Recreational swims provide a fun and engaging way for children and parents to practice new skills, stay active, and enjoy quality time together in the pool with the MIT community.No Z Center (MIT Recreation - Zesiger Sports and Fitness Center) membership is required to participate.A parent or caregiver must accompany children in the water. Per Z Center policy, each adult may supervise up to two children at a time.Children must be at least 6 months old to join. If younger, they must be able to hold their head up comfortably. Registration is here. Only for MIT Spouses and Partners Connect members.

Speaker: Zander Kelley (IAS)Title: Bounds for 3-ProgressionsAbstract:Suppose you have a set A of integers from {1,2,...,N} that contains at least N/C elements. Then, for large enough N, must A contain three equally spaced numbers (i.e., a 3-term arithmetic progression)? In 1953, Roth showed that this is indeed the case when C is roughly loglog(N), while Behrend in 1946 showed that C can be at most 2^(√(log N)) by giving an explicit construction of a large set with no 3-term arithmetic progressions. Since then, the problem has been a cornerstone of the area of additive combinatorics. Following a series of remarkable results, a celebrated paper from 2020 due to Bloom and Sisask improved the lower bound on C to C = (log N)^(1+c), for some constant c>0.This talk will describe our work which shows that the same holds when C is roughly 2^((log N)^(1/12)), thus getting closer to Behrend's construction.Based on joint work with Raghu Meka.

Preparing for a research talk, investor pitch, or interview? Eager to polish your three-minute thesis video, podcast, or public talk? In this NEW, six-session workshop series, learn to refine your speaking and presentation skills across a range of contexts. Whether your audience is intimate or enormous, expert or novice, we will help you find strategies to capture and keep their attention. Each interactive session will invite you to implement tips on tailoring your content, delivery, and visual aids to develop your confidence, clarity, and charisma. At the end of six meetings, you will have solid advice and experience with introducing yourself and your topic, tailoring your talk to diverse audiences, structuring your content, streamlining your flow, practicing effectively, and fielding questions.Session 1: Tue, April 1, 4:00-5:30 p.m. First Impressions Session 2: Thu, April 3, 4:00-5:30 p.m. Engage Your Audience Session 3: Tue, April 8, 4:00-5:30 p.m. Structure Your Presentation Session 4: Thu, April 10, 4:00-5:30 p.m. Tell Your Story Session 5: Tue, April 15, 4:00-5:30 p.m. Enhance Your Presentation Session 6: Thu, April 17, 4:00-5:30 p.m. Finish Strong: Conclusions and Q&A

The discovery of Flash Processing—has opened unprecedented avenues in field assisted material consolidation: sintering, surface science, hyper-catalysis, joining of dissimilar materials, far from equilibrium new materials of complex chemistries, and even refractory alloys like tungsten consolidated outside a furnace in seconds and with properties that cannot be achieved in other ways. For example, powders of several primitive oxides can be casually mixed and flashed to yield a single phase of a multicomponent ceramic in a few seconds. Compounds not accessible can be made quickly, easily, and inexpensively. Examples like non-stoichiometric oxides which may have unusual properties for ceramic electrolytes and cathode materials in Li+ batteries and fuel cells. Advanced multi phase ceramic composites and high entropy perovskites made in under 1 minute vs many hours for alternative processing. New ways to join metals and ceramics and recent progress on new electromagnetic touch free flash techniques that provide a path to scalable large complex geometry processing. Field assisted processing is on the cusp of a breakout. If we can address current challenges, this can become one of the cornerstones of modern materials processing. If you or your group is working on metals, ceramics, high entropy alloys, new states of matter and is interested in a fascinating discussion about the physics of off-equilibrium consolidation this is a must attend seminar.Biography: Rishi Raj, Professor of Mechanical Engineering, UC Boulder and Distinguished Life Member by The American Ceramic Society. Rishi received his PhD in 1970 under the tutelage of Michael Ashby and David Turnbull. Rishi is well known for his contributions to metallurgy and ceramics including the developed some of the first high temperature metal processing maps and techniques like sinter forging as well as his significant contributions to polymer derived ceramics but his greatest contribution is the discovery of Flash sintering in 2010, a field that is now building a rapid following as interest in off-equilibrium processing of metals and ceramics grows in importance .

Fair and Efficient Combinatorial Assignment | Alex Teytelboym (Oxford)

Speaker: Themis Sapsis (MIT)Title: Optimal sampling and extreme-event aware learning for probabilistic modeling of extremes in complex systemsAbstract: Analysis of real-world physical and engineering systems is characterized by unique computational challenges associated with high dimensionality of parameter spaces, large cost of simulations or experiments, as well as existence of uncertainty. For a wide range of these problems the goal is to either quantify uncertainty and compute risk for critical events, optimize parameters or control strategies, and/or making decisions. In this talk we will discuss two aspects related to the modeling of extreme events: i) optimal sampling of data for the characterization of extreme events, and ii) extreme-event aware learning, i.e. learning methods that do not necessarily assume the existence of extreme events in the training data.In the first part of the talk, we will discuss how Bayesian active learning provides a flexible framework for i) identifying the most informative data for extremes, which is relevant for engineering problems where optimal experimental design is feasible, or ii) quantify the value of data in a prescribed dataset, e.g. relevant for climate modeling. Despite its attractive properties, the Bayesian framework is often prohibitively expensive in terms of the required simulations or experiments, even in the active learning setting. We introduce a new class of acquisition functions that utilize a likelihood-weighted ratio that accounts for the importance of the output relative to the input. This ratio acts essentially as a probabilistic sampling weight and guides the sampling algorithm towards regions of the input space where the objective function assumes abnormal values, resulting in significant savings of computational or experimental resources needed for convergence. We show that the adopted acquisition functions can be rigorously derived as the asymptotic limit of an optimal acquisition function that has a minimax form over a functional space. Subsequently, we demonstrate their favorable properties compared to standard methods on benchmark functions commonly used in the optimization community as well as real world applications involving turbulence, fluid-structure interaction problems and optimal sensor placement.In the second part of the talk, we examine the problem of learning for extremes without the assumption of extreme events in the training data. We introduce the framework of Extreme Event Aware (e2a or eta) or η-learning which does not assume the existence of extreme events in the available data. η-learning reduces the uncertainty even in `unchartered' extreme event regions, by enforcing the extreme event statistics of a few observables during training, which can be available or assumed through qualitative arguments or other forms of analysis. This type of statistical regularization results in models that fit the observed data, but also enforces consistency with the prescribed or assumed statistics of some observables, enabling the generation of unprecedented extreme events even when the training data lack extremes therein. A series of theoretical results based on optimal transport theory offers a rigorous justification and highlights the optimality of the introduced method. The favorable properties η-learning are demonstrated in synthetic examples as well as a real world example involving modeling extreme precipitation events.