Events for January 17, 2024

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Canceled TILOS Seminar: Medical image reconstruction via deep learning: new architectures, data reduction and theoretical guarantees

TILOS Seminar Series
Computer Science & Engineering Building (CSE), Room 2154 3234 Matthews Ln, La Jolla

In this talk I will discuss the challenges and opportunities for using deep learning in medical image reconstruction. Contemporary techniques in this field rely on convolutional architectures that are limited by the spatial invariance of their filters and have difficulty modeling long-range dependencies. To remedy this, I will discuss our work on designing new transformer-based architectures called HUMUS-Net that lead to state of the art performance and do not suffer from these limitations. In the next part of the talk I will report on techniques to significantly reduce the required data for training. Finally, I will briefly discuss our recent attempts to develop rigorous theory for simple end-to-end training methods used in image reconstruction problems which is surprisingly quite challenging even for simple target functions. Notability, our theory will be in the rich (or beyond NTK regime) that conforms with practical choice of hyperparameters. Time permitting I will discuss other exciting directions for the use of deep learning in MR.

Inference and Decision-Making amid Social Interactions | Shuangning Li

Special Seminar Series
Computer Science & Engineering Building (CSE), Room 4140 3234 Matthews Ln, La Jolla

From social media trends to family dynamics, social interactions shape our daily lives. In this talk, I will present tools I have developed for statistical inference and decision-making in light of these social interactions.

(1) Inference: I will talk about estimation of causal effects in the presence of interference. In causal inference, the term “interference” refers to a situation where, due to interactions between units, the treatment assigned to one unit affects the observed outcomes of others. I will discuss large-sample asymptotics for treatment effect estimation under network interference where the interference graph is a random draw from a graphon. When targeting the direct effect, we show that popular estimators in our setting are considerably more accurate than existing results suggest. Meanwhile, when targeting the indirect effect, we propose a consistent estimator in a setting where no other consistent estimators are currently available.

(2) Decision-Making: Turning to reinforcement learning amid social interactions, I will focus on a problem inspired by a specific class of mobile health trials involving both target individuals and their care partners. These trials feature two types of interventions: those targeting individuals directly and those aimed at improving the relationship between the individual and their care partner. I will present an online reinforcement learning algorithm designed to personalize the delivery of these interventions. The algorithm's effectiveness is demonstrated through simulation studies conducted on a realistic test bed, which was constructed using data from a prior mobile health study. The proposed algorithm will be implemented in the ADAPTS HCT clinical trial, which seeks to improve medication adherence among adolescents undergoing allogeneic hematopoietic stem cell transplantation.