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BWSI Python Core 2022

Outline

The focus of this online section is the essentials of Python and NumPy. The outline can be broken down into the following components:

  • Learning the essentials of Python and NumPy, via Python Like You Mean It
    • Module 1: Introduction to Python
    • Module 2: Essentials of Python
    • Module 3: Essentials of NumPy

Requirements

It is beneficial for students to have some previous experience programming. While a relatively comprehensive introduction to Python is presented here, this course is not designed to serve as an introduction to programming - it is a bit faster-paced than is ideal for a first-introduction to programming.

Course Staff

Course Staff Image #1

Ryan Soklaski

Ryan Soklaski is a technical staff member of Lincoln Laboratory’s Artificial Intelligence Technologies group. There, he researches machine learning techniques that are performant under data-restricted circumstances, and works as a core developer for a lab-internal machine learning library. Prior to joining the laboratory, Ryan earned his PhD in theoretical condensed matter physics at Washington University in St. Louis. His doctoral thesis involved conducting physics simulations on high-performance computing clusters to study the physical mechanisms that drive the glass formation process in metallic liquids. Ryan’s background in education includes working as a lead-instructor for an undergraduate physics course at Washington University, and as a graduate-level teaching assistant. His interests include methods of numerical analysis, developing software in Python, and quantum mechanics.

David Mascharka

David Mascharka

David Mascharka is a research engineer at covariant.ai, where he focuses on perception-related tasks. His work is aimed at enabling robots to see, understand, and interact with the real world in complex ways. Prior to his role at covariant, David was a computer vision researcher in Lincoln Laboratory's Intelligence and Decision Technologies group. His academic interests include trust and interpretability in machine systems, acting under uncertainty, and the philosophical intersections of technology and everyday life.

David Mascharka

Petar Griggs

Petar Griggs is an undergraduate student at Harvard University, studying math and physics. He is currently a Student Technical Assistant in Lincoln Laboratory’s Artificial Intelligence Technology group, developing models to design and predict materials and their physical properties. In his course work, Petar’s interests lie primarily in stochastic processes and differential geometry, and their applications in physics and statistics. A member of CogWorks’ inaugural 2017 cohort, Petar has been involved with BWSI and CogWorks ever since.

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