We invite you to submit your latest and greatest research/science to IEEE EMBS Body Sensor Networks (BSN) 2024. Deadline for paper submissions are June 24th (FIRM DEADLINE).

Why you should attend BSN 2024:
-Meet expert scientists in the field of sensing and AI. Many of them are humble, excited to talk, and willing to help guide you.
-Exciting keynotes in Sensor Innovation, AI, and Social Responsibility.
-Discussions on advancing women and minorities in engineering. (How do we break down barriers and build more bridges?)
-Discussions on how to get your technology in the right hands for the right reasons (succeeding with your technology).
-Discussions on moving from bench to bedside and beyond.
-Amazing food in ChiTown (Chicago)!
-Walking distance to renowned labs, medical centers, historical architectural marvels, and attractions!

Highlights for Junior Faculty, Postdocs, and Students:
-Career Panel (Opportunities to network and find your next lab/career!)
-NIH Grant Writing Workshop
-Funded Student Travel Awards
-Meet new mentors and future colleagues!

Current stellar organizing committee: Wenyao Xu, Bjoern Eskofier, Wei Chen, Bobak Mortazavi, Nivedita Arora, Nanshu Lu, VP Nguyen, Ye (Sarah) Sun, Karan Ahuja, Chris Romano, Stephen Xia, @Bonnie Nolan, Brandon Oubre, Mahdi Pedram, Bashima Islam, Siyi Xu, Rahim Esfandyarpour, Matthew Flavin, Lama Nachman, Alex Adams, Glenn Fernandes, Kunal Mankodiya, Maia Jacobs, Josiah Hester!

Steering committee: Roozbeh Jafari, Paolo Bonato, Omer Inan, Canan Dagdeviren, Sunghoon Ivan Lee

Website for more details: https://bsn.embs.org/

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CIERA has partnered with the Pittsburgh Supercomputing Center as a satellite site for their Machine Learning and Big Data workshop, sponsored by ACCESS.

This workshop will focus on topics including big data analytics and machine learning with Spark, and deep learning using Tensorflow.

Please note that registration is required via the link below.

Room location and access details are provided upon registration.

Register Now

Wednesday / CS Seminar
October 16th / 12:00 PM
Hybrid / Mudd 3514

Speaker
Christos Dimoulas, Northwestern University

Talk Title
The Rational Programmer, A Method for Investigating Programming Language Pragmatics

Abstract
"The work of software developers directly depends on decisions that
language creators make for them. Therefore, when language creators choose
from a number of design alternatives, they should consider how their
choices affect the developers. Specifically, language creators should
consider the use of a language feature in particular work contexts, an
idea analogous to what linguists call ``pragmatics.'' However, so far,
there are only a few instruments for investigating Programming Language
(PL) pragmatics.

To address this gap, I have developed a new scientific instrument called
the Rational Programmer. At the technical level, the Rational Programmer
method puts the idea of simulation, a technique with a long history in
Computer Science, to new use in PL research. The heart of a
rational-programmer simulation is an algorithmic abstraction of using a
language feature for information gathering, interpretation and action in a
work context. Typically, a rational-programmer simulation produces a
recommendation for a use strategy that a developer can employ while
working in the given context. It may also identify a problematic aspect of
a feature's design with concrete evidence, which then the creators and
developers can leverage to address the problem. Finally, a
rational-programmer simulation can inform instructors how to teach
students the effective use of a feature. In this talk, I will demonstrate
the workings of the Rational Programmer method with examples."

Biography
Christos is an assistant professor of Computer Science at Northwestern
University. The goal of his research is to understand how Programming
Language techniques can improve the work life of software developers.

Research/Interest Areas:
Programming Languages

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Zoom: https://northwestern.zoom.us/j/99788665335
Panopto: https://northwestern.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=44a7f2e8-bcaf-4f82-beba-b1fd014323d7
DEI Minute: TBA

Speaker:

Oscar Stuhler, Assistant Professor, Department of Sociology, Northwestern University

Title:

Studying Textual Representations of Social Structures

Abstract:

Textual data contain rich accounts of actions taken by different actors, of relationships, and of particular identities’ attributes. In other words, texts contain representations of social structures. Sociology has a long tradition of formally studying such structures. However, with the embrace of computational text analysis, sociologists' focus has shifted from grammar-based approaches to ones based on co-occurrence, such as word embeddings. Time to bring grammar back in! In this talk, I will first present a framework for extracting grammatically structured information like the above from text. I then show how this allows us to study larger discursive patterns by presenting both past and ongoing research. In particular, I will be talking about gender and agency in fiction writing (1850-2010) and contending conceptions of refugees in German news. I will close with some work in progress that leverages generative large language models to extend this ongoing research program.

Speaker Bio:

Oscar Stuhler is a sociologist studying discourse with formal, quantitative methods. Much of his work focuses on how to measure, analyze, and theorize textual representations of social structures. Oscar is an Assistant Professor at Northwestern University’s Department of Sociology. He completed his PhD in sociology at New York University.

Location:

In person: Chambers Hall, 600 Foster Street, Lower Level
Remote option: https://northwestern.zoom.us/j/93590489365
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.

Related Info

We invite you to submit your latest and greatest research/science to IEEE EMBS Body Sensor Networks (BSN) 2024. Deadline for paper submissions are June 24th (FIRM DEADLINE).

Why you should attend BSN 2024:
-Meet expert scientists in the field of sensing and AI. Many of them are humble, excited to talk, and willing to help guide you.
-Exciting keynotes in Sensor Innovation, AI, and Social Responsibility.
-Discussions on advancing women and minorities in engineering. (How do we break down barriers and build more bridges?)
-Discussions on how to get your technology in the right hands for the right reasons (succeeding with your technology).
-Discussions on moving from bench to bedside and beyond.
-Amazing food in ChiTown (Chicago)!
-Walking distance to renowned labs, medical centers, historical architectural marvels, and attractions!

Highlights for Junior Faculty, Postdocs, and Students:
-Career Panel (Opportunities to network and find your next lab/career!)
-NIH Grant Writing Workshop
-Funded Student Travel Awards
-Meet new mentors and future colleagues!

Current stellar organizing committee: Wenyao Xu, Bjoern Eskofier, Wei Chen, Bobak Mortazavi, Nivedita Arora, Nanshu Lu, VP Nguyen, Ye (Sarah) Sun, Karan Ahuja, Chris Romano, Stephen Xia, @Bonnie Nolan, Brandon Oubre, Mahdi Pedram, Bashima Islam, Siyi Xu, Rahim Esfandyarpour, Matthew Flavin, Lama Nachman, Alex Adams, Glenn Fernandes, Kunal Mankodiya, Maia Jacobs, Josiah Hester!

Steering committee: Roozbeh Jafari, Paolo Bonato, Omer Inan, Canan Dagdeviren, Sunghoon Ivan Lee

Website for more details: https://bsn.embs.org/

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Artificial intelligence (AI) is becoming increasingly integral to national and global security strategies. How are researchers leveraging AI to help predict terrorist attacks, reshape terror group behavior and combat misinformation? How could AI help us achieve more effective outcomes in diplomatic negotiations? How are new AI techniques helping to predict cyber-attacks before they occur and mitigate them in real time when they do?

Gain insights from leading experts in AI, cybersecurity and national security at the annual Conference on AI & National Security hosted by the Northwestern Security & AI Lab (NSAIL). Jointly housed at Northwestern University’s Roberta Buffett Institute for Global Affairs and McCormick School of Engineering, NSAIL conducts fundamental research in AI relevant to issues of cybersecurity and counterterrorism and presents at venues for public policy discussion and negotiation, including the United Nations, Capitol Hill and the Mumbai Stock Exchange.

This year's conference will feature a range of presentations showcasing new AI technologies and panel discussions offering insights from leading researchers, security strategists and more. Conference attendees are also invited to attend a working lunch with live demonstrations of advanced AI technologies and tools developed at NSAIL.

This event will be livestreamed. Register to join the livestream via Zoom and view the full conference agenda here.

Register Now

In this workshop, we discuss how to use Quest GPU resources effectively. First, we show how to use virtual environments and containers to ensure you have all the GPU-enabled software you need for your application. Second, we discuss how to request GPU resources on Quest. This includes how to determine how much GPU memory you need and the type of GPU resource you should request. Finally, we demonstrate running a number of applications including training neural networks with PyTorch and Tensorflow as well as running molecular dynamics simulations.

Prerequisites: Before attending this workshop, researchers new to Quest should:  

• Apply for a Quest allocation. We recommend “Research Allocation I” if you are new to Quest. If your lab has a joint Quest allocation, you can also apply to join that allocation.

•Watch the Introduction To Quest video series to get an overview of the system, learn how to submit jobs, and become familiar with best practices. To learn more about Quest, see  About Quest.

• Become familiar with Unix command line: see our Intro Command Line and Bash Scripting workshops and check Online Resource Guide for learning basic command line skills.

The Topics in Research Computing Series includes a variety of workshops covering introductory topics like how to use the Quest cluster computer and submit array jobs, as well as more advanced computing topics such as profiling code on the cluster and running parallel R and Python code. The specific workshops offered may vary each quarter, so please check back for updates each quarter.

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All biological systems are learning systems. Moreover, all biological systems are physical systems. Therefore, there exists a direct connection between physics and learning. To tease out these statements, we will discuss efforts to uncover new learning mechanisms, or algorithms, from a physics perspective, aka physical learning, such as multi-mechanism learning (MML) and frequency propagation (FP). MML can be implemented to recognize static patterns as well as dynamic ones and provides a simple physical explanation for the underlying basis of bidirectionality observed in neuronal circuits, for example. While MML and FP are supervised physical learning algorithms, efforts are also underway to develop unsupervised physical learning algorithms given the recent observation that physical learning can be viewed as feedback-based aging in a glassy landscape. All in all, these combined efforts point towards a unified framework of learning with and without brains that may help us build both better artificial intelligence systems and biological intelligence systems, including a network of brain organoids.

Jennifer Schwarz, Professor, Syracuse University

Host: Istvan Kovacs

Advancing arrhythmia patient care with digital twins and AI

ABSTRACT:

Precision medicine is envisioned to provide therapy tailored to each patient. The rapidly increasing ability to capture extensive patient data, coupled with machine learning, is a pathway to achieving this vision. A different pathway towards precision medicine is the increasing ability to encode known physics laws and physiology knowledge within mathematical equations and to adapt such models to represent the behavior of a specific patient.

Wouldn’t it be great to have a digital representation of ourselves that allows doctors to simulate our personal medical history and health conditions using relationships learned both from data and from biophysics knowledge? That virtual replica of ourselves would integrate data-driven machine learning and multiscale physics-based modeling to continuously update itself as our health condition changes and more information about our interaction with the environment is acquired. These digital twins would forecast the trajectory of the patient’s disease, estimate risk of adverse events, and predict treatment response so that the potential outcome would inform treatment decision.

This presentation explores the synergies that have been achieved between machine learning and mechanistic physics-based heart models towards enabling precision medicine in cardiology. It showcases how machine learning and multiscale cardiac modeling complement each other in engineering your heart’s health. A highlight is the robust prediction of sudden cardiac death risk in different heart diseases. Another application of the heart digital twin technology is illustrated by the development of a precise treatment for patients suffering from arrhythmias. This application prevents future re-hospitalizations and repeat procedures, shifting the treatment selection from being based on the state of the patient today to optimizing the state of the patient tomorrow.

BIO:

Dr. Trayanova holds the inaugural Murray B. Sachs Professorship in the Department of Biomedical Engineering at Johns Hopkins University. She is also a Professor of Medicine at the Johns Hopkins School of Medicine, and a Professor of Applied Mathematics and Statistics. She envisioned, created, and directs the Alliance for Cardiovascular Diagnostic and Treatment Innovation. She is also the Director for AI Research in Health and Medicine at Johns Hopkins University under the Data Science and AI Institute, where she is responsible for directing efforts across the university in developing and deploying AI applications that advance healthcare delivery and improve patient outcomes. She also directs the Computational Cardiology Laboratory.

Dr. Trayanova is internationally recognized as a leader in personalized multi-scale computational modeling of whole heart electrophysiology and arrhythmias (heart digital twinning). Her research output includes 450 published papers and book chapters. She has published extensively in the most prestigious journals, such as The Lancet, Nature Cardiovascular Medicine, Nature Communications, Nature BME, Science Advances, Science TM, Physiological Reviews, Nature Reviews Cardiology, eLife, and others.

Trayanova’s work has received world-wide recognition, and she is the recipient of numerous honors and awards. She is the recipient of an NIH Director’s Pioneer Award in 2013; in 2019, she was inducted in the Women of Technology International Hall of Fame, an honor conferred only on 5 women each year from around the world. Also in 2019, she received the Distinguished Scientist Award from Heart Rhythm Society. This was followed by the Zipes Distinguished Award by the same society in 2020, and by the Gordon Moe Award by the Cardiac Electrophysiology Society in 2023. In 2025, Trayanova will be the recipient of the Hodgkin-Huxley-Katz Award by the Physiological Society. Trayanova has been named a Fellow of every American and European clinical cardiology society, testifying to her impact in clinical practice. She is also a Fellow of AIMBE, BMES, IAMBE, and IUPS. She has given over 380 invited lectures, majority of them keynotes or plenary lectures. Dr. Trayanova’s work has received widespread media coverage and recognition (see recent article in the Wall Street Journal, and she has also given a TEDx talk. Dr. Trayanova is also the inventor on numerous patents and patent applications filed world-wide. In recognition of her innovation, she was named Fellow of the National Academy of Inventors in 2020.

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A holistic and critical look at language agents

Yu Su, Assistant Professor, Department of Computer Science and Engineering, The Ohio State University

Abstract: How are the contemporary AI agents powered by LLMs different from those of the earlier generations? I argue that their most distinct trait is a new capability of using language as a vehicle of both 'thought' and communication, and therefore they are best called "language agents." I will describe a conceptual framework for these language agents, followed by a more in-depth discussion on several core competencies, including memory, planning, and tool use. I will conclude the talk with interesting future directions.

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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

Related Info

Register Now

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.

Register Now

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

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

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Talk Title: How is it Possible to Train Deep Neural Networks?

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.

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.

Related Info

Learn more about the writing and tutoring activities and prompts shared in this series. Draft your own prompts to fit your students' needs.

Register Now

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: TBA

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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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.

Register Now

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

Related Info

TBA

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.

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.

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

Register Now

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.

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.

Register Now

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.

TBA

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.

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TBA

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.

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TBA

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.

Related Info