MATRIX Spring Seminar Series – Emily Hannigan

Robotic Neural Networks Predicting the Change in a System

Emily Hannigan

Robotic Manipulation and Mobility Lab

Columbia University

3/3/2023

11AM – 12PM CST

https://utsa.webex.com/utsa/j.php?MTID=mebdf9475a3c660cf65f2d72f41ab598d

Abstract:  Robots have the potential to aid humans from search and rescue work to space exploration, to in-home assistance for those with limited mobility. One of the main milestones required to achieve these ends is a robust ability to manipulate objects and locomote in cluttered and changing environments. To achieve this, a robot needs an internal model of the dynamics of itself and the world: given the current state of the world (the robot and the environment), how will any given sequence of actions change the state of the robot and its environment? This predictive model allows a controller to test out actions “in its head” before actually taking any actions. One of the most promising avenues for creating robust and accurate forward models is to use a neural network to approximate the dynamics of the system. However, the state of the art still falls short in a few areas. While it is easy to learn a data driven dynamics model to predict the change of a system over a short horizon, predicting farther into the future is still an open challenge. Additionally, the more complex a robotic system is, the more variables the model needs to keep track of and predict (we call this the state or observation of the system); the higher the dimensionality of the state, the more data and computation power we need to learn a model. This work investigates ways to improve the long term accuracy of data driven dynamics models using temporal data. It also evaluates methods of reducing state space dimensionality by learning this lower dimensional representation alongside a forward model using a method we dub “Context Training”. Throughout the development of these ideas, we also argue for improved evaluation metrics for learned forward models.

MATRIX Spring Seminar Series – Dr. Roberto Perdisci

IoTFinder: Efficient Large-Scale Identification of IoT Devices via Passive DNS Traffic Analysis

Roberto Perdisci

Patty and D.R. Grimes Distinguished Professor in Computer Science

Director, Institute for Cybersecurity and Privacy

School of Computing

University of Georgia

3/10/2023

11am – 12pm CST

WebEx: https://utsa.webex.com/utsa/j.php?MTID=m48256e669a2ad8e4769466e25d886773

Being able to identify and enumerate potentially vulnerable IoT devices across the Internet is important, because it allows for assessing global Internet risks and enables network operators to check the hygiene of their own networks. Towards this goal, in this talk I’ll present IoTFinder, a network-based system for efficient, large-scale passive identification of IoT devices. To develop IoTFinder, we leveraged distributed passive DNS data collection, and developed a machine learning-based system that aims to accurately identify a large variety of IoT devices based solely on their DNS traffic fingerprints. IoTFinder can efficiently detect IoT devices independently of whether they reside behind a NAT or other network middleboxes, or whether they are assigned an IPv4 or IPv6 address. We designed IoTFinder as a multi-label classifier, and evaluated its accuracy in several different settings, including DNS traffic collected at a US-based ISP hosting more than 40 million clients. The experimental results show that our approach allows for accurately and efficiently detecting many diverse IoT devices, even when they are hosted behind a NAT and their traffic is “mixed” with traffic generated by other IoT and non-IoT devices hosted in the same local network.

Spring Seminar Series – Dr. Douglas Brooks

Applications of Machine Learning in Industry R&D

Dr. Douglas Brooks, PhD

Southwest Research Institute (SWRI)

3/24/2023

11AM – 12PM CST

Webex: https://utsa.webex.com/utsa/j.php?MTID=mc5ef9409265da73d5d53c136180aa35f

This discussion will highlight past and present projects, showcasing the approaches and results to solving industry focused R&D challenges. The discussion will specifically highlight the unique efforts within the R&D sector. Additionally, the discussion will include some potential future concepts and thoughts.

LINK

Spring Seminar Series – Dr. Irene Chen

Beyond Bias Audits: Bringing Equity to the Machine Learning Pipeline

Dr. Irene Chen

Microsoft Research

3/31/2023

11AM – 12PM CST

WebEx: https://utsa.webex.com/utsa/j.php?MTID=m97203d6036ae3b1f22fd0fb98da3c2d1

Advances in machine learning and the explosion of clinical data have demonstrated immense potential to fundamentally improve clinical care and deepen our understanding of human health. However, algorithms for medical interventions and scientific discovery in heterogeneous patient populations are particularly challenged by the complexities of healthcare data. Not only are clinical data noisy, missing, and irregularly sampled, but questions of equity and fairness also raise grave concerns and create additional computational challenges.

In this talk, I present two approaches for leveraging machine learning towards equitable healthcare. First, I examine how to incorporate differences in access to care into the modeling step. Using a deep generative model, we examine the task of disease phenotyping in heart failure and Parkinson’s disease. Second, I demonstrate how to address one specific health disparity through the early detection of intimate partner violence from clinical indicators. Using a time-based model with noisy labels, we can correct for biases in data measurement to learn more clinically useful subtypes and improve prediction. The talk concludes with a discussion about how to rethink the entire machine learning pipeline with an ethical lens to building algorithms that serve the entire patient population