Six NYU Tandon researchers garner prestigious CAREER Awards

faculty headshots of winners

Top left clockwise: Rumi Chunara, Yury Dvorkin, Damon McCoy, Quanyan Zhu, Julia Stoyanovich, and Andrea Silverman

This year, six young principal investigators from NYU Tandon garnered prestigious National Science Foundation (NSF) CAREER Awards, given in support of early-career faculty members who have the potential to serve as academic role models and to lead advances in the mission of their department or organization. That’s an especially noteworthy feat considering that the school’s engineering faculty has just 80 members. It’s an impressive percentage; an inspiring group of engineers; an exciting array of world-changing research; and, given that three of the six are women, solid proof of the strides Tandon has made in closing the STEM gender gap.     

Rumi Chunara

Assistant Professor of Computer Science and Engineering

If you’ve ever worn a FitBit or other such tracker, you know how valuable the information it generates can be for evaluating your personal progress. New data sources like this offer unprecedented potential to study lifestyle, environmental, and social factors that can improve our understanding of well-being and health. This will dramatically change the healthcare paradigm in this country and significantly reduce costs and illnesses, more so than a solely reactive focus on disease diagnosis and treatment. However, inconsistencies between how people use these tools, when people use them, and who uses them, challenge public health researchers' abilities to make inferences, draw comparisons, and evaluate change.

The NSF has honored Rumi Chunara, an Assistant Professor of Biostatistics at NYU’s College of Global Public Health in addition to her post at Tandon, for her work with person-generated data (PGD) from Internet and mobile data sources such as social media sites and wearables. This award builds on her existing work, which demonstrates how information from these tools opens a window into how people respond to local crises, such as epidemics, as well as providing low-cost views into other health risk factors and outcomes — enabling precise and focused interventions. By addressing inconsistencies of how, when and by whom the data is generated, her project will improve the validity and reliability of measures extracted from PGD and enable improved understanding of high-granularity health risks and outcomes for augmenting public health research and practice.

Yury Dvorkin

Assistant Professor of Electrical and Computer Engineering

Thanks to recent advances in smart grid technologies, the U.S. power grid is undergoing much-needed nation-wide modernization, and that turn of events is giving consumers a high level of autonomy, freeing them from their utility companies and allowing them the freedom to choose an alternative supplier or deploy their own solar panels and energy storage units. This customer-driven exodus toward what are called distributed-energy resources (DER) reduces the utility companies’ revenue streams and undermine their financial viability. In response, utilities in many regions are increasing electricity tariffs, pushing even more consumers to defect to other options — a death spiral for major utilities. This spiral scares a vast majority of power executives who anticipate a complete transformation of their business model by 2030.

Yury Dvorkin aims to fundamentally rethink and re-engineer the current U.S. power grid to accommodate a high penetration level of DERs, while improving the overall reliability, resiliency, and energy efficiency of the power sector. He is designing a peer-to-peer platform that will manage customer-end DERs using the utility's own network infrastructure and control policies. Coupled with a system to calculate appropriate network usage charges, it would be a win-win situation for electricity consumers and utility companies, made possible thanks to Dvorkin’s expertise and the NSF’s generous support.  

Damon McCoy

Assistant Professor of Computer Science and Engineering

Cryptocurrencies, such as Bitcoin, offer the promise of increased efficiency in the financial system along with decreased fees and costs for processes like making international money transfers and raising investment capital. Unfortunately, they are also misused as a way of facilitating a rogue’s gallery of illegal activities, including extortion, drug dealing, human trafficking, and cybercrime. Improved forensic tools could go a long way towards identifying wrongdoers and capturing valuable information about nefarious transactions, but much of the research into advanced cryptocurrency forensic techniques is performed by companies and integrated into their own closed platforms. As a result, law enforcement officials, non-profit organizations, and the general public are left largely in the dark, with little knowledge of how to detect, understand, and guard against the illicit activities occurring within these shadowy cryptocurrency ecosystems.

Damon McCoy has embarked on a research project that uniquely blends improvements to data collection and archiving with advanced machine learning-based algorithms in order to devise a set of open and improved tools that can be used on a broad range of cryptocurrencies, making both the online world and the physical world much safer places.

Andrea Silverman

Assistant Professor of Civil and Urban Engineering

First came word that Andrea Silverman and her colleagues had garnered a grant from the Marron Institute of Urban Management for their study on the impact of flooding on the urban microbiome (as scientists call the community of micro-organisms living together in a particular habitat) and city residents’ exposure to sewage pathogens (which can include disease-causing bacteria and viruses). Following close on the heels of that good news came her CAREER Award, given for her study of the degradation of antibiotic resistance genes in the environment when exposed to sunlight.

Antibiotic resistance genes are the DNA molecules found within bacteria that code for resistance to antibiotic treatment, which is a growing global public health threat. Resistance genes have been found to pass through wastewater treatment facilities and enter environmental waters unscathed, heightening the concern that the environment is a transmission route for antibiotic-resistant bacteria. The goal of Silverman’s research is to evaluate the persistence and decay of antibiotic resistance genes in the environment, to help fill critical gaps in our understanding of the role of the environment in the risk of disease transmission. This work builds upon her previous research investigating disinfection processes in natural wastewater treatment systems, such as manmade treatment ponds and wetlands designed specifically to treat sewage.

Silverman, who holds a joint appointment with NYU’s College of Global Public Health, has explained that engineers who design water and wastewater treatment systems need to understand the genesis and behavior of waterborne microbial and chemical contaminants that present public health risks, and how those characteristics affect treatment efficiency.

“Similarly,” she added, “public health professionals who focus on water-related illnesses should have a good understanding of the benefits and limitations of technologies used to treat water and assure its safety.” With the support of organizations like the NSF and the Marron Institute, she is successfully straddling both of those worlds.

Julia Stoyanovich

Assistant Professor of Computer Science and Engineering

Almost everyone remembers plotting points on blue-ruled pieces of graph paper in middle school — charting simple data like sports scores or pieces of variously colored candy in a bag — but graphs are used to represent a plethora of complex phenomena, including the Web, social networks, biological pathways, transportation networks, and semantic knowledge bases. Many interesting and important questions about graphs concern their evolution rather than their static state: Which Web pages are becoming increasingly popular? How does influence propagate in social networks? How do the utilization of transportation options and the cost of ridership in a city change during the day and throughout the week? How does knowledge evolve?

Formulating these questions as programs is currently beyond the skills of most data scientists, and executing those programs poses tremendous efficiency challenges, especially for graphs with billions of edges or significant evolution rates.

While much of the research now being done is aimed at developing sophisticated graph analytics and efficiently implementing them for static graphs, Julia Stoyanovich, who has a joint appointment with NYU’s Center for Data Science, is focused on querying and analysis of evolving graphs – functionality that she explains is urgently needed because of the scalability challenges inherent to evolving graph analysis, and usability and ease of dissemination. The initial results of her work are available in an open-source platform called Portal at  A prominent set of use cases for her work will come from data science for social-good applications, including urban homelessness and analysis of transportation utilization and cost in cities – areas that have long been important to Stoyanovich, who was appointed by Mayor Bill de Blasio to New York City’s first-ever Automated Decision Systems Task Force. The Task Force is responsible for providing recommendations to City agencies, to help ensure fairness and equity in data-driven algorithmic systems used to determine such issues as where to locate firehouses, how to allocate public housing units, and which students will be sent to which schools.

Quanyan Zhu

Assistant Professor of Electrical and Computer Engineering

Cyber-physical systems (CPS), as the name implies, can be exceedingly complex, with multiple layers of interacting cyber, physical, and even human components. Because the layers can be tightly integrated and highly interdependent, traditional analysis tools are insufficient to cope with their full complexity. That spells bad news for those defending these systems against cyberwarfare.

Quanyan Zhu explains that it is essential to develop a multidisciplinary approach to working with CPS — which include smart grid utilities, medical monitoring devices, and autopilot avionics, among other vital systems — especially because those complex, coupled layers leave such systems vulnerable to hacking by malefactors seeking to inflict harm on our infrastructure.

He points to the infamous Stuxnet attack, which caused billion-dollar losses, as something of a poster child for how failure of a component at one layer can lead to the failure of another layer, and precisely why a holistic system-level view is needed. An attack could start with social engineering (the human element) to gather information and obtain unauthorized credentials before exploiting the cyber system and, ultimately, damaging the physical components. That type of advanced persistent threat (APT) attack — accomplished through a set of multi-stage stealthy and continuous hacking processes — highlights the fact that perfect security is not always possible, making it imperative that a system be as resilient as possible in the wake of an attack. Using electric power systems and cloud-enabled autonomous systems as his case studies, Zhu aims to establish an integrated game-theoretic framework to engineer resilient, high-confidence CPS. With his CAREER Award, the NSF displays its high confidence in his work.