Events
Upcoming Event
Statistics and Data Science Seminar: "Data Augmentation for Graph Regression"
Department of Statistics and Data Science
11:00 AM
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Ruan Conference Room – lower level, Chambers Hall
Details
Data Augmentation for Graph Regression
Meng Jiang, Associate Professor, Department of Computer Science and Engineering, University of Notre Dame
Abstract: Graph regression plays a key role in materials discovery by enabling the prediction of numerical properties of molecules and polymers. However, graph regression models often rely on training sets with only a few hundred labeled examples, and these labels are typically imbalanced. While a large number of unlabeled examples are available, they are often drawn from diverse domains, making them less effective for improving target label predictions. In machine learning, data augmentation refers to techniques that increase the size of the training set by generating slightly modified or synthetic versions of existing data. These methods are simple yet effective. In this talk, I will introduce three graph data augmentation techniques tailored for supervised learning, imbalanced learning, and transfer learning in graph regression tasks. The first technique leverages the mutual enhancement between model rationalization and data augmentation, improving both accuracy and interpretability in molecular and polymer property prediction. This approach demonstrates that graph data augmentation can be effectively performed in latent spaces. The second technique generates representations of additional data points with underrepresented labels to balance the training set. The third technique introduces a graph diffusion transformer (Graph DiT) that facilitates data-centric transfer learning, addressing the limitations of self-supervised methods when dealing with unlabeled graph data. Graph DiT integrates multiple properties such as synthetic score and gas permeability as condition constraints into diffusion models for multi-conditional polymer generation. Lastly we will discuss foundation model approaches for materials discovery.
Time
Friday, May 23, 2025 at 11:00 AM - 12:00 PM
Location
Ruan Conference Room – lower level, Chambers Hall Map
Contact
Calendar
Department of Statistics and Data Science
Five Things New Graduates Should Know About AI, but Won't Learn in School
AI@NU
12:00 PM
Details
The Artificial Intelligence Graduate Group (AIGG) welcomes Derek Ferguson, Chief Software Officer of the Fitch Group, for an AI industry talk.
Time
Thursday, May 22, 2025 at 12:00 PM - 1:00 PM
Calendar
AI@NU
Appl Math: Yiping Lu on "Two Tales, One Resolution: Physics-Informed Test Time Scaling and Precondition"
McCormick-Engineering Sciences and Applied Mathematics (ESAM)
11:15 AM
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M416, Technological Institute
Details
Title: Two Tales, One Resolution: Physics-Informed Test Time Scaling and Precondition
Speaker: Yiping Lu, Northwestern University
Abstract: In this talk, I will introduce a novel framework for physics-informed debiasing of machine learning estimators, which we call Simulation-Calibrated Scientific Machine Learning (SCaSML). This approach leverages the structure of physical models to achieve two key objectives:
Unbiased Predictions: It produces unbiased predictions even when the underlying machine learning predictor is biased.
Overcoming Dimensionality Challenges: It mitigates the curse of dimensionality that often affects high-dimensional estimators.
The SCaSML paradigm integrates a (potentially) biased machine learning algorithm with a de-biasing procedure that is rigorously designed using numerical analysis and stochastic simulation. Our methodology aligns with recent advances in inference-time computation—similar to those seen in the large language model literature—demonstrating that additional computation can enhance ML estimates. Furthermore, we establish a surprising equivalence between our framework and another research direction that utilizes approximate (linearized) solvers to precondition iterative methods. This connection not only bridges two distinct areas of study but also offers new insights into improving estimation accuracy in complex, high-dimensional (PDE) settings.
Zoom: https://northwestern.zoom.us/j/94570889326
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Time
Tuesday, May 27, 2025 at 11:15 AM - 12:15 PM
Location
M416, Technological Institute Map
Contact
Calendar
McCormick-Engineering Sciences and Applied Mathematics (ESAM)
Research-In-Progress: Xueying Wang
NSF-Simons National Institute for Theory and Mathematics in Biology
3:00 PM
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Suite 3500
Details
Members of the NITMB community are invited to join us for Research-In-Progress meetings, an informal venue for members of the NITMB to discuss ongoing and/or planned research.
Xueying Wang is a Postdoctoral Fellow at the National Institute for Theory and Mathematics in Biology. Earning her Ph.D. in Physics from the University of Illinois, Urbana-Champaign, her research tackles the dynamical properties of complex, chaotic, and out-of-equilibrium systems, including fluid turbulence, biological and artificial neural networks, ecological systems, and active matter. In her doctoral work, she developed a spatially extended stochastic ecological model of energy flow in a fluid undergoing the transition to turbulence and predicted the four different phases encountered during the progression to fully developed turbulence in the quasi-one-dimensional flow. She employs a combination of computational and analytical techniques derived from statistical physics in her research. She has widespread research interests ranging from fluid turbulence to generalized learning & adaptation and structural stability & emergent functionality. For more information, see personal website and Google Scholar page.
Learn more about Xueying Wang's research and engage in discussion with the NITMB community. Research-In-Progress talks take place on Wednesdays at 3pm at the NITMB office (875 N Michigan Ave., Suite 4010). Snacks and coffee will follow.
Time
Wednesday, May 28, 2025 at 3:00 PM - 4:00 PM
Location
Suite 3500
Contact
Calendar
NSF-Simons National Institute for Theory and Mathematics in Biology
Urban & Community Workshop: Evie Lopoo (Northwestern) & Sophia Costa (University of Chicago)
Sociology Department
4:00 PM
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222, Parkes Hall
Details
The Department of Sociology's Urban & Community Workshop presents:
Evie Lopoo (NU): Understanding Creative Labor in the Indie MusicScene: Competition and Community in the Age of AI
Sophia Costa (U Chicago): Responding to Poverty Ungovernance During the COVID-19 Pandemic
(A. Fahlberg, C. Martins, A. C. Araujo, L. Santos & J. L. da Silva)
Parkes Hall, Room 222 | Zoom
4:00 PM to 5:30 PM
Time
Monday, June 2, 2025 at 4:00 PM - 5:30 PM
Location
222, Parkes Hall Map
Contact
Calendar
Sociology Department
2024-2025 Commencement Ceremony
University Academic Calendar
All Day
Details
2024-2025 Commencement Ceremony
Time
Sunday, June 15, 2025
Contact
Calendar
University Academic Calendar
Northwestern Engineering PhD Hooding and Master's Degree Recognition Ceremony
McCormick School of Engineering and Applied Science
9:00 AM
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2705 Ashland Ave
Details
McCormick School of Engineering PhD Hooding and Master's Degree Recognition Ceremony. The most up to date information can be found on our graduation webpage.
Time
Monday, June 16, 2025 at 9:00 AM - 11:00 AM
Location
2705 Ashland Ave
Contact
Calendar
McCormick School of Engineering and Applied Science
Northwestern Engineering Undergraduate Convocation
McCormick School of Engineering and Applied Science
2:00 PM
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2705 Ashland Ave
Details
McCormick School of Engineering Undergraduate Convocation. The most up to date information can be found on our graduation webpage.
Time
Monday, June 16, 2025 at 2:00 PM - 4:00 PM
Location
2705 Ashland Ave
Contact
Calendar
McCormick School of Engineering and Applied Science
Open SkAI 2025
SkAI Institute
All Day
Details
The Open SkAI 2025 conference is the U.S. National Science Foundation and Simons Foundation-funded SkAI Institute's inaugural conference. The main aim of the conference is to enhance and generate new Astro-AI research directions. Conference themes will include: Astro-AI research across survey astronomy, from stars and transients to galaxy formation, evolution, and the dark sector. We encourage researchers from all levels to apply to attend.
Time
Tuesday, September 2, 2025
Contact
Calendar
SkAI Institute
Open SkAI 2025
SkAI Institute
All Day
Details
The Open SkAI 2025 conference is the U.S. National Science Foundation and Simons Foundation-funded SkAI Institute's inaugural conference. The main aim of the conference is to enhance and generate new Astro-AI research directions. Conference themes will include: Astro-AI research across survey astronomy, from stars and transients to galaxy formation, evolution, and the dark sector. We encourage researchers from all levels to apply to attend.
Time
Wednesday, September 3, 2025
Contact
Calendar
SkAI Institute