Events
CS Distinguished Lecture: Tong Zhang
Department of Computer Science (CS)
12:00 PM
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3514, Mudd Hall ( formerly Seeley G. Mudd Library)
Details
Wednesday / CS Distinguished Lecture
May 1st / 12:00 PM
Hybrid / Mudd 3514
Speaker
Tong Zhang, University of Illinois Urbana-Champaign
Talk Title
Machine Learning Methods for Finetuning and Alignment of Large Language Models
Abstract
Large Language Models (LLMs) represent a significant milestone in the development of artificial general intelligence. Numerous pretrained models are available within the open-source community, yet they often require further training to be effectively utilized in downstream applications. The enhancement of these models is primarily accomplished through two methods: finetuning and Reinforcement Learning from Human Feedback (RLHF). Finetuning is crucial for tailoring LLMs to specialized topics or for developing capabilities such as instruction following. Meanwhile, RLHF aims to align LLMs with human preferences.
This talk presents various machine learning problems and algorithms for both finetuning and RLHF. For finetuning, we will introduce techniques that minimize hallucinations and enhance resource efficiency in optimization processes. For RLHF, we will explore both the theoretical frameworks that establish principled approaches and the practical algorithms that implement these theories. The presentation highlights some key questions and solutions in optimizing LLMs for enhanced functionality and alignment with human preferences.
Biography
Tong Zhang is currently a professor in the Computer Science department at the University of Illinois Urbana Champaign. He is a fellow of the IEEE, American Statistical Association, and Institute of Mathematical Statistics. His research interests include machine learning theory, algorithms, and applications. Tong Zhang has served as the chair or area chair in major machine learning conferences such as NeurIPS, ICML, and COLT, and has also served on the editorial boards of leading machine learning journals such as PAMI, JMLR, and the Machine Learning Journal.
Research Area/Interests
Data Mining; Machine Learning; Natural Language Processing
Zoom: https://northwestern.zoom.us/j/99555833103?pwd=TEtaakVGL2xUYytDa2Nvbk14Z3VhUT09
Time
Wednesday, May 1, 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)
McC Engineering Master of Science Program Fair May 2024
Office of Professional Education (OPE)
12:00 PM
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Main Lobby, Technological Institute
Details
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
Time
Wednesday, May 1, 2024 at 12:00 PM - 2:00 PM
Location
Main Lobby, Technological Institute Map
Contact
Calendar
Office of Professional Education (OPE)
WED@NICO SEMINAR: Chris Bail, Duke University "Bridging Divides with Generative AI"
Northwestern Institute on Complex Systems (NICO)
12:00 PM
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Lower Level, Chambers Hall
Details
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.
Time
Wednesday, May 1, 2024 at 12:00 PM - 1:00 PM
Location
Lower Level, Chambers Hall Map
Contact
Calendar
Northwestern Institute on Complex Systems (NICO)
Thought Leader Dialogue: AI and the Future of Work
Center for Human-Computer Interaction + Design (HCI+D)
4:00 PM
Details
With the explosion of AI platforms, what is the future of work? Will we need “humans in the loop”? Although these platforms are still in the early stages, implications are broadly significant. In this dialogue, we bring together researchers with distinct perspectives to discuss approaches for designing a desirable future of work for everyone. Tackling questions related to ethics, governance, and competition, this dialog will highlight the role of thoughtful design in achieving the vision of a desirable future of work for everyone.
Time
Thursday, May 2, 2024 at 4:00 PM - 5:15 PM
Contact
Calendar
Center for Human-Computer Interaction + Design (HCI+D)
BME Seminar Series: Dr. Xuanhe Zhao
McCormick - Biomedical Engineering Department (BME)
4:00 PM
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Tech M345, Technological Institute
Details
“Merging Humans and Machines: Innovation and Translation”
ABSTRACT:
Whereas human tissues and organs are mostly soft, wet, and bioactive, machines are commonly hard, dry, and abiotic. Merging humans and machines is of imminent importance in addressing grand societal challenges in health, sustainability, security, education, and happiness of living. However, merging humans and machines is extremely challenging due to their fundamentally contradictory properties. At MIT Zhao Lab, we invent, understand, and facilitate the translation of soft materials to form long - term, robust, non - fibrotic, and high - efficacy interfaces between humans and machines. In this talk, I will discuss two examples of merging humans and machines, including tough and fast bioadhesives that can replace sutures and wearable devices that can continuously image diverse deep organs. I will propose two challenges in science and technology:
• Can we develop tissue - implant interfaces that fully eliminate fibro tic encapsulation?
• Can we image the full human body continuously over days to months?
I will conclude the talk with a vision for future human - machine convergence – aided by and synergized with modern technologies such as artificial intelligence, synthetic biology, and precision medicine.
BIOGRAPHY:
Xuanhe Zhao is a Professor of Mechanical Engineering at MIT. The mission of Zhao Lab is to advance science and technology between humans and machines to address grand societal challenges in health and sustainability. A major current focus is the study and development of soft materials and systems. Dr. Zhao has won early career awards from NSF, ONR, ASME, SES, AVS, Adhesion Society, JAM, EML, and Materials Today. He has been a Clarivate Highly Cited Researcher since 2018. Bioadhesive ultrasound, based on Zhao Lab’s work published in Science, was named one of TIME Magazine's Best Inventions of the year in 2022. SanaHeal Inc., based on Zhao Lab’s work published in Nature, was awarded the 2023 Nature Spinoff Prize. Over ten patents from Z hao Lab have been licensed by companies and have contributed to FDA - approved and widely - used medical devices.
Time
Thursday, May 2, 2024 at 4:00 PM - 5:00 PM
Location
Tech M345, Technological Institute Map
Contact
Calendar
McCormick - Biomedical Engineering Department (BME)
Statistics and Data Science Seminar: "Audience Choice: Bayesian Workflow / Causal Generalization / Modeling of Sampling Weights"
Department of Statistics and Data Science
11:00 AM
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Ruan Conference Room – lower level, Chambers Hall
Details
Audience Choice: Bayesian Workflow / Causal Generalization / Modeling of Sampling Weights
Andrew Gelman, Department of Statistics and Department of Political Science, Columbia University
The audience is invited to choose among three possible talks:
Bayesian Workflow: The workflow of applied Bayesian statistics includes not just inference but also building, checking, and understanding fitted models. We discuss various live issues including prior distributions, data models, and computation, in the context of ideas such as the Fail Fast Principle and the Folk Theorem of Statistical Computing. We also consider some examples of Bayesian models that give bad answers and see if we can develop a workflow that catches such problems. For background, see here: http://www.stat.columbia.edu/~gelman/research/unpublished/Bayesian_Workflow_article.pdf
Causal Generalization: In causal inference, we generalize from sample to population, from treatment to control group, and from observed measurements to underlying constructs of interest. The challenge is that models for varying effects can be difficult to estimate from available data. We discuss limitations of existing approaches to causal generalization and how it might be possible to do better using Bayesian multilevel models. For background, see here: http://www.stat.columbia.edu/~gelman/research/published/KennedyGelman_manuscript.pdf and here: http://www.stat.columbia.edu/~gelman/research/published/causalreview4.pdf and here: http://www.stat.columbia.edu/~gelman/research/unpublished/causal_quartets.pdf
Modeling of Sampling Weights: A well-known rule in practical survey research is to include weights when estimating a population average but not to use weights when fitting a regression model—as long as the regression includes as predictors all the information that went into the sampling weights. But what if you don’t know where the weights came from? We propose a quasi-Bayesian approach using a joint regression of the outcome and the sampling weight, followed by poststratifcation on the two variables, thus using design information within a model-based context to obtain inferences for small-area estimates, regressions, and other population quantities of interest. For background, see here: http://www.stat.columbia.edu/~gelman/research/unpublished/weight_regression.pdf
Topic will be chosen live by the audience attending the talk.
Time
Friday, May 3, 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
Russ Tedrake, MIT and Toyota Research Institute, "Dexterous Manipulation with Diffusion Policies"
Center for Robotics and Biosystems (CRB)
11:30 AM
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M416, Technological Institute
Details
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.
Time
Friday, May 3, 2024 at 11:30 AM - 12:30 PM
Location
M416, Technological Institute Map
Contact
Calendar
Center for Robotics and Biosystems (CRB)
Scikit-Learn Workshop Series (Virtual): Part 3 - Supervised Learning – Classification
Northwestern IT Research Computing and Data Services
1:00 PM
Details
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.
Time
Monday, May 6, 2024 at 1:00 PM - 2:00 PM
Contact
Calendar
Northwestern IT Research Computing and Data Services
Human-AI Interaction in the Age of Large Language Models
SOC - Department of Communication Studies
1:30 PM
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1-122, Frances Searle Building
Details
The Technology & Social Behavior PhD program is excited to welcome Professor Diyi Yang of Stanford University to campus on Monday, May 6 for our Spring Colloquium. Professor Yang will give a talk titled Human-AI Interaction in the Age of Large Language Models which will be in the Center for Human-Computer Interaction + Design (Frances Searle 1-122) in-person, live only. We hope to see you there!
Abstract: Large language models have revolutionized the way humans interact with AI systems, transforming a wide range of fields and disciplines. In this talk, we discuss several approaches to enhancing human-AI interaction using LLMs. The first one looks at social skill training with LLMs by demonstrating how we use LLMs to teach conflict resolution skills through simulated practice. The second part develops efficient learning methods for adapting LLMs to low-resource languages and dialects to reduce disparity in language technologies. We conclude by discussing how human-AI interaction via LLMs can empower individuals and foster positive change.
Bio: Diyi Yang is an assistant professor in the Computer Science Department at Stanford University. Her research focuses on human-centered natural language processing and computational social science. She is a recipient of IEEE “AI 10 to Watch” (2020), Microsoft Research Faculty Fellowship (2021), NSF CAREER Award (2022), an ONR Young Investigator Award (2023), and a Sloan Research Fellowship (2024). Her work has received multiple paper awards or nominations at top NLP and HCI conferences, (e.g., Best Paper Honorable Mention at ICWSM 2016, Best Paper Honorable Mention at SIGCHI 2019, and Outstanding Paper at ACL 2022).
Time
Monday, May 6, 2024 at 1:30 PM - 2:30 PM
Location
1-122, Frances Searle Building Map
Contact
Calendar
SOC - Department of Communication Studies
WED@NICO SEMINAR: Daniel Harris, Brown University "At the interface: physical analogy with interfacial fluid mechanics"
Northwestern Institute on Complex Systems (NICO)
12:00 PM
//
Lower Level, Chambers Hall
Details
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.
Time
Wednesday, May 8, 2024 at 12:00 PM - 1:00 PM
Location
Lower Level, Chambers Hall Map
Contact
Calendar
Northwestern Institute on Complex Systems (NICO)
Introduction to the HathiTrust Research Center (Online)
Northwestern Libraries
1:00 PM
Details
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.
Time
Wednesday, May 8, 2024 at 1:00 PM - 2:00 PM
Contact
Calendar
Northwestern Libraries
Extending Care: A Conversation about Conservation and Futurity
Block Museum of Art
6:00 PM
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Block Museum of Art, Mary and Leigh
Details
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.
Time
Wednesday, May 8, 2024 at 6:00 PM - 7:30 PM
Location
Block Museum of Art, Mary and Leigh Map
Contact
Calendar
Block Museum of Art
Visions of Tomorrow: Building Responsible AI
The Garage
5:15 PM
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The Garage, North Campus Parking Garage/Academic Building
Details
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.
Time
Thursday, May 9, 2024 at 5:15 PM - 6:00 PM
Location
The Garage, North Campus Parking Garage/Academic Building Map
Contact
Calendar
The Garage
Statistics and Data Science Seminar: "T-Stochastic Graphs"
Department of Statistics and Data Science
11:00 AM
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Ruan Conference Room – lower level, Chambers Hall
Details
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.
Time
Friday, May 10, 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
CS Distinguished Lecture: Trust, Backdoor Vulnerabilities and Possible Mitigations (Shafi Goldwasser)
Department of Computer Science (CS)
12:00 PM
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3514, Mudd Hall ( formerly Seeley G. Mudd Library)
Details
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
Time
Friday, May 10, 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)
Scikit-Learn Workshop Series (Virtual): Part 4 - Unsupervised Learning and Beyond
Northwestern IT Research Computing and Data Services
1:00 PM
Details
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.
Time
Monday, May 13, 2024 at 1:00 PM - 2:00 PM
Contact
Calendar
Northwestern IT Research Computing and Data Services
WED@NICO SEMINAR: Eleni Katifori, University of Pennsylvania "TBA"
Northwestern Institute on Complex Systems (NICO)
12:00 PM
//
Lower Level, Chambers Hall
Details
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.
Time
Wednesday, May 15, 2024 at 12:00 PM - 1:00 PM
Location
Lower Level, Chambers Hall Map
Contact
Calendar
Northwestern Institute on Complex Systems (NICO)
IRB BROWN BAG | Responsible Use of Artificial Intelligence (AI) in Human Research
Institutional Review Board (IRB)
12:00 PM
Details
Artificial intelligence (AI) is already changing society and impacting the nature of research, raising important questions about how data is gathered, analyzed, and reported, who is considered a researcher, and the ethics behind the conduct of research. To help us unpack some of these important and timely questions, the Northwestern University Institutional Review Board Office is proud to host Christina Maimone, Northwestern Information Technology Research Computing and Data Services Associate Director, in a Brown Bag session focused on the ethical and compliant use of AI in human research.
Dr. Mainmone will address everything from technological principles to security and compliance rules to integrity and ethical considerations for using AI tools in human research. For those exploring the use of AI technologies in their work, this session will also cover potential uses of AI throughout the research process, from project development to the analysis and publication of research results. Finally, attendees will leave with resources and support available at Northwestern as well as knowledge on principles guiding the evaluation of the use of AI tools. We look forward to hosting you!
Time
Wednesday, May 15, 2024 at 12:00 PM - 1:00 PM
Contact
Calendar
Institutional Review Board (IRB)
MLDS Exchange 2024
Master of Science in Machine Learning and Data Science (MLDS)
9:00 AM
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633 N St Clair Street
Details
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!
Time
Friday, May 17, 2024 at 9:00 AM - 5:00 PM
Location
633 N St Clair Street
Calendar
Master of Science in Machine Learning and Data Science (MLDS)
Statistics and Data Science Seminar: "An Automatic Finite-Sample Robustness Check: Can Dropping a Little Data Change Conclusions?"
Department of Statistics and Data Science
11:00 AM
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Ruan Conference Room – lower level, Chambers Hall
Details
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.
Time
Friday, May 17, 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
CIERA Astronomy Night Out Featuring a Lecture by Dr. Caitlin Witt "Tuning into the Cosmic Symphony: Pulsar Timing, the Gravitational Wave Background, and Beyond"
CIERA - Annual Public Lecture Series
7:00 PM
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LR3, Technological Institute
Details
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.
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Time
Friday, May 17, 2024 at 7:00 PM - 9:00 PM
Location
LR3, Technological Institute Map
Contact
Calendar
CIERA - Annual Public Lecture Series
Next Steps in Python: Parsing text with NLTK
Northwestern IT Research Computing and Data Services
12:00 PM
Details
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.
Time
Wednesday, May 22, 2024 at 12:00 PM - 1:00 PM
Contact
Calendar
Northwestern IT Research Computing and Data Services
WED@NICO SEMINAR: Serguei Saavedra, MIT "How Do Ecological Systems Become (re)Assembled?"
Northwestern Institute on Complex Systems (NICO)
12:00 PM
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Lower Level, Chambers Hall
Details
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.
Time
Wednesday, May 22, 2024 at 12:00 PM - 1:00 PM
Location
Lower Level, Chambers Hall Map
Contact
Calendar
Northwestern Institute on Complex Systems (NICO)
Visions of Tomorrow: Navigating AI's Uncharted Territory
The Garage
5:15 PM
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The Garage, North Campus Parking Garage/Academic Building
Details
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.
Time
Thursday, May 23, 2024 at 5:15 PM - 6:00 PM
Location
The Garage, North Campus Parking Garage/Academic Building Map
Contact
Calendar
The Garage
WED@NICO SEMINAR: Joseph Paulsen, Syracuse University "TBA"
Northwestern Institute on Complex Systems (NICO)
12:00 PM
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Lower Level, Chambers Hall
Details
Speaker:
Joseph Paulsen, Associate Professor, Department of Physics, Syracuse University
Title:
TBA
Abstract:
TBA
Speaker Bio:
Joseph Paulsen earned a bachelor's degrees in Mathematics and Physics from St. Olaf College in Northfield, MN, and he completed his PhD in Physics at the University of Chicago with Sidney Nagel. He won a National Science Foundation CAREER Award for his work that studies connections between geometry and mechanics in thin materials. Outside of science, one of his passions is trying to squirrel away as much time as possible to ski with his 7-year-old daughter (his son and his wife are not skiers... yet).
Location:
In person: Chambers Hall, 600 Foster Street, Lower Level
Remote option: https://northwestern.zoom.us/j/94291553667
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.
Time
Wednesday, May 29, 2024 at 12:00 PM - 1:00 PM
Location
Lower Level, Chambers Hall Map
Contact
Calendar
Northwestern Institute on Complex Systems (NICO)