Industry Experts

We’ve worked with experts from the industry to put together course materials that will introduce you to the exciting and fast moving world of quant trading!

Jonathan Larkin

Jonathan is an expert in quantitative investment strategies. He has a ton of experience! He was Global Head of Equities at Millennium Management and Co-Head of Americas Equity Derivatives Trading at JPMorgan!

Gordon Ritter

Gordon Ritter is a Professor at NYU Courant and Tandon, Baruch College, and Rutgers. He is an elite buy-side quantitative trader and portfolio manager, and was named Buy-Side Quant of the Year 2019 by Risk.net.

Justin Sheetz

Justin has held a variety of roles; he was an investment strategist in BlackRock’s Scientific Active Equity Group, and most recently he was a quant research analyst at MUFG/HighMark Capital.

Instructors

Let’s introduce you to our instructors, in order of their appearance:

Arpan Chakraborty

Arpan is a computer scientist with a PhD from North Carolina State University. He teaches at Georgia Tech (within the Masters in Computer Science Program), and is a coauthor of the book Practical Graph Mining with R.

Juan Delgado

Juan is a computational physicist with a Masters in Astronomy and it is currently finishing his PhD. He previously worked at NASA developing space instruments and writing software to analyze large amounts of scientific data using machine learning techniques.

Luis Serrano

Luis was formerly a Machine Learning Engineer at Google. He holds a PhD in mathematics from the University of Michigan and a Postdoctoral Fellowship at the University of Quebec at Montreal.

Mat Leonard

Mat is the product lead for Udacity's School of AI and a former content developer as well. He received his PhD in physics from UC Berkeley where he spent most of his time as an experimental neuroscientist. During that time, he gained a love of Python, machine learning, neural networks, and Bayesian statistics. At Udacity, Mat has built courses on Hadoop, data visualization, Python tools such as Conda and Jupyter, and deep learning.

In this course, Mat will be teaching you the basics of PyTorch and how to use new features for deploying models.

Cezanne Camacho

Cezanne is a computer vision and deep learning expert, with a Masters in Electrical Engineering from Stanford University. As a former genomics and biomedical imaging researcher, she’s applied machine learning to the field of medical diagnostics. She's mostly interested in how humans reason and how to use that logic to become a better teacher and programmer!

In this course, Cezanne will teach you about using PyTorch to define and train convolutional and recurrent neural networks.

Liz Otto Hamel

Liz completed her PhD in applied physics and experimental neuroscience from Stanford University, where she used optical and analytical techniques to study activity patterns of large ensembles of neurons. She was formerly a data science instructor at The Data Incubator.

Brok Bucholtz

Brok has a background of over five years of software engineering experience from companies like Optimal Blue. Brok designed several coding projects that students build in Udacity’s Deep Learning, Self-Driving Car, and Artificial Intelligence Nanodegree programs.

Eddy Shyu

Eddy worked at BlackRock, Thomson Reuters, and Morgan Stanley, and has an MS in Financial Engineering from HEC Lausanne. Eddy also taught data analytics at UC Berkeley.

Parnian Barekatain

Parnian is a self-taught AI programmer and researcher. Previously, she interned at OpenAI on multi-agent Reinforcement Learning and organized the first OpenAI hackathon. She also runs a ShannonLabs fellowship to support the next generation of independent researchers.