This is the home page for the Machine Learning Lunch Series, sponsored by EOG Resources, at the School of Engineering. The coordinators are Michael Weylandt, Tan Nguyen, Chen Luo, Lorenzo Luzi, and Cannon Lewis, and the faculty coordinator is Reinhard Heckel. The mailing list is: ml-l@rice.edu.
Unless otherwise noted, the meetings are on Wednesdays at 12:00pm. 

Details about the ML lunch presentation are below. Lunch is provided for graduate students, postdoctoral scholars, and faculty.


Building Tools for Data-Driven Discovery: Graphical Models & Data Integration

Sep. 19, 12:00 pm- 1:00 pm in DCH 3092
Speaker: Genevera Allen
Please indicate interest, especially if you want lunch, here.
Abstract:  In this two part talk, I will present two major research projects motivated by large and complex data sets in neuroscience and genomics.  First, and following up from last week’s presentation, I will present current work in my group on graphical models for estimating functional connectivity from large-scale neural recordings.  Second, I will present recent work on graphical models and dimension reduction methods for integration of data from diverse data sources.

 

Inferring Interactions between Neurons, Stimuli, and Behavior

Sep. 12, 12:00 pm- 1:00 pm in DCH 3092
Speaker: Genevera Allen (STAT + BCM), Ankit Patel (BCM + ECE), Xaq Pitkow (ECE + BCM), Krešimir Josić (UH + BIOS), and Andreas Tolias (BCM + ECE)
Please indicate interest, especially if you want lunch, here.
Abstract:  Our understanding of the properties of individual neurons in the neocortex and their role in brain computations has advanced significantly over the last decades. However, we are still far from understanding how large assemblies of cells interact to process stimulus information and generate behavior. These causal interactions are not a static property of neural circuits, but can change rapidly depending on the current task and global brain state. As the BRAIN Initiative develops critical experimental tools to record massive numbers of neural activities in functioning brains, we need mathematical tools to analyze and interpret this data. Currently there are no high-throughput methods available to measure neural interactions in cortical circuits in vivo, and relate them to stimuli and behavior. We will develop statistical tools to achieve this goal. We propose to infer the interactions between neurons, stimuli, and behavior, while incorporating both sparse connectivity constraints and accounting for latent common inputs. Our first aim is to optimize our statistical method of inferring synaptic efficacy in silico by applying it to large, realistic artificial neural networks. We will empirically validate the method in mouse cortex by combining high-speed 3D two-photon imaging in vivo with multi-cell patching experiments on the same tissue in vitro. Our second aim is to generalizes this statistical tool to measure neural computation, by inferring nonlinear interactions simultaneously between the stimulus and neural activity (encoding), and neural activity and behavior (decoding). We will test these techniques using a variety of models that perform realistic inference in the presence of nuisance variables. This will include deep convolutional networks that achieve or exceed human performance. While related statistical methods have been used in other disciplines, our approach is novel in neuroscience. To achieve these goals we have assembled a collaborative team of systems neuroscientists, mathematicians, computational neuroscientists and statisticians. We are committed to sharing code and data, and we have also assembled a group of End Users to be early adopters and testers of our methods. Our combination of revolutionary experimental and mathematical tools will provide neuroscience with unprecedented insights into the brains distributed neural computations.

 

Data Science for Networks

Sep. 5, 12:00 pm- 1:00 pm in DCH 3092
Speaker: Santiago Segarra
Please indicate interest, especially if you want lunch, here.
Abstract:  Understanding networks and networked behavior has emerged as one of the foremost intellectual challenges of the 21st century. While we obviously master the technology to engineer transformational networks — from communication infrastructure to online social networks — our theoretical understanding of fundamental phenomena that arise in networked systems remains limited. My goal is to combine network science and signal processing in order to leverage the structure of networks to better understand data defined on them. In this context, the term Data Science for Networks can be understood as a joint effort to understand both network structures and network data. I will introduce the fundamental building blocks of graph signal processing (GSP) as a toolbox to study network data, and delve deeper into the problem of inferring a graph from nodal observations. I will then discuss ongoing work and potential future research directions.

 

Predicting mental health and measuring sleep using machine learning and wearable sensors/mobile phones

Aug. 28, 12:00 pm- 1:00 pm in DCH 3092
Speaker: Akane Sano
Please indicate interest, especially if you want lunch, here.
Abstract:  Sleep, stress and mental health have been major health issues in modern society. Poor sleep habits and high stress, as well as reactions to stressors and sleep habits, can depend on many factors. Internal factors include personality types and physiological factors and external factors include behavioral, environmental and social factors. What if 24/7 rich data from mobile devices could identify which factors influence your bad sleep or stress problem and provide personalized early warnings to help you change behaviors, before sliding from a good to a bad health condition such as depression? In my talk, I will present a series of studies and systems we have developed to investigate how to leverage multi-modal data from mobile/wearable devices to measure, understand and improve mental well-being. First, I will talk about methodology and tools I developed for the SNAPSHOT study, which seeks to measure Sleep, Networks, Affect, Performance, Stress, and Health using Objective Techniques. To learn about behaviors and traits that impact health and well-being, we have measured over 200,000 hours of multi-sensor and smartphone use data as well as trait data such as personality from about 300 college students exposed to sleep deprivation and high stress. Second, I will describe statistical analysis and machine learning models to characterize, model, and forecast mental well-being using the SNAPSHOT study data. I will discuss behavioral and physiological markers and models that may provide early detection of a changing mental health condition. Third, I will introduce recent projects that might help people to reflect on and change their behaviors for improving their well-being.

 

The Spatial Proximity and Connectivity (SPC) Method for Measuring and Analyzing Residential Segregation

Aug. 22, 12:00 pm- 1:00 pm in DCH 3092
Speaker: Elizabeth Roberto
Please indicate interest, especially if you want lunch, here.
Abstract: In recent years, there has been increasing attention to the spatial dimensions of residential segregation—including the spatial arrangement of segregated neighborhoods and the geographic scale or relative size of segregated areas. However, the methods used to measure segregation do not incorporate features of the built environment, such as the road connectivity between locations or the physical barriers that divide groups. My talk will introduce the Spatial Proximity and Connectivity (SPC) method for measuring and analyzing segregation. The method addresses the limitations of current approaches by taking into account how the physical structure of the built environment affects the proximity and connectivity of locations. I will describe the method and demonstrate its application for studying segregation and spatial inequality. The SPC method contributes to scholarship on residential segregation by capturing the effect of an important yet understudied mechanism of segregation—the connectivity, or physical barriers, between locations—on the level and spatial pattern of segregation, and it enables further consideration of the role of the built environment in segregation processes.

 

 


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