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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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Feb4
EVENT DETAILSmore info
lessFor Dario Robleto, the practice of art shares a key aspiration with scientific endeavor: both artists and scientists strive to increase the sensitivity of their observations. In her contribution to The Heart’s Knowledge catalogue, Jennifer Roberts (Professor of the Humanities, Harvard University) writes that “the act of measurement cannot be separated from the search for meaning.” What are the tools that artists and scientists use to observe and measure the unknown? How might we use those tools collaboratively to construct new pathways of human understanding across time and distance? How might shared values of empathy, care, and curiosity guide such pursuits?
In this opening conversation, Robleto and Roberts will be joined by Lucianne Walkowicz, astronomer and co-founder of the JustSpace Alliance, and Michael Metzger, Pick-Laudati Curator of Media Arts and curator of The Heart’s Knowledge, to reflect on these questions. Join us for a discussion that reaches across boundaries to examine the shared pursuit of greater understanding that binds artists and scientists.
Drop by The Block early and join the Block Museum Student Associates in the galleries for a look at the exhibition.
Programs are open to all, on a first-come first-served basis. RSVPs not required, but appreciated.
TIME Saturday, February 4, 2023 at 2:00 PM - 3:30 PM
LOCATION McCormick Auditorium, Norris University Center map it
CONTACT Block Museum of Art block-museum@northwestern.edu EMAIL
CALENDAR Block Museum of Art
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Feb6
EVENT DETAILS
lessMonday / CS Seminar
February 6th / 10:00 AM
Mudd 3514Title: Aligning Machine Learning, Law, and Policy for Responsible Real-World Deployments
Speaker: Peter HendersonAbstract:
Machine learning (ML) is being deployed to a vast array of real-world applications with profound impacts on society. ML can have positive impacts, such as aiding in the discovery of new cures for diseases and improving government transparency and efficiency. But it can also be harmful: reinforcing authoritarian regimes, scaling the spread of disinformation, and exacerbating societal biases. As we rapidly move toward systemic use of ML in the real world, there are many unanswered questions about how to successfully use ML for social good while preventing its potential harms. Many of these questions inevitably require pursuing a deeper alignment between ML, law, and policy. Are certain algorithms truly compliant with current laws and regulations? Is there a better design that can make them more in tune to the regulatory and policy requirements of the real world? Are laws, policies, and regulations sufficiently informed by the technical details of ML algorithms, or will they be ineffective and out-of-sync? In this talk, I will discuss ways to bring together ML, law, and policy to address these questions. I will draw on real-world examples throughout the talk, including a unique real-world collaboration with the Internal Revenue Service. I will show how investigating questions of alignment between ML, law, and policy can advance core research in ML, as well as how we might develop new algorithms to expand policy and regulatory options. It is my hope that the tools discussed in this talk will help us lead to more effective and responsible ways of deploying ML in the real world, so that we steer toward positive impacts and away from potential harms.
Biography:
Peter Henderson is a joint JD-PhD (Computer Science) candidate at Stanford University advised by Dan Jurafsky. He is also an OpenPhilanthropy AI Fellow, a Graduate Student Fellow at the Stanford RegLab working closely with Daniel E. Ho, and technical advisor at the Institute of Security and Technology. Previously, he received his M.Sc. at McGill University, advised by Joelle Pineau and David Meger. In the past he has worked at the California Supreme Court, Amazon AWS & Alexa, and Meta Fundamental AI Research.TIME Monday, February 6, 2023 at 10:00 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)
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Feb6
EVENT DETAILS
lessMonday / CS Seminar
February 6th / 12:00 PM
Mudd 3514Title: Toward Deep Semantic Understanding: Event-Centric Multimodal Knowledge Acquisition
Speaker: Manling LiAbstract:
Traditionally, multimodal information consumption has been entity-centric with a focus on concrete concepts (such as objects, object types, physical relations, e.g., a person in a car), but lacks ability to understand abstract semantics (such as events and semantic roles of objects, e.g., driver, passenger, mechanic). However, such event-centric semantics are the core knowledge communicated, regardless whether in the form of text, images, videos, or other data modalities.
At the core of my research in Multimodal Information Extraction (IE) is to bring such deep semantic understanding ability to the multimodal world. My work opens up a new research direction Event-Centric Multimodal Knowledge Acquisition to transform traditional entity-centric single-modal knowledge into event-centric multi-modal knowledge. Such a transformation poses two significant challenges: (1) understanding multimodal semantic structures that are abstract (such as events and semantic roles of objects): I will present my solution of zero-shot cross-modal transfer (CLIP-Event), which is the first to model event semantic structures for vision-language pretraining, and supports zero-shot multimodal event extraction for the first time; (2) understanding long-horizon temporal dynamics: I will introduce Event Graph Model, which empowers machines to capture complex timelines, intertwined relations and multiple alternative outcomes. I will also show its positive results on long-standing open problems, such as timeline generation, meeting summarization, and question answering. Such Event-Centric Multimodal Knowledge starts the next generation of information access, which allows us to effectively access historical scenarios and reason about the future. I will lay out how I plan to grow a deep semantic understanding of language world and vision world, moving from concrete to abstract, from static to dynamic, and ultimately from perception to cognition.Biography:
Manling Li is a Ph.D. candidate at the Computer Science Department of University of Illinois Urbana-Champaign. Her work on multimodal knowledge extraction won the ACL'20 Best Demo Paper Award, and the work on scientific information extraction from COVID literature won NAACL'21 Best Demo Paper Award. She was a recipient of Microsoft Research PhD Fellowship in 2021. She was selected as a DARPA Riser in 2022, and a EE CS Rising Star in 2022. She was awarded C.L. Dave and Jane W.S. Liu Award, and has been selected as a Mavis Future Faculty Fellow. She led 19 students to develop the UIUC information extraction system and ranked 1st in DARPA AIDA evaluation in 2019 and 2020. She has more than 30 publications on multimodal knowledge extraction and reasoning, and gave tutorials about event-centric multimodal knowledge at ACL'21, AAAI'21, NAACL'22, AAAI'23, etc. Additional information is available at https://limanling.github.io/.TIME Monday, February 6, 2023 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)
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Feb8
EVENT DETAILS
lessWednesday / CS Seminar
February 8th / 10:00 AM
Hybrid / Mudd 3514Title: Controlling Large Language Models: Generating (Useful) Text from Models We Don’t Fully Understand
Speaker: Ari HoltzmanAbstract:
Generative language models have recently exploded in popularity, with services such as ChatGPT deployed to millions of users. These neural models are fascinating, useful, and incredibly mysterious: rather than designing what we want them to do, we nudge them in the right direction and must discover what they are capable of. But how can we rely on such inscrutable systems?This talk will describe a number of key characteristics we want from generative models of text, such as coherence and correctness, and show how we can design algorithms to more reliably generate text with these properties. We will also highlight some of the challenges of using such models, including the need to discover and name new and often unexpected emergent behavior. Finally, we will discuss the implications this has for the grand challenge of understanding models at a level where we can safely control their behavior.
Biography:
Ari Holtzman is a PhD student at the University of Washington. His research has focused broadly on generative models of text: how we can use them and how can we understand them better. His research interests have spanned everything from dialogue, including winning the first Amazon Alexa Prize in 2017, to fundamental research on text generation, such as proposing Nucleus Sampling, a decoding algorithm used broadly in deployed systems such as the GPT-3 API and academic research. Ari completed an interdisciplinary degree at NYU combining Computer Science and the Philosophy of Language.TIME Wednesday, February 8, 2023 at 10:00 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)
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Feb8
EVENT DETAILS
lessWednesday / CS Seminar
February 8th / 12:00 PM
Mudd 3514Title: Cryptography, Security, and Law
Speaker: Sunoo ParkAbstract:
My research focuses on the security, privacy, and transparency of technologies in societal and legal context. My talk will focus on three of my recent works in this space, relating to (1) preventing exploitation of stolen email data, (2) enhancing accountability in electronic surveillance, and (3) legal risks faced by security researchers.Biography:
Sunoo Park is a Postdoctoral Fellow at Columbia University and Visiting Fellow at Columbia Law School. Her research interests range across cryptography, security, and technology law. She received her Ph.D. in computer science at MIT, her J.D. at Harvard Law School, and her B.A. in computer science at the University of Cambridge. She has also been affiliated with Cornell Tech's Digital Life Initiative, the Berkman Klein Center for Internet & Society at Harvard University, the MIT Media Lab's Digital Currency Initiative, and MIT's Internet Policy Research Initiative.TIME Wednesday, February 8, 2023 at 12:00 PM - 1:00 PM
LOCATION Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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Feb13
EVENT DETAILS
lessWednesday / CS Seminar
February 13th / 10:00 AM
Mudd 3514Title: Distance-Estimation in Modern Graphs: Algorithms and Impossibility
Speaker: Nicole WeinAbstract:
The size and complexity of today's graphs present challenges that necessitate the discovery of new algorithms. One central area of research in this endeavor is computing and estimating distances in graphs. In this talk I will discuss two fundamental families of distance problems in the context of modern graphs: Diameter/Radius/Eccentricities and Hopsets/Shortcut Sets.The best known algorithm for computing the diameter (largest distance) of a graph is the naive algorithm of computing all-pairs shortest paths and returning the largest distance. Unfortunately, this can be prohibitively slow for massive graphs. Thus, it is important to understand how fast and how accurately the diameter of a graph can be approximated. I will present tight bounds for this problem via conditional lower bounds from fine-grained complexity.
Secondly, for a number of settings relevant to modern graphs (e.g. parallel algorithms, streaming algorithms, dynamic algorithms), distance computation is more efficient when the input graph has low hop-diameter. Thus, a useful preprocessing step is to add a set of edges (a hopset) to the graph that reduces the hop-diameter of the graph, while preserving important distance information. I will present progress on upper and lower bounds for hopsets.
Biography:
Nicole Wein is a Simons Postdoctoral Leader at DIMACS at Rutgers University. Previously, she obtained her Ph.D. from MIT advised by Virginia Vassilevska Williams. She is a theoretical computer scientist and her research interests include graph algorithms and lower bounds including in the areas of distance-estimation algorithms, dynamic algorithms, and fine-grained complexity.TIME Monday, February 13, 2023 at 10:00 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)
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Feb13
EVENT DETAILS
lessWednesday / CS Seminar
February 13th / 12:00 PM
Mudd 3514Title: Reading to Learn: Improving Generalization by Learning From Language
Speaker: Victor ZhongAbstract:
Traditional machine learning systems are trained on vast quantities of annotated data or experience. These systems often do not generalize to new, related problems that emerge after training, such as conversing about new topics or interacting with new environments. In this talk, I present Reading to Learn, a new class of algorithms that improve generalization by learning to read language specifications, without requiring any actual experience or labeled examples. This includes, for example, reading FAQ documents to learn to answer questions about new topics and reading manuals to learn to play new games. I will discuss new algorithms and data for Reading to Learn applied to a broad range of tasks, including policy learning in grounded environments and data synthesis for code generation, while also highlighting open challenges for this line of work. Ultimately, the goal of Reading to Learn is to democratize AI by making it accessible for low-resource problems where the practitioner cannot obtain annotated data at scale, but can instead write language specifications that models read to generalize.Biography:
Victor Zhong is a PhD student at the University of Washington Natural Language Processing group. His research is at the intersection of natural language processing and machine learning, with an emphasis on how to use language understanding to learn more generally and more efficiently. His research covers a range of topics, including dialogue, code generation, question answering, and grounded reinforcement learning. Victor has been awarded the Apple AI/ML Fellowship as well as an EMNLP Outstanding Paper award. His work has been featured in Wired, MIT Technology Review, TechCrunch, VentureBeat, Fast Company, and Quanta Magazine. He was a founding member of Salesforce Research, and has previously worked at Meta AI Research and Google Brain. He obtained a Masters in Computer Science from Stanford University and a Bachelor of Applied Science in Computer Engineering from the University of Toronto.TIME Monday, February 13, 2023 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)
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Feb15
EVENT DETAILS
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Join us for a series of professional development events hosted by McCormick HR. The first event of the series will focus on conflict resolution.
Conflict is an unavoidable part of life, both at home and at work. Knowing how to resolve conflict – and, in many cases, reap the benefits that conflict can bring – is a valuable skill. Participants in this workshop will learn how to iron out differences before they escalate, they will explore the dynamics of conflict, develop awareness of their role in conflict situations, and acquire tips for dealing with hostile individuals.
This event is voluntary and open to all Northwestern Engineering staff, faculty, postdocs, and research staff.
Training provided by SupportLinc, your Employee Assistance Program
TIME Wednesday, February 15, 2023 at 12:00 PM - 1:00 PM
LOCATION The Hive, Room 2-350, Ford Motor Company Engineering Design Center map it
CONTACT Kimberly Higgins kimberly.higgins@northwestern.edu EMAIL
CALENDAR McCormick School of Engineering and Applied Science
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Mar11
EVENT DETAILS
lessWinter Classes End
TIME Saturday, March 11, 2023
CONTACT Office of the Registrar nu-registrar@northwestern.edu EMAIL
CALENDAR University Academic Calendar
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Mar18
EVENT DETAILS
lessSpring Break Begins
TIME Saturday, March 18, 2023
CONTACT Office of the Registrar nu-registrar@northwestern.edu EMAIL
CALENDAR University Academic Calendar
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Mar24
EVENT DETAILS
lessWinter Degrees Conferred
TIME Friday, March 24, 2023
CONTACT Office of the Registrar nu-registrar@northwestern.edu EMAIL
CALENDAR University Academic Calendar
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Mar27
EVENT DETAILS
lessSpring Break Ends
TIME Monday, March 27, 2023
CONTACT Office of the Registrar nu-registrar@northwestern.edu EMAIL
CALENDAR University Academic Calendar
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Mar28
EVENT DETAILS
lessSpring Classes begin 8 a.m. (Northwestern Monday: Classes scheduled to meet on Mondays meet on this day)
TIME Tuesday, March 28, 2023
CONTACT Office of the Registrar nu-registrar@northwestern.edu EMAIL
CALENDAR University Academic Calendar