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Parsa joins three-year, $2.4 million 3D chip creation project

Teresa Donnellan — Fri, 10 Nov 2023 21:10:09 +0000

Parsa joins three-year, $2.4 million 3D chip creation project Teresa Donnellan Fri, 11/10/2023 - 16:10

In This Story

The hippocampus stores memories and makes connections between them, recognizing trends and helping the brain to learn and adapt to its environment. One Mason professor has joined a cross-university, interdisciplinary team to create a chip that can do the same.

Maryam Parsa, an assistant professor in electrical and computer engineering, is one of the four principal investigators on a three-year, $2.4 million project funded by the National Science Foundation to create chips that processes information like the hippocampus.

Maryam Parsa with her ��AV research group.

The project, DEJA-VU, involves four principal investigators: Maryam Parsa from ��AV, Akhilesh Jaiswal from the University of Wisconsin, Madison (project PI), Babak Shahbaba, and Norbert Fortin both from the University of California, Irvine. Each collaborator will contribute their special skills to create 3D Solid-State Learning Machines for Various Cognitive Use-Cases. The project will model and quantify key information processing steps in the hippocampus. These key hippocampal functions will then be embedded on to solid-state computing chips through state-of-the-art hardware design techniques. A hippocampal-aware, hardware-aware algorithmic framework will augment the chip design efforts to enable online learning and decision-making in resource constrained environments.

“The project has potential disruptive applications in the field of robotics and autonomous systems spanning industrial, consumer and defense sectors,” said Parsa. She added, “The transformative potential of the project emerges from research conducted at three different levels of abstractions of neuroscience, hardware, and algorithm.”

Parsa’s portion of the project is $550,000 for the development of the hippocampal-aware, hardware-aware learning algorithm.

Topics

Department of Electrical and Computer Engineering

National Science Foundation

Research

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Quantum conundrums: Navigating noise and enhancing expertise

Nathan Kahl — Wed, 23 Aug 2023 14:12:07 +0000

Quantum conundrums: Navigating noise and enhancing expertise Nathan Kahl Wed, 08/23/2023 - 10:12

There’s a joke, playing on the quantum world’s unique properties, that goes, “There are three types of people in this world: Those who understand quantum computing, those who don’t understand quantum computing, and those who simultaneously do and do not understand quantum computing.” All kidding aside, Weiwen Jiang sees a world in which quantum computing is in widespread use; with new funding from the National Science Foundation (NSF), he is taking steps toward that goal.

Weiwen Jiang. Photo provided

Jiang, an assistant professor in ��AV’s Department of Electrical and Computer Engineering, is leading two recently awarded NSF projects—worth a total $900,000—for work on the development of these complex devices and on building the quantum workforce of tomorrow.

Quantum computers differ from classical computers in that they use elements of quantum mechanics to perform calculations, allowing them to operate much faster and crunch more data. While there are several operational quantum computers in use—IBM and Google are among the top manufacturers—they currently are far from their promised potential and simply cannot yet make the large-scale calculations predicted of them.

Jiang said one key problem is, “They are not ‘stable.’ We can use them for computations, but you might get one answer today and then get an entirely different answer tomorrow.”

Quantum devices are notoriously susceptible to “noise”—specifically, things like cosmic rays, changes in the Earth's magnetic field, radiation, and even mobile Wi-Fi signals. The noise contributes to the devices’ instability.

The $600,000 collaborative grant will fund the work of Jiang and his collaborators from Kent State University in developing an adaptor that will adjust to fluctuating noise, improving the performance of applications on quantum devices. Jiang is well versed on the topic, having recently won the Best Poster Award for “System-level optimizations in improving the robustness of quantum applications on unstable quantum devices” at an event at Oak Ridge National Lab.

According to Jiang’s preliminary works, the deployment of the quantum applications faces several challenges, including sustainability—on one quantum processor, most quantum applications are sensitive to the temporal changes of quantum noise; portability—different quantum processors (even from the same vendor) with specific properties will lead to variation of model uncertainty; and transparency—a lack of visualization tools can block users from tailoring their quantum applications to quantum computers for higher reliability. The NSF project will systematically provide solutions in response to these challenges.

Jiang is optimistic about the future of quantum computing: “Every year, we see a lot of breakthroughs. Just a couple of months ago IBM published a paper on noise reduction. And every year, we see that the number of qubits in quantum computers increases from five in the year 2000 to over 400 on a new computer from IBM.” (A qubit is the basic unit of information used in quantum computing, much like a 1 and 0 for traditional computing.)

Another grant, which Jiang shares with collaborators Mingzhen Tian and Jessica Rosenberg in the College of Science, provides $300,000 from NSF to bolster the quantum workforce pipeline. The grant is for “an end-to-end quantum system integration training program.” The faculty members are developing a new course at Mason, organizing workshops at the IEEE International Conference on Quantum Computing in September (where Jiang is the quantum system track co-chair), and conducting tutorials at international conferences. Recently the team, led by Rosenberg, coordinated a summer immersion program at Mason for high school students. In addition, in the coming months, Jiang will be conducting seminars at a variety of minority-serving institutions in the DC region.

Jiang said the opportunities for quantum-trained engineers are robust and growing. “I have collaborations locally with Leidos and MITRE, for example, and they have needs in this field. Further, we know that quantum will make a difference in everything from finance to drug discovery to machine learning and beyond.”

He is encouraged about the quantum future—both in the world and here at Mason. He stressed that as student demand grows for this technology, “we need to provide the appropriate materials for our students because we’re seeing a lot of strong interest in this field.”

Explore research at Mason

In This Story

Weiwen Jiang

Topics

Electrical and Computer Engineering

Khaled Khasawneh receives NSF Awards in collaboration with University of California

Rena Malai — Mon, 15 Aug 2022 19:23:16 +0000

Khaled Khasawneh receives NSF Awards in collaboration with University of California Rena Malai Mon, 08/15/2022 - 15:23

In This Story

Khaled Khasawneh

Khaled N. Khasawneh, assistant professor in the department of Electrical and Computer Engineering and the director of the Computer Architecture, Machine Learning, and Security (CAMLsec) Lab, has been awarded two NSF grants. These grants are in collaboration with University of California (UC) Davis and UC Riverside, and total $2.4M.

“These are timely awards to allow my group to continue pursuing cutting edge research in machine learning security, cloud security, and hardware security fields,” says Khasawneh. “Special thanks to NSF for their funding. I appreciate the efforts of my collaborators, the endless support from our department, and the efforts of the talented students in my group.”

Award 1: Collaborative Research: SaTC: CORE: Medium: Targeted Microarchitectural Attacks and Defenses in Cloud Infrastructure

Cloud computing paradigms have emerged as a major facility to store and process massive amounts of data produced by various business units, public organizations, Internet-of-Things, and cyber-physical systems. The cloud scheduler is the component responsible for deciding which computer a cloud application should run. The current design of cloud schedulers only focuses on meeting the performance requirements of submitted applications without security considerations.

This project, in collaboration with professor Houman Homayoun’s lLab at UC Davis, examines how cloud schedulers can be exploited by attackers to facilitate targeted micro-architectural attacks in cloud environments. The project also explores novel approaches to defend against targeted micro-architectural attacks in the cloud.

Award 2: Collaborative Research: SHF: Medium: Approximate Computing for Machine Learning Security: Foundations and Accelerator Design

Advances in Deep Neural Networks (DNN) have enabled a wide range of promising applications. However, DNNs are vulnerable to Adversarial Machine Learning attacks, with potentially dangerous outcomes, such as mistaking a stop sign for a speed limit sign.

This project, in collaboration with professors Nael Abu-Ghazaleh and Samet Oymak at UC Riverside, will explore the use of approximate computing to improve the robustness of DNNs against adversarial attacks. Approximate computing is a design paradigm that trades results precision for simpler hardware.

Topics

grants

NSF

NSF Grant

Electrical and Computer Engineering

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Two papers gain recognition in the hardware security field

Rena Malai — Fri, 25 Feb 2022 19:28:12 +0000

Two papers gain recognition in the hardware security field Rena Malai Fri, 02/25/2022 - 14:28

In This Story

Khaled Khasawneh

Khaled N. Khasawneh (at right), assistant
professor in the Electrical and Computer Engineering Department, works alongside PHD student Behnam Omidi.

Khaled N. Khasawneh, assistant professor in the Electrical and Computer Engineering Department at ��AV, contributed significant research and work to two papers which have been recognized in the area of hardware security.

Researchers in Khasawneh’s CAMLsec Lab Identified a New Vulnerability in the Cloud Scheduler

A paper entitled “Repttack: Exploiting Cloud Schedulers to Guide Co-Location Attacks” was written as part of a collaborative research effort with University of California (UC)- Davis and ��AV. Along with his collaborators at UC Davis, Khasawneh helped to discover a new vulnerability in cloud schedulers, which could inadvertently allow targeted micro-architectural attacks in the cloud. This paper pointed out that certain features in the cloud scheduler enables arbitrary users to influence scheduling results. This can help attackers co-locate attacker’s code with a targeted victim’s code in a heterogeneous cloud, which enables a wide variety of micro-architectural attacks that leak sensitive data.

“The current design of scheduling algorithms in the cloud focuses on enhancing workloads performance, resource utilization, and load-balancing without security considerations, which may bring new vulnerability as we showed in our work,” says Khasawneh.

The paper is accepted for publication at the 2022 Network and Distributed System Security Symposium (NDSS), and will be presented there in April.

This paper is co-authored with Behnam Omidi, a second year PHD student within the Electrical and Computer Engineering Department. His research focuses on discovering systems vulnerabilities and hardware support to secure computing systems.

Khasawneh—Omidi’s advisor-- says this paper was a great opportunity to gain useful knowledge as it was a tremendous collaborative effort between both schools.

“I’m more of an expert in microarchitecral attacks, and our co-collaborators at UC Davis, led by Dr. Homayoun, brings expertise of cloud computing and resource scheduling,” says Khasawneh.

Khasawneh’s Paper Selected as a Top Pick in Architecture and Hardware Security 2021

A paper co-authored by Khasawneh was selected as a Top Pick in Architecture and Hardware Security 2021.

The top picks in architecture and embedded security represent the top and most impactful papers that have been published in the area in the last six years, from 2015 to 2020. Top picks are selected from conference papers that have appeared in leading hardware security conferences including but not limited to DAC, ICCAD, DATE, ASPDAC, HOST, Asian HOST, GLSVLSI, VLSI Design, CHES, ETS, VTS, ITC, S&P, Usenix Security, CCS, NDSS, ISCA, MICRO, ASPLOS, HPCA, HASP, ACSAC, Euro S&P, and Asia CCS.

The top pick paper is titled “Spectre Returns! Speculation Attacks using the Return Stack Buffer ” and had previously won the best paper award in the USENIX Workshop on Offensive Technologies (WOOT) in 2018. This paper discovers a new class of Spectre attack, called SpectreRSB, that exploits the return stack buffer (RSB), which is used in modern CPUs to help predict return addresses, instead of the branch predictor unit. SpectreRSB allows malicious software to steal passwords, keys, and other sensitive information, from memory it shouldn't be allowed to touch.

According to Khasawneh, Linux kernel released a patch to protect against SpectreRSB vulnerability (CVE-2018-15572).

Topics

grants

Department of Electrical and Computer Engineering

College of Engineering and Computing

data

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Weiwen Jiang wins IEEE Best Paper Award

Tama Moni — Mon, 13 Dec 2021 19:38:48 +0000

Weiwen Jiang wins IEEE Best Paper Award Tama Moni Mon, 12/13/2021 - 14:38

In This Story

Weiwen Jiang

A paper from, Weiwen Jiang, assistant professor of electrical and computer engineering at ��AV, has been selected by the IEEE Transactions on Computer-Aided Design for the 2021 Donald O. Pederson Best Paper Award.

The paper, “Hardware/Software Co-Exploration of Neural Architectures,” proposes a novel hardware and software framework for efficient neural architecture search (NAS), a technique to automate machine learning systems.

The IEEE TCAD Editorial Board and the IEEE Council on Electronic Design Automation selected the paper as one of two 2021 winners from over 800 papers published by the journal in the last two years, based on the overall quality, originality, level of contribution, subject matter, and timeliness of the research. The award was presented at the Design Automation Conference in December 2021.

“The awarded work is the first work that demonstrates that the best tradeoff between neural network performance and hardware efficiency requires the co-exploration of hardware design and neural network architecture. It provides fundaments of a series of my co-design neural network system works, among which several works have been nominated for best paper at top EDA conferences (e.g., DAC, CODES+ISSS, and ASP-DAC),” says Jiang.

Topics

Electrical and Computer Engineering

College of Engineering and Computing

C-TASC

Maryam Parsa

Taylor Nguyen — Thu, 15 Jul 2021 01:53:35 +0000

Maryam Parsa Taylor Nguyen Wed, 07/14/2021 - 21:53

Tenure-Track Assistant Professor, Department of Electrical and Computer Engineering

Contact Information

Phone: 703-993-6097
Campus: Fairfax
Building: Nguyen Engineering Building
Room 3215
Mail Stop: 1G5
Email: mparsa@gmu.edu

Personal Websites

Personal Website

In the News

College of Engineering and Computing launches graduate certificate programs in AI, microfabriction, smart grid technology, and machine learning
February 29, 2024
Parsa joins three-year, $2.4 million 3D chip creation project
November 10, 2023

Biography

Maryam Parsa is an assistant professor in the Electrical and Computer Engineering department at ��AV. Prior to joining Mason, she was a postdoctoral researcher at Oak Ridge National Laboratory in the Beyond Moore Computing group. She received her PhD in electrical and computer engineering from the Center for Brain-Inspired Computing (C-BRIC) at Purdue University in December 2020 with a prestigious four-year Intel Corporation and Semiconductor Research Corporation (SRC) PhD fellowship.

Parsa has broad interests in the areas of neuromorphic computing, neural architecture search, and Bayesian optimization across the full stack of materials, devices, circuits, systems, algorithms, and applications. Her research involves developing causal and physics-based machine learning and Bayesian optimization to accelerate materials discovery. Further, in her focus on neuromorphic computing, her goal is to not only enable accurate, fast, energy-efficient, and resilient intelligence at the edge through algorithm-hardware codesign but also to develop novel hierarchical learning/training approaches based on Bayesian optimization, evolutionary optimization, and synaptic learning rules. She is interested in a wide range of applications such as computational neuroscience, smart healthcare diagnosis, and cyber-physical systems.

Degrees

PhD, Electrical and Computer Engineering, Purdue University
MS, Civil Engineering, Purdue University
MS, Electrical and Computer Engineering, University of Ottawa
BS, Electrical and Computer Engineering, Khaje Nasir Toosi University of Technology

Research Interests

Neuromorphic learning
Bio-inspired robotics
Distributed learning
Algorithm hardware co-design

Publications

Google Scholar

Weiwen Jiang

Taylor Nguyen — Sat, 03 Jul 2021 01:07:14 +0000

Weiwen Jiang Taylor Nguyen Fri, 07/02/2021 - 21:07

Tenure-track Assistant Professor, Department of Electrical and Computer Engineering

Contact Information

Phone: TBD
Campus: Fairfax
Building: Nguyen Engineering Building
Room 3247
Mail Stop: 1G5
Email: wjiang8@gmu.edu

In the News

Quantum conundrums: Navigating noise and enhancing expertise
August 23, 2023
Weiwen Jiang wins IEEE Best Paper Award
December 13, 2021

Personal Websites

Personal Website

Biography

Weiwen Jiang joined Mason in 2021. Prior to this, he was a post-doctoral research associate at the University of Notre Dame. He received his PhD from Chongqing University in 2019. From 2017 to 2019, he conducted research at the University of Pittsburgh and was co-advised by the professor there for his PhD thesis. He works on hardware and software co-design; in particular, the co-design of neural networks and different hardware accelerators, including mobile devices, FPGA, and ASIC. His recent work brings the co-design philosophy to quantum computing and demonstrates that quantum advantage can be achieved for the neural network for the first time.

Jiang’s research works have been published in prestigious journals and conferences, including Nature Electronics, Nature Communications, IEEE/ACM Transactions, DAC, ICCAD, and ESWEEK. He is the recipient of the Best Paper Award in ICCD’17 and Best Paper Nominations in DAC'19, CODES+ISSS'19, ASP-DAC'16, and ASP-DAC'20. In addition, he works closely not only with academic societies (such as Notre Dame, UPITT, Northeastern, Rice, and UConn) but also with industries (such as IBM, Xilinx, Facebook, and Edgecortix).

Degrees

PhD, Computer Science, Chongqing University
Joint PhD, Electrical and Computer Engineering, University of Pittsburgh

Researchers receive over $1.5 million from DARPA to optimize security and energy tradeoff

Anonymous — Tue, 02 Feb 2021 17:47:20 +0000

Researchers receive over $1.5 million from DARPA to optimize security and energy tradeoff Anonymous (not verified) Tue, 02/02/2021 - 12:47

Topics

Cyber Security Research

Cybersecurity

Department of Electrical and Computer Engineering

In This Story

Brian Mark

Khaled Khasawneh

Kai Zeng

Sai Manoj Pudukotai Dinakarrao

Sai Manoj Pudukotai Dinakarrao, Kai Zeng, Khaled Khasawneh, and Brian Mark are collaborating with researchers at Virginia Tech to optimize the safety and energy-efficient tradeoff.

The capabilities and reach of 5G are expanding, but with new capabilities come new security challenges. Four Mason Engineering researchers received a $1.6 million grant from DARPA to tackle one of the many security issues that 5G poses as part of a larger DARPA initiative called Open, Programmable, Secure 5G (OPS-5G).

The grant, entitled EPIC SWaPD: Energy Preserving Internet of Things (IoT) Cryptography for Small Weight and Power Devices, aims to optimize the security and energy efficiency tradeoff by creating a low-energy security architecture for various types of IoT devices.

“In a network, there are many different devices of different sizes and capabilities. Many of these devices don’t have much computing power or battery life, and a common cybersecurity attack on these devices is to drain their battery life,” says Brian Mark, co-principal investigator on the grant.

Mark, Khaled Khasawneh, Kai Zeng, and Sai Manoj Pudukotai Dinakarrao in the Department of Electrical and Computer Engineering are collaborating with the company Kryptowire and Assistant Professor Matthew Hicks from Virginia Tech for the project.

These small IoT devices could be as simple as a thermometer or humidity sensor that is part of a larger smart home system. “These sensors can be very tiny, and they have limited computation and communication capabilities. The first step for each of these types of sensors would be to bootstrap, or link, a secure connection to the network, which requires authentication without pre-shared secrets. This is where the vulnerabilities lie,” says Kai Zeng.

When sensors or other types of small IoT devices must automatically authenticate themselves, attackers have many paths of attack. They can drain the device's battery, rendering it useless, or steal sensitive information or data from the device. Because of the many pathways that attackers could use to harm these devices, the team is combining cryptography, network protocol design, and machine learning to assure the success and scalability of their efforts.

“Something really important for this effort is that the security architecture operates on the principles of zero trust and least privilege,” says Mark. “Zero trust means that when a device comes in and wants to join the network, the assumption is that there is no prior trust or information shared between the devices, while least privilege implies the minimum permissions are granted to the entity to perform its task. So, we needed to bootstrap the security association and grant an entity just enough authority to access the devices or data that it needs but no more than that.”

One way they are looking at securing the connection is through gait-inspired authentication, which leverages the kinetic energy generated by a human user. “Every device harvests energy in a different way, and we use the harvesting pattern for authentication of the device, which preserves energy and accomplishes a security task at the same time,” says Dinakarrao.

Another approach is to exploit the “always-on” sensors in some devices like smartphones. “We plan to employ always-on sensing to perform the exchange of device identification and cryptographic material,” says Khasawneh.

They are also using machine learning to ensure both authentication and authorization of different devices. “We not only have to authenticate the devices and entities that are allowed on a network, but we have to verify their different roles and privileges. I might be granted access to the information that tells me the temperature of a room, but perhaps I might not have the authorization to change the thermostat setting,” says Mark. “Using graph-based models, we can verify the trust relationships of different devices or entities and this process can be accelerated using machine learning techniques,” adds Dinakarrao.

Long-term, the team aspires for their security architecture to easily be applied to other devices. “When we talk about the energy and security tradeoff, we have to think about how we can do things in a smart way to conserve energy while enhancing security. But we also need to think about how to make our architecture scalable to larger, more geographically distributed networks. Right now, this work is with smaller devices, but the hope is that our overall security architecture can apply to a variety of devices with different capabilities,” says Mark.

Sai Manoj Pudukotai Dinakarrao

Martha Bushong — Sun, 09 Feb 2020 03:41:30 +0000

Sai Manoj Pudukotai Dinakarrao Martha Bushong Sat, 02/08/2020 - 22:41

Assistant Professor, Department of Electrical and Computer Engineering

Contact Information

Phone: 703-993-5083
Campus: Fairfax
Building: Nguyen Engineering Building
Room 3247

Email: spudukot@gmu.edu

Personal Websites

Personal Website

ORCID PROFILE

Biography

Sai Manoj Pudukotai Dinakarrao, an assistant professor in the Department of Electrical and Computer Engineering at Mason, received a BTech degree in electronics and communication engineering from the Jawaharlal Nehru Technological University, Anantapur, India, in 2010; an MTech in information technology with a specialization in networking and communication from International Institute of Information Technology Bangalore, India, in 2012; and PhD degree in electrical engineering from the Nanyang Technological University, Singapore in 2015. He worked as a post-doctoral researcher at TU Wien in Vienna, Austria, between 2015 and 2017. He worked as a post-doctoral researcher and research assistant professor at ��AV in 2017 and 2018.

Pudukotai Dinakarrao’s research interests include hardware security, adversarial machine learning, Internet of Things networks, deep learning in resource-constrained environments, in-memory computing, accelerator design, algorithms, design of self-aware many-core microprocessors, and resource management in many-core microprocessors. He was a recipient of The Young Research Fellow Award in the Design Automation Conference (DAC) 2013. His student won the Xilinx Open Hardware Contest 2017 (Student Category).

His works won best paper awards in top-tier conferences like ICDM and ICCE and were also nominated for best paper awards in conferences like DATE and ICCAD.

Degrees

PhD, Electrical and Electronic Engineering, Nanyang Technological University
MTech, Information Technology, International Institute of Information Technology Bangalore
BTech, Electronics and Communication Engineering, Jawaharlal Nehru Technological University, Anantapur

Kai Zeng

admin_alpha — Tue, 20 Oct 2015 23:30:01 +0000

Kai Zeng admin_alpha Tue, 10/20/2015 - 19:30

Professor, Department of Electrical and Computer Engineering

Contact Information

Phone: 703-993-5933
Campus: Fairfax
Building: Nguyen Engineering Building
Room 3252
Mail Stop: 1G5

Email: kzeng2@gmu.edu

Personal Websites

Personal Website

In the News

Message manipulation tricks text thieves
December 12, 2022
University researchers receive grants from the Commonwealth Cyber Initiative for cyber and arts and design research
February 3, 2021
Researchers receive over $1.5 million from DARPA to optimize security and energy tradeoff
February 2, 2021

Biography

Kai Zeng is a Professor in the Department of Electrical and Computer Engineering at the College of Engineering and Computing. He is also affiliated with the Department of Computer Science and Cyber Security Engineering. Prior to coming to Mason, he was a faculty member in the Department of Computer and Information Science at the University of Michigan - Dearborn and a postdoctoral scholar in the Computer Science Department at the University of California, Davis (UCD). His research interests include 5G wireless security, CPS/IoT security, spectrum sharing, machine learning, and physical layer security.

Research Spotlight:

Kai Zeng's research focuses on secure connections in wireless communications and network forensics. As a PI, he collaborates with other faculty and organizations to develop "WindTexter." WindTexter is a commercialized product that helps users establish a secure and covert connection in areas where 5G connections are unsecure. Watch the video below to learn more about this product.

Research

2019 - 2021: Secure 5G Wireless Communications, funded by Commonwealth Cyber Initiative (CCI).

2019 - 2020: Best Practices in Cybersecurity for Utilities: Secure Remote Access, funded by Protect Our Power.

2018 - 2020: Cyber P3 Scholarship Program, funded by NSA.

2016 - 2019: TWC: Small: Secure Near Field Communications between Mobile Devices, funded by National Science Foundation.

2016 - 2018: COTS Approach to Information Security, funded by Progeny Systems (ONR STTR, Phase II).

2016 - 2017: USAR's Cyber Solider Development, funded by NSA.

2015 - 2018: EARS: Collaborative Research: Intelligence Measure of Cognitive Radio Networks, funded by the National Science Foundation.

2012 - 2018: CAREER: Towards Reliable and Efficient Network Monitoring in White Space, funded by the National Science Foundation.

2015: Secure Communications in Wireless Environments, funded by MITRE.

Research Interests

5G Wireless Security, Spectrum Sharing, CPS/IoT Security, Machine Learning, Physical Layer Security, Network Forensics

Degrees

PhD, Electrical and Computer Engineering, Worcester Polytechnic Institute

C-TASC

Parsa joins three-year, $2.4 million 3D chip creation project

In This Story

The hippocampus stores memories and makes connections between them, recognizing trends and helping the brain to learn and adapt to its environment. One Mason professor has joined a cross-university, interdisciplinary team to create a chip that can do the same.

Topics

Quantum conundrums: Navigating noise and enhancing expertise

Explore research at Mason

In This Story

Related News

Topics

Khaled Khasawneh receives NSF Awards in collaboration with University of California

In This Story

Topics

Two papers gain recognition in the hardware security field

In This Story

Topics

Weiwen Jiang wins IEEE Best Paper Award

In This Story

Topics

Maryam Parsa

Contact Information

Personal Websites

In the News

Biography

Degrees

Research Interests

Publications

Weiwen Jiang

Contact Information

In the News

Personal Websites

Biography

Degrees

Researchers receive over $1.5 million from DARPA to optimize security and energy tradeoff

Topics

In This Story

Sai Manoj Pudukotai Dinakarrao

Contact Information

Personal Websites

Biography

Degrees

Kai Zeng

Contact Information

Personal Websites

In the News

Biography

Research Spotlight:

Research

Research Interests

Degrees