Impact: Connecting Computational Math Research with Practice and in turn, Practice with its Consequences
Abstract: Efficient machine learning models play a critical role in enabling on-device experiences. However, there is a trade-off between computational efficiency, such as latency, memory footprint, and power consumption, and model accuracy, where increased compression leads to lower accuracy. In this presentation, we will review recent approaches to optimize the efficiency-accuracy trade-off. Specifically, we will explore how we can harness the power of over-parameterization to facilitate learning and create more accurate and compact models. Join us to discover the latest strategies that can enhance on-device experiences with efficient machine learning models.
Bio: Hadi Pouransari is a Machine Learning Researcher and tech lead at Apple's MIND team. His interests include distributed large-scale training, optimization, and efficient models. Previously, he was a senior ML engineer at Apple Special Project Group. He earned his PhD in 2017 from ICME under the supervision of Professors Darve and Mani, with a PhD minor in Computer Science. His thesis on fast numerical linear algebra with applications to scientific computing was awarded the Juan Simo thesis award. Hadi completed his dual degrees in Computer Science and Mechanical Engineering from Sharif University in 2011.