MEAM Seminar: "Mixing with Activity: Transport of Swimming Microorganisms in Complex Flows"
2022-07-05 10:00 - 23:30 (America/New_York) | Towne 313, 220 S. 33rd Street, Philadelphia, PA, 19104, United States
How will swimming microorganisms affect the mixing of a diffusive contaminant in chaotic flows? Answers to this question can lead to improved understanding of the spread of pollutants (e.g. algal blooms) in oceans and lakes, as well as potentially useful applications in biofuel and vaccine productions. In this talk, I present recent experiments on the chaotic mixing of a passive pollutant in dilute suspensions of swimming Escherichia coli. I will first discuss the results in a two-dimensional (2D) time-periodic flow that preserves spatial symmetry. Second, I will extend these results to a more realistic case of spatially disordered 2D vortical flows that resemble geological flows in nature. Overall, these results show that the interaction between active swimming particles and dynamical structures of the flow (i.e. Lagrangian coherent structures) can lead to profound changes in the transport and mixing of passive scalar quantities.

Quantum Engineering Summer Seminar Series: Jon Felbinger, PhD [private-public partnerships] (Quantum Economic Development Consortium)
2022-07-08 12:00 - 13:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
About the Series: The Quantum Engineering Summer Seminar Series is hosted by the Quantum Engineering Graduate Association (QEGA) every Friday at 12:00 – 1:00 pm EDT throughout the summer months and will be followed by a separate Fall series. The series invites leading world leading experts across academia, industry, and government working on experimental, theoretical, and policy aspects of quantum science and engineering. Each seminar will be followed by a 10 minute networking session with the invited speaker.
Speaker List:
July 8th, 2022 – Jon Felbinger, PhD [private-public partnerships] (Quantum Economic Development Consortium)
July 15th, 2022 – William Oliver, PhD [superconducting qubits] (MIT and MIT Lincoln Labs)
July 22nd, 2022 – Sinead Griffin, PhD [novel qubit materials design] (Lawrence Berkeley National Laboratory)
July 29th, 2022 – Hannes Bernien, PhD [trapped ion systems] (University of Chicago)
August 5th, 2022 – Prineha Narang, PhD [computational quantum dynamics] (Harvard University)
August 12th, 2022 – Peter McMahon, PhD [quantum annealing] (Cornell University)
August 19th, 2022 – Nick Bronn, PhD [NISQ era quantum computing] (IBM)
August 26th, 2022 – Riccardo Manenti, PhD [scalable quantum computing systems] (Rigetti)
Meeting Information: The seminar series will be a hybrid event with talks broadcasted on campus (Wu and Chen Auditorium, Levine Hall) and available online via Zoom. The Zoom link will be posted weekly for each individual talk.
Join QEGA: The Quantum Engineering Graduate Association (QEGA) is a new organization at Penn founded by Nima Leclerc and Noah Johnson, two Penn ESE PhD students in the Sigillito Group. QEGA’s goal is to increase graduate student and faculty engagement in quantum engineering at Penn through school-wide events such as this seminar series, quantum-career networking opportunities, and a quantum engineering outreach program program. If you would like to stay connected with us, please email Nima at nleclerc@seas.upenn.edu with the subject line ‘qega listserv’ to get added to our mailing list!

PSOC@Penn: “Fat accumulation induces heterogeneous regions of softness in fatty liver” (David Li)
2022-07-11 12:00 - 13:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Contact manu@seas.upenn.edu for the Zoom link.

MEAM Seminar: "Sedimentation of Active Suspensions: Hindrance and Phase-separation"
2022-07-12 10:00 - 11:30 | Towne 313, 220 S. 33rd Street, Philadelphia, PA, 19104, United States
Sedimentation of active matter is found in many natural and industrial processes. It plays an important role on the distribution of plankton in oceans, which is key part of the carbon cycle (i.e. ocean’s biological pump) that transports carbon from the ocean’s surface to depth. In this seminar, I will present an investigation on the dynamics of the sedimentation of spherical colloids in presence of swimming microorganisms at various concentration within the dilute regime. In the first part of this talk, I will show how bacterial activity hinders the sedimentation speeds of passive particle. These results can be described by an advection-diffusion equation with an added population dynamics term. In the second part of this talk, I will show the appearance of two sedimentation fronts, particle and bacterial. Even passive systems of poly-dispersed (by size) particles are known to show segregating sedimentation fronts; larger (and faster) settling particles will separate from smaller (and slower) ones, given enough time. Here, I will show how the increase of activity affects sedimentation speeds and the timescales associated with the appearance of the bacteria front. These timescales associated with the second front leads to a phenomenological model that captures the sedimentation of passive particles in active fluids.

ESE Ph.D. Thesis Defense: "Optical and Spin Dynamics of Quantum Emitters in Hexagonal Boron Nitride at Room Temperature"
2022-07-13 10:00 - 11:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Hexagonal boron nitride (h-BN) is a van der Waals material that hosts defect-based quantum emitters (QEs) at room temperature, providing an unparalleled platform for realizing devices for quantum technologies and studying light-matter interactions. Recent observations suggest the existence of multiple distinct defect structures responsible for QEs. Theoretical proposals suggest vacancies, substitutional atoms, and their complexes as likely defect candidates. However, experimental identification of the QEs’ electronic structure is lacking, and key details of the QEs’ charge and spin properties remain unknown. This thesis focuses on understanding the optical and spin dynamics of QEs in h-BN at room temperature. Starting with the motivation for studying quantum systems and QEs, this thesis introduces QEs in h-BN and discusses its current understanding. Next, it discusses the materials and methods developed and utilized during the course of this thesis. Next, it discusses the optical dynamics acquired using photoluminescence spectroscopy and photon emission correlation spectroscopy (PECS) and shows several QEs exhibit pure single-photon emission. It discusses the complex optical dynamics associated with excitation and relaxation through multiple electronic excited states – revealed by PECS and polarization-resolved excitation and emission. Following, it presents the optical dynamics simulations of electronic structure models that are consistent with the observations, and discusses the results in the context of ab initio theoretical calculations. Next, it discusses magnetic-field-dependent PECS that can be used as a framework to probe the presence of single spins that are otherwise elusive. Following, it presents detection and confirmation of single-spin using optically detected magnetic resonance. Finally, it discusses the spin dynamics and time-domain measurements acquired using optical and microwave pulse protocols crucial to developing methods to coherently control the QE’s spin. To conclude, it discusses the future directions that can help identify the chemical nature of QEs in h-BN and establish it as a scalable material platform for quantum technologies.

ESE Ph.D. Thesis Defense: "Learning Environmental Models with Multi-Robot Teams Using a Dynamical Systems Approach"
2022-07-14 10:00 - 12:00 | PERCH 303, Pennovation 3rd Floor, 3401 Grays Ferry Avenue, Bldg 6176, Philadelphia, PA, 19146, United States
Robots have the capability to sense and track natural phenomena for environmental monitoring, deepening our understanding of the world. Robotic modeling of such phenomena is essential to operating in complex environments, allowing robots to perform in more realistic scenarios. Thus, representing complex environments is paramount to the success of multi-robot teams. While considerable efforts have been made for modeling with multi-robot teams, specifically in coordination and distributed methods, these techniques have limitations in spatiotemporal, complex environments. These environments can be vastly different, such as fluid flows, oceans, and space. Robots operating in these environments typically create representations of their surroundings using computationally expensive techniques or by leveraging human expert knowledge.
Interestingly, though these environmental processes may seem unrelated, they can all be analyzed with dynamical systems theory. This thesis presents methods for representing the environment as a dynamical system with machine learning techniques. We formulate machine learning methods that lend to data-driven modeling of the phenomena for robotic applications, specifically using dimensionality reduction techniques and kernel methods. The data-driven modeling explicitly leverages theoretical foundations of dynamical systems theory. Dynamical systems theory offers mathematical and physically interpretable intuitions about the environmental representation. The procedures presented include distributed algorithms, online adaptation, uncertainty quantification, and feature extraction to allow for the actualization of these techniques on-board robots. The environmental representations guide robot behavior in developing strategies such as optimal sensing and energy-efficient navigation. The methods and procedures provided in this thesis were verified across prototypical environments and on experimental robots.

ESE Ph.D. Thesis Defense: "Creating dynamical robots of different morphologies and sizes through automatic origami design"
2022-07-14 12:30 - 14:30 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Origami robots are machines whose morphologies and functions are created by folding locally flat sheets. This thesis makes three contributions to the design and fabrication of origami robots aimed at the development of an automated computational pipeline for the specification and construction of widely different morphologies and body sizes capable of highly dynamic operation. The initial contribution recruits recent advances in the design of compliant folded structures to build the first soft robots that exhibit highly dynamic behavior. Specifically, the proof-of-concept robots reported here achieve their juggling and hopping behaviors by actuating their origami springs as power-cascading devices. Second, this thesis advances the origami design literature by automating the construction of compliant origami kinematic chains. The “Kinegami” algorithm reported here accepts a Denavit-Hartenberg kinematic specification and uses a catalog of tunably compliant origami modules to generate a crease pattern that folds into the prescribed serial robot mechanism. Finally, the thesis addresses the problem of scalability in general (not just origami) robot design by studying the simultaneous interaction of structural integrity and actuator affordance. Four contrasting abstract task domains impose different scaling criteria that reveal the relative advantages and disadvantages of three distinct structural principles combined with three different actuator types. For example, applying the unloaded dynamic task criterion to a direct drive actuation type reveals that the origami-style shell structure supports superior length scale-up. An accompanying empirical study confirms that structural alternatives cannot achieve a one-degree-of-freedom hopping task at the same five-fold scale-up of the original hopper design exhibited by the shell structure design.
Considered in isolation, these contributions advance, respectively, the recent soft robotics literature, the older origami design literature, and the traditional engineering scaling literature. Considered together, they advance the agenda for the rapid, computer-assisted design of customized, high-performance robots.

Quantum Engineering Summer Seminar Series: William Oliver, PhD [superconducting qubits] (MIT and MIT Lincoln Labs)
2022-07-15 12:00 - 13:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States

PSOC@Penn Talk: David Basanta Gutierrez & Conor C. Lynch (Moffit Cancer Center)
2022-07-18 12:00 - 13:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States

MEAM Seminar: "Modboats: Building an Aquatic Modular Self-Reconfigurable Robotic System out of Underactuated Modules"
2022-07-19 10:00 - 11:30 | Towne 313, 220 S. 33rd Street, Philadelphia, PA, 19104, United States
Modular robots offer the promise of flexibility; we will be able to deploy teams of generalist modules and reconfigure them into various specialists to perform whatever task is necessary. Surface swimming aquatic modular robots in particular are not well studied, however, because the literature assumes that the modules need to be holonomic, which makes them expensive and difficult to build and maintain. We disrupt this assumption with the Modboat – an inexpensive, underactuated surface swimmer that uses only one motor to propel and steer itself along the ocean surface, and is capable of all the behaviors required of a modular self-reconfigurable robotic system (MSRR). In this seminar, I present my work on designing the Modboat modules, achieving near holonomic motion capabilities using temporal multiplexing, and developing the capabilities needed for modular self-reconfiguration.

BE Doctoral Dissertation: "Rapid, scalable, amplified RNA detection in situ with clampFISH 2.0" (Ian Dardani)
2022-07-21 13:00 - 15:00 | Smilow Center for Translational Research, Room 09-146
The Department of Bioengineering at the University of Pennsylvania and Dr. Arjun Raj are pleased to announce the Doctoral Dissertation Defense of Ian Dardani.

Title: “Rapid, scalable, amplified RNA detection in situ with clampFISH 2.0″

Date: Thursday, July 21, 2022
Time: 1pm ET

Add to Google Calendar
Location: In-person: Smilow Center for Translational Research, Room 09-146
Zoom: https://upenn.zoom.us/j/94182106925

Quantum Engineering Summer Seminar Series: Sinead Griffin, PhD [novel qubit materials design] (Lawrence Berkeley National Laboratory)
2022-07-22 12:00 - 13:00 | https://upenn.zoom.us/j/96715197752

Physical Sciences Onco-development Colloquium (PSOC): "From physics to therapeutics – An entrepreneurial journey across disciplines" (Kandaswamy Vijayan)
2022-07-25 12:00 - 13:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Physical Sciences Onco-development Colloquium (PSOC)
Hybrid talk in Raisler Lounge and on Zoom (contact manu@seas.upenn.edu for the Zoom link).

MEAM Ph.D. Thesis Defense: "The Design of a Community-Informed Socially Interactive Humanoid Robot and End-effectors for Novel Edge-Rolling"
2022-07-25 15:00 - 16:00 | Room 337, Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
This dissertation discusses my work in building an HRI platform called Quori and my once separate now integrated work on a manipulation method that can enable robots like Quori, or any more capable robot, to move large circular cylindrical objects.
Quori is a novel, affordable, socially interactive humanoid robot platform for facilitating noncontact human-robot interaction (HRI) research. The design of the system is motivated by feedback sampled from the HRI research community. The overall design maintains a balance of affordability and functionality. Ten Quori platforms have been awarded to a diverse group of researchers from across the United States to facilitate HRI research to build a community database from a common platform.
This dissertation concludes with a demonstration of Quori transporting a large cylinder for which Quori does not have the power to lift nor the range of motion to dexterously manipulate. Quori is able to achieve this otherwise insurmountable tasks through a novel robotic manipulation technique called robotic edge-rolling. Edge-rolling refers to transporting a cylindrical object by rolling on its circular edge, as human workers maneuver a gas cylinder on the ground for example. This robotic edge-rolling is achieved by controlling the object to roll on the bottom edge in contact with the ground, and to slide on the surface of the robot’s end-effector. It can thus be regarded as a form of robotic dexterous, in-hand manipulation with nonprehensile grasps. This work mainly addresses the problem of grasp planning for edge-rolling by studying how to design appropriately shaped end-effectors with zero internal mobility and how to find feasible grasps for stably rolling the object with the simple end-effectors.

MEAM Seminar: "Motion Planning for a Variable Topology Truss Robot"
2022-07-26 10:00 - 11:30 | Towne 313, 220 S. 33rd Street, Philadelphia, PA, 19104, United States
Variable Topology Trusses (VTTs) are a new type of modular, self-reconfigurable robot (MSRR). Like other self-reconfigurable robot systems, a VTT can change its shape to adapt to a variety of tasks, offering a wider range of capabilities than a single robot configuration. While most MSRRs consist of robot modules that are connected in a chain or lattice architecture, VTTs use a truss-based architecture instead. A VTT can be thought of as a variable geometry truss—a set of linear actuator modules that are connected together to form a rigid structure—with the additional ability to reconfigure by merging and splitting the truss nodes. This truss architecture gives improved structural efficiency and scalability, but it presents unique challenges for motion planning and reconfiguration planning.
In this talk, I will first introduce the Variable Topology Truss concept and show our recent progress on developing the robot hardware. Then, I will present my work on motion planning for reconfiguration and shape morphing of VTTs. Traditional probabilistic motion planning techniques are ineffective because the collision constraints generate a configuration space with many narrow passageways and inaccessible regions. I will present a technique that guides a motion planner by leveraging the topological and knot-theoretic information embedded in a robot configuration.

BE Doctoral Dissertation: "Fundamentals and Application of 3D Suspension Bath Bioprinting for the Engineering of Meniscal Tissue" (Margaret Prendergast)
2022-07-29 09:00 - 11:00 | Room 337, Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
The Department of Bioengineering at the University of Pennsylvania and Dr. Jason Burdick are pleased to announce the Doctoral Dissertation Defense of Margaret Prendergast.

Title: Fundamentals and Application of 3D Suspension Bath Bioprinting for the Engineering of Meniscal Tissue
Date: Friday, July 29th, 2022
Time: 9 AM ET
Location: Towne Building, Room 337, 107 Towne Bldg, Philadelphia PA 19104
(If you wish to join virtually, Please reach out to Maggie (prendm@seas.upenn.edu) for zoom link and passcode.)

Quantum Engineering Summer Seminar Series: Hannes Bernien, PhD [trapped ion systems] (University of Chicago)
2022-07-29 12:00 - 13:00 | https://upenn.zoom.us/j/96715197752

BE Doctoral Dissertation Defense: "Designing Hyaluronic Acid Granular Hydrogels for Biomaterials Applications" (Victoria Grace Muir)
2022-07-29 12:00 - 14:00 | Class of 62 Auditorium, John Morgan Building, 3620 Hamilton Walk, Philadelphia, PA, 19104
The Department of Bioengineering at the University of Pennsylvania and Dr. Jason Burdick are pleased to announce the Doctoral Dissertation Defense of Victoria Grace Muir.

Title: Designing Hyaluronic Acid Granular Hydrogels for Biomaterials Applications
Date: Friday, July 29th, 2022
Time: 12pm ET
Location: John Morgan Building, Class of 62 Auditorium, 3620 Hamilton Walk, Philadelphia, PA 19104.

The public is welcome to attend.

MEAM Ph.D. Thesis Defense: "Imperfect Adhesion Between Elastic Solids"
2022-08-01 10:30 - 12:00 | Room 337, Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Surface force-mediated adhesion, i.e. via van der Waals forces, is critical for the direct bonding of bulk solids in the absence of an adhesive layer. However, no two surfaces are perfectly flat nor conformal and some have intentional patterns, which leads to imperfect adhesion, i.e. interface adhesion strength that is below its ideal strength. In the case of non-conformal patterned interface, regions of tensile and compressive stresses exist in the adhered solids at the equilibrium state. Imperfect adhesion also can arise from edge effects. The understanding of imperfect adhesion is important in controlling interfacial strength and toughness for various applications including MEMS/NEMS, micro-transfer printing, and soft robotic grippers.
The overall goal of this thesis is to investigate the mechanics of surface force-mediated adhesion by examining the interplay between intrinsic traction-separation relation (TSR), interface topography, and elastic bulk properties. The TSR developed accounts for strong repulsion to avoid material interpenetration. The effective interface properties, including the overall adhesion strength and work of separation, are determined from numerical calculations using finite element analysis.
The first study exploits surface patterning for adhesion control. A cohesive model for a periodic unit cell with non-conformal patterned interfaces is developed to analyze the joint effects of non-uniform interface separation and elastic bulk deformation. The second study investigates edge effects. In general, interface shear tractions coupled with Poisson contraction reduces the interface adhesion significantly and result in fracture-based failure. Understanding the interaction between the normal and the shear tractions is important but has not been studied extensively thus far. The second study concerns the detachment of an elastic pillar from a rigid substrate. A non-dimensional parameter is defined to describe the transition between strength-based and fracture-based failure. In a third study, the mechanics that govern interface failure in electroadhesives is investigated using a TSR derived from Coulomb’s law. This last study highlights the importance of interface crack growth in the design of electroadhesives through modeling an elastic cylinder electrostatically adhered to a rigid substrate. The adhesion strength of the electroadhesives becomes imperfect when a non-dimensional parameter reaches a critical value.

MEAM Seminar: "Neural Network Assisted Open Boundaries for Molecular Dynamics Simulations"
2022-08-02 10:00 - 11:30 | Towne 313, 220 S. 33rd Street, Philadelphia, PA, 19104, United States
Molecular dynamics simulations allow for the visualization and analysis of atoms and molecules. The choice of boundary conditions in these simulations can impact the overall system, and is an important design decision. In particular, open boundary molecular dynamics simulations is one type of methodology that allows for the treatment of atomistic models with non equilibrium conditions. In this seminar, I introduce an approach to the treatment of open boundaries using machine learning. A neural network-assisted design will be presented that can emulate the physics and reduce the computational cost of open boundary simulations. Particle influxes and neural network-derived forces are applied at the boundaries of an open domain consisting of explicitly modeled Lennard-Jones atoms in order to represent the effects of an unmodeled surrounding fluid. Canonical ensemble simulations with periodic boundaries are used to train the neural network and to sample boundary fluxes. The method, as implemented in the LAMMPS molecular simulation package, yields results comparable to those calculated using periodic molecular dynamics and runs two orders of magnitude faster than a comparable grand canonical molecular dynamics system.

Quantum Engineering Summer Seminar Series: Prineha Narang, PhD [computational quantum dynamics] (Harvard University)
2022-08-05 12:00 - 13:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States

MEAM Seminar: "Data-Driven Model Discovery for Non-equilbrium Processes via Stochastic Thermomechanics"
2022-08-09 10:00 - 11:30 | Towne 313, 220 S. 33rd Street, Philadelphia, PA, 19104, United States
Non-equilibrium phenomena are ubiquitous across material systems and of great technological relevance. Examples of such phenomena include diffusion processes in liquid and gases, viscoelasticity and plasticity in solids, and rheological behavior of colloidal and granular media. Despite their ubiquity and importance, the understanding of non-equilibrium phenomena remains in its infancy compared with classical equilibrium thermodynamics and statistical mechanics from both theoretical and computational aspects. As a consequence, current modeling and simulation strategies, including multiscale paradigms, are mostly trapped within a compromise between computational efficiency and physical fidelity.
In this talk, I will leverage several different recent advances in non-equilibrium physics, together with emerging machine learning techniques, to develop data-driven computational strategies for learning continuum evolution equations. First, I will present a new strategy for continuum model discovery to systems that have both a reversible (elastic) and dissipative evolution, by using fluctuation theorems, particularly, the Jarzynski equality. Second, I will talk about a proposed machine learning architecture called Variational Onsager Neural Networks (VONNs) to learn the non-equilibrium evolution PDEs based on the Onsager’s variational approach. Finally, I will introduce a statistical mechanics framework with quantified uncertainty to extrapolate material behavior to different loading conditions (including far-from-equilibrium conditions) or material systems.

Quantum Engineering Summer Seminar Series: Peter McMahon, PhD [quantum annealing] (Cornell University)
2022-08-12 12:00 - 13:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States

MEAM Seminar: "Toward Agile Legged Locomotion in a Novel Quadrupedal Robot with an Axially Twisting Spine"
2022-08-16 10:00 - 11:30 | Towne 313, 220 S. 33rd Street, Philadelphia, PA, 19104, United States
With the advent of more capable and cheaper legged robots, robotic legged locomotion is becoming an increasingly attractive solution for locomotion and embodiment. Legs offer expanded capability over wheeled robots, albeit at the cost of complexity and efficiency. For example, walking robots explore rubble-filled caves, inspect industrial sites, move around human spaces (e.g., stairs), and travel in natural environments. Still, the current capabilities of most robots pale in comparison to most biological-legged locomotors. For instance, the arboreal squirrel can run from branch to branch, climb up and down trees, and leap from surface to surface, composing strings of maneuvers without slowing or stopping. Taking biology as inspiration, collaborations with mathematicians, biomechanists, paleontologists, and geologists help us distill down the essential locomotor primitives and morphologies for new, more agile, quadrupedal robots.
My research focuses on the benefits of adding novel internal degrees of freedom to quadrupeds (namely an axially twisting spine), especially in spatial, transitional maneuvers. In this talk, I will show some results and preliminary work using physical models and optimization to suggest that investigating spinal degrees of freedom is vital in opening up the capabilities of legged quadrupeds. Next, I will introduce a novel quadrupedal robot, Twist, walk through its design process, describe the context of its design, and elaborate on some questions we hope to solve within and outside the field of robotics. Finally, I will discuss my work toward these transitional (parkour) behaviors in robotics, particularly regarding moving from a sagittal plane model and robot to an intrinsically spatial, three-dimensional model and robot.

Quantum Engineering Summer Seminar Series: Nick Bronn, PhD [NISQ era quantum computing] (IBM)
2022-08-19 12:00 - 13:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States

MEAM Seminar: "Disease Indicator Prediction in Vascular Flows via Physics-Informed Deep Learning"
2022-08-23 10:00 - 11:30 | Towne 313, 220 S. 33rd Street, Philadelphia, PA, 19104, United States
Unlike diseases with strong genetic predisposition, such as cancer, there exist no accurate personalized diagnostic tools for disorders such as Hypertensive Pregnancy Disorders (HPD) and its mechanism remain understudied. For the purpose of performing diagnostics, we require disease indicators (biomarkers) that are strongly correlated with the underlying condition, i.e. absolute vascular pressure for hypertension. Measuring absolute pressure requires an invasive procedure and it is impossible for small vessels and in pregnant subjects, but an informed estimate is possible via the use of Computational Fluid Dynamics. Computational Fluid Dynamics can also be employed for studying the relation between disease outcomes and biomarkers, by constructing virtual representations of a human body called Digital Twins and performing simulations for different parameters. Unfortunately the computational cost of CFD is very high thus studying the physiology of large cohorts of patients or making real time predictions is impossible. Therefore, fast surrogates that generalize across different patients and parameters are required.
In this talk, I will leverage aspects of both computational mechanics and deep learning to develop methods for predicting complex biomarkers that cannot be measured in the clinic, such as absolute pressure, for performing patient specific diagnostics. First, I will discuss the feasibility of estimating vascular parameters for assessing Hypertensive Pregnancy Disorders using a combination of Bayesian inference and a reduced order Navier-Stokes model and show that despite the accuracy of this procedure, its computational complexity makes it impossible to be employed in clinical practice. Second, I will discuss leveraging Physics-Informed Neural Networks (PINNs) for designing Deep Learning surrogates to alleviate computational drawbacks of the above pipeline for one patient. Third, I will discuss machine learning inference in function spaces, called Operator Learning, and more specifically the LOCA method and the potential of online biomarker predictions for different patients employing operator learning on manifolds and quantifying the uncertainty of predictions.

CBE Dissertation Defense: "Visualizing Genetics: Quantifying Gene Expression Through Genetic and Epigenetic Modifications"
2022-08-23 10:00 - 14:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
“Visualizing Genetics: Quantifying Gene Expression Through Genetic and Epigenetic Modifications”
Abstract:
Precise control over gene expression is essential for proper development of an organism. However, genes are regulated in a variety of ways, the mechanisms of which are not well understood. This thesis explores gene regulation at two different levels: genetic control and epigenetic control. Genetic control focuses on components of the genome such as enhancers, promoters, gene composition, and gene length. Each of these facets can potentially affect either the timing, amount, or location of gene expression. Epigenetic control looks at how different DNA markers, which do not modify the DNA sequence, are able to manipulate gene production. Using quantitative live-imaging in Drosophila embryos and immunofluorescence staining in human fibroblasts, we are able to capture and analyze the impact each of these factors have on gene expression. In genetic control, we see that enhancers play a dominant role in controlling expression, specifically through the individual transcription factor binding sites within an enhancer. Based on the binding affinity and type of binding site, the enhancer can influence when genes are active as well as the amount of mRNA produced. The level of mRNA production is also influenced by the rate of RNA polymerase II (Pol II) elongation. The speed that Pol II moves along a gene body can impact how much mRNA is made within a certain developmental time period. We find that not only do enhancers play a role in controlling Pol II elongation rate, but the composition of the gene itself also contributes to modulating the rate of elongation. In epigenetic control, there are many different markers interacting with DNA. Here we identified H3K9me3 as a key epigenetic modification that controls DNA compaction, and thus can silence a large number of genes. As a result, specific epigenetic markers can ultimately control gene expression and dramatically impact a cell’s ability to reprogram itself. Slight changes in gene regulatory mechanisms can cause extreme changes in gene expression and ensuring that organisms develop properly is contingent upon having a better understanding of how different factors influence expression.

BE Doctoral Dissertation Defense: "Molecular Imaging Biomarkers and Regulation Approaches for Next-Generation Cellular Therapies" (Iris Lee)
2022-08-23 15:00 - 17:00 | BRB 0252
The Department of Bioengineering at the University of Pennsylvania and Dr. Mark Sellmeyer are pleased to announce the Doctoral Dissertation Defense of Iris Lee.

Title: Molecular Imaging Biomarkers and Regulation Approaches for Next-Generation Cellular Therapies
Date: Tuesday, August 23rd, 2022
Time: 3 PM ET
Location: Biomedical Research Building (BRB) 0252

The public is welcome to attend.

MEAM Seminar: "High-performance Electroadhesives for Materials and Robots with Programmable Stiffness"
2022-08-25 10:00 - 11:30 | Towne 313, 220 S. 33rd Street, Philadelphia, PA, 19104, United States
Materials with electroprogrammable stiffness and adhesion can enhance the performance of robotic systems but achieving large changes in stiffness and adhesive forces in real time is an ongoing challenge. Electroadhesive clutches can rapidly adhere high stiffness elements, although their low force capacities, high activation voltages, and inability to separate and turn off stiffness changes reliably have limited their applications. A major challenge in realizing stronger electroadhesive clutches is that current parallel-plate models poorly predict clutch force capacity and cannot be used to design better devices. Furthermore, soft material interfaces have not been utilized for stronger electroadhesive clutches due to latent adhesion at the contact interface that prevents programmable release.
In this talk, a fracture mechanics framework to understand the relationship between clutch design, force capacity and contact area is discussed. This mechanics-based framework predicts clutch performance across multiple geometries and applied voltages. Based on this approach, a Coulombic electrostatic clutch with 94 times the force capacity per unit electrostatic force of state-of-the-art electroadhesive clutches is realized. These electroadhesive clutches are used to increase the load capacity of a soft, pneumatic finger by a factor of 62 times compared to a finger without an electroadhesive. Finally, this mechanics-based design methodology is applied to the design of low-voltage ionoelastomer clutches with soft material interfaces for wearable robotic applications with increased force capacities and programmable release at reduced device sizes.

Quantum Engineering Summer Seminar Series: Riccardo Manenti, PhD [scalable quantum computing systems] (Rigetti)
2022-08-26 12:00 - 13:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
