Chemistry Student Seminar (CSS) is a student-organized seminar series that host graduate students and postdocs to share their research in a friendly and informal environment. Free donuts and coffee are provided.

Meet other MS&PC members from all over the world, get resources and information about life at MIT/Cambridge/Boston, exchange ideas, and engage in cultural conversations in a friendly and casual environment, while working on English fluency.Please email ecgatmit@gmail.com for more information.

Thinking territorially centers design’s ethical commitments to nature and future generations. Territorial design invites engagement with a variety of built and unbuilt environments in response to the multi-scalar, multifaceted challenges of climate change—spanning urban hinterlands, rural villages, jungles, deserts, and oceans. While disrupting the rural-urban and nature-human divides, territorial design also challenges the disciplinary boundaries between architecture, landscape and urban design.This design roundtable convenes four researchers, designers, and educators working across rural and urban contexts in different parts of the world—from Massachusetts to the Mekong Delta. The first part of the event discusses research. In the face of climate change’s complexity, uncertainty, and immense scale, what role does design play? How can designers define and scope their work to remain both manageable and impactful? The second part shifts to design pedagogy, examining how research and teaching influence one another. Student projects from the speakers’ courses will be exhibited during the event.

Speaker: Alex CohenTitle: Fractal uncertainty in higher dimensions

Speaker: Dennis Shen (University of Southern California)Abstract: One dominant approach to evaluate the causal effect of a treatment is through panel data analysis, whereby the behaviors of multiple units are observed over time. The information across time and units motivates two general approaches: (i) horizontal regression (i.e., unconfoundedness), which exploits time series patterns, and (ii) vertical regression (e.g., synthetic controls), which exploits cross-sectional patterns. Conventional wisdom often considers the two approaches to be different. We establish this position to be partly false for estimation but generally true for inference. In the absence of any assumptions, we show that both approaches yield algebraically equivalent point estimates for several standard estimators. However, the source of randomness assumed by each approach leads to a distinct estimand and quantification of uncertainty even for the same point estimate. This emphasizes that researchers should carefully consider where the randomness stems from in their data, as it has direct implications for the accuracy of inference.Biography: Dennis Shen is an assistant professor in the Data Sciences and Operations Department at the USC Marshall School of Business. Before joining USC, he was a FODSI postdoctoral fellow at the Simons Institute at UC Berkeley. He also served as a technical consultant for Uber Technologies and TauRx Therapeutics. He has received several recognition for his work, including the INFORMS George B. Dantzig Dissertation Award (2nd to his esteemed colleague, Somya) and MIT George Sprowls PhD Thesis Award in Artificial Intelligence & Decision-making.

All members of the MIT community are welcome to join us in the MIT Chapel for a Christian worship service commemorating Good Friday. The service will center around the story of the passion and death of Jesus Christ accompanied by prayer and music. Hosted by the Lutheran Ministry at MIT and other MIT Christian chaplains.