[bioundgrd] FW: 22.S095 Radiation and Life; 22.S097 Applied Machine Learning for Nuclear Engineers

[Joshua Stone]

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From: Brandy J Baker <brandyb at mit.edu<mailto:brandyb at mit.edu>>
Subject: 22.S095 Radiation and Life; 22.S097 Applied Machine Learning for Nuclear Engineers
Date: November 5, 2020 at 4:09:07 PM EST


22.S095       Radiation and Life – Applications of Radiation Sources in Medicine, Research, & Industry
What are the myriad of uses of radiation sources? How do we control their uses to protect workers? In this discovery subject, students will be introduced to the basics of ionizing and non-ionizing radiation, radiation safety and protection and an overview of the variety of health physics applications especially as it pertains to the medical field and to radioactive materials research in academia. This class will cover the basic physics of ionizing and non-ionizing radiation, known effects on the human body and the techniques to measure those effects. This class will introduce common radiation-based medical imaging techniques and therapies. Students will engage in a variety of hands-on projects, demonstrations and experiments that will introduce them to standard techniques and practices in typical medical and MIT research lab environments where radiation is used. This is a great class to take if you are currently doing in medical, biological, or nuclear areas at MIT, or if you are interested in later doing a UROP, Co-Op or Internship that will involve working with radiation sources and detectors. The course is geared toward undergraduates as an introduction to how radiation sources and radioactivity are used by researchers in most majors at MIT.

Course Credits - 3 Units

Prerequisites - None

Course Meeting Times (In-person required)
Lectures: 2 sessions / week, 1.5 hours / session,  N52 – 495
Tuesday (Lecture): 9:30 AM – 11:00 AM
Thursday (Lab): 9:30 AM – 11:00 AM

Instructor - Tolga Durak, PhD, PE, Managing Director, Environment Health and Safety
For more information, email tdurak at mit.edu<mailto:tdurak at mit.edu>

Limited enrollment.  Preference is given to freshman and NSE sophomore students.


22.S097 Applied Machine Learning for Nuclear Engineers
(see attached poster)

In this course, freshman and sophomore students have an opportunity to explore both machine learning (ML) techniques and nuclear science and engineering in an interesting and applied form. Students learn how to apply most popular ML methods to analyze real-world applications relevant to nuclear scientists and engineers in the areas of nuclear fission, fusion, security, and radiology (Health Physics). You will use ML to optimize the fuel inside nuclear fission reactors, to build surrogate models to simulate nuclear fusion reactors, to process signals from nuclear detectors to identify special nuclear materials, and to detect cancer tumors in patients. The course includes two lectures per week. The first lecture emphasizes the theory and methodology of a machine learning topic. The second lecture is a lab session where students practice and complete programming exercises related to that machine learning topic. Lab exercises are designed to promote active learning, high-level thinking, and basic problem-solving skills, and can be completed within the lab session virtually using a computing cloud. In addition to the physics knowledge about nuclear science and engineering, this course is a pure computational/programming course, designed to teach students practical ML techniques to build data science skills helpful for their tenure in college of engineering and computing.

Course Credits - 3 Units

Prerequisites - No perquisite required. Students are preferred to have basic background in math and computer programming. Lab work will involve Python programming.

Course Meeting Times (Virtual)
Lecture/Lab: Thursday + Friday, 3-4:30pm

Instructor – Dr. Majdi I. Radaideh, Nuclear Science and Engineering
For more information, email radaideh at mit.edu<mailto:radaideh at mit.edu>

Limited enrollment.  Preference is given to freshman and sophomores, and NSE students.



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