I was born in Northampton, MA while my parents were attending the University of Massachusetts Amherst. We moved back home to Staten Island, NY when I was 2 and I grew up there, attending P.S. 4 elementary school, I.S. 75 intermediate school, and St. Joseph by the Sea High school before returning to western MA in 2000 to attend Amherst College.

In the summer of 2004 I received a B.A. in Computer Science and Psychology from Amherst College under the advisorship of Professor Catherine McGeoch (CS) and Professor Sarah Turgeon (Psych). My senior thesis in Computer Science was completed under the supervision of Professor Andrew Barto at UMass Amherst. For my thesis, I developed a computational model of the partial reinforcement extinction effect observed in many animal learning studies involving schedules of partial reinforcement.

During that time I was also involved in research with Professor John Moore and  Dr. Robert Polewan of the UMass Department of Neuroscience and Behavior. We developed a human eyeblink conditioning paradigm (the Cartesian Reflex Project) for studying the effects of different stimuli (e.g., faces vs. geometric shapes) on cognitive processing time in traditional classical conditioning tasks with voluntary unconditioned responses. My primary contribution to this endeavor was the development of the hardware/software interface and protocol design software used in the paradigm.

I joined the Department of Computer Science at the University of Massachusetts Amherst in the fall of 2004 and began research as a member of the Autonomous Learning Laboratory under the advisorship of Professor Andrew Barto. I worked both as a Teaching and Research Assistant for the department throughout that time. I received an M.S. in Computer Science in 2008 and continued work in the Ph.D. program. My doctoral research focused on using reinforcement learning algorithms to model intrinsically motivated behavior in humans and animals; i.e., behavior that is rewarding for its own sake, rather than because it solves a specific problem. The algorithms developed in that work were primarily used to illustrate methods for intrinsically motivated learning of skill hierarchies in artificial agents interacting with structured environments.

In May of 2010 I joined HitPoint Inc., a video game development studio in Hatfield, MA, as a part-time software engineer. In August of 2011, I joined full-time as a Senior Engineer and worked as sole engineer on a few iPhone titles and as lead engineer on the episodic puzzle adventure series Adera, which HitPoint created for Microsoft’s launch of Windows 8. In 2013 I became Director of Engineering at HitPoint and led a team of a dozen engineers on various independent and third-party titles for companies including Microsoft, Disney, and EA.

In April of 2015 I decided to leave HitPoint to focus on finishing up my PhD, but was immediately presented with a job opportunity at a new machine learning startup in San Francisco called OSARO. OSARO was working on interesting problems in machine learning and reinforcement learning, which meshed perfectly with my academic background. I moved to San Francisco in June of 2015, finished up my PhD remotely and defended my thesis that December.

I worked for several years as a research engineer at OSARO, helping to build the team and software infrastructure that went into their early product offerings. We solved several hard problems in computer vision and dexterous robotic manipulation, with applications in industrial manufacturing and supply chain logistics. It was a treat to work with such a talented team on these cutting-edge challenges, and I’m excited to watch OSARO continue to grow and succeed.

In 2019, I started missing the time I spent in the digital art and storytelling spaces at HitPoint, but also knew that I wanted to continue innovating in applied machine learning. I thought about ways to combine the two and began exploring applications of generative models to synthetic media creation. After some experimentation, I decided to start a company building tools and platforms for making media creation more accessible using machine learning.

After a long search, I found the perfect co-founder and we started work on our current endeavor, Storytime. We raised a small VC round in 2021 and are now growing our team to help build the first public version of our product, which we’ll release in 2022. I’m super excited to be building something new in such a fun and fast-growing space, and can’t wait to share our products with creative storytellers everywhere.

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