Justin Lindsey is a signal finder. As he explains it, he did not start out that way. he started out as builder. His Dad was a ham radio guy and it’s just the way it was around our home. . . from home construction, to solder, to embedded controllers, to early PC building, to programing, even some early robotics. He just built things.
This building led him to MIT where his building evolved to solving complex things simply, occasionally even elegantly. He spent the 90’s designing and building fast distributed systems and the companies that housed them. He gained fundamental skills in distributed high-speed computation on large data sets. Computational speed became a passion.
Then the world changed on September 11. After which, he was asked to be the CTO for the FBI and my path shifted to applied analytics. What mattered was finding signals that could both explain what had or was happening and to predict what might happen. He began to believe that encoded in the large, diverse, and dynamic data sources of our world were traces of reality or signals that could be decoded or revealed through computing, quantitative methods, and directed efforts. These signals could tell him how to act. In many ways it felt as though they allowed him to speed through and learn from the past, slow down the present, and simultaneously consider many futures. Signal finding was a rush and thread of meaningful impact that defined him. He could see signals.
His quest is to find signals and encapsulate them into operational analytic systems to dramatically increase the impact of analytics on the world.
In This Episode You'll Learn:
- Select A Target That Matters
- A target gives meaning to signals
- Data is the raw material from which signal is extracted
- Statistical methods, machine learning, and computational techniques are the methods not the END
- With enough samples Human Behavior is probabilistically predictable
Links and Resources Mentioned in This Episode:
