Director of
2023 NC State Quantum Workshop.
This year's workshop will have a tutorial flavor with a quantum machine learning theme.
Please email me if you have any questions. (And registration is coming up soon.)
I'm increasingly working with the
IBM quantum hub at NC State.
In addition to research collaborations, I'm helping determine the future educational approach
in quantum information science and engineering.
H. Liu,
C. Rush, and
D. Baron,
"Rigorous State Evolution Analysis for Approximate Message Passing with Side Information,"
to appear in IEEE Trans. Inf. Theory, November 2022
(arxiv).
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.
I created my first YouTube
video on personal finance. In this video, I'm analyzing the amount of time it should take you to reach
financial independence, under some assumptions. The first half of the video motivates the results and
discusses them, and the second half offers the mathematical derivation.
I gave a quantum computing tutorial
seminar, August 2022
(slides).
Teaching
ECE 592
(topics in data science), August 2022.
Press release
about our work on using neural tangent kernels in federated learning.
K. Yue,
R. Jin,
R. Pilgrim,
C.-W. Wong,
D. Baron, and
H. Dai,
“Neural Tangent Kernel Empowered Federated Learning,”
Int. Conf. Machine Learning, Baltimore, MD, June 2022
(arxiv).
P. Kashyap,
Y. Choi,
S. Dey,
C.-W. Wong,
D. Baron,
C. Cheng,
T. Wu, and
P. D. Franzon,
"Modeling of Adaptive Receiver Performance Generative Adversarial Networks,"
IEEE Electrical Components and Technology Conference (ECTC),
San Diego, June 2022
(pdf).
I am spending the 2021-2022 academic year on remote sabbatical at Harvard,
where I am studying quantum computing and information with
Yue Lu.
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 am the director of the 2023 NC State Quantum Workshop.
This year's workshop will have a tutorial flavor with a quantum machine learning theme.
Please email me if you have any questions. (And registration is coming up soon.)
Quite a few people have been asking me how they can learn more about quantum computing
and information. Here are some links.
I suggest to start by watching a quantum computing tutorial video. One option is my
seminar, August 2022
(slides). Here's a link to the recording:
I also enjoyed watching this
presentation
hosted by Microsoft in 2018.
Next, you could read through the interesting
tutorial
by Andy Matuschak and Michael Nielsen.
What impressed me is that they "quiz" the reader with many questions,
and later send you reminder questions from time to time, in order to
establish long-term memory.
Here is a course on
quantum mechanics and quantum computation by Unmesh Vazirani.
This course is great for beginners, and you should have some initial feel for the material in roughly 15 hours.
A more in-depth understanding of the material likely requires to go through a textbook.
The "classical" text is Quantum Computation and Quantum Information by Michael Nielsen and Isaac Chuang.
Another text that focuses on information aspects is Quantum Information Theory by Mark Wilde.
Prospective students who are interested in studying quantum computing and information with
me are welcome to read how to approach me (please see below).
Research
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:
Education
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.
H. J. Trussell and
D. Baron
"Creating Analytic Online Homework for Digital
Signal Processing,"
to appear in IEEE Signal Proc. Mag., Sept. 2015
(pdf).
Jin Tan
(M.S. 2012; Ph.D. 2015; currently with CGG Service Inc.;
dissertation).
Joe Young
(B.S. 2015; currently Ph.D. student at Rice University).
Danielle Carmon (B.S. 2011; M.S. nonthesis 2012; currently with IBM).
Ilya Poltorak
(B.Sc. 2011 at
Technion;
later completed M.Sc. at Tel Aviv University;
currently with GM).
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.
(Last updated
.)
