Large Language Models (LLMs) are AI-driven algorithmic frameworks initially developed to capture mathematical representations of textual data. Many forms of healthcare and medical data also exhibit inherent temporal correlations. Examples include DNA (genetics and genomics), RNA (transcriptomics), 4D CT (time-resolved CT), and eye-tracking sequences (fixation eye motions). This talk explores

In this talk we will be going through various applications of Artificial Intelligence (AI) algorithmic systems using Retinal video sequence data. Temporal modeling of sequence data using Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) based approaches has been investigated, as compared to conventional ML methods such as Logistic Regression,

Defeating Dementia: New Directions from the Biology of Aging Symposium, presented by Bakar Labs/QB3 and the Alzheimer’s Drug Discovery Foundation (ADDF) on Thursday, the 13th of April, at Bakar Labs in Berkeley.

Multiple Sclerosis (MS) is a chronic immune-mediated inflammatory disease (IMID) of the central nervous system (CNS). Early identification of MS, especially as a screening method for at-risk individuals, is crucial to delay disease progression and improve patient outcomes by preventing future irreversible neurologic damage. In this work, we utilize well-validated

While both the heterogeneity and complexity of the hardware and software system consisting of autonomous cars is increasing significantly, a robot car is presented with tremendous amount of combinations of possibilities in decision-making processes when it comes to the problem of how to drive in a world where human drivers

Systematic methodologies to build and train an AI safety platform dedicated for self-driving vehicles The AI platform enables behavior learning from real-world human driver, human roadagents’ behaviors, and thus generates so called long-tail events, risky behaviors, andcollisions in a scalable manner. Prediction of human uncertainty, human intentions, and rationalizing humanbehaviors

While most of the time, human drivers can predict the simple intentions of other drivers and various on-road behaviors a few seconds in advance, thus rationalizing the associated risks, such reasoning capabilities can be challenging and difficult for an autonomous driving system. Predicting driver intention and behavior is of great

Invited talk at Tsinghua University on “Applications of Deep Neural Network”. As the tech. lead of deep learning task force at NIO USA INC, I was invited to give this colloquium talk on general applications of deep neural network.