Events
Upcoming Event
McCormick Engineering Fall 2024 MS Program Fair
Office of Professional Education (OPE)
11:00 AM
//
First Floor Main Lobby, Technological Institute
Details
Please join us for a casual fair-style event to learn about grad school opportunities here at Northwestern!
Admissions teams and other program faculty and staff will be in attendance to share information and answer questions about Master's level programs in Engineering at Northwestern. We will have materials to hand out and will be open to speaking one-on-one and looking at resumes. You are welcome to join whether you know which program interests you or you’d just like to learn more about the possbilities available in graduate engineering education at Northwestern.
Lunch is provided. We look forward to seeing you there!
The Master of Science programs featured include:
Master of Science in Biotechnology (MBP)
Master of Science in Machine Learning and Data Science (MLDS)
Master of Science in Artificial Intelligence (MSAI)
Master of Science in Project Management (MPM)
Master of Science in Robotics (MSR)
Master of Science in Engineering Design Innovation (EDI)
Master of Science in Theoretical and Applied Mechanics (TAM)
Master of Science in Mechanical Engineering
Master of Science in Material Science and Engineering
Master of Science in Computer Engineering
Master of Science in Computer Science
Master of Science in Electrical Engineering
Master of Science in Biomedical Engineering
Master of Science in Chemical and Biological Engineering
Master of Science in Energy and Sustainability (MSES)
Master of Science in Law Program
Time
Wednesday, October 23, 2024 at 11:00 AM - 1:00 PM
Location
First Floor Main Lobby, Technological Institute Map
Contact
Calendar
Office of Professional Education (OPE)
Introduction to Statistical Power Analysis (Virtual)
Northwestern IT Research Computing and Data Services
12:00 PM
Details
Proper planning for quantitative research is a crucial and sometimes overlooked step in the research process. Studies with sample sizes that are too small to have a good chance of finding realistic effects waste time and effort and can increase the likelihood of incorrect conclusions. This workshop will review basic concepts for hypothesis testing, statistical power, and precision analysis and help participants understand what they need to know to properly plan their statistical analyses with appropriate sample sizes. It will include a short demonstration using R software for power analysis.
Prerequisites: Participants should be familiar with basic statistical concepts and methods like those taught in an introductory statistics course.
This workshop complements our other data science services and support for researchers. All Northwestern researchers (student, staff, and faculty in all fields) can request free consultations with our Data Scientists and Statisticians to guide you through your data project, from considering the feasibility and choosing a method to troubleshooting the code and suggesting improvements. If you're looking for more extensive support, we partner on faculty-sponsored research through our project collaboration service.
Time
Wednesday, October 23, 2024 at 12:00 PM - 1:00 PM
Contact
Calendar
Northwestern IT Research Computing and Data Services
ECE Meet-the-Faculty Seminar - Karan Ahuja
Department of Electrical and Computer Engineering (ECE)
2:00 PM
//
L440, Technological Institute
Details
The rapid proliferation of consumer devices has created an unprecedented opportunity to revolutionize how we understand and quantify human behavior and movement in real-world settings. Today’s consumer devices – like smartphones and smartwatches – provide a glimpse of this potential, offering coarse digital representations of users with metrics such as step count, heart rate, and a handful of human activities like running and biking. Even these very low-dimensional representations are already bringing value to millions of people's lives, but there is significant potential for improvement. In my research, I introduce the "Human API" that enables consumer devices to query rich and continuous representations of our physical lives to sense, track, and understand users to augment their interactions and assist them in daily life. By driving advances in machine learning and sensing, sensor fusion, and edge computing, we aim to transform consumer devices into sophisticated user digitization and motion capture systems. Armed with such knowledge, our future devices could offer longitudinal health and wellness tracking, more productive work environments, full-body avatars in extended reality, and embodied telepresence experiences, to name just a few domains. Critically, these advances cannot come at the expense of user practicality, meaning my work must be strategic in developing new sensors and making use of existing sensors and edge computation.
Time
Wednesday, October 23, 2024 at 2:00 PM - 3:00 PM
Location
L440, Technological Institute Map
Contact
Calendar
Department of Electrical and Computer Engineering (ECE)
BMG Seminar: Leng Han, PhD, Indiana University School of Medicine
Biochemistry & Molecular Genetics Seminar Series
10:00 AM
//
Simpson Querrey Auditorium, Simpson Querrey Biomedical Research Center
Details
The Department of Biochemistry & Molecular Genetics presents:
Leng Han, PhD
David Brown Chair Professor
Brown Center for Immunotherapy, Department of Biostatistics and Health Data Science
Indiana University School of Medicine
Presentation:
"Harnessing big data for precision medicine”
Abstract:
Despite advancements in treatment options for cancer, a majority of cancer types continue to lack fully characterized and effective targeted therapies to improve disease diagnostics, prognoses, and patient survival outcomes. Therefore, there is an urgent need to gain a more comprehensive understanding of the molecular basis of diseases and develop novel prognostic and therapeutic strategies. Our lab utilizes cutting-edge techniques in systems biology to understand the molecular mechanisms of complex diseases. We conducted a series of pan-cancer analyses to provide clinical insights into cancer therapy, including RNA targeted therapy (Journal of the National Cancer Institute, 2018; Genome Medicine, 2019a; Genome Medicine, 2019b; Nature Communications, 2019; Cancer Research, 2022), chronotherapy (Cell Systems, 2018), hypoxia-targeted therapy (Nature Metabolism, 2019), target therapy (Genome Medicine, 2020a), autophagy-targeted therapy (Nature Communications, 2022), and immunotherapy (Nature Immunology, 2019; Nature Communications, 2020a; Nature Communications, 2020b; Genome Medicine, 2020b; Advanced Science, 2020; Journal of the National Cancer Institute, 2021; Cancer Cell, 2021; The Innovation, 2021; Journal for Immunotherapy of Cancer, 2022; Nature Reviews Clinical Oncology, 2022; Cell Metabolism, 2023; The Innovation, 2023; Nature Reviews Clinical Oncology, 2023). These studies shed light on future clinical considerations for the development of innovative therapies for cancer types currently lacking effective treatment options. We will further develop highly innovative prognostic and therapeutic strategies with the potential to produce a major impact on biomedical research.
Host: Dr. Ruli Gao, Assistant Professor of Biochemistry and Molecular Genetics
Time
Thursday, October 24, 2024 at 10:00 AM - 11:00 AM
Location
Simpson Querrey Auditorium, Simpson Querrey Biomedical Research Center Map
Contact
Calendar
Biochemistry & Molecular Genetics Seminar Series
MLDS Online Information Session
Master of Science in Machine Learning and Data Science (MLDS)
10:00 AM
Details
QUALIFY FOR INNOVATIVE AND TECHNICAL JOBS AT TOP COMPANIES
With more companies using data, the demand for data scientists continues to soar. Register for our Master of Science in Machine Learning and Data Science online information session to learn how you can take the next step in your career as an effective, knowledgeable leader in a rapidly growing field.
Learn more or register
Time
Thursday, October 24, 2024 at 10:00 AM - 11:00 AM
Calendar
Master of Science in Machine Learning and Data Science (MLDS)
TAM Seminar- Machine Learning Enabled Inverse and Forward Problems for Polymer-Bonded Energetic Materials- WaiChing Sun
McCormick - Civil and Environmental Engineering (CEE)
11:00 AM
//
A230, Technological Institute
Details
Abstract: Energetic materials are solids that can release significant energy upon stimuli. They can constitute propellants, explosives, fuels, and pyrotechnics used in aerospace, mining, and defense industries. To reduce the sensitivity of the materials and enhance safety, energy materials are often manufactured as two-phase composites, with a softer binder as the host matrix that holds the explosive crystals, such as HMX (High-melting Explosive) in place. This design, referred to as Polymer-bonded explosives (PBX), enables the molding, shaping, and uniformity of the materials, leading to improved predictable performance. Nevertheless, the characterization of the energy localization often requires a material model capable of handling extremely large deformation of phase transformation. This talk reports recent progress on forward and inverse problems for modeling the HMX and PBX enabled by machine learning. For the forward modeling problems, we attempt to create mathematical models of HMX expressed in symbolic form. To avoid the difficulty of training the Kolmogorov-Arnold network, we introduce an alternative technique to learn neural additive basis in projected feature space to control the expressivity-speed trade-off. For the inverse problem, we introduce a generative AI that enables us to create highly realistic PBX microstructures as well as the granular microstructures of crystals by separately handling the generation of grain geometry and topology of the granular structures via conditional latent diffusion and graph recurrent neural network. Benchmark numerical examples of material point simulations for shock loading in b-HMX are performed to assess the practicality of using the discovered machine learning models for high-fidelity simulations.
Bio: Dr. Sun has been an associate professor at Columbia University since 2020. He obtained his BS from UC Davis (2005), MS in civil engineering (geomechanics) from Stanford (2007), MA (Civil Engineering) from Princeton (2008), and Ph.D. in theoretical and applied mechanics from Northwestern (2011). Sun’s research focuses on theoretical, computational, and data-driven mechanics for porous and energetic materials. He is the recipient of a few awards, including the Walter Huber Civil Engineering Research Prize (2023), the IACM John Argyris Award (2020), the EMI Leonardo da Vinci Award (2018), the Zienkiewicz Numerical Methods Engineering Prize (2017), and early career awards from NSF, AFOSR, and ARO.
Time
Thursday, October 24, 2024 at 11:00 AM - 12:00 PM
Location
A230, Technological Institute Map
Contact
Calendar
McCormick - Civil and Environmental Engineering (CEE)
NITMB Seminar Series - Jorge Nocedal
NSF-Simons National Institute for Theory and Mathematics in Biology
10:00 AM
//
Suite 4010
Details
Talk Title: How is it Possible to Train Deep Neural Networks?
Abstract:
In 1961, Minsky, one of the founders of AI, perceived a fundamental flaw within the burgeoning field of artificial neural networks. He doubted that such a nonlinear system could be effectively trained using gradient methods, because unless the “structure of the search space is special, the optimization may do more harm than good.” Fast forward to today, and we observe deep neural networks — far more complex than those envisioned at the field's inception — being successfully trained with methods akin to gradient descent. It has, indeed, become evident that the objective function displays a highly benign structure that we are only starting to comprehend. In this lecture, I aim to summarize our current understanding of this enigmatic optimization process. I will discuss several themes, including intrinsic dimensionality, the optimization landscape, and implicit regularization, all within the context of deep networks and large language models.
Speaker Bio:
Jorge Nocedal is the Walter P. Murphy Professor of Industrial Engineering and Management Sciences and (by courtesy) Engineering Sciences and Applied Mathematics at Northwestern University. Nocedal is also the Director of the Center for Optimization and Statistical Learning. Nocedal's main area of research is optimization, with applications in machine learning, engineering design, and the physical sciences. Research activities range from the design of new algorithms, to their software implementation and mathematical analysis. Areas of emphasis include large scale problems (with millions of variables), optimization under uncertainty, and parallel computing.
Learn more about Jorge Nocedal'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.
Time
Friday, October 25, 2024 at 10:00 AM - 11:00 AM
Location
Suite 4010
Contact
Calendar
NSF-Simons National Institute for Theory and Mathematics in Biology
Statistics and Data Science Seminar: "Subsampling for Big Data Regression with Measurement Constraints"
Department of Statistics and Data Science
11:00 AM
//
Ruan Conference Room – lower level, Chambers Hall
Details
Subsampling for Big Data Regression with Measurement Constraints
Lin Wang, Assistant Professor of Statistics, Purdue University
Abstract: Despite the availability of extensive data sets, it is often impractical to observe the labels for all data points due to various measurement constraints in many applications. To address this challenge, subsampling approaches can be employed to select a subset of design points from a large pool for observation, resulting in substantial savings in labeling costs. In this presentation, I will introduce our recent research on computationally feasible subsampling techniques. Our primary focus is on regression with labeled data, which includes linear regression, ridge regression, and nonparametric additive regression. For these regression tasks, we have developed sampling approaches that aim to minimize the mean squared error in estimations and predictions. We will demonstrate the effectiveness of our proposed approaches through theoretical analysis and extensive numerical results.
Time
Friday, October 25, 2024 at 11:00 AM - 12:00 PM
Location
Ruan Conference Room – lower level, Chambers Hall Map
Contact
Calendar
Department of Statistics and Data Science
Writing and Studying with Generative AI
Northwestern IT Teaching and Learning Technologies
12:00 PM
Details
Learn more about the writing and tutoring activities and prompts shared in this series. Draft your own prompts to fit your students' needs.
Time
Friday, October 25, 2024 at 12:00 PM - 1:00 PM
Calendar
Northwestern IT Teaching and Learning Technologies
CS Seminar: Research ethics and generative artificial intelligence (Mohammad Hosseini)
Department of Computer Science (CS)
12:00 PM
//
3514, Mudd Hall ( formerly Seeley G. Mudd Library)
Details
Friday / CS Seminar
October 25th / 12:00 PM
Hybrid / Mudd 3514
Speaker
Mohammad Hosseini, Northwestern University
Talk Title
Research ethics and generative artificial intelligence
Abstract
Scientists and engineers are increasingly using generative artificial intelligence in research. Some use cases have merely yielded efficiency gains while others have enabled new types of and directions in research that would be impossible otherwise. In this interactive session, the impacts of generative artificial intelligence on the overall integrity of research, and ethical ambiguities resulting from further integration of these tools in research, will be discussed.
Biography
Mohammad Hosseini is an assistant professor of ethics at the Department of Preventive Medicine at Northwestern University in Chicago. Born in Iran, he obtained an MA in applied ethics from Utrecht University (Netherlands), and a PhD in research ethics and integrity from Dublin City University (Ireland).
Research/Interest Areas:
AI Ethics, Research Ethics and Integrity
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Zoom: https://northwestern.zoom.us/j/94422150214
Panopto: https://northwestern.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=56ac3607-8f71-49a2-986e-b1f6016aaf22
DEI Minute: Implicit Bias in Technology
Time
Friday, October 25, 2024 at 12:00 PM - 1:00 PM
Location
3514, Mudd Hall ( formerly Seeley G. Mudd Library) Map
Contact
Calendar
Department of Computer Science (CS)
Appl Math: Pedram Hassanzadeh on "Integrating the Spectral Analyses of Neural Networks and Nonlinear Physics for Explainability, Generalizability, and Stability"
McCormick-Engineering Sciences and Applied Mathematics (ESAM)
11:15 AM
//
M416, Technological Institute
Details
Title: Integrating the Spectral Analyses of Neural Networks and Nonlinear Physics for Explainability, Generalizability, and Stability
Speaker: Pedram Hassanzadeh, The University of Chicago
Abstract: In recent years, there has been substantial interest in using deep neural networks (NNs) to improve the modeling and prediction of complex, multiscale, nonlinear dynamical systems such as turbulent flows and Earth’s climate. In idealized settings, there has been some progress for a wide range of applications from data-driven spatio-temporal forecasting to long-term emulation to subgrid-scale modeling. However, to make these approaches practical and operational, i.e., scalable to real-world problems, a number of major questions and challenges need to be addressed. These include 1) instabilities and the emergence of unphysical behavior, e.g., due to how errors amplify through NNs, 2) learning in the small-data regime, 3) interpretability based on physics, and 4) out-of-distribution generalization (e.g., extrapolation to different parameters, forcings, and regimes) which is essential for applications to non-stationary systems such as a changing climate. While some progress has been made in addressing (1)-(4), e.g., doing transfer learning for generalization, these approaches have been often ad-hoc, as currently there is no rigorous framework to analyze deep NNs and develop systematic and general solutions to (1)-(4). In this talk, I will discuss some of the approaches to address (1)-(4), for example, once we identify spectral bias as the cause of instabilities in state-of-the-art weather models like Pangu-weather, GraphCast, and FourCastNet. Then I will introduce a new framework that combines the spectral (Fourier) analyses of NNs and nonlinear physics, and leverages recent advances in theory and applications of deep learning, to move toward rigorous analysis of deep NNs for applications involving dynamical systems. For example, this approach can guide and explain transfer learning and pruning in such applications. I will use examples from turbulence modeling and weather/climate prediction to discuss these methods and ideas.
Zoom: https://northwestern.zoom.us/j/99466696080
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Time
Tuesday, October 29, 2024 at 11:15 AM - 12:15 PM
Location
M416, Technological Institute Map
Contact
Calendar
McCormick-Engineering Sciences and Applied Mathematics (ESAM)
Next Steps in R: Writing Your Own Functions (Virtual)
Northwestern IT Research Computing and Data Services
12:00 PM
Details
In this workshop, you will learn how to write your own custom functions in R. We will cover the purpose and syntax of functions in R, how to declare a function, how to declare inputs (or arguments) to functions, and how to get outputs (or return values) from functions. We will also cover the Tidyverse curly-curly operator {{}} to learn how to pass columns of dataframes as arguments to R functions.
Prerequisites: Participants should be familiar with R at the level of the R Fundamentals Bootcamp, another introductory R workshop, or be a self-taught R coder.
Next Steps in R is a series of workshops that will help you improve your research code through new packages, skills, and other tips. You do not need to attend each session to participate. Each one-hour session meets via Zoom on a Tuesday at noon, CDT.
This workshop series complements our other data science services and support for researchers. All Northwestern researchers (student, staff, and faculty in all fields) can request free consultations with our Data Scientists and Statisticians to guide you through your data project, from considering the feasibility and choosing a method to troubleshooting the code and suggesting improvements. If you're looking for more extensive support, we partner on faculty-sponsored research through our project collaboration service.
Time
Tuesday, October 29, 2024 at 12:00 PM - 1:00 PM
Contact
Calendar
Northwestern IT Research Computing and Data Services
Data Science Nights - October 2024
Northwestern Institute on Complex Systems (NICO)
5:15 PM
//
Lower Level, Chambers Hall
Details
OCTOBER MEETING: Tuesday, October 29, 2024 at 5:20pm (US Central)
LOCATION:
In person: Chambers Hall, Lower Level
600 Foster Steet, Evanston Campus
AGENDA:
TBA
SPEAKERS:
TBA
DATA SCIENCE NIGHTS are monthly talks on data science techniques or applications, organized by Northwestern University graduate students and scholars. Aspiring, beginning, and advanced data scientists are welcome! For more information: http://bit.ly/nico-dsn
Time
Tuesday, October 29, 2024 at 5:15 PM - 7:00 PM
Location
Lower Level, Chambers Hall Map
Contact
Calendar
Northwestern Institute on Complex Systems (NICO)
CS Seminar: Co-design and evolution of mobile hardware and operating systems (Lars Bergstrom)
Department of Computer Science (CS)
12:00 PM
//
3514, Mudd Hall ( formerly Seeley G. Mudd Library)
Details
Wednesday / CS Seminar
October 30th / 12:00 PM
Hybrid / Mudd 3514
Speaker
Lars Bergstrom, Google
Talk Title
Co-design and evolution of mobile hardware and operating systems
Abstract
Every year, owners of mobile devices want them to cost less, last longer, be upgraded for longer, increase security, and to run more demanding applications and services. In this talk, focusing on the Android ecosystem I'll cover some examples of how we work all the way from instruction set and hardware architecture up to operating system and application features in order to deliver on these demands. I will also discuss some of the critical gaps where today there are opportunities to greatly improve the way we measure, model and improve the products and software we design and build.
Biography
Lars Bergstrom is a Director of Engineering at Google on the Android team, working on their platform tools and libraries. He manages the tools that update the Android operating system as well as the Java, C/C++, and Rust toolchains and the supporting libraries. He also serves as Google’s Corporate Director to RISC-V International and is Chair of the Board of Directors of the Rust Foundation. Before Google, he was at Mozilla Research, initially contributing to the Servo browser project and directing the integration of Rust into Firefox and the partner ecosystem. Later, he led Mozilla’s AR and VR work, shipping software and building OEM relationships on many different devices. He received his Ph.D. in Computer Science from the University of Chicago in 2013.
Research/Interest Areas:TBA
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Zoom: TBA
Panopto: TBA
DEI Minute: TBA
Time
Wednesday, October 30, 2024 at 12:00 PM - 1:00 PM
Location
3514, Mudd Hall ( formerly Seeley G. Mudd Library) Map
Contact
Calendar
Department of Computer Science (CS)
ECE Meet-the-Faculty Seminar - Koray Aydin
Department of Electrical and Computer Engineering (ECE)
2:00 PM
//
L440, Technological Institute
Details
Metasurfaces and metamaterials emerged as promising nanophotonic material and device platforms to control light-matter interactions at the nanoscale. In such materials, the collective and effective optical response is dictated by individual building blocks and controlled by the geometrical parameters forming the crystal structure. In this talk, I will provide an overview of the research program in the Metamaterials and Nanophotonic Devices Laboratory at Northwestern University. I will introduce machine learning and inverse electromagnetic design methods to model optical metasurfaces. Inverse-designed free-space and on-chip metasurfaces realized with direct laser writing technique will be discussed and their applications in imaging and spectroscopy will be highlighted. Metasurface spatial filters for broadband optical edge-detection and bound-states-in-continuum (BIC) metasurfaces integrated with quantum dots will be introduced. I will also highlight collaborative research in designing and realizing metasurfaces based on DNA-assembled nanoparticle superlattices and their potential applications.
Time
Wednesday, October 30, 2024 at 2:00 PM - 3:00 PM
Location
L440, Technological Institute Map
Contact
Calendar
Department of Electrical and Computer Engineering (ECE)
AI@NU Graduate Student Group Speaker: Industry AI Expert Agus Sudjianto
AI@NU
9:00 AM
Details
Agus Sudjianto, Co-creator of PiML (Python interpretable Machine Learning) and Senior Vice President of Risk & Technology at H2O.ai, will speak as an AI industry expert for the AI@NU community.
The AI@NU Graduate Student Group seeks to connect AI enthusiasts across the campus to realize opportunities for collaboration and build community among graduate students. The group is not limited to computer scientists, rather the members range across many disciplines and schools.
Time
Friday, November 1, 2024 at 9:00 AM - 10:00 AM
Contact
Calendar
AI@NU
NITMB Seminar Series - Sonja Petrović
NSF-Simons National Institute for Theory and Mathematics in Biology
10:00 AM
//
Suite 4010
Details
Talk Title: Testing model/data fit for networks arising in biological contexts
Sonja Petrović is a Professor of Applied Mathematics at the Illinois Institute of Technology. Petrović's research is in nonlinear algebra and nonlinear statistics. Petrović develops, analyzes, and applies statistical models for discrete relational data such as networks. Petrović also studies randomized algorithmic approaches to computational algebra problems whose expected runtimes are much lower than the well-known worst-case complexity bounds, develop probabilistic models to study average and extreme behavior of algebraic objects, and use machine learning to predict and improve the behavior of algebraic computation.
Learn more about Sonja Petrović'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.
Time
Friday, November 1, 2024 at 10:00 AM - 11:00 AM
Location
Suite 4010
Contact
Calendar
NSF-Simons National Institute for Theory and Mathematics in Biology
Statistics and Data Science Seminar: "Autonomous Learning: Unifying OOD Detection and Continual Learning"
Department of Statistics and Data Science
11:00 AM
//
Ruan Conference Room – lower level, Chambers Hall
Details
Autonomous Learning: Unifying OOD Detection and Continual Learning
Bing Liu, Distinguished Professor and Peter L. and Deborah K. Wexler Professor of Computing at the University of Illinois Chicago
Abstract: Continual learning (CL) focuses on incrementally learning a sequence of tasks, with class incremental learning (CIL) being one of the most challenging settings. This talk begins by presenting a theoretical study of the CIL problem. The key result is that the necessary and sufficient conditions for effective CIL are strong within-task prediction and reliable out-of-distribution (OOD) detection. The theory unifies CIL and OOD detection, which are regarded as two completely different problems. Building on the theory, new CIL methods have been developed, which significantly outperform existing baselines. However, traditional CIL operates in a closed-world context. We then extend the theory to the open world—where unknown and out-of-distribution objects are encountered—leading to the learning paradigm of open-world CIL, or open-world continual learning (OWCL), enabling autonomous learning. In the last part of the talk, I will discuss challenges in OWCL and present a prototype system that learns on the fly continually and autonomously after deployment.
Time
Friday, November 1, 2024 at 11:00 AM - 12:00 PM
Location
Ruan Conference Room – lower level, Chambers Hall Map
Contact
Calendar
Department of Statistics and Data Science
Chronicling the 2024 Presidential Election: Faculty Predictions and Perspectives
SPS: Special Events
12:00 PM
Details
Join us ahead of the 2024 presidential election for a dynamic discussion featuring faculty from Northwestern University School of Professional Studies (SPS).
During this online panel, faculty from the MS in Data Science and MA in Public Policy and Administration programs will draw upon their diverse areas of expertise to provide insight into the tools used to determine the social and political impacts of this historic election. Topics will include the behavioral science of voting and the processing of misinformation, as well as polling trends and election forecasting.
This event is part of the Northwestern University SPS Thought Leadership Series, a program of events featuring a wide range of compelling topics and thoughtfully led conversations throughout the 2024–25 academic year.
Date: Friday, November 1
Time: Noon to 1 p.m. CT
Location: Online (Zoom)
* A link to join the webinar will be shared with event registrants the day before the event.
Moderator:
Mollie Foust, MPA
Director at Afton Partners; MPPA Faculty Member
Expertise: local government and public policy
Panelists:
Angela Fontes, PhD
Managing Partner at Fontes Research; MPPA Faculty Member
Expertise: behavioral economics and sociology
Stephen Kleinschmit, PhD
Founder of Midwest Public Affairs Conference; MPPA Faculty Member
Expertise: policy analysis and civic analytics
Tom Miller, PhD
Creator of The Virtual Tout®; MSDS Faculty Director
Expertise: data analytics and election forecasting
If you have questions about this event, please contact Melanie Galván at melanie.galvan@northwestern.edu
Time
Friday, November 1, 2024 at 12:00 PM - 1:00 PM
Contact
Calendar
SPS: Special Events
EES Seminar- Environmental Engineers Are the Circular Carbon Engineers- Jason Ren
McCormick - Civil and Environmental Engineering (CEE)
2:00 PM
//
A230, Technological Institute
Details
Abstract: Achieving carbon circularity and net zero emissions requires a comprehensive understanding of emissions and re-valorization of waste carbon, while keeping fossil carbon underground. Environmental engineers are uniquely positioned to lead sustainable carbon management as the “circular carbon engineers”. For example, modern wastewater treatment is evolving from “removal-centered” to a “recovery-oriented” approach, and the increasing availability of low-cost renewable electricity presents new opportunities to convert "electrons to molecules". This presentation will highlight recent efforts to address knowledge gaps in the complex GHG emission profiles of the wastewater sector, utilizing both field studies and data science methods. It will also discuss technologies for electrifying treatment processes, co-valorizing wastewater and CO2, performing electrolysis of impaired water, and producing commodity chemicals using electroactive membranes.
Bio: Dr. Zhiyong Jason Ren (https://ren.princeton.edu) is a professor in the department of Civil and Environmental Engineering and the Andlinger Center for Energy and the Environment at Princeton University. He leads the Princeton Water & Energy Technologies (WET) Lab with research focusing on water sector decarbonization and digitalization. His group uses electrochemistry, microbiology, and data science tools to gain insights into the fundamental determining factors, and they develop models and technologies for resource recovery during environmental and chemical processes. Dr. Ren has received numerous recognitions including the Walter J. Weber, Jr. AEESP Frontier in Research Award (2024), the Paul L. Busch Award (2021), and Walter L. Huber Research Prize (2020). He is a Fellow of Royal Society of Chemistry and International Water Association. He is an Associate Editor for Environmental Science & Technology (ES&T) and ES&T Letters. Ren received his Ph.D. in environmental engineering from Penn State University.
Time
Friday, November 1, 2024 at 2:00 PM - 3:00 PM
Location
A230, Technological Institute Map
Contact
Calendar
McCormick - Civil and Environmental Engineering (CEE)
CS Distinguished Lecture: Kristin Lauter
Department of Computer Science (CS)
12:00 PM
//
3514, Mudd Hall ( formerly Seeley G. Mudd Library)
Details
Time
Monday, November 4, 2024 at 12:00 PM - 1:00 PM
Location
3514, Mudd Hall ( formerly Seeley G. Mudd Library) Map
Contact
Calendar
Department of Computer Science (CS)
Next Steps in Python: Virtual Environments (Virtual)
Northwestern IT Research Computing and Data Services
12:00 PM
Details
Python has a wide array of libraries with different versions and many dependencies that you can install on your computer to aid in your research. What do you do when you want to install a new library with dependencies that would conflict with another library you use? The answer is to define self-contained virtual environments. In this workshop, we will cover why and how to create virtual environments for your Python code development.
Prerequisites: Participants should be familiar with Python at the level of the Python Fundamentals Bootcamp, another introductory Python workshop, or be a self-taught Python coder.
Next Steps in Python is a series of workshops that will help you improve your research code through new packages, skills, and other tips. You do not need to attend each session to participate. Each one-hour session meets via Zoom on a Monday at noon, CDT.
This workshop series complements our other data science services and support for researchers. All Northwestern researchers (student, staff, and faculty in all fields) can request free consultations with our Data Scientists and Statisticians to guide you through your data project, from considering the feasibility and choosing a method to troubleshooting the code and suggesting improvements. If you're looking for more extensive support, we partner on faculty-sponsored research through our project collaboration service.
Time
Monday, November 4, 2024 at 12:00 PM - 1:00 PM
Contact
Calendar
Northwestern IT Research Computing and Data Services
From Academia to Finance: Research in Supervised Learning, Reinforcement Learning, and Bandits
AI@NU
9:00 AM
Details
Alec Koppel, AI Research Lead at JP Morgan AI Research, is the featured speaker for the AI@NU Graduate Student Group's Industry Talk Series. The topic for this talk is titled, "From Academia to Finance: Research in Supervised Learning, Reinforcement Learning, and Bandits."
The AI@NU Graduate Student Group seeks to connect AI enthusiasts across the campus to realize opportunities for collaboration and build community among graduate students. The group is not limited to computer scientists, rather the members range across many disciplines and schools.
Time
Wednesday, November 6, 2024 at 9:00 AM - 10:00 AM
Contact
Calendar
AI@NU
CS Distinguished Lecture: Nate Foster
Department of Computer Science (CS)
12:00 PM
//
3514, Mudd Hall ( formerly Seeley G. Mudd Library)
Details
Time
Wednesday, November 6, 2024 at 12:00 PM - 1:00 PM
Location
3514, Mudd Hall ( formerly Seeley G. Mudd Library) Map
Contact
Calendar
Department of Computer Science (CS)
WED@NICO SEMINAR: Yingdan Lu, Northwestern School of Communication "The Evolution of Authoritarian Propaganda in the Digital Age"
Northwestern Institute on Complex Systems (NICO)
12:00 PM
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Lower Level, Chambers Hall
Details
Speaker:
Yingdan Lu, Assistant Professor, Department of Communication Studies, Northwestern University
Title:
The Evolution of Authoritarian Propaganda in the Digital Age
Abstract:
TBA
Speaker Bio:
Yingdan Lu is an Assistant Professor in the Department of Communication Studies at Northwestern University, and co-director of the Computational Multimodal Communication Lab. Her research focuses on digital technology, political communication, and information manipulation. She uses computational and qualitative methods to understand the evolution and engagement of digital propaganda in authoritarian regimes and how individuals encounter and communicate multimodal (mis)information in AI-mediated environments. Her work has appeared in leading peer-reviewed journals across communication, political science, and human-computer interaction. Before joining Northwestern, Yingdan received her Ph.D. in Communication and a Ph.D. minor in Political Science from Stanford University.
Location:
In person: Chambers Hall, 600 Foster Street, Lower Level
Remote option: https://northwestern.zoom.us/j/92346340083
Passcode: NICO24
About the Speaker Series:
Wednesdays@NICO is a vibrant weekly seminar series focusing broadly on the topics of complex systems, data science and network science. It brings together attendees ranging from graduate students to senior faculty who span all of the schools across Northwestern, from applied math to sociology to biology and every discipline in-between. Please visit: https://bit.ly/WedatNICO for information on future speakers.
Time
Wednesday, November 6, 2024 at 12:00 PM - 1:00 PM
Location
Lower Level, Chambers Hall Map
Contact
Calendar
Northwestern Institute on Complex Systems (NICO)
CS Seminar
Department of Computer Science (CS)
12:00 PM
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3514, Mudd Hall ( formerly Seeley G. Mudd Library)
Details
Time
Monday, November 11, 2024 at 12:00 PM - 1:00 PM
Location
3514, Mudd Hall ( formerly Seeley G. Mudd Library) Map
Contact
Calendar
Department of Computer Science (CS)
AI for Research: Choosing a LLM for Your Project (Virtual)
Northwestern IT Research Computing and Data Services
12:00 PM
Details
The rapid advancement in Large Language Models has led to a proliferation of model options. It can be daunting to sort through variations in model creators, versions, sizes, and resource requirements. Fear not – we have you covered. This workshop will walk you through the basics of how to choose a model for your project, including aspects such as what type of data you can give to the model, how many words and other characters you can input, and which tasks the model is optimized for.
Prerequisites: Some familiarity with Python and machine learning will be useful for this workshop, but you don’t need to be an expert in either.
The Artificial Intelligence for Research (AIR) workshop series provides practical advice and examples on how to effectively and securely utilize AI in your research. From writing code to training Large Language Models (LLMs), our Data Scientists will walk you through the tools and tips you'll need.
This workshop series complements our other data science services and support for researchers. All Northwestern researchers (student, staff, and faculty in all fields) can request free consultations with our Data Scientists and Statisticians to guide you through your data project, from considering the feasibility and choosing a method to troubleshooting the code and suggesting improvements. If you're looking for more extensive support, we partner on faculty-sponsored research through our project collaboration service.
Time
Tuesday, November 12, 2024 at 12:00 PM - 1:00 PM
Contact
Calendar
Northwestern IT Research Computing and Data Services
WED@NICO SEMINAR: Guy Aridor, Kellogg School of Management "The Value of Belief Data in Online Recommendation Systems"
Northwestern Institute on Complex Systems (NICO)
12:00 PM
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Lower Level, Chambers Hall
Details
Speaker:
Guy Aridor, Assistant Professor of Marketing, Kellogg School of Management, Northwestern University
Title:
The Value of Belief Data in Online Recommendation Systems
Abstract:
Designing algorithmic recommendation systems on online platforms is simultaneously a data collection and an algorithmic problem, though most work has focused on the algorithmic aspects. In this talk, I’ll describe several recent papers that show the value of collecting data not just on consumption behavior, but also on pre-consumption attitudes — what consumers think about items they have not consumed. I’ll discuss how an economic model of user consumption choices in environments that incorporates these attitudes can rationalize empirical consumption patterns on a movie recommendation platform, MovieLens. We then test the assumptions of and the hypotheses generated by this model in a field experiment on MovieLens that collects such belief data in order to decompose the mechanisms that drive the effectiveness of recommendations. Finally, I’ll discuss a practical procedure and a resulting open-source dataset collected via this procedure that we implement on the MovieLens platform that allows for the collection of such data at scale that can enable the incorporation of such data into the design of recommendation systems.
Speaker Bio:
Guy Aridor is an Assistant Professor of Marketing at Northwestern Kellogg School of Management and a research affiliate at CESifo. His research employs tools from economics and quantitative marketing to investigate policy and antitrust issues in the digital economy, as well as the effects of new technologies on consumer behavior. He has a particular interest in consumer privacy, recommendation systems, and social media platforms. He holds a PhD in economics from Columbia University and a BA in pure/applied mathematics, computer science, and economics from Boston University.
Location:
In person: Chambers Hall, 600 Foster Street, Lower Level
Remote option: https://northwestern.zoom.us/j/99906995637
Passcode: NICO24
About the Speaker Series:
Wednesdays@NICO is a vibrant weekly seminar series focusing broadly on the topics of complex systems, data science and network science. It brings together attendees ranging from graduate students to senior faculty who span all of the schools across Northwestern, from applied math to sociology to biology and every discipline in-between. Please visit: https://bit.ly/WedatNICO for information on future speakers.
Time
Wednesday, November 13, 2024 at 12:00 PM - 1:00 PM
Location
Lower Level, Chambers Hall Map
Contact
Calendar
Northwestern Institute on Complex Systems (NICO)
Statistics and Data Science Seminar: "Quantum Computation and Statistics"
Department of Statistics and Data Science
11:00 AM
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Ruan Conference Room – lower level, Chambers Hall
Details
Quantum Computation and Statistics
Yazhen Wang, Department of Statistics, University of Wisconsin-Madison
Abstract: Quantum computation and quantum information are of great current interest across various fields, including computer science, mathematics and statistics, physical sciences and engineering. As the theory of quantum physics is fundamentally stochastic, quantum computation and quantum information are inherently infused with elements of randomness and uncertainty. Consequently, quantum algorithms are random in nature. This highlights the important role for statistics to play in the realm of quantum computation, which in turn offers great potential to revolutionize computational statistics. In this talk, I will provide an overview of quantum computation and statistics, covering the fundamental concepts and exploring quantum advantage along with the role of statistics and the implications for statistics.
Time
Friday, November 15, 2024 at 11:00 AM - 12:00 PM
Location
Ruan Conference Room – lower level, Chambers Hall Map
Contact
Calendar
Department of Statistics and Data Science
Research-In-Progress: Ben Kuznets-Speck
NSF-Simons National Institute for Theory and Mathematics in Biology
2:00 PM
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Suite 4010
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. Ben Kuznets-Speck is a postdoctoral research fellow in the Goyal Lab at Northwestern University. Kuznets-Speck leverages and develops theory, simulation and machine learning techniques to work at the interface of statistical physics, biology and evolution. Learn more about Ben Kuznets-Speck’s research and engage in discussion with the NITMB community. Research-In-Progress talks take place on Fridays at 2pm at the NITMB office (875 N Michigan Ave., Suite 4010). Snacks and coffee will follow.
Time
Friday, November 15, 2024 at 2:00 PM - 3:00 PM
Location
Suite 4010
Contact
Calendar
NSF-Simons National Institute for Theory and Mathematics in Biology
Fall Seminar 2024: Adoption of AI in the Travel Industry Featuring Sergey Shebalov
Master of Science in Machine Learning and Data Science (MLDS)
3:30 PM
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Krebs Classroom, Henry Crown Sports Pavilion
Details
Join us for an insightful seminar featuring Sergey Shebalov about the resurgence of Artificial Intelligence. We will delve into real-world use cases, the challenges and opportunities in AI adoption, and the impact of AI on the travel industry.
Lecture Summary: Artificial Intelligence is experiencing resurrection over the last several years. The number of use cases already implemented in practice and the amount of investment allocated to development and adoption of this technology indicates that this time it's here to stay. While several challenges still remain and some new ones are anticipated the path towards Al becoming as common as computer, smartphone or internet is clear. We will discuss this journey on the example of the travel industry. We'll consider several main applications of Al that already provided significant benefits, a typical process these applications go through from an idea to a full-scale adoption, and the skills required to support that process from the new generation of leaders, scientists, engineers and practitioners.
Sergey Shebalov is a VP of Data Science and Head of Research at Sabre. He leads the Sabre Labs team responsible for development and implementation of complex Al decision support systems. Sergey holds PhD in mathematics from University of Illinois and has two decades of experience in the travel industry IT. His area of expertise is intelligent retailing,
resource optimization and adoption of Al in practice.
Location: Krebs Classroom, McCormick Education Center, 2311 Campus Dr
Time
Friday, November 15, 2024 at 3:30 PM - 4:30 PM
Location
Krebs Classroom, Henry Crown Sports Pavilion Map
Calendar
Master of Science in Machine Learning and Data Science (MLDS)
CS Distinguished Lecture: Kyros Kutulakos
Department of Computer Science (CS)
12:00 PM
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3514, Mudd Hall ( formerly Seeley G. Mudd Library)
Details
Time
Monday, November 18, 2024 at 12:00 PM - 1:00 PM
Location
3514, Mudd Hall ( formerly Seeley G. Mudd Library) Map
Contact
Calendar
Department of Computer Science (CS)
Corrina Schlombs - "Data Entry, Labor, and Gender: Office Automation in Capitalist and Socialist Economies"
Science in Human Culture Program - Klopsteg Lecture Series
4:30 PM
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Hagstrum 201, University Hall
Details
Speaker
Corrina Schlombs, History, Rochester Institute of Technology
Title
"Data Entry, Labor, and Gender: Office Automation in Capitalist and Socialist Economies"
Abstract
In 1949, MIT mathematician Norbert Wiener warned US labor leader Walther Reuther that, in the US capitalist economy, automation technologies would cause massive unemployment. But a closer look at labor changes from computing technologies reveals a more complex picture: electronic computing also required new manual routine labor for data entry. Data occurred on paper, such as checks, insurance contracts or phone notes, and before it could be processed by a computer, it needed to be transferred into a computer-legible format—often punch cards or tape. In my talk, I examine mid-twentieth century office automation in capitalist and socialist economies, with a focus on the East German financial sector. In an economy promising full employment and lacking sufficient numbers of workers, officials promoted automation technologies with the goal of releasing workers. However, computing technologies were implemented in ways that heavily drew on women’s labor for data entry. Investigating how questions of technological change, employment, labor, and identity played out in different economic contexts, the talk calls technological promises into question at a time when artificial intelligence technologies are (again) expected to uproot the balance between human and machine labor.
Biography
Dr. Schlombs’s research focuses on technology and capitalism in transatlantic relations. In her current book project, she investigates transatlantic transfers of productivity culture and technology in the two decades before and after World War II. Productivity, a statistical measure of output per worker, came to encapsulate the American economic system, and transatlantic debates about productivity called into question the notion of the capitalist West during the Cold War conflict.
Time
Monday, November 18, 2024 at 4:30 PM - 6:00 PM
Location
Hagstrum 201, University Hall Map
Contact
Calendar
Science in Human Culture Program - Klopsteg Lecture Series
Introduction to SQL (In-Person)
Northwestern IT Research Computing and Data Services
12:00 PM
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421, Wieboldt Hall North Entrance
Details
This workshop covers the basics of SQL syntax required to query databases, particularly basic commands such as SELECT, LIMIT, OFFSET, WHERE, BETWEEN, IN, IS NULL, ORDER BY, DISTINCT, GROUP BY, HAVING, and CASE WHEN.
Prerequisites: None. No installations are required.
This workshop complements our other data science services and support for researchers. All Northwestern researchers (student, staff, and faculty in all fields) can request free consultations with our Data Scientists and Statisticians to guide you through your data project, from considering the feasibility and choosing a method to troubleshooting the code and suggesting improvements. If you're looking for more extensive support, we partner on faculty-sponsored research through our project collaboration service.
Time
Tuesday, November 19, 2024 at 12:00 PM - 1:30 PM
Location
421, Wieboldt Hall North Entrance Map
Contact
Calendar
Northwestern IT Research Computing and Data Services
Webinar: AI and the Global Economy | From Kellogg Executive Education and Kellogg Insight
Kellogg Insight
1:00 PM
Details
Today’s AI models can do a lot of things. But how did they become so powerful—and in what ways might they reshape the economy? In this complimentary webinar from Kellogg Executive Education and Kellogg Insight, Sergio Rebelo will take us on a journey into the past and present of AI. A leading macroeconomist, he will also offer his perspective on how AI stands to impact society, jobs, and the broader economy.
Speaker bio: Sergio Rebelo is the MUFG Bank Distinguished Professor of International Finance at Kellogg, where he has published widely in leading economics journals. He has served as a consultant to the World Bank, the International Monetary Fund, the Board of Governors of the Federal Reserve System, and the European Central Bank. He’s also an award-winning teacher.
Time
Tuesday, November 19, 2024 at 1:00 PM - 2:00 PM
Contact
Calendar
Kellogg Insight
CS Seminar
Department of Computer Science (CS)
12:00 PM
//
3514, Mudd Hall ( formerly Seeley G. Mudd Library)
Details
Time
Wednesday, November 20, 2024 at 12:00 PM - 1:00 PM
Location
3514, Mudd Hall ( formerly Seeley G. Mudd Library) Map
Contact
Calendar
Department of Computer Science (CS)
Intermediate SQL (In-Person)
Northwestern IT Research Computing and Data Services
12:00 PM
//
421, Wieboldt Hall North Entrance
Details
This workshop covers intermediate SQL syntax required to query databases, including joins, UNION, EXCEPT, INTERSECT, subqueries, common table expressions, and window functions.
Prerequisites: Basic SQL at the level of the Introduction to SQL workshop. No installations are required.
This workshop complements our other data science services and support for researchers. All Northwestern researchers (student, staff, and faculty in all fields) can request free consultations with our Data Scientists and Statisticians to guide you through your data project, from considering the feasibility and choosing a method to troubleshooting the code and suggesting improvements. If you're looking for more extensive support, we partner on faculty-sponsored research through our project collaboration service.
Time
Wednesday, November 20, 2024 at 12:00 PM - 1:30 PM
Location
421, Wieboldt Hall North Entrance Map
Contact
Calendar
Northwestern IT Research Computing and Data Services