Smartwatch-like wearables have enabled seamless tracking of vital signs and physical activities, but still lack a significant feature: they are currently unable to provide any information about brain states or to modulate brain function for optimizing human health and performance. This project aims to make it possible for wearables to feature such capabilities. Being aware of brain states is not only extremely valuable in clinical studies but is also crucial to improving human performance in various everyday life activities. While recording neural signals directly from the scalp region is possible, it is impractical for use in everyday life. In order to fill this gap, the goal of this project is to pioneer a closed-loop brain-aware wearable architecture called MINDWATCH. This enables (1) decoding multidimensional brain states from noninvasive wearable devices and (2) applying corrective control. MINDWATCH will transform healthcare delivery (e.g., aging, autism, dementia) as well as human performance and productivity enhancement (e.g., online learning, smart workplaces). For instance, knowledge of mental health and cognitive engagement can enable detecting if a student is depressed or is not cognitively engaged/learning, which makes it possible to take corrective action early on. The research is integrated with educational and outreach activities with an emphasis on increasing the participation of minorities in science and engineering. These activities include hosting hands-on STEM K12 events, supervising undergraduate research interns and capstone senior design projects, creating educational videos, and interdisciplinary course development.
I am an associate professor of Biomedical Engineering at the New York University (NYU) where I direct the Computational Medicine Laboratory located within the NYU Langone Health's Tech4Health Institute. Prior to joining NYU, I was an assistant professor of Electrical and Computer Engineering at the University of Houston. The goal of my research is to develop algorithms for designing a MINDWATCH, i.e., smartwatches of the future that can monitor brain activity and use simple everyday actuators (e.g., music or light) to improve mental health and performance. I received my bachelor’s degree (summa cum laude) in Electrical Engineering (Honors Program Citation) from the University of Maryland, and S.M. and Ph.D. degrees in Electrical Engineering and Computer Science with a minor in Mathematics from MIT, where I was a member of the MIT Laboratory for Information and Decision Systems as well as the MIT-Harvard Neuroscience Statistics Research Laboratory. I completed my postdoctoral training at the Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory at MIT as well as the Department of Anesthesia, Critical Care and Pain Medicine at the Massachusetts General Hospital. I am a senior member of IEEE and recipient of different awards such as an MIT Technology Review 2020 Innovator Under 35 award, a 2020 National Science Foundation CAREER Award, a 2020 Research Excellence award as well as a 2020 Teaching Excellence Award from the University of Houston's Cullen College of Engineering, a 2016 IEEE-USA New Face of Engineering award, a National Science Foundation Graduate Research Fellowship, an MIT Graduate Fellowship, and the University of Maryland's Department of Electrical and Computer Engineering Chair's Award. Moreover, I was selected by the National Academy of Engineering for the 2019 U.S. Frontiers of Engineering Symposium. In 2020, I was featured by the IEEE Women in Engineering Magazine as a “Woman to Watch”. Furthermore, I have been inducted into various honor societies including Phi Kappa Phi, Tau Beta Pi, and Eta Kappa Nu. My research interests include wearable technologies, medical cyber-physical systems, and control, estimation and system identification of biomedical and neural systems.
For more information, please visit my lab’s website: https://wp.nyu.edu/cml/
Origami-inspired structures that fold flat sheets along creases with designed patterns to create transformable structures have been widely applied in science and engineering, especially in space operations, e.g., for deployment of folded solar panels equipped on launched satellites. Although the deformation process plays an essential role in transitions between the origami states, few studies focus on the control and actuation of the origami folding mechanism toward high autonomy of the deformation process. This project aims to develop an autonomous origami-inspired transformable system to enable high-performance deformation maneuvering in space operations requiring frequent and/or time-responsive shape changes. The integrative research incorporating theory, analysis, algorithm development, and experimental verification will contribute to a theoretical and experimental platform to advance the autonomy of origami system operations in challenging environments. The research products will have significant impacts on the proliferated satellite marketplace where low mass, small volume, and adaptable structures/subsystems of space vehicles are in demand. Going beyond the applications in space missions, origami-inspired transformable systems have much broader applications in science and engineering. Moreover, the collaboration of experts in both cyber and physical areas promotes the creation of interdisciplinary products that bridge different disciplines.
This research will create and validate new approaches for optimally managing mobile observational networks consisting of a renewably powered ?host? agent and ?satellite? agents that are deployed from and recharged by the host. Such networks can enable autonomous, long-term measurements for meteorological, climate change, reconnaissance, and surveillance applications, which are of significant national interest. While the hardware exists for such networks, the vast majority of existing mission planning and control approaches treat energy as a finite resource and focus on finite-duration missions. This research will represent a paradigm shift, wherein the energy resource available to the network is renewable, but the instantaneously available power is limited. This demands strategies that continuously trade off energy harvesting and scientific information gathering. This research will establish a comprehensive framework for managing the aforementioned tradeoffs, with both simulation-based and experimental demonstrations. The specific observational framework considered in this work will involve a fleet of solar-powered autonomous surface vessels, unoccupied aerial vehicles, and undersea gliders to for characterizing atmospheric and oceanic interactions between the deep-ocean and near-shore waters adjacent to North Carolina's Outer Banks. The research will be complemented with targeted internship activities, K-12 outreach activities at The Engineering Place at NC State, and outreach activities with the Detroit Area Pre-College Engineering Program.
We are entering an age of unprecedented access to information, where transformational methodologies are demonstrating a clear vision of an autonomy-driven future. Self-driving cars, precision agriculture, robotic monitoring, and infrastructure inspection are but a few areas experiencing an autonomy revolution. To continue in this promising direction, it is critical that we facilitate the safe and reliable coordination of diverse cyber-physical systems (CPS).
Unfortunately, at present there is a wide gap in our understanding that limits this goal: a stark divide exists between algorithms for decision-making, sensing, and motion, and underlying computational resources. This project therefore seeks to define computation-aware autonomy by answering the following questions: (1) How does an environment impact computation? (2) How should autonomy adapt to improve computational awareness? (3) How are computational resources optimized at run-time in support of autonomy? and (4) How is autonomy software rendered resilient to errors? This project aims to answer these questions through optimization, computational resource management, and software resilience, with evaluation in an outdoor robotic testbed. Finally, the broader impacts of this work include: (1) K-12 academic experiences for underrepresented students in collaboration with Virginia Tech's Center for Enhancement of Engineering Diversity; (2) autonomy curriculum and design projects; and (3) participation in a series of symposiums through the Ridge and Valley chapter of the Association for Unmanned Vehicle Systems International.
This project explores frameworks, tools, and methodologies that enable fairness-aware, privacy-aware, society-in-the-loop, personalized Internet-of-Things (IoT) systems. Thanks to the rapid growth in mobile computing and wearable technologies, monitoring complex human context becomes feasible, which paves the way to develop human-in-the-loop IoT systems that naturally evolve to adapt to the human and environment state autonomously. Nevertheless, as we move forward towards a long-standing desire to build effective, pervasive computing systems that are both autonomous and personalized, we can push the envelope of these systems to have a positive collateral effect on society. We can design the personalized systems that are aware of and can mutually adapt to the context of their surroundings for the collective benefit of the users. This requires new computation paradigms and algorithms to achieve our vision for society-in-the-loop personalized computing.
Rapid growth in additive manufacturing (AM) has improved the accessibility, customizability and affordability of making products using personal printers. Designs can be developed by consumers, if they have enough knowledge of mechanical design and 3D modeling, or they can be obtained from third parties. However, the process of translating a design to a program that can successfully executed by a 3D printer often requires specialized domain knowledge that many end-users currently lack. In the meantime, lots of objects, which may be very similar or identical to what the non-technical user aims to design and print, have been produced by experts in industry and, hence, millions of proven part designs already exist. This research aims to fill the above-mentioned gap by developing a theoretically sound and practically deployable, domain-specific online search engine, called Srch3D, for 3D models. Srch3D will provide the non-technical end-users with a user-friendly solution to efficiently search for their components in a large repository of existing proven part designs.
Saman Zonouz is an Associate Professor at Georgia Tech in the Schools of Cybersecurity and Privacy (SCP) and Electrical and Computer Engineering (ECE). Saman directs the Cyber-Physical Security Laboratory (CPSec). His research focuses on security and privacy research problems in cyber-physical systems including attack detection and response capabilities using techniques from systems security, control theory and artificial intelligence. His research has been awarded by Presidential Early Career Awards for Scientists and Engineers (PECASE), the NSF CAREER Award in Cyber-Physical Systems (CPS), Significant Research in Cyber Security by the National Security Agency (NSA), and Faculty Fellowship Award by the Air Force Office of Scientific Research (AFOSR). His research group has disclosed several security vulnerabilities with published CVEs in widely-used industrial controllers such as Siemens, Allen Bradley, and Wago. Saman is currently a Co-PI on President Biden’s American Rescue Plan $65M Georgia AI Manufacturing (GA-AIM) project. Saman was invited to co-chair the NSF CPS PI Meeting as well as the NSF CPS Next Big Challenges Workshop. Saman has served as the chair and/or program committee member for several conferences (e.g., IEEE Security and Privacy, CCS, NDSS, DSN, and ICCPS). Saman obtained his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign.
This project aims to transform the software development process in modern cars, which are witnessing significant innovation with many new autonomous functions being introduced, culminating in a fully autonomous vehicle. Most of these new features are indeed implemented in software, at the heart of which lies several control algorithms. Such control algorithms operate in a feedback loop, involving sensing the state of the plant or the system to be controlled, computing a control input, and actuating the plant in order to enforce a desired behavior on it. Examples of this range from brake and engine control, to cruise control, automated parking, and to fully autonomous driving. Current development flows start with mathematically designing a controller, followed by implementing it in software on the embedded systems existing in a car. This flow has worked well in the past, where automotive embedded systems were simple ? with few processors, communication buses, and simple sensors. The control algorithms were simple as well, and important functions were largely implemented by mechanical subsystems. But modern cars have over 100 processors connected by several miles of cables, and multiple sensors like cameras, radars and lidars, whose data needs complex processing before it can be used by a controller. Further, the control algorithms themselves are also more complex since they need to implement new autonomous features that did not exist before. As a result, both computation, communication, and memory accesses in such a complex hardware/software system can now be organized in many different ways, with each being associated with different tradeoffs in accuracy, timing, and resource requirements. These in turn have considerable impact on control performance and how the control strategy needs to be designed. As a result, the clear separation between designing the controller, followed by implementing it in software in the car, no longer works well. This project aims to develop both the theoretical foundations and the tool support to adapt this design flow to emerging automotive control strategies and embedded systems. This will not only result in more cost-effective design of future cars, but will also help with certifying the implemented controllers, thereby leading to safer autonomous cars.
This project aims to create a cyber physical system for remotely controlling cellular processes in real time and leverage the biomedical potential of synthetic biology and microrobotics to create pancreatic tissue. With 114,000 people currently on the waitlist for a lifesaving organ transplant in the United States alone, the ability to directly produce patient-compatible organs, obviating the need for animal and clinical studies can revolutionize personalized medicine. Tissues in the human body such as liver, kidney, and pancreatic islets comprise cells arranged in complex patterns spanning both 2D and 3D structures. However, scaffold- and microgel-based tissue engineering approaches along with 3D bioprinting are often unable to create these complex 3D structures. In this project, the team focuses on the pancreas, which has a unique anatomical structure composed of the regular arrangement of circular cell clusters called islets. The proposed research aims at overcoming the hurdle of recreating these spatial patterns in vitro by developing a cyber physical process by which swarms of microrobots will be steered in 3D to regulate the differentiation of genetically engineered stem cells and drive these cells into forming desired pancreatic tissue. The broader impacts of this line of work are significant because it is a key first step in the synthesis of new, or the repair of ailing, human organs, providing for interactive behavior between computer controlled microrobots and genetically programmed stem cells. Manufacturing living tissue is revolutionary as it could act as a bridge between preclinical and clinical trials, to ensure better drug testing models and develop more personalized precision medicine. For pancreatic components, in particular, generating human organoids compliant with pharmaceutical standards is an exceptional challenge, and current methods are laborious, time-consuming, expensive, and irreproducible, which has caused industry to shy away from this organ. The education and outreach activities that complement the research component of this project address the need to increase underrepresented minorities (that is, women and under-served populations) in problem-solving research careers, like Engineering in K-12.
Today, operators of cellular networks and electricity grids measure large volumes of data, which can provide rich insights into city-wide mobility and congestion patterns. Sharing such real-time societal trends with independent, external entities, such as a taxi fleet operator, can enhance city-scale resource allocation and control tasks, such as electric taxi routing and battery storage optimization. However, the owner of a rich time series and an external control authority must communicate across a data boundary, which limits the scope and volume of data they can share. This project will develop novel algorithms and systems to jointly compress, anonymize, and price rich time series data in a way that only shares minimal, task-relevant data across organizational boundaries. By emphasizing communication efficiency, the developed algorithms will incentivize data sharing and collaboration in future smart cities.
Smart home products have become extremely popular with consumers due to the convenience offered through home automation. In bridging the cyber-physical gap, however, home automation brings a widening of the cyber attack surface of the home. Research towards analyzing and preventing security and safety failures in a smart home faces a fundamental obstacle in practice: the poor characterization of home automation usage. That is, without the knowledge of how users automate their homes, it is difficult to address several critical challenges in designing and analyzing security systems, potentially rendering solutions ineffective in actual deployments. This project aims to bridge this gap, and provide researchers, end-users, and system designers with the means to collect, generate, and analyze realistic examples of home automation usage. This approach builds upon a unique characteristic of emerging smart home platforms: the presence of "user-driven" automation in the form of trigger-action programs that users configure via platform-provided user interfaces. In particular, this project devises methods to capture and model such user-driven home automation to generate statistically significant and useful usage scenarios. The techniques that will be developed during the course of this project will allow researchers and practitioners to analyze various security, safety and privacy properties of the cyber-physical systems that comprise modern smart homes, ultimately leading to deployments of smart home Internet of Things (IoT) devices that are more secure. The project will also produce and disseminate educational materials on best practices for developing secure software with an emphasis on IoT devices, suitable for integration into existing computer literacy courses at all levels of education. In addition, the project will focus on recruiting and retaining computer science students from traditionally underrepresented categories.
This project is centered on three specific goals. First, it will develop novel data collection strategies that allow end-users to easily specify routines in a flexible manner, as well as techniques based on Natural language Processing (NLP) for automatically processing and transforming the data into a format suitable for modeling. Second, it will introduce approaches for transforming routines into realistic home automation event sequences, understanding their latent properties and modeling them using well-understood language modeling techniques. Third, it will contextualize the smart home usage models to make predictions that cater to security analyses specifically and develop tools that allow for the inspection of a smart home?s state alongside the execution of predicted event sequences on real products. The techniques and models developed during the course of this project will be validated with industry partners and are expected to become instrumental for developers and researchers to understand security and privacy properties of smart homes.