Dr. Tiffany Zhang is committed to advancing national security, bringing a wealth of experience and expertise to help tackle some of the most critical challenges. Through extensive research and analysis, Dr. Zhang has honed her ability to anticipate potential risks and vulnerabilities, enabling her to develop proactive cybersecurity strategies. Whether assessing the implications of technological advancements, tracking geopolitical developments or identifying asymmetric threats, Dr. Zhang possesses a keen awareness of the multifaceted nature of contemporary security challenges.
Dr. Zhang’s background in cybersecurity and machine learning, specifically object tracking and identification along with large-scale network congestion control for disaggregated storage systems, have equipped her with the specialized skills to address complex security issues effectively. Prior to joining the University of Nebraska at Omaha (UNO), she collaborated with the Brookhaven National Laboratory Deep Learning Team to help the International Atomic Energy Agency (IAEA) develop the Object Identification/Object Tracking Software. This software has significantly helped IAEA inspectors automate the video inspection process. Dr. Zhang also collaborated with Air Force Research Laboratory on a machine learning-based transmitter identification project. Additionally, she has collaborated with Samsung to develop efficient network congestion control solutions for large-scale disaggregated storage systems. These experiences have not only demonstrated her capacity to deliver tangible results but have also instilled a deep sense of responsibility to help protect the Nation and its citizens.
Q&A
Q: Why are you interested in supporting national security?
A secure environment promotes economic development, upholds democratic values and protects the rights and freedoms of individuals. By participating in national security initiatives, I can help counter threats such as cyberattacks, terrorism and global conflicts while promoting domestic and international peace and cooperation. My motivation stems from a dedication to take on complex challenges, protecting critical infrastructure and ensuring that future generations inherit a safe and prosperous world.
Although I have not worked directly in the DOD space, I have made research contributions that support national security via other national organizations. I was involved in innovative and significant projects during my three years of collaboration with the Air Force Research Laboratory (AFRL) and Brookhaven National Laboratory (BNL). My work with AFRL centered on advancing radio frequency detection research using machine learning techniques. I also created user-friendly software for auto-tracking and detecting suspicious objects, specifically designed for nuclear field inspectors of the International Atomic Energy Agency (IAEA). This project incorporated advanced deep learning algorithms, such as the newest versions of the You Only Look Once algorithm (YOLO) and PyTorch Re-identification, to automate frame analysis and improve operational efficiency. My contributions earned recognition with the esteemed Department of Energy (DOE) National Nuclear Security Administration (NNSA) Joule Awards in 2020 and 2022.
Some of Dr. Zhang's research is available via the NSRI publications database.
Q: What national security challenges do you think you could offer your expertise to solve?
My expertise plays a key role in tackling several urgent national security issues. Through my research on optimizing network performance for large-scale storage systems, I can enhance the reliability and efficiency of vital data infrastructure, crucial for secure communications and decision making. My focus on machine learning-based rate adaptation algorithms for Internet of Things (IoT) wireless networks is especially pertinent for improving real-time communication and adaptability in defense and surveillance systems. Furthermore, my background in cloud computing enables me to create scalable and secure architectures that can manage extensive national security operations. My work on object tracking and identification, employing cutting-edge deep learning algorithms, provides sophisticated solutions for automated monitoring, detection and tracking—essential for addressing threats in areas like border security, nuclear nonproliferation and the protection of critical infrastructure. Collectively, these areas of expertise position me to deliver innovative, technology-driven solutions to help solve national security challenges.
Q: What do you see in the next five or 10 years in your space that you think is important for national security leaders to consider?
In the next five to ten years, various trends will be crucial for national security leaders to address:
- The swift growth of data-intensive applications will necessitate strong network performance optimization for large-scale storage systems to enable secure and efficient data management.
- With the rise of IoT devices, there will be a pressing need for advanced rate adaptation algorithms to facilitate wireless networks in dynamic, resource-constrained environments, particularly for defense and real-time operational applications.
- Moreover, the increasing dependence on cloud computing will call for innovative strategies to secure cloud infrastructures against more elaborate cyber threats while ensuring scalability and resilience.
- Artificial intelligence and machine learning will be instrumental in national security, especially in improving automated surveillance, threat detection and decision-making processes. Leaders should prioritize investment in advanced algorithms for object-tracking and identification to enhance capabilities in border security, critical infrastructure protection and counterterrorism.
- Lastly, as the threat landscape changes, it will be vital to integrate ethical considerations and strong cybersecurity measures in the development and deployment of AI and networked systems to reduce risks and foster trust in technological solutions. Actively engaging with these advancements will be essential to effectively counter emerging threats.
Q: What are you working on now that excites you and why?
I am currently engaged in creating extensive network infrastructure solutions tailored for large-scale storage systems. This project is especially exciting as it addresses the urgent need for efficient and reliable data management in complex environments. By enhancing performance and ensuring secure, high-capacity networks, this research plays a crucial role in the advancement of data-intensive applications and supports high-performance computing clusters.
Additionally, I am developing machine learning-driven rate adaptation algorithms designed to improve agricultural IoT wireless networks. This initiative captivates me because it combines cutting-edge technology with practical applications in precision agriculture. Our primary focus area is Nebraska, where the challenging terrain creates unique obstacles for establishing effective network communications between farm sensors and central servers. By leveraging machine learning, we aim to create adaptive solutions that improve connectivity and data exchange in rural communities. This enables farmers to make informed, data-driven decisions, enhancing productivity, sustainability and resilience in agriculture. The transformative potential of this work on farming practices and its contribution to global food security make it both impactful and deeply fulfilling.
Learn more about Dr. Zhang via her UNO bio.
