News & EventsDepartment Events & Announcements
Events
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Apr22
EVENT DETAILS
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Abstract:
The rapid advancement of artificial intelligence presents many opportunities to improve law and society. At the same time, AI presents risks and potential harms. These opportunities and challenges can be separated into two categories. First, the law and regulations that apply to technology. Second, the use of technology to improve legal-services delivery and justice systems. Additionally, each category offers opportunities to use technology to promote the rule of law.
The deployment of AI raises many interesting questions about the application of existing law and regulation. AI also raises questions about the need for new law and regulation, including to ensure fairness, accountability, and transparency. AI also creates the possibility of implementing basic rules of democracy, human rights, and the rule of law into innovation processes and AI systems by design and default. The challenge is to use law and regulation to guide the deployment of AI toward societal improvements, without slowing innovation.
Technology has also shown the potential to revolutionize legal-services delivery, thus improving access to law and legal services for everyone. In the U.S., estimates are that more than 80% of the impoverished and more than 50% of the middle class lack access to legal services. Even some legal needs of businesses, large and small, go unmet. Computational technologies hold great promise for automating the delivery of various legal services across this spectrum. For basic legal needs, in the future smartphones could provide all users with an inventory of their legal rights and obligations, as well as solutions to common legal problems.
Innovation in legal-services delivery has been slow, however, including because regulations prohibit lawyers from sharing fees with anyone who is not a lawyer. Consequently, lawyers and technologists rarely collaborate on legal-services delivery projects. But this is changing. An increasing number of lawyers are working with allied professionals to improve processes, better manage projects, become data-driven, and leverage technology to improve legal services. Basic technologies and AI are slowly making their way into the legal industry, from legal aid and courts to large law firms and corporate legal departments.
If we are to realize the potential for AI to improve society, we must have law, regulation, and ethical principles in mind at every stage, from problem definition, design, and data collection to training, deployment, and maintenance of systems. To achieve this, technologists and lawyers must collaborate and share a common vocabulary. Lawyers must learn about technology, and technologists must learn about law. Multidisciplinary teams with a shared commitment to law, regulation, and ethics can proactively address the challenges AI presents today, and advance our collaborative problem-solving capabilities to address tomorrow’s challenges and create a better future.
Bio:
Daniel W. Linna Jr. is a Visiting Professor of Law at Northwestern Pritzker School of Law. Dan is also an affiliated faculty member at CodeX – The Stanford Center for Legal Informatics.
Dan’s teaching and research focus on innovation and technology, including computational law, artificial intelligence, data analytics, leadership, operations, and innovation frameworks. At Northwestern, Dan has taught Artificial Intelligence and Legal Reasoning, Law of Artificial Intelligence and Robotics, Assessing Artificial Intelligence and Computational Technologies, and the Innovation Lab, an interdisciplinary class with computer science faculty and students.
Dan joined Northwestern from Michigan State University College of Law, where he was a Professor of Law in Residence and the founder and Director of LegalRnD – The Center for Legal Services Innovation. At Michigan State, Dan taught Quantitative Analysis for Lawyers, Litigation {Data, Theory, Practice, & Process}, Entrepreneurial Lawyering, and Lawyer Ethics and Regulation in a Technology-Driven World. Dan has also been an adjunct professor at the University of Michigan Law School, where he has taught Negotiation and Legal Technology & Innovation: Legal-Service Delivery in the 21st Century.
TIME Monday, April 22, 2019 at 12:00 PM - 1:00 PM
LOCATION Room 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Brianna Mello brianna.mello@northwestern.edu EMAIL
CALENDAR Department of Computer Science
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Apr24
EVENT DETAILS
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Wednesday / CS Seminar
April 24th / 12:00 PM
In Person / Mudd 3514Speaker
R. James Cotton, Northwestern University & Shirley Ryan AbilityLabTalk Title
AI Powered Movement Analysis, Big Rehabilitation Data and a Path to Precision RehabilitationAbstract
This talk will discuss multiple methodological lines of work making movement and gait analysis more clinically accessible and biomechanically grounded. This includes reconstruction from synchronized multiview videos, smartphone videos, and wearable sensors. We will also discuss how implicit functions provide a powerful representation to map from time to joint angles, and GPU accelerated methods that enable end-to-end biomechanical fits from these different modalities. It will discuss some of the opportunities that large movement data enables, including the use of self-supervised learning to discover gait representations that can function as both diagnostic and response biomarkers. Finally, we will outline a vision for a Causal Framework for Precision Rehabilitation that can model this data to link from impairment to function and identify the optimal dynamic treatment policies to improve rehabilitation outcomes.Biography
I am an electrical engineer, neuroscientist, and physiatrist working as a physician-scientist at Shirley Ryan AbilityLab and Assistant Professor in the Northwestern University Department of Physical Medicine and Rehabilitation. I completed my residency in PM&R at Shirley Ryan AbilityLab (formers Rehabilitation Institute of Chicago) where I remained as faculty. Prior to that I obtained a B.S. in Electrical Engineering from Rice University followed by an MD and PhD in systems neuroscience from Baylor College of Medicine. My lab works at the intersection of artificial intelligence, wearable sensors, computer vision, causal and biomechanical modeling, and novel technologies to more precisely monitor and improve rehabilitation outcomes.Research Area/Interests
AI, computer vision, gait analysis, wearable sensors, rehabilitationTIME Wednesday, April 24, 2024 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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Apr25
EVENT DETAILS
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Spring is finally here. Enjoy the good vibes to come with some free bagels and coffee courtesy the Computer Science Department. Mix and mingle with fellow CS students and faculty every last Thursday of the month.
TIME Thursday, April 25, 2024 at 9:00 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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Apr29
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Monday / CS Seminar
April 29th / 12:00 PM
Hybrid / Mudd 3514Speaker
Kaize Ding, Northwestern UniversityTalk 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 ProcessingZoom: https://northwestern.zoom.us/j/99555833103?pwd=TEtaakVGL2xUYytDa2Nvbk14Z3VhUT09
TIME Monday, April 29, 2024 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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May1
EVENT DETAILS
lessWednesday / CS Distinguished Lecture
May 1st / 12:00 PM
Hybrid / Mudd 3514Speaker
Tong Zhang, University of Illinois Urbana-ChampaignTalk Title
TBAAbstract
TBABiography
TBAResearch Area/Interests
TBAZoom: TBA
TIME Wednesday, May 1, 2024 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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May8
EVENT DETAILS
lessWednesday / CS Distinguished Lecture
May 8th / 12:00 PM
Hybrid / Mudd 3514Speaker
Monica Lam, Stanford UniversityTalk Title
TBAAbstract
TBABiography
TBAResearch Area/Interests
TBAZoom
TBATIME Wednesday, May 8, 2024 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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May8
EVENT DETAILSmore info
lessThe 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.
TIME Wednesday, May 8, 2024 at 6:00 PM - 7:30 PM
LOCATION Block Museum of Art, Mary and Leigh map it
CONTACT Block Museum of Art block-museum@northwestern.edu EMAIL
CALENDAR Block Museum of Art
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May10
EVENT DETAILS
lessFriday / CS Distinguished Lecture
May 10th / 12:00 PM
Hybrid / Mudd 3514Speaker
Shafi Goldwasser; Simons Institute for the Theory of Computing, University of California BerkeleyTalk Title
Trust, Backdoor Vulnerabilities and Possible MitigationsAbstract
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 AlgorithmsZoom: TBA
TIME Friday, May 10, 2024 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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May30
EVENT DETAILS
lessTBA
TIME Thursday, May 30, 2024 at 9:00 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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May30
EVENT DETAILS
lessTBA
TIME Thursday, May 30, 2024 at 3:00 PM - 5:00 PM
LOCATION TBA, 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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Jun10
EVENT DETAILSmore info
lessMcCormick School of Engineering PhD Hooding and Master’s Degree Recognition Ceremony
TIME Monday, June 10, 2024 at 9:00 AM - 11:00 AM
LOCATION Welsh-Ryan Arena
CONTACT Amy Pokrass amy.pokrass@northwestern.edu EMAIL
CALENDAR McCormick School of Engineering and Applied Science
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Jun10
TIME Monday, June 10, 2024 at 2:00 PM - 4:00 PM
LOCATION Welsh-Ryan Arena
CONTACT Amy Pokrass amy.pokrass@northwestern.edu EMAIL
CALENDAR McCormick School of Engineering and Applied Science