Research News

Zhong-Ping Jiang, professor of electrical and computer engineering (ECE) and member of the C2SMART transportation research center at NYU Tandon, directed this research. Leilei Cui, a Ph.D. student in the ECE Department is lead author. Zhengyou Zhang and Shuai Wang from Tencent are co-authors.

This paper studies the trajectory tracking and balance control problem for an autonomous bicycle — one that is ridden like a normal bicycle before automatically traveling by itself to the next user — that is a non-minimum phase, strongly nonlinear system.

As compared with most existing methods dealing only with approximate trajectory tracking, this paper solves a longstanding open problem in bicycle control: how to develop a constructive design to achieve asymptotic trajectory tracking with balance. The crucial strategy is to view the controlled bicycle dynamics from an interconnected system perspective.

More specifically, the nonlinear dynamics of the autonomous bicycle is decomposed into two interconnected subsystems: a tracking subsystem and a balancing subsystem. For the tracking subsystem, the popular backstepping approach is applied to determine the propulsive force of the bicycle. For the balancing subsystem, optimal control is applied to determine the steering angular velocity of the handlebar in order to balance the bicycle and align the bicycle with the desired yaw angle. In order to tackle the strong coupling between the tracking and the balancing systems, the small-gain technique is applied for the first time to prove the asymptotic stability of the closed-loop bicycle system. Finally, the efficacy of the proposed exact trajectory tracking control methodology is validated by numerical simulations (see the video).  

"Our contribution to this field is principally at the level of new theoretical development," said Jiang, adding that the key challenge is in the bicycle's inherent instability and more degrees of freedom than the number of controllers. "Although the bicycle looks simple, it is much more difficult to control than driving a car because riding a bike needs to simultaneously track a trajectory and balance the body of the bike. So a new theory is needed for the design of an AI-based, universal controller." He said the work holds great potential for developing control architectures for complex systems beyond bicycles.

The work was done under the aegis of the Control and Network (CAN) Lab led by Jiang, which consists of about 10 people and focuses on the development of fundamental principles and tools for the stability analysis and control of nonlinear dynamical networks, with applications to information, mechanical and biological systems.

The research was funded by the National Science Foundation (Grant number 10.13039/100000001).

Jin Kim Montclare, professor of chemical and biomolecular engineering, with affilations at NYU Langone Health and NYU College of Dentistry, directed this research with first author Michael Meleties, fellow Ph.D. student Dustin Britton, postdoctoral associate Priya Katyal, and undergraduate research assistant Bonnie Lin.

Owing to their tunable properties, hydrogels comprising stimuli-sensitive polymers are among the most appealing molecular scaffolds because their versatility allows for applications in tissue engineering, drug delivery and other biomedical fields.

Peptides and proteins are increasingly popular as building blocks because they can be stimulated to self-assemble into nanostructures such as nanoparticles or nanofibers, which enables gelation — the formation of supramolecular hydrogels that can trap water and small molecules. Engineers, to generate such smart biomaterials, are developing systems that can respond to a multitude of stimuli including heat. Although thermosensitive hydrogels are among widely studied and well-understood class of protein biomaterials, substantial progress is also reportedly being made in incorporating stimuli-responsiveness including pH, light, ionic strength, redox, as well as the addition of small molecules.

The NYU Tandon researchers, who previously reported a responsive hydrogel formed using a coiled-coil protein, Q, expanded their studies to identify the gelation of Q protein at distinct temperatures and pH conditions.

Using transmission electron microscopy, rheology and structural analyses, they observed that Q self-assembles and forms fiber-based hydrogels exhibiting upper critical solution temperature (UCST) behavior with increased elastic properties at pH 7.4 and pH 10. At pH 6, however, Q forms polydisperse nanoparticles, which do not further self-assemble and undergo gelation. The high net positive charge of Q at pH 6 creates significant electrostatic repulsion, preventing its gelation. This study will potentially guide the development of novel scaffolds and functional biomaterials that are sensitive towards biologically relevant stimuli

Montclare explained that upper critical solution temperature (UCST) phase behavior is characterized by a solution that will form a hydrogel when it is cooled below a critical temperature.

"In our case, it is due to the physical crosslinking/entanglement of fibers that our fiber-based hydrogel forms when cooled," she said, adding that when the temperature is raised above the critical temperature, the hydrogel transitions back into solution and most of the fibers should disentangle.

"In our study, we looked at how this process is affected by pH. We believe that the high net charge of the protein at pH 6 creates electrostatic repulsions that prevent the protein from assembling into fibers and further into hydrogels, while at higher pH where there would be less electrostatic repulsion, the protein is able to assemble into fibers that can then undergo gelation."

The team reporting on this research was led by André D. Taylor, a professor of chemical and biomolecular engineering at NYU Tandon, and post-doctoral associate Jaemin Kong.

Perovskite solar cells have progressed in recent years with rapid increases in power conversion efficiency (from 3% in 2006 to 25.5% today), making them more competitive with silicon-based photovoltaic cells. However, a number of challenges remain before they can become a competitive commercial technology.

One of these challenges involves inherent limitations in the process of p-type doping of organic hole-transporting materials within the photovoltaic cells.

This process, wherein doping is achieved by the ingress and diffusion of oxygen into hole transport layers, is time intensive (several hours to a day), making commercial mass production of perovskite solar cells impractical. The Tandon team, however, discovered a method of vastly increasing the speed of this process through the use of carbon dioxide instead of oxygen.

In perovskite solar cells, doped organic semiconductors are normally required as charge-extraction interlayers situated between the photoactive perovskite layer and the electrodes. The conventional means of doping these interlayers involves the addition of lithium bis(trifluoromethane)sulfonimide (LiTFSI), a lithium salt, to spiro-OMeTAD, a π-conjugated organic semiconductor widely used for a hole-transporting material in perovskite solar cells, and the doping process is then initiated by exposing spiro-OMeTAD:LiTFSI blend films to air and light. Besides being time consuming, this method largely depends on ambient conditions. By contrast, Taylor and his team reported a fast and reproducible doping method that involves bubbling a spiro-OMeTAD:LiTFSI solution with carbon dioxide (CO2) under ultraviolet light.

They found that the CO2 bubbling process rapidly enhanced electrical conductivity of the interlayer by 100 times compared to that of a pristine blend film, which is also approximately 10 times higher than that obtained from an oxygen bubbling process. The CO2 treated film also resulted in stable, high-efficiency perovskite solar cells without any post-treatments.

The lead author Jaemin Kong explained that “Employing the pre-doped spiro-OMeTAD to perovskite solar cells shortens the device fabrication and processing time. Further, it makes cells much more stable as most detrimental lithium ions in spiro-OMeTAD:LiTFSI solution were stabilized to lithium carbonate, created while the doping of spiro-OMeTAD happened during CO2 bubbling process. The lithium carbonates end up being filtered out when we spincast the pre-doped solution onto the perovskite layer. Thus, we could obtain fairly pure doped organic materials for efficient hole transporting layers.”

Moreover, the team found that the CO2 doping method can be used for p-type doping of other π-conjugated polymers, such as PTAA, MEH-PPV, P3HT, and PBDB-T. Taylor said the team is looking to push the boundary beyond typical organic semiconductors used for solar cells.

"We believe that wide applicability of CO2 doping to various π-conjugated organic molecules stimulates research ranging from organic solar cells to OLEDs and OFETs even to thermoelectric devices that all require controlled doping of organic semiconductors,” and added that “Since this process consumes a quite large amount of CO2 gas during the process, it can be also considered for CO2 capture and sequestration study in the future. We are hoping that the CO2 doping technique could be a stepping stone for overcoming existing challenges in organic electronics and beyond.”

This research was led by Brandon Reagen, assistant professor of computer science and electrical and computer engineering, with Nandan Kumar Jha, a Ph.D. student under Reagen, and Zahra Ghodsi, who obtained her Ph.D. at NYU Tandon under Siddharth Garg, Institute associate professor of electrical and computer engineering.

Concerns surrounding data privacy are having an influence on how companies are changing the way they use and store users’ data. Additionally, lawmakers are passing legislation to improve users’ privacy rights. Deep learning is the core driver of many applications impacted by privacy concerns. It provides high utility in classifying, recommending, and interpreting user data to build user experiences and requires large amounts of private user data to do so. Private inference (PI) is a solution that simultaneously provides strong privacy guarantees while preserving the utility of neural networks to power applications.

Homomorphic data encryption, which allows inferences to be made directly on encrypted data, is a solution that addresses the rise of privacy concerns for personal, medical, military, government and other sensitive information. However, the primary challenge facing private inference is that computing on encrypted data levies an impractically high penalty on latency, stemming mostly from non-linear operators like ReLU (rectified linear activation function).

Solving this challenge requires new optimization methods that minimize network ReLU counts while preserving accuracy. One approach is minimizing the use of ReLU by eliminating uses of this function that do little to contribute to the accuracy of inferences.

“What we are to trying to do there is rethink how neural nets are designed in the first place,” said Reagen. “You can skip a lot of these time and computationally-expensive ReLU operations and still get high performing networks at 2 to 4 times faster run time.”

The team proposed DeepReDuce, a set of optimizations for the judicious removal of ReLUs to reduce private inference latency. The researchers tested this by dropping ReLUs from classic networks to significantly reduce inference latency while maintaining high accuracy.

The team found that, compared to the state-of-the-art for private inference DeepReDuce improved accuracy and reduced ReLU count by up to 3.5% (iso-ReLU count) and 3.5× (iso-accuracy), respectively.

The work extends an innovation, called CryptoNAS. Described in an earlier paper whose authors include Ghodsi and a third Ph.D. student, Akshaj Veldanda, CryptoNAS optimizes the use of ReLUs as one might rearrange how rocks are arranged in a stream to optimize the flow of water: it rebalances the distribution of ReLUS in the network and removes redundant ReLUs.

The investigators will present their work on DeepReDuce at the 2021 International Conference on Machine Learning (ICML) from July 18-24, 2021.

Julia Stoyanovich, director of the Center for Responsible AI (R/AI) at NYU Tandon, and assistant professor of computer science and engineering and of data science, co-authored this paper with Armanda Lewis, a graduate student pursuing her master’s at the NYU Center for Data Science.

The authors detail their development of and pedagogy for a technical course focused on responsible data science, which tackles the issues of ethics in AI, legal compliance, data quality, algorithmic fairness and diversity, transparency of data and algorithms, privacy, and data protection.

The ability to interpret machine-assisted decision-making is an important component of responsible data science that gives a good lens through which to see other responsible data science topics, including privacy and fairness. The researchers’ study includes best practices for teaching technical data science and AI courses that focus on interpretability, and tying responsible data science to current learning science and learning analytics research.

The work also explores the use of “nutritional labels” — a family of interpretability tools that are gaining popularity in responsible data science research and practice — for interpreting machine learning models.

• In the paper, the investigators offer a description of a unique course on responsible data science that is geared toward technical students, and incorporates topics from social science, ethics and law.
• The work connects theories and advances within the learning sciences to the teaching of responsible data science, specifically, interpretability — allowing humans to understand, trust and, if necessary, contest the computational process and its outcomes. The study asserts that interpretability is central to the critical study of the underlying computational elements of machine learning platforms.
• The collaborators assert that they are among the first to consider the pedagogical implications of responsible data science, creating parallels between cutting-edge data science research and cutting-edge educational research within the fields of learning sciences, artificial intelligence in education, and learning analytics and knowledge.

Additionally, the authors propose a set of pedagogical techniques for teaching the interpretability of data and models, positioning interpretability as a central integrative component of responsible data science.

This research was led by Monica Menendez, Global Network professor of civil and urban engineering, and Joseph Chow, deputy director of the C2SMART University Transportation Center at NYU Tandon.

This project proposes a flexible bus dispatching system using automated modular vehicle technology, and considers multimodal interactions and congestion propagation dynamics.

This study proposes a novel flexible bus dispatching system in which a fleet of fully automated modular bus units, together with conventional buses, serves the passenger demand. These modular bus units can either operate individually or combined (forming larger modular buses with a higher passenger capacity). This provides enormous flexibility to manage the service frequencies and vehicle allocation, reducing thereby the operating cost and improving passenger mobility.

The investigators developed an optimization model to determine the optimal composition of modular bus units and the optimal service frequency at which the buses (both conventional and modular) should be dispatched across each bus line. They explicitly accounted for the dynamics of traffic congestion and complex interactions between the modes at the network level, based on a recently proposed three-dimensional macroscopic fundamental diagram (3D-MFD). To the best of Chow and Menendez' knowledge, this is the first application of the 3D-MFD and modular bus units for the frequency setting problem in the domain of bus operations.

Using this system of analysis, the researchers were able to show improved costs across the system by adjusting the number of combined modular bus units and their dispatching frequencies to changes in car and bus passenger demand. A comparison with the commonly used approach that considers only the bus system (neglecting the complex multimodal interactions and congestion propagation) reveals the value of the proposed modeling framework.

This research was led by Joseph Chow, deputy director of the C2SMART University Transportation Center at NYU Tandon. Co-authors included Kaan Ozbay, Director, and Shri Iyer, Managing Director of C2SMART. Chow and Ozbay are professors in the department of Civil and Urban Engineering. 

The COVID-19 pandemic has affected travel behaviors and transportation system operations, and raised new challenges for public transit. Cities are grappling with what policies can be effective for a phased reopening shaped by social distancing.

The C2SMART researchers used a baseline model for pre-COVID conditions to create a new model representing travel behavior during the COVID-19 pandemic. They achieved this both by recalibrating the population agendas to include work-from-home, and by re-estimating the mode choice model (to fit observed traffic and transit ridership data) for the Center’s MATsim-NYC platform, a multi-agent simulation test bed for evaluating emerging transportation technologies and policies. They then analyzed the increase in car traffic due to the phased reopen plan guided by the state government of New York.

Analyzing four reopening phases and two reopening scenarios (with and without transit capacity restrictions), they found that a reopening with 100% transit capacity may only see as much as 73% of pre-COVID ridership and an increase in the number of car trips by as much as 142% of pre-pandemic levels. They also discovered that limiting transit capacity to 50% would decrease transit ridership further from 73% to 64% while increasing car trips to as much as 143% of pre-pandemic levels.

They noted that, while the increase appears small, the impact on consumer surplus is disproportionately large due to already increased traffic congestion. Many of the trips also get shifted to other modes like micromobility.

The findings imply that a transit capacity restriction policy during reopening needs to be accompanied by (1) support for micromobility modes, particularly in non-Manhattan boroughs, and (2) congestion alleviation policies that focus on reducing traffic in Manhattan, such as cordon-based pricing.

This research was led by Joseph Chow, deputy director of the C2SMART University Transportation Center at NYU Tandon and professor of civil and urban engineering, with researchers Brian Yueshuai He, Jinkai Zhu, Ziyi Ma, and Ding Wang.

Evaluation of the demand for emerging transportation technologies and policies can vary by time of day due to spillbacks on roadways, rescheduling of travelers’ activity patterns, and shifting to other modes that affect the level of congestion. These effects are not well-captured with static travel demand models.

Chow and his team calibrated and validated the first open-source multi-agent simulation model for New York City, called MATSim-NYC, to support agencies in evaluating policies such as congestion pricing. The simulation-based virtual test bed is loaded with a “synthetic” 2016 population of over eight million people, calibrated in a prior study. Model validation using transit stations and road links is comparable to NYPBM.

In a study published in Transport Policy the researchers used the model to evaluate a congestion pricing plan proposed by the Regional Plan Association, and found a much higher (127K) car trip reduction compared to the RPA report (59K). The team discovered that the Association’s pricing policy would impact the population segment making trips within Manhattan differently from the population segment of trips outside Manhattan: benefits from congestion reduction benefit the former by about 110%+ more than the latter.

The simulation can show that 37.3% of the Manhattan segment would be negatively impacted by the pricing compared to 39.9% of the non-Manhattan segment, which has implications for redistribution of congestion pricing revenues. The citywide travel consumer surplus decreases when the congestion pricing goes up from $9.18 to $14 both ways even as it increases for the Charging-related population segment. This implies that increasing pricing from $9.18 to $14 benefits Manhattanites at the expense of the rest of the city.

RPA congestion pricing policy would have net increase in consumer surplus. The results suggest toll revenue redistribution should focus on outer boroughs.

This research, under direction of Theodore (Ted) S. Rappaport, was led by graduate students Shihao Ju, Yunchou Xing, and Ojas Kanhere.

Driven by ubiquitous usage of mobile devices and the explosive growth and diversification of the Internet of Things (IoT), sixth-generation (6G) wireless systems will need to offer unprecedented high data rate and system throughput, which can be achieved in part by deploying systems transmitting and receiving at millimeter-wave (mmWave) and Terahertz (THz) frequencies (i.e., 30 GHz - 3 THz). These regions of the electromagnetic spectrum are capable of massive data throughput at near zero latency, key to future data traffic demand created by such wireless applications as augmented/virtual reality (AR/VR) and autonomous driving.

Importantly, the linchpin for successful deployment of mmWave and THz systems for 6G wireless communications will be their performance in indoor scenarios. Therefore, accurate THz channel characterization for indoor environments is essential to realizing the designs of transceivers, air interface, and protocols for 6G and beyond.

To this end, NYU WIRELESS has introduced NYUSIM 3.0, the latest version of its MATLAB®-based open-source mmWave and sub-THz statistical channel simulation software, enabling the indoor MIMO channel simulations for frequencies from 500 MHz to 150 GHz with RF bandwidth of 0 to 800 MHz. The new NYUSIM 3.0 is publicly available with a simple MIT-style open source acknowledgement license. To date, NYUSIM has been downloaded over 80,000 times.

NYUSIM 3.0 implemented a 3-D indoor statistical channel model for mmWave and sub-THz frequencies following the mathematical framework of the 3-D outdoor statistical channel model adopted in earlier versions of NYUSIM. The indoor 3-D statistical channel model for mmWave and sub-THz frequencies, was developed from extensive radio propagation measurements conducted in an office building at 28 GHz and 140 GHz in 2014 and 2019 — in both line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. The team carefully measured over 15,000 power delay profiles to study temporal and spatial channel statistics such as the number of time clusters, cluster delays, and lobe angular spreads.

The adopted channel models for Version 3.0 are elaborated in a upcoming paper, “Millimeter Wave and Sub-Terahertz Spatial Statistical Channel Model for an Indoor Office Building” (to appear in IEEE Journal on Selected Areas in Communications, Special Issue on Terahertz Communications and Networking in the second quarter 2021) by a team of three students at NYU WIRELESS, and the Department of Electrical and Computer Engineering under Rappaport’s guidance, led by Ph.D. student Shihao Ju. Besides proposing a unified indoor channel model across mmWave and sub-THz bands based on the team’s indoor channel measurements, the work provides a reference for future standards development above 100 GHz.

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 are versatile 3-dimensional macromolecular structures with an astonishing variety of potential applications, many of them in medicine, including tissue engineering and wound healing. Because of their hydrophilic properties and internal architecture, these compounds can even trap and deliver drugs directly to targets, opening up a host of potential applications involving safe delivery of cytotoxic compounds that are standard treatment for cancer and other diseases.

To have such “Swiss Army Knife” capabilities, these typically soft materials must be imbued with properties conferring static and dynamic mechanical strength that enables them to carry a molecular payload and know when to release it.

Taking up this challenge, Montclare and her former students built upon recent work developing a photo-crosslinkable triblock copolymer protein hydrogel called CEC-D, a compound with limited viscoelastic mechanical and moderate sustained release properties. In the new work they explored the potential of transition metal cations (positively charged ions) to enhance the mechanical properties of CED-D, including its ability to encapsulate and release the small molecule curcumin, known for its anti-inflammatory properties.

In the paper, “Effect of Divalent Metal Cations on the Conformation, Elastic Behavior, and Controlled Release of a Photocrosslinked Protein Engineered Hydrogel,” published in the ACS publication Applied Bio Materials, the investigators found that the hydrogels coordinated with divalent metal ions such as Zn2+, Cu2+, and Ni2+ demonstrated control over the encapsulation and release of curcumin, a discovery suggesting that cation-tuned hydrogels constitute a promising drug delivery platform with tunable physicochemical properties.

“Depending on the metal, we can control the structure, mechanical stiffness and small molecule delivery of the hydrogel,” said Montclare. “This has important implications for drug delivery and this knowledge can be used to tailor vehicles to deliver specific therapeutics. For example, we can tailor these materials to fabricate wound dressings that improve healing by triggering drug release in the presence of metals.”