Lincoln Laboratory

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Hands on Full Duplex Radio - IAP
Jan/29 Mon 12:00AM

Design, build and test your own full-duplex radio with real-world hardware/software engineering!

Full-duplex technology is revolutionizing the wireless world! This system concept is fundamentally different that traditional radios that divide transmission and reception in either time and/or frequency. Future networks will leverage this emerging technology to improve efficiency and enhance mobile user experiences. This course will introduce students to the various self-interference cancellation techniques that enable full-duplex operation in wireless systems and will allow them to create their own full-duplex radios through hands-on engineering with real-world hardware/software.  Must register by 1/22/2024

Email Ken Kolodziej to register for the class at

Hands on Holography IAP
Jan/08 Mon 10:00AM–12:00PM
Jan/10 Wed 10:00AM–12:00PM
Jan/12 Fri 10:00AM–12:00PM
Jan/17 Wed 10:00AM–12:00PM
Jan/19 Fri 10:00AM–12:00PM

What is holography? It's not just beautiful art – it's also a range of measurement techniques that let you record a 3D light field. Come learn the theory of wave optics, interference, and diffraction, and then make your own holograms in our hands-on lab! See what your favorite image looks like when turned into a computer-generated hologram. We'll also do demos and visit the newly renovated MIT Museum, home of the world's most comprehensive collection of holographic art. No prior background required. Must register by 12/22/2023.


Email to register. Limit 30 students. NOTE: All 5 class sessions are required.



Mathematics of Big Data & Machine Learning
Jan/09 Tue 10:00AM–12:00PM
Jan/16 Tue 10:00AM–12:00PM
Jan/23 Tue 10:00AM–12:00PM
Jan/30 Tue 10:00AM–12:00PM

Enrollment: Limited: Advance sign-up required Limited to 35 participants

Attendance: Participants must attend all sessions

Prereq: Matrix Mathematics

Big Data describes a new era in the digital age where the volume, velocity, and variety of data created across a wide range of fields is increasing at a rate well beyond our ability to analyze the data.  Machine Learning has emerged as a powerful tool for transforming this data into usable information.  Many technologies (e.g., spreadsheets, databases, graphs, matrices, deep neural networks, ...) have been developed to address these challenges.  The common theme amongst these technologies is the need to store and operate on data as tabular collections instead of as individual data elements.  This class describes the common mathematical foundation of these tabular collections (associative arrays) that apply across a wide range of applications and technologies.  Associative arrays unify and simplify Big Data and Machine Learning.  Understanding these mathematical foundations allows the student to see past the differences that lie on the surface of Big Data and Machine Learning applications and technologies and leverage their core mathematical similarities to solve the hardest Big Data and Machine Learning challenges.

This interactive course will involve significant interactive student participation and a small amount of homework.   Those students who fully participate and complete the homework will receive a certificate of completion.

The MIT Press book "Mathematics of Big Data" that will be used throughout the course will be provided.

E-mail the instructor to sign up.


Hayden Jananthan - Research Scientist MIT Supercomputing Center -

Jeremy Kepner - Fellow & Head MIT Supercomputing Center -

Signup Deadline: Dec 15

Practical High Performance Computing - IAP
Jan/16 Tue 10:00AM–01:00PM
Jan/18 Thu 10:00AM–01:00PM
Jan/23 Tue 10:00AM–01:00PM
Jan/25 Thu 10:00AM–01:00PM

Overview: The focus of this workshop is to introduce the role of High Performance Computing (HPC) in research. Students will learn when to scale from their laptops to HPC, what challenges that introduces, and how to address those challenges with efficient HPC workflows. The MIT SuperCloud will be used for hands-on examples. Students should bring an existing research problem/application that they would like to scale as a project.

Pre-recorded lectures will be available before class and class time will be spent on hands-on activities and student research project work. Students taking the class for MIT credit must complete a short report on their project.

Jan 16 Introduction to Supercomputing Workflows and Systems

Jan 18 Serial Optimization and Parallel Speedup

Jan 23 Building and Running Parallel Workflows

Jan 25 Distributed Computing

Instructors: Lauren Milechin; Julie Mullen; Chris Hill


Enrollment: advance sign-up required, sign-up by 01/09, limited to 20 participants
Enroll by emailing