CIS Seminar: "Decentralized Mechanism Design: Cryptography Meets Game Theory"
2025-01-28 15:30 - 16:30 (America/New_York) | Wu & Chen Auditorium
In classical auction design, we take it for granted that the auctioneer is trusted and always implements the auction’s rules honestly. This assumption, however, no longer holds in modern auctions based on blockchains, or those mediated by third-party platforms such as Google. For example, in blockchain-based auctions, the consensus nodes that partly act as the auctioneer are incentivized to deviate from honest behavior if profitable. Third-party auction platforms such as Google have also been involved in high-profile anti-trust lawsuits for manipulating their auctions.

In this talk, I will describe our recent work on decentralized mechanism design, where we aim to build a new scientific foundation for emerging auctions that are not backed by a trusted auctioneer. I will characterize the mathematical landscape of decentralized mechanism design, by showing several infeasibility and feasibility results. I will also highlight how cryptography can play an essential role for bypassing impossibility results in decentralized mechanism design, leading to a new class of auctions that not only incentivize bidders to act honestly, but also incentivize the auctioneer to play by the book.

ASSET Seminar: "Efficient Sharing of AI Infrastructures with Specialized Serverless Computing"
2025-01-29 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Abstract:
The efficient sharing of AI infrastructures is becoming increasingly important in both public and private data centers. This demand is driven by two key factors: the proliferation of specialized AI models tailored for different users and applications, and the highly dynamic nature of requests, which are often on-demand. Dedicated GPU allocation in such scenarios results in prohibitively high costs and inefficient resource utilization.
In this talk, I will introduce serverless computing as a promising paradigm for addressing these challenges by enabling efficient, on-demand sharing of AI infrastructures. I will highlight its use cases and discuss key barriers to broader adoption. Following this, I will present ServerlessLLM, a state-of-the-art system designed to tackle key challenges in serverless large language model (LLM) inference, particularly cold-start latency. Specifically, I will cover ServerlessLLM’s novel contributions, including its checkpoint format design, locality-aware scheduling, and inference request live migration. Finally, I will outline open challenges beyond efficiency, such as fairness, privacy, and sustainability, which are critical for the future of serverless AI systems.
Zoom Link (if unable to attend in-person): https://upenn.zoom.us/j/95090162762

Spring 2025 GRASP SFI: Guandao Yang, Stanford University, “Toward Spatial Intelligence with Limited Data”
2025-01-29 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
Modern artificial intelligences (AIs) rely heavily on internet-scale data with unified representations. However, such large-scale homogeneous data isn’t readily available for spatial computing applications involving 3D geometry, hindering the development of spatial intelligence— AIs that can generate and understand 3D spatial data. In this talk, I will present ideas toward building spatial intelligence systems with limited 3D data. I will discuss my work combining existing mathematical models in graphics with foundation models in machine learning to generate and analyze 3D shapes. Finally, I will conclude with a discussion about the future opportunities and challenges in developing data-efficient AIs for spatial computing and beyond.

CBE Seminar: "Engineering Technologies for Enhanced Modeling, Detection, and Treatment of Neurological Disease" (Alice Stanton, MIT)
2025-01-29 15:30 - 16:30 | Wu & Chen Auditorium
Abstract:
Neurological conditions are the leading cause of illness worldwide, though over 92% of clinically tested CNS drug candidates fail to become treatments. Contributing to this high failure rate is a lack of understanding of human disease mechanisms, technologies to address them, and the restrictive blood-brain barrier (BBB), which most compounds fail to cross. New models are critically needed that more faithfully recapitulate human neurological disease, providing a tool for enhanced discovery of biomarkers and targets, effective therapeutic development, and personalized drug screening. In this seminar, I will present my work in (1) developing biomaterials-based platforms to mimic physiological conditions, probe fundamental questions, and utilize as cell-carrying scaffolds, (2) establishing a multicellular human cell-based model of the brain, miBrain, that incorporates neuronal, glial, vascular, and immune components into 3D brain tissue with structural organization, combining the power of induced pluripotent stem cell (iPSC) technology and tissue engineering, and (3) utilizing the multicellular brain model to interrogate disease pathogenesis and delivery across the BBB, harnessing a microfluidic platform I developed to enable 3D vascular perfusion within the miBrain. In my doctoral work, I developed biomaterials-based platforms to probe fundamental questions of stem cell mechanotransduction and to utilize as cell-carrying scaffolds in tissue engineering approaches. In my postdoctoral work, I applied these engineering tools to develop an advanced preclinical brain model, extensively characterized and validated the platform, and leveraged this system to model disease and BBB transport. I differentiate iPSCs into each of the six major brain cell types and assemble them in the Neuromatrix Hydrogel I developed to incorporate brain-niche cues and support cell network co-formation, and culture them in high throughput well format or perfusable chip format. miBrains form integrated 3D immune-glial-neurovascular units with enhanced cell- and tissue-scale phenotypes inclusive of myelinated neuronal networks, microglial immune cells, and BBB. To enable perfusable vasculature within the miBrain, I developed a novel microfluidic platform, the GelChip, via a 3D printing fabrication strategy, that supports 3D network formation within complex co-cultures and engineered hydrogels to form the miBrain-on-Chip. Harnessing iPSCs from patient lines and genome editing to isolate the functional consequences of specific mutations, I can form multicellular brain models across patient cohorts, assess disease susceptibility, probe mechanisms, and screen putative interventions. I have harnessed the miBrain to model APOE4 risk for Alzheimer’s Disease, recapitulating canonical disease hallmarks of increased reactive astrocytes, amyloid aggregates, and neuronal tau phosphorylation. Further, I found that APOE4 astrocytes are sufficient to increase neuronal tau phosphorylation via crosstalk with microglia. I have thus established a novel preclinical brain model with broad utility for dissecting disease mechanisms, assessing delivery to the brain, and probing barrier function and other hallmarks in contexts of disease. In the conclusion of this seminar, I will explore how multicellular human cell-based brain models and their underlying technologies and approaches can be leveraged to accelerate mechanistic understanding, therapeutic target identification, and drug candidate optimization for treatment delivery and efficacy.

MSE Seminar: "Tuning Nanostructured Materials for Combustion Applications" (Kerri-lee A. Chintersingh, Ph.D., New Jersey Institute of Technology)
2025-01-30 10:30 - 12:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Metals powders like aluminum and boron are attractive potential fuel additives for pyrotechnics, propellants and explosives due to their high energy release upon oxidation. However, they tend to agglomerate, have lengthy ignition delays, and low combustion rates/efficiencies. This work aims to design metal powders with tuned surface, micro-structure, morphology, or chemistries to mitigate these challenges and favor the formation of desired products and high energy release; without jeopardizing thermochemical performance, safety, and stability. One approach used is to incorporate elements (like Fe) that can act as oxygen shuttle catalysts for surface reactions or to introduce elements to form exothermic intermetallic products (like Zr). Ball-milling allows a simple one pot technique to incorporate these elements to form metastable nanocomposite powders with lower ignition thresholds and improved combustion efficiencies. Novel experimental and diagnostic tools like x-ray phase contrast imaging (XPCI) and snapshot hyper-spectral imager for emissions and reactions (SHEAR) have been coupled to capture condensed phase/internal particle features and external optical emissions, temperatures, and gas phase species from combustion reactions, respectively. Machine learning is also used to obtain quantitative data: identify trends, detect anomalies, and classify particle events from the videos produced from combustion scenes. Results show that surface modification to boron reduces ignition delays and doping boron with as low as 1wt% Fe improves surface reaction rates in air. Other additives like Bi and Co also help to change boron’s oxidation mechanism. For aluminum, powders can be modified by emulsion assisted milling to produce spheres of various sizes by changing the milling parameter space with process control agents like acetonitrile and hexane, and the addition of Zr can accelerate combustion in thermite formulations and allow Al powders to burn in multiple phases with improved combustion efficiencies.

IDEAS/STAT Optimization Seminar
2025-01-30 12:00 - 13:00 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States

Herman P. Schwan Distinguished Lecture: "Engineering Proteins, Genomes, Viruses & Organs" (George Church, Harvard & MIT)
2025-01-30 15:30 - 16:45 | Zoom
Our exponential technologies for reading, writing genomes and epigenomes combined with AI-ML has enabled large libraries and selections for radical new functions — e.g. resistance to all viruses, novel delivery vectors for gene therapies, xeno-transplantation, de-extinction, and de-aging.
This lecture will be held remotely.
Zoom Link: https://upenn.zoom.us/j/94497096918?pwd=[REDACTED]

Spring 2025 GRASP Seminar: Nat Trask, University of Pennsylvania, "Geometric structure preservation in probabilistic digital twins"
2025-01-31 10:30 - 11:45 | Wu & Chen Auditorium
This will be a hybrid event with in-person attendance in Wu and Chen and virtual attendance on Zoom.
ABSTRACT
Motivated by the ever-increasing success of machine learning in language and vision models, many aim to build AI-driven tools for scientific simulation and discovery. Contemporary techniques drastically lag behind their comparatively mature counterparts in modeling and simulation however, lacking rigorous notions of convergence, physical realizability, uncertainty quantification, and verification+validation that underpin prediction in high-consequence engineering settings. One reason for this is the use of “off-the-shelf” ML architectures designed for language/vision without specialization to scientific computing tasks. In this work, we establish connections between graph neural networks and the finite element exterior calculus (FEEC). FEEC forms the backbone of modern mixed finite element methods, tying the discrete topology of geometric descriptions of space (cells, faces, edges, nodes and their connectivity) to the algebraic structure of conservations laws (the div/grad/curl theorems of vector calculus). By building a differentiable learning architecture mirroring the construction of Whitney forms, we obtain a de Rham complex supporting FEEC, allowing us to learn models combining the robustness of traditional FEM with the drastic speedups and data assimilation capabilities of ML. We then introduce a novel UQ framework based on optimal recovery in reproducing Hilbert spaces, allowing the model to quantify epistemic uncertainty, providing practical notions of trust where the model may be reliably employed.

MEAM Seminar: “Digital Twins for the Earth System”
2025-02-04 10:15 - 11:15 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Reliable forecasts of the Earth system are crucial for human progress and safety from natural disasters. Artificial intelligence offers substantial potential to improve prediction accuracy and computational efficiency in this field, however this remains underexplored in many domains. Here we introduce Aurora, a large-scale foundation model for the Earth system trained on over a million hours of diverse data. Aurora outperforms operational forecasts for air quality, ocean waves, tropical cyclone tracks, and high-resolution weather forecasting at orders of magnitude loss computational cost than dedicated existing systems. With the ability to fine-tune Aurora to diverse application domains at only modest computational cost, Aurora represents significant progress in making actionable Earth system predictions accessible to anyone.

CBE Doctoral Dissertation Defense: "A molecular understanding of the structure-property relationships of model end-linked polymer networks" (Han Zhang)
2025-02-04 11:00 - 13:00 | Room 35, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
Abstract:
Polymer networks, including thermoplastics, thermosets, elastomers and gels, are among the most versatile and widely utilized polymeric materials, with applications spanning drug delivery systems, membranes and implantable devices. A comprehensive understanding of the intricate relationship between the macroscopic properties and the structure and topology of polymer networks is crucial for advancing their utilization and facilitating the design of new materials. Molecular simulations have proven invaluable in exploring the structure-property relationships across various material systems. However, compared to other polymeric systems, such as polymer melts and glasses, polymer networks have received comparatively less attention in molecular simulation studies. A primary challenge lies in the inherent inhomogeneities of polymer networks, such as loop defects and dangling ends. Nevertheless, many fundamental theories about polymer networks are based on idealized network models assuming homogeneous and defect-free structures. The understanding of polymer networks from a molecular perspective has remained limited, as methods for construct models that accurately reflect real polymer network topologies have only recently begun to emerge.
This dissertation presents a computational framework that integrates molecular dynamics simulations, Monte Carlo simulations and network analysis to explore the structure-property relationships in model end-linked polymer networks containing topological defects. We demonstrate that constructing networks from engineered tapered copolymers significantly expands the composition range in which co-continuous morphologies form. These co-continuous morphologies exhibit great potential to combine typically incompatible material properties within a single sample, unlocking exciting opportunities for advanced materials design. Additionally, we investigate the elastic properties of these networks, providing valuable physical insights into how these properties are influenced by the key structural parameters of the networks, including polymer mole fractions, chain lengths and solvent quality.
Advanced computational tools are also employed to investigate the fracture process of end-linked polymer networks from a molecular perspective. Network analysis proves to be a simple yet powerful approach for identifying potential failure locations within polymer networks based solely on their initial undeformed configurations. Furthermore, by leveraging molecular simulations, we deliver a quantitatively refined, molecular-level understanding of the fracture process, tracking energy storage and dissipation at the bond, chain, generation and tree levels within the networks. These findings offer a molecular-level perspective on the influence of structure parameters and topological defects in the networks, providing valuable insights for the mechanism behind the failure process and the inverse design strategies of network materials with tailored properties.
Meeting ID: 966 7815 7284
Passcode: [REDACTED]

CIS Seminar: "Thinking Outside the GPU: Systems for Scalable Machine Learning Pipelines"
2025-02-04 15:30 - 16:30 | Wu & Chen Auditorium
Scalable and efficient machine learning (ML) systems have been instrumental in fueling recent advancements in ML capabilities. However, further scaling these systems requires more than simply increasing the number and performance of accelerators. This is because modern ML deployments rely on complex pipelines composed of many diverse and interconnected systems.
In this talk, I will emphasize the importance of building scalable systems across the entire ML pipeline. In particular, I will explore how large-scale ML training pipelines, including those deployed at Meta, require distributed data storage and ingestion systems to manage massive training datasets. Optimizing these data systems is essential as data demands continue to grow. To achieve this, I will demonstrate how synergistic optimizations across the training data pipeline can unlock performance and efficiency gains beyond what isolated system optimizations can achieve. While these synergistic optimizations are critical, deploying them requires navigating a large system design space. To address this challenge, I will next introduce cedar, a framework that automates the optimization and orchestration of ML data processing for diverse training workloads. Finally, I will discuss further opportunities in advancing the scalability, security, and capabilities of the hardware and software systems that continue to drive increasingly sophisticated ML training and inference pipelines.

ASSET Seminar: "Steering Machine Learning Ecosystems of Interacting Agents"
2025-02-05 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Abstract:
Modern machine learning models—such as LLMs and recommender systems—interact with humans, companies, and other models in a broader ecosystem. However, these multi-agent interactions often induce unintended ecosystem-level outcomes such as clickbait in classical content recommendation ecosystems, and more recently, safety violations and market concentration in nascent LLM ecosystems.
In this talk, I discuss my research on characterizing and steering ecosystem-level outcomes. I take an economic and statistical perspective on ML ecosystems, tracing outcomes back to the incentives of interacting agents and to the ML pipeline for training models. First, in LLM ecosystems, we show how analyzing a single model in isolation fails to capture ecosystem-level performance trends: for example, training a model with more resources can counterintuitively hurt ecosystem-level performance. To help steer ecosystem-level outcomes, we develop technical tools to assess how proposed policy interventions affect market entry, safety compliance, and user welfare. Then, turning to content recommendation ecosystems, we characterize a feedback loop between the recommender system and content creators, which shapes the diversity and quality of the content supply. Finally, I present a broader vision of ML ecosystems where multi-agent interactions are steered towards the desired algorithmic, market, and societal outcomes.
Zoom Link (if unable to attend in-person): https://upenn.zoom.us/j/95467348262

Spring 2025 GRASP & CIS Seminar: Yossi Gandelsman, University of California, Berkeley, “Interpreting the Inner Workings of Vision Models”
2025-02-05 15:00 - 16:00 | Heilmeier Hall (Room 100), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
This will be a hybrid event with in-person attendance in Towne 100 Heilmeier Hall and virtual attendance on Zoom.
ABSTRACT
In this talk, I present an approach for interpreting the internal computation in deep vision models. I show that these interpretations can be used to detect model bugs and to improve the performance of pre-trained deep neural networks (e.g., reducing hallucinations from image captioners and detecting and removing spurious correlations in CLIP) without any additional training. Moreover, the obtained understanding of deep representations can unlock new model capabilities (e.g., novel identity editing techniques in diffusion models and faithful image inversion in GANs). I demonstrate how to find common representations across different models (discriminative and generative) and how deep representations can be adapted at test-time to improve model generalization without any additional supervision. Finally, I discuss future work on improving the presented interpretation techniques and their application to continual model correction and scientific discovery.

CBE Seminar: "Toward Efficient and Synthesizable In-silico Molecular Design" (Wenhao Gao, MIT)
2025-02-05 15:30 - 16:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Abstract:
The discovery of functional molecules plays a fundamental role in advancing chemical science and engineering, yet it remains a costly and time-intensive process. Recent advances in computational methods, particularly in generative artificial intelligence, have introduced a new approach—generative molecular design—which holds the promise of efficiently identifying molecules with desired properties. However, despite significant progress, their practical impact in real-world applications has been limited. In this talk, I will present our efforts to address critical bottlenecks in generative molecular design, namely synthetic accessibility and sample efficiency. I will present the development of benchmarks that capture real-world complexity and the development of chemistry-tailored solutions to enhance the practicality of generative algorithms. Taken together, these advances aim to close the gap between computational innovation and practical feasibility, paving the way for the accelerated, AI-driven discovery of novel functional molecules.

IDEAS/STAT Optimization Seminar
2025-02-06 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Zoom link: https://upenn.zoom.us/j/98843354016

BE Seminar - "Engineering cell state-specific gene regulation with AI and synthetic biology" (Sebastián Castillo Hair, University of Washington)
2025-02-06 15:30 - 16:30
Cells across tissues, developmental stages, and disease conditions adopt distinct intracellular states – epigenomic, transcriptomic, and proteomic profiles – to compartmentalize function in time and space. The ability to write DNA- and RNA-encoded programs that sense and interface with cellular states has transformative potential for biotechnology, for example in designing gene therapies with cell type- and disease-specificity to minimize off-target effects. However, limited understanding of how cellular processes respond to cellular states remains a major bottleneck. Machine learning (ML) models that learn the regulatory grammar of cell state regulation from large datasets offer a powerful tool to overcome these barriers. In this talk I will present advances in combining ML and high throughput experiments to study and engineer DNA and RNA sequences that recognize cell types, tissues, and developmental stages. First, I will focus on enhancers – DNA sequences that regulate transcription with cell type-specificity. We trained ML models on databases of genomic accessibility – an imperfect proxy of enhancer activity that is available for hundreds of cell types and tissues – and used them to design enhancers targeted to multiple cell types. Experimentally, these enhancers drove stronger and more specific gene expression than endogenous controls when tested on 10 human cell lines, including from muscle, liver, breast, and embryonal tissues, and even in vivo in mouse retinas. In the second part, I will focus on mRNA sequence engineering. We used ML predictors of translation to optimize performance of an mRNA-delivered gene editor, and developed predictors of mRNA degradation in response to microRNAs—non-coding RNAs that are differentially expressed across cell types. Using these, we designed mRNAs with programmable specificity across 10 cell lines. Finally, I will focus on gene regulation during the dramatic cell state transitions that occur during development. By training ML predictors of chromatin accessibility and mRNA regulation during zebrafish embryogenesis and applying explainable AI methods, we uncovered regulatory grammar driving differentiation and gene expression dynamics. Our work shows the potential of combining ML, high throughput experiments, and synthetic biology to decode and engineer cell state-responsive biological systems.

Spring 2025 GRASP Seminar: Hersh Sanghvi, University of Pennsylvania, "Online Controller Adaptation with Meta-Learned Models"
2025-02-07 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 and Chen and virtual attendance on Zoom.
ABSTRACT
Control tuning and adaptation present a significant challenge to the usage of robots in diverse environments. It is often nontrivial to find a single set of control parameters by hand that work well across the broad array of environments and conditions that a robot might encounter. Automated adaptation approaches must utilize prior knowledge about the system while adapting to significant domain shifts to find new control parameters quickly. In this talk, I will present our work to develop a general framework that deals with these challenges. I’ll discuss how we can train predictive models of controller performance that quickly adapt to online data and can be used as cost functions within efficient sampling-based optimization routines to find new control parameters online that maximize performance. I’ll also demonstrate how our framework can be used to adapt controllers for four diverse systems: a simulated race car, a simulated quadrupedal robot, and a simulated and physical quadrotor.

MSE Thesis Defense: "Cryogenic Microscopies of Energy Storage Materials: Insights into Metal Anodes, Solid-Electrolyte Interfaces, and Mxenes"
2025-02-07 11:30 - 13:00 | LRSM Reading Room, 3231 Walnut St., Philadelphia, PA, 19104, United States
The growing global energy demand has driven the development of advanced batteries, particularly lithium metal anodes and anode-free systems, due to their potential for higher energy density at reduced costs. However, characterizing the interfaces within these systems presents a critical challenge due to their susceptibility to decomposition during conventional atomic-resolution analysis. The emergence of Cryogenic Electron Microscopy (cryo-EM), recognized by the 2017 Nobel Prize in Chemistry, has revolutionized structural characterization by enabling near-atomic resolution imaging at liquid nitrogen temperatures. Originally transforming biomolecule imaging, this technique has now extended to materials science, making it ideal for investigating degradation-prone battery components and other sensitive materials while significantly reducing beam-induced damage.
In this work, we developed and implemented an integrated characterization approach combining multiple advanced techniques. Our methodology merges aberration-corrected (Scanning) Transmission Electron Microscopy ((S)TEM) with spectroscopic tools including
Energy-Dispersive X-ray Spectroscopy (EDS) and Electron Energy Loss Spectroscopy (EELS) for atomic-level structural and chemical analysis. For sample preparation and protection, we pioneered a novel approach combining Time-of-Flight Secondary Ion Mass Spectrometry (TOF-SIMS) with cryogenic Focused Ion Beam (cryo-FIB), enabling robust detection and mapping of degradation-prone materials such as lithium hydride in battery anodes. Our systematic investigation revealed crucial insights into battery interfaces and materials. Using an integration of cryo-FIB-SEM, cryo-(S)TEM, EDS, and 4D-STEM, we uncovered the unique characteristics of solid-electrolyte interfaces (SEI) in sodium metal batteries with solid polymer electrolytes. The analysis revealed a distinctive thin SEI layer with reduced carbon content, resulting in favorable “round” sodium nucleation patterns that contrast with the dendritic formations typical in liquid electrolyte systems. We extended this approach to examine electron beam-induced defects in titanium carbides and carbonitrides MXenes, where our observations led to implementing cryo-STEM as a solution for preserving the pristine material structure and exam it as a potential current collector for the batteries.
This comprehensive study demonstrates the transformative potential of cryogenic electron microscopy techniques in advancing our understanding of sensitive battery materials and interfaces, thereby contributing to the development of next-generation energy storage solutions.

MEAM Ph.D. Thesis Defense: "Bio-inspired Architected Materials/structures with Enhanced Failure Characteristics"
2025-02-07 15:30 - 16:30 | Towne 319, 220 S. 33rd Street, Philadelphia, 19104, United States
Structural failure is a critically important design consideration in many engineering applications. Nature provides a number of interesting examples of lightweight structural features that exhibit outstanding failure characteristics. Recent progress in additive manufacturing has facilitated precise control over geometric features, allowing for the implementation of bio-inspired structural designs on a layer-by-layer basis. In this thesis, we will introduce the role of geometry in the failure characteristics of bio-inspired architected materials/structures together with a new bio-mimic manufacturing method designed for these bio-inspired architected materials/structures. We will first present the use of bamboo-inspired void patterns to geometrically improve the failure properties of structures made from brittle polymers under flexural bending. Then, we will extend this study into a material system with tunable plasticity to study the effect of plasticity on the fracture behavior of different architected voids. Lastly, we will showcase the concept of employing simulated “swarms” of bio-inspired agents, such as those mimicking the behavior of bees and ants, to create novel designs of architected materials/structures that exhibit enhanced fracture properties, including improved energy dissipation to failure and damage tolerance under complex loading conditions.

Energy Week: Joseph Bordogna Forum
2025-02-10 15:00 - 16:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
“Creating Lasting Impact at the Interface of Energy, Technology, and Society”
Join Vijay Kumar, Nemirovsky Family Dean of Penn Engineering for a discussion with David Turk – the former Deputy Secretary of the U.S. Department of Energy and Vanessa Chan, the former Chief Commercialization Officer and Director of the Office of Technology Transitions at the U.S. Department of Energy and the inaugural Vice Dean of Innovation & Entrepreneurship at Penn Engineering.
This event will be followed by a reception.

MEAM Seminar: "Flow Architectures, From Capillary Networks to Blood Transport through Organs"
2025-02-11 10:15 - 11:15 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Flow systems in nature, whether animate or inanimate, have evolved in time toward greater efficiency by adapting, or ‘morphing’, their configuration to decrease resistance to the currents flowing through them. Observations at various scales indicate that the distribution of flow, i.e. the connection of a point (source/sink) to a volume (sink/source) or vice-versa, is the most efficient when it happens through a dendritic architecture. Flow channels function in concert with the structures around them, as a combination of long and fast flows along the channels, with short and slow flows through the surrounding medium. The ability to predict flow patterns enables engineers to propose flow designs for heat, mass, and fluid flows. Our previous work theorized the deterministic nature of morphing and showed how to obtain efficient flow configurations for combined and sometimes competing objectives.
In this talk we will discuss the blood flow architecture of the liver, the largest organ in the body. The superimposition of three different types of networks (hepatic artery and portal vein trees as inflows, and hepatic vein tree as outflow) leads to a very complex hierarchical structure made of several millions to billions of blood vessels. Out of the complexity of the blood flow system, invoking the principles mentioned above, we can predict the main features of this flow system and represent the hepatic blood circulatory system as a deterministic combination of dendritic networks and porous systems made of rigid or elastic vessels.
Next, we will consider the design of capillary networks for the cooling of high-power electronic components and how, incidentally, we understood that our theoretical approaches could also predict hydrotropism, the growth of plant roots towards areas with high moisture level. While the mechanisms are complex and involve several drivers, we extracted a conceptual understanding of how a plant root system evolves in time to connect more and more water sources while morphing its entire vasculature at every growth step. The network volume grows with the network, but the volume distribution throughout all the channels morphs to allow enough capillary pressure in each branch and minimum friction losses to pull the maximum flow rate out of the system.

ESE Spring Seminar - "Machine Learning: Algorithmic and Economic Perspectives"
2025-02-11 11:00 - 12:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Algorithms are increasingly integrated into various societal applications, often directly interacting with people and communities. This highlights the importance of understanding the interplay between algorithmic decisions and economic incentives when designing machine learning algorithms. In this talk, I will explore two examples of this dynamic through the lens of privacy in data markets and fairness in dynamic resource allocation.
The first part of the talk investigates a data marketplace involving users, platforms, and data buyers. Users benefit from platform services in exchange for data, incurring privacy loss when their data, albeit noisily, is shared with the buyer. The user chooses platforms to share data with, while platforms decide on data privacy levels and pricing before selling to the buyer. The buyer finally selects platforms to purchase data from. Using a multi-stage game-theoretic framework, I demonstrate how platform competition and buyer valuation shape user participation, platform viability, and overall welfare. I also discuss privacy regulatory interventions that can enhance user utility in mixed markets of high- and low-cost platforms.
The second part of the talk focuses on designing fair resource allocation algorithms through multi-round auctions, where an auctioneer sells indivisible goods to groups of buyers while adhering to group fairness constraints. I demonstrate that optimal mechanisms can be characterized using a dynamic programming approach and involve dynamic subsidization policies that balance revenue maximization with fairness guarantees. Additionally, I develop efficient approximations for computing these mechanisms, providing insights into the computational challenges of designing fair resource allocation algorithms.

Energy Week: "Catalyzing Clean Energy Investments in the Private Sector: The Role of the Federal Government"
2025-02-11 14:00 - 15:00 | Berger Auditorium (Room 13), Skirkanich Hall, 210 South 33rd Street, Philadelphia, PA, 19104, United States
In this panel, senior members of the Biden-Harris Administration will discuss the work they did to catalyze the private sector to commercialize critical technologies for the clean energy transition. Through the Bipartisan Infrastructure Law and the Inflation Reduction Act, the U.S. Department of Energy (DOE) created over 900,000 new jobs, catalyzed over 900 new and expanded manufacturing facilities and for every dollar invested by the DOE, six dollars was brought to the table by the private sector. Learn more from key leaders on how this was accomplished.
The panel discussion will be moderated by Vanessa Chan, the inaugural Vice Dean of Innovation & Entrepreneurship at Penn Engineering and the former Chief Commercialization Officer at DOE under President Biden.
Featured panelists:

Maria Robinson, former Director of the Grid Deployment Office (GDO)
Jigar Shah, former Director of the Loan Programs Office (LPO)
Giulia Siccardo, former Director of the Office Manufacturing Energy & Supply Chain (MESC)

The event will be followed with light refreshments.

CIS Seminar: "Leveraging the Wisdom of Clouds for Internet Security"
2025-02-11 15:30 - 16:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Over the past decade, networked systems have consolidated under just a handful of hyperscale cloud providers (e.g., AWS, Azure). While this offers logistical and economic advantages, attackers specifically target providers and their customers, a shift that has left traditional network vantage points blind to the most sophisticated adversaries. In this talk, I’ll explore how we adapt Internet measurement to these new deployment models to regain situational awareness and defend modern service deployments. I’ll introduce DScope, a new Internet telescope that continuously relocates its vantage point across public cloud infrastructure. Unlike prior approaches that use a fixed vantage point, this allows us to observe the most sophisticated attackers that actively avoid existing measurement infrastructure. Our dynamic approach also achieves a statistically representative view of cloud-based attacks, a property that we prove for the first time.

Using data from DScope, I’ll also discuss how the shared networking environment of public clouds leads to new vulnerabilities. We’ll examine the problem of latent configuration, which occurs when cloud customers reference network resources that are then reused by other tenants. This new security risk is uniquely enabled by public clouds, but through rigorous analysis and systems design we can make cloud deployments more secure in practice. I’ll conclude by discussing open problems and future work in leveraging Internet vantage points for security, with a focus on intelligent interactivity and rapid response to emergent threats.

ASSET Seminar: "Beyond Scaling: Frontiers of Retrieval-Augmented Language Models"
2025-02-12 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Abstract:
Large Language Models (LMs) have demonstrated remarkable capabilities by scaling up training data and model sizes. However, they continue to face critical challenges, including hallucinations and outdated knowledge, which particularly limit their reliability in expert domains such as scientific research and software development. In this talk, I will urge the necessity of moving beyond the traditional scaling of monolithic LMs and advocate for Augmented LMs—a new AI paradigm that designs, trains, and deploys LMs alongside complementary modules to address these limitations. Focusing on my research on Retrieval-Augmented LMs, one of the most impactful and widely adopted forms of Augmented LMs today, I will begin by presenting our systematic analyses of current LM shortcomings and demonstrate how Retrieval-Augmented LMs offer a more effective and efficient path forward. I will then discuss my work to establish new foundations for further reliability and efficiency by designing and training new LMs and retrieval systems to dynamically adapt to diverse inputs. Finally, I will demonstrate the real-world impact of such Retrieval-Augmented LMs through OpenScholar, our fully open Retrieval-Augmented LM designed to assist scientists in synthesizing scientific literature, now used by more than 25,000 researchers and practitioners worldwide. I will conclude by outlining my vision for the future of Augmented LMs, emphasizing advancements in their abilities to handle heterogeneous and diverse modalities, more efficient and effective integration with diverse components, and advancing evaluations with interdisciplinary collaboration.
Zoom Link (if unable to attend in-person): https://upenn.zoom.us/j/95663463468

CBE Seminar: "Genetically Encodable Designer Biomaterials" (Shunzhi Wang, University of Washington)
2025-02-12 15:30 - 16:30 | Wu & Chen Auditorium
Abstract:
Nature harnesses genetic sequences to encode structural information and emergent physical and chemical transformations required by life processes. Such orchestration is achieved through hierarchical self-assemblies driven by weak molecular interactions and optimized via evolution. However, emulating this level of programmability to create designer biomolecular materials remains a grand challenge. In this seminar, I will present our recent progress in developing integrated computational and experimental methods to create de novo protein nanomaterials with previously unattainable architectures and properties. I will first introduce a general approach to design 3D de novo protein crystals as genetically encodable materials with promising intracellular applications. Next, inspired by AlphaGo’s success in playing board games, we trained AI agents to generate proteins by creating custom rules and rewards. Our approach enables the top-down blueprinting of complex protein architectures with desired system properties and demonstrates the power of reinforcement learning in protein design. Finally, I will describe a modular bond-centric approach to streamline protein nanomaterial generation from standardized building blocks using simple geometric principles, which also enables reconfigurable assembly networks. Further, by deliberately introducing geometric frustration to break perfect point-group symmetry, we show the predictable formation of quasi-symmetric protein particles with tailorable sizes ranging from 40 to over 200 nm. Thus, the quasi-symmetry that has long fascinated structural biologists can now be designed, with immediate applications in biologics delivery and molecular cell biology. Our findings highlight the potential of computational protein design to develop programmable biomaterials, rapidly approaching and potentially surpassing the capabilities of DNA nanotechnology, paving the way for transformative advances in human health and sustainable development.

Energy Week: Viewing of Legion 44 Documentary Film
2025-02-12 18:00 - 20:00 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Vanessa Chan, the Jonathan and Linda Brassington Practice Professor in Materials Science and Engineering (MSE) and Vice Dean of Innovation and Entrepreneurship at Penn Engineering, is hosting the premiere of the documentary Legion 44. Special guests include Leila Conners, the film’s director and producer, and Dr. Max Pisciotta, a featured climate scientist in the film and a Penn Engineering alumnus.
About Legion 44:
Legion 44 chronicles young visionaries, from all over the world, who are inventing groundbreaking solutions to combat climate disruption. From Tuvalu’s fight to preserve its culture amid rising seas to innovations in carbon removal using rocks, water, and carbon, the film showcases human ingenuity and collaboration across the globe. Narrated by Walton Goggins, with stunning visuals and a hopeful score, this love letter to humanity journeys through Oman, Kenya, Morocco, St. Vincent, and more, asking: can we scale these solutions in time to save our planet? Directed by Leila Conners, it’s her third climate-focused film following The 11th Hour and Ice on Fire.

CBE & VIEST Joint Seminar: "Lignin Conversion to Performance-advantaged Fuels, Chemicals, and Polymers" (Gregg Beckham, NREL)
2025-02-13 10:30 - 11:30 | Vagelos Institute for Energy Science and Technology, Room 121, 231 S 34th Street, Philadelphia, PA, 19104, United States
Abstract:
Terrestrial plants harbor renewable carbon resources in their cell walls that can directly contribute to decarbonization of the transportation and materials sectors, but the heterogeneous, aromatic lignin polymer found in plants has long obstructed both natural- and humankind-driven endeavors to deconstruct plant biomass to valuable products. However, viable methods to valorize lignin are essential to ultimately enable second-generation biorefining, given that lignin can comprise up to 40% of the carbon in lignocellulose, but today it is merely burned for low-value heat and power. To this end, this talk will cover our efforts (with many collaborators) to separate the lignin polymer from the plant cell wall and use both chemical and biological catalysis to convert it into molecules and polymers that are either drop-in replacements for products sourced from fossil carbon today or that exhibit performance-advantaged properties relative to petrochemical incumbents. Specifically, we are focused on catalytic oxidation to depolymerize lignin into bio-available intermediates coupled to metabolic engineering approaches that convert mixtures of aromatic compounds to single products, which can find use in performance-advantaged biopolymers. In parallel, we are also developing and scaling methods to remove lignin from the cell wall through reductive catalysis and to selectively conduct hydrodeoxygenation to produce sustainable aviation fuel blendstocks.

Energy Week: "Climate 101: Climate Change and Artificial Intelligence"
2025-02-13 11:00 - 12:00 | Annenberg School for Communication, Room 300, 3620 Walnut Street, Philadelphia, PA, 19104, United States
The Environmental Innovations Initiative’s Climate 101 series demystifies climate change and its link with a diversity of disciplines. Experts from across Penn share how they think about climate through the lens of their research and teaching, helping to raise our climate literacy and prepare us to tackle one of the planet’s greatest challenges: the climate crisis.
Join us for February’s Climate 101 event with Benjamin Lee, Professor of Electrical and Systems Engineering and Computer and Information Science, where he will talk about Climate Change and Artificial Intelligence. This event takes place during Energy Week at Penn.
All events are Hybrid, and are held in Annenberg School for Communication, Room 300.
Register Now

IDEAS/STAT Optimization Seminar: "Foundations of Deep Learning: Optimization and Representation Learning"
2025-02-13 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Deep learning’s success stems from the ability of neural networks to automatically discover meaningful representations from raw data. In this talk, I will describe some recent insights into how optimization enables this learning process. First, I will show how optimization algorithms exhibit surprisingly rich dynamics when training neural networks, and how these complex dynamics are actually crucial to their success – enabling them to find solutions that generalize well, navigate challenging loss landscapes, and efficiently adapt to local curvature. I will then explore how optimization enables neural networks to adapt to low-dimensional structure in the data, how the geometry of the loss landscape shapes the difficulty of feature learning, and how these ideas extend to in-context learning in transformers.

Zoom link: https://upenn.zoom.us/j/93151261686 (Meeting ID: 931 5126 1686)
