Calendar

Mar
26
Tue
AMS Special Seminar: Dr. Angela Jarrett (University of Texas- Austin) @ Maryland 109
Mar 26 @ 10:30 am – 11:30 am

Title: Guiding clinical and preclinical investigations of breast cancer with mathematical modeling and analyses

Abstract: One of the great challenges for cancer treatment is the inability to optimize therapy. Without a reasonable mathematical framework, our ability to select treatment regimens for the individual patient is fundamentally limited to trial and error. Presented here are examples of data-driven, integrated experimental-mathematical approaches to studying breast cancer’s response to therapy for both pre-clinical and clinical investigations. The preclinical model, consisting of ODEs, connects various experiments for an in vivo mouse system to better understand the interactions of the immune response and targeted therapy for breast cancer. The clinical model is a 3D PDE system for predicting tumor response to neoadjuvant therapy using patient-specific data that lays the groundwork for optimizing chemotherapeutic dosing and scheduling. In both examples, the results of uncertainty and sensitivity analyses are discussed to show how they can be used to generate experimentally testable hypotheses, narrow the scope for experimental investigations, and evolve mathematical models. Additionally, multi-scale models are proposed that bridge the gap between in vitro and in vivo experiments to step towards clinical translation.

Mar
28
Thu
The Distinguished Goldman Lecture Series- AMS Seminar: Gerard Cornuejols (Carnegie Mellon University) @ Hodson 213
Mar 28 @ 1:30 pm – 2:30 pm

Title: Min-Max Relations for Packing and Covering

Abstract: We consider a family M of subsets of a finite set E. A “cover” is a subset of E that intersects every member of the family M. A “packing” is a set of members of M no two of which intersect. Clearly, the cardinality of a packing is at most that of a cover. We study conditions under which the maximum cardinality of a packing equals the minimum cardinality of a cover. We present recent results obtained jointly with Ahmad Abdi and Dabeen Lee.

Bio:  Gerard Cornuejols is professor of Operations Research at Carnegie Mellon University. His research interests are in integer programming and combinatorial optimization. He received the Lanchester Prize twice (1978 and 2015), the Fulkerson Prize (2000), the Dantzig Prize (2009) and the von Neumann Theory Prize (2011).

Apr
4
Thu
The Acheson J. Duncan Lecture Series: AMS Seminar: Rina Foygel Barber (University of Chicago) @ Hodson 213
Apr 4 @ 1:30 pm – 2:30 pm

Title: Robust inference with the knockoff filter.

Abstract: In this talk, I will present ongoing work on the knockoff filter for inference in regression. In a high-dimensional model selection problem, we would like to select relevant features without too many false positives. The knockoff filter provides a tool for model selection by creating knockoff copies of each feature, testing the model selection algorithm for its ability to distinguish true from false covariates to control the false positives. In practice, the modeling assumptions that underlie the construction of the knockoffs may be violated, as we cannot know the exact dependence structure between the various features. Our ongoing work aims to determine and improve the robustness properties of the knockoff framework in this setting. We find that when knockoff features are constructed using estimated feature distributions whose errors are small in a KL divergence type measure, the knockoff filter provably controls the false discovery rate at only a slightly higher level. This work is joint with Emmanuel Candès and Richard Samworth.

This is joint work with Emmanuel Candès, Aaditya Ramdas, and Ryan Tibshirani.

Bio: TBA

Apr
5
Fri
The Acheson J. Duncan Lecture Series: AMS Seminar: Rina Foygel Barber (University of Chicago) @ Hodson 213
Apr 5 @ 1:30 pm – 2:30 pm

Title: Distribution free prediction: Is conditional inference possible?

Abstract: We consider the problem of distribution-free predictive inference, with the goal of producing predictive coverage guarantees that hold conditionally rather than marginally. Existing methods such as conformal prediction offer marginal coverage guarantees, where predictive coverage holds on average over all possible test points, but this is not sufficient for many practical applications where we would like to know that our predictions are valid for a given individual, not merely on average over a population. On the other hand, exact conditional inference guarantees are known to be impossible without imposing assumptions on the underlying distribution. In this work we aim to explore the space in between these two, and examine what types of relaxations of the conditional coverage property would alleviate some of the practical concerns with marginal coverage guarantees while still being possible to achieve in a distribution-free setting.

This is joint work with Emmanuel Candès, Aaditya Ramdas, and Ryan Tibshirani.

Bio: TBA

Apr
11
Thu
AMS Seminar: Thalia Zariphopoulou (University of Texas) @ Whitehead 304
Apr 11 @ 1:30 pm – 2:30 pm

Title: TBA

 

Abstract: TBA

Apr
18
Thu
AMS Seminar: Lori Brady (JHU-Civil Eng) @ Whitehead 304
Apr 18 @ 1:30 pm – 2:30 pm

Title: TBA

 

Abstract: TBA

Apr
25
Thu
The John C. & Susan S.G. Wierman Lecture Series- AMS Seminar: Lianne Sheppard (University of Washington) @ Shaffer 304
Apr 25 @ 1:30 pm – 2:30 pm

Title: TBA

 

Abstract: TBA

May
2
Thu
AMS Seminar: Elana Fertig (JHU-BioMed) @ Whitehead 304
May 2 @ 1:30 pm – 2:30 pm

Title: TBA

 

Abstract: TBA

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