Events
Past Event
Biological Systems that Learn
NSF-Simons National Institute for Theory and Mathematics in Biology
9:00 AM
Details
Many biological systems have evolved to embody solutions to complex inverse problems to produce desired outputs or functions; others have evolved strategies to learn solutions to complex inverse problems on much shorter (e.g. physiological or developmental) time scales. Another class of systems that solves complex inverse problems for desired outputs is artificial neural networks. A critical distinction between the biological systems and neural networks is that the former are not associated with processors that carry out algorithms such as gradient descent to solve the inverse problems. They must solve them using local rules that only approximate gradient descent. Thus a key challenge is to understand how biological systems encode local rules for experience-dependent modification in physical hardware to implement robust solutions to complex
inverse problems. A similar challenge is faced by a growing community of researchers interested in developing physical systems that can solve inverse problems on their own. Despite the differences between physical/biological systems that solve inverse problems via local rules on one hand, and neural networks that solve them using global algorithms like gradient descent on the other hand, each has the potential to inform the other. For example, the insight that overparameterization is important for obtaining good solutions generalizes from neural networks to physical/biological learning systems.
Examples of biological systems that learn at different scales include (1) biological filament networks such as the actin cortex, collagen extracellular matrix and fibrin blood clots, which maintain rigidity homeostasis as an output under constantly varying and often extreme stresses as inputs. (2) Epithelial tissues during various stages of development, which can undergo large shape changes and controlled cellular flows as desired outputs. (3) Immune systems, which constantly adapt to bind to invading pathogens as desired outputs. (4) Ecological systems, in which species can change their interactions (eg. learn to consume new species) in order to bolster their population as an output.
The goal of this workshop is to discover new core principles and mathematical tools/approaches shared across physical learning systems, biological learning systems and neural networks that will inform deeper understanding and future discovery in all 3 fields. To this end, we will bring together researchers interested in viewing biological problems through the lens of inverse problems, researchers working on physical learning, and researchers studying neural networks for a week of intensive discussion and cross-fertilization.
We envision a unique format for this workshop, focused on framing and discussing open questions rather than on recitations of recent results. We will ask a subset of participants to present pedagogical overviews of key topics to help members of disparate communities establish common intellectual ground for discussion.
Time
Tuesday, January 7, 2025 at 9:00 AM - 5:00 PM
Contact
Calendar
NSF-Simons National Institute for Theory and Mathematics in Biology
Northwestern Engineering PhD Hooding and Master's Recognition Ceremony
McCormick School of Engineering and Applied Science
4:00 PM
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Pick-Staiger Concert Hall
Details
The ceremony will take place on Saturday, December 13 in Pick-Staiger Concert Hall, 50 Arts Circle Drive.
Time
Saturday, December 13, 2025 at 4:00 PM - 6:00 PM
Location
Pick-Staiger Concert Hall Map
Contact
Calendar
McCormick School of Engineering and Applied Science
Inderpal Bhandari
Northwestern Network for Collaborative Intelligence (NNCI)
12:00 PM
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Guild Lounge, Scott Hall
Details
Join us for NNCI's first Distinguished Speaker event featuring Inderpal Bhandari.
Lunch will be provided. Registration is required.
Time
Tuesday, January 13, 2026 at 12:00 PM - 1:30 PM
Location
Guild Lounge, Scott Hall Map
Contact
Calendar
Northwestern Network for Collaborative Intelligence (NNCI)
U.S. District Court Judge Xavier Rodriguez - NNCI & Pritzker School of Law
Northwestern Network for Collaborative Intelligence (NNCI)
12:15 PM
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Thorne Auditorium, Arthur Rubloff Building
Details
Join us for an in-person Distinguished Speaker event, co-hosted with Pritzker School of Law, featuring Judge Xavier Rodriguez.
Location: Thorne Auditorium, Pritzker School of Law, Chicago Campus
Lunch will be provided. Registration is required.
Time
Tuesday, January 20, 2026 at 12:15 PM - 1:30 PM
Location
Thorne Auditorium, Arthur Rubloff Building Map
Calendar
Northwestern Network for Collaborative Intelligence (NNCI)
"Big-Data Algorithms That Are Not Machine Learning" - Jeffrey Ullman
Northwestern Network for Collaborative Intelligence (NNCI)
12:00 PM
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Ruan Conference Center, Chambers Hall
Details
Join us for an in-person Distinguished Speaker event featuring Jeffrey Ullman, a prominent figure in computer science whose contributions have shaped the foundations of algorithms, databases, and theoretical computing.
Title: "Big-Data Algorithms That Are Not Machine Learning"
Abstract: We shall introduce four algorithms that run very fast on large amounts of data, although typically the answers they give are approximate rather than precise. (1) Locality-sensitive hashing (2) Approximate counting (3) Sampling (4) Counting triangles in graphs.
Lunch will be provided. Registration is required.
Jeff Ullman is the Stanford W. Ascherman Professor of Engineering (Emeritus) in the Department of Computer Science at Stanford and CEO of Gradiance Corp. He received the B.S. degree from Columbia University in 1963 and the PhD from Princeton in 1966. Prior to his appointment at Stanford in 1979, he was a member of the technical staff of Bell Laboratories from 1966-1969, and on the faculty of Princeton University between 1969 and 1979. From 1990-1994, he was chair of the Stanford Computer Science Department. Ullman was elected to the National Academy of Engineering in 1989, the American Academy of Arts and Sciences in 2012, the National Academy of Sciences in 2020, and has held Guggenheim and Einstein Fellowships. He has received the Sigmod Contributions Award (1996), the ACM Karl V. Karlstrom Outstanding Educator Award (1998), the Knuth Prize (2000), the Sigmod E. F. Codd Innovations award (2006), the IEEE von Neumann medal (2010), the NEC C&C Foundation Prize (2017), and the ACM A.M. Turing Award (2020). He is the author of 16 books, including books on database systems, data mining, compilers, automata theory, and algorithms.
Time
Tuesday, February 10, 2026 at 12:00 PM - 1:30 PM
Location
Ruan Conference Center, Chambers Hall Map
Contact
Calendar
Northwestern Network for Collaborative Intelligence (NNCI)