Monday, February 6, 2023 | 5:00pm – 7:00pm ET
Speaker: Alex Sludds, MIT PhD Candidate
Veiw a recording of the thesis defense here.
Abstract: In this virtual thesis defense Alexander Sludds will discuss an innovative way for edge devices to perform advanced machine learning by using the cloud to stream data, which will allow devices with limited power and memory to compute at high speeds.
Speaker Bio: Alexander Sludds, is an MIT PhD student in integrated photonics. His research interests include the application of optics to data management and access for computation as well as novel techniques for interfacing CMOS and Photonic Systems. Most recently his research has focused on large scale Silicon Photonic systems demonstrating Optical Neural Networks more energy efficient and faster than possible with electrical CMOS technology. In practice of this research Sludds has lead large system tapeouts in commercial Photonic CMOS foundry processes. Sludds received a Bachelors of Science in Electrical Engineering and Computer Science from MIT in 2018.
Explore
AI Tool Generates High-Quality Images Faster Than State-of-the-Art Approaches
Adam Zewe | MIT News
Researchers fuse the best of two popular methods to create an image generator that uses less energy and can run locally on a laptop or smartphone.
New Security Protocol Shields Data From Attackers During Cloud-based Computation
Adam Zewe | MIT News
The technique leverages quantum properties of light to guarantee security while preserving the accuracy of a deep-learning model.
New Technique Helps Robots Pack Objects into a Tight Space
Adam Zewe | MIT News
Researchers coaxed a family of generative AI models to work together to solve multistep robot manipulation problems.