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

Links Publications Software Videos Bio/Contact CV Collaborators Other

New

• D. Baron, H. P. Patil, and H. Zhou, "Maximum Likelihood Quantum Error Mitigation for Algorithms with a Single Correct Output," Feb. 2024 (arxiv). This work was presented at the ITA Workshop (slides).
• Teaching ECE592 (signal processing and quantum), January 2024.
• Youtube video on how long it takes to reach financial independence with Chad May and Deveshwar Hariharan.

• P. Kashyap, P. P. Ravichandiran, L. Wang, D. Baron, C.-W. Wong, T. Wu, and P. Franzon, "Thermal Estimation for 3D-ICs through Generative Networks," IEEE Int. 3D Systems Integration Conf., 2023.
• K. Yue, R. Jin, C.-W. Wong, D. Baron, and H. Dai, "Gradient obfuscation gives a false sense of security in federated learning," USENIX Security Symposium, August 2023 (arxiv, slides).
• Teaching ECE 512 (topics in data science), August 2023.
• S.-J. Cao R. Goenka, C.-W. Wong, A. Rajwade, and D. Baron, "Group Testing with Side Information via Generalized Approximate Message Passing," to appear in IEEE. Trans. Signal Proc., June 2023 (arxiv).
• Tutorial video on group testing with side information, May 2023. (You can also look at the slides.)

• Teaching ECE592 (signal processing and quantum), January 2023.
• 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.
• M. Abdelkhalek, D. Baron, and C.-W. Wong, "Mismatched Estimation in the Distance Geometry Problem," Asilomar Conf. Signals, Systems, Computers, November 2022 (arxiv, video of presentation).
• 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.
• First invitation to our quantum workshop.
• 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 512 (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.
• Links to our work from 2021, 2020, 2019, 2018, 2017, 2016, 2015, 2014, 2013, and 2010-2012.

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.

• 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 advanced graduate course on quantum computing and quantum information was taught by Ryan O'Donnell at CMU. This course will likely require around 30 hours.
• Another graduate course is introduction to quantum information science by Artur Ekert at Oxford. Again, this course will likely require around 30 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:

• MDL signal estimation and universal denoising.
• Compressed sensing (CS) - signal reconstruction algorithms, information theoretic results, distributed CS, hardware projects in CS, and other unrelated CS topics.
• Fast algorithms for universal lossless compression.
• Universality in channel coding and distributed compression.
• Worst-case constraints in compression and prediction.
• Compression and represenation of multi-dimensional functions with smooth discontinuities.
• Delay-constrained performance in ALOHA networks.

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:

• ECE 512 (Topics in Data Science; graduate course), 2016-2020, 2022, 2023, ECE Dept., NC State University.
• ECE 592 (Signal processing and quantum computing; graduate course), 2023, ECE Dept., NC State University.
• ECE 308 (Elements of Control Systems; undergraduate course) - 2014 and 2015, ECE Dept., NC State University.
• ECE 421 (Introduction to Signal Processing; undergraduate course) - 2013-2021, ECE Dept., NC State University.
• ECE 514 (Random Processes; graduate course) - 2010-2013, ECE Dept., NC State University.
• ECE 792 (Universal Algorithms in Communications and Signal Processing), 2012, ECE Dept., NC State University.
• EE 044130 (Signals and Systems; undergraduate), 2009, EE Dept., Technion - Israel.
• ECE 418 [formerly ECE 318] (Introduction to Digital Image and Video Processing; mixed graduate/undergraduate), 2003, ECE Dept., University of Illinois at Urbana-Champaign.

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).

Students

• Yanting Ma (Ph.D. 2017; currently with MERL; dissertation).
• Ryan Pilgrim (M.Sc. 2017; currently with Vadum; dissertation, slides).
• Junan Zhu (Ph.D. 2016; currently with JPMorgan Chase; dissertation).
• 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.

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