Highlights

Spear-phishing is a type of cyber-attack that sends personalized emails to targeted individuals and organizations attempting to convince the victims to perform some action, such as transferring money, logging into a website or sharing data, which the attacker can then use illicitly.

Li and Shen, both assistant professors, are developing a new system to automatically detect spear-phishing emails, so the damage to an individual or organization can be contained. They are supported by a $150,000 Microsoft Security Research Artificial Intelligence Award and will work with the company to test their new system. The researchers are taking a novel approach to what has become a billion-dollar problem. “We will model the email communications between senders and recipients as a social graph and apply graph-learning models to classify the emails,” said Li. “To keep our models adapted to the new benign and malicious email patterns that emerge in an organization, we’ll also apply online learning, a very efficient method to update the model.” In spear-phishing, the attacker usually writes an email tailored to the background and roles of the victims and sends it to a small number of recipients, a more stealth approach than other email-based attacks like spam, which is sent to a large number of recipients without customization. The attacker often impersonates someone the victim knows, using a similar email address and a compromised email account. Because of such impersonation, the email is more likely to be read and processed by the victim. These emails are often evasive and difficult to capture with existing approaches that are based on malware detection, sender/domain blacklisting, among others.

“To address this problem, we will explore how to adapt state-of-the-art graph learning algorithms,” said Shen. “Machine learning over graphs is an area I am very excited about as it provides algorithmic and theoretical tools for understanding and learning from data collected in networked systems. We expect this project to have a profound impact on email security and research in graph learning.”

“We are thrilled to be selected as one of the only two winning teams for this award,” said Li. “This is also a great opportunity for our research to make realworld impact and protect numerous email users.”

In November 2020, the University of Texas at San Antonio formally launched CyManII, a $111 million public-private partnership funded by the U.S. Department of Energy, to engage in collaborative research and development that will help U.S. manufacturers become more resilient against cyber threats. UTSA will lead the consortium of 59 proposed member institutions.

U.S. manufacturers are among the top targets for cyber criminals and nation-state adversaries, impacting the production of energy technologies such as electric vehicles, solar panels and wind turbines. Integration across the supply chain network and an increased use of automation applied in manufacturing processes can make industrial infrastructures vulnerable to cyber-attacks.

To protect American manufacturing jobs and workers, CyManII will transform U.S. advanced manufacturing and make manufacturers more energy efficient, resilient and globally competitive.

“Our role is to align smart manufacturing with cyber secure manufacturing and renewables to provide truly sustainable solutions,” said CALIT2 director G.P. Li, professor of electrical engineering and computer science.

UCI scientists and engineers seek to develop wireless digital transformation platforms to help U.S. manufacturers become more competitive while fighting climate change and continuing to sustain their growth. These digital approaches can transform small and medium-sized manufacturers in business and technical practices related to improving work productivity; reduce energy consumption, greenhouse gas emissions and waste; enhance logistic and supply chain management; and protect proprietary information and privacy – all while creating a happy, healthy working environment.

As part of its national strategy, CyManII focuses on four priority areas where collaborative research and development can help U.S. manufacturers: securing automation, securing the supply chain network, improving energy efficiency, and building a national program for education and workforce development.

“Our target group of manufacturing companies are small and medium size. Upgrading their efficiency by leveraging data will allow for a surge of demand from internal markets,” Li said. “At the same time, we want to ensure cybersecurity and provide energy efficiencies that support the nation’s goal to combat climate change and its impact.”

of Agriculture. The funds will help the Platforms for Advanced Wireless Research program establish a new facility in central Iowa dedicated to driving innovation and improving broadband connectivity in sparsely inhabited regions of the U.S.

Systems working group principal investigator Ozdal Boyraz, UCI professor of electrical engineering and computer science, will lead a team focusing on free-space optical backbone technologies associated with the initiative. FSO uses infrared laser beams to transmit digital data – including internet messages, video, computer files and radio signals – across vast distances without using fiber-optic cables.

“It would be cost-prohibitive to hardwire every location in the nation’s vast rural regions with broadband fiber, so one solution is to use line-of-sight light beam transmitters and receivers to cover the territory,” Boyraz said. “The challenge for our team is to develop technologies that are robust and highly reliable.” Academic researchers in the Iowa hub, called ARA: Wireless Living Lab for Smart and Connected Rural Communities, will work with representatives from an industry consortium of 35 wireless companies to build a programmable infrastructure across Iowa State University, the city of Ames, and nearby farms and communities. The systems will provide a technological backbone for precision agriculture and livestock operations and, potentially, autonomous vehicles and drones.

Said Boyraz: “This project aims to improve the quality of life in rural America through better internet access, benefiting sectors as diverse as agriculture, business, healthcare, education and culture.”

grant. Considered an early career award, NSF CRII grants honor new professors with the goal of initiating and encouraging independent research. Elmalaki’s research titled “Society-in-the-Loop Personalized Computing” includes $175,000 for two years, beginning June 15, 2021.

“As an early career academician, this award provides validation to my research ideas and the required support for my Ph.D. students,” said Elmalaki.

Elmalaki’s research focuses on enabling fairness-aware, privacy-preserving, society-in-the-loop, personalized internet of things (IoT) systems by providing frameworks, tools and methodologies that understand their fundamental properties and guide their systematic design.

“This research will have a far-reaching impact, as it will evolve the IoT systems from a one-size-fits-all approach to a personalized process in which learning and adaptation agents are tailored toward humans’ individual needs,” she explained.

“This macroscopic view of the design of personalized systems to enhance societallevel fairness without compromising the individual-level privacy will contribute toward understanding and building such human-technology relationships, considered to be one of the NSF’s 10 big ideas.”

NSF CISE’s mission is to enable the U.S. to uphold its leadership in computing, communications, information science and engineering; promote understanding of the principles and uses of advanced computing, communications and information systems in service to society; support advanced cyberinfrastructure that enables and accelerates discovery and innovation across all science and engineering disciplines; and contribute to universal, transparent and affordable participation in an information-based society.

the ears.” According to the American Tinnitus Association, an estimated 50 million people in the U.S. suffer from the condition, which can interfere with the ability to work, socialize and sleep.

A UCI multidisciplinary research team, led by Dr. Hamid Djalilian, professor of otolaryngology, includes Michael Green, professor of electrical engineering and computer science; Fan-Gang Zeng, professor of otolaryngology; and Harrison Lin, associate professor of otolaryngology. The researchers found that electronic stimulation of the inner ear can alleviate tinnitus. Based on the success of their initial studies – which involved inserting a miniature electrode in the ear canal and through a small opening of the ear drum – the team has recently been developing an innovative implantable device that is placed behind the ear drum. Patients use a compact hand-held controller to turn electric stimulation on and off as symptoms occur and subside.

“Anyone who has the condition knows the frustration of dealing with the general lack of hope for a cure. Coming to UCI was a real breath of fresh air in a world of pessimism,” said Fargo, who is a patient of Djalilian. “I spent years researching and traveling the world looking at different solutions. One of the things that was abundantly clear was the lack of money being put into solving this problem. That’s why I’ve decided to step up and help accelerate the doctor’s work.”

Fargo’s gift is a $1 million challenge. He will match all donations one-on-one until $2 million is raised to fund the next phases of research and, ultimately, bring a device to market.

Although the exact cause of tinnitus is unclear, medical experts believe that inadequate stimulation of the cochlea – the organ in the inner ear that senses sound – or the cochlear nerve, which carries signals, causes over-sensitization of the auditory cortex, which is located in the brain’s temporal lobe and is responsible for processing sound. The result is hearing sound when there is no actual external noise.

“Outcomes so far have been very exciting, and Brian’s generous gift will help us continue this important work,” Djalilian said. “Our implantable electronic stimulation device shows great promise for bringing a life-changing breakthrough to millions of people.”

in analyzing the scans. Their research was recently published in the Journal of Cardiovascular Magnetic Resonance.

Cardiac magnetic resonance imaging, or MRI, is the method of choice for assessing heart function and anatomy in children with complex congenital heart diseases (CHD). Segmenting and analyzing individual heart chambers in these children are essential steps toward understanding their conditions. But hearts in children with CHD differ from those in healthy children and adults, and analyzing their MRI data is highly challenging, timeconsuming and prone to variability in interpretation by physicians.

The research team’s artificial intelligence platform is based on deeplearning algorithms that can automatically and efficiently analyze cardiac MRIs for this growing group of patients.

“Our learning-based framework provides an automated, fast and accurate model for left ventricle and right ventricle segmentation, and its outstanding performance in children with complex CHDs implies its potential to be used in clinics across the pediatric age group,” said Dr. Arash Kheradvar, professor of biomedical engineering.

Compared to the existing automated approaches, UCI’s platform does not make any assumption about the image or structure of the heart, but instead performs segmentation while learning features of the image on its own, fully automating the process without requiring any predefined input. This makes its results more reliable than those from commercially available platforms. The researchers trained and validated their algorithm on a dataset of 64 pediatric patients with complex cases of CHD from Children’s Hospital of Los Angeles. They found no significant statistical difference in accuracy between the advanced AI method and manual segmentation.

“A major challenge in AI-based segmentation and analysis of cardiac MRI of children with congenital heart disease is lack of a diverse dataset. To mitigate that, for the first time, we employed a novel deep-learning method that synthetically generates new segmented MRI data from noise,” said Saeed Karimi-Bidhendi, the article’s first author and a UCI doctoral candidate in electrical engineering and computer science. Karimi-Bidhendi works in the EECS lab of Chancellor’s Professor Hamid Jafarkhani, who is also a member of the research team.

“This means that machines can segment and analyze the cardiac MRI data of these patients as well as a pediatric cardiologist,” said Kheradvar. “Eventually machines will be able to perform the analyses, replacing physicians. This paper indicates one more step toward fully automating the diagnostic imaging.”