Seminars

Robust Autonomous Vehicle Localization using GPS: from Tandem Drifting Cars to “GPS” on the Moon

Grace X. Gao

Assistant Professor
Department of Aeronautics and Astronautics
Stanford University
Stanford, CA

Autonomous vehicles often operate in complex environments with various sensing uncertainties. On Earth, GPS signals can be blocked or reflected by buildings; and camera measurements are susceptible to lighting conditions. While having a variety of sensors is beneficial, including more sensing information can introduce more sensing failures as well as more computational load. For space applications, such as localization on the Moon, it can be even more challenging.

In this talk, I will present our recent research efforts on robust vehicle localization under sensing uncertainties. We turn sensing noise and even absence of sensing into useful navigational signals. Inspired by cognitive attention in humans, we select a subset of “attention landmarks” from sensing measurements to reduce computation load and provide robust positioning. I will also show our localization techniques that enable various applications, from autonomous tandem drifting cars to a GPS-like system for the Moon.

Grace X. Gao is an assistant professor in the Department of Aeronautics and Astronautics at Stanford University. She leads the Navigation and Autonomous Vehicles Laboratory (NAV Lab). Prof. Gao has won a number of awards, including the National Science Foundation CAREER Award, the Institute of Navigation Early Achievement Award and the RTCA William E. Jackson Award. Prof. Gao and her students won Best Presentation of the Session/Best Paper Awards 29 times at Institute of Navigation conferences over the past 17 years. She also won various teaching and advising awards, including the Illinois College of Engineering Everitt Award for Teaching Excellence, the Engineering Council Award for Excellence in Advising, AIAA Illinois Chapter’s Teacher of the Year, and most recently Advisor of the Year Award and Teacher of the Year Award by AIAA Stanford Chapter in 2022 and 2023, respectively.

Wednesday, January 24, 2024

3:30 PM

Zumberge Hall of Science, Room 252 (ZHS 252)

The Zoom webinar is at https://usc.zoom.us/j/95892885119?pwd=QXZOZUhrcTJRYk5qZzZwVThrTytVZz09.

host: Nguyen

Enabling Sustainable Propulsion and Clean Energy Transitions: Reacting Flow Modeling across Molecular to Continuum Scales

Rui Xu

Postdoctoral Scholar
Department of Chemistry
Stanford University
Stanford, CA

While the global demand for air travel continues to rise, the aerospace carbon footprint is increasingly concerning. In the near term, it is highly desirable for the rational design, efficient certification, and massive production of carbon-neutral fuels to mitigate greenhouse emissions. Furthermore, future aerospace vehicles will be integrated with highly efficient and high-speed propulsion devices using clean and renewable energy sources. The design of these sustainable and high-speed propulsion systems requires a fundamental understanding of reacting flow physics across multiple scales, featuring interactions between the molecular and the continuum flow scale. In this talk, I will present state-of-the-art approaches to multiscale reacting flow modeling of sustainable aerospace energy carriers. The modeling spans from molecular scale using GPU-enabled quantum chemistry computation to continuum scale gas dynamics and turbulence-resolved flow modeling. I will first emphasize the reacting flow modeling of bio-derived sustainable aviation fuel (SAF), which is considered a near-term alternative to conventional jet fuels. I will then discuss the study of methane and natural gas as potential transition fuels, along with hydrogen and battery technologies for the long-term future. I envision that the presented approach will help not only to enable sustainable aviation but also to advance a clean and sustainable transition in the future energy landscape.

Rui (Ray) Xu is a Postdoctoral Scholar in the Department of Chemistry at Stanford University and the PULSE Institute in the SLAC National Accelerator Laboratory. His research centers around multiscale reacting flow modeling to enable sustainable aerospace propulsion and clean energy transitions. Dr. Xu obtained his Ph.D. in Mechanical Engineering from Stanford University, his M.S. in Mechanical Engineering from Northwestern University, and his B.S. in Mechanical Engineering from Shanghai Jiao Tong University. He is the recipient of the ACS Wiley Computers in Chemistry Outstanding Postdoc Award in 2024 and the ACTC AFOSR Scholar Award in 2022.

Wednesday, January 31, 2024

3:30 PM

Zumberge Hall of Science, Room 252 (ZHS 252)

The Zoom webinar is at https://usc.zoom.us/j/95892885119?pwd=QXZOZUhrcTJRYk5qZzZwVThrTytVZz09.

host: Ronney

Microfluidics with Macro-Impact: Advancing Sustainability through Nanoparticle - Enhanced Foams for Optimized CO₂ Sequestration

Negar Nazari

Postdoctoral Fellow
School of Engineering and Applied Sciences
Harvard University
Cambridge, MA

The contemporary global challenge centers on ensuring water and energy access for a growing population while minimizing environmental impacts and promoting sustainability. Porous media play a crucial role in this, facilitating processes like carbon sequestration, hydrogen storage, and geothermal energy extraction within geological formations. The Paris Climate Accord emphasizes reducing greenhouse gas emissions, with carbon sequestration in geological formations being a potential solution. However, challenges like ensuring safe storage and preventing leaks remain.

Utilizing a foaming solution alongside CO₂ injection emerges as a promising method to reduce the mobility of CO₂, enhancing the blockage of CO₂ in more permeable areas and thus bolstering storage safety. A significant hurdle in this technique is the thermodynamic instability of the bubble interface in the high salinity brines found in host formations. The introduction of nanoparticles enhances the interface's stability, counteracting the capillary forces that destabilize the foam's lamellae. The dynamics of gas-liquid interfaces differ between aqueous surfactants and nanoparticles. Nanoparticles impact the drag on elongated bubbles at low capillary numbers by establishing monolayer formations at the fluid interface, which in turn increases the interfacial dilatational viscoelasticity. This enhancement in viscoelasticity strengthens the interface's dynamic resistance to changes in surface area, whether through stretching or compressing, thereby improving the stability of the interface.

Negar Nazari is a Postdoctoral fellow at the school of engineering and applied sciences at Harvard University. Her research focuses on understanding complex fluid flow and transport in porous media with particular emphasis on topics relevant to energy and sustainability including but not limited to carbon and hydrogen storage. Prior to her postdoc, she completed her PhD at the energy science and engineering department at Stanford University. Her PhD research focused on microscale analysis of fluid-fluid interactions and complex multiphase flow in fractured systems and channels. Her research interests lie in energy and sustainability, microfluidics, and data-driven and programming techniques to upscale flow studies. Negar received the Trailblazing Researcher Award from the California Institute of Technology for exceptional contributions and frontier research in Energy and Sustainability.

Wednesday, February 7, 2024

3:30 PM

Zumberge Hall of Science, Room 252 (ZHS 252)

The Zoom webinar is at https://usc.zoom.us/j/95892885119?pwd=QXZOZUhrcTJRYk5qZzZwVThrTytVZz09.

host: Ronney

Computational Paradigms Towards Sustainable Aeronautics

Zhenyu Gao

Postdoctoral Fellow
Department of Aerospace Engineering and Engineering Mechanics
The University of Texas at Austin
Austin, TX

As technology and the environment rapidly evolve, the aerospace industry is actively seeking solutions to three significant opportunities and challenges. First, the data-intensive transformation will reframe the aerospace industry with big data technologies, analytical methods, and high-performance computation. Second, future aerospace systems must be environmentally, socially, and economically sustainable. Third, aerospace systems of diverse types and capabilities will grow robustly and operate at larger scales. In this talk, I will share a series of recent studies which leverage data-driven and computational methods for the design and analysis of sustainable aeronautical systems. This includes (1) a machine learning approach for efficient and accurate aviation environmental impact modeling, (2) a data-driven optimization approach for holistic and equitable advanced air mobility noise management, and (3) a modeling and simulation approach for sustainable and safe 3D urban airspace design. This research highlights the significance of data-driven approaches for the sustainable development of novel aerospace systems.

Zhenyu Gao is a Postdoctoral Fellow in the Department of Aerospace Engineering and Engineering Mechanics at The University of Texas at Austin. His areas of research encompass sustainable aviation, data-driven aerospace engineering, and intelligent transportation systems. He earned his Ph.D. in Aerospace Engineering and an M.S. in Operations Research from Georgia Institute of Technology, and a B.S. in Aerospace Engineering from the University of Illinois at Urbana–Champaign. His doctoral dissertation was awarded the 2023 Georgia Tech Sigma Xi Best Ph.D. Thesis Award. He has also served as a visiting researcher at the National University of Singapore and the Institute of Science and Technology Austria (ISTA). During his time at Georgia Tech and UT Austin, he has contributed to over ten research projects funded by entities such as the FAA, NASA, and industry corporations like The Boeing Company.

Wednesday, February 14, 2024

3:30 PM

Zumberge Hall of Science, Room 252 (ZHS 252)

The Zoom webinar is at https://usc.zoom.us/j/95892885119?pwd=QXZOZUhrcTJRYk5qZzZwVThrTytVZz09.

host: Ronney

Blending Soft and Rigid for Physical Intelligence in Robotics

Zach Patterson

Postdoctoral Associate
MIT Computer Science & Artificial Intelligence Lab
Massacussetts Institute of Technology
Cambridge, MA

Most large animals have a blend of soft and rigid materials in their load-bearing structures. This design principle is largely overlooked by traditional robotics, which favors rigid materials, and by soft robotics, which predominantly uses soft components. Inspired by the natural integration of these materials in the animal kingdom, my research aims to develop robotic systems that combine soft and rigid elements harmoniously, leading to inherent "physical intelligence.” I will begin with an exploration of manipulators that embody this innovative soft-strong paradigm, followed by a discussion on the critical role of advanced control algorithms in harnessing physical intelligence effectively. Next, I will showcase the application of this soft-rigid hybrid approach in creating biomimetic robots, drawing inspiration from marine creatures like sea turtles and echinoderms. These biomimetic robots serve as versatile experimental platforms, enabling us to explore and elucidate questions in biomechanics and paleobiology that are otherwise challenging to address. I will finally discuss how these diverse categories of robots could revolutionize the interactions of intelligent machines with the environment.

Zach Patterson is a Postdoctoral Associate at the MIT Computer Science & Artificial Intelligence Lab. His research sits at the intersection of robot design, control, and biomimetics with a focus on utilizing soft robotic technologies. Zach received his B.S. in Mechanical Engineering from the University of Pittsburgh in 2017 and his Ph.D. in Mechanical Engineering from Carnegie Mellon University in 2022.

February 26, 2024 (Monday)

1:30 PM

Laufer Conference Room, Olin Hall of Engineering, Room 406 (OHE 406)

The Zoom webinar is at https://usc.zoom.us/j/95892885119?pwd=QXZOZUhrcTJRYk5qZzZwVThrTytVZz09.

host: Ronney

Intelligent Wearable Systems for Synergistic Human-Robot Interactions

Keya Ghonasgi

Postdoctoral Fellow
Exoskeleton and Prosthetic Intelligent Controls (EPIC) Lab
Georgia Institute of Technology
Atlanta, GA

Wearable robots hold immense, untapped potential to enhance human performance through physical interactions combining human and robot abilities. For instance, assistive robots can follow user intent while overcoming limitations like reduced strength due to neurological injuries. Surgical robots can enhance expert surgeons’ skill with precision and accuracy. In the future, wearable robots could become as ubiquitous as smart watches and phones. However, current state-of-the-art solutions face challenges – providing limited improvements in performance, being expensive and impractical for the real-world, or causing discomfort, leading to abandonment. This talk showcases three avenues to unlock the promised synergistic potential of humans and robots.

First, I explore the role of understanding human interaction behaviors in the development of responsive robots. For example, combining data-driven and model-based approaches can help us characterize behaviors and identify generalizable movement patterns. Next, I discuss how robots can be tailored to suit human biomechanics and abilities. For instance, can diverse users easily interact with the device? If not, can humans be taught to interact with non-intuitive robots? Finally, I motivate the need for simultaneous learning in the individual and the robot. Such co-evolving systems enable personalized interactions, especially beneficial for individualized rehabilitation or skill training applications. These research areas are interlinked, requiring an interdisciplinary approach at the intersection of human neuroscience and biomechanics, artificial intelligence, and robot design and control. This research empowers synergistic robot interactions and paves the way for the seamless integration of wearable robots into human life.

Keya Ghonasgi is a postdoctoral fellow at the Georgia Institute of Technology where she works with lower limb assistive devices. She received her Ph.D. in Mechanical Engineering from the University of Texas at Austin (UT) in 2023 and her M.S. in Mechanical Engineering from Columbia University in 2018. Keya’s research on robotic exoskeletons has led to honors including being selected as a Rising Star in Mechanical Engineering (2022) and a CalTech Young Investigator Lecturer (2023). Keya’s work has been funded through various sources including a UT graduate student fellowship award, an NSF M3X grant, and industry collaborations with Meta Reality Labs and Google Brain. Keya is passionate about developing the next generation of human-interactive technology in the form of wearable robots that harness synergistic human and robot capabilities.

Wednesday, February 28, 2024

3:30 PM

Zumberge Hall of Science, Room 252 (ZHS 252)

The Zoom webinar is at https://usc.zoom.us/j/95892885119?pwd=QXZOZUhrcTJRYk5qZzZwVThrTytVZz09.

host: Ronney

To Err is Robotic: Enabling Robust Autonomy with Risk-Sensitivity

Preston Culbertson

Postdoctoral Scholar
AMBER Lab
California Institute of Technology
Pasadena, CA

Despite significant recent advances in robot learning and perception, achieving robust robot behavior for real-world, dynamic tasks like dexterous manipulation remains elusive. This challenge stems from the uncertainty inherent in robots' geometric models, perception systems, and controllers, particularly during dynamic interactions with the environment. This talk explores how risk-sensitivity can provide a principled, practical approach to addressing these robustness issues directly. First, I will discuss our work showing Neural Radiance Fields (NeRFs) — typically trained for novel view synthesis — can be used for both collision avoidance and localization, repurposing them as a versatile, probabilistic occupancy model for robotics. Next, we will turn to the problem of real-time, risk-sensitive planning more broadly. Specifically, I will present work combining stochastic control barrier functions (CBFs), which provide rigorous probabilistic safety/performance guarantees, with deep generative dynamics models to yield a lightweight, data-driven approach to risk-sensitive control. We have demonstrated that our method (running onboard a quadrotor at 100Hz) enables aggressive, yet safe flight with a completely unmodeled and uninstrumented slung load. The talk will conclude with a discussion of some lessons learned and future directions in risk-sensitive robotics.

Preston Culbertson is a postdoctoral scholar in the AMBER Lab at Caltech. His research interests lie at the intersection of robotics, machine learning, optimization, and computer vision. Specifically, his research explores how to enable robust robot behavior for dynamic, contact-rich tasks like manipulation, locomotion, and navigation, emphasizing new tools for understanding risk and uncertainty for autonomous systems. Preston earned his PhD from Stanford University, mentored by Prof. Mac Schwager, where his work on collaborative manipulation and robot assembly was awarded the NASA Space Technology Research Fellowship and the 'Best Manipulation Paper' award at ICRA 2018.

Monday, March 4, 2024

1:30 PM

Laufer Conference Room, Olin Hall of Engineering, Room 406 (OHE 406)

The Zoom webinar is at https://usc.zoom.us/j/95892885119?pwd=QXZOZUhrcTJRYk5qZzZwVThrTytVZz09.

host: Ronney

Physics-Aware Data-Driven Modeling and Uncertainty Quantification for Large-Scale Environmental Problems

Hannah Lu

Postdoctoral Associate
Department of Aeronautics and Astronautics, Department of Civil Environmental Engineering, Earth Resources Laboratory and Laboratory for Information and Decision Systems
MIT
Cambridge, MA

Data-driven modeling of complex systems is a rapidly evolving field facilitated by the concurrent rise of data science. To alleviate the prohibitively expensive computational costs of repeated full-model simulations in uncertainty quantification, data-driven modeling is often used to describe the behaviors of the complex system by predicting the quantities of interest directly. In this talk, I will present my contributions to this field with an emphasis on (1) improving model performance by using physics-aware machine learning techniques, (2) quantifying uncertainties in the system’s response, and (3) inferring the key parameters of the physics-based models from measured data. Examples of applications will be focused on large-scale geological carbon sequestration—an important strategy for reducing greenhouse gas emissions to the atmosphere and mitigating climate change. The objective is to develop a convenient computing toolbox to provide more accurate scientific information at cheaper computational costs for better environmental management and decision-making.

Hannah Lu is a postdoc associate at MIT, affiliated with the Department of Aeronautics and Astronautics, Department of Civil Environmental Engineering, Earth Resources Laboratory and Laboratory for Information and Decision Systems. She obtained her Ph.D. from Energy Science and Engineering at Stanford Doerr School of Sustainability. Her research interests lie in the field of scientific computing, reduced order modeling, uncertainty quantification and machine learning in applications of environmental fluid mechanics. She received EDGE Doctoral Fellowship, Frank G. Miller Fellowship Award and Henry J. Ramey, Jr. Fellowship Award from Stanford University; Student Travel Award from SIAM Conference on UQ; NSF Fellowship from MMLDT-CSET Conference; Travel Grant from NSF-funded HydroML Symposium; and a first-place USNCCM17 Best Presentation Award in postdoc category.

Wednesday, March 6, 2024

3:30 PM

Zumberge Hall of Science, Room 252 (ZHS 252)

The Zoom webinar is at https://usc.zoom.us/j/95892885119?pwd=QXZOZUhrcTJRYk5qZzZwVThrTytVZz09.

host: Ronney

Dexterous Decision-Making for Real-World Robotic Manipulation

Rachel Holladay

Doctoral Candidate
Electrical Engineering and Computer Science Department
Massachusetts Institute of Technology
Cambridge, MA

For a robot to prepare a meal or clean a room, it must make a large array of decisions, such as what objects to clean first, where to grasp each ingredient and tool, how to open a heavy, overstuffed cabinet, and so on. To enable robots to tackle these tasks, I decompose the problem into two interdependent layers: generating a series of subgoals (i.e., a strategy) and solving for the robot behavior that achieves each of these subgoals. Critically, to accomplish a rich set of manipulation tasks, these subgoal solvers must account for force, motion, deformation, contact, uncertainty and partial observability.

My research contributes models and algorithms that enable robots to reason over both the geometry and physics of the world in order to solve long-horizon manipulation tasks. In this talk, I will first discuss how this approach has enabled robots to perform tasks that require reasoning over and exerting force, like opening a childproof medicine bottle with a single arm. Next, I will present an abstraction for the complex physics of frictional pushing and demonstrate its application within the context of in-hand manipulation. Finally, I will illustrate how robots can make robust choices in the face of uncertainty. For example, this empowers robots to reliably chop up fruit of unknown ripeness!

Rachel Holladay is a Ph.D. student in the Electrical Engineering and Computer Science Department at the Massachusetts Institute of Technology. Her research focuses on developing algorithms and models that enable robots to robustly perform long-horizon, contact-rich manipulation tasks in everyday environments. She received her B.S. in Computer Science and Robotics from Carnegie Mellon University.

Monday, March 18, 2024

1:30 PM

Laufer Conference Room, Olin Hall of Engineering, Room 406 (OHE 406)

The Zoom webinar is at https://usc.zoom.us/j/95892885119?pwd=QXZOZUhrcTJRYk5qZzZwVThrTytVZz09.

host: Ronney

Walking Droplets and Galloping Bubbles

Pedro Sáenz

Assistant Professor
Physical Mathematics Laboratory
Department of Mathematics
University of North Carolina at Chapel Hill
Chapel Hill, NC

In the first part of this talk, we present a classical wave-particle analog of Anderson localization with walking droplets, which self-propel across the surface of a vibrating fluid bath through a resonant interaction with its own guiding wave fild. These walking droplets, or 'walkers', push the boundaries of classical mechanics to include behaviors previously thought to be exclusive to the quantum realm. By investigating the erratic motion of walkers over submerged random topographies, we demonstrate the emergence of localized statistics analogous to those of quantum particles. Consideration of an ensemble of walker trajectories reveals a suppression of diffusion when the guiding wave field extends over the disordered topography. The emergent statistics are rationalized mechanistically by virtue of the wave-mediated resonant coupling between the droplet and topography, which generates an attractive wave potential about the localization region. This hydrodynamic quantum analog demonstrates how a classical particle may localize like a wave, and so suggests new directions for future research.

The second part of this talk is dedicated to introducing a new symmetry-breaking instability inspired by walking droplets that causes bubbles to self-propel. We illustrate how a vertically vibrated bubble may spontaneously start to "gallop" along solid boundaries due to a parametric instability in its shape oscillations. A systematic exploration of the key system parameters reveals a myriad of domain exploration modes, including rectilinear, circular, and run-and-tumble motions. We use direct numerical simulations to extend the galloping instability to hemispherical bubbles. The bubble's spectrum and a perturbative stability analysis shows that the galloping instability results from the coupling between symmetric and asymmetric modes about the vibration axis. By computing the inertial reaction of an inviscid external liquid to the bubble deformation, we describe the bubble's propulsion as a manifestation of Saffman's mechanism of swimming in inviscid flow. We present a series of proof-of-concept experiments showcasing the potential of galloping bubbles for practical applications.

Pedro Sáenz is an Assistant Professor and the director of the Physical Mathematics Laboratory (www.pml.unc.edu) in the Department of Mathematics at UNC. From 2015 to 2019, he was an Instructor in Applied Mathematics at MIT. Pedro received his Ph.D. from the University of Edinburgh in 2014, and was a post-doctoral fellow at Imperial College London in 2015. Pedro was awarded an Alfred P. Sloan Research Fellowship in 2023, and an NSF CAREER award in 2021. Pedro has also received multiple scientific visualization awards, including the American Physical Society Gallery Fluid of Motion award in 2017 and 2022. His research blends experiments, numerical simulations and theory to address fundamental problems that find motivation in physics and engineering.

Wednesday, March 20, 2024

3:30 PM

Zumberge Hall of Science, Room 252 (ZHS 252)

The Zoom webinar is at https://usc.zoom.us/j/95892885119?pwd=QXZOZUhrcTJRYk5qZzZwVThrTytVZz09.

host: Kanso

Ultrasound-Responsive Intelligent Material Systems

Shima Shahab

 
Associate Professor
and
Mary V. Jones Faculty Fellow

Department of Mechanical Engineering
Virginia Tech
Arlington, VA

Intelligent material systems, often known as smart materials, may adapt their behavior in response to changes in external stimuli. The use of smart materials in numerous sensitive applications has increased the demand for a remote, wireless, efficient, and physiologically safe stimulus. These needs will be addressed in this presentation by using Focused Ultrasound (FUS) as an external trigger. To achieve the desired response of an ultrasound-responsive smart structure, FUS has the unique property of maintaining both spatial and temporal control and propagating over large distances with low losses. Shape Memory Polymers (SMPs) and piezoelectric (PZT) materials will be discussed as ultrasound-responsive smart materials. First, we will look into the acoustic-thermoelastic dynamics of ultrasound-stimulated SMPs in order to develop next-generation delivery, sensing, and morphing devices. When activated by FUS, SMPs can be manipulated into any temporary shape and then recover to their stress-free permanent shape. FUS is a promising stimulus with the unique and superior capacity to cause localized heating, activate various intermediate shapes, and enable noninvasive shape recovery in polymers. Second, we'll go through the fundamentals of PZT-based Ultrasonic Power Transfer (UPT) systems. UPT along with acoustic holograms is a new technique that relies on piezoelectric receivers to receive FU in selective patterns. UPT is used to wirelessly charge modest to high-power electronics in biomedical implants and enclosed electronic devices working in unmanned aerial and undersea vehicles. Finally, holographic lenses, also referred to as acoustic holograms, will be discussed. These lenses are utilized to generate complicated FUS fields. They save the desired wavefront's phase profile, which is utilized to reconstruct the acoustic pressure field when illuminated by a single acoustic source. Because of its robustness, simplicity, and low cost, the use of holographic lenses for sound modification in medical applications has attracted interest in recent years. Ultrasound-guided thermal therapy is one such application that use the absorbed acoustic field to generate a therapeutic effect within the human body.

Shima Shahab is Mary V. Jones Faculty Fellow and an Associate Professor in the Department of Mechanical Engineering at Virginia Tech. She completed her Ph.D. and M.S. in Mechanical Engineering at Georgia Institute of Technology. Dr. Shahab is the Director of Multiphysics Intelligent and Dynamical Systems (MInDS) laboratory and an Associate Editor of Journal of Intelligent Material Systems and Structures (JIMSS). Her theoretical and experimental research program focuses on the intersection of smart materials and dynamical systems for various interdisciplinary applications such as contactless ultrasound power transfer, ultrasound responsive polymer-based systems, ultrasound atomization, and acoustic holograms. Dr. Shahab has served as principal investigator on research grants from the National Science Foundation, Alpha Foundation, Oakridge National Laboratory, and Ford Motor Company. In addition to a recent NSF CAREER award, Dr. Shahab is the recipient of ASME Gary Anderson Early Achievement Award. The award recognizes a young researcher on the rise who has already made significant contributions to the field of Adaptive Structures and Material Systems.

More at https://me.vt.edu/people/faculty/shahab-shima.html .

Wednesday, March 27, 2024

3:30 PM

Zumberge Hall of Science, Room 252 (ZHS 252)

The Zoom webinar is at https://usc.zoom.us/j/95892885119?pwd=QXZOZUhrcTJRYk5qZzZwVThrTytVZz09.

host: Maghsoodi

Role of Arterial Stiffness and Pulsatile Hemodynamics in Target Organ Damage: Implications for Human Health

Julio A. Chirinos

Professor of Medicine
Cardiovascular Division
Perelman School of Medicine
University of Pennsylvania
Philadelphia, PA

The normal aorta exerts a powerful cushioning function, which limits arterial pulsatility and protects the microvasculature from excessive fluctuations in pressure and blood flow. Large-artery stiffening, which occurs with aging and various pathologic states, impairs this cushioning function, and has important consequences on target organs, including the brain, the heart, the kidneys and the placenta. Arterial stiffness also appears to be implicated in the pathogenesis of cardiometabolic disease. Large-artery stiffness represents a high-priority therapeutic target to ameliorate the global burden of cardiovascular disease in the next several decades. We will discuss key physiologic and biophysical principles related to arterial stiffness and the impact of aortic stiffening on target organs and associated disease states.

Julio A. Chirinos, MD, PhD is a Professor of Medicine in the Cardiovascular Division, Co-Director of the Clinical Research T32 Training Program in Cardiovascular Biology and Medicine, and Adjunct Faculty at the Center for Magnetic Resonance and Optical Imaging, at the University of Pennsylvania Perelman School of Medicine. He is a specialist in cardiac imaging (echocardiography and cardiac magnetic resonance imaging). He is also adjunct Faculty at the University of Ghent in Belgium, where he maintains an active collaboration with the Asklepios Investigators. His PhD was focused on the non-invasive assessment of arterial hemodynamics. He is the President of the North American Artery Society.

He directs an NIH-funded research program focused on the role of arterial stiffness and pulsatile hemodynamics in cardiovascular disease, mechanisms of human heart failure with preserved ejection fraction (HFpEF) and the use of proteomics to discern mechanisms of human heart failure. He currently leads clinical studies designed to therapeutically target the arterial tree in order to reduce maladaptive cardiac remodeling, diastolic dysfunction, and to treat patients with HFpEF, an epidemic condition for which limited effective proven pharmacologic therapies are currently available. He also leads various cohort studies with deep cardiovascular phenotyping aimed at characterizing phenotypic profiles in humans. He co-leads a Global Heart Failure biomarker consortium, an industry-academic collaboration investigating proteomics and genomics in human heart failure. He is the University of Pennsylvania Principal Investigator and a Steering Committee member of Heart Share, a multicenter research consortium funded by the NHLBI aimed at discerning mechanisms of disease in human Heart Failure with Preserved Ejection Fraction.

Dr. Chirinos has published >250 papers in high-impact journals, including the New England Journal of Medicine, New England Journal of Medicine Evidence, The Lancet, The Lancet Respiratory Medicine, Journal of the American Medical Association (JAMA), Circulation, Hypertension, and Journal of the American College of Cardiology (JACC). He has received awards or honorary fellowships from the American Heart Association, the Inter-American Society of Cardiology, the American Society of Hypertension and the European Society of Cardiology. He is a member of the American Society of Clinical Investigation and an honorary member of the Korean Society of Cardiology. He has also received multiple research grants from the National Heart, Lung and Blood Institute (NHLBI), the National Institute on Aging (NIA), the National Institutes for Advancing Translational Sciences (NCATS), the American College of Radiology (ACR), and the American Heart Association (AHA), among others. He was one of 20 global members of the Lancet Commission for Hypertension, in charge of developing strategies and recommendations to reduce the global burden of hypertension. He has participated in various clinical expert committees for the American Heart Association, American Society of Echocardiography, European Society of Cardiology, American Society of Hypertension and European Association of Cardiovascular Imaging. During the COVID-19 pandemic, Dr. Chirinos led 2 international multicenter trials testing therapeutic strategies related to the intersection of COVID-19 and cardiovascular disease. He is also the co-PI of ongoing cohort studies to study the long-term cardiovascular consequences of COVID-19, co-chair of the Global Heart Failure Biomarker Consortium, and a Steering Committee Member of HeartShare. Both of these multicenter consortia are focused on the study of heart failure with preserved ejection fraction (HFpEF).

He is currently the President of the North American Artery society, which promotes the study of arterial function as a determinant of cardiovascular disease. He was also an Associate Editor of the American Heart Association Journal Circulation: Heart Failure, The Journal of Clinical Hypertension, Editor of the Cochrane Group (Cochrane Collaboration), Senior Consulting Editor of the Journal of the American College of Cardiology: Cardiovascular Imaging and a member of the editorial board of the Journal of the American Heart Association, Pulse and the Journal of Geriatric Cardiology. He is the editor of a textbook on Arterial Stiffness and Pulsatile Hemodynamics (Arterial Stiffness and Pulsatile Hemodynamics in Health and Disease; Elsevier, 2022).

Dr. Chirinos also directs a core analysis laboratory for assessments of cardiac and arterial structure and function with non-invasive imaging, which has served as the core lab for various multicenter studies, including population studies, American College of Radiology Network studies and industry-funded studies. He has been an invited speaker in >160 scientific sessions.

Wednesday, April 3, 2024

3:30 PM

Zumberge Hall of Science, Room 252 (ZHS 252)

The Zoom webinar is at https://usc.zoom.us/j/95892885119?pwd=QXZOZUhrcTJRYk5qZzZwVThrTytVZz09.

host: Pahlevan

Do We Really Need All That Data? Learning and Control for Contact-Rich Manipulation

Michael Posa

Assistant Professor
Department of Mechanical Engineering and Applied Mechanics
University of Pennsylvania
Philadelphia, PA

For all the promise of big-data machine learning, what will happen when robots deploy to our homes and workplaces and inevitably encounter new objects, new tasks, and new environments? If a solution to every problem cannot be pre-trained, then robots will need to adapt to this novelty. Can a robot, instead, spend a few seconds to a few minutes gathering information and then accomplish a complex task? Why does it seem that so much data is required, anyway? I will first argue that the hybrid or contact-driven aspects of manipulation clashes with the inductive biases inherent in standard learning methods, driving this current need for large data. I will then show how contact-inspired implicit learning, embedding convex optimization, can reshape the loss landscape and enable more accurate training, better generalization, and ultimately data efficiency. Finally, I will present our latest results on how these learned models can be deployed via real-time multi-contact MPC for dexterous robotic manipulation, where the robot must autonomously make and break contact and initiate stick-slip transitions.

Michael Posa is an Assistant Professor in Mechanical Engineering and Applied Mechanics at the University of Pennsylvania. He leads the Dynamic Autonomy and Intelligent Robotics (DAIR) lab, a group within the Penn GRASP laboratory. His group focuses on developing computationally tractable algorithms to enable robots to operate both dynamically and safely as they interact with their environments. Michael received his Ph.D. in Electrical Engineering and Computer Science from MIT in 2017 and received his B.S. in Mechanical Engineering from Stanford University in 2007. Before his doctoral studies, he worked as an engineer at Vecna Robotics. He received the NSF CAREER Award in 2023, the RSS Early Career Spotlight in 2023, a Google Faculty Research Award, and a Young Faculty Researcher Award from the Toyota Research Institute. His work has also received awards recognition at TRO, ICRA, Humanoids, and HSCC.

Wednesday, April 10, 2024

3:30 PM

Zumberge Hall of Science, Room 252 (ZHS 252)

The Zoom webinar is at https://usc.zoom.us/j/95892885119?pwd=QXZOZUhrcTJRYk5qZzZwVThrTytVZz09.

host: Nguyen

Colloidal Protein Analogs

Jasna Brujic

Professor
Center for Soft Matter Research at New York University
Physics Department
New York University
New York, NY

Our group is inspired by Nature’s strategy of folding biopolymers into specific protein and RNA structures to build a toy model of polymeric chains of droplets, i.e. “colloidomers”, that are designed via DNA interactions to fold into well-defined architectures. Indeed, simple alternating chains (up to 14 droplets long) with only two droplet flavors (ABABAB…) are sufficient to uniquely encode a dozen “foldamers”, constituting 1% of all possible rigid structures in 2D. Subsequently, these 2D foldamers can self-assemble into larger nets that, upon density-matching, are able to further fold into unique 3D geometries, for example viral capsids. These hierarchical protocols circumvent the vast phase space of the nominal folding landscape, in which a random cluster of 12 particles has tens of thousands or rigid folds to choose from. Once the colloidal protein analog is formed, it can then be further programmed by the polymerase-exonuclease-nickase (PEN) toolbox of enzymes that interact with droplet-droplet DNA bonds, to produce highly non-linear dynamical systems. These "mayonnaise robots” promise to offer a bright and functional future on the colloidal length scale.

Jasna Brujic is a Professor of Physics at New York University. She is one of the core faculty in the Center for Soft Matter Research. Brujic is an experimental physicist, who received her Ph.D. for work on the statistical mechanics of granular matter at the Cavendish Laboratory of the University of Cambridge, UK. She then conducted post-doctoral research at Columbia University in the area of single molecule proteins. Since 2007, Brujic has led a research group at the interface between soft matter physics and biophysics. The group uses biomimetic emulsion systems to study jammed matter, cellular organization in tissues in 3D, protein-protein adhesion, and programmable self-assembly of materials with custom designs.

Wednesday, April 17, 2024

3:30 PM

Zumberge Hall of Science, Room 252 (ZHS 252)

The Zoom webinar is at https://usc.zoom.us/j/95892885119?pwd=QXZOZUhrcTJRYk5qZzZwVThrTytVZz09.

host: Kanso