Monday / CS Seminar
April 29th / 12:00 PM
Hybrid / Mudd 3514

Speaker
Kaize Ding, Northwestern University

Talk Title
Data-Efficient Graph Learning

Abstract
The world around us -- and our understanding of it -- is rich in relational structure: from atoms and their interactions to objects and entities in our environments. Graphs, with nodes representing entities and edges representing relationships between entities, serve as a common language to model complex, relational, and heterogeneous systems. Despite the success of recent deep graph learning, the efficacy of existing efforts heavily depends on the ideal data quality of the observed graphs and the sufficiency of the supervision signals provided by the human-annotated labels, leading to the fact that those carefully designed models easily fail in resource-constrained scenarios.

In this talk, I will present my recent research contributions centered around data-efficient learning for relational and heterogeneous graph-structured data. First, I will introduce what data-efficient graph learning is and my contributions to different research problems under its umbrella, including graph few-shot learning, graph weakly-supervised learning, and graph self-supervised learning. Based on my work, I will elucidate how to push forward the performance boundary of graph learning models especially graph neural networks with low-cost human supervision signals. I will also touch upon the real-world applications of data-efficient graph learning to different domains and finally conclude my talk with a brief overview of my future research agenda.

Biography
Kaize Ding is an Assistant Professor in the Department of Statistics and Data Science at Northwestern University. Before joining Northwestern, he obtained his Ph.D. degree in Computer Science at Arizona State University in 2023 under the supervision of Prof. Huan Liu. His research interests are generally in data mining, machine learning, and natural language processing, with a particular focus on graph machine learning, data-efficient learning, and reliable AI. His work has been published in top-tier conferences and journals (e.g., AAAI, EMNLP, IJCAI, KDD, NeurIPS, TheWebConf, and TNNLS), and has been recognized with several prestigious awards and honors, including the AAAI New Faculty Highlights, SDM Best Posters Award, Best Paper Award at the Trustworthy Learning on Graphs workshop, etc.

Research Area/Interests
Data Mining; Machine Learning; Natural Language Processing

Zoom: https://northwestern.zoom.us/j/99555833103?pwd=TEtaakVGL2xUYytDa2Nvbk14Z3VhUT09

Scikit-Learn is one of the major libraries for machine learning in Python. This series comprises four workshops designed to give you a map of Scikit-Learn’s different functionalities and place you on firm ground to start using it for your machine-learning projects.

Part 2 - Supervised Learning – Regression
Regression analysis is a powerful tool for uncovering the associations between features of your data (known as independent variables) and dependent variables (usually denoted by Y). In this workshop, you will learn to identify ML tasks suited for regression analysis, appropriately process independent variables, train and evaluate models, and generate predictions. We will also discuss some common pitfalls and assumptions of the chosen modeling techniques.

Prerequisites: Basic familiarity with Python is required. Familiarity with NumPy is highly recommended. No previous machine learning or statistics experience is necessary, but it will be helpful.

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Join Tom Wagner, Ph.D., the founder & CEO of an AI and robotics company named Berkshire Grey, as he shares elements of his journey, and that of his team, to have commercial impact with technology. During the talk, Wagner will take you along the path that started with a simple goal to have impact with technology, and then moved to a specific idea, to building a team, to venture capital investment, to material business, to taking the company public, to being acquired and taken private again as the play continues. During this talk Tom will offer candid thoughts and take aways in technology and markets, building a team, working with venture capitalists, customer adventures, creating choices when there seem to be none, what terms like “work life balance” really mean in this context, and the importance of embracing uncertainty and ambiguity from time to time. On a semi-technical front, some of the topics Wagner will cover include sparse data, search heuristics, bounded rationality, and the path from sensor inputs to information-based decision making.

For those interested in learning about starting a technology company, or simply looking for possibly entertaining and useful anecdotes, please Register here!

Speaker Bio:

Tom Wagner would generally describe a portion of his existence as the quest to have impact with technology. He has served as CEO of Berkshire Grey since the company’s inception in 2013. Previously, he had the privilege of serving as the Chief Technology Officer of iRobot Corporation, a publicly traded robotics company, from 2008 to 2012. Prior to joining iRobot, Wagner served at the Defense Advanced Research Projects Agency (DARPA), the research and development agency of the U.S. Department of Defense, where he created, managed, and transitioned programs in artificial intelligence, robotics, logistics, communications, command and control, telehealth, connected devices, and connected intelligent assistants. Earlier in his career, Wagner served as a principal lead at Honeywell, a professor at the University of Maine, spent time at General Motors, and held various roles in small & startup companies. He holds a Ph.D. in artificial intelligence and computer science from the University of Massachusetts Amherst, a M.S. from the University of New Hampshire, and a B.S. from Michigan State University.

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Please join us for a casual fair to learn about grad school opportunities here at Northwestern!

Admissions teams and other program faculty and staff will be there to share information and answer questions. 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 possibilities!

Lunch will be provided. We look forward to seeing you there!

The Master of Science programs featured:

- 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

- MS in Chemical and Biological Engineering

- Master of Science in Energy and Sustainability (MSES)

- Master of Science in Law Program

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Speaker:

Chris Bail, Professor of Sociology, Political Science, and Public Policy, Duke University

Title:

Bridging Divides with Generative AI

Abstract:

Political discourse is the soul of democracy, but misunderstanding and conflict can fester in divisive conversations. The widespread shift to online discourse exacerbates many of these problems and corrodes the capacity of diverse societies to cooperate in solving social problems. Scholars and civil society groups promote interventions that make conversations less divisive or more productive, but scaling these efforts to online discourse is challenging. This talk will describe a large-scale experiment that demonstrates how online conversations about divisive topics can be improved with AI tools. Specifically, my colleagues and employ a large language model to make real-time, evidence-based recommendations intended to improve participants’ perception of feeling understood. These interventions improve reported conversation quality, promote democratic reciprocity, and improve the tone, without systematically changing the content of the conversation or moving people’s policy attitudes. These findings replicate during a half year experiment on a large social media platform.

Speaker Bio:

Chris Bail is Professor of Sociology, Political Science, and Public Policy at Duke University, where he founded the Polarization Lab. He studies how artificial intelligence shapes human behavior in a range of different settings—and social media platforms in particular. Chris is passionate about building the field of computational social science. He is the Editor of the Oxford University Press Series in Computational Social Science and the Co-Founder of the Summer Institutes in Computational Social Science. Chris received his PhD from Harvard University in 2011.

Location:

In person: Chambers Hall, 600 Foster Street, Lower Level
Remote option: https://northwestern.zoom.us/j/97722631639
Passcode: NICO2024

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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Speaker: Russ Tedrake, Toyota Professor of EECS, Aero/Astro, ME at Massachusetts Institute of Technology and VP of Research at Toyota Research Institute

Title: Dexterous Manipulation with Diffusion Policies

Date and Time: Friday, May 3 at 11:30 AM CT

Location: Tech ESAM M416 and Zoom

Zoom Link: https://tinyurl.com/CRBSeminar

• NU-authenticated attendees will be automatically admitted. Others, please email amy.nedoss@northwestern.edu to be admitted from the waiting room.

Abstract:

At the Toyota Research Institute (TRI), we've been working on behavior cloning for dexterous manipulation. Building on the Diffusion Policy framework that we've recently developed in collaboration with Shuran Song, we now have a very solid pipeline for taking ~50-100 bimanual haptic teleop demonstrations and turning that into a surprisingly effective visuomotor (+tactile) policy. Because there is no explicit state representation required, these skills work equally well manipulating deformable, liquid, or other difficult to model tasks as they do for more traditional rigid-object manipulation. We're actively scaling this up into the multi-task setting and now see a plausible path towards "Large Behavior Models". This behavior cloning pipeline is working incredibly well, and must be understood deeply in the broader context of output-feedback control. Time permitting, I'll also tell you a bit about some new results in optimization-based planning and control, and where they might fit in the age of foundation models.

Bio:

Russ Tedrake is the Toyota Professor at the Massachusetts Institute of Technology (MIT) in the Department of Electrical Engineering and Computer Science, Mechanical Engineering, and Aero/Astro, and he is a member of MIT’s Computer Science and Artificial Intelligence Lab (CSAIL). He is also the Vice President of Robotics Research at Toyota Research Institute (TRI). He received a B.S.E. in Computer Engineering from the University of Michigan in 1999, and a Ph.D. in Electrical Engineering and Computer Science from MIT in 2004. Dr. Tedrake is the Director of the MIT CSAIL Center for Robotics and was the leader of MIT’s entry in the DARPA Robotics Challenge. He is a recipient of the NSF CAREER Award, the MIT Jerome Saltzer Award for undergraduate teaching, the DARPA Young Faculty Award in Mathematics, the 2012 Ruth and Joel Spira Teaching Award, and was named a Microsoft Research New Faculty Fellow. His research has been recognized with numerous conference best paper awards, including ICRA, Robotics: Science and Systems, Humanoids, Hybrid Systems: Computation and Control, as well as the inaugural best paper award from the IEEE RAS Technical Committee on Whole-Body Control.

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Scikit-Learn is one of the major libraries for machine learning in Python. This series comprises four workshops designed to give you a map of Scikit-Learn’s different functionalities and place you on firm ground to start using it for your machine-learning projects.

Part 3 - Supervised Learning – Classification
Classification is the problem of identifying which class or category (label) an observation (features) belongs to within a pre-defined set of categories. In this workshop, you will learn to identify classification problems, prepare the features and label data for modeling, train and evaluate models, and generate predictions. We will also discuss some common pitfalls and assumptions of the chosen modeling techniques.

Prerequisites: Basic familiarity with Python is required. Familiarity with NumPy is highly recommended. No previous machine learning or statistics experience is necessary, but it will be helpful.

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Speaker:

Daniel Harris, Assistant Professor of Engineering, Brown University

Title:

At the Interface: Physical Analogy with Interfacial Fluid Mechanics

Abstract:

Maxwell describes physical analogy as a "partial similarity between the laws of one science and those of another which makes each of them illustrate the other." Hydrodynamics has long since been a source of physical analogy, sharing similar equations with other seemingly disparate fields of physics. The focus of this talk will be on physical analogies with interfacial fluid systems, where accessible tabletop experiments can be used to investigate and communicate physical phenomena at vastly different scales. Following a brief review of some historical examples of analogy in interfacial fluid mechanics, I will describe two recent tabletop experiments developed in our lab that share similarities with certain microscopic colloidal systems. While physical analogy can be fruitfully used to advance science across disciplines, it can also be leveraged to enhance scientific communication and pedagogy.

Speaker Bio:

Daniel M. Harris is an Assistant Professor of Engineering at Brown University in the Fluids and Thermal Sciences group. Before joining Brown, Dan was a Postdoctoral Research Associate and Lecturer at the University of North Carolina at Chapel Hill in the Department of Mathematics. Dan received his B.S. in Mechanical Engineering from Cornell University in 2010 and his Ph.D. in Applied Mathematics from MIT in 2015.

Dan’s primary research interests are in interfacial phenomena, microfluidics, and transport phenomena. His research involves an integrated experimental and theoretical approach. Dan has also received numerous awards for his scientific visualizations, including being selected as the winner of the 2016 NSF/Popular Science Visualization Challenge in Photography, as well as numerous prizes from the American Physical Society’s Gallery of Fluid Motion and Gallery of Soft Matter.

Location:

In person: Chambers Hall, 600 Foster Street, Lower Level
Remote option: https://northwestern.zoom.us/j/93585934682
Passcode: NICO2024

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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The HathiTrust Research Center (HTRC) enables beginner-to-advanced text analysis on the HathiTrust Digital Library collection. This workshop will focus on the beginner features of the HTRC, including building a workset and running the built-in algorithms.

This workshop is presented online via Zoom by Jamie Carlstone, Authority Metadata Librarian.

A Northwestern Zoom account is required to access this session.

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The Center for Scientific Studies in the Arts is a collaboration between the Art Institute of Chicago and materials science-related departments at Northwestern University to pursue objects-based and objects-inspired scientific research. Materials research benefits ongoing work in conservation, archaeology, art history, and curatorial scholarship.

Learn how the Center uses materials research to care for art objects in sustainable, innovative ways with Maria Kokkori, Senior Scientist in the Center for Scientific Studies in the Arts. She will be in conversation with Corey Byrnes, Northwestern Associate Professor of Chinese Culture and co-founder/co-director of the Environmental Humanities Workshop in Kaplan Humanities Center.

This event is presented by the McCormick School of Engineering and Applied Science in conjunction with exhibition Actions for the Earth: Art, Care & Ecology.

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Join us at The Garage to hear from Arjun Ravi Kannan, Director of Data Science Research at Discover, who will provide insight on the near future and the ideas pushing it forward.

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T-Stochastic Graphs

Karl Rohe, Professor of Statistics, University of Wisconsin-Madison

Abstract: Previous statistical approaches to hierarchical clustering for social network analysis all construct an "ultrametric" hierarchy. While the assumption of ultrametricity has been discussed and studied in the phylogenetics literature, it has not yet been acknowledged in the social network literature. We show that "non-ultrametric structure" in the network introduces significant instabilities in the existing top-down recovery algorithms. To address this issue, we introduce an instability diagnostic plot and use it to examine a collection of empirical networks. These networks appear to violate the "ultrametric" assumption. We propose a deceptively simple class of probabilistic models called T-Stochastic Graphs which impose no topological restrictions on the latent hierarchy. Perhaps surprisingly, this model generalizes the previous models. To illustrate this model, we propose six alternative forms of hierarchical network models and then show that all six are equivalent to the T-Stochastic Graph model. These alternative models motivate a novel approach to hierarchical clustering that combines spectral techniques with the well-known Neighbor-Joining algorithm from phylogenetic reconstruction. We prove this spectral approach is statistically consistent.

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Friday / CS Distinguished Lecture
May 10th / 12:00 PM
Hybrid / Mudd 3514

Speaker
Shafi Goldwasser; Simons Institute for the Theory of Computing, University of California Berkeley

Talk Title
Trust, Backdoor Vulnerabilities and Possible Mitigations

Abstract
Cryptographic tools and models enable to trust the use of technology platforms controlled by worst case computationally bounded adversaries. In this talk I will use cryptographic modeling and tools to view trust dilemmas in various phases of the machine learning pipelines. We will touch on privacy in the training stage, verification of properties of
machine learning models, and the possibility of achieving robustness in presence of backdoors.

Biography

Shafi Goldwasser is Director of the Simons Institute for the Theory of Computing, and Professor of Electrical Engineering and Computer Science at the University of California Berkeley. Goldwasser is also the RSA Professor (post tenure) of Electrical Engineering and Computer Science at MIT and Professor Emeritus of Computer Science and Applied Mathematics at the Weizmann Institute of Science, Israel. Goldwasser holds a B.S. Applied Mathematics from Carnegie Mellon University (1979), and M.S. and Ph.D. in Computer Science from the University of California Berkeley (1984).

Goldwasser's contributions include the introduction of probabilistic encryption, interactive zero knowledge protocols, elliptic curve primality testings, hardness of approximation proofs for combinatorial problems, combinatorial property testing, and pseudo deterministic algorithms.

Goldwasser was the recipient of the ACM Turing Award in 2012, the Gödel Prize in 1993 and in 2001, the ACM Grace Murray Hopper Award in 1996, the RSA Award in Mathematics in 1998, the ACM Athena Award for Women in Computer Science in 2008, the Benjamin Franklin Medal in 2010, the IEEE Emanuel R. Piore Award in 2011, the Simons Foundation Investigator Award in 2012, and the BBVA Foundation Frontiers of Knowledge Award in 2018. Goldwasser is a member of the NAS, NAE, AAAS, the Russian Academy of Science, the Israeli Academy of Science, the London Royal Mathematical Society and a Foreign Member of the Royal Society. Goldwasser holds honorary degrees from Ben Gurion University, Bar Ilan University, Carnegie Mellon University, Haifa University, University of Oxford, and the University of Waterloo, and has received the UC Berkeley Distinguished Alumnus Award and the Barnard College Medal of Distinction.

Research Area/Interests
Cryptography, Complexity, Probabilistic Algorithms

Zoom: TBA

Scikit-Learn is one of the major libraries for machine learning in Python. This series comprises four workshops designed to give you a map of Scikit-Learn’s different functionalities and place you on firm ground to start using it for your machine-learning projects.

Part 4 - Unsupervised Learning and Beyond
Unsupervised learning uses machine learning to analyze unlabeled datasets without human supervision. Several real-world problems require discovering hidden patterns in data. In this workshop, you will learn about different unsupervised learning methods, such as dimensionality reduction and clustering, and how to process your data to apply these algorithms. We will also discuss other machine learning methods and future steps.

Prerequisites: Basic familiarity with Python is required. Familiarity with NumPy is highly recommended. No previous machine learning or statistics experience is necessary, but it will be helpful.

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Speaker:

Eleni Katifori, Associate Professor, Department of Physics & Astronomy, University of Pennsylvania

Title:

TBA

Abstract:

TBA

Speaker Bio:

Eleni Katifori is an Associate Professor in the Department of Physics & Astronomy, University of Pennsylvania. Prof Katifori’s research group are interested in understanding the physics behind the morphological and functional attributes of living organisms. They primarily focus on questions inspired by and related to biological transport networks and the elasticity and geometry of thin sheets. Professor Katifori received her Ph.D from Harvard University in 2008 and a B.S. from the University of Athens, Greece in 2002.

Location:

In person: Chambers Hall, 600 Foster Street, Lower Level
Remote option: https://northwestern.zoom.us/j/92857810876
Passcode: NICO2024

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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The Northwestern University Institutional Review Board Office is proud to host the Northwestern Information Technology Research Computing and Data Services team in a session focused on the Ethical and Compliant Use of Artificial Intelligence (AI) in Human Research.

Christina Maimone

Associate Director of Research Data Services, Northwestern IT Research Computing and Data Services

This session will discuss security, compliance, integrity, technological, and ethical considerations for using AI tools as a part of human research. We’ll cover the potential uses of AI throughout the research process, from project development through to the analysis and publication of results. Learn about principles to guide the evaluation of the use of AI tools, as well as the resources and support available at Northwestern for using AI tools.

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New Developments, Industrial Applications, and Opportunities in Generative AI

Join us for an exciting event exploring the latest advancements in Generative AI! This in-person gathering will take place at the Hyatt Centric Chicago Magnificent Mile in Chicago, IL. Discover how Generative AI is revolutionizing varied industries and uncover the endless possibilities it offers. Whether you're a tech enthusiast, researcher, or industry professional, this event is a must-attend. Network with like-minded individuals, engage in thought-provoking discussions, and gain valuable insights from expert speakers. Don't miss out on this unique opportunity to stay ahead of the curve in the world of AI!

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An Automatic Finite-Sample Robustness Check: Can Dropping a Little Data Change Conclusions?

Tamara Broderick, Associate Professor, Department of Electrical Engineering and Computer Science at MIT

Abstract: Practitioners will often analyze a data sample with the goal of applying any conclusions to a new population. For instance, if economists conclude microcredit is effective at alleviating poverty based on observed data, policymakers might decide to distribute microcredit in other locations or future years. Typically, the original data is not a perfect random sample from the population where policy is applied -- but researchers might feel comfortable generalizing anyway so long as deviations from random sampling are small, and the corresponding impact on conclusions is small as well. Conversely, researchers might worry if a very small proportion of the data sample was instrumental to the original conclusion. So we propose a method to assess the sensitivity of statistical conclusions to the removal of a very small fraction of the data set. Manually checking all small data subsets is computationally infeasible, so we propose an approximation based on the classical influence function. Our method is automatically computable for common estimators. We provide finite-sample error bounds on approximation performance and a low-cost exact lower bound on sensitivity. We find that sensitivity is driven by a signal-to-noise ratio in the inference problem, does not disappear asymptotically, and is not decided by misspecification. Empirically we find that many data analyses are robust, but the conclusions of several influential economics papers can be changed by removing (much) less than 1% of the data.

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Northwestern University's Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA) presents the 3rd CIERA ASTRONOMY NIGHT OUT featuring Dr. Caitlin Witt

• A free, keynote lecture given by CIERA astronomer, Dr. Caitlin Witt
• After the lecture, engage in family astronomy activities, and look through telescopes (weather permitting)!
• Come see electrified gasses through light-splitting glasses, or take home your own crafted constellation! We'll have CIERA astronomers ready to answer all of your spacey questions, and a local artist showcasing how he bridges science and the arts! This and much more at CIERA's Astronomy Night Out!
• Campus parking lots are free and unrestricted in the evenings.
• All are welcome! Content tailored to a general audience.

Caitlin Witt
CIERA, Northwestern University and Adler Planetarium

Talk Title:
Tuning into the Cosmic Symphony: Pulsar Timing, the Gravitational Wave Background, and Beyond

About this Talk: In the cosmic symphony of gravitational waves, supermassive black hole binaries provide the bass notes. However, when these colossal duos play all at once, their individual melodies overlap into an indistinct rumble. After 15 years of effort, astronomers announced last summer that they have at last found evidence for this background hum of gravitational waves using an observatory that spans our entire Milky Way galaxy. Tune in with us to hear more about gravitational wave symphonies, monstrous black holes, colliding galaxies, dead stars, and more!

About the Presenter:
Dr. Caitlin A. Witt is the CIERA-Adler Postdoctoral Fellow at CIERA and the Adler Planetarium. Her main research interests are supermassive black hole binaries (SMBHBs), the low frequency gravitational waves they emit, and their effects on their host galaxies. As a member of the North American Nanohertz Observatory for Gravitational Waves (NANOGrav), Caitlin leads efforts to detect continuous gravitational waves from individual SMBHBs with pulsar timing arrays and develop methods to use information learned in electromagnetic searches for SMBHBs in multi-messenger searches for these elusive pairs. She is also interested in searches for periodicity in AGN light curves, efficient computational analysis of large data sets, and data visualization. At Adler, Caitlin is working with the Public Observing team to develop a research program for Chicago students using Adler’s Doane Observatory.

To receive updates about this and other CIERA Events, please subscribe to our e-mail list.

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Next Steps in Python Workshop Series is a seven-part series covering intermediate Python skills, tips, and tricks guaranteed to make your coding life easier. You do not need to attend each session to participate - there is a new lesson each week.

Each one-hour session meets via Zoom on Wednesdays at noon, CDT.  

Parsing text with NLTK
NLTK is one of Python's main libraries for natural language processing (NLP). This workshop introduces the library by focusing on how to parse text to create bags of words. In other words, this workshop teaches how to go from raw text (a string) to a list of words that can be used for different NLP methods.

Prerequisites: Participants should be familiar with Python at an introductory level.

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Speaker:

Serguei Saavedra, Associate Professor, Department of Civil and Environmental Engineering, MIT

Title:

How Do Ecological Systems Become (re)Assembled?

Abstract:

One of the most iconic thought experiments in biology is what would happen if we could rewind the tape of life on Earth and play it again. Would the tape have a different story in every replay? Or is there a general order of events? The relevance of this thought experiment is not just philosophical or counterfactual, because (re)assembly processes undergone by ecological systems, from microbes to mega-fauna, are continuously replicating the experiment. By integrating theoretical and empirical work, in this talk I will provide a guideline to increase our understanding about the (re)assembly possibilities of ecological systems. Explaining and predicting the (re)assembly of ecological systems underpins our ability to develop successful interventions in bio-restoration, bio-technologies, and bio-medicine.

Speaker Bio:

Serguei Saavedra is an Associate Professor at MIT in the Department of Civil and Environmental Engineering. He is also an external faculty at Santa Fe Institute. Serguei is a theoretical ecologist focused on understanding the feasibility of observing the emergence, transformations, and regeneration of ecological systems under environmental changes. Before joining MIT in 2016, Serguei studied systems engineering in Mexico; specialized in mathematical modeling at Genoa University; completed his PhD in engineering science at Oxford University; and did his postdoctoral work at the NICO (under the mentorship of Brian Uzzi), Doñana Biological Station, and in the department of environmental systems at ETH.

Location:

In person: Chambers Hall, 600 Foster Street, Lower Level
Remote option: https://northwestern.zoom.us/j/91082510906
Passcode: NICO2024

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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Join us at The Garage to hear from Alex Castrounis, Founder & CEO at Why of AI, who will provide insight on the near future and the ideas pushing it forward.

Castrounis is the founder and CEO of Why of AI, a book author on AI, and an adjunct professor of AI for Northwestern University's Kellogg & McCormick MBAi program. He has over two decades of experience advising startups to Fortune 100 companies on using data, analytics, and AI models to drive business growth and customer success.

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