University Researchers Team Up to Develop Technology Innovations for NYC in the face of COVID

Nov 10 2020 | By Holly Evarts

Thanks to a generous Columbia Engineering alumni donor, 10 faculty teams have each won an $85,000 award to develop technology innovations for urban living in the face of COVID-19. Each team includes a SEAS faculty member working with collaborators across the University to generate solutions that will not only engineer a speedy recovery for New York, but also will transform the city’s infrastructure and systems, define urban redevelopment, and minimize future disruptions—whether from the next pandemic, superstorms and sea-level rise, energy shortages, or other severe events or disasters.

“The COVID-19 crisis has dramatically altered the flow of everyday living—from work, education, and transportation to food, entertainment, and health and services—challenging us to rethink many aspects around the design, infrastructure, and functionality of our cities,” said Columbia Engineering Dean Mary Boyce. “Urban centers like New York, including our Columbia campus, require a wide range of innovations as we reemerge into a new “normal” and a resilient future. The greatness of New York City coupled with innovative minds across Columbia is a winning team and we are deeply appreciative of our donor’s generosity in helping us to ignite this passion.”

The funding for this initiative—Urban Living Tech Innovations—will support creative cross-disciplinary research projects that develop and deploy advanced technologies. These include artificial intelligence, machine learning, computational systems, algorithms, predictive modeling, design thinking, simulations, robotics, materials, chemicals, sensors, and devices to speed New York’s recovery, establish a new normal, and advance public health and medicine through new technologies and scalable models.

The initiative is also providing student ignition awards to develop prototypes and start new ventures. These teams will be given opportunities to use the new Columbia MakerSpace for experimentation and testing. Rolling submissions are currently ongoing until December 15, 2020. More information here.

The 10 winning research teams are:

Real-Time Crowd Management to Prepare Subway Stations for Future Pandemics

Sharon Di, Assistant Professor, Civil Engineering & Engineering Mechanics (SEAS)
Fred Jiang, Associate Professor, Electrical Engineering and Computer Engineering (SEAS)
Jeffrey Shaman, Professor, Environmental Health Sciences (SPH)

The goal of this proposal is to equip public transit communities for greater preparedness and resilience to future pandemics with a crowd management system leveraging sensing, data science, and digital twin. The research question is: how should crowds be managed in real-time during the pandemic to mitigate risk and ease the public fear of taking mass transit? This question will be addressed by bridging engineering and scientific models at the intersection of indoor pedestrian mobility, epidemiology, and travel behavior. Rather than investing in transit infrastructure and services in a short timeframe (which is especially challenging when transit agencies are struggling with staffing shortage and financial crisis), reshaping public transport mobility could be a more viable way.

 

Optimizing Emergency Response during a Pandemic in Urban Environments

Andrew Smyth, Professor, Civil Engineering & Engineering Mechanics (SEAS)
Henry Lam, Professor, Industrial Engineering and Operations Research (SEAS)
Jay Sethuraman, Professor, Industrial Engineering and Operations Research (SEAS)
Kat Thomson, Assistant Commissioner, Heads the FDNY Data Analytics Team (FDNY)

This proposal seeks to develop implementable policies which can alleviate the load imbalance on hospitals while still maintaining optimized outcomes for individual patients. Such a system will require actual or indirectly estimated hospital real-time “census” data heretofore unused in the EMS world. The hospital capacity data can then be integrated in an optimization setting to balance the travel time and hospital wait time considerations. It is important to note that this consideration is not just made for each individual patient, because a longer EMS trip away from a cluster region will also mean the EMS units are less optimally positioned for the next call from that cluster region.

 

Preparing for the Next Pandemic and Super-Storm

Daniel Bienstocks, the Liu Family Professor, Industrial Engineering and Operations Research and APAM (SEAS)
George Deodatis, the Santiago and Roberta Calatrava Family Professor, Civil Engineering and Engineering Mechanics (SEAS)
Kyle Mandli, Associate Professor, Applied Physics and Applied Mathematics (SEAS)
Jonathan Sury, Project Director, National Center for Disaster Preparedness (NCDP) / (EI)

A main objective of the work will be the development of a “risk dashboard,” a software tool to inform decision-makers and the public concerning risks (e.g. high concentration of people at a subway station), threats (e.g. a hospital experiencing congestion) and ongoing difficulties (e.g. a major roadway to a hospital impeded), as well as projecting future risks (etc) by means of analytics.

 

Low-Cost Continuous Multi-Person Fever Detection for a Safer COVID-19 and Post-COVID-19 World

Xiaofan (Fred) Jiang, Associate Professor, Electrical Engineering (SEAS)
Andrew Rundle, Associate Professor, Epidemiology (SPH)
Teresa Spada, Director of Practice Operations (ColumbiaDoctors Midtown)

The vision of this project is to equip buildings and mass transits around the world with the capability to continuously screen occupants for fever, thus improving the safety and resilience of our cities against future pandemics. One of the key objectives of this project is to create a technology that can monitor many occupants simultaneous without disrupting their normal activities, which is essential in helping to restore some amount of normality to our vibrant cities. Another goal is to keep the cost to be within a few hundred dollars, so that it is affordable for everyone. Collectively, these “smarter” buildings and transit systems become parts of epidemic early warning systems, enabling cities to quickly respond to future novel viruses, as well as flu seasons.

 

Designing Safe Elevator Systems amidst a Pandemic

Adam Elmachtoub, Assistant Professor, Industrial Engineering & Operations Research (SEAS)
Cliff Stein, Professor, Industrial Engineering & Operations Research, and Computer Science (SEAS)
Charles Branas, Professor & Chair, Epidemiology (SPH)
Neal Parikh, Director, Artificial Intelligence (Mayor’s Office of the CTO)
Department of Citywide Administrative Services (DCAS)

In this proposal, we shall consider two major forms of interventions based on (i) changing passenger behaviors and (ii) elevator AI. In many elevator systems, changing the algorithms and technology of how the elevators navigate through the building is challenging due to outdated technology and would require long-term planning and expensive modifications. Thus, focusing on how passengers use and board elevators is often the only option for an intervention. Currently, many elevator systems take a hands-off approach to managing the flow of people to elevators, resulting in something that resembles first-come first-served.

 

Low-cost monitor for verification of UV Sterilization systems

Ioannis Kymissis, Professor, Electrical Engineering (SEAS)
Elizabeth Hillman, Professor, Biomedical Engineering (SEAS); Department of Radiology (CUIMC)

Ultraviolet (UV) sterilization is a method that can neutralize a range of pathogens including SARS-CoV-2. However, UV light can also be dangerous to humans, causing cataracts and skin cancer through direct exposure, as well as environmental hazards such as generation of toxic ozone gas. Although UV sterilization systems are broadly categorized in terms of their wavelength band (e.g. A, B or C), the effectiveness of UV sterilization, and the risk of UV to humans, depends critically on the precise wavelength of UV used, and the intensity and duration of UV exposure. The wide array of existing and emerging UV sterilization systems currently being deployed to decontaminate public spaces are likely to have widely varying illumination properties, and are prone to shifts in UV emissions as lamps degrade over time. This project is focused on developing a system to track and wirelessly report UV wavelength and exposure in a space and to precisely quantify both the effectiveness of UV sterilization for removal of pathogens and their acute and cumulative risk of exposure to humans. The project will further develop a low-cost monitor and accompanying mobile app that will report on the spectrally resolved dose that the unit has received, allowing users to be confident that the sterilization of a space has been conducted properly.

 

Testing the Efficacy of Far-UVC Light to Safely Inactivate Airborne and Surface Viruses in Real-Life Demonstration Projects

David J. Brenner, Professor of Radiation Biophysics, Center for Radiological Research, Department of Radiation Oncology (CUIMC)
Gordana Vunjak-Novakovic, Professor of Biomedical Engineering and Medical Sciences (SEAS)
Manuela Buonanno, Associate Research Scientist, Center for Radiological Research and the Radiological Research Accelerator Facility (CUIMC)
David Welch, Associate Research Scientist, Center for Radiological Research (CUIMC)

Overhead far-UVC light (222 nm) has emerged as a potentially safe and efficient approach for continuously reducing the level of active virus, including coronavirus, in the air and on surfaces in occupied indoor spaces. Prior to large-scale implementation, several real-life demonstration projects are being undertaken. A knowledge gap within these demonstration projects is a methodology for assessing reductions in the infectious viral load in the room – in short, does it work? This work aims to fill that gap by designing and validating an experimental approach for measuring active viral load - as opposed to total (active + inactive) viral load - in the air and on surfaces, and then using the approach in the demonstration projects, with vs. without far-UVC.

 

Development and Field-Testing a Mobile App for Tracking Home-Based COVID-19 Rapid Test Results

Samuel Sia, Professor, Biomedical Engineering (SEAS)
Shih-Fu Chang, the Richard Dicker Professor of Electrical Engineering, and Computer Science (SEAS)
Jessica Justman, Associate Professor of Medicine in Epidemiology (CUIMC) and Senior Technical Director, ICAP (SPH)
Wafaa El-Sadr, University Professor of Epidemiology and Medicine, Founder and Director of ICAP (CUIMC/SPH)

We propose to develop and field-test on at least 20 users in an underserved community (e.g. patients at Harlem Hospital): 1) a mobile app that will aid users (both providers and consumers) to perform and interpret rapid COVID-19 tests, and 2) a cloud-hosted platform that will track the results in real time, matched to geolocation and co-morbidities, to increase the value of the data for epidemiology and public policy. We will build on our previous experience where we field-tested a mobile app for tracking HIV and sexually transmitted disease rapid tests among high-risk groups in an NIH R01-funded project. We will work with partners (starting at Harlem Hospital) across New York City. We now know there are significant and ongoing variations from neighborhood to neighborhood, depending on race and income, with the hardest hit areas including East New York in Brooklyn, Far Rockaway, Flushing and Elmhurst in Queens, and Baychester and Co-op City in the Bronx. Continued collection of granular data in these communities will be crucial, to monitor the infection rate as well as to better understand co-morbidities and other correlates (e.g. density of housing) to disentangle the drivers of these inequalities.

 

Direct bioelectronic detection of SARS-CoV-2 from saliva using single-molecule field-effect transistor (smFET) arrays

Kenneth Shepard, the Lau Family Professor, Electrical Engineering, and Professor, Biomedical Engineering (SEAS)
Henry Colecraft, the John C. Dalton Professor, Physiology and Cellular Biophysics, and Professor, Pharmacology (CUIMC)

Direct testing for the virus, which also reduces requirements for multiple reagents, is a necessary step to improving diagnostic testing. While two such antigen test have been approved for detection of SARS-CoV-2 based on immunoassays to the S protein, specificity is poor because of reliance on a single (or in a some cases two) detection antibodies, and no quantitation of viral load is possible. Sample preparation is still required for these assays. We will address this gap by using an in development rapid POC platform for direct, real-time, multiplexed, quantitative bioelectronic detection of biomolecules that employs an all-electronic detection device that functions at the single-molecule level. These single-molecule field-effect transistors (smFETs) are arrayed on a complementary metal-oxide-semiconductor (CMOS) integrated circuit chip mounted on a USB-stick-form-factor device.

 

Education Through Crisis and Disruption: Inquiry Based STEM Learning Via Enhanced Text Message

Paulo Blikstein, Associate Professor (TC)
Lydia Chilton, Assistant Professor, Computer Science (SEAS)

An essential component of STEM education is participation in authentic inquiry-learning activities. Providing students with (a) access to these activities and (b) adequate support while engaged in these activities has proven challenging in the new reality of remote learning. Our goal is to systematically examine and develop a solution to this problem using a low-cost, mobile phone-based approach to at-home, inquiry-driven science learning called the “STEM Messaging System” (SMS+). SMS+ will support real-time, interactive, message-based STEM activities for which the only computational resource required is the family’s mobile phone.