Name: John Allspaw on Problem Detection
Start: 2018-10-15T18:30:00-04:00
End: 2018-10-15T21:00:00-04:00
Location: Two Sigma

**Please Note: We cannot accommodate +1s on your RSVP. Everyone must register and RSVP on their own.**

We are excited to host John Allspaw, Principal Researcher at Adaptive Capacity Labs, presenting Problem Detection (https://www.researchgate.net/publication/220579480_Problem_detection) by Gary Klein, Rebecca Pliske, Beth Crandall, and David Woods.

Talk:

Published in 2005 in the journal Cognition, Technology and Work, "Problem Detection" explores the "process by which people first become concerned that events may be taking an unexpected and undesirable direction that potentially requires action." While this paper primarily centers on empirically rebutting previous theories of how problems are detected, it also puts forth many important observations and concepts for software engineering to pay close attention to. This talk won't just be a re-statement of the paper's core views; I will place these into a software engineering and operations context and connect them to SRE and DevOps worlds in ways that may be consequential.

The paper's authors are Gary Klein, Rebecca Pliske, Beth Crandall, and David Woods.

Paper: https://www.researchgate.net/publication/220579480_Problem_detection

Bio:

John Allspaw has worked in software systems engineering and operations for over twenty years in many different environments: biotech, government, online media, social networking, and e-commerce. John’s publications include the books The Art of Capacity Planning (2009) and Web Operations (2010) as well as the forward to “The DevOps Handbook”. His 2009 Velocity talk with Paul Hammond, “10+ Deploys Per Day: Dev and Ops Cooperation” helped start the DevOps movement.

John served as CTO at Etsy, holds an MSc in Human Factors and Systems Safety from Lund University, and is currently a Principal Researcher at Adaptive Capacity Labs.

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Lightning Talk:

Lydia Gu on A Tutorial on Thompson Sampling

Abstract:

Multi-armed bandits is an online machine learning framework which trades off exploitation, selecting the current best choice, and exploration, gathering data on unknown options. One strategy for implementing this tradeoff is Thompson sampling. First proposed in 1933 in the context of clinical trials, Thompson sampling was mostly forgotten in academic literature until the recent decade. Around 2010, a couple of papers demonstrated empirically its competitive performance, prompting a flurry of academic work. In this lightning talk, we will give an overview of the multi-armed bandits problem and the Thompson sampling algorithm, and see how it has been used by companies for personalization.

Paper: https://arxiv.org/pdf/1707.02038.pdf

Bio:

Lydia Gu is a tech lead at B12, a startup that's changing the way websites are made using humans + AI. She has an MEng from MIT and lives in New York, where she enjoys rock climbing, escape the rooms, and escaping the city.

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Details:

Doors open at 6:30 pm; the presentations will begin right around 7:00 pm; and, yes, there will be refreshments of all kinds and pizza.

You'll have to check-in with security with your Name/ID. Definitely sign-up if you’re going to attend–unfortunately people whose names aren’t entered into the security system in advance won’t be allowed in.

After John's presentation, we will open up the floor to discussion and questions.

We hope that you'll read some of the papers and references before the meetup, but don't stress if you can't. If you have any questions, thoughts, or related information, please visit #pwlnyc (https://paperswelove.slack.com/messages/pwlnyc/) on slack (http://papersweloveslack.herokuapp.com/), our GitHub repository (https://github.com/papers-we-love/papers-we-love), or add to the discussion on this event's thread.

Andrew Gross

Darren Newton

David Ashby

Andrew Turley

Lydia Gu

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