BE Doctoral Dissertation Defense: "Developing Translational Multimodal Neurotechnology Across Multiple Scales Using Novel Materials And Approaches" (Sneha Shankar)
2024-09-05 12:00 - 14:00 (America/New_York) | Glandt Forum, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
The Department of BIoengineering and Dr. Flavia Vitale proudly announce the Doctoral Dissertation Defense of Sneha Shankar. Details are as follows:

Title: “Developing Translational Multimodal Neurotechnology Across Multiple Scales Using Novel Materials And Approaches”
Date: September 5th, 2024
Time: 12pm
Location: Glandt Forum, Singh Center for Nanotechnology
Advisor: Flavia Vitale
Zoom option: https://upenn.zoom.us/j/97587304173?pwd=[REDACTED]

The public is welcome to attend.

BE Seminar: "Designing Programmable Protein Therapeutics with Generative Language Models" (Pranam Chatterjee, Duke University)
2024-09-05 15:30 - 16:30 | 216 Moore Building
CRISPR has revolutionized biotechnology by enabling the simple design of guide RNAs to target and edit almost any DNA sequence. By developing new generative protein design algorithms, my hybrid lab focuses on extending this CRISPR-like programmability to proteins and other key molecules. In this talk, we will first delve into our algorithms that design binders to undruggable proteins, such as those driving pediatric cancers (alveolar rhabdomyosarcoma and Ewing’s sarcoma) and neurodegenerative diseases (Huntington’s and Alexander Disease). Our generative language models, including SaLT&PepPr, PepPrCLIP, and PepMLM, design short binding peptides from target sequence alone, with no dependence on stable 3D structures, and by fusing these “guide” peptides to E3 ubiquitin ligases, deubiquitinases, and other modifying enzymes, we have created a CRISPR-analogous system to edit these proteins. To be even more specific, we train isoform-specific targeting models such as PTM-Mamba for PTM-specific binding, FusOn-pLM for fusion oncoprotein-specific degradation, and moPPIt for motif-specific targeting of protein-protein interactions. Inspired by the power of language models, we further show how we can extend this programmability to DNA with our PAM-free CRISPR enzymes and our recent DPAC model, which allows us to design proteins that can bind and modulate any DNA sequence. Moreover, we are creating binders for heavy metals through our MetaLATTE algorithm, aiming to sequester and detoxify metals from contaminated environments, and addressing chemical pollutants, such as PFAS, by leveraging our generative models to develop proteins that can bind and degrade these persistent environmental toxins. Finally, we will explore our long-term goal of generating new cell states with model-designed proteins, highlighting our recent work on transcription factor-directed stem cell differentiation to ovarian cell types, such as granulosa cells and oogonia. By combining generative design with experimental engineering, our hybrid lab aims to translate these advances into practical applications for treating intractable diseases and addressing environmental challenges.

MEAM Seminar: "Elasticity of Fluids in Nanopores: Molecular Modeling and Ultrasonic Experiments"
2024-09-10 10:15 - 11:15 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Fluids confined in nanopores are ubiquitous in nature and technology. In recent years, the interest in confined fluids has grown, driven by research on unconventional hydrocarbon resources — shale gas and shale oil, much of which are confined in nanopores. When fluids are confined in nanopores, many of their properties differ from those of the same fluid in the bulk. These properties include density, freezing point, transport coefficients, thermal expansion coefficient, and, as it was shown recently, elastic properties.
The elastic modulus of a fluid confined in the pores contribute to the overall elasticity of the fluid-saturated porous medium and determine the speed at which elastic waves traverse through the medium. In this talk I will show how elastic modulus of a confined fluid in a nanopore can be calculated based on Monte Carlo and molecular dynamics simulations and illustrate it with calculations for various fluids. Additionally, I will present our recent experimental measurements of elastic properties of water confined in nanoporous glass samples. Our results suggest that some of the models widely used for describing elasticity of fluid-saturated porous solids need to be revised.

Biomedical Data Science Seminar Series - "Unlocking Brain Insights: Machine Learning for Neuroimaging Studies"
2024-09-10 10:30 - 12:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Modern neurotechnologies generate vast and intricate imaging data across multiple modalities, capturing nuanced aspects of brain structure and function in both healthy and diseased states, thus propelling neuroimaging into the ‘big data’ era. The quantitative analysis of such extensive neuroimaging datasets presents unprecedented opportunities for uncovering novel insights into various neuroscience problems. Machine learning and AI have emerged as promising tools for these analyses, capable of producing personalized, quantitative imaging-based indices with diagnostic and prognostic value, potentially revolutionizing healthcare delivery. However, technical challenges persist due to the high dimensionality of imaging data, disease heterogeneity, and the integration of these technologies into clinical workflows. In this talk, I will discuss novel computational approaches that harness advanced machine learning techniques to tackle these challenges. First, I will present progress toward a scalable unsupervised non-negative matrix factorization framework for interpretable and reproducible dimensionality reduction of neuroimaging data. Second, I will delve into a deep learning framework designed to parse disease heterogeneity and generate subject-specific abnormality maps. Finally, I will explore deep learning models aimed at overcoming obstacles in implementing AI-enabled clinical workflows. I will illustrate the broad impact of these approaches through their applications in diverse settings, with a focus on Alzheimer’s disease.

BE Doctoral Dissertation Defense: "Guided organogenesis using mosaic patterns of progenitors" (Catherine Porter)
2024-09-11 10:00 - 12:00 | BRB Auditorium
The Department of Bioengineering at the University of Pennsylvania and Dr. Alex Hughes are pleased to announce the Doctoral Dissertation Defense of Catherine Porter.

Title: Guided organogenesis using mosaic patterns of progenitors

Date: September 11, 2024
Time: 10:00 AM
Location: BRB Auditorium

Zoom info:
Topic: Catherine Porter’s Dissertation Defense
Time: Sep 11, 2024 10:00 AM Eastern Time (US and Canada)
Join Zoom Meeting
https://upenn.zoom.us/j/99041449653?pwd=[REDACTED]
Meeting ID: 990 4144 9653
Passcode: [REDACTED]

The public is welcome to attend.

ASSET Seminar: "Machine Learning and Brain Imaging: Contributions to Diagnostics, Prognostication, and Treatment Guidance"
2024-09-11 12:00 - 13:15 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Abstract:
Neuroimaging has significantly expanded our understanding of brain changes in neuropsychiatric disorders as well as in aging and neurodegenerative diseases. However, it wasn’t until the advent of machine learning tools that imaging signatures that can be detected in individuals, rather than groups, were constructed. More importantly, imaging signatures derived via machine learning models have shown promise in prognostication, as well as in guiding personalized treatments. This talk will present work on deriving imaging signatures of diagnostic and predictive value. It will then focus on weakly-supervised machine learning methods for analysis of the heterogeneity of brain imaging phenotypes, arriving at a dimensional representation reflecting the heterogeneity of brain aging and of various brain diseases. Finally, international consortia pooling and harmonizing large numbers of brain MRIs from many studies are presented as means for creating sufficiently large datasets for robust machine learning training and heterogeneity analysis, but also pose new challenges, including that or harmonization and domain adaptation across studies.
Zoom Link (if unable to attend in-person): https://upenn.zoom.us/j/92905415705

BE Doctoral Dissertation Defense: "Quantitative Intracranial EEG Methods for Targeted Therapy in Drug-resistant Epilepsy" (Akash Pattnaik)
2024-09-11 13:30 - 15:30 | 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. Brian Litt proudly announce the Doctoral Dissertation Defense of Akash Pattnaik.

Title: Quantitative Intracranial EEG Methods for Targeted Therapy in Drug-resistant Epilepsy

Date: Wednesday September 11
Time: 1:30pm
Location: Glandt Forum, Singh Center for Nanotechnology
Advisor: Dr. Brian Litt
Zoom option: https://upenn.zoom.us/j/93665199428?pwd=[REDACTED]

The public is welcome to attend.

GRASP Seminar: Shlomi Laufer, Technion – Israel Institute of Technology, "Enhancing Surgical Skill Assessment Through Computer Vision"
2024-09-11 14:00 - 15:00 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
*This seminar will be held in-person in Levine 307 as well as virtually via Zoom.
ABSTRACT
Medical education traditionally employs the apprenticeship model, where trainees learn directly under the supervision of experienced practitioners. This model necessitates close follow-up and typically provides extremely subjective and non- standardized feedback. Over the years, efforts to introduce more objective assessment tools have gained momentum, although these tools often remain time-consuming and can still be influenced by subjective evaluations. Recently, the integration of motion sensors with medical simulators has provided a more objective form of feedback. However, they are typically limited to different aspects of motion economy. In this presentation, I will explore how advancements in computer vision can be utilized to create more informative assessments and feedback on surgical skills. I will also briefly discuss automatic assessment of anesthesiologists. Additionally, I will demonstrate how introducing cameras into the operating room provides a new avenue
for analyzing surgical workflows.

MSE Seminar: "Sustainability Efforts at Dow: Vision, Strategy, Case Studies, and Challenges,” Kumar Sanketh, Dow
2024-09-12 10:30 - 12:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Materials and chemicals impact daily life, as they enable modern healthcare, preservation of food, and delivering performance and efficiency of transportation and infrastructure. The talk will focus on Dow’s vision and path for carbon neutrality. An overview of Dow Chemical’s portfolio and the breadth of chemistries being practiced will be discussed. Dow has a vision of being carbon neutral by 2050 and recycling 3MM MT of plastics by 2030. To meet this ambitious goal, innovation is being focused on four different fronts related to sustainable chemistry and Engineering. They include Reducing Hazards, Atom economy, Energy footprint, & Holistic design. Case studies will be presented on how innovation in these spaces is being developed and commercialized. To address the complex challenges in sustainable materials and circularity there is a need for collaborative efforts across Industry, academia, and government policy. In this regard, some of the challenges which can be considered for collaboration will be presented.

BE Doctoral Dissertation Defense: "Mechanistic Elucidation and Amelioration of RNA Lipid Nanoparticle Side Effects for Therapeutic Applications" (Serena Omo-Lamai)
2024-09-12 14:00 - 16:00 | BRB 1412
The Department of Bioengineering at the University of Pennsylvania and Dr. Jacob Brenner proudly announce the Doctoral Dissertation Defense of Serena Omo-Lamai.

Title: Mechanistic Elucidation and Amelioration of RNA Lipid Nanoparticle Side Effects for Therapeutic Applications
Advisor: Jacob Brenner, MD, PhD

Date: Thursday September 12th
Time: 2:00pm
Location: BRB 1412

Zoom Option Info: Serena Omo-Lamai is inviting you to a scheduled Zoom meeting.
Topic: Serena Omo-Lamai’s Thesis Defense Time: Sep 12, 2024 02:00 PM Eastern Time (US and Canada)
Join Zoom Meeting https://upenn.zoom.us/j/99710440253 Meeting ID: 997 1044 0253

The public is welcome to attend.

CIS Seminar: "Trustworthy Forecasting Algorithms"
2024-09-12 15:30 - 16:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Algorithms are increasingly tasked with forecasting the probabilities of uncertain events: a creditor repaying a loan, a user clicking an advertisement, or a word appearing next in a stream of text, for example. Such forecasts are trustworthy if their users can be sure they won’t regret treating the predicted probabilities as if they were actual distributions from which outcomes would be sampled. The term “calibration” refers to various measures of forecast accuracy that formalize this property of trustworthiness. Defining calibration, and designing algorithms to achieve it, turns out to be a tightrope walk between strong definitions, which ensure reliable results for users but are computationally and statistically harder to achieve, and weak definitions, which have the opposite benefits and drawbacks. I will report on some recent research aimed at locating a sweet spot between these two extremes: calibration properties that are achievable using no more samples or computation than the easiest statistical learning tasks, while providing guarantees for downstream users that are, in many cases, as powerful as the strongest notions of calibration.
This talk is based on joint work with Michael Kim, Princewill Okoroafor, Renato Paes Leme, Jon Schneider, and Yifeng Teng.

MEAM Seminar: "Natural Structural Materials: Lessons on Toughening Mechanisms, Weight Reduction, and Multifunctionality"
2024-09-17 10:15 - 11:15 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Structural materials that are damage-tolerant, lightweight, multifunctional, and sustainable are highly desirable for many engineering applications. Such combinations of properties are often found in the biological world. Organisms from nature construct various biological structural materials for protection, predation, body support, camouflage, etc. Despite the fact that these materials are made from limited constituent materials with usually poor intrinsic mechanical properties, such as brittle minerals and soft biopolymers, biological materials are often able to achieve remarkable mechanical properties while offering additional functionalities simultaneously, such as low density, coloration, transparency, flexibility, visual sensitivity, etc. In this talk, I will present our recent work in elucidating the structure-property relationships in some natural structural materials by focusing on their strategies for achieving damage tolerance, weight reduction, and multifunctionality. For example, I will present a unique damage-tolerant, dual-scale, single-crystalline, low-density microlattice we recently discovered in an echinoderm skeletal system. Our research combines quantitative multiscale 3D structural analysis, in-situ mechanical analysis, theoretical and computational modeling, and design and manufacturing of bio-inspired materials. I hope this talk will stimulate more discussions in research areas such as materials, mechanics, biomimetics, biology, and manufacturing.

ESE Fall Seminar - "Big AI for Small Devices"
2024-09-17 11:00 - 12:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
As artificial intelligence (AI) transforms industries, state-of-the-art models have exploded in size and capability. However, deploying these models on resource-constrained edge devices remains a significant challenge. Smartphones, wearables, and IoT sensors face stringent limitations on compute, memory, power, and communication, creating a gap between demanding AI models and edge hardware capabilities that hinders the deployment of intelligence. In this talk, we will re-examine techniques to bridge this gap and embed big AI on small devices. We will begin by discussing how the properties of various hardware platforms impact the design strategies of efficient deep neural network (DNN) models, such as quantization and pruning. Next, we will discuss techniques aimed at reducing the inference and training costs of distributed collaborative edge AI systems. Finally, we will delve into the underlying design philosophies and their evolution toward efficient, scalable, robust, and secure edge computing systems.

CIS Seminar: "Recent Advances in Spatial Generative AI"
2024-09-17 15:30 - 16:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Generative spatial AI offers unprecedented capabilities for photorealistic scene representation, generation, and novel-view synthesis, among other tasks. In this talk, we discuss recent advances in large-scale 3D scene representations, efficient neural rendering approaches, and generative AI strategies that allow us to generate photorealistic multi-view-consistent digital humans and general 3D scenes.

ASSET Seminar: "Representation-based Learning and Control for Dynamical Systems"
2024-09-18 12:00 - 13:15 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Abstract:
The explosive growth of machine learning and data-driven methodologies have revolutionized numerous fields. Yet, the translation of these successes to the domain of dynamical physical systems remains a significant challenge. Closing the loop from data to actions in these systems faces many difficulties, stemming from the need for sample efficiency and computational feasibility, along with many other requirement such as verifiability, robustness, and safety. In this talk, we bridge this gap by introducing innovative representations to develop nonlinear stochastic control and reinforcement learning methods. Key in the representation is to represent the stochastic, nonlinear dynamics linearly onto a nonlinear feature space. We present a comprehensive framework to develop control and learning strategies which achieve efficiency, safety, robustness, and scalability with provable performance. We also show how the representation could be used to close the sim-to-real gap. Lastly, we will briefly present some concrete real-world applications, discussing how domain knowledge is applied in practice to further close the loop from data to actions.
Zoom Link (if unable to attend in-person): https://upenn.zoom.us/j/99029916016

BE Doctoral Dissertation Defense: "Quantitative Informatics Approaches to Characterize and Predict Childhood Genetic Epilepsies" (Peter Galer)
2024-09-18 13:00 - 15:00 | Glandt Forum, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
The Department of Bioengineering at the University of Pennsylvania along with Drs. Brian Litt and Ingo Helbig proudly announce the Doctoral Dissertation Defense of Peter Galer.

Title: Quantitative Informatics Approaches to Characterize and Predict Childhood Genetic Epilepsies
Date: Wednesday September 18
Time: 1:00pm
Location: Glandt Forum, Singh Center for Nanotechnology
Advisors: Dr. Brian Litt & Dr. Ingo Helbig
Zoom link: https://upenn.zoom.us/j/97035487377

The public is welcome to attend.

Fall 2024 GRASP SFI: Dian Wang, Northeastern University, “Equivariant Learning for Robotic Manipulation”
2024-09-18 15:00 - 16:00 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
This will be a hybrid event with in-person attendance in Levine 307 and virtual attendance on Zoom.
ABSTRACT
Despite recent advances in machine learning for robotics, current approaches often lack sample efficiency, posing a significant challenge due to the enormous time consumption to collect real-robot data. In this talk, I will present our innovative methods that tackle this challenge by leveraging the inherent symmetries in the physical environment. Specifically, I will outline a comprehensive framework of equivariant policy learning and its application across various problem settings, including reinforcement learning, behavior cloning, and grasping. Our methods not only significantly outperform state-of-the-art baselines but also achieve these results with far less data, both in simulation and in real-world scenarios. Furthermore, our approach demonstrates robustness in the presence of symmetry distortions, such as variations in camera angles.

CBE Seminar: "Autonomous Soft and Colloidal Matter Fueled by Chemical Reactions" (Taylor Woehl, University of Maryland)
2024-09-18 15:30 - 16:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Abstract:
Biological systems utilize networks of biochemical reactions to drive autonomous and dynamic processes. Examples include the dynamic polymerization of microtubules and the dynamic min protein system in E. Coli. In contrast, synthetic soft and colloidal matter generally requires active stimulation to undergo reconfiguration, such as application of external fields or manual changes in solution chemistry. These materials exhibit singular responses to stimulation, such as shape change or self-assembly, but lack temporal control. In recent years, synthetic chemical reaction cycles have been discovered that enabled active soft materials that exhibit transient responses to a single chemical stimulus. This seminar will describe recent work in Professor Woehl’s lab that utilize chemical reactions to drive transient and autonomous responses in colloids and hydrogels. In the first of three vignettes, I will discuss the colloidal interactions occurring during chemically fueled assembly of micron sized colloids. Here, aqueous colloids are exposed to a carbodiimide chemical fuel that temporarily renders their surface hydrophobic. We systematically identify ranges of fuel concentration that lead to no assembly, transient assembly, and irreversible assembly of colloids. Experimental measurements of colloid surface charge and hydrophobicity inform a colloidal interaction model that corroborates the experimental observations. This work identifies electrostatic and hydrophobic forces as critical for controlling transient assembly and establishes a colloidal interaction model for rationally choosing fuel concentrations for colloids of varying size. In the second vignette, I will discuss the use redox reaction cycles to generate transient protein hydrogels. Here, a strong oxidizer and weak reducing agent are concurrently added to denatured serum albumin, which causes formation of a transient disulfide crosslinked hydrogel. We found that increasing the denaturant concentration decreased the lifetime of the hydrogel. Biophysical measurements revealed this to be due to changes in the unfolding state of the protein, which mediated the concentration of free cysteines available to react with the oxidizer. This work uncovered a novel approach to tune the lifetime of transient protein hydrogels and established a connection between molecular protein structure and macroscopic hydrogel properties. In the final vignette, I will present recent work on chemically fueled shape changes in porous hydrogels. Prior work has demonstrated slow responses of polymer hydrogels to chemical fuels, typically on the order of ~10 hours. Here we utilize a combination of co-polymer hydrogels, porosity, and pH clock reactions to reduce the reconfiguration time to ~10 minutes. This work demonstrates the most rapid autonomous shape change in chemically fueled hydrogels to date.

MEAM Ph.D. Thesis Defense: "Contacts with Dynamically Tunable Adhesion and Friction via Active Materials with Thermally Modulated Stiffness"
2024-09-19 10:00 - 11:00 | Room 337, Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Contact interactions, including adhesion and friction, are critical to the design of many engineered systems. Currently, most systems rely on materials with static mechanical properties, requiring careful selection of materials to realize effective systems for specialized tasks. However, with advances in smart materials, system design is no longer limited to materials with static properties. There is a significant potential to exploit active materials for dynamic control of mechanical behaviors, including adhesion and friction, to enable the design of systems with improved performance and new functionalities. Example applications of such systems include the gripping and manipulation of objects in robotics and manufacturing, the temporary attachment of wearable devices, and the creation of tactile interfaces for virtual reality. In this work, active control of adhesion and friction is realized using materials with tunable stiffness. In particular, thermally responsive polymers, which exhibit substantial changes in stiffness, provide significant potential for adhesion and friction control. We demonstrate the use of a conductive thermoplastic and a shape memory polymer, both with thermally modulated stiffness, to dynamically tune adhesion and friction. Through a combination of experimentation and finite element analysis, we present a composite microstructured adhesive with high strength and adhesion switchability, while highlighting the role of scale in achieving fast response times. Through further experimentation, we investigate the ability to tune friction, using stiffness modulation to enable a transition from Coulomb friction to adhesion-dominated friction. This ability to dynamically control adhesion and friction offers new opportunities for the design of engineered systems.

MSE Seminar: "Materials, Mechanics, and Performance of Flexible Polymeric Actuators in Robotics" (Sameh Tawfick - University of Illinois, Urbana-Champaign)
2024-09-19 10:30 - 12:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
My group is developing a roadmap for soft actuating materials to replace bulky electric motors in miniature robots requiring large mechanical work output.
First, I will describe the materials microstructure and mechanics of polymeric coiled muscles made by twisting nylon fishing lines, and how these actuators use internal strain energy to achieve a “record breaking” performance. Then I will describe intriguing hierarchical super-, and hyper-coiled artificial muscles which, inspired by natural muscles, exploit the interplay between nonlinear mechanics and material microstructure.
Next, I will describe their use to actuate the dynamic snapping of insect-scale jumping robots. The combination of strong but slow muscles with a fast-snapping beam gives rise to dynamic buckling cascade phenomena leading to effective robotic jumping mechanisms. These examples shed light on the future of robotics propelled by new bioinspired materials, nonlinear mechanics, and unusual manufacturing processes.

CIS Seminar: "Learning Controllers for multi-robot Teams"
2024-09-19 15:30 - 16:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
We have recently demonstrated the possibility of learning controllers that are zero-shot transferable to groups of real quadrotors via large-scale, multi-agent, end-to-end reinforcement learning. We train policies parameterized by neural networks that can control individual drones in a group in a fully decentralized manner. Our policies, trained in simulated environments with realistic quadrotor physics, demonstrate advanced flocking behaviors, perform aggressive maneuvers in tight formations while avoiding collisions with each other, break and re-establish formations to avoid collisions with moving obstacles, and efficiently coordinate in pursuit-evasion tasks. The model learned in simulation transfers to highly resource-constrained physical quadrotors performing station-keeping and goal-swapping behaviors. Motivated by these results and the observation that neural control of memory-constrained, agile robots requires small yet highly performant models, the talk will conclude with some thoughts on coaxing learned models onto devices with modest computational capabilities.

BE Doctoral Dissertation Defense: "Extracting Insights From Electronic Health Records Using Optimized Large Language Models" (Kevin Xie)
2024-09-20 09:00 - 11:00 | 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. Brian Litt are pleased to announce the Doctoral Dissertation Defense of Kevin Xie.

Title: Extracting Insights From Electronic Health Records Using Optimized Large Language Models
Date: September 20th, 2024
Time: 9:00 AM
Location: Glandt Forum, Singh Center for Nanotechnology
Advisor: Dr. Brian Litt
Zoom link: https://upenn.zoom.us/j/91430247073?pwd=[REDACTED]

The public is welcome to attend.

GRASP Industry Talk: Amazon Robotics - Scanless Technologies, “Amazon Robotics Tech Talk”
2024-09-20 10:30 - 11:45 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
This will be an in-person event only with in-person attendance in Wu and Chen. This seminar will not be recorded or available on Zoom.
Amazon Robotics Tech Talk & Networking Event

Amazon Robotics’ Scanless Technologies Team is excited to invite you to join us for a Tech Talk presentation followed by a Networking Event on Friday, 9/20 starting at 10:30am in Wu & Chen Auditorium, 101 Levine Hall.
Join us for the Tech Talk from 10:30-11:45am to get the unique opportunity to learn about how our applied science research work in computer vision addresses real-world challenges within Amazon’s fulfillment centers in the areas of action detection/recognition and item identification.
Connect in person and engage in meaningful discussions with Amazon Robotics applied scientists, software managers, and campus recruiters at our Networking Event in Levine 512 immediately following the Tech Talk. The Networking Event will run from 12:00pm – 1:00pm.
Meet our Amazon Robotics Scanless Technologies speakers for this event:

Nick Hu, Applied Scientist II (GRASP alum)
Mansi Agarwal, Applied Scientist
Frank Preiswerk, Principal Applied Scientist
Cara Garber, Software Development Manager

We look forward to meeting you all!

ASSET & Warren Center Research Mixer
2024-09-20 13:00 - 18:00 | Glandt Forum, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
The ASSET and Warren Center will be hosting a research mixer to welcome new PhD students and expose new and current students to the wide range of research in Penn Engineering in AI/ML. The program will consist of (short) faculty talks, poster presentations by students/postdocs, and a reception to end the night!

PICS Colloquium: Motion-based rules and solitary waves: ameloblasts and birds
2024-09-20 14:00 - 15:00 | PICS Conference Room 534 – A Wing , 5th Floor, 3401 Walnut Street, Philadelphia, PA, 19104, United States
Information is usually expected to propagate across a population of active agents such as living cells or birds in a diffusive manner. Yet waviness in the microstructure of dental enamel implies coherent wave motion among the generative ameloblast cells over exceptionally long periods, from a week in the mouse to months in the human; and trajectory data for starling flocks show solitary wave disturbances that propagate in much shorter times but with constant speed and amplitude across the entire flock. Necessary conditions for coherent wave behavior include persistent network invariance, i.e., neighbor sets not changing too quickly, and some form of non-mass inertia.
In a top-down strategy, a wave equation is conjectured for 2D sheets of ameloblasts with a single unknown parameter supplying rate inertia. Optimizing only , the wave equation can predict the observed cross-sectional shape of the enamel body in the mouse incisor. Nonlinearity in the wave equation gives rise to solitary waves, which also predict the time of a key transformation in ameloblast behavior. Most interestingly for modeling biological development, solitary waves coupled explicitly to shape or motility transformations in cells have characteristics determined internally and independent of remote conditions. Complex pattern formation, e.g., segmentation, can be derived from coupled systems of transformations, using rules based only on displacements and their derivatives.
Governing equations for solitary waves can be constructed for ameloblasts and birds that deal with different variables but are isomorphic and account successfully for observations in both populations, an instance of substrate independence in rules for emergent behavior.

ESE Fall Seminar - "Beyond the Exit of the Device Miniaturization Tunnel"
2024-09-23 11:00 - 12:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
For the past fifty years, researchers of semiconductor technology have felt like walking inside a tunnel. There was a single path forward – two-dimensional down-scaling of device sizes, also referred to as 2D miniaturization. With device features approaching atomic scale, semiconductor technology has reached the exit of this tunnel. The future is bright at the exit of the tunnel as there are many possible paths that create new opportunities for architectures that are extremely difficult (or even impossible) to implement using existing technology approaches that rely mainly on 2D miniaturization. It is no longer possible to draw a clear boundary between design and fabrication. Many innovations across the entire system stack – from architectures to circuits, devices, fabrication processes, and materials – will provide large multiplicative benefits at the system level. And there will be a change in the supplier-integrator and fabless-foundry ecosystems that have been in place for over 30 years.
Specifically, AI computation benefits tremendously simply by having more on-chip memory capacity. To derive the highest energy efficiency, not only the chip architecture needs to be application domain specific, but the memory technologies must also be application domain-specific to capture the highest benefits while incurring the lowest impact on latency, bandwidth and cost. As an example, I will describe the use of ultra-low leakage transistors (< 10-18 A/µm) with large band gap oxide semiconductor materials to build gain cell memory. Gain cell memory has only two transistors and can be built in a 3D stack (thus small cell size and high density) that can complement SRAM to provide larger on-chip memory capacity [1]. More importantly, these memory technologies can be integrated on Si logic chips to arrive at a computing system that integrates multiple heterogeneous device technologies in a 3D monolithic, stacked, assembled integrated circuit: 3D MOSAIC [2]. These Differentiated Access Memory (DAM) systems will require us to revisit and re-examine computing at all levels, from the design of new memory technologies to high-level algorithms.
[1] S. Liu et al., “Gain Cell Memory on Logic Platform – Device Guidelines for Oxide Semiconductor Transistor Materials Development,” 2023 International Electron Devices Meeting (IEDM), San Francisco, CA, USA, 2023, pp. 1-4, doi: 10.1109/IEDM45741.2023.10413726.
[2] R. M. Radway et al., “The Future of Hardware Technologies for Computing: N3XT 3D MOSAIC, Illusion Scaleup, Co-Design,” 2021 IEEE International Electron Devices Meeting (IEDM), San Francisco, CA, USA, 2021, pp. 25.4.1-25.4.4, doi: 10.1109/IEDM19574.2021.9720647.
Acknowledgements: This presentation benefits from a long-time collaboration with Prof. Subhasish Mitra (Stanford). This work is supported in part by Department of Defense Microelectronics Commons California-Pacific-Northwest AI Hardware Hub, Eccalon/DoD, National Science Foundation (award number 2235329), SRC JUMP 2.0 CHIMES Center and PRISM Center, and member companies of the Stanford SystemX Alliance, Stanford Non-Volatile Memory Technology Research Initiative (NMTRI), and the Stanford Differentiated Access Memory (DAM) industry affiliate programs.

MEAM Seminar: "Controlling Adhesion and Friction of Soft Interfaces by Meso-Scale Structures"
2024-09-24 10:15 - 11:15 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Discoveries of unique adhesive and frictional properties in biological attachment systems have, over the past two decades, demonstrated how near-surface architecture at lengths between the molecular (a few nm) and continuum (mm) scales can be used to achieve interesting and unique surface mechanical properties. This has spurred considerable research activity in design of meso-scale, near-surface architecture (typically at micron to mm length scales). This talk will present some of our group’s contributions to this field, including design and understanding of film-terminated fibrillar structures for adhesion and friction enhancement, shape-complementary interfaces for controlled adhesion and friction, enhancement of elasto-hydrodynamic friction by surfaces with periodic modulation of properties, and understanding of transitions in lubricated friction of soft solids.

ESE Ph.D. Thesis Defense: "Design and control of dynamical systems: a classical to quantum perspective"
2024-09-24 14:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
The design and control of dynamical systems have long been core objectives of engineering. In this thesis, we tackle the complexities of design and control across paradigms ranging from Boolean models of genetic networks, to thermally driven stochastic systems, to quantum-mechanical systems. These disparate domains share common challenges, including the large dimensionality of the design space and the computational intractability of objective functions. For classical systems, we draw inspiration from optimization heuristics and genetic programming, leveraging the inherent symmetries within these problems. This approach led to the discovery of a novel symmetry in biological systems, which we term dynamical mirror symmetry, and the subsequent design of artificial mechanical structures that emulate the behavior of biological prions. Quantum systems introduce an additional layer of complexity: the exponential growth in the dimensionality of the Hilbert space, which makes classical simulations impractical. As a test platform, we develop control sequences tailored for nitrogen vacancy centers to achieve precise control. Our approach begins with the use of standardized quantum control sequences, demonstrating their capability to infer the parameters of a quantum Hamiltonian. We then develop a more general method inspired by diagrammatic path integrals, which enables full differentiability and supports perturbative expansions for optimization and control.

CIS Seminar: "Machine learning for discovery: deciphering RNA splicing logic"
2024-09-24 15:30 - 16:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Recent advances in machine learning such as deep learning have led to powerful tools for modeling complex data with high predictive accuracy. However, the resulting models are typically black box, limiting their usefulness in scientific discovery. I will describe an “interpretable-by-design” machine learning model capturing a fundamental cellular process known as RNA splicing. Our model provides a systematic understanding of RNA splicing logic, recapitulating and extending existing domain knowledge.
The talk will not assume any prior knowledge, and should be accessible to a broad audience.

ESE Fall Seminar - "Power to the People (and to the Datacenters)! Achieving the dream of a clean and reliable electricity supply"
2024-09-25 11:00 - 12:00 | Towne 327
Most of us think of electricity as a clean and reliable source of energy, which flows out of the plug whenever we need it. The reality is far more complex. The reliability of our electricity supply is increasingly threatened by extreme weather, causing more frequent power outages. The cleanliness of our electricity supply varies greatly between different locations and hours of the day, depending on the availability of solar and wind power.
Our research combines insights from power engineering with tools from linear programming, stochastic optimization and risk analysis to develop methods that address pressing questions related to our electricity supply. In this talk, I will present results from two main topics. First, I will discuss how consumers can reduce their carbon footprint by using electricity where and when low carbon power is available. We will focus on hyperscale data centers as an example, though the insights apply to anyone consuming electricity (including you and me!). Second, as an example of extreme weather impacts, I will discuss the intersection between power grids and wildfires. Specifically, we will look at how utilities can optimize their operations to minimize both the risk of wildfire ignitions and the extent of power outages.
