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.
P. Kashyap,
W. S. Pitts,
D. Baron,
C.-W. Wong,
T. Wu, and
P. D. Franzon,
"High Speed Receiver Modeling Using Generative Adversarial Networks,"
IEEE 30th Conf. Electrical Perf. Electronic Packaging & Systems (EPEPS), Oct. 2021
(pdf).
Y. Ma,
M. Kang,
J. Silverstein, and
D. Baron,
"Local Convergence of an AMP Variant to the LASSO Solution in Finite Dimensions,"
Proc. Int. Symp. Inf. Theory (ISIT), Melbourne, Australia, July 2021
(arxiv).
Jesse Cramer
and I recently put together a writeup about
the 1% rule.
This heuristic lets one balance luxury spending on their path to financial independence.
R. Goenka,
S. Cao,
C.-W. Wong,
A. Rajwade, and
D. Baron,
"Contact Tracing Enhances the Efficiency of COVID-19 Group Testing,"
to appear in Conf. Inf. Sciences & Systems (CISS), March 2021.
R. Goenka,
S. Cao,
C.-W. Wong,
A. Rajwade, and
D. Baron,
"Contact Tracing Enhances the Efficiency of COVID-19 Group Testing,"
to appear in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Process.
(ICASSP), June 2021
(pdf,
arxiv).
Teaching
ECE 421
(introduction to signal processing), January 2021.
Op-ed for
Technician,
NC State University's student paper,
about how the university's messed-up reopening for the fall semester was preventable, August 2020.
(link,
pdf.)
Our work on pooled testing was featured in this
interview
in the Technician, NC State's student paper, August 2020.
Teaching
ECE 592
(topics in data science), August 2020.
Our work on pooled coronavirus testing was featured in this
Nature interview, July 2020.
I was on a panel on safely reopening schools. The main speakers were
Darius Lakdawalla, an economist with expertise in epidemiology, and
Paul Romer, the 2018 Nobel prize winner in economics. June 2020.
The following interview in IEEE Spectrum discussed our work on pooled coronavirus testing in detail.
T. Perry, "Researchers are using algorithms to tackle the coronavirus test shortage:
The scramble to develop new test kits that deliver faster results,"
IEEE Spectrum, June 2020, page 4.
Our work on pooled coronavirus testing with side information was presented
at the Paris Machine Learning Meetup
(pdf file with slides), May 2020.
D. Baron,
C. Rush, and
Y. Yapici,
"mmWave Channel Estimation via Approximate Message Passing with Side Information,"
IEEE SPAWC, May 2020
(arxiv,
slides).
Our work on group testing was featured in this
interview
with
IEEE Spectrum,
April 2020.
H. Liu,
C. Rush, and
D. Baron,
"Rigorous State Evolution Analysis for Approximate Message Passing with Side Information,"
submitted, March 2020
(arvix).
Teaching
ECE 421
(introduction to signal processing), January 2020.
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:
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.
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
.)
