Podcast MIN

Commercial ML Opportunities Lie Everywhere

Michael runs SFL Scientific, a data science consulting organization that he defines as having all the fun of working on projects for other companies looking to solve challenges. The group has been the Nvidia Partner of the Year, a huge distinction for the work SFL does. Their goal is to solve issues in a novel way, which makes it perfect for utilizing the power of ML. Today they work on things from surgical transplant diagnostic issues to drones and autonomous vehicles, and beyond.

SFL is full of former STEM professionals which makes healthcare an easy fit. They aim to offer novel solutions to traditional problems. An example is total heart transplants in children, which is, in many ways, archaic in its approach. Michael describes it as more of an art than a science, which is where SFL comes in. They want to take it from art to science to take much of the guesswork and natural skill out of the process. This isn’t to say transplants are going blind, they have facets and stats they utilize to determine much of the work of transplants, but SFL offers more concrete data points to optimize the choices of organs to transplant and whom to transplant them to. This ultimately could save lives and cut down the 30% of organs out there that are wasted because of lack of sophistication in transplant matching. In the private sector, Michael sees a continual problem of lack of belief and trust in ROI when it comes to investing in ML. AutoML might win a Kaggle competition, but it can’t help investigate nuanced issues and solutions that help companies and their employees grow. Michael describes AutoML like a calculator – it’s a tool but not itself the solution to a novel challenge.

In the public sector, companies not out to make a profit, are lagging in the adoption of technology. The US public sector isn’t nimble — thinking about programs in a matter of decades and losing the progress of tech that can happen in a matter of months. SFL has tried to help to adopt private-sector technological solutions into the public sector in situations that are mission-critical. Michael is passionate about doing work “at the edge”, where access to tools is limited and tools need to be able to function autonomously. SFL Scientific sees this kind of work as incredibly important for the Department of Defense who needs drones and unmanned vehicles that can potentially function on AI. One of the issues we don’t have the human resources for this work is the lack of capital and revenue available when someone can make 10x in the private sector. But, the people I personally know who work in this sector are incredibly passionate, incredibly skilled, and have job security across decades because of the slow and steady pace of progress.

Michael describes his usual every day as “crazy”. He describes it as a bifurcated focus: how do we build an organization and how do we have fun in work execution? Michael focuses on the former more often throughout his day, where he networks and “sells” their work which is deeply technical and incredibly varied. Michael’s responsibility is keeping up on all trends in the tech space and bringing them back to the team that can then apply them in the solutions they’re seeking out for their clients. The good news is, SFL Scientific is hiring across many positions and Michael shared what they’re looking for. That’s people who can wear multiple hats and be incredibly adaptive in research and approach. You need curiosity and the ability to interact with clients as well as technical skills in Python, machine learning, and general data modalities.

We shifted into looking at Michael’s professional journey. He participated in the work at the CERN’s Large Hadron Collider, working in data science in an academic setting. He was part of the analysis team that helped confirm the discovery of the Higgs boson. After his academic career, Michael wanted to take these skills and knowledge to real-world, on the ground problems, taking the same techniques that solved fundamental physics problems to solve problems in medicine, farming, and other seemingly mundane issues.

Michael is excited about the booms in information and data sharing that can make difficult problems easier to tackle. COVID-19 has pushed some of this work, showing the need for rapid information sharing and the need to reorganize and reevaluate the red tape we put around data sharing when lives are on the line and the clock is ticking.
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