- DDoS: a 20-year journey from compromised workstations to IoT attacks
- Building noise robust machine listeners with data and inspiration from humans
- Driving Enterprise Transformation with Virtual & Augmented Reality
- Eager Execution in TensorFlow
- Applied AI Techniques
- Energy Management as a Service (EmaaS): Design, Analysis and Realization
- Slides for talk on Ontology-based Classification and Faceted Search Interface for APIs
- Ontology-based Classification and Faceted Search Interface for APIs
- NYC, Media and Technology: What’s Hot
- Bio-inspired Computation Approach for Tumor Growth with Spatial Randomness Analysis of Kidney Cancer Xenograft Pathology Slides
Monthly Archives: November 2014
Software Engineering Seminar Series:
Software at Scale
Computer Systems Technology
New York City College of Technology
300 Jay St.
Brooklyn, NY 11201
Tuesday, November 25 1-2pm
Light refreshments will be served!
Can your app handle an appearance on the front page of TechCrunch? In this talk, we’ll compare common design patterns and strategies for building software that can scale to millions of users and beyond, such as concurrency, caching, CDNs, compression, immutability, sharding, partial ordering, and read optimization. We’ll discuss why the REST paradigm has become such a natural fit for building web and app backend services, as well as how to test your app for scalability so you can be confident that it will survive an unexpected spike in traffic.
Michael Barnathan is a Director of Engineering at Amplify Access, which deploys educational tablets to K-12 schools across the country. Prior to joining Amplify, Michael founded Clipless, the first contextual deals startup, which survived two appearances on the front page of TechCrunch, gained massive traction in a short period of time, and was acquired 8 months from founding. Michael’s prior experience also includes a Senior Software Engineer position at Google, where he worked on their build pipeline, creating software to compile millions of lines of source code in under 1 second. He holds a Ph. D. in machine learning from Temple University, with a particular emphasis on using computer vision techniques to automatically diagnose medical scans. In his spare time, Michael enjoys genetic engineering, piano, and composition.