Lester Mackey

Affiliation: Microsoft Researcher

Lester Mackey grew up in Long Island, New York; went to college in Princeton, New Jersey; moved to Berkeley, California, to pursue a PhD; spent a year as a postdoc and three years as a professor at Stanford University; and now lives outside Boston, Massachusetts.

His interest in statistics unfolded while a student immersed in the Ross Mathematics Program, an intense eight-week summer program in number theory designed to expose teenagers to proof-based mathematics. There, Mackey learned about axioms, induction, and rigorous proof. He also learned to think deeply about simple subjects such as integers, fractions, and arithmetic. He even found a new way to learn, as each student taught him or herself the material by working through a daily sequence of problem sets. Mackey was exposed to a side of mathematics he had never encountered and loved it.

The following year, Mackey had his first taste of mathematical research at the Research Science Institute, where he enrolled in a free summer program that allowed high-school students to learn the skills and tools of academic research while conducting independent research.

Mackey was encouraged to attend both programs by Chrysi Notskas, a teacher from his school district who took it upon herself to mentor him.

Mackey’s interest in machine learning, data science, and statistics intensified when he joined his friends in the Netflix Prize competition. During his senior year in college, his buddies David Weiss and David Lin burst through his door with the Slashdot announcement: “Build a Better Netflix, Win a Million Dollars?” They decided to compete, and that’s how he learned as much as he could about machine learning. In the end, his team lost the million-dollar prize by 20 minutes (they tied the winning team, and the tie-breaker was time of submission), but the experience sparked their imaginations and opened the door to the world of machine learning for Mackey.

“Every day,” MacKey noted, “I reflect on my good fortune at finding a field (machine learning) and a job (researcher) that I truly love. My work scarcely seems like work, as it’s exactly what I would be doing even if no one was paying me.”