Penn Engineering Events

ESE Seminar: "Towards Socially-Aware Autonomy for Mobility-Efficient Smart Cities"
2019-02-28 11:00 - 12:00 (America/New_York) | Room 337, Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
As cities grow everywhere, and urban roadways become overburdened, efficient strategies are required for improving mobility. With the prevalence of smart sensing and Internet of Things (IoT) devices, such as smartphones and smart intersections, the physical infrastructures of our cities are being connected to the cyber world. As a result, cities are becoming smart. Moreover, with the emergence of new and inevitable technologies, such as autonomous and connected vehicles, mobility on demand systems, and electric vehicles, smart cities are rapidly evolving. As we experience the arrival of such technologies, there is an opportunity to reclaim urban mobility. However, a blind utilization of these technologies may deflect us from reaching this goal. In my research, I leverage the connectivity that is inherent in smart cities as well as the opportunities that new technologies such as autonomous and connected vehicles provide, to study the efficient operation of smart cities via management strategies that can guarantee overall societal benefits.
In this talk, I will focus on the societal-scale mobility implications of the increased deployment of autonomous and connected vehicles in mixed-autonomy traffic networks, where both human-driven and autonomous vehicles will coexist on the roads. I will first talk about the mobility implications of selfish autonomy, in which autonomous cars are not aware of their overall impact and simply attempt to optimize their own travel benefits. In this context, I will introduce conditions under which an increase in the fraction of autonomous vehicles on a traffic network, even when operating selfishly, results in increased societal mobility benefits. Conversely, I will show that if these conditions do not hold, overall network mobility may degrade as the fraction of autonomous vehicles increases. Having shown the negative consequences that the increased deployment of autonomous and connected vehicles may have on the operation of traffic networks, I will further discuss the use of traffic management strategies, such as pricing, which can guarantee the overall societal-scale efficiency of traffic networks with mixed vehicle autonomy.

CBE Seminar: "Mechanisms of Cellulose Activation and Implications for Bioenergy"
2019-02-27 15:00 - 16:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States

CIS Seminar: "Perceiving Humans in the 3D World"
2019-02-26 15:00 - 16:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Since the dawn of civilization, we have functioned in a social environment where we spend our days interacting with other humans. As we approach a society where intelligent systems and humans coexist, these systems must also interpret and interact with humans that reside in the 3D world. While computer vision systems today work well for finding 2D patterns in images or reconstructing rigid objects in 3D, they still struggle to perceive non-rigid objects in 3D, like moving human bodies. My goal is to build a system that can perceive and understand embodied agents in the 3D world from visual input. Such systems can enable motion capture in-the-wild, robots that learn to act by visually observing people, and ultimately, socially intelligent machines that understand human behavior.

In this talk, I will discuss my work in reconstructing 3D non-rigid, deformable objects such as humans and animals from everyday photographs and video, and show how such systems can be used to train a simulated character to learn to act by watching YouTube videos. I will discuss the challenges related to the limited availability and quality of ground-truth 3D data and how we can overcome these challenges using weakly supervised approaches.

ESE Seminar: "New Designer Materials: Sculpting Electromagnetic Fields on the Atomic Scale"
2019-02-26 11:00 - 12:00 | Room 337, Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
New optical nanomaterials hold the potential for breakthroughs in a wide range of areas from ultrafast optoelectronics such as modulators, light sources and hyperspectral detectors, to efficient upconversion for energy applications, bio-sensing and quantum information science. An exciting opportunity to realize such new nanomaterials lies in controlling the local electromagnetic environment on the atomic- and molecular-scale (~1-10 nm), which enables extreme local field enhancements. We use creative nanofabrication techniques at the interface between chemistry and physics to realize this new regime together with ultrafast optical techniques to probe the emerging phenomena. Here, I will provide an overview of our recent research including high-speed thermal photodetectors, ultrafast spontaneous emission and metasurface-enhanced biosensors.

MEAM Seminar: "Interfacial Soft Matter"
2019-02-26 10:30 - 12:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Surface tension plays a critical role in a wide range of biological, environmental, technological and geophysical settings. In this talk, I will present three different problems dealing with interfacial soft matter that find motivation in markedly diverse areas. First, I will discuss the evaporation kinetics and flow dynamics of non-spherical sessile drops undergoing phase change. While previous investigations have been restricted to axisymetric drops, I will illustrate a number of new geometry-induced effects emerging from consideration of a range of non-spherical drops. Second, I will describe the non-equilibrium dynamics and statistical behavior of a hydrodynamic pilot-wave system, a liquid drop self-propelling on the surface of a vibrating fluid bath through a resonant interaction with its own wave field. In particular, I will consider the wave-mediated interaction of this active system with variable bottom topography to illustrate the emergence of wavelike statistics around defects, and spontaneous collective order and phase transitions in spin lattices. The final part of this talk will deal with active colloids. Specifically, I will consider the realignment of a Janus drop, a double-emulsion drop formed by two immiscible fluids, in response to an externally-imposed temperature gradient. The development of dynamically reconfigurable microlenses based on this mechanism will also be discussed.

PRiML Seminar: "Optimizing probability distributions for learning: sampling meets optimization"
2019-02-22 15:00 - 16:00 | Room 401B, 3401 Walnut, 3401 Walnut Street, Philadelphia, PA, 19104, United States
Optimization and sampling are both of central importance in large-scale machine learning problems, but they are typically viewed as very different problems. This talk presents recent results that exploit the interplay between them. Viewing Markov chain Monte Carlo sampling algorithms as performing an optimization over the space of probability distributions, we demonstrate analogs of Nesterov’s acceleration approach in the sampling domain, in the form of a discretization of an underdamped Langevin diffusion. In the other direction, we view stochastic gradient optimization methods, such as those that are common in deep learning, as sampling algorithms, and study the finite-time convergence of their iterates to an invariant distribution.

CIS Seminar: "Towards Generalization and Efficiency in Reinforcement Learning"
2019-02-22 11:00 - 12:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Abstract:

In classic supervised machine learning, a learning agent behaves as a passive observer: it receives examples from some external environment which it has no control over and then makes predictions. Reinforcement Learning (RL), on the other hand, is fundamentally interactive : an autonomous agent must learn how to behave in an unknown and possibly hostile environment, by actively interacting with the environment to collect useful feedback. One central challenge in RL is how to explore an unknown environment and collect useful feedback efficiently. In recent practical RL success stories, we notice that most of them rely on random exploration which requires large a number of interactions with the environment before it can learn anything useful. The theoretical RL literature has developed more sophisticated algorithms for efficient learning, however, the sample complexity of these algorithms has to scale exponentially with respect to key parameters of underlying systems such as the dimensionality of state vector, which prohibits a direct application of these theoretically elegant RL algorithms to large-scale applications. Without any further assumptions, RL is hard, both in practice and in theory.

In this work, we improve generalization and efficiency on RL problems by introducing extra sources of help and additional assumptions. The first contribution of this work comes from improving RL sample efficiency via Imitation Learning (IL). Imitation Learning reduces policy improvement to classic supervised learning. We study in both theory and in practice how one can imitate experts to reduce sample complexity compared to RL approaches. The second contribution of this work comes from exploiting the underlying structures of the RL problems via model-based learning approaches. While there exist efficient model-based RL approaches specialized for specific RL problems (e.g., tabular MDPs, Linear Quadratic Systems), we develop a unified model-based algorithm that generalizes a large number of RL problems that were often studied independently in the literature. We also revisit the long standing debate on whether model-based RL is more efficient than model-free RL from a theoretical perspective, and demonstrate that model-based RL can be exponentially more sample efficient than model-free ones, which to the best of our knowledge, is the first that separates model-based and model-free general approaches.

CIS Seminar: "Cataloging the Visible Universe through Bayesian Inference at Petascale"
2019-02-21 15:00 - 16:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Abstract:
A key task in astronomy is to locate astronomical objects in images and to characterize them according to physical parameters such as brightness, color, and morphology. This task, known as cataloging, is challenging for several reasons: many astronomical objects are much dimmer than the sky background, labeled data is generally unavailable, overlapping astronomical objects must be resolved collectively, and the datasets are enormous — terabytes now, petabytes soon. Existing approaches to cataloging are largely based on algorithmic software pipelines. In this talk, I present a new approach to cataloging based on inference in a fully specified probabilistic model. I consider two inference procedures: one based on variational inference (VI) and another based on MCMC. A distributed implementation of VI, written in Julia and run on a supercomputer, achieves petascale performance — a first for any high-productivity programming language. The run is the largest-scale application of Bayesian inference reported to date. In an extension, using new ideas from variational autoencoders and deep learning, I avoid many of the traditional disadvantages of VI relative to MCMC, and improve model fit

ESE Seminar: "Physics-Driven Sensing and Processing: From Computational Periscopy to Particle Beam Microscopy"
2019-02-21 11:00 - 12:00 | Room 337, Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
In many areas of science and engineering, novel signal acquisition methods allow unprecedented access to physical measurements. From digital cameras to microscopes and nano-scale biosensors, the data generated are shaped by both the underlying physics of the phenomena and characteristics of the acquisition device. Meanwhile, in many practical scenarios, the useful signals are remarkably weak, the measurements sparse, or even the acquisition process itself may damage the observed sample. These realities therefore necessitate the development of techniques that combine signal processing with physics-driven modelling to transcend current capabilities and enable, for instance: imaging of hidden scenes (or computational periscopy), the algebraic inversion of physical fields, and the reduction of sample damage in particle beam microscopy.
This concept of combining physics with signal processing will be the main theme of my talk. First, I will show that computational periscopy with ordinary digital cameras can be made possible by judiciously exploiting the physics of light transport to analyze subtle shadows, in a photograph of a visible surface. Second, I will briefly present an algebraic inversion method for fields constrained by partial differential equations and highlight its application to load-balancing in processors. Finally, by developing a detailed model and analysis for particle beam microscopy, I will show how introducing time-resolution into the acquisition process can significantly reduce beam dose, and sample damage, without compromising on imaging quality.

MSE Faculty Candidate Seminar: "Atom-Scale Engineering using 2D Materials"
2019-02-21 10:45 - 11:45 | Auditorium, LRSM Building, 3231 Walnut Street, Philadelphia, PA, 19104, United States

CBE Seminar: "C4E – Computational Chemistry of Compounds for Catalysis and Energy"
2019-02-20 15:00 - 16:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States

ESE Seminar: "Hybrid Quantum Networks: Interfacing Photons, Phonons, and Superconducting Qubits"
2019-02-19 11:00 - 12:00 | Room 337, Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Quantum information science strives to utilize the fundamental laws of physics to achieve revolutionary improvement in computation, communication, and sensing. Existing quantum protocols rely on a wide variety of physical platforms for storing, transferring, and processing of quantum information. Optical photons are the ideal carriers of information because of their low loss, large bandwidth of transmission, and resilience to thermal noise. However, the task of processing quantum information is exceedingly difficult to achieve in the optical domain because of the weakness of optical nonlinearities. Alternatively, superconducting quantum circuits provide a scalable means of storing and processing quantum information in the microwave regime but lack a mechanism for long-range information transfer.
Hybrid quantum networks promise to combine such essential functionalities in a system where superconducting processing nodes are joined via optical communication links. An integral element in this architecture is a quantum interconnect capable of interfacing the electrical and optical components across an immense frequency gap. I provide a summary of my past and current research on optical and microwave quantum systems and outline my future research directions, which aim to develop nano-engineered devices for entangling superconducting qubits with telecom-band optical photons and long-lived phonons.

MSE Faculty Candidate Seminar: “Tuning Nanoscale Materials Using the Local Environment”
2019-02-19 10:45 - 11:45 | Auditorium, LRSM Building, 3231 Walnut Street, Philadelphia, PA, 19104, United States

CIS Seminar:" Physical Scene Understanding"
2019-02-15 11:00 - 12:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Abstract:
Human intelligence is beyond pattern recognition. From a single image, we’re able to explain what we see, reconstruct them in 3D, predict what’s going to happen, and plan our actions. In this talk, I will present our recent work on physical scene understanding—reverse-engineering these capacities to make machines that are versatile, data-efficient, and have better generalization ability. The core idea is to exploit the scene’s compositional structure by integrating deep recognition networks with generative, approximate simulation engines. I’ll focus on a few topics: building an object representation for both its geometry and physics; learning compact, interpretable dynamics models for planning and control; perception and reasoning beyond vision.

ESE seminar: "Engineering the Quantum Vacuum"
2019-02-15 09:30 - 10:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
The vacuum of space may seem empty and boring; however, this void is actually teeming with activity. According to the laws of quantum mechanics, fluctuations of electromagnetic fields are omnipresent even in empty space. These fluctuations can manifest themselves in a variety of ways, including the generation of nanoscale forces between objects—a phenomenon known as the Casimir effect. In this talk, I will discuss our development of novel measurement techniques to probe these interactions and how we can engineer and control such quantum effects for useful devices. I will demonstrate our ability to tailor the sign and magnitude of the force, as well as how we can induce rotations (i.e. a Casimir torque) between optically birefringent materials. Beyond interesting science, our ability to control these interactions will give us new opportunities for nanoscale devices and to modify chemistry and electronics in ways not previously possible. Finally, I will briefly outline a few additional research areas from our lab related to novel optical phenomena, materials, and devices.

CIS Seminar: "Deep Learning Models for Language: What they learn, where they fail, and how to make them more robust"
2019-02-14 15:00 - 16:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Abstract:
Deep learning has become pervasive in everyday life, powering language applications like Apple’s Siri, Amazon’s Alexa, and Google Translate. The inherent limitation of these deep learning systems, however, is that they often function as a “black box,” preventing researchers and users from discerning the roles of different components and what they learn during the training process. In this talk, I will describe my research on interpreting deep learning models for language along three lines. First, I will present a methodological framework for investigating how these models capture various language properties. The experimental evaluation will reveal a learned hierarchy of internal representations in deep models for machine translation and speech recognition. Second, I will demonstrate that despite their success, deep models of language fail to deal even with simple kinds of noise, of the type that humans are naturally robust to. I will then propose simple methods for improving their robustness to noise. Finally, I will turn to an intriguing problem in language understanding, where dataset biases enable trivial solutions to complex language tasks. I will show how to design models that are more robust to such biases, and learn less biased latent representations.

MSE Faculty Candidate Seminar: "Engineering Hierarchical Polymers to Control Biomolecular Transport"
2019-02-14 10:45 - 11:45 | Auditorium, LRSM Building, 3231 Walnut Street, Philadelphia, PA, 19104, United States

CBE Seminar: "Pixelated Polymers: Programming Function into Liquid Crystalline Polymer Networks and Elastomers"
2019-02-13 15:00 - 16:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States

CIS Seminar: "Visualization for People + Systems"
2019-02-12 15:00 - 16:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Abstract:

While computers can help us manage data, human judgment and domain expertise is what turns it into understanding. Meeting the challenges of increasingly large and complex data requires methods that richly integrate the capabilities of both people and machines. In response to these challenges, my research combines methods from visualization, data management, human-computer interaction, and programming languages to enable effective methods for data analysis and communication.

In my talk, I will present new languages and models that power interactive systems for scalable data exploration. Vega-Lite is a high-level declarative language for rapidly creating interactive visualizations, while also providing a representation for tools that generate visualizations. Draco is a model of visualization design that extends Vega-Lite with design guidelines, formal reasoning over the design space, and visualization recommendation. Falcon and Pangloss enable scalable interaction and exploration of large data volumes by making principled trade-offs among people’s latency tolerance, precomputation, and the level of approximation. A recurring strategy across these projects is to leverage an understanding of people’s tasks and capabilities to inform system design and optimization.

My future research will contribute systems that automatically reason over domain-specific representations of interactive multi-view graphics, visualizations of large data, uncertainty representations, and data analysis. This reasoning can inform how to efficiently run data science pipelines and enhance our ability to analyze and communicate data.

MEAM Seminar: "Fluid Mechanics and Turbulence in Extended Wind Farms"
2019-02-12 10:45 - 11:45 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
In this presentation we discuss several properties of the flow structure and turbulence in the wind turbine array boundary layer (WTABL). This particular type of shear flow develops when the atmospheric boundary layer interacts with an array of large wind turbines. Based on such understanding, we aim to develop reduced order, analytically tractable models. These are important engineering tools for wind energy, both for design and control purposes. We will focus on two fluid mechanical themes relevant to wind farm design and control. The first topic deals with spectral characteristics of the fluctuations in power generated by an array of wind turbines in a wind farm. We show that modeling of the spatio-temporal structure of canonical turbulent boundary layers coupled with variants of the Kraichnan’s random sweeping hypothesis can be used to develop analytical predictions of the frequency spectrum of power fluctuations of wind farms. In the second part we describe a simple (deterministic) dynamic wake model, its use for wind farm control, and its extension to the case of yawed wind turbines. The work to be presented arose from collaborations with Juliaan Bossuyt, Johan Meyers, Richard Stevens, Tony Martinez, Michael Wilczek, Carl Shapiro and Dennice Gayme. We are grateful for National Science Foundation support.

ESE Seminar: "Local Geometric Spectral Data Analysis"
2019-02-08 11:00 - 12:00 | Berger Auditorium (Room 13), Skirkanich Hall, 210 South 33rd Street, Philadelphia, PA, 19104, United States
Modern technological developments have enabled the acquisition and storage of increasingly large-scale, high-resolution, and high-dimensional data in many fields. Yet in domains such as biomedical data, the complexity of these datasets and the unavailability of ground truth pose significant challenges for data analysis and modeling. In this talk, I present new unsupervised geometric approaches for extracting structure from large-scale high-dimensional data. By looking deep within the spectrum of the graph-Laplacian, we define a new robust measure, the Spectral Embedding Norm, to separate clusters from background, and demonstrate its application to both outlier detection and data visualization. This measure further motivates a new greedy clustering approach based on Local Spectral Viewpoints for identifying high-dimensional overlapping clusters while disregarding noisy clutter. We demonstrate our approach on two-photon calcium imaging data, successfully extracting hundreds of individual cells. Finally, to address the computational complexity of applying spectral approaches to large-scale data, we present a new randomized near-neighbor graph construction. Compared to the traditional k-nearest neighbor graph, using our near-neighbor graph for spectral clustering on datasets of a few million points is two orders of magnitude faster, while achieving similar clustering accuracy.
Joint work with Ronald Coifman, Jackie Schiller, Maria Lavzin, Xiuyuan Cheng, George Linderman, Ariel Jaffe, Yuval Kluger and Stefan Steinerberger.

ESE Seminar: "Liquid Silicon: A New Computing Paradigm Enabled by Monolithic 3D Cross-Point Memory"
2019-02-07 11:00 - 12:00 | Room 337, Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Almost every subfield of electrical engineering and computer science are undergoing disruptive times. With Moore’s Law coming to an end, an expanded roadmap for semiconductors beyond traditional CMOS scaling becomes unclear. At the other end, traditional application software development is being replaced by emerging machine learning techniques whose success will, in turn, rely on the availability of powerful, efficient and flexible computer systems. Due to these emerging applications, architecture is transitioning from mainstream CPU to heterogeneous and diverse options such as GPU, TPU, etc. The confluence of these key trends has created a wide efficiency gap, due to the mismatch between emerging application requirements and the relatively slow evolutionary improvements in existing CMOS-based computer hardware.
To close the gap, in this talk, I will present a reconfigurable memory-oriented computing fabric, namely Liquid Silicon (L-Si) by leveraging the monolithic 3D stacking capability of RRAM. L-Si addresses several key fundamental limitations of state-of-the-art reconfigurable architectures including FPGA, etc. in supporting emerging data-/search-intensive applications (e.g., machine learning and neural networks) through a series of innovations. It, for the first time, extends the configuration capabilities of existing reconfigurable architectures (FPGA, CGRA) from computation to the whole spectrum, from full memory to full computation, or intermediate states in between (partial memory and partial computation). Thus, it allows users more flexibility in customizing hardware to better match an application’s characteristics, for higher performance and energy efficiency. The talk will consist of four parts, technology, architecture, compiler tool, and algorithm, with a combined EE and CS flavor.

MSE Faculty Candidate Seminar: "Energy storage and neuromorphic computing using electrochemical ion insertion"
2019-02-07 10:45 - 11:45 | Auditorium, LRSM Building, 3231 Walnut Street, Philadelphia, PA, 19104, United States

CBE Faculty Candidate Seminar: "Novel Electrolyte Design to Control Electrochemistry in Energy Storage Systems"
2019-02-06 15:00 - 16:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States

MSE Faculty Candidate Seminar: "Disorder and Superconductivity in 2D TMD Heterostructures"
2019-02-05 10:45 - 11:45 | Glandt Forum, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States

MEAM Seminar: "Hairy Hydrodynamics"
2019-02-05 10:45 - 11:45 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Flexible slender structures in flow are everywhere. While a great deal is known about individual flexible fibers interacting with fluids, considerably less work has been done on fiber ensembles — such as fur or hair — in flow. These hairy surfaces are abundant in nature and perform multiple functions from thermal regulation to water harvesting to sensing. Motivated by these biological systems, we consider several examples of hairy surfaces interacting with flow including drinking bats and diving sea otters. In the first example we consider viscous dipping, a feeding method utilized by many nectar drinking animals. This mechanism is reminiscent of Landau-Levich-Derjaguin (LLD) dip coating, and has been analyzed through the LLD framework in previous studies. However, many viscous dippers have hairy structures on their tongues that enhance fluid uptake. In this study, we investigate the impact of mesoscale hairy structures on feeding efficiency. In the second example, we take inspiration from semi-aquatic mammals (such as fur seals, otters, and beavers) which have specially adapted fur that serves as an effective insulator both above and below water. Many of these animals have evolved pelts that naturally entrap air when they dive. Here we investigate diving conditions and fur properties which amplify air entrainment.

SEAS Lecturers' Seminar on Teaching and Learning: "The Process of Becoming: Identity in Engineering Education"
2019-01-31 15:00 - 16:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Identity is an enduring and continuous sense of who one is and is often thought of as the answer to the questions, “Who am I, Who can I be, and Where do I belong?” Research shows that developing a robust engineering identity is important for academic and personal development, integration into engineering, and retention within engineering programs. Identity plays animportant role in the ways students see themselves and are recognized or positioned by others as the kind of people who can or cannot become an engineer. This positioning can exclude women, minorities, and first-generation college students who may have more difficulty adopting engineering identities in a culture that often does not position them as engineers. This talk will explore the current research on engineering identity and provide actionable ways for faculty to support students’ processes of becoming engineers.

CIS Seminar: "Made to Order: Verifying Correctness and Security of Hardware through Event Orderings"
2019-01-29 15:00 - 16:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Correctness and security problems in modern computer systems can result from problematic hardware event orderings and interleavings during an application’s execution. Since hardware designs are complex and since a single user-facing instruction can exhibit a variety of different hardware execution sequences, analyzing and verifying systems for correct event orderings is challenging. My work addresses these challenges by combining hardware architecture and systems approaches with formal methods to support the specification, analysis, and verification of implementation-aware event ordering scenarios, with the specific goal of automatically synthesizing implementation-aware programs capable of violating correctness or security guarantees. In this talk, I will present two formal, early-stage verification tools and techniques rooted in this approach. TriCheck conducts axiomatic full-stack memory consistency model (MCM) verification (from high-level programming languages down through hardware implementations). Using rigorous and efficient formal approaches, TriCheck identified flaws in RISC-V’s draft MCM specification and two counterexamples to a previously proven-correct compiler mapping scheme from C11 to IBM Power and ARMv7. Noting that MCM and security analysis are amenable to similar approaches, CheckMate uses related axiomatic techniques to evaluate susceptibility of a hardware design and its related system support to formally-specified classes of security exploits; in response, it synthesizes proof-of-concept exploit code when a design is susceptible. CheckMate automatically synthesized programs representative of Meltdown and Spectre attacks as well as new exploits, MeltdownPrime and SpectrePrime, that I have demonstrated on Intel hardware.

MEAM Seminar: "Elastomeric Materials for Autonomic Force Transmission, Optoelectric Sensing, and 3D Printing Soft Robots"
2019-01-29 10:45 - 11:45 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
This talk will present multidisciplinary work from material composites and robotics. We have created new types of actuators, sensors, displays, and additive manufacturing techniques for soft robots and haptic interfaces. For example, we now use stretchable optical waveguides as sensors for high accuracy, repeatability, and material compatibility with soft actuators. For displaying information, we have created stretchable, elastomeric light emitting displays as well as texture morphing skins for soft robots. We have created a new type of soft actuator based on molding of foams, and stereolithography printing of elastomer based soft robots, and implemented deep learning in stretchable membranes for interpreting touch. All of these technologies depend on the iterative and complex feedback between material and mechanical design. I will describe this process, what is the present state of the art, and future opportunities for science in the space of additive manufacturing of elastomeric robots.
