See "https://sps-it.ucsd.edu/symposium.html" for program schedule and register ASAP!

Dear Colleagues,

SPS-IT is presenting a symposium highlighting how UC San Diego researchers can advance their work through shared computing resources, streamlined lab data practices, and access to national platforms. In today’s challenging funding environment, discover how you can leverage community, free, and subsidized resources to sustain research, scale discoveries, and accelerate innovation. The event will conclude with a hands-on NVIDIA GPU workshop—bring your laptop and explore new ways to accelerate your science.

SPS-IT Research IT Symposium
Resources and Services Supporting UC San Diego
Location: UC San Diego – Natural Sciences Building Auditorium (NSB 1205)
Date/Time: Monday, September 15, 2025 | 8:45 AM – 1:00 PM

Register now to secure your spot—seating limited to 90 participants.

Program Highlights:

• Welcome & Opening Remarks – Christine Hrycyna, Dean, School of Physical Sciences

• Scientific Data Workflows & FAIR Principles – Tools and strategies for instruments and repositories, including options with free or subsidized costs for long-term storage

• SecureConnect – Strengthening research IT security and productivity

• Compute Everywhere – Access to NAIRR, SDSC systems (Expanse, TSCC, Voyager, Cosmos), the National Research Platform, and other free or subsidized computing resources

• Hands-On Workshop: NVIDIA & Applied Data Systems – Four ways to start GPU computing:

  • GPU-enabled applications

  • Drop-in accelerated libraries

  • Portable programming models

  • CUDA for maximum control

Morning refreshments and a networking lunch are provided.

As multi-dimensional data arrays, tensors play a central role in data science and machine learning. In this talk, I will discuss efficient tensor algorithms and their applications in data science. In the first part of the talk, I will introduce a stochastic mixed-precision method for large-scale tensor computations. By leveraging block sampling together with low-precision arithmetic, this approach significantly reduces both memory usage and computational cost for gradient evaluations, thereby accelerating tensor decomposition algorithms. Neural networks have grown rapidly in size in recent years, leading to substantial memory and computation costs that pose major challenges for training and deployment. In the second part of the talk, I will discuss how tensor methods and tensor decompositions can be used to compress large neural networks, thereby accelerating both training and inference.