Dror Baron

Dror Baron  
Associate Professor
Department of Electrical and Computer Engineering
North Carolina State University

Links Publications Software Videos Bio/Contact CV Collaborators Other


Quantum Computing and Information
I spent the 2021-2022 academic year on remote sabbatical at Harvard, where I studied quantum computing and information with Yue Lu. I am also collaborating with Bojko Bakalov on group theory problems in quantum computing, and Carlos Ortiz Marrero on quantum machine learning. As part of my quantum learning curve, during the Spring 2023 semester I will be teaching a new course that combines signal processing and quantum computing. The course will be available to both undergraduate and graduate students; here is a flyer that provides some details about the course. Please contact me if you want to learn more about the course.

Part of my role in quantum computing and information is to stimulate the growth of this research area at NC State University. In addition to leading discussions in the ECE Department about our evolving educational and research programs in quantum, I served as the director of the 2023 NC State Quantum Workshop. The January 2023 workshop had a tutorial flavor with a quantum machine learning theme. You may want to watch video recordings from the workshop. We plan follow up annual workshops with different themese; please email me if you have any questions.

Resources for studying quantum computing and information. Quite a few people have been asking me how they can learn more about quantum computing and information. Here are some links.

Prospective students who are interested in studying quantum computing and information with me are welcome to read how to approach me (please see below).

During the last several years, we have been inundated by a deluge of data in applications including distributed networked systems, finance, medical imaging, and seismics. My interest lies in fundamental research for problems involving vast amounts of data that must be processed effectively and rapidly in order to extract useful - potentially "actionable" - information. To approach these problems, we must use a multi-disciplinary approach, and I combine tools from information theory, statistical signal processing, machine learning, and computer science. I call this computational information processing.

Specific research directions that I have worked on include:

During the pandemic, I started partitioning material in my courses (information about courses I've taught appears below) into modules. While teaching in Fall 2022, I'm uploading modules to YouTube, in order to make the material publicly accessible. If people have questions about the material, modules, or courses, you're welcome to contact me.

Here is information about courses that I have taught:

In addition to teaching, Joel Trussell and I developed software for automating questions in ECE 421 (Introduction to Signal Processing; undergraduate course) using WeBWorK software. Each student receives a customized version of each of the questions, and the student is allowed several attempts to solve the question. The student may also request another version of the question (with different numbers). We used these for homeworks and quizzes during the 2015 spring semester. Students solved the quizzes in class using laptops, tablets, or even smart-phones; they received quiz grades immediately. Overall, students provided favorable feedback about the WeBWorK-based system, especially because it allowed many small homeworks sets followed by brief quizzes, which forced them to study consistently throughout the semester. You are invited to check out some examples on our demo using a guest login. To learn more, please take a look at the paper below. We would be glad to hear from you.


Prospective Students
Regretfully, I cannot respond to most inquiries regarding openings for graduate and postdoctoral positions in my group. To get my attention, I suggest that you browse through my webpage, see whether some of the research directions seem interesting, and explain how it caught your attention. I will very likely respond to such inquiries, especially inquiries by students who are interested in studying quantum computing and information. In contrast, prospective students who send the same letter to dozens or even hundreds of potential advisors should realize that this approach is unlikely to succeed.

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