Past Events

Developing Online Learning Experiments Using Doenet (2023)

Apply to attend


In this five-day workshop, participants will learn how to create and implement online learning experiments using the Distributed Open Education Network (Doenet, Doenet is designed to help faculty critically evaluate how different content choices influence student learning in their classrooms. Doenet enables instructors to quickly test hypotheses regarding the relative effectiveness of alternative approaches by providing tools to assign different variations of an activity and analyze the resulting data.

Following brief introductions and demos of features of the Doenet platform, participants will work in small groups to develop learning experiments that can be used in the college classroom, assisted by the developers of Doenet. The expectation is that participants will leave the workshop with a learning experiment that they can use in their classroom the following year.

The workshop will run from 9 AM on Monday, May 22 through 4 PM on Friday, May 26. All organized activities will occur between 9 AM and 4 PM each day.

The workshop is open to faculty at all levels teaching STEM courses.

To apply, please submit the following documents through the Program Application link at the top of the page:

  1. A personal statement briefly (200 words or less) stating what you hope to contribute to the discussion on learning experiments and what you hope to gain from this workshop. Include courses you teach for which you'd like to develop learning experiments. Priority will be given to those able to run learning experiments in their courses in the following year.
  2. A brief CV or resume. (A list of publications is not necessary.)

This workshop is fully funded by the National Science Foundation. All accepted participants who request funding for travel and/or local expenses will receive support. There is no registration fee.

Participants who perform learning experiments on Doenet during the following academic year will be eligible to receive a small stipend to support their work.

Deadline for full consideration: April 17, 2023.

Supported by NSF grant DUE 1915363.

ScreeNOT: Optimal Singular Value Thresholding and Principal Component Selection in Correlated Noise

Data Science Seminar

Elad Romanov (Stanford University)


Principal Component Analysis (PCA) is a fundamental and ubiquitous tool in statistics and data analysis.

The bare-bones idea is this. Given a data set of n points y_1, ..., y_n, form their sample covariance S. Eigenvectors corresponding to large eigenvalues--namely directions along which the variation within the data set is large--are usually thought of as "important"  or "signal-bearing"; in contrast, weak directions are often interpreted as "noise", and discarded along the proceeding steps of the data analysis pipeline. Principal component (PC) selection is an important methodological question: how large should an eigenvalue be so as to be considered "informative"?

Our main deliverable is ScreeNOT: a novel, mathematically-grounded procedure for PC selection. It is intended as a fully algorithmic replacement for the heuristic and somewhat vaguely-defined procedures that practitioners often use--for example the popular "scree test".

Towards tackling PC selection systematically, we model the data matrix as a low-rank signal plus noise matrix Y = X + Z; accordingly, PC selection is cast as an estimation problem for the unknown low-rank signal matrix X, with the class of permissible estimators being singular value thresholding rules. We consider a formulation of the problem under the spiked model. This asymptotic setting captures some important qualitative features observed across numerous real-world data sets: most of the singular values of Y are arranged neatly in a "bulk", with very few large outlying singular values exceeding the bulk edge. We propose an adaptive algorithm that, given a data matrix, finds the optimal truncation threshold in a data-driven manner under essentially arbitrary noise conditions: we only require that Z has a compactly supported limiting spectral distribution--which may be a priori unknown. Under the spiked model, our algorithm is shown to have rather strong oracle optimality properties: not only does it attain the best error asymptotically, but it also achieves (w.h.p.) the best error--compared to all alternative thresholds--at finite n.

This is joint work with Matan Gavish (Hebrew University of Jerusalem) and David Donoho (Stanford).

Some Elementary Economics (& Physics) of the Electricity Grid

Industrial Problems Seminar

Sriharsha (Harsha) Veeramachaneni (WindLogics)


The simplified tutorial-style talk will delve into the fundamentals of how electricity is transacted in the north-American grid: How electricity prices are determined; How the physics of electricity flow affects prices, and some counterintuitive consequences thereof; And how all this relates to the business of profitably operating a power plant.

Squishy Mathematical Reasoning in a Robotics Start-up

Industrial Problems Seminar

Michelle Snider (Service Robotics & Technologies)


Service Robotics & Technologies (SRT Labs) brings legacy infrastructure, smart sensors, and collaborative robotics into a unified data management ecosystem in order to monitor, analyze and automate systems.  Applications range from smart laboratories to smart buildings to smart cities. The smart technology space provides a wealth of interesting projects which may not immediately sound like math problems but whose solutions often greatly benefit from a mathematical perspective. In this talk, I will discuss some different projects where my team applied mathematical approaches to find realistically implementable solutions, interspersed with career lessons learned along the way.

A Varied and Winding Math Career in Industry

Industrial Problems Seminar

Laura Lurati (Edward Jones)


In this talk, I'll share my personal career path as an applied mathematician both from the perspective of the various industries I've worked in (aerospace, finance, real estate, and software engineering) and my own transition from an individual contributor to management. I'll give an overview of the types of problems I worked on in each of these fields and the common skills that have helped me throughout my career. Finally, I will share some of my work as a builder of high-performing teams, the rewards of management, and what I look for in candidates when hiring new teammates.  As a key message, I hope to share that a career in applied mathematics can take very interesting turns if you are open to new possibilities and continual learning. 

Learning in Stochastic Games

Data Science Seminar

Muhammed Omer Sayin (Bilkent University)


Reinforcement learning (RL) has been the backbone of many frontier artificial intelligence (AI) applications, such as game playing and autonomous driving, by addressing how intelligent and autonomous systems should engage with an unknown dynamic environment. The progress and interest in AI are now transforming social systems with human decision-makers, such as (consumer/financial) markets and road traffic, into socio-technical systems with AI-powered decision-makers. However, self-interested AI can undermine the social systems designed and regulated for humans. We are delving into the uncharted territory of AI-AI and AI-human interactions. The new grand challenge is to predict and control the implications of AI selfishness in AI-X interactions with systematic guarantees. Hence, there is now a critical need to study self-interested AI dynamics in complex and dynamic environments through the lens of game theory.

In this talk, I will present the recent steps we have taken toward the foundation of how self-interested AI would and should interact with others by bridging the gap between game theory and practice in AI-X interactions. I will specifically focus on stochastic games to model the interactions in complex and dynamic environments since they are commonly used in multi-agent reinforcement learning. I will present new learning dynamics converging almost surely to equilibrium in important classes of stochastic games. The results can also be generalized to the cases where (i) agents do not know the model of the environment, (ii) do not observe opponent actions, (iii) can adopt different learning rates, and (iv) can be selective about which equilibrium they will reach for efficiency. The key idea is to use the power of approximation thanks to the robustness of learning dynamics to perturbations. I will conclude my talk with several remarks on possible future research directions for the framework presented.

IMA Data Science Seminar - Learning in Stochastic Games

Muhammed Omer Sayin (Bilkent University) will give a presentation entitled Learning in Stochastic Games.

Continuous-time probabilistic generative models for dynamic networks

Data Science Seminar

Kevin Xu (Case Western Reserve University)


Networks are ubiquitous in science, serving as a natural representation for many complex physical, biological, and social systems. Probabilistic generative models for networks provide plausible mechanisms by which network data are generated to reveal insights about the underlying complex system. Such complex systems are often time-varying, which has led to the development of dynamic network representations to enable modeling, analysis, and prediction of temporal dynamics.

In this talk, I introduce a class of continuous-time probabilistic generative models for dynamic networks that augment statistical models for network structure with multivariate Hawkes processes to model temporal dynamics. The class of models allows an analyst to trade off flexibility and scalability of a model depending on the application setting. I focus on two specific models on opposite ends of the tradeoff: the community Hawkes independent pairs (CHIP) model that scales up to millions of nodes, and the multivariate Community Hawkes (MULCH) model that is flexible enough to replicate a variety of observed structures in real network data, including temporal motifs. I demonstrate how these models can be used for analysis, prediction, and simulation on several real network data sets, including a network of militarized disputes between countries over time.


Working as an Artificial Intelligence Advisor to the US Government

Industrial Problems Seminar

Mitchell Kinney (The MITRE Corporation)


Though Artificial Intelligence (AI) has progressed rapidly, many areas of government remain wary of upending legacy systems to capitalize on the technology. MITRE serves as a trusted advisor to government agencies and is a conduit between private industry and government through the management of multiple Federally Funded Research and Development Centers (FFRDCs). As a member of the AI and Autonomy Innovation Center, my role is to help government understand the potential positives and pitfalls of implementing AI technology.

I will discuss my background, my company, my responsibilities and give an overview of a project I worked on to help highlight how machine learning could be used to transfer paper-based systems engineering models into modern software. The prototype we developed uses computer vision techniques to build an internal graph representation of the diagram that can be translated to commercial tools.


Lecture: Adil Ali

Industrial Problems Seminar

Adil Ali (CH Robinson)