generated forecasts of changes in health. What are some of these patterns telling us about neurological activity, mood, stress and brain health and how accurately? This talk will highlight some of the latest results from our studies at MIT, focusing on both scientific and ethical challenges in using AI to foster good mental health.
Biography
Dr. Rosalind Picard, MIT and Empatica. Dr. Picard is a professor, inventor, engineer, and scientist. She wrote the book Affective Computing that outlines how to give machines the skills of emotional intelligence, which inspired the growing field of Affective Computing. She co-founded two companies that commercialized inventions by her and her team at MIT: Empatica, providing the first AI-smartwatch recognized by the FDA for monitoring seizures, and Affectiva, providing emotion AI software. At MIT, she is a full professor teaching and directing research at the Media Lab, and serves as founding faculty chair for MindHandHeart, MIT’s campus-wide wellbeing initiative. She serves also as chief scientist and Chairman for Empatica, where working with many medical partners, they recently released the first medically approved (in the EU) non-invasive smartwatch running AI for forecasting a positive PCR test for a viral respiratory infection (Influenza H1N1, Rhinovirus, or COVID-19).
TIME Wednesday, April 21, 2021 at 12:00 PM - 1:00 PM
CONTACT Pamela Villalovoz pmv@northwestern.edu EMAIL
CALENDAR Department of Computer Science
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Apr28
TIME Wednesday, April 28, 2021 at 12:30 PM - 1:00 PM
CONTACT Northwestern Engineering Events northwestern-engineering-events@northwestern.edu EMAIL
CALENDAR McCormick School of Engineering and Applied Science
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May10
EVENT DETAILS
TIME Monday, May 10, 2021 at 2:00 PM - 3:00 PM
CONTACT Northwestern Engineering Events northwestern-engineering-events@northwestern.edu EMAIL
CALENDAR McCormick School of Engineering and Applied Science
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May12
EVENT DETAILS
lessSpeaker: Joseph Holtgreive, assistant dean for undergraduate engineering
TIME Wednesday, May 12, 2021 at 12:00 PM - 12:30 PM
CONTACT Northwestern Engineering Events northwestern-engineering-events@northwestern.edu EMAIL
CALENDAR McCormick School of Engineering and Applied Science
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May31
EVENT DETAILS
lessMemorial Day (no classes)
TIME Monday, May 31, 2021
CONTACT Office of the Registrar nu-registrar@northwestern.edu EMAIL
CALENDAR University Academic Calendar
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Jun7
EVENT DETAILS
lessSpring examinations begin
TIME Monday, June 7, 2021
CONTACT Office of the Registrar nu-registrar@northwestern.edu EMAIL
CALENDAR University Academic Calendar
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Jun12
EVENT DETAILS
lessSpring examinations end
TIME Saturday, June 12, 2021
CONTACT Office of the Registrar nu-registrar@northwestern.edu EMAIL
CALENDAR University Academic Calendar