Causal Machine Learning: Continuous Structure Learning and Identifiability of Causal Invariances
Published: | 9:31 pm | Posted in: Events
Speaker: Kevin Bello Date: Mar 1, 11:45am–12:45pm Abstract: Interpretability and causality are key desiderata in modern machine learning systems. Graphical models, and more specifically directed acyclic graphs (DAGs, a.k.a. Bayesian networks), serve as a well-established tool for expressing interpretable causal relationships. However, the task of estimating DAG structures from data poses a significant challenge, given […]
Michael Gubanov (PI) and Grigory Fedyukovich (co-PI) Awarded a $550,000 NSF Grant to Support Web-scale Knowledge Graph Construction for Data Science
Published: | 7:54 pm | Posted in: News
FSU Department of Computer Science faculty Michael Gubanov and Grigory Fedyukovich have been awarded a new NSF grant for the project “Search for the Unknown – A Hybrid Scalable Data Management System Providing Deep Access to the Scientific Knowledge in Data Science”. The project is a collaborative effort between FSU and USF. It is expected to […]
Securing Embedded Systems Using Compartmentalization
Published: | 4:16 pm | Posted in: Events
Speaker: Arslan Khan Date: Feb 29, 11:45am–12:45pm Abstract: Embedded systems are low-power resource-constrained devices implementing specialized tasks, unlike general-purpose computers. Embedded systems find applications in various domains, from the Internet of Things (IoT) to general purpose Personal Computers (PC). Unfortunately, due to the resource constraints of embedded systems, developers often sacrifice security in favor of […]
Toward Secure Federated Learning
Published: | 1:17 pm | Posted in: Events
Speaker: Minghong Fang Date: Feb 28, 11:45am–12:45pm Abstract: Federated learning is a distributed machine learning approach that enables multiple clients (e.g., smartphones, IoT devices, and edge devices) to collaboratively learn a model with help of a server, without sharing their raw local data. Due to its potential promise of protecting private or proprietary user data, […]
Security of AI-enabled Perception Systems in Autonomous Driving
Published: | 1:16 pm | Posted in: Events
Speaker: Yi Zhu Date: Feb 27, 11:45am–12:45pm Abstract: Autonomous vehicles (AVs) are visioned as a revolutionary power for future transportation. A fundamental function of AV systems is perception, which aims to understand the surrounding driving environment using the sensors such as cameras, radar, and LiDAR, to help the AVs make critical driving decisions. However, some […]
An Adversarial Perspective on the Machine Learning Pipeline
Published: | 2:43 pm | Posted in: Events
Speaker: Fnu Suya Date: Feb 26, 11:45am–12:45pm Abstract: Machine learning models are often vulnerable to attacks during both training and test phases, yet the risks in adversarial environments are frequently misjudged. In this talk, I will first demonstrate that black-box test time attacks, which require only API access to the victim model, are more potent […]
Trustworthy and Scalable Machine Learning
Published: | 1:35 pm | Posted in: Events
Speaker: Yang Zhou Date: Feb 23, 11:45am–12:45pm Abstract: Machine learning (ML), a powerful tool for automatically extracting, managing, inferencing, and transferring knowledge, has been proven to be extremely useful in understanding the intrinsic nature of real-world big data. Despite achieving remarkable performance, ML models, especially deep learning models, suffer from severe trustworthiness and scalability challenges: […]
From Theory to Application: Overparameterization and Machine Learning at the Edge
Published: | 1:29 pm | Posted in: Events
Speaker: Peizhong Ju Date: Feb 21, 11:45am–12:45pm Abstract: Machine Learning (ML), a vital branch of Artificial Intelligence (AI), has seen rapid advancements in recent years. As ML continues to evolve, it faces two major challenges: the need for deeper theoretical understanding and the complexities of deployment at the edge. In this talk, I will present […]
Exploring the Adversarial Robustness of Language Models
Published: | 1:48 am | Posted in: Events
Speaker: Muchao Ye Date: Feb 19, 11:45am–12:45pm Abstract: Language models built by deep neural networks have achieved great success in various areas of artificial intelligence, which have played an increasingly vital role in profound applications including chatbots and smart healthcare. However, since deep neural networks are vulnerable to adversarial examples, there are still concerns about […]
Resource-Efficient Machine Learning: Reduce the Cost of Graph Learning and Beyond
Published: | 3:24 am | Posted in: Events
Speaker: Xiaotian (Max) Han Date: Feb 16, 11:45am–12:45pm Abstract: In this talk, I will present my research on resource-efficient machine learning techniques for graph neural networks (GNNs) and beyond. These techniques aim to reduce the computational resources required by these models, making them more practical for real-world applications. i) I will discuss accelerating the training […]