Picture of Ramesh Sridharan

About me

I'm an engineering leader, researcher, and educator working on the potential of machine learning, data science, and artificial intelligence to improve people's lives.

I'm currently a Machine Learning Architect at Manifold, where I lead AI development projects for leading companies. My team and I apply machine learning to solve critical business problems for our customers, and deliver production-grade AI solutions.

I'm also involved in a number of teaching and education initiatives. Currently, I'm teaching UC Berkeley's capstone data science course, Data 102 with my colleague Yan Shuo Tan. In the past, I've also taught Berkeley's introductory data science course, Data 8.

You can also find me on github and LinkedIn.

A brief history

Before Manifold, I was Head of Machine Learning at Vidado (formerly known as Captricity), where I led a team of machine learning engineers building algorithms to digitize and make sense of data from paper forms and documents.

I've volunteered with several organizations using education to help bring about positive social change. Most recently, I've worked as a tutor for the Prison University Project, helping to provide a rigorous education to incarcerated people. I've also worked as an instructor for MEET, bringing together Palestinian and Israeli youth through education in computer science and entrepreneurship.

In the not-so-distant past, I completed my PhD in the Medical Vision Group at CSAIL, where I was supervised by Polina Golland. For my PhD, I worked on applications of machine learning and artificial intelligence to problems in medical imaging. I was funded by the NSF Graduate Research Fellowship.

In the more distant past, I was an undergraduate in EECS at UC Berkeley. While I was there I worked with Stan Klein's Visual Processing Lab on signal processing in vision science, and with Dan Garcia and Brian Harvey on a (now-defunct) curriculum development project involving parallelism in introductory computer science courses.


* Indicates equal contribution