Penn Engineering Events

CBE Dissertation: "DNA Mediated Particle Adhesion"
2020-01-27 13:00 - 15:00 (America/New_York) | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Advisor: John Crocker, PhD; Committee Members: Talid Sinno, PhD; Scott Diamond, PhD; and Daniel Hammer, PhD

MSE Faculty Candidate Seminar: "Defect Structure Process Maps for Laser Powder Bed Fusion Additive Manufacturing"
2020-01-23 10:45 - 11:45 | Auditorium, LRSM Building, 3231 Walnut Street, Philadelphia, PA, 19104, United States
Additive manufacturing (AM) greatly expands the design freedom and near-net shape production of metallic components across multiple length scales. However, defects arising from starting materials, processing conditions, and post-processing may significantly affect the structural integrity and operational performance of metal AM parts. This paper seeks to elucidate common defects and defect formation mechanisms encountered in typical laser powder bed fusion (LPBF) AM processes. While the defect structures of conventional joining processes such as laser welding have been studied extensively, this talk primarily focuses on the nature of porosity transfer to the finished part exclusively for metal AM powder bed processing techniques. Multiple starting powders and analysis methods are summarized which demonstrate that the manifestation of defects within metal AM builds largely stems from the particular choice of process settings, with some influence of powder feedstock choice and post-processing heat treatments. Practical build strategies to limit the occurrence of defects by the use of process mapping and geometric modeling are also evaluated utilizing this fundamental understanding of defect formation. Such explorations may enable the validation and calibration of models to permit process qualification without the reliance on costly trial and error type experimentation currently employed.

CBE Seminar: "Collaboration and Competition Between Active Sheets for Self-Propelled Particles"
2020-01-22 15:00 - 16:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Abstract:
Biological species routinely collaborate for their mutual benefit or compete for available resources, thereby displaying dynamic behavior that is challenging to replicate in synthetic systems. Here, we use computational modeling to design microscopic, chemically active sheets and self-propelled particles encompassing the appropriate synergistic interactions to exhibit bio-inspired “feeding”, “fleeing” and “fighting”. This design couples two different mechanisms for chemically generating motion in fluid-filled microchambers: solutal buoyancy and diffusiophoresis. Catalyst-coated sheets, which resemble crabs with four distinct “claws”, convert reactants in solution into products, and thereby create local variations in the density and chemical composition of the fluid. Via the solutal buoyancy mechanism, the density variations generate fluid flows, which modify the shape and motility of the “crabs”. Concomitantly, the chemical variations propel the motion of the particles via diffusiophoresis and, thus, the crabs’ and particles’ motion becomes highly interconnected. For crabs with restricted lateral mobility, these two mechanisms can be modulated to either drive a crab to catch and appear to “feed” on all the particles or enable the particles to “flee” from this sheet. Moreover, by adjusting the sheet’s size and the catalytic coating, two crabs can compete and “fight” over the motile, diffusiophoretic particles. Alternatively, the crabs can temporally “share” resources by shuttling the particles back and forth between themselves. With completely mobile sheets, four crabs can collaborate to perform a function that one alone cannot accomplish. These findings provide design rules for creating chemically-driven soft robotic sheets that significantly expand the functionality of microfluidic devices.

CIS Seminar: "Networked Systems in the Era of Programmable Dataplanes"
2020-01-21 15:00 - 16:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Abstract:
Emerging networking architectures are allowing for flexible and reconfigurable packet processing at line rate both on the switch and the NIC. Despite their promising new functionality, programmable switches and NICs are not all-powerful; they have limited state, support limited types of operations, and limit per-packet computation to operate at line rate. In this talk, I will describe how to mask resource limitations using approximation techniques and new scheduling algorithms and how to build a general framework for exposing in-network computing capability to distributed applications. In addition to presenting case studies of optimizing networked systems, I will reflect on the role of programmable dataplanes in datacenter computing.

MEAM Seminar: "Plate Mechanical Metamaterials and their Applications"
2020-01-21 10:30 - 12:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Recently, we introduced the concept of plate mechanical metamaterials—cellular plates with carefully controlled periodic geometry and unique mechanical properties—as well as its initial realization in the form of freestanding corrugated plates made out of an ultrathin film. We used atomic layer deposition (ALD) and microfabrication techniques to make robust plates out of a single continuous ALD layer with cm-scale lateral dimensions and thicknesses between 25 and 100 nm, creating the thinnest freestanding plates that can be picked up by hand. We also fabricated and characterized nanocardboard – plate metamaterials made from multiple layers of nanoscale thickness, whose geometry and properties are reminiscent of honeycomb sandwich plates or corrugated paper cardboard. Ultralow weight, mechanical robustness, thermal insulation, as well as chemical and thermal stability of alumina make plate metamaterials attractive for numerous applications, including structural elements in flying microrobots and interstellar light sails, high-temperature thermal insulation in energy converters, photophoretic levitation, as well as ultrathin sensors and resonators. I will briefly discuss our experimental progress on all these applications, including demonstrations of extremely robust thermal insulators that can sustain a temperature difference of ~1000 K across a micron-scale gap, hollow AFM cantilevers that offer greatly enhanced sensitivity and data acquisition rates, and macroscopic plates that levitate when illuminated by light.

MSE Grace Hopper Lecture: "Artificial Intelligence for Generating Materials Science Knowledge"
2020-01-16 10:45 - 11:45 | Auditorium, LRSM Building, 3231 Walnut Street, Philadelphia, PA, 19104, United States
The process of scientific inquiry involves observing a signal (data) and interpreting it to generate information (knowledge). For example, in electron microscopy the signal may be a diffraction pattern from which information on crystal orientation may be deduced by applying diffraction theory. Science advances both through improvements in gathering data and in techniques for extracting knowledge from it. Artificial intelligence (AI) – a broad term comprising data science, machine learning (ML), neural network computing, computer vision, and other technologies – opens new avenues for extracting information from high-dimensional materials data. In that sense, AI offers the possibility to advance materials science in the same way as a new imaging modality or a new theoretical model. The applications of AI in materials science cut a broad swath, from large, labelled data sets the fit naturally in the Big Data paradigm to small, sparse, multimodal data sets that test the limits of cutting-edge AI.
This presentation will focus on AI applications in the context of image-based data, including both visual features and composition, processing, or properties metadata. Computer vision (CV) representations are developed to numerically encode the visual information contained in images. ML tools are then selected based on the characteristics of the data set and the desired outcome. For example, a large, homogeneous data set of steel inclusions is best suited to a Deep Learning approach involving a purpose-built convolutional neural network. In contrast, a random-forest method can find significant trends in a small, multi-modal data set that includes microstructural, crystallographic, and micromechanical data. Complex image segmentation leverages a convolution neural network that has been trained using images very different from those it is applied to. These case studies will motivate a discussion of AI method selection based on data set characteristics and desired outcomes. The ultimate goal is to develop AI as a new tool for information extraction and knowledge generation in materials science.

MSE Materials in Practice Seminar: "So You're an Engineer...Now What?"
2020-01-15 15:00 - 16:00 | LRSM Reading Room, 3231 Walnut St., Philadelphia, PA, 19104, United States
Materials in Practice is a seminar series where technologists who are using materials science to shape the world we live in share their secret to doing this. We hope that anyone who wants to apply the research they are doing to solve real world problems will be able to attend this Seminar Series.

MEAM Seminar: "Confined Curved Shells and their Elaborate Conformations"
2020-01-14 10:30 - 12:00 | Glandt Forum, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
Curved shells, when confined, can deform to a broad assortment of large scale shapes and smaller scale wrinkling and folding patterns quite unlike what produced by their flat counterparts. The intrinsic, natural curvature of shells is the central element that allows for this rich and very interesting morphological landscape, but it is also the source of geometric nonlinearities that renders an analytic treatment of non‐Euclidean shells, even under small load, very difficult. In this talk we examine some snapshots of this morphological landscape. Inspired by the natural folding and unfolding of polled grains, we use theory, simulations and experiments to explore the large scale deformation of a confined thin spherical shell with an opening. We then proceed to investigate the surface topography of shallow doubly curved shells resting on a fluid substrate. The frustration due to the competing geometry of the flat substrate and the curved shell produces a wealth of highly reproducible and ordered, or random and disordered patterns. From these examples, we see Gaussian curvature emerging as a powerful tool that can generate complex patterns.

REACT@Penn 2020: Global Pathways to enable Innovative Materials Solutions for Urban Challenges
2020-01-13 10:00 - 16:00 | Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
How can science and technology help cities solve their problems? Are there common urban air, water and energy challenges for science to tackle?

10:00 am
Urban Challenges with (potential) Materials Solutions
Model cities of Grenoble, France, Philadelphia, USA and Seoul, Korea

1:30 pm
Adoption and Adaptation of New Technologies for Urban Challenges
Joshua Sperling, National Renewable Energy Laboratory

2:30 pm
Facilitating New Technology-based Solutions for Urban Challenges
Panel discussion that includes experts from the Water Center at Penn, City of Philadelphia’s Department of Planning and Development and Office of Transportation, Infrastructure & Sustainability

https://react.seas.upenn.edu/event/react-at-penn-2020/

Thesis Defense: "Transcriptional Control of Endothelial Cell Motility, Extracellular Matrix Sensing, and Vasculogenesis"
2020-01-10 14:00 - 15:00 | Class of 62 Auditorium, John Morgan Building, 3620 Hamilton Walk, Philadelphia, PA, 19104
The Department of Bioengineering at the University of Pennsylvania and Dr. Joel Boerckel are pleased to announce the Doctoral Dissertation Defense of Devon E. Mason.
This event is open to the public.

BE Seminar: "High-throughput T cell repertoire profiling enabled systems immunology and immune engineering"
2020-01-09 12:00 - 13:00 | Room 337, Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
T cells are important to the initiation, prevention, and cure of many diseases. For example, various T cells based cancer immunotherapies have been quite effective in treating several types of cancers. However, a significant fraction of patients do not respond. A comprehensive understanding of the complexity of the T cells repertoire in health and diseases not only provide underlying mechanisms but also new therapeutic targets. In the past several years, we have developed several tools to profile the T cell repertoire from T cell receptor diversity to T cell receptor affinity to multi-dimensional profiling of single T cells in high-throughput. In this talk, I will first introduce these tools and then give examples on how we use them to answer some of the fundamental questions in systems immunology, which in turn help us design new approaches in immune engineering.

CBE Dissertation Defense: "Controllable, Synthetic Membrane-Less Organelles from Recombinant Proteins"
2019-12-17 13:00 - 15:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Committee: Dr. Daniel A. Hammer, Advisor; Drs. Kathleen J. Stebe, Daeyeon Lee and Elizabeth Rhoades

MEAM Doctoral Dissertation Defense: "Estimation, Mapping and Navigation with Micro Aerial Vehicles for Infrastructure Inspection"
2019-12-10 15:00 - 16:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Multi-rotor Micro Aerial Vehicles (MAV) have become popular robotic platforms in the last decade due to their manufacturability, agility and diverse payload options. Amongst the most promising applications areas of MAVs are inspection, air delivery, surveillance, search and rescue, real estate, entertainment and photography to name a few. While GPS offers an easy solution for outdoor autonomy, using onboard sensors is the only solution for autonomy in constrained indoor environments. In this work, we study onboard state estimation, mapping and navigation of a small MAV equipped with a minimal set of sensors inside GPS-denied axisymmetric tunnel-like environments such as penstocks. We primarily focus on state estimators formulated for different sensor suits which include 2D/3D lidars, cameras and Inertial Measurement Units (IMU). Penstocks are pitch dark environments and offer very weak visual texture even with onboard illumination, hence our estimators primarily rely on lidars and IMU. The point cloud data returned by the lidar consists of either elliptical contours or indiscriminate partial cylindrical patches making localization along the tunnel axis theoretically impossible. Cameras track features on the walls using the onboard illumination to estimate the velocity along the tunnel axis unobservable to range sensors. Information from all sensors are then fused in a central Kalman Filter for 6 Degrees-of-Freedom (DOF) state estimation. These approaches are validated through onsite experiments conducted in four different dams demonstrating state estimation, environment mapping, autonomous and shared control.

ESE PhD Thesis Defense: "Affordances and Control of a Spine Morphology for Robotic Quadrupedal Locomotion"
2019-12-06 15:00 - 16:30 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Abstract: How does a robot’s body affect what it can do? This talk explores this question with respect to a morphology common to biology but rare in robotics: the presence of a bendable back. Using the Canid and Inu quadrupedal robots, I describe and quantify several advantages afforded by this morphological design choice for legged machines that can be evaluated against the spine’s added weight and complexity. Additionally, I discuss control strategies related to quadrupedal running with a spine.

MSE Seminar: "In-Situ Resistance Degradation & Switching of Bulk Fe-doped SrTiO3 and Yttria-Stabilized Zirconia"
2019-12-05 10:45 - 11:45 | Auditorium, LRSM Building, 3231 Walnut Street, Philadelphia, PA, 19104, United States
Perovskite-type titanates are the standard dielectric used in multilayer ceramic capacitors (MLCCs), and yttria-stabilized (YSZ) is the prototypical fast oxygen conductor used in solid oxide fuel cells (SOFCs) and oxygen sensors. Under a DC electric field, the resistance of YSZ and Fe-doped strontium titanate (Fe-STO) degrades in an indistinguishable way. This is surprising because, unlike YSZ, Fe-STO is a p-type semiconductor, so they cannot possibly degrade by the same mechanism. For decades, it is known that the degradation of perovskite titanates is caused by the migration of oxygen vacancies towards the cathode, where they are blocked, and thus pile up, which changes the region to n-type. In contrast, through highly accelerated in situ lifetime tests, we determined, for the first time, that the DC voltage induces in YSZ a metal-insulator transition that propagates from the cathode to the anode, which lowers the resistance by orders of magnitude. In situ studies have further provided direct observations and mechanisms of resistance switching of both YSZ and Fe-STO. This is also of great importance as they relate to another technologically relevant device: resistance random access memory (ReRAM), which is currently explored for neuromorphic computing.

MEAM Special Seminar: "Differences in Material Properties of Trabecular Bone Tissue from Modeling- and Remodeling-Based Bone Formation in Rats"
2019-12-03 12:00 - 13:00 | CRB Auditorium, 415 Curie Boulevard, Philadelphia, PA, 19104, United States
Bone undergoes continuous changes during life through processes of bone modeling and remodeling. Modeling-based bone formation (MBF) i.e. bone formation without prior activation of osteoclastic resorption, only occurs during growth, healing, and in response to external mechanical loading. Remodeling-based bone formation (RBF), which is tightly coupled with bone resorption by osteoclasts, plays a predominant role maintaining skeletal health. Recent studies identified the activation of MBF as an important mechanism by which anabolic agents, such as intermittent parathyroid hormone (PTH), rapidly elicit new bone formation. Due to the challenge of differentiating between MBF and RBF on a thick bone specimen, the quality of the bone tissue generated through these two distinct cellular mechanisms is unknown. Therefore, the goal of this study is to (1) develop an imaging method that can be coupled with a mechanical testing platform for reliable identification and examination of material properties of MBF and RBF on thick bone sections, and (2) define the differences in material properties of trabecular bone tissue from MBF and RBF.

ESE Seminar: "Beyond Supervised Learning for Biomedical Imaging"
2019-12-03 11:00 - 12:00 | Smilow Center Auditorium, 3400 Civic Center Blvd, Philadelphia, PA, 19104, United States
Abstract: Today, many biomedical imaging tasks, such as 3D reconstruction, denoising, detection, registration, and segmentation, are solved with machine learning techniques. In this talk, I will present a flexible learning-based framework that has allowed us to derive efficient solutions for a variety of such problems, without relying on heavy supervision. I will primarily employ image registration as a concrete application and present the details of VoxelMorph, our unsupervised learning-based image registration tool. I will show empirical results obtained by co-registering thousands of brain MRI scans where VoxelMorph has yielded state-of-the-art accuracy with runtimes that are orders of magnitude faster than conventional tools. Finally, I will present some recent results where we used VoxelMorph to learn conditional deformable templates that can reveal population variation as a function of factors of interest, such as aging or genetics. Our code is freely available at https://github.com/voxelmorph/voxelmorph.

MEAM Seminar: "Physics-Informed Neural Networks (PINNs) for Physical Problems & Biological Problems"
2019-12-03 10:30 - 12:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
We will present a new approach to develop a data-driven, learning-based framework for predicting outcomes of physical and biological systems and for discovering hidden physics from noisy data. We will introduce a deep learning approach based on neural networks (NNs) and generative adversarial networks (GANs). We also introduce new NNs that learn functionals and nonlinear operators from functions and corresponding responses for system identification. Unlike other approaches that rely on big data, here we “learn” from small data by exploiting the information provided by the physical conservation laws, which are used to obtain informative priors or regularize the neural networks. We will also make connections between Gauss Process Regression and NNs and discuss the new powerful concept of meta-learning. We will demonstrate the power of PINNs for several inverse problems in fluid mechanics, solid mechanics and biomedicine including wake flows, shock tube problems, material characterization, brain aneurysms, etc, where traditional methods fail due to lack of boundary and initial conditions or material properties.

PSOC Seminar : 'Deconstructing tumor architecture'
2019-12-02 12:00 - 13:00 | Room 337, Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States

ESE Seminar: "Management Strategies for Hyperscale Datacenters"
2019-11-26 11:00 - 12:00 | PICS Conference Room 534 – A Wing , 5th Floor, 3401 Walnut Street, Philadelphia, PA, 19104, United States
Abstract: Hyperscale datacenters provide critical infrastructure for the information economy, refining data to extract value. Sharing datacenters improves energy efficiency but whether strategic users participate depends on management policies. I address these challenges by integrating practical insights from computer architecture with rigorous methods in algorithmic economics and machine learning. I illustrate this approach for power allocation and performance diagnosis. First, I allocate server power by anticipating strategic behavior and incentivizing participation. I design mechanisms in which users selfishly draw power to boost performance yet avoid oversubscribing the shared supply. Second, I diagnose performance anomalies by inferring interpretable causal relationships. I design statistical frameworks that learn performance models and extract recurring semantic structure from these models with natural language processing. Finally, I conclude by describing future directions for efficient and secure architectures in datacenters and beyond.

MEAM Seminar: "Micro/Nanomanufacturing of 3D Functional Coatings via Self-Limiting Electrospray Deposition"
2019-11-26 10:30 - 12:00 | Glandt Forum, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
Recent developments in nanostructured materials have demonstrated myriad desirable properties ranging from optical and mechanical metamaterials to biomanipulative surfaces. To bring these properties from the lab to the commercial space will require innovative nanomanufacturing strategies focused on scalable and cost-effective techniques. My lab, the Hybrid Micro/Nanomanufacturing Laboratory, applies the manipulation of fundamental driving forces to this challenge through combinations of top-down and bottom-up techniques for new hybrid lithographic strategies. In this seminar, I will highlight one such strategy: self-limiting electrospray deposition (SLED) of thin film microcoatings. Electrospray deposition is a well-established technique for the creation of thin films from the spray of highly charged droplets loaded with the materials to be deposited. In SLED, specific manipulation of the electrostatic repulsion, hydrodynamic forces, and evaporation kinetics can be employed to conformally cover 3D architectures with microcoatings. The generated coatings are hierarchical, possessing either nanoshell or nanowire microstructure. Having demonstrated the mechanism of the self-limiting effect, we have developed the ability to employ materials that would be otherwise incompatible with self-limiting. In this way we have incorporated a wide variety of functional systems, including: (1) biocompatible, (2) plasmonic, (3) elastomerically-toughened composite, (4) anti-corrosive epoxy or sol gel, and (5) electrically conductive coatings. We have also characterized the geometric limits of features that can be coated through this approach, showing that the 3D capabilities increase with decreasing feature size to the micron-scale. This property, combined with the hierarchical structure of the coatings, shifts the burden of micro/nanoscale resolution from a costly or slow technique to a more scalable method, thereby removing barriers for integration into advanced manufacturing techniques such as roll-to-roll or additive manufacturing.

CBE Dissertation Defense: "Fabrication of Superhydrolic Nanostructured Membranes for Oil/Water Separation
2019-11-25 15:00 - 17:00 | LRSM REading Room 106/107
Committee: Daeyeon Lee, PhD and Shu Yang, PhD, Co-Advisors; Kathleen Stebe, PhD and Amish Patel, PhD

ESE Thesis Defense: Zhe Xuan
2019-11-25 11:00 - 12:30 | Room 307, Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Title: “Towards Advanced Photonic Integrated Systems through Electronic-Assisted Design”
Abstract: Optical systems have applications ranging from traditional areas such as communication and spectroscopy to emerging fields such as quantum computing and cryptography. Bench-top optical systems, using free-space optics or fiber optics with standalone devices, have been successful. However, they tend to occupy a large space and are costly to build. With the advance of the photonic integrated circuit, many of those systems are being integrated on-chip, promising portable and energy-efficient implementations with orders of magnitude reduction in cost and volume. Similar to the scaling of the electronics, the scaled photonic devices also give rise to better device performance and eventually better system performance. In this work, I will leverage the bandwidth, the low latency and phase coherence of the photonic integrated circuit, in combination with the processing power and the control capabilities of the electronic integrated circuit, to enable new functionalities and new architectures of the optical systems.
Dissertation Committee:
Chair: Prof. Jan Van der Spiegel
Supervisor: Prof. Firooz Aflatouni
Member: Prof. Nader Engheta
Member: Prof. Bo Zhen

PICS Seminar: "Molecular Simulation and Machine Learning as Routes to Exploring Structure and Phase Behavior in Atomic and Molecular Crystals"
2019-11-22 14:00 - 15:00 | PICS Conference Room 534 – A Wing , 5th Floor, 3401 Walnut Street, Philadelphia, PA, 19104, United States
Abstract: Organic molecular crystals frequently exist in multiple forms known as polymorphs. Structural differences between crystal polymorphs can affect desired properties, such as bioavailability of active pharmaceutical formulations, lethality of pesticides, or electrical conductivity of organic semiconductors. Crystallization conditions can influence polymorph selection, making an experimentally driven hunt for polymorphs difficult. Such efforts are further complicated when polymorphs initially obtained under a particular experimental protocol “disappear” in favor of another polymorph in subsequent repetitions of the experiment. Consequently, theory and computational can potentially play a vital role in mapping the landscape of crystal polymorphism. Traditional crystal structure prediction methods face their own challenges, and therefore, new approaches are needed. In this talk, I will show, by leveraging concepts from mathematics and statistical mechanics in combination with techniques of molecular simulation, traditional methods, and machine learning, that a new paradigm in crystal structure prediction may be emerging. Examples demonstrating prediction of structures of crystals, co-crystals, and phase transitions will be presented.

CIS Seminar: " Flash Boys 2.0: Frontrunning, Transaction Reordering, and Consensus Instability in Decentralized Exchanges"
2019-11-21 15:00 - 16:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Abstract:
Cryptocurrencies and blockchains are often heralded as the keys to a new era of financial transparency and fairness. Decentralized exchanges (DEXes), in particular, run directly on blockchains. They manage custody of traded assets, preventing theft by exchange operators, and provide a globally visible and accessible trading platform that seems egalitarian.
Our research, though, has revealed that DEXes are in rife with Wall-Street-like manipulation—and worse. Arbitrage bots, automated trading programs written as smart contracts, continuously exploit DEXes to profit at the expense of ordinary users. I’ll talk about the increasingly sophisticated strategies used by arbitrage bots, where their gains come from, and how game-theoretic modeling closely reflects their real-world behavior. I’ll also recount their rise as an inadvertent side-effect of our research.
DEX arbitrage doesn’t just impact DEX users, but is suggestive of widespread malfeasance in cryptocurrency exchanges and also poses an unexpected, systemic threat to the foundational stability of blockchains such as Ethereum today.

BE Seminar: "Nanofluidic Technologies for Biomolecule Manipulation"
2019-11-21 12:00 - 13:00 | PICS Conference Room 534 – A Wing , 5th Floor, 3401 Walnut Street, Philadelphia, PA, 19104, United States
In the last 20 years, microfabrication techniques have allowed researchers to miniaturize tools for a plethora of bioanalytical applications. In addition to better sensitivity, accuracy and precision, scaling down the size of bioanalytical tools has led to the exploitation of new technologies to further manipulate biomolecules in ways that has never before been achieved. For example, when microfluidic channels are on the same order of magnitude of the electric double layers that form due to localized charge at the surfaces, there exists unique physics that create different flow phenomenon, such as analyte concentration and/or separation, mainly due to the couples physics of electrostatics and fluid dynamics. This talk will outline the basis of such interesting phenomena, such as nanofluidic separation and concentration, and well as probe the applications of such coupled systems, for example, handheld DNA detection. Most importantly, we will focus on the most recent work in the Pennathur lab in this field — biopolar electrode (BPE)-based phenomenon. Bipolar electrodes (BPE) have been studied in microfluidic systems over the past few decades, and through rigorous experimentally-validated modeling of the rich combined physics of fluid dynamics, electrokinetics, and electrochemistry at BPEs, I will show the potential of utilizing microfluidic-based BPEs for the design and development of low power, accurate, low volume fluid pumping mechanisms, with the ultimate goal of integration into wearable drug delivery and µTAS systems.

MSE Seminar: "Exploring Photonic Materials with Symmetry and Topology"
2019-11-21 10:45 - 11:45 | Auditorium, LRSM Building, 3231 Walnut Street, Philadelphia, PA, 19104, United States
Symmetry and topology are fundamental notions existing in all kinds of natural systems, from spiral galaxies and hurricanes to amino acids in molecules and non-trivial topologically protected electronic states in condensed matter. A stream of photons is typically topologically trivial, nevertheless, its full-vector nature intrinsically endows light with full capability of creating and carrying unique symmetry and topology, especially non-Hermitian symmetries that cannot be easily implemented in condensed matter. Explorations of symmetry and topology on a photonic platform not only deepen our understanding of fundamental physics, but also enable novel material properties to facilitate technological breakthroughs for photonic applications. In this seminar, I will present our recent efforts on investigating the complex optical potentials with the non-Hermitian parity-time symmetry for the next generation of optical communication and information technology. We demonstrated an orbital angular momentum (OAM) microlaser that structures and twists the lasing radiation at the microscale, which can provide an additional OAM-based information dimension to meet the growing demand for information capacity. By strategically interfacing non-Hermitian photonic materials and topological physics, we realized the dynamic control of robust topological transmission links of light inside the bulk of a photonic topological insulator, routing optical signals in a highly flexible and scalable manner.

CBE Seminar: "Soft, Wet, and Sticky: Viscous Forces and Elasticity in Wet Adhesion”
2019-11-20 15:00 - 16:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Abstract:
Understanding and harnessing the coupling between lubrication pressure, elasticity, and surface interactions provides materials design strategies for applications such as adhesives, coatings, microsensors, and biomaterials. This presentation will discuss our efforts to understand how soft materials make contact and adhere under dynamic conditions in fluid environments. Measurements of interactions between soft surfaces will show how elastic films deform due to viscous forces and influence adhesion. In particular, we will discuss conditions under which elasticity favors both dynamic and static adhesion in fluid environments. In the second part of the presentation, we will show practical implications for adhesives on soft surfaces such as skin. More specifically, we will discuss how the presence of water influences contact formation and the performance of adhesives. We will also show qualitative differences in debonding mechanism caused by the elasticity of the substrate.

ESE Thesis Defense: Cassiano Becker
2019-11-20 10:00 - 11:30 | Room 307, Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Title: “Data-Driven Modeling, Analysis and Design of Networked Dynamical Systems with Applications in Neuroscience”
Abstract: In this thesis, we provide contributions to the modeling, analysis and design of networked dynamical systems from a data-driven perspective. Our approach is grounded on the integration of concepts and tools from graph theory, control theory, optimization, and statistics. In particular, we develop methodologies that can be applied to challenging, high-dimensional problems where only partial or summarized information about the structure and function of a complex system is available. We validate our methods across different application domains, most remarkably with large-scale neuroimaging datasets that follow state-of-the-art acquisition techniques and span multiple individuals and experimental paradigms.
Structurally, the contributions of this thesis have been divided in three parts. In the first part, we address the topic of inference and analysis of complex networks. In particular, we establish data-driven models and algorithms that approximate measures of functional connectivity, derived from a system’s potentially complex dynamics, as a matrix mapping based on structural connectivity information. Using tools from spectral graph theory, we propose a mapping technique that is able to systematically account for the role of indirect structural walks in the generation of functional associations. We then apply our method to obtain accurate structural-to-functional connectivity mappings for brain networks derived from functional magnetic resonance (fMRI) and diffusion imaging measurements, and perform a series of analyses of the generated mappings in terms of their spectral characteristics.
In the second part, we devote our attention to the problem of identification of system dynamics. First, we generate data-driven dynamic models that capture the influence of cognitive inputs on brain signals that are observed through fMRI measurements. More specifically, we extend subspace system identification methods to create large-scale, linear time-invariant representations for both single and multiple-individual configurations of a motor task-fMRI experiment, and apply control-theoretic tools to analyze the dynamical characteristics of the resulting models. Subsequently, to abate some general limitations of linear time-invariant models, we propose three novel system identification approaches. Specifically, we (i) enable the identification of state-space linear time-varying models through a generalized expectation maximization method, (ii) perform the estimation of linear parameter-varying models having a latent parameter space via Bayesian variational inference, and (iii) implement blind subspace identification using Riemannian optimization.
In the last part of the thesis, we focus on the topic of network design for dynamic performance. More specifically, we address the problem of finding edge weights of a linear networked dynamical system such that certain bounds on its controllability metrics are satisfied. In particular, we consider the worst-case (i.e., minimum eigenvalue of the controllability Gramian) and average-case (i.e., trace of the inverse controllability Gramian) metrics. Algorithmically, we provide a principled convex relaxation approach that exhibits guaranteed convergence and known global optimal value. Our methodology includes the possibility to consider cost functions over the edge weights which can be used, for example, to promote solutions with higher sparsity in the required edge modifications. Finally, we verify our results with numerical simulations over many random network realizations, and with multi-agent and power system topologies.
Advisor:
Victor M. Preciado, Associate Professor of Electrical and System Engineering
Graduate Chair of Electrical and System Engineering
Department of Electrical and Systems Engineering
Department of Applied Mathematics and Computational Science
University of Pennsylvania
Committee:
George J. Pappas (chair), Full Professor and Department Chair
Department of Electrical and Systems Engineering, Department of Computer and Information Sciences, Department of Mechanical Engineering and Applied Mechanics
University of Pennsylvania
Danielle S. Bassett (member), J. Peter Skirkanich Professor
Department of Bioengineering, Department of Electrical and Systems Engineering Department of Physics and Astronomy, Department of Neurology, Department of Psychiatry
University of Pennsylvania
Sergio Pequito (member), Assistant Professor
Department of Industrial and Systems Engineering
Department of Electrical, Computer, and Systems Engineering
Rensselaer Polytechnic Institute

ESE Dissertation Defense: David Q. Sun
2019-11-19 16:00 - 18:00 | Room 307, Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Title: “Understanding Coalition Dynamics in Multiparty Conflicts: An Agent-Based Approach with Multi-Objective Spatial Model”
Abstract: Through this research, we explore the dynamics of coalition formation in multi-agent competitive games where each agent has its unique characteristics. We do so by constructing an abstract formal model, and a more complex agent-based model. In the broader context of multiparty competitive games (which are closely related to civil wars), we make several critical assumptions regarding the motivations for coalition formation (and dis-integration) and view coalitions as an outcome of rational, utilitarian choices made by the agents. Accordingly, we investigate two specific types of such problems. The first problem is the existence and characteristics of the stable states. We investigate when such stable states are viable, and potential path dependency on initial states. The second problem is the influence of certain changes in agent properties (objective position in Euclidean space, normalized power size, etc.) on the dynamics of coalition formations. In particular, we are interested in learning how such characteristics influence the evolution surrounding stable states.
We approach the problems using two set of methods. First, we present a formal, mathematical model inspired by theories from non-cooperative games and the legislative coalition formations, to explore the existence and characteristics of the stable states. Later, we develop an agent-based counterpart that extends the formal model. We leverage computational modeling to explore the system dynamics at scale. The findings on the characteristics of coalition dynamics model are further validated through both statistical analysis and empirical case studies, where we also compare the model predictions with those from alternative coalition theories from relevant fields.
Committee Members:
Prof. Tony E. Smith (Chair) Professor of Electrical and Systems Engineering, University of Pennsylvania
Barry G. Silverman (Advisor) Professor of Electrical and Systems Engineering, University of Pennsylvania
Steven O. Kimbrough (Reader) Professor of Operations, Information and Decisions, Wharton School, University of Pennsylvania
Dr. Alexander Kalloniatis (External Reader) Defense Science and Technology Organisation, Australian Department of Defense
