When we design algorithms or implement them into computing systems, we rarely think about the policies that they instantiate. We rarely think beyond trite generalizations, of the countless users who may be at the receiving end of the vagaries of our algorithms. If our project becomes immensely successful, then we will count our users in […]
Category: Computer Science-y
Is Computing Innovation Getting Harder?
The angst, or perhaps irritation, that we feel at times about the rate of innovation was captured pithily by Peter Thiel, “We wanted flying cars, instead we got 140 characters.” I am going to confine my thoughts to the world I know best, innovations in the field of computing. Many of us, inside the ivory […]
Your Job Can Be Done Better By My Algorithm
Citation Reams have been written on jobs being replaced by algorithms and by robots running on algorithms. Much of the most impactful writing has come from economists — my two favorite ones are Joseph Stiglitz and Daron Acemoglu, or for a more lay person perspective read this NYTimes article that covers their work. Some of […]
Three algorithms to live by: LSTM, FedAverage, C-W
Citation We come across many algorithms in our education and work. Here I look at three relatively recent ones from the area of Machine Learning (ML), and more specifically from my vantage point of reliability and security of ML. There have been reams written on each, at various levels of technical depth. So obviously I […]
Big Data and Security: Oxymoron?
Citation Big data technologies have dramatically changed the world we live in, and in double quick time. And you know that unless you have been living under a Martian rock. We take it for granted in many of our daily interactions — in our personal lives as well as at work. Big data technologies fuel […]
Is Computing a Team Sport?
Most of us, in the field of computing, like to believe we are good team players. This seems not just the politically correct line, but also makes our work more feel more enjoyable [1]. I am encompassing in my discussion a fairly wide swath, those who are in research, both academic and industrial, in the […]
Short Take: Continuous Computer Vision on Mobiles
The area of continuous computer vision algorithms that can run on mobile or embedded or edge or take your pick of resource-constrained platform, has seen a great outpouring of work. This post is a look at how this field has been marching along, seen from the eyes of a computer systems person, as opposed to […]
Computer Systems Research: The Joys, the Perils, and How to Count Beans Well
This post was first written for the ACM SIGARCH blog and appeared there on Nov 30, 2020. Thanks to Rajeev (Balasubramonian, University of Utah) for instigating this post and then guiding with helpful prods and suggestions. Citation This post is broadly meant for computer systems researchers, and that is a big tent, including members of […]
Does Computer Systems have a Reproducibility Problem?
And Should you Care? This is about the reproducibility of results in Computer Systems. The papers that we shed blood, sweat, and tears for getting into our hyper-competitive conferences (definitely the latter two, the first is not widely documented). Are they helping us progress as fast and as efficiently as they could? Are our software […]
Internet at 50 Years: Hills to Climb
Citation In this second of my two part reflection on the internet at its 50th birthday, I turn my eyes toward three challenges the medium has to solve. I then list from a bird’s eye view some of the most important solutions being investigated in academia to fix these. In customary braggadocio, I include some […]
Distant Whispers from an Academic Engineer's World 