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With Braxton McKee (Founder, Ufora) & Jeremy Freeman (Neuroscientist).
Tue, Jun 28, 2016 @ 07:00 PM   FREE   Rise NYC, 43 W 23rd St, 2nd Fl
 
   
 
 
              

    
 
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EVENT DETAILS
Agenda

Scaling Machine Learning Systems - (Braxton McKee, CEO & Founder, Ufora)

Braxton is the technical lead and founder of Ufora, a software company that develops Pyfora, an automatically parallel implementation of the Python programming language that enables data science and machine-learning at scale. Before founding Ufora with backing from Two Sigma Ventures and others, Braxton led the ten-person MBS/ABS Credit Modeling team at Ellington Management Group, a multi-billion dollar mortgage hedge fund. He holds a BS (Mathematics), MS (Mathematics), and M.B.A. from Yale University.

Braxton will discuss scaling machine learning applications using the open-source platform Pyfora. He will describe both the general approach and also some specific engineering techniques employed in the implementation of Pyfora that make it possible to produce large-scale machine learning and data science programs directly from single-threaded Python code.

State of Modern Neuroscience + Machine Learning - (Jeremy Freeman)

Jeremy Freeman is a neuroscientist working at the intersection of biology and technology. He wants to know how the brain works, and how understanding it can change the design of intelligent systems. He's passionate about open source and open science, and he helps develop a variety of tools for data analysis, visualization, collaboration, and reproducibility, including binder (http://mybinder.org/), thunder (http://thunder-project.org/), and lightning (http://lightning-viz.org/). He leads a group at Janelia Research Center (https://www.janelia.org/), and collaborates actively with scientists, developers, and designers across a range of fields.

Jeremy is going to describe the state of modern neuroscience, and how it might relate to machine intelligence. After more then a century of studying the brain, we know frustratingly little about how it works. At the same time, we're building artificial systems with remarkable capabilities and performance in an ever growing variety of domains. How can these two worlds inform one another? What are the limits of current machine intelligence? What do we not yet understand about biological intelligence? What tools and concepts do we need to make progress? Jeremy won't answer these questions, but will try to capture our current thinking, and show why he is excited where these two fields are heading.

Other links:

http://jeremyfreeman.net/

http://thefreemanlab.com/

Rapid Q/A

Social event after: TBD



See you at the event!

- Rizwan, Maryam, Kishan, & Matt
 
 
 
 
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