CDL and MLDS are proud to welcome Adam Stokes, Director of Data Science, Modeling, and Segmentation at Ahold Delhaize USA for his seminar "Demand Modeling Overview – Examples and reflections from a career in forecasting"

Abstract: Adam will cover his journey through his Data Science career – how I landed in data science and the projects I have worked on. Additionally, Adam will showcase some example of projects he and his teams have worked on throughout his data science journey. Along with project examples, Adam will provide his commentary on popular business problems, implementation issues, and what trends he sees for the future.

Bio: Adam has spent most of his post-graduate career in demand modeling and the use cases attached to demand. He has led data science teams responsible for building and maintaining the demand models for all McDonald’s restaurants across North America and Japan, was the Development lead for McDonald’s A/B testing platform for mobile offers, led the development of Machine Learning capabilities in Demand Forecasting across the globe for Kraft Heinz, and currently leads a team at Ahold Delhaize responsible for customer level modeling, segmentation, and forecasting.

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The Slippage: 3D Humanities Series aims to nurture the formation of an interdisciplinary learning community that critically engages the connections between performance, history, theater, and emerging technologies. The 3-part performance and workshop series is designed to expose Northwestern University audiences to professional artists with technology-enhanced practices, and to provide participants with the skills and on-campus resources that enable innovative humanities research. Each event will pair a performance and discussion led by a technology-focused professional performance artist, with a hands-on skills-based workshop led by Northwestern University faculty or staff. The workshops will allow participants to engage technologies such as: motion capture, 3D digitization and printing, artificial intelligence and mechatronics – merging technological skill sets with critical discussions around artistry, humanity, and social possibilities.

Artists:

April 16 - Ben Baker, Assistant Professor of Philosophy at Colby College

April 30- Eto Otitigbe, Assistant Professor of Art at Brooklyn College

May 14- LaJuné McMillian, Multidisciplinary Artist and Educator

Join us from 5:00-7:00PM on any or all of the dates for these innovative programs:

Introduction: 5:00 - 5:10PM
Presentation: 5:10 - 5:30PM
Moderated Q&A: 5:30 - 5:50PM
Break: 5:50 - 6:00PM
Workshop: 6:00 - 7:00PM
Group Reflection: 7:00 - 7:30PM
Reception 7:30-8:30PM

Workshop Leaders: Thomas F. DeFrantz, Ted Quiballo, Craig Stevens, Michael A. Peshkin, Nick Marchuk, Darren Gergle

Series Convenors: Thomas F. DeFrantz (SLIPPAGE Lab/Performance Studies), Craig Stevens (Northwestern IT: Media & Technology Innovation/Anthropology), Ted Quiballo (Northwestern Libraries)

Sponsors: The Alice Kaplan Institute for the Humanities; The Alumnae of Northwestern University; Northwestern Libraries; Northwestern Depts. of IT and Performance Studies; and SLIPPAGE Lab.

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David Freedman is the Chair of Neurobiology and the Stahl Professor of Neurobiology in the Wallman Society of Fellows of Neurobiology at the University of Chicago.

Research summary: Through experience, we learn to interpret the sights and sounds around us and to make decisions that move us closer to achieving our goals. Our ability to learn from and adapt to our ever changing environment is a foundation of complex behavior, as it allows us to make sense of incoming sensory stimuli and to plan successful actions. To study these questions, our laboratory uses advanced neurophysiological and behavioral techniques, in parallel with machine learning approaches for studying cognitive computations in artificial neural networks. Together, our work is providing insights into the brain mechanisms of visual learning, recognition and decision making.

Learn more about David Freedman's research

The NSF-Simons National Institute for Theory and Mathematics in Biology Seminar Series aims to bring together a mix of mathematicians and biologists to foster discussion and collaboration between the two fields. The seminar series will take place on Fridays from 10am - 11am at the NITMB offices in the John Hancock Center in downtown Chicago. There will be both an in-person and virtual component.

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May 16th speaker is TBD

Welcome to the Northwestern Medicine Healthcare AI Forum, where we dive into cutting-edge developments in the field of AI for healthcare. We present the latest published research and technology innovation, and facilitate open discussion among attendees.

Open to Northwestern Medicine professionals and the broader research and patient community within Northwestern University and the Greater Chicago area, our mission is to establish a thriving healthcare AI ecosystem that fosters collaboration and supports a network of dedicated scholars and professionals. We are here to promote research, innovation, and leadership while facilitating the seamless translation of AI advancements into the realm of biomedicine.

To ensure our forum delivers the most pertinent insights into AI's application across various aspects of healthcare, we have assembled a distinguished advisory board. This board comprises esteemed faculty from the fields of medicine, engineering, art and science, as well as senior leaders of the healthcare system. The advisory board is chaired by Prof. Yuan Luo, Chief AI Officer at I.AIM and Northwestern University Clinical and Translational Sciences Institute (NUCATS).

Join us in shaping the future of healthcare through the power of artificial intelligence, as we foster a vibrant community of scholars, patients, professionals and leaders dedicated to driving innovation in this critical field.

Meetings take place remotely via Zoom on the third Friday of each month, 10-11am CT.

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The Artificial Intelligence Graduate Group (AIGG) welcomes Derek Ferguson, Chief Software Officer of the Fitch Group, for an AI industry talk.

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.

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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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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

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2024-2025 Commencement Ceremony

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