Research News

Edward Wolf, professor emeritus at the NYU Tandon School of Engineering led this research

The 2020 hurricane season had a record breaking 30 named storms, 13 of which became hurricanes and six became major hurricanes. Compare that to the average storm season, which historically produces 12 named storms, six hurricanes, and three major hurricanes. It’s clear that hurricanes are increasing in both number and intensity.

Researchers have been studying the relation between water temperature and hurricane frequency for years now. There have been suggestions that as temperature increases, storms become more powerful.

Now, Tandon researcher Edward Wolf has released new research that confirms both facts. In studying recent hurricane data, Wolf was able to pinpoint the ocean temperature where it becomes possible for hurricanes to form. That temperature is approximately 26.5 degrees Celsius. Much like how water boils at 100 degrees Celsius (and not a degree lower), weather patterns cannot phase change into a hurricane until this water temperature is met.

Wolf also developed a simple algorithm that can predict the severity of a storm by measuring the temperature of the water beneath it. As the temperature goes up, the severity of the storm increases in a consistent and measurable manner. Not only does this prove that water temperature and storm strength are directly linked, it could be a tool to efficiently gauge the strength of a storm — an early warning system that could help communities in its path prepare.

Wolf’s research also provides one surprising detail: the algorithm describing how storm severity increases in proportion to ocean temperatures finds a direct analog in ferromagnetism — the strength of an iron magnet’s field: the temperature-defined phase change of ferromagnetism follows the same critical exponent formula T-Tc ^⅓ determining shift to magnetism at specific temperatures.

Researchers are now able to use the vast scientific literature on ferromagnetism in order to study hurricane formation, which by its nature has less raw data to work with. Wolf was able to use previous iron studies to fine tune his algorithm, producing even finer data.

This research was conducted by Jin Kim Montclare, Professor of Chemical and Biomolecular Engineering; and former students Yao Wang, a recent Ph. D. graduate, and Xiaole Wang, a former M.S. student.

Protein hydrogels, 3-dimensional macromolecular structures that do not dissolve in water (in spite of being hydrophilic), can hold large quantities of aqueous solutions due to the network formed from chemical or physical crosslinking. Partly because of this they have many medical applications including tissue engineering, wound healing and drug delivery.

These materials can be synthesized by crosslinking polymers chemically via covalent bonds, or physically via non-covalent interactions, or a mixture of both. One way of doing this is through photo-initiated crosslinking, wherein chemically inert groups become photo-reactive once exposed to certain wavelengths of light.

“The advantage of employing photochemical reactive groups over traditional chemical reagents is that they give the user spatiotemporal control over polymerization,“ said Montclare. “In other words, the biopolymers bearing the photocrosslinkers can be generated and crosslinked at some later step under various conditions to achieve control over encapsulation and release of therapeutic agents.”

In this research, the team designed a macromolecular triblock polymer comprising two different self-assembling domains derived from elastin protein (E) and coiled-coil protein (C), that can be photopolymerized due to an a NHS-diazirine (D) crosslinker to produce a CEC-D hydrogel.  

In the work, “Free-Standing Photocrosslinked Protein Polymer Hydrogels for Sustained Drug Release,” in Biomacromolecules, a publication of the American Chemical Society, the investigators determined the best photocrosslinker concentration and exposure time necessary to create this independent hydrogel.

The researchers found that, overall, CEC-D hydrogel exhibits comparable characteristics, including stability, drug release profile, and elastic behavior to other hydrogels.

Because it can be used for drug delivery with high encapsulation and a low but significant release of curcumin, CEC-D has been proven to be capable of a sustained release of a given drug over a week's time.

This research is led by Vikram Kapila, professor of mechanical and aerospace engineering. Principal authors are Ph.D. students Ashwin Rajkumar, Master's students Fabio Vulpi and Satish Reddy Bethi, and Preeti Raghavan of Johns Hopkins University School of Medicine and Rusk Rehabilitation at NYU School of Medicince.

Recent years have seen a brisk rise in development and deployment of digital health systems using such technologies as wearable sensors and embedded controllers to enhance access to medical diagnostics and treatments. Because of an accelerating trend in the number of stroke survivors requiring rehabilitation, healthcare services worldwide are considering technological solutions to enhance accessibility to assessment and treatment, particularly during the past year’s period of enforced quarantine due to COVID-19.

Some of the challenges faced by these technologies are clinical acceptance, high equipment cost, accuracy, and ease of use.

To address these limitations, the researchers designed wearable inertial sensors for exergames (WISE), a system that includes an animated virtual coach to deliver instruction, and a subject- model whose movements are animated by real-time sensor measurements from the WISE system worn by a subject. The paper examines the WISE system’s accuracy and usability for the assessment of upper limb range of motion (ROM).

The system uses five wearable sensor modules affixed to a user’s upper body: above the wrist on the left and right forearms, above the elbow on the left and right arms, and on the back. Each WISE sensor module consists of a sensor interfaced with a microcontroller soldered to a printed circuit board, which is connected to a lithium-ion battery. A storage and calibration cube is designed and 3D-printed to house the five WISE system modules and simultaneously calibrate all the sensors prior to placement on a subject. The microcontroller retrieves absolute orientation measurement from the sensor and wirelessly streams it to a computer. The investigators used a Unity3D-based exergame interface to animate the sensor data into a 3D-human model.

Seventeen neurologically intact subjects were recruited to participate in a usability study of the WISE system. The subjects performed a series of shoulder and elbow exercises for each arm instructed by the animated virtual coach; accuracy of the ROM measurements obtained with the WISE system were compared with those obtained with a system using the Microsoft Kinect markerless motion capture system (a platform used for exergames and often tested for rehabilitation capabilities). The results suggest the WISE system performs as well as Kinect.

The researchers plan future studies with patient populations in clinical and tele-rehabilitation settings.

This work is supported in part by the National Science Foundation DRK-12 Grant DRL-1417769, RET Site Grant EEC-1542286, and ITEST Grant DRL-1614085; NY Space Grant Consortium Grant 48240-7887; and Translation of Rehabilitation Engineering Advances and Technology (TREAT) grant NIH P2CHD086841.

This research is led by Vikram Kapila, professor of mechanical and aerospace engineering. Principal authors are Ph.D. students Hassam Khan Wazir and Christian Lourido, and Sonia Mary Chacko, a researcher and recent Ph.D. graduate under Kapila.

Healthcare workers, at risk of contracting COVID-19 when in close proximity to infected patients, may transmit the virus to other hospital-bound patients, including those on dialysis. In order to circumvent this risk, the researchers proposed a remote control system for dialysis machines. The proposed setup uses dialysis machines fitted with robotic manipulators connected wirelessly to tablets allowing remote control by workers outside of patients’ rooms.

The system (see video here) comprises an off-the-shelf four degrees of freedom (DoF) robotic manipulator equipped with a USB camera. The robot base and camera stand are fixed on a platform, making the system installation and operation simple, just requiring the user to properly locate the robot in front of its workspace and point the camera to a touchscreen (representing a dialysis machine instrument control panel touchscreen) with which the robot manipulator is required to interact. The user interface consisting of a mobile app is connected to the same wireless network as the robot manipulator system. To identify the surface plane of action of the robot, the mobile app uses the camera’s video-feed which includes a 2D image marker located in the plane of the instrument control panel touchscreen, in front of the robot manipulator. The robotic arm can facilitate complicated sequences of button and slider manipulation thanks to a complex series of algorithms that automate some functions of the machine.

The machine attached to the already in-use dialysis machine livestreams data and results directly to a tablet or computer operated by a remote user in another room. Users tend to report a more consistent performance from the system when the user interaction is performed with a computer versus a tablet, though no significant difference has been recorded between the two modes of operation.

One of the most significant features of this new technology is that creating a custom user interface is not required to operate it, given that the user is interacting directly with the video feed from the instrument control panel touchscreen. Thus, the system works on any device with a touchscreen. The proposed device can be administered almost instantly, which can make it useful in an emergency situation. In the future, options of applying AR (augmented reality) features to the system in order to optimize user experience and efficiency may be explored.

This work is supported in part by the National Science Foundation under ITEST grant DRL-1614085, RET Site grant EEC-1542286, and DRK- 12 grant DRL-1417769.

This research was based on a program developed by Vikram Kapila, professor of mechanical and aerospace engineering.  

Sparking interest in STEM education is a critical step toward creating a diverse workforce that can confer key advantages to any country in the global tech world. The number of US jobs required in STEM fields has increased nearly 34% over the past decade, but the number of students opting to pursue STEM as a major and career is declining. Many educators, researchers, and funding agencies have devoted significant efforts towards promoting students’ motivation and interests for learning and academic performance at all levels of STEM ecosystem to catalyze students’ entry on the pathways for STEM professions.

For students, robotics seems to be one of the best entryways to the field of engineering and STEM education in general. For example, students using a robot kit made by LEGO in a classroom can have a joyful and entertaining experience as they feel like playing with toys, which can encourage them to participate in robotic-based learning activities. Still, teachers can be reluctant. These programs can require specialized knowledge and teachers often do not have models or understanding of pedagogical approaches to implement technology-integrated courses, in general, and robotics-integrated courses, in particular.

A new study from Tandon researchers describes a professional development (PD) program designed to support middle school teachers in effectively integrating robotics in science and mathematics classrooms. The PD program encouraged the teachers to develop their own science and mathematics lessons, aligned with national standards, infused with robotic activities.

The study is based on a program developed by Vikram Kapila, Professor of Mechanical and Aerospace Engineering, and it involves Sonia Mary Chacko, a recent doctoral graduate from NYU Tandon. The lead author of the study, Hye Sun You, served as a research associate in the program and is now an Assistant Professor of Science Education at Arkansas Tech University. The study proposed that a multi-week summer PD and sustained academic year follow-up imparted to the teachers the technical knowledge and skills of robotics as well as an understanding of when and how to use robotics in science and mathematics teaching.

The 41 participants of study consisted of 20 mathematics and 20 science teachers and one teacher who teaches both subjects. Three instruments were administered to the teachers during the PD, and follow-up interviews were conducted to further examine benefits and possible impacts on their teaching resulting from the PD. The data were analyzed by both statistical and qualitative methods to identify the effectiveness of the PD.

This study found the technology integration of LEGO robotics tools has the potential to enhance teaching and learning, and that thoughtful PD programs and ongoing support for teachers can provide specific and practical ways to reduce the barrier to embedding technology in educational curricula. It is expected that the PD focused on improving teachers’ knowledge level, confidence, and attitudes towards technology helps teachers overcome barriers that make it difficult to integrate technology into their instruction and ultimately, transforms the performance of students by effective use of technology.

This work is supported in part by the National Science Foundation under ITEST grant DRL-1614085, RET 632 Site grant EEC-1542286, and DRK-12 grant DRL-1417769, and NY Space Grant Consortium grant 76156-10488.

In a new study led by Institute Professor Maurizio Porfiri at NYU Tandon, researchers showed a novel principle of actuation — to transform electrical energy into motion. This actuation mechanism is based on solvation, the interaction between solute and solvent molecules in a solution. This phenomenon is particular important in water, as its molecules are polar: oxygen attracts electrons more than hydrogen, such that oxygen has a slightly negative charge and hydrogen a slightly positive one. Thus, water molecules are attracted by charged ions in solution, forming shells around them. This microscopic phenomenon plays a critical role in the properties of solutions and in essential biological processes such as protein folding, but prior to this study there was no evidence of potential macroscopic mechanical consequences of solvation.

The group of researchers, which also included Alain Boldini, a Ph.D. candidate in the Department of Mechanical and Aerospace Engineering at NYU Tandon, and Dr. Youngsu Cha of the Korea Institute of Science and Technology, proposed that solvation could be exploited to produce macroscopic deformations in materials. To this end, Porfiri and his group utilized ionomer membranes, unique polymeric materials in which negative charges cannot move. Positive ions can easily enter these membranes, while negative ions are repulsed by them. To demonstrate actuation, ionomer membranes were immersed in a solution of water and salt, between two electrodes. Applying a voltage across the electrodes caused the membrane to bend. The paper, "Solvation-Driven Electrochemical Actuation," is published in the American Physical Society's Physical Review Letters.

According to the model developed by Porfiri and his group, the voltage caused a current of positive ions toward the negative electrode. These ions entered the membrane from one side, along with the water molecules in their solvation shells. On the other side of the membrane, positive ions and their solvation shells were dragged outside. The membrane responded like a sponge: the side full of water expanded, while the side with less water shrank. This differential swelling produced the macroscopic bending of the membrane. Studying actuation with different ions helps understand this phenomenon, as different ions attract a different number of water molecules around them.

The discovery of macroscopic mechanical consequences of solvation paves the way for more research on membranes. The group expects applications in the field of electrochemical cells (batteries, fuel cells, and electrolyzers), which often rely on the membranes utilized in this study. These membranes also share similarities with natural membranes, such as cell membranes, on which the mechanical effects of solvation are largely unknown.

The work was supported by the National Science Foundation under grant No. OISE-1545857 and the National Research Foundation of Korea (NRF) funded by the Korea government (MSIT) under grant No. 2020R1A2C2005252.

This research was led by Siddharth Garg, Institute Associate Professor of electrical and computer engineering, and included Benjamin Tan, a research assistant professor of electrical and computer engineering, and Kang Liu, a Ph.D. student.  

Machine learning (ML) systems are being proposed for use in domains that can affect our day-to-day lives, including face expression recognition systems. Because of the need for privacy, users will look to use privacy preservation tools, typical produced by a third party. To this end, generative adversarial neural networks (GANs) have been proposed for generating or manipulating images. Versions of these systems called “privacy-preserving GANs” (PP-GANs) are designed to sanitize sensitive data (e.g., images of human faces) so that only application-critical information is retained while private attributes, such as the identity of a subject, are removed — by, for example, preserving facial expressions while replacing other identifying information.

Such ML-based privacy tools have potential applications in other privacy sensitive domains such as to remove location-relevant information from vehicular camera data; obfuscate the identity of a person who produced a handwriting sample; or remove barcodes from images. In the case of GANs, the complexity involved in training such models suggests the outsourcing of GAN training in order to achieve PP-GANs functionality.

To measure the privacy-preserving performance of PP-GANs researchers typically use empirical metrics of information leakage to demonstrate the (in)ability of deep learning (DL)-based discriminators to identify secret information from sanitized images. Noting that empirical metrics are dependent on discriminators’ learning capacities and training budgets, Garg and his collaborators argue that such privacy checks lack the necessary rigor for guaranteeing privacy.

In the paper “Subverting Privacy-Preserving GANs: Hiding Secrets in Sanitized Images,” the team formulated an adversarial setting to "stress-test" whether empirical privacy checks are sufficient to guarantee protection against private data recovery from data that has been “sanitized” by a PP-GAN. In doing so, they showed that PP-GAN designs can, in fact, be subverted to pass privacy checks, while still allowing secret information to be extracted from sanitized images.

While the team’s adversarial PP-GAN passed all existing privacy checks, it actually hid secret data pertaining to the sensitive attributes, even allowing for reconstruction of the original private image. They showed that the results have both foundational and practical implications, and that stronger privacy checks are needed before PP-GANs can be deployed in the real-world.

“From a practical stand-point, our results sound a note of caution against the use of data sanitization tools, and specifically PP-GANs, designed by third-parties,” explained Garg.

The study, which will be presented at the virtual 35^th AAAI Conference on Artificial Intelligence, provides background on PP-GANs and associated empirical privacy checks; formulates an attack scenario to ask if empirical privacy checks can be subverted, and outlines an approach for circumventing empirical privacy checks.

• The team provides the first comprehensive security analysis of privacy-preserving GANs and demonstrate that existing privacy checks are inadequate to detect leakage of sensitive information. 

• Using a novel steganographic approach, they adversarially modify a state-of-the-art PP-GAN to hide a secret (the user ID), from purportedly sanitized face images. 

• They show that their proposed adversarial PP-GAN can successfully hide sensitive attributes in “sanitized” output images that pass privacy checks, with 100% secret recovery rate. 


“Our experimental results highlighted the insufficiency of existing DL-based privacy checks, and potential risks of using untrusted third-party PP-GAN tools,” said Garg, in the study.

COVID 19 has wreaked havoc across the planet. As of January 1, 2021, the WHO has reported nearly 82 million cases globally, with over 1.8 million deaths. In the face of this upheaval, public health authorities and the general population are striving to achieve a balance between safety and normalcy. The uncertainty and novelty of the current conditions call for the development of theory and simulation tools that could offer a fine resolution of multiple strata of society while supporting the evaluation of “what-if” scenarios.

The research team led by Maurizio Porfiri proposes an agent-based modeling platform to simulate the spreading of COVID-19 in small towns and cities. The platform is developed at the resolution of a single individual, and demonstrated for the city of New Rochelle, NY — one of the first outbreaks registered in the United States. The researchers used New Rochelle not only because of its place in the COVID timeline, but because agent-based modelling for mid-size towns are relatively unexplored despite the U.S. being largely composed of small towns.

Supported by expert knowledge and informed by officially reported COVID-19 data, the model incorporates detailed elements of the spreading within a statistically realistic population. Along with pertinent functionality such as testing, treatment, and vaccination options, the model also accounts for the burden of other illnesses with symptoms similar to COVID-19. Unique to the model is the possibility to explore different testing approaches — in hospitals or drive-through facilities— and vaccination strategies that could prioritize vulnerable groups. Decision making by public authorities could benefit from the model, for its fine-grain resolution, open-source nature, and wide range of features.

The study had some stark conclusions. One example: the results suggest that prioritizing vaccination of high-risk individuals has a marginal effect on the count of COVID-19 deaths. To obtain significant improvements, a very large fraction of the town population should, in fact, be vaccinated. Importantly, the benefits of the restrictive measures in place during the first wave greatly surpass those from any of these selective vaccination scenarios. Even with a vaccine available, social distancing, protective measures, and mobility restrictions will still key tools to fight COVID-19.

The research team included Zhong-Ping Jiang, professor of electrical and computer engineering; post-docs Agnieszka Truszkowska, who led the implementation of the computational framework for the project, and Brandon Behring; graduate student Jalil Hasanyan; as well as Lorenzo Zino from the University of Groningen, Sachit Butail from Southern Illinois University, Emanuele Caroppo from the Università Cattolica del Sacro Cuore, and Alessandro Rizzo from Turin Polytechnic. The work was partially supported by National Science Foundation (CMMI1561134 and CMMI-2027990), Compagnia di San Paolo, MAECI (“Mac2Mic”), the European Research Council, and the Netherlands Organisation for Scientific Research.

This research project is led by Department of Electrical and Computer Engineering Professors Farshad Khorrami and Ramesh Karri, who is co-founder and co-chair of the NYU Center for Cybersecurity, and Prashanth Krishnamurthy, a research scientist at NYU Tandon; and Jörg Henkel and Hussam Amrouch of the Computer Science Department of the Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.

The project builds upon on-going research, funded by a $1.3 million grant from the Office of Naval Research, to create algorithms for detecting Trojans — deliberate flaws inserted into chips during fabrication — based on the short term aging phenomena in transistors.

It will focus on this physical phenomenon of short-term aging as a route to detecting hardware Trojans. The efficacy of short-term aging-based hardware Trojan detection has been demonstrated through simulations on integrated circuits (ICs) with several types of hardware Trojans through stochastic perturbations injected into the simulation studies. This DURIP project seeks to demonstrate hardware Trojan detection in actual physical ICs.

Khorrami explained that the new $359,000 grant will support the design and fabrication of 28nm chips with and without built-in trojans

"The supply chain in manufacturing chips is complex and most foundries are overseas. Once a chip is fabricated and returned to the customer, the question is if additional hardware has been included on the chip die for most likely malicious purposes," he said.

For this purpose, this DURIP project is proposing a novel experimental testbed consisting of:

• A specifically designed IC that contains Trojan-free and Trojan-infected variants of multiple circuits (e.g., cryptographic accelerators and micrcontrollers). This IC will be used for evaluation of the efficacy and accuracy of the hardware short-term aging based Trojan detection methods. To validate the Trojan detection methodology the team will use 3mm×3mm ICs with both Trojan-free and Trojan-infected variants of multiple circuits.

• AnFPGA-based interface module to apply clock signal and inputs to the fabricated IC and collect outputs.

• A fast switching programmable power supply for precise application of supply voltage changes to the IC’s being tested. The unit will apply patterns of supply voltages to the test chips to induce controllable and repeatable levels of short-term aging.

• Finally, a data analysis software module on a host computer for machine learning based device evaluation and anomaly detection (i.e., detection of hardware Trojans).

This testbed, a vital resource in the physical validation of the proposed NYU-KIT hardware Trojan detection methodology will also be a valuable resource for evaluating and validating other hardware Trojan detection techniques developed by NYU and the hardware security researchers outside of NYU. The testbed will therefore be a unique experimental facility for the hardware security community by providing access to (i) physical ICs with Trojan- free and Trojan-infected variants of circuits ranging from moderate-sized cryptographic circuits to complex microprocessors plus (ii) a generic FPGA-based interface to interrogate and test these ICs for Trojans according to their detection method.

This project is led by S. Farokh Atashzar, assistant professor of electrical and computer engineering at NYU Tandon; and John-Ross Rizzo, assistant professor in the Departments of Rehabilitation Medicine and Neurology at NYU Langone Health, and of mechanical and aerospace and biomedical engineering at NYU Tandon.

Stroke, the leading cause of motor disabilities, is putting tremendous pressure on healthcare infrastructures because of an imbalance between an aging society and available neurorehabilitation resources. Thus, there has been a surge in the production of novel rehabilitative technologies for accelerating recovery. Despite the successful development of such devices, lack of objective standards besides clinical investigations using subjective measures have led to controversial recommendations regarding several devices, including robots.

This NSF/FDA Scholar-in-Residence project, designed to address the need for effective rehabilitative technologies, is focused on the design, implementation, and evaluation of a novel, objective, and robust algorithmic biomarker of recovery. Called Delta CorticoMuscular Information-based Connectivity (D-CMiC), the proposed algorithm-based protocol quantifies the connectivity between the central nervous system (CNS) and the peripheral nervous system (PNS) by simultaneously measuring electrical activity from the brain and an ankle muscle on the affected side of recovering post-stroke patients. The system will quantify both spectrotemporal neurophysiological connectivity between the CNS (using electroencephalography (EEG) and PNS (using high-density surface electromyography (HD-sEMG).

The goal of the collaborative project, beyond clarifying the neurophysiology of recovery, is to expedite availability of more effective rehabilitation devices to patients for a range of neurological disorders beyond stroke (such as Parkinson's disease, Essential Tremor and Ataxia). For educational impact, the project will generate a unique transdisciplinary educational environment by conducting workshops about emerging Brain-Computer Interface (BCI) technologies in medicine, and undergraduate team projects for human-machine interfacing, with a focus on promoting STEM activities within underrepresented groups.



The predictive capability, precision, and efficiency of the developed D-CMiC metric will be analyzed by collecting data from recovering stroke patients and healthy subjects alike. Unique D-CMiC features include: (1) accurately and objectively tracking corticomuscular functional connectivity in the Delta/low frequency band; (2) computationally modeling of corticomuscular connectivity; (3) building the basis for the first medical device development tool for the systematic, objective, and transparent evaluation of pre-market rehabilitation devices, aligned with the FDA's mission.