ESP Biography



DARYL SEW, Cornell sophomore studying Computer Science




Major: Computer Science

College/Employer: Cornell University

Year of Graduation: 2017

Picture of Daryl Sew

Brief Biographical Sketch:

I'm a sophomore studying Computer Science at Cornell University's College of Engineering with a passion for artificial intelligence/machine learning and game design. I grew up in New York City and attended Stuyvesant High School, where I had the pleasure of participating in FIRST robotics' FTC and FRC programs as well as mentoring an FLL team. Here at Cornell, I've joined the software subteam of the Autonomous Underwater Vehicle project team where I've been working on computer vision stuff I've also started doing research with the Autonomous Systems Lab where I've been working on sensor calibration and data visualization.

I've worked on many interesting machine learning and computer vision problems across my roles at electronic parts search engine startup Octopart, electric carmaker Tesla Motors, and Cornell's autonomous robotics labs, and I hope to get you excited about computer science and robotics with a glimpse into what these fields are capable of accomplishing.



Past Classes

  (Look at the class archive for more.)


Machine Learning & Audio Analysis with Python in Splash 2014 (Nov. 22 - 23, 2014)
Machine learning is a field of computer science that concerns writing programs that can make and improve predictions or behaviors based on some data. The applications of machine learning are very diverse - they range from self driving cars to spam filters to autocorrect algorithms and much more. Using scikit-learn, an open source machine learning library for Python, we'll cover reinforcement learning (the kind used to create artificial intelligence for games like chess), supervised learning (the kind used in handwriting recognition), and unsupervised learning (the kind eBay uses to group its products). We'll then cover audio analysis through Fourier transforms with numpy, an open source general purpose computational library for Python, and we'll use our newfound audio analysis and machine learning skills to write very basic speech recognition software. Applications of machine learning to the fields of multitouch gesture recognition and computer vision will also be discussed, drawing from my work at Tesla and research on self driving cars & autonomous submarines.


Machine Learning & Audio Analysis with Python in Splash! 2013 (Nov. 23 - 24, 2013)
Machine learning is a field of computer science that concerns writing programs that can make and improve predictions or behaviors based on some data. The applications of machine learning are very diverse - they range from self driving cars to spam filters to autocorrect algorithms and much more. Using scikits-learn, an open source machine learning library for Python, we'll cover reinforcement learning (the kind used to create artificial intelligence for games like chess), supervised learning (the kind used in handwriting recognition), and unsupervised learning (the kind eBay uses to group its products). We'll then cover audio analysis through Fourier transforms with numpy, an open source general purpose computational library for Python, and we'll use our newfound audio analysis and machine learning skills to write very basic speech recognition software.