Adam Drake leads technical business transformations in global and multi-cultural environments through leadership, technology, and data architecture guidance. He has served as a White House Presidential Innovation Fellow, and is an IEEE Senior Member.
Adam’s professional background spans a variety of industries including e-commerce, online travel, online marketing, financial services, healthcare, and oil and gas. His recognition as a Senior Member of the Institute for Electrical and Electronics Engineers (IEEE) places him in the top 10% of over 400,000 members in over 160 countries.
Adam has a passion for helping companies become more productive by improving internal leadership capabilities, and accelerating product development through technology and data architecture guidance. His work in technology roles since the 90s has included a wide range of technical and leadership roles, including performing in-depth technical due-diligence and funding analysis for investors, and mentoring new technical and operational executives.
His technical interests include online learning systems, high-frequency/low-latency data processing systems, recommender systems, distributed systems, and functional programming. He has a background in Applied Mathematics.
Selected talks in reverse chronological order.
Developing Your AI BS Detector (v2)
In March of 2019 I was lucky to be invited to give a lecture to Army Futures Command on AI and chose to give an updated version of my BS Detector lecture. The event was conducted in person and was also attended remotely by other commands via secure videoteleconference.
As I usually do, I have written up an accompanying article for the lecture and you can also view the corresponding slides if you find them valuable.
Data Science and Agile: Can or not?
Data Science Singapore hosted this wonderful event alongside the STACK conference organized by GovTech Singapore. The goal was to discuss the goals of Data Science teams and Agile work and development processes and how those two areas may or may not work well together. My general opinion is that they are completely compatible, though some would argue that more structured development processes do not lend themselves to the research-focused nature of many Data Science/Machine Learning teams. It was a get discussion and crowd, and the venue was unique as well!
Novel Results Considered Harmful
For full details, see my post Novel Results Considered Harmful
The appetite for applications of Artificial Intelligence (AI) has been growing at an astounding rate, producing a flood of hype but only a trickle of results. In this lecture I will present a brief survey and situation report of technical needs in industry, examples of solving common problems uncommonly well, and make the case for focusing on Intelligence Amplification (IA) instead of AI. A working example of machine learning techniques in resource constrained environments will be presented, along with performance results demonstrating non-optimized training speeds of over 350,000 requests per second on a commodity laptop.
High-Performance Machine Learning on a Single Node (a.k.a. Big Data, Small Machine)
I was honored to be invited by DevTO to give a talk at their May meetup. The organizers were keen to have someone speak about high-performance machine learning, and I was happy to oblige.
The general thesis of the talk is that, for the purposes of machine learning, setting up large compute clusters is wholly unnecessary. Furthermore, it should generally be considered harmful as those efforts are extremely time consuming and detract from solving the actual machine learning problem at hand.
To illustrate the point, I showed an online learning approach to binary classification problems using logistic regression with adaptive learning rates.
If you want more details, I expand on this topic in Big Data, Small Machine
Developing Your AI BS Detector, Rational AI in the Enterprise, Toronto, 5 April 2018
From the event overview page:
Join us as an experienced lineup of AI experts share their stories from the field, cut through the AI fog and help you build a strategy to optimize your investment in machine learning projects, companies and technologies.
Whether you’re looking to develop them as a startup or tech leader, deploy them as a business user, or fund them as an investor, you’ll leave with key insights to help you prioritize resources towards your most valuable — and solvable — AI-powered applications.
This event was put on by Steve O’Neil and his team, who all did an excellent job. The venue was standing-room only with a fantastic audience of 300-400 people.
The goal of the event was to have a discussion around “Rational AI in the Enterprise” and I think all of the speakers did a wonderful job of doing so. We wanted to present the facts as they are, on the ground, in real-world projects and situations.
My talk was aimed at a mixed audience of technologists, investors, and executives. The goal was to instill a sense of skepticism when considering if and when a given business problem dictates an AI solution. I also sought to increase people’s ability to have a critical conversation with someone pitching an AI solution to a business problem.
Machine Learning with Fully Anonymized Data, Machine Intelligence Toronto, 14 December 2017
Sharing data for any purposes, including for machine learning, is fraught with problems related to ethics, organizational policy and dynamics, and regulatory restrictions. In this talk I will discuss the topic of feature hashing and how, with a slight modification from the standard approach, we can satisfy many ethical and other requirements by training our models without any knowledge of the underlying data. In addition to presenting the ethical and regulatory benefits of building machine learning models using completely anonymized data, if time permits we may explore and discuss the performance characteristics and benefits of such an approach.
So You Want to be a Data Scientist, Singapore, 15 March 2017
A panel discussion arranged by General Assembly Singapore. For the full writeup, see this excellent article.
Considering a career as a data scientist or data analyst? Curious about what a day-to-day job in data looks like? Join General Assembly and NTUC to learn about what a day in the life entails for a data expert!
Hear from a panel of data experts as they tell all about their professional experience and see what it’s like to work as one. We will explore different options for branching into data and discuss how data experts are found and hired. We will also talk about the expectations you can have throughout your career and key areas of growth within the industry.
How to Lead and Win in a Data Life, Singapore, 6 October 2016
From the Data Science Singapore’s Meetup page:
Hi all! Adam (who spoke in June on Big Data, Small Machine) is back to share with us his thoughts on data leadership and building a successful career in the data science. We will follow his talk with a panel discussion between Adam and Koo (who is Co-founder/Practicum Director of Data Science Rex and Adjunct Lecturer at NUS-ISS, SMU & UniSIM). Kai Xin (data scientist from Lazada) will be moderating the panel.
Chief Data Officer Summit, Singapore, 30 June 2016
Organisations and boards have now realised they need someone in a position who can oversee the strategic business application of its information assets enterprise wide. The Chief Data Officer Summit will focus on this leadership function and how it’s changing businesses globally. With presentations & case studies from today’s data experts, you’ll return to the office with the inspiration to innovate your data strategy.
Download CDO Summit 2016 slides
Data Science Singapore, Singapore, 16 June 2016
I gave a tech talk to a wonderful audience at from Data Science Singapore. Many thanks to the organizers and the host (Singapore Management University) for their efforts.
Datasets too large to fit into RAM are increasingly common, even in simple environments like Kaggle competitions. Adam will introduce some ways of dealing with this issue in addition to demonstrating some scalable machine learning techniques which are production ready and capable of processing over 10s of thousands of events per second on an old laptop.
Download DSSG June 2016 slides
Aon Center for Innovation and Analytics, Singapore, 16 June 2016
I had the honor of participating as an expert panelist on the topic of Corporate Innovation & the Road to Entrepreneurship. The internal-only event attracted approximately 80 attendees from Aon’s global and Asia Pacific executive team in addition to some of their up and coming technology and business leaders. It was a wonderful event and the organizers did a fantastic job.
Free and Open Source Software Asia (FOSSAsia), Singapore, 19 March 2016
FOSSASIA is the premier Free and Open Source technology event in Asia for developers, start-ups, and contributors. Projects at FOSSASIA range from open hardware, to design, graphics and software, big data, and internet of things. FOSSASIA was established in 2009. Previous events took place in Cambodia and Vietnam.
Chairman, Moderators, Free and Open Source Software Asia (FOSSAsia), Singapore, March 2016
I served as the chair for the moderators of the event, covering all tracks and presentations. I provided training, guidelines, and support in order to ensure that all of the lectures were kept to proper time constraints and questions shepherded appropriately. On time. On target.
Big Data and Analytics Innovation Summit, Singapore, 2 March 2016
Download Big Data and Analytics Innovation Summit slides
Chief Data Officer Summit, Singapore, 18 June 2015
Download CDO Summit 2015 slides
Big Data Singapore, Singapore, 4 March 2015
I don’t have the slides and no photos or videos of this one, unfortunately.
Edinburgh Parallel Computing Center (EPCC), Edinburgh, 2 February 2015
This event was organized by the EPCC to discuss data topics in prominent companies and industrial research centers in order to share experience on high-performance data processing.
Big Data Berlin, Berlin, 28 August 2014
Download Big Data Berlin slides
PyData Berlin, Berlin, 25-27 July 2014
Panel Discussion: The Challenges and Frontiers of Data Science in Europe