Penn Engineering Events

ESE Ph.D. Thesis Defense: "Statistical Learning for System Identification, Prediction, and Control"
2022-01-20 10:00 - 12:00 (America/New_York) | Zoom – Meeting ID 949 5950 4530
Despite the recent widespread success of machine learning, we still do not fully understand its fundamental limitations. Going forward, it is crucial to better understand learning complexity, especially in critical decision making applications, where a wrong decision can lead to catastrophic consequences. In this thesis, we focus on the statistical complexity of learning unknown linear dynamical systems, with focus on the tasks of system identification, prediction, and control. We are interested in sample complexity, i.e. the minimum number of samples required to achieve satisfactory learning performance. Our goal is to provide finite-sample learning guarantees, explicitly highlighting how the learning objective depends on the number of samples. A fundamental question we are trying to answer is how system theoretic properties of the underlying process can affect sample complexity.
Using recent advances in statistical learning, high-dimensional statistics, and mini-max theory, we provide finite-sample guarantees in the following settings. i) System Identification. We provide the first finite-sample guarantees for identifying a stochastic partially-observed system; this problem is also known as the stochastic system identification problem. ii) Prediction. We provide the first end-to-end guarantees for learning the Kalman Filter, i.e. for learning to predict, in an offline learning architecture. We also provide the first logarithmic regret guarantees for the problem of learning the Kalman Filter in an online learning architecture, where the data are revealed sequentially. iii) Difficulty of System Identification and Control. Focusing on fully-observed systems, we investigate when learning linear systems is statistically easy or hard, in the finite sample regime. Statistically easy to learn linear system classes have sample complexity that is polynomial with the system dimension. Statistically hard to learn linear system classes have worst-case sample complexity that is at least exponential with the system dimension. We show that there actually exist classes of linear systems which are hard to learn. Such classes include indirectly excited systems with a large degree of indirect excitation. Similar conclusions hold for both the problem of system identification and the problem of learning to control.

CBE Seminar: "Tackling Disease-Associated Biomolecules: From RNA Therapeutics to Single-Molecule Detection"
2022-01-19 15:30 - 16:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Abstract
The functionality versatility of polymeric and nucleic acid materials provides diverse engineering opportunities for the detection and therapeutic targeting of disease-associated biomolecules. My work has encompassed nanoscale and microscale technologies to address challenges in cancer therapeutics and diagnostics, from targeting the expression of dysregulated genes to probing rare protein biomarkers. I will first describe the design of a highly potent small interfering RNA nanoparticle delivery system that leverages approaches in nucleic acid engineering and polymer chemistry. Towards overcoming analytical barriers in clinical diagnostics, I will then describe the development of ultrasensitive single-molecule detection technologies to measure low abundance proteins. By utilizing powerful molecular amplification methods and simple signal readouts, these digital enzyme-linked immunosorbent assay (ELISA) platforms enable the detection of low attomolar (10-18 M) protein concentrations, with over four orders-of-magnitude improvements in sensitivity over conventional protein detection methods. Importantly, these methods democratize ultrasensitive protein detection, enabling high-throughput, high-multiplex digital measurements to be carried out with common laboratory instrumentation.
Finally, I will highlight the application of digital ELISA technology towards the development of an ultrasensitive blood test for a retrotransposon-encoded protein as a highly specific multi-cancer biomarker. Overall, the development of these technologies can pave the way towards the future integration of materials and biomolecular engineering approaches with ultrasensitive bioanalytical tools for accelerating biomarker signature discovery and precision medicine.

CEMB Future Leaders: "The place of plant chromatin in sensing mechanical stress"
2022-01-19 11:00 - 12:00 | https://upenn.zoom.us/j/96715197752
Launched in May 2021, the Future Leaders in Mechanobiology is a monthly seminar series featuring up-and-coming leaders in mechanobiology–PhD students and postdocs from a wide range of fields, backgrounds, and institutions. By providing an international stage to share one’s work and opportunities to interact with researchers at all career stages, we aim to create an inclusive and valuable series for early-stage researchers and the mechanobiology community as a whole.
Register HERE for access to the Zoom link and visit the CEMB website for more information.

MEAM Seminar: "Assembly Engineering of Patchy Particles into Complex Structures, and Beyond"
2022-01-18 10:00 - 11:30 | Zoom – Email MEAM for Link, peterlit@seas.upenn.edu
The ability to predict, design and make the perfect material with just the right properties to do what we want, how we want, and when we want is the holy grail of materials research. Such “materials on demand” require control over thermodynamics, kinetics, nonequilibrium behavior, and structure across many length and time scales. With continuing advances in computer simulation capabilities, we have never been closer to the goal, but many challenges – and opportunities – remain. Many of those are at the boundaries of the subfields of materials research, where ideas from one area spur advances in others, and where computational tools and concepts are transferable across domains and scales. At the same time, foundational understanding at one scale can help understand new discoveries at different scales, regardless of the nature of the material and the forces holding it together. In this seminar, we show how atomic and molecular crystal structures – made possible by chemical bonds – can be realized for nanoparticles and colloids via enthalpic and entropic bonds. We show how these bonds can be designed and engineered for self-assembly.

BE Seminar: "Self-Assembling Nanotechnologies for Precision Biomaterials" (Santiago Correa, Stanford)
2022-01-17 12:00 - 13:00 | PA
Self-assembled materials with defined nanoscale architectures can engage with biological systems in fundamentally new ways, providing unprecedented biomedical opportunities. In particular, the ability to more precisely control both the location and timing of drug release makes these biomaterials especially useful for delivering potent or sensitive cargo, which has major implications for cancer therapy and immuno-engineering. Whether it is drug delivery, gene therapy, or tissue regeneration, these materials are pushing the boundaries for engineering our own biology.
In this seminar, I will discuss how self-assembled biomaterials are used to manipulate the tumor microenvironment to achieve gene silencing, non-invasive tumor detection, and localized immunostimulation in vivo. By leveraging non-covalent interactions to build composite nanomaterials, we constructed multi-functional biomaterials capable of dynamic stimuli-responsive behaviors. I will detail the development of nanoparticle coatings that target ovarian tumors and preferentially traffic to specific subcellular compartments. These coatings enabled the development of both theranostic and immunostimulatory nanomedicines that required specific subcellular trafficking to carry out their functions. I will also discuss the development of nanoparticle-based, supramolecular hydrogels, which provide injectable, clinically relevant strategies for localized cancer immunotherapy. Together, these applications demonstrate the ability for self-assembled biomaterials to accomplish complex goals in vivo and mediate highly specific biomedical functions.

CIS Seminar: "Encrypted Computation"
2022-01-11 13:00 - 14:15 | Room 337, Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Can we use cryptography to protect data while simultaneously enabling computation over it? Such cryptosystems have the potential to enable all of the amazing benefits of cloud computing without forcing users to give up control over their private data. The breakthrough development of fully homomorphic encryption (FHE) took the first step in this direction by showing how to perform arbitrary computations over encrypted data, without revealing anything about the data itself. In this talk, I will survey some of my work that goes beyond FHE to enable computation over cryptographically protected data in many other scenarios. We will discuss Fully Homomorphic Signatures, Multi-Key FHE, Laconic Function Evaluation, and Program Obfuscation.

BE Seminar: "Neural Engineering and the Primate Brain: Working at the Electrical and Optical Interface" (Bijan Pesaran)
2021-12-16 15:30 - 16:30 | Moore 216, 200 S. 33rd Street, Philadelphia, PA, 19104, United States
This seminar will be held live and broadcast on zoom – link coming soon.
Neural engineering is enjoying an era of transformative growth. Classical methods that dominated the neurosciences for decades are being replaced by powerful new technologies. In this talk, I will discuss how to engineer electrical and optical interfaces to the primate brain. I will first present work on electrode interfaces that stimulate and record at the surface of and within the brain. I will show how simultaneously measuring and manipulating neurons immediately beneath electrode contacts during behavior delivers ground-truth data. The results have implications for electrode interface design and new generations of implantable biomedical devices. I will then turn to optical neural interfaces. Two-photon fluorescence microscopy images the activity of neurons expressing genetically-encoded calcium indicators and is most often performed in small animal models, such as the mouse, worm and fly. I will present a cellular-resolution robotic imaging platform to investigate the non-human primate brain at scale. I will finish by discussing potential applications of this technology to a range of scientific and clinical goals.

BE Doctoral Dissertation Defense: "Computational imaging biomarkers for precision medicine: characterizing heterogeneity in breast cancer" (Rhea Chitalia)
2021-12-16 13:15 - 15:15 | BRB 253
The Department of Bioengineering at the University of Pennsylvania and Dr. Despina Kontos are pleased to announce the Doctoral Dissertation Defense of Rhea Chitalia.

Title: “Computational imaging biomarkers for precision medicine: characterizing heterogeneity in breast cancer”
Date: Thursday, December 16, 2021
Time: 1:15 PM
Location: BRB 0253 and Zoom.

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RHEA CHITALIA is inviting you to a scheduled Zoom meeting.
Topic: Doctoral Dissertation Defense of Rhea Chitalia
Time: Dec 16, 2021 01:15 PM Eastern Time (US and Canada)
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ESE Fall Colloquium - "Harnessing Piezoelectricity in Novel Microsystems for Classical and Quantum Information Processing"
2021-12-16 11:00 - 12:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Piezoelectricity is the intrinsic coupling between electric fields and strains in materials. While piezoelectric sensors, actuators, and RF filters are ubiquitous and important components of existing microsystems, their potential is still largely underutilized in many application spaces. In this talk, I will discuss the physics, engineering, and applications of two novel classes of piezoelectrically enabled microsystems developed by the MEMS-Enabled Quantum Photonics group at Sandia National Labs: (1) piezoelectrically modulated photonic, phononic, and optomechanical integrated circuits and 2) piezoelectric-semiconductor hybrids for all-acoustic radio frequency signal processing and acoustoelectrically enhanced optomechanics. I will discuss these systems in detail and show how they are being used to make significant advances in both classical and quantum information processing applications.

Fall 2021 GRASP Seminar: Heng Yang, "Certifiable Outlier-Robust Geometric Perception: Robots that See through the Clutter with Confidence"
2021-12-15 14:00 - 15:00 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
*This will be a HYBRID Event with in-person attendance for Dr. Malikopoulos’ in-person talk in Wu & Chen Auditorium and Virtual attendance via Zoom Webinar here.
Geometric perception is the task of estimating geometric models (e.g., object pose and 3D structure) from sensor measurements and priors (e.g., point clouds and neural network detections). Geometric perception is a fundamental building block for robotics applications ranging from intelligent transportation to space autonomy. The ubiquitous existence of outliers —measurements that tell no or little information about the models to be estimated— makes it theoretically intractable to perform estimation with guaranteed optimality. Despite this theoretical intractability, safety-critical robotics applications still demand trustworthiness and performance guarantees on perception algorithms. In this talk, I present certifiable outlier-robust geometric perception, a new paradigm to design tractable algorithms that enjoy rigorous performance guarantees, i.e., they return an optimal estimate with a certificate of optimality for a majority of problem instances, but declare failure and provide a measure of suboptimality for worst-case instances. Particularly, I present two general-purpose algorithms in the certifiable perception toolbox: (i) an estimator that uses graph theory to prune gross outliers and leverages graduated non-convexity to compute the optimal model estimate with high probability of success, and (ii) a certifier that employs sparse semidefinite programming (SDP) relaxation and a novel SDP solver to endow the estimator with an optimality certificate or escape local minima otherwise. The estimator is fast and robust against up to 99% random outliers in practical perception applications, and the certifier can compute high-accuracy optimality certificates for large-scale problems beyond the reach of existing SDP solvers. I showcase certifiable outlier-robust perception on robotics applications such as scan matching, satellite pose estimation, and vehicle pose and shape estimation. I conclude by remarking opportunities for integrating certifiable perception with big data, machine learning, and safe control towards trustworthy autonomy.

BE Doctoral Dissertation Defense: "Biofabrication approaches with hyaluronic acid hydrogels for cartilage repair" (Jonathan Galarraga)
2021-12-15 12:00 - 14:00 | Glandt Forum, Singh Center for Nanotechnology, 3205 Walnut 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 Jonathan Galarraga.

Title: “Biofabrication approaches with hyaluronic acid hydrogels for cartilage repair”
Date: Wednesday, December 15, 2021
Time: 12:00 PM
Location: Glandt Forum, Singh Center for Nanotechnology and Zoom

Zoom Information:
JONATHAN GALARRAGA is inviting you to a scheduled Zoom meeting.
Topic: Doctoral Dissertation Defense of Jonathan Galarraga
Time: Dec 15, 2021 12:00 PM Eastern Time (US and Canada)
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CEMB Future Leaders: "Immuno-stromal axes in pulmonary and biomaterial-mediate fibrosis"
2021-12-15 11:00 - 12:00 | https://upenn.zoom.us/j/96715197752

CBE PhD Dissertation Defense | "Selective Catalytic Reactions for Taking Furan-Based Compounds to Useful Chemicals"
2021-12-15 09:00 - 10:30 | Zoom – Email CBE for link
Abstract:
“Efforts have been devoted to understanding the upgrading of plant-based biomass waste into useful chemicals to replace the current petrochemical production. An extensive amount of work has been accomplished in the past few decades but there are still many uncertainties. The transformation of furanic compounds, which present a major fraction in lignocellulosic biomass, is one of them. Furans are important solvent molecules in industry, and they are also critical feedstock to produce other valuable chemicals. The work in this presentation mainly focused on bridging the gap between different chemistry for a complete industrial production process. Two reactions that I have extensively studied were the acylation of furans and the dehydra-decyclization of cyclic ethers to corresponding conjugated dienes.
Selective Friedel-Crafts acylation of furans to a type of alkyl furan ketone molecules is very useful for increasing the molecular weight of biomass in a controlled manner, and product from this reaction can be applied in many fields. The acylation reaction was classically performed with acid anhydride or chloride, using strong Lewis acids as catalysts (such as AlCl3) through homogeneous reactions. To enhance the reaction efficiency and reduce separation cost, I used non-branched long-chain carboxylic acids as acylation agent and Brønsted acidic zeolites as catalyst. With proper reaction conditions demonstrated for the direct acylation reaction, solvent was found to play an important role during reaction, which can be utilized for further reaction performance improvement.
The second reaction is to produce conjugated dienes from cyclic ethers, which has been studied over various Brønsted zeolitic materials. Conjugated dienes are the backbone monomers in synthetic rubber production. Previous works on zeolites have either low selectivity to the desired conjugated diene products or low reaction rates, and having protons as active sites, oligomerization of unsaturated hydrocarbons on the Brønsted catalysts was another big issue. In my work, ZrO2 was identified to be a promising catalyst to produce C4~C5 conjugated dienes from corresponding cyclic ethers at quantitative conversion. ZrO2 has shown a significant performance compared to other common-seen Lewis acids, and a possible reaction mechanism on ZrO2 was tabulated for further catalyst design.”

ESE Fall Colloquium - "Processing in Memory: Past, Present, and Future"
2021-12-14 11:00 - 12:00 | Zoom – Meeting ID 996 4057 1041
Applications are increasingly data-intensive and bound by the performance of the memory and/or storage system. This “memory wall” arises from several factors: the volume of data is increasing exponentially, outstripping cache capacities; many applications extensively use streaming data with little or no temporal reuse; as algorithms become more sophisticated, access patterns are often unfriendly to effective caching; and the computation intensity of many of these algorithms is low–we often spend more time and energy moving data to the processor than we spend computing on the data. All these factors motivate breaking down the classic von Neumann architecture that separates processing and memory, and computing as close to the data as possible, with processing elements either tightly coupled with memory or storage, or possibly even embedded directly in the memory chips.
The memory wall has been a concern for decades, with numerous proposals over the years for processing-in-memory and near-data architectures. This talk will review the motivation for processing in memory and some prior proposals, then provide an overview of the current landscape, take a deep dive into the Fulcrum processing-in-memory architecture developed by my research group, and then conclude with some suggestions for promising applications and directions for in-memory/near-data design, together with some necessary operating-system and middleware capabilities.

MEAM Seminar: "Architected Nanoscale Plates for Thermionic Energy Conversion and Relativistic Interstellar Travel"
2021-12-14 10:00 - 11:30 | Zoom – Email MEAM for Link, peterlit@seas.upenn.edu
Architected plates feature geometrical patterns that provide advantageous mechanical properties, such as an enhanced bending stiffness or a reduced tendency to tear. In this talk I will introduce our research group’s multiscale hexagonally patterned corrugated plates, which have nano-scale film thicknesses, micron-scale total heights, and square-centimeter-scale lateral area dimensions. I will first explain how we use these plates in direct thermionic heat-to-electricity generators and show that new corrugation modalities provide several-fold reductions in the plates’ thermal conductivity, leading to greater device efficiency. I will then show that our plates can be tailored to become ultra-reflective membranes for photon-propelled space travel near the speed of light, and explain mechanical guidelines for the design of relativistic light sails.

BE Doctoral Dissertation Defense: "Single-component optogenetic tools for cytoskeletal rearrangements" (Erin Berlew)
2021-12-10 10:00 - 12:00 | Room 337, Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
The Department of Bioengineering at the University of Pennsylvania and Dr. Brian Chow are pleased to announce the Doctoral Dissertation Defense of Erin Berlew.

Title: “Single-component optogenetic tools for cytoskeletal rearrangements”
Date: Friday, December 10, 2021
Time: 10:00 AM
Location: Towne 337 and Zoom

Zoom information:
ERIN BERLEW is inviting you to a scheduled Zoom meeting.
Topic: Erin Berlew’s Dissertation Defense
Time: Dec 10, 2021 10:00 AM Eastern Time (US and Canada)
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Meeting ID: 915 3327 7922
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BE Seminar: "Tissue-Inspired Synthetic Biomaterials" (Shelly Peyton)
2021-12-09 13:30 - 16:30 | Moore 216, 200 S. 33rd Street, Philadelphia, PA, 19104, United States
This seminar will be held live and broadcast on zoom – check your email for the zoom link or contact ksas@seas.upenn.edu.
Improved experimental model systems are critically needed to better understand cancer progression and bridge the gap between lab bench proof-of-concept studies, validation in animal models, and eventual clinical application. Many methods exist to create biomaterials, including hydrogels, which we use to study cells in contexts more akin to what they experience in the human body. Our lab has multiple approaches to create such biomaterials, based on combinations of poly(ethylene glycol) (PEG) with peptides and zwitterions. In this presentation, I will discuss our synthetic approaches to building life-like materials, how we use these systems to grow cells and understand how a cell’s environment, particularly the extracellular matrix regulates cancer cell growth, dormancy, and drug sensitivity.

MSE Seminar: "Topological Quantum Phases, Novel Superconductors, and Ultra-Thin Films Beyond Graphene"
2021-12-09 12:30 - 13:30 | Auditorium, LRSM Building, 3231 Walnut Street, Philadelphia, PA, 19104, United States
Dr. Bansil will discuss some of his recent work aimed at understanding the electronic structure and spectroscopy of novel superconductors, topological materials, and atomically thin 2D films and battery materials. [1-5] Illustrative examples will include: (i) How by exploiting electronic structure techniques we have been able to successfully predict and understand the characteristics of many new classes of topologically interesting materials, including magnetic topological materials; (ii) How atomically thin beyond graphene 2D and layered materials offer exciting new possibilities for manipulating electronic structures and provide novel platforms for fundamental science studies and applications; And, (iii) with regard to the high-Tc’s, he will discuss recent breakthroughs in modeling the insulating pristine compounds and their transition from the insulating to the metallic state with doping without invoking free parameters such as the Hubbard U parameter. A first-principles description of the competing stripe and magnetic phases in the cuprates also then becomes possible, providing a new pathway for modeling correlated materials more generally.
[1] A. Gao, et al., Nature 595, 521 (2021).
[2] H. Hafiz, et al., Nature 594, 213 (2021).
[3] Y. Zhang et al., Proceedings of the National Academy of Sciences 117, 68 (2020).
[4] D. J. Trainer et al., ACS Nano 14, 2718 (2020).
[5] A. Bansil, H. Lin and T. Das, Reviews of Modern Physics 88, 021004 (2016).

ESE Fall Colloquium Seminar - "Data Compression: From Classical to Modern"
2021-12-09 11:00 - 12:00 | Zoom – Meeting ID 968 2448 5695
Lossy data compression is a vital, if hidden, enabling technology. This virtual seminar would be impossible without data compression! Existing compression standards for images and audio rely on a “classical” theory of compression that models sources as stationary Gaussian processes. This theory is quite mature, and it provides remarkable insights into how to compress Gaussian sources in a variety of settings, including those in which the data to be compressed is distributed among multiple users, as occurs in federated learning. At the same time, compressors based on artificial neural networks (ANNs) have recently begun to outperform those based on the classical theory for images and other practical sources. Given that the classical methods are provably near-optimal for Gaussian sources, it is evident that ANNs are able to exploit non-Gaussianity in the data and that a new theory of data compression is required to explain their performance. I will present recent results on both fronts, specifically the latest advances on distribution compression of Gaussian sources and some first steps toward the development of a “modern” theory. No background in data compression or information theory will be assumed.
The talk contains results obtained in collaboration with Omer Bilgen and Johannes Ballé.

CIS Seminar: “Synthetic Data: Anonymisation Groundhog Day”
2021-12-07 12:00 - 13:00 | Zoom – Email CIS for link, cherylh@cis.upenn.edu
Synthetic data has been advertised as a silver-bullet solution to privacy-preserving data publishing that addresses the shortcomings of traditional anonymisation techniques. The promise is that synthetic data drawn from generative models preserves the statistical properties of the original dataset but, at the same time, provides perfect protection against privacy attacks. In this work, we present the first quantitative evaluation of the privacy gain of synthetic data publishing and compare it to that of previous anonymisation techniques.
Our evaluation of a wide range of state-of-the-art generative models demonstrates that synthetic data either does not prevent inference attacks or does not retain data utility. In other words, we empirically show that synthetic data does not provide a better tradeoff between privacy and utility than traditional anonymisation techniques.
Furthermore, in contrast to traditional anonymisation, the privacy-utility tradeoff of synthetic data publishing is hard to predict. Because it is impossible to predict what signals a synthetic dataset will preserve and what information will be lost, synthetic data leads to a highly variable privacy gain and unpredictable utility loss. In summary, we find that synthetic data is far from the holy grail of privacy-preserving data publishing.

ESE Fall Colloquium Seminar - "Alpha-loss: A Tunable Class of Loss Functions for Robust Learning"
2021-12-07 11:00 - 12:00 | Zoom – Meeting ID 916 0331 6605
Machine learning has dramatically enhanced the role of automated decision making across a variety of domains. There are three ingredients that are at the heart of designing of sound ML algorithms: data, learning architectures, and loss functions. In this talk, we focus on loss functions and the role of information theory in understanding the choice of loss functions in learning. We introduce alpha-loss as a parameterized class of loss functions that resulted from operationally motivating information-theoretic measures. Tuning the parameter alpha from 0 to infinity allows continuous interpolation between known and oft-used losses: log-loss (alpha=1), exponential loss (alpha=1/2), and 0-1 loss (alpha=infinity).
Beginning with the classification properties of alpha-loss and its information-theoretic interpretations, we will focus on a specific model, namely the logistic model, and quantify the optimization landscape of the average loss as viewed through the lens of Strict-Local-Quasi-Convexity. We discuss how different regimes of the parameter alpha enables the practitioner to tune the sensitivity of their algorithm towards two emerging challenges in learning: robustness and fairness. Finally, we comment on ongoing and future work on different applications of alpha-loss including GANs and boosting

MEAM Seminar: "Continuum Mechanics of Non-equilibrium Phenomena: A Journey Through Space and Time Scales"
2021-12-07 10:00 - 11:30 | Zoom – Email MEAM for Link, peterlit@seas.upenn.edu
The fascinating diversity of material behavior at the macroscopic scale, including plasticity, phase transformations, viscoelasticity or diffusion, can only emerge from the underlying atomistic or particle behavior. Yet, the direct connection between these two scales for non-equilibrium phenomena remains an extremely challenging quest from both a theoretical and computational perspective. This knowledge gap currently hinders predictive simulations and material discovery, and leads to large economic losses and barriers to innovation in many industrial sectors. In this talk, we will discuss several advances in the predictive modeling of non-equilibrium mechanics, by combining tools from continuum mechanics, statistical physics, applied mathematics, as well as machine learning and data-driven strategies.

Keeping Up the Good Work: Honoring 40 Years of Excellence in Diversity
2021-12-03 15:00 - 17:00 | Heilmeier Hall (Room 100), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
This year marks the 40th anniversary of Penn Engineering’s diversity efforts and we invite you to join us on Friday, December 3, at 3:00 p.m. EST to celebrate Penn Engineering’s new Office of Diversity, Equity, and Inclusion (ODEI), formerly known as the Office of Minority Programs.

We plan to meet in Heilmeier Hall (Towne 100) from 3:00-3:30 p.m. for remarks and we will then move upstairs to 211 Towne to officially unveil and tour the brand new ODEI Suite.

The new suite that will be unveiled honors and builds upon the remarkable work of Ms. Cora Ingrum, ODEI’s former Director and her Administrative Assistant, Ms. Donna Hampton. Over the years, the office has been instrumental in identifying and retaining diverse engineering talent and helping students successfully transition into graduate and professional programs as well as productive professional careers. ODEI has a legacy of rich programs and partnerships with academia and industry that has continued to be strengthened under the leadership of Dr. Laura Stubbs, the current Director of ODEI.

The recording of this event is posted here for your viewing.
https://youtu.be/y7-cTd03e3Y

PICS Colloquium: "Computational and Experimental Modeling of Membrane Distillation"
2021-12-03 14:00 - 15:00 | PICS Conference Room 534 – A Wing , 5th Floor, 3401 Walnut Street, Philadelphia, PA, 19104, United States
Abstract:
Computational fluid dynamics simulations and experiments were conducted to study the performance of distillation membrane process for water treatment and desalination applications. Polarizations and fouling/scaling in a direct contact membrane distillation module were examined. Laminar and turbulent flow regimes in the feed and permeate channels were considered. The hydrophobic membrane separates feed and permeate channels. The membrane is treated as a functional surface where the permeate flux is modeled as a function of local temperature, concentration using the Dusty-Gas diffusion model. The combination of Knudsen and viscous diffusion governs the vapor transport through the membrane. Vaporization occurs in the feed channel at the entrance of pores of the membrane due to the well-known confinement (surface tension) effect. Vapor is pushed through the membrane by the partial pressure difference created by the temperature concentration difference across the membrane. The permeability (resistance) of the membrane depends strongly on the membrane thickness, porosity, pore size, and tortuosity. Mitigation of polarizations and fouling/scaling and the optimization of module design will be presented and discussed.

GRASP on Robotics: "Toward Object Manipulation Without Explicit Models"
2021-12-03 10:30 - 11:45 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
*This will be a HYBRID Event with in-person attendance in Wu & Chen Auditorium and Virtual attendance via Zoom Webinar here.
The prevalent approach to object manipulation is based on the availability of explicit 3D object models. By estimating the pose of such object models in a scene, a robot can readily reason about how to pick up an object, place it in a stable position, or avoid collisions. Unfortunately, assuming the availability of object models constrains the settings in which a robot can operate, and noise in estimating a model’s pose can result in brittle manipulation performance. In this talk, I will discuss our work on learning to manipulate unknown objects directly from visual (depth) data. Without any explicit 3D object models, these approaches are able to segment unknown object instances, pickup objects in cluttered scenes, and re-arrange them into desired configurations. I will also present recent work on combining pre-trained language and vision models to efficiently teach a robot to perform a variety of manipulation tasks. I’ll conclude with our initial work toward learning implicit representations for objects.

CIs Seminar: "Modeling cancer progression with neural networks"
2021-12-02 15:30 - 16:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
The exponential growth of biological datasets in recent years has increased the dependency of cancer research on AI and computational analysis. Cancer evolves through the accumulation of genetic events over time, which are represented through different types of data. Integration of machine learning methods with temporal modeling and genomic methods has the potential to improve our understanding and treatment of cancer. In this talk, we will discuss emerging data science challenges for cancer research and opportunities to address these challenges using integrated AI frameworks.

Fall 2021 GRASP Seminar: Gregory S. Chirikjian, "Robot Imagination: Affordance-Based Reasoning about Unknown Objects"
2021-12-02 14:00 - 15:00 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Today’s robots are very brittle in their intelligence. This follows from a legacy of industrial robotics where robots pick and place known parts repetitively. For humanoid robots to function as servants in the home and in hospitals they will need to demonstrate higher intelligence, and must be able to function in ways that go beyond the stiff prescribed programming of their industrial counterparts. A new approach to service robotics is discussed here. The affordances of common objects such as chairs, cups, etc., are defined in advance. When a new object is encountered, it is scanned and a virtual version is put into a simulation wherein the robot “imagines’’ how the object can be used. In this way, robots can reason about objects that they have not encountered before, and for which they have no training using. Videos of physical demonstrations will illustrate this paradigm, which the presenter has developed with his students Hongtao Wu, Meng Xin, Sipu Ruan, and others.

MSE Seminar: "Data-driven materials design in the quantum regime: motif-centric learning framework and local-symmetry-guided material discovery"
2021-12-02 12:30 - 13:30 | Auditorium, LRSM Building, 3231 Walnut Street, Philadelphia, PA, 19104, United States
Materials design in the quantum regime call for the integration of multi-tier materials information that go beyond atomic structures. Especially, many quantum behaviors are greatly controlled by local symmetries and local bonding environments. In this talk, motivated by Pauling’s rules, Dr. Yan will show that local bonding environments (motifs) can be incorporated in a graph-based machine learning architecture to make reliable property predictions for solid-state quantum materials including complex metal oxides. The proposed atom-motif dual network model demonstrates the feasibility to incorporate beyond-atom materials information in a graph network framework and achieves the state-of-the-art performance in predicting the electronic structure properties of complex metal oxides. As an example of quantum material design by local bonding symmetry, he will discuss how data-driven material science can be combined with symmetry-based physical principles to guide the search for quantum defects in two-dimensional (2D) materials for quantum information processing and quantum computing. The use of local bonding symmetry (irreducible representations) as a material design hypothesis enables the identification of anion antisite defects as promising spin qubits and quantum emitters in six monolayer transition metal dichalcogenides. The work creates a technically accessible 2D platform for the fabrication of defect-based multi-qubit systems for quantum computing. At the end of the talk, he will discuss the continued development of machine learning models that embrace symmetries and symmetry-based interactions and the discovery of quantum defects in a vast space of 2D material systems.

CBE Seminar: "From Molecular Simplicity to Supramolecular Complexity: Low Symmetry Packings of Ionic Spherical Micelles"
2021-12-01 15:30 - 16:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Abstract
A delicate balance of noncovalent interactions drives hydrated molecular amphiphiles to self-assemble into lyotropic liquid crystals (LLCs) of varied topologies, the exquisite nanodomain structures of which suggest applications as separations membranes, mesoporous materials synthesis templates, and therapeutic delivery vehicles. Based on hard sphere colloidal crystals, spherical micelles are intuitively expected to form high symmetry face-centered cubic (FCC) and hexagonally close-packed (HCP) LLCs. However, we recently discovered that ionic surfactant micelles also form a zoo of low symmetry, tetrahedrally close-packed Frank-Kasper (FK) phases that mimic the structures of elemental metals and their alloys. The emergence of complex FK s, A15, C14, and C15 LLCs arises from a frustrated non-covalent force balance that minimizes local variations in amphiphile solvation, while maximizing electrostatic cohesion in the ionic micelle ensemble. We describe how chemical features of the amphiphiles and the path-dependent processing of their aqueous dispersions drive LLC sphere packing symmetry selection, culminating in our discovery of the ability of oil-swollen ionic micelles to aperiodically order into lyotropic dodecagonal quasicrystals (DDQCs). Thus, engineering molecular frustration into simple amphiphiles begets high fidelity, (a)periodic pattern formation at length scales far exceeding those of their constituent molecular and supramolecular building blocks.

Fall 2021 GRASP SFI: Lucas Manuelli, NVIDIA, “Robot Manipulation with Learned Representations”
2021-12-01 15:00 - 16:00 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
We would like to have robots which can perform useful manipulation tasks in real-world environments. This requires robots that can perceive the world with both precision and semantic understanding, methods for communicating desired tasks to these systems, and closed loop visual feedback controllers for robustly executing manipulation tasks. This is hard to achieve with previous methods: prior work hasn’t enabled robots to densely understand the visual world with sufficient precision to perform robotic manipulation or endowed them with the semantic understanding needed to perform tasks with novel objects. This limitation arises partly from the object representations that have been used, the challenge in extracting these representations from the available sensor data in real-world settings, and the manner in which tasks have been specified. The talk will have two sections. In the first section I will focus on object-centric representations and will present a family of approaches that leverage self-supervision, both in the visual domain and for learning physical dynamics, to enable robots to perform manipulation tasks. Specifically we (i) demonstrate the novel application of dense visual object descriptors to robotic manipulation and provide a fully self-supervised robot system to acquire them (ii) introduce the concept of category-level manipulation tasks and develop a novel object representation based on semantic 3D keypoints along with a task specification that uses these keypoints to define the task for all objects of a category, including novel instances, (iii) utilize our dense visual object descriptors to quickly learn new manipulation skills through imitation and (iv) use our visual object representations to learn data-driven models that can be used to perform closed loop feedback control in manipulation tasks. The second part of the talk will discuss an alternative action-centric approach that enables the incorporation of language-instructions in our manipulation pipelines.
