Colloquium Archive

Live Ask Me Anything with Professional Programmer and Businessperson

Cameron Tully-Smith
Senior Software Engineer


An SSU CS alum (who returned to get his MBA) that grew up programming, Cameron Tully-Smith has worked in a wide range of roles spanning early-stage startups to the largest multinationals in multiple industries.  In addition to writing great software, he is passionate about empowering people, being on great teams and coming up with unconventional solutions.  Come by and ask him anything you want, from what it's really like to work in various roles in the industry to what comes up in many interviews (from the hiring manager as well as the interviewee perspectives) and anything else you'd like.  He enjoys unusual, well thought out questions as well as spur of the moment discussions.  Outside of CS, his latest hacking includes taking Wilderness First Aid training and running ultramarathons which has so far resulted in his qualifying for the Western States Endurance Run (100 mile race) and running a 24-hour race.

The Snap! (Build Your Own Blocks) Programming Environment

Dan Garcia
University of California, Berkeley


Snap! (formerly BYOB) is a visual, drag-and-drop programming language. It is an extended reimplementation of "Scratch" (a project of the Lifelong Kindergarten Group at the MIT Media Lab) that allows users to Build Your Own Blocks. It features first class lists, first class procedures, and continuations. These added capabilities make it suitable for a serious introduction to computer science for high school or college students. This talk (usually offered as a workshop) will introduce the language, and walk users from their first "mobile app" authorable in 90 seconds through the vast array of incredible features the language has to offer.

Beyond Restart: Checkpointing for the Exascale Era

Rebecca Hartman-Baker
User Engagement Group Leader
National Energy Research Scientific Computing Center


The National Energy Research Scientific Computing Center (NERSC) is the mission high-performance computing center for the United States Department of Energy Office of Science (DOE-SC). NERSC operates cutting-edge, large-scale supercomputers for users performing simulations and data analysis in science areas of interest to DOE-SC. Experimental data analysis, in which data from large scientific instruments such as telescopes or light sources is computationally analyzed, is a growing portion of the compute workload at NERSC. Many of these scientific instruments increasingly require real-time or fast computing turnaround. NERSC's challenge is to support this urgent workload within its existing operations. Checkpoint/restart (C/R) can enable this new workload pattern and address other operational challenges, such as resiliency. In this presentation, we describe our efforts to create a robust offering of checkpointing approaches and to simplify and incentivize their use, in order to optimize the user experience, minimize wasted cycles, and maximize compute-system utilization.

Distance functions on computable graphs

Jennifer Chubb
Associate Professor
University of San Francisco


An infinite graph---one made of nodes and edges---is computable if there is an algorithm that can decide whether or not a given pair of nodes is connected by an edge.  So, for example, the internet is a computable graph which is, for all intents and purposes, infinite.  Now, given two nodes on a connected, computable graph, a natural question to ask is, What is the length of the shortest path between them, i.e., the distance between the nodes?  Of course, for a connected graph we can always find such a path and determine its length, but the question of finding a shortest path is harder, and is not in general something we can compute for infinite computable graphs.

In this talk, we will see what it means for something to be non-computable via a classic example called the halting problem.   Next we will see that it's possible to encode non-computable information into even very simple, computable mathematical objects.  Finally, we will see how non-computable information can be encoded into the distance function, the function which outputs the shortest distance between two nodes of a given graph.  So, the answer to What is the shortest path between two nodes?  Well, we may never know for sure.

This work is joint with Wesley Calvert and Russell Miller.

Deep Learning for Medical Imaging

Geetha Chauhan
AI Practice Head / CTO


The talk covers use cases, special challenges and solutions for Deep Learning for Medical Image Analysis. You will learn about:

  • Use cases for Deep Learning in Medical Image Analysis
  • Different DNN architectures used for Medical Image Analysis
  • Special purpose compute / accelerators for Deep Learning (in the Cloud / On-prem)
  • How to parallelize your models for faster training of models and serving for inferenceing.
  • Optimization techniques to get the best performance from your cluster (like Kubernetes/ Apache Mesos / Spark)
  • How to build an efficient Data Pipeline for Medical Image Analysis using Deep Learning
  • Resources to jump start your journey - like public data sets, common models used in Medical Image Analysis