MEAM Ph.D. Thesis Defense: "Real-Time Perception and Mixed-Integer Footstep Control for Underactuated Bipedal Walking on Rough Terrain"
2025-05-06 15:00 - 16:00 (America/New_York) | Towne 319, 220 S. 33rd Street, Philadelphia, 19104, United States
The promise of bipedal robots is to go where people go, serving as surrogates for human labor in dangerous, unstructured environments. For the most part, this promise remains unrealized. The primary challenge for controlling bipedal locomotion is underactuation. Standing on a single leg limits control authority, requiring appropriate foot placement to generate or absorb momentum and maintain balance. Rough terrain exacerbates this challenge by introducing restrictions on where the robot can step. These restrictions must be identified from onboard sensing modalities and accounted for in the footstep plan, all while meeting the strict real-time requirements of feedback control. In this thesis, we examine systems, modeling choices, and algorithms for solving this problem, ultimately enabling dynamic bipedal walking over previously unseen discontinuous terrain.
Conventional approaches decouple the problem of walking over rough terrain into separate modules for footstep planning and motion control, limiting walking speed and online adaptability. The beginning of this thesis introduces a new model-predictive-control-style footstep planner which eliminates this decomposition. We jointly optimize over the robot’s dynamics and discrete choice of stepping surface in real time to stabilize underactuated walking over constrained footholds.
Our footstep controller depends on approximating the safe terrain as a union of convex planar polygon “stepping stones”. In order to generate such an approximation from onboard sensors in real time, we propose novel safe terrain segmentation and convex decomposition algorithms. Our segmentation approach avoids the common design choice of plane segmentation, which we argue makes segmentation algorithms slower and less reliable. Instead, we classify terrain as safe based only on local features, yielding a segmentation which is both fast to compute and temporally consistent. We present full stack perceptive locomotion experiments on the underactuated biped Cassie, leveraging our novel footstep controller and perception pipeline to walk over previously unseen discontinuous terrain.
Finally, we present an exploratory study of a cascaded-fidelity model predictive footstep controller, which combines elements of our first footstep planner with whole-body model predictive control in order to navigate even more challenging terrains.

AI at the Crossroads: Ethics, Innovation and Impact
2025-05-07 14:00 - 15:30 | Amy Gutmann Hall, Auditorium, 3333 Chestnut Street, Philadelphia, PA, 19104, United States
As part of Philly Tech Week and AI Month, this panel explores the balance between cutting-edge AI advancements and responsible development, with a focus on the broader societal impact of artificial intelligence.
Panelists:

Chris Callison-Burch, Professor, CIS; Program Director, Online Master of Science in Engineering in Artificial Intelligence
Melissa Kelly, Deputy Managing Director, Penn Center for Innovation
Aaron Roth, Henry Salvatori Professor of Computer and Cognitive Science, CIS

CBE Seminar: "Redox-responsive Interfaces for Selective Electrochemical Separations" (Xiao Su, University of Illinois, Urbana-Champaign)
2025-05-07 15:30 - 16:30 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Abstract:
Electric fields and electrochemical reactions can unlock new separation pathways. While electrochemical separations have been explored previously for water desalination, their translation to value-added chemical manufacturing or resource recovery has been hampered by the lack of molecular selectivity. Here, we present the molecular design of redox-responsive materials for enabling selective electrochemical separations.
Redox electron-transfer can be leveraged to tune interfacial selectivity, reversibility, and even synergistic coupling of reaction and separations. We combine synthetic design, in-situ interfacial measurements, and computational simulations to investigate the binding mechanisms of target ions at electrodes, and elucidate the contributing effects of charge-transfer, electrostatics, and solvation. These mechanistic insights can guide the discrimination of structurally similar ions, for applications ranging from critical element recovery to even enantioselective separations. Separately, we demonstrate how copolymer design and electrochemical systems engineering can innovate redox-electrodialysis. These integrated processes can have an impact on multicomponent separations, including the simultaneous abatement of ultra-short-, short-, and long-chain PFAS from semiconductor manufacturing.
Finally, we highlight the generalizability of the redox-mediated separations beyond adsorption, and extend the concepts to electrochemical liquid-liquid extraction platforms. We discuss how these continuous technologies can be scalable, and provide a path for industrial translation in e-waste recycling and mineral processing.

Spring 2025 GRASP Seminar: Mike Shou, National University of Singapore, "Video intelligence in the era of multimodal"
2025-05-08 10:00 - 11:00 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
This will be a hybrid event with a VIRTUAL speaker. The GRASP seminar will be streamed for in-person attendance in Levine 307 and virtual attendance on Zoom.
ABSTRACT
The past few years have witnessed great success in video intelligence, as supercharged by multimodal models. In this talk, I will start with a brief sharing of our efforts, in building video-language models for understanding and diffusion models for video generation. Yet, video understanding and generation have always been two separate research pillars, despite their strong synergy. This motivates us to develop Show-o, one unified single transformer that can do both multimodal understanding and generation. Show-o is the first to unify autoregressive and discrete diffusion modeling, flexibly supporting a wide range of vision-language tasks of any input/output format, including visual question-answering, text-to-image/video generation, and generation of video keyframes with captions, all within one single 1.3B transformer. Show-o sheds light for building the next-generation multimodal video foundation model, and has sparked many follow-up works already.

CPE4H Seminar: “Engineering Native Biological Complexity from the Inside–out and Outside–in” (Cole A. DeForest, University of Washington)
2025-05-08 10:30 - 11:30 | Glandt Forum, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
Abstract:
Engineering heterogenous multicellular tissue with native complexity remains one of the holy grails of regenerative medicine and basic biological research. As success in this regard would yield powerful bioengineered constructs useful in functional transplantation, high-throughput drug screening, and fundamental biology investigation, research efforts in our lab have centered around developing and implementing tools to spatiotemporally customize living cell function both from the “outside–in” and from the “inside–out”. In this talk, I will discuss some of our group’s recent successes in reversibly modifying the chemical and physical aspects of synthetic cell culture platforms with user-defined and grayscale control, regulating cell-biomaterial interactions through user-programmable Boolean logic, engineering microvascular networks that span nearly all size scales of native human vasculature (including capillaries), irreversibly photoassembling bioactive proteins within living cells, and driving biomolecular condensate formation using de novo-designed proteins. Results will highlight our ability to modulate intricate cellular behavior including stem cell differentiation, protein secretion, and cell-cell interactions in 4D.

CBE Doctoral Dissertation Defense: "Bridging Transcription and Signaling to Study c-MYC Function and Regulation in Cancer Cells" (Reshma Kalyan Sundaram)
2025-05-13 13:30 - 15:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Abstract:
The transcription factor c-MYC (MYC) is a master regulator of gene expression and is frequently deregulated in human cancers. Despite the prevalent role of MYC in cancers, no MYC inhibitors are currently available for clinical use. In this work, we investigated the molecular mechanisms underlying MYC’s transcriptional function and deregulation using an integrated approach combining bioinformatics analyses and kinetic modeling. In studying MYC’s regulation of transcriptional function, we analyzed publicly available next-generation sequencing datasets (ChIP-seq and RNA-seq) in various cancer cell lines to characterize MYC’s DNA binding patterns and gene targets. We discovered that MYC indirectly binds the TRE sites specifically at enhancers over promoters. We also found that MYC co-occupied these TRE enhancer sites synergistically with the AP-1 family of TFs, and that MYC binding to these sites varied with MYC levels. Gene Ontology analysis revealed that MYC binding to TRE sites contributes to transcriptional rewiring of cells by modulating several cancer hallmarks like proliferation, apoptosis, and cell adhesion. We also investigated upstream regulatory mechanisms contributing to MYC deregulation. We built an Ordinary Differential Equation (ODE) based systems model incorporating extracellular growth and matrix signals (received by EGFR and integrins, respectively) and intracellular signaling pathways (MAPK, Rho/ROCK, and PI3K/Akt) that regulate MYC. The modeling results revealed that MYC regulation is primarily driven by EGFR in normal cells, whereas both EGFR and integrin signaling play a combined role in regulating MYC in cancerous conditions. Our findings highlight a novel role played by extracellular matrix (ECM) based microenvironmental cues in addition to the well-known growth signaling cues on MYC regulation. In summary, we identify an enhancer-specific mechanism through which MYC functions in concert with AP-1 to regulate gene expression, and demonstrate how extracellular cues, including ECM signaling, contribute to MYC regulation. These newly uncovered mechanisms provide deeper insights into MYC’s oncogenic functions, and suggest potential avenues for therapeutic targeting of MYC-driven cancers.

ESE Guest SEminar - "Dynamical control of tip-induced quantum light-matter interactions at the nanoscale"
2025-05-22 11:30 - 12:30 | Greenberg Lounge (Room 114), Skirkanich Hall, 210 South 33rd Street, Philadelphia, PA, 19104, United States
The controllable manipulation of bandgap, radiative emission, and energy transfer in low-dimensional quantum materials provides a versatile platform for a range of quantum photonic devices. Moreover, the understanding and precise regulation of nanoscale behaviors exhibited by excitonic quasiparticles, such as excitons and trions, in low-dimensional semiconductors are paramount for the development of highly efficient nano-excitonic devices. In this talk, we introduce a tip-induced nano-spectroscopic approach for dynamically controlling light-matter interactions at the nanoscale. We then demonstrate a series of tip-induced nano-engineering experiments exhibiting plasmon-exciton interactions in quantum dots and atomically thin semiconductors. Our research shows a novel strategy for the creation of robust, tunable, and ultracompact nano-excitonic devices utilizing low-dimensional semiconductors.

MEAM Seminar: "The Role of YAP and TAZ in Regulating Mechanical Load-induced Bone Adaptation and Osteocytes Mechanosensing"
2025-06-03 10:15 - 11:15 | Room 337, Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Fetal movements and physical activities generate mechanical signals that regulate musculoskeletal development. It is widely accepted that a bone’s adaptive response occurs within an optimal strain range that stimulates bone formation, exceeding typical daily activity levels. This principle has led to models predicting how bones respond to mechanical loads, as insufficient mechanical signals can result in bone loss, while high signals can stimulate new bone tissue formation.
The discussions on the mechanical adaptation of tissue have primarily focused on changes in size or shape under load. However, a key question remains: how do cells sense these loads and convert them into biochemical events leading to bone gain or loss? Osteocytes, the primary mechanosensors in bone, detect mechanical and hormonal stimuli, coordinating osteoblast and osteoclast activities. Mechanical signals activate osteocyte mechanosensors, triggering pathways that regulate transcription factors like YAP (Yes-associated protein) and TAZ (Transcriptional co-activator with PDZ-binding motif). These factors induce gene expression by binding to transcription factor TEAD, directing the signaling that regulates osteoblast and osteoclast function—a process known as mechanotransduction. In this study, we hypothesize that mechanical loading regulates prenatal bone development and adult bone remodeling through YAP/TAZ signaling and osteocyte mechanosensing. Therefore our goal is to determine the roles of YAP and TAZ in mechanical load-induced prenatal and postnatal bone formation.

CBE Doctoral Dissertation Defense: "Leveraging confinement and surface effects to control polymer phase behavior and transport phenomena in polymer-infiltrated nanoparticle films" (Trevor Devine)
2025-06-03 14:00 - 15:30 | Vagelos Institute for Energy Science and Technology, Room 121, 231 S 34th Street, Philadelphia, PA, 19104, United States
Abstract:

Highly loaded, polymer-infiltrated nanoparticle films (PINFs) enable the synergistic combination of polymers with the functionality of nanoscale fillers. Extensive studies have found that their behavior deviates markedly from bulk polymers due to extreme confinement and high interfacial area within the interstitial pore network. However, incorporating polymer blends in these PINFs (blend-PINFs) is unexplored. Confinement and nanoparticle surface interactions may substantially alter phase behavior from bulk expectations. Additionally, the prevalence of adsorbed polymer layers within PINFs present an opportunity to engineer a polymeric material dominated by interfacial effects, with little or no bulk region. In this thesis, we investigate how confinement and polymer-nanoparticle interaction asymmetry impact phase behavior, solvent resistance, and transport phenomena in blend-PINFs. Using a combination of optical microscopy, spectroscopic ellipsometry, scanning electron microscopy, small-angle neutron scattering (SANS), and resonant soft X-ray scattering (RSoXS), we examine how blend morphology deviates under nanoconfinement. To probe solvent resistance, we employ ex situ solvation experiments to track polymer removal and determine how confinement and surface chemistry influence polymer retention. We also introduce a novel fabrication method, Sequential Capillary Rise Infiltration (SCaRI), which sequentially infiltrates individual polymers. We find that strong asymmetry in polymer-nanoparticle interactions can suppress macroscopic phase separation by inducing pore-scale segregation. In blends with symmetric interactions, confinement produces more complex, system-specific effects, leading to either compatibilization or phase separation depending on blend type. We find that confinement enhances solvent resistance in PINFs, but surprisingly, resistance is not governed solely by polymer-solvent interactions: solvent-nanoparticle interactions emerge as a dominant factor in displacing adsorbed chains. Through SCaRI, we demonstrate that the infiltration sequence can significantly alter the final infiltration amount, and that phase morphology resembles fully infiltrated, CaRI-produced structures only when the second polymer has a stronger nanoparticle affinity. Overall, our results reveal several fundamental findings that allow more intelligent design of PINF and blend-PINFs that synergistical combine the aspects of its constituent polymers and nanoparticles, while also unlocking novel properties unachievable without the highly loaded nature of the PINFs.

Summer 2025 GRASP Seminar: "Brain orchestra in resting-state: Identifying communication modules from the functional architecture of area V1"
2025-06-09 12:00 - 13:00 | Amy Gutmann Hall, Room 306, 3317 Chestnut Street, Philadelphia, PA, 19104, United States
This is a hybrid event with in-person attendance in AGH 306 and virtual attendance via Zoom.
ABSTRACT
How does the brain perform the complicated computations that allow us to learn about and interact with the environment? The rapid advances in optical imaging, machine learning, and the availability of computational resources, provide a unique opportunity to decipher this fundamental question. Although much has been learned about the computational properties of single neurons, we remain far from understanding how networks of cortical cells coordinate and interact with each other to process information. Several pioneering works have proposed theories regarding how the configuration of neuronal ensembles encodes information in the cortex. Ensembles of neurons that fire in synchrony are likely to be more efficient at relaying shared information to downstream targets as well as more likely to belong to networks of neurons subserving similar functions. Spontaneous patterns of activity reflect the intrinsic dynamics of the brain in the absence of external stimulation or task performance.
We imaged essentially simultaneously thousands of pyramidal neurons from granular and supragranular layers in mouse primary visual cortex (area V1) and mapped the functional connectivity within and across layers. We found that under resting-state conditions, 19-34% of neuronal pairs at distances < 300μm exhibited significant functional connections, decreasing to ∼10% by 1mm. Orientation-tuning similarity had a weak influence on correlations measured during resting-state conditions. In contrast, internal brain state, reflected in modulations of aggregate neuronal activity or pupil diameter, played a much stronger role. Overall, V1 laminae display small-world architecture, yet layers show different connectivity structure: Layer 4 exhibits stronger pairwise correlations and flatter degree-of-connectivity distribution compared to supragranular layers, whose degree-of-connectivity distribution decays exponentially.
We argue that neurons, together with their first-order functionally connected partners, define basic multi-neuronal ensembles (modules) that serve as fundamental information processing primitives both within and across cortical laminae. Across cortical layers, the firing probability of Layer 2/3 pyramidal neurons can be predicted by the co-firing of their first-order functionally connected partners in Layer 4, following a ReLU-like activation pattern. Typically, Layer 2/3 neuron firing probability rises sharply, when ≥ 13% of its Layer 4 partners co-fire, a nonlinear behavior that ensures reliable transmission of supra-threshold activity as well as sparse firing. Furthermore, module-to-module information flow from Layer 4 to Layer 2/3 displays increased specificity, sensitivity, accuracy, and precision relative to module-to-neuron information transfer. Interestingly, modules of different sizes exhibit different response properties as well as different strengths of coupling to behavioral brain-state parameters, distributed across a continuum. In general, responses of Layer 2/3 neurons adapt to the dynamic range of the aggregate input they receive from their co-firing Layer 4 partners. These findings on the behavior of first-order functionally connected multi-neuronal ensembles (modules) remain robust even when functional connectivity is assessed under stimulation conditions, where signal correlations predominate.

MSE Ph.D. Thesis: "Metasurfaces For Environmental Refractive Index Sensing: Design, Fabrication And Interrogation"
2025-06-09 15:00 - 17:00 | Towne 327 the Active Learning Classroom
Metasurfaces are artificial materials composed of sub-wavelength building blocks whose size, shape, periodicity and composition are tailored to engineer their optical response and achieve arbitrary control of their interactions with light. Their phase discontinuities or resonances are critically dependent upon the local dielectric or refractive index environment, thus making metasurfaces excellent candidates as refractive index sensors. A unique application of such passive metasurface refractive index sensors is in agricultural and environmental sensing to conduct in-situ measurements of crop health conditions with high spatiotemporal resolution, thus maximizing crop yield. This application necessitates compact, low-cost, biocompatible metasurface sensors that can be distributed en-masse. In this thesis we study low-cost, scalable, combined top-down and bottom-up meatasurface fabrication techniques involving nanoimprint lithography and solution-processible metallic and dielectric colloidal nanocrystals. We then study metasurface sensor monitoring or interrogation techniques utilizing conventional RGB/hyperspectral remote imaging, as well as novel polarimetry techniques, to prove the real-world implementation feasibility of these metasurface refractive index sensors and develop their evaluation metrics.

MEAM Seminar: "SLAM in Hard Places"
2025-06-10 10:15 - 11:15 | Room 337, Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Simultaneous Localization and Mapping is a fundamental problem for robots interacting with a novel environment and has been a densely studied area of research for several decades. The modern paradigm of feature extraction and matching coupled with advancements in sensor technology have allowed robots to achieve sub meter localization accuracy over kilometer long trajectories in controlled indoor and urban environments. Despite these advancements, as roboticists endeavour to deploy agents in more unstructured outdoor settings to perform search and rescue or geological survey, the standard assumptions adopted by the majority of the community start to break down. In this talk we will discuss the SLAM paradigm at a high level, how these assumptions break down in the outdoor-unstructured setting, existing strategies for mitigation, and finally present work from the lab for performing SLAM in an underwater ocean setting.

ESE Ph.D. Thesis Defense: "Tunable Dielectric Nanocrystal Metasurfaces for Colorimetric Sensing"
2025-06-11 13:30 | Room 313, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
Optical metasurfaces enable strong light–matter interactions, making them ideal platforms for high–figure-of-merit (FOM) sensing. When fabricated from colloidal nanocrystal dispersions, these metasurfaces offer unique advantages in fabrication flexibility, reconfigurability, and cost-effectiveness. However, conventional fabrication approaches often rely on toxic material systems. In this thesis, we enhance the FOM of titanium dioxide (TiO₂)-based dielectric metasurfaces by integrating biocompatible materials and a low-temperature, solution-processable fabrication method. We address three key challenges: (1) scalable fabrication of TiO₂metasurfaces using environmentally friendly materials and low-temperature processes, (2) enhancement of humidity sensitivity for environmental sensing, and (3) realization of dual-band operation through three-dimensional metasurface architectures.
We develop a room-temperature, water-based nanoimprint lithography method using aqueous TiO₂ nanocrystal (NC) inks to fabricate metasurfaces that support quasi-guided mode resonances (QGMs). By tuning geometric parameters, we engineer high quality factor QGM resonances that serve as baselines for sensing. We introduce chitosan biopolymer as a responsive filler into the void spaces between NCs. The moisture uptake properties of chitosan dynamically alter the refractive index of metasurface film, enhancing sensitivity to relative humidity by up to 250%. We model this TiO2 NC – Chitosan composite system using effective medium approximations and experimentally validate the impact of polymer incorporation on device performance and hysteresis behavior. Finally, we demonstrate dual-band optical humidity sensing by fabricating double-sided TiO₂ metasurfaces with independently tunable gratings on opposite sides of a shared waveguide. We imprint gratings of different periodicities and spatial orientations. A decoupling interlayer of PDMS allows one metasurface to remain humidity-insensitive, acting as an in-situ optical standard. This enables differential sensing with improved accuracy.

MEAM Seminar: "Discrete and Continuous Modeling of Fibrous Biological Materials: Compressible Large Deformations, Damage, and Crack Propagation"
2025-06-12 10:15 - 11:15 | Room 337, Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Random fiber networks are integral to biological materials such as the extracellular matrix, cytoskeleton, and blood clots. A random fiber network is the main structural component of the extracellular matrix, the cytoskeleton, and our blood clots. The mechanical behavior of these materials is characterized by large deformations, non-linear stress-strain response, and large compressibility. Experimental and computational studies have shown that macroscopic mechanical properties are strongly influenced by changes in network topology and fiber properties. Similarly, the rupture of these networks is dictated by their microstructural organization. However, a complete understanding of the failure mechanisms and their origins remains elusive.
In this talk, we will present computational models of discrete random fiber networks to understand the mechanical response and rupture of random fiber networks. In addition, we will introduce continuum constitutive models that capture the stress-strain relations and damage accumulation for network materials.

MEAM Seminar: "Mechanical Interfaces for Health: From Mechanobiology to Tactile Perception"
2025-06-17 10:15 - 11:15 | Room 337, Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Our lab combines adhesion and tribology with modern polymers and surface coatings to understand soft interfaces in biology towards improving human health and accessibility. On the scale of cells, mechanical stiffness of cells and tissue can indicate diseases like fibrosis or osteoarthritis. On the scale of the human body, mechanical forces generated by friction form the tactile stimuli used to perceive touch. Here, we will show how leveraging soft matter phenomena like elastohydrodynamics and soft sliding friction can overcome existing challenges to access mechanical markers in disease modeling, or to develop new methods to control the sense of touch in haptic devices for people who are blind. We will conclude with some future directions where engineering of mechanical interfaces is the basis towards improving healthcare outcomes.

Summer 2025 GRASP Seminar: Antonino Furnari, University of Catania, "Towards an Embodied Understanding of Human Behaviour with Egocentric Vision"
2025-06-17 11:30 - 12:30 | Amy Gutmann Hall, Room 306, 3317 Chestnut Street, Philadelphia, PA, 19104, United States
This is a hybrid event with in-person attendance in AGH 306 and virtual attendance via Zoom.
ABSTRACT
The last few years are witnessing a major shift in the way we think of computing, with humans relying more and more on AI assistants which speak their language and understand images and videos. While this revolution has largely been possible by wrapping the world with a digital layer allowing artificial systems to natively ingest information and produce content, future AI systems will need to conquer the same physical space in which humans live. Towards this direction, egocentric vision has established itself as a powerful paradigm, placing a camera where humans have eyes, making it possible to perceive the world from their unique point of view. In this talk, I will first discuss the role that the community envisions for egocentric vision in the AI revolution, then highlight the foundational role of large-scale egocentric datasets, such as EPIC-KITCHENS, EGO4D, and Ego-Exo4D in advancing our ability to understand human behavior. I will next present research efforts aimed towards developing deep learning models to perceive, understand, and anticipate human actions and interactions from this unique first-person perspective. Finally, I will discuss how these capabilities can pave the way for assistive technologies on wearable devices designed to provide direct support to users in procedural activities.

ESE Ph.D. Thesis Defense: "Statistical Limits and Efficient Algorithms for Learning-Enabled Control"
2025-06-18 09:00 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
As the adoption of large-scale learning for control continues to grow, developing sample-efficient algorithms has become critical. Yet, even in simple settings, algorithms achieving optimal sample complexity for specific problem instances often remain unknown. Motivated by this limitation, we discuss recent progress toward understanding sample-efficient methods in learning-enabled control. We first examine the statistical limits of offline reinforcement learning with continuous state, action, and observation spaces by deriving lower bounds on the cost of a learned controller that characterize inherently challenging problem instances. We then introduce efficient algorithms and establish tight finite-sample bounds on the cost they incur for controlling a general class of nonlinear dynamical systems. These results underscore the critical role of dataset quality and motivate our subsequent exploration of optimal task-oriented experiment design. Finally, we consider large-scale pre-trained models for control, analyzing how models trained across diverse tasks can be fine-tuned for new control objectives with limited data. We approach this problem through the lens of representation learning in adaptive control and provide upper bounds on the incurred regret.

AI Across the Care Spectrum: From Bench to Bed (Webinar)
2025-06-18 14:00 - 15:00 | PA
Join Penn AI on June 18 @ 2PM for an exciting webinar exploring how artificial intelligence is transforming every corner of healthcare—from dental exams to home care, medical imaging to gerontology, and beyond. Hear directly from leading experts including as they dive into the real-world impact of AI in their fields, the challenges of implementing AI responsibly in clinical settings, and the bold opportunities ahead—such as the possibility of a foundation model for medicine. This dynamic and forward-looking conversation is open to the public—don’t miss the chance to see how AI is reshaping the future of care.
Speakers include:

Marylyn Ritchie, Edward Rose, M.D. and Elizabeth Kirk Rose, M.D. Professor, PSOM/Genetics (co-moderator)
René Vidal, Rachleff University Professor, PSOM/Radiology, SEAS/ Electrical & Systems Engineering (co-moderator)
Markus Blatz, Professor of Restorative Dentistry, Penn Dental Medicine
Patricia Brennan, Provost’s Distinguished Visiting Faculty Fellow 2024-2025
George Demiris, Penn Integrates Knowledge University Professor, Penn Nursing, PSOM/Biostatistics and Epidemiology
Alison Pouch, Assistant Professor of Radiology, PSOM/Radiology, SEAS/Bioengineering
Gary Weissman, Assistant Professor, PSOM/Medicine

Register Now

MEAM Ph.D. Thesis Defense: "Elastomeric Strain Limitation for Design of Soft Pneumatic Actuators"
2025-06-20 10:00 - 11:00 | Room 337, Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Modern robots embody power and precision control, yet as robots undertake tasks that apply forces on humans this power brings risk of injury. Soft robotic actuators use deformation to produce smooth, continuous motions and conform to delicate objects while imparting forces capable of safely pushing humans. This thesis presents strategies for the design, modeling, and strain-based control of human-safe elastomeric soft pneumatic actuators (SPA) for force generation, focusing on embodied mechanical response to simple pressure inputs.
We investigate electroadhesive (EA) strain limiters for variable shape generation, rapid force application, and targeted inflation trajectories. We attach EA clutches to a concentrically strain-limited elastomeric membrane to alter the inflation trajectory and rapidly reorient the inflated shape. We expand the capabilities of EA for soft robots by encasing them in elastomeric sheaths and varying their activation in real time, showing applications in variable trajectory inflation under identical pressure sweeps.
We then address the problem of trajectory control in the presence of external forces by modeling the pressure-trajectory relationship for a concentrically strain-limited class of silicone actuators. We validate theoretical models based on material properties and energy minimization using active learning and automated testing. We apply our ensemble of neural networks for inverse membrane design, specifying quasi-static mass lift trajectories from a simple pressure sweep. Finally, we begin analysis of experimentally informed active learning optimization for additional mechanical design tasks, moving towards future works in larger system design.

MSE Ph.D. Defense: "Wet Spinning Responsive Filaments: Assembly and Processing with Anisotropic Building Blocks"
2025-06-25 12:00 - 14:30 | Auditorium, LRSM Building, 3231 Walnut Street, Philadelphia, PA, 19104, United States
Fibers are among the most versatile material forms in contemporary applications, including textiles, biomedicine, and aerospace. Wet spinning, the process of coagulating a polymer solution into tangible fibers, remains one of the oldest and most reliable methods for continuously fabricating fibers from a wide range of materials. Contemporary research in fiber science has begun to re-imagine filaments as environmentally-responsive materials, capitalizing on novel classes of polymers and nanomaterials to amplify sensing, electrical, and mechanical properties. A promising class is aromatic organic materials, which can offer distinctive processability and structural advantages with tunable intermolecular interactions such as aromatic stacking and hydrogen bonding. In this thesis, we focus on the controlled assembly and processing of anisotropic building blocks, from thermotropic LC monomers to aramid nanofibers, for the preparation of responsive filaments. In understanding their interactions and ordering, we formulate and design wet spinning processes to scalably fabricate thermoresponsive filaments for applications in active textiles, artificial muscles, and atmospheric water harvesting.

CBE Doctoral Dissertation Defense: "Reconfigurable Metasurfaces Based on Multistable Elastic Pixels" (Jed-Joan S. Edziah)
2025-06-25 14:00 - 15:30 | Glandt Forum, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
Abstract:
Metamaterials are engineered materials designed to manipulate and tailor electromagnetic (EM) waves. Metasurfaces are planar metamaterials that rely on inclusions whose optical properties and spatial arrangements are designed to interact with incident EM waves to yield desired reflected or transmitted waveforms. A reconfigurable metasurface in which the relative positions of inclusions can be controlled could yield a range of EM responses from a single device. Building such metasurfaces remains a major challenge. In current methods, one device state is typically defined by the spatial position of inclusions absent external forces or fields, and the other, volatile state (or states) are achieved via application of external forces or fields to alter inclusion position. Such reconfigurable metasurfaces require continuous energy input to maintain volatile device states, impacting device energy efficiency. A method to define distinct non-volatile device states using inclusions designed to have multiple equilibrium loci separated by energy barriers that are large compared to thermal energies could allow for efficient device operation and for reconfiguration. Each stable state would yield a non-volatile device configuration. A switching field could allow the inclusions to move from one stable location to another, defining distinct device states. The switching field could be removed, and the reconfigured state could persist until it became desirable to reconfigure the device again.
In this thesis, I define and develop the concept of a multistable elastic pixel (MEP). A MEP consists of an inclusion (i.e, a colloidal particle, disk, or chip) placed in a nematic liquid crystal (NLC) filled pore. In my research, rather than exploiting the birefringence of the NLC, the NLC’s elastic free energy is exploited to control the inclusion position within the pore. By changing inclusion positions in a metasurface comprising an array of MEPs, the EM response of the metasurface can be altered in a controlled fashion. The pore shape, anchoring energies, and those of the inclusions are designed to mold the NLC director field around spherical or disk-shaped inclusions. The NLC molecules within the pores are distorted from their preferred, uniform spatial organization. These distortions in the nematic director field define an elastic free energy landscape that depends on colloidal particle position. Inclusions within the MEPs move to equilibrium locations within the pore where the distortions, and hence the elastic free energy, are minimized. By designing pore shapes to have multiple equilibrium loci separated spatially by zones with elastic energy barriers, multiple inclusion docking sites and device states can be defined. In principle, the MEPs concept can be demonstrated in the homogenized limit (for inclusion sizes and periodicities much smaller than the incident wavelength) or in the diffractive limit (for inclusion sizes and periodicities similar to the incident wavelength). However, since the MEPs design relies on lithographic processes, for ease of fabrication, I focus on MEPs-based diffractive devices.
I develop and explore two bistable MEP designs, a ‘pillbox’ shaped pore and a ‘peanut’ shaped pore. Both pores feature curved ends connected either by straight walls (pillbox) or by a constricted region (peanut). I characterize the elastic energy landscape within these bistable MEP structures for colloids immersed in the NLC 4-Cyano-4′-pentylbiphenyl (5CB). I focus on inclusions (Ag-coated silica colloids) confined within pores fabricated atop a borosilicate substrate via photolithography. Pores were confined with a top borosilicate substrate via a spacer, such as a Cu electrode, which was used to apply an electric switching field. The required switching fields were on the order of 103-104 V/m. In the absence of the switching fields, inclusions remained in their stable locations. Energy landscapes were explored by displacing the colloidal inclusions from their equilibrium locations, and observing their trajectories as they returned to equilibrium. In the limit of negligible particle inertia, the trajectories are analyzed to reveal the forces and energy dissipated along a trajectory. Experiments agree with simulations of the NLC elastic free energy in the Landau-de Gennes framework, which show that a spherical or disk-shaped inclusion finds a minimum energy configuration near the curved ends of the pores, with an energy barrier to reconfiguration that is smallest for the straight-sided pillbox and becomes more significant for peanuts with narrower waists or greater antagonistic curvature. This design affords control of equilibrium inclusion locations and of the switching fields required to move between them. Furthermore, the NLC elastic energy landscape is highly non-linear. Topological defects can arise that alter the energy barriers to reconfiguration.
We develop diffraction-limited MEP-based devices that enable reconfigurable optical states. By arranging multiple MEPs on a surface, I design metasurfaces with nonvolatile, reconfigurable scattering cross-sections. I demonstrate our two-state device design in which inclusions are tuned from a lattice (State A) to a chain (State B) configuration. First, static ~10 μm Ag chip inclusions arranged in the State A and B configurations were fabricated on Si wafers using direct-write lithography and lift-off. These showed distinct far-field diffraction patterns in reflection mode under ~630 nm illumination in air, consistent with Fraunhofer theory. We fabricated arrays of MEP and circular pores within which static 9 μm Ag chip inclusions were deposited and assembled into NLC cells, which we refer to as MEP cells. The near-field optical responses of the two device states in the MEP cells were probed via reflected light microscopy using a partially coherent LED source filtered to emit green light. These responses were modeled by convolution with a circular kernel, whose outer radius r increases along z such that r = z · NA_Illumination. We successfully modeled the observed power distributions for each device state. States A and B exhibited clearly distinguishable near-field scattering signatures. Using the same cells, we also collected far-field diffraction patterns in reflection mode under normal-incidence 630 nm laser light with linear polarization filtering. The observed diffraction patterns corresponded to the unit cell periodicities of each state, again confirming distinct EM responses. Together, these results demonstrate that MEP-based architectures enable experimentally discrete optical states with both near-field and far-field distinctions.
To realize tunability, we demonstrate that chip inclusions can serve as reconfigurable elements in the two-state device. We showed that a single ~500 nm thick Ag chip inclusion can be electrically reconfigured within an MEP pore. The inclusion translated toward the positive electrode and reversed direction with polarity switching, similar to the behavior of colloidal inclusions, though no defect dynamics were observed. The switching voltage (100 V) was lower than that required for colloidal inclusions, likely due to reduced elastic forces, which may be attributed to the chip’s thinner edges. We also designed a magnetically and electrically tunable chip MEP unit; the magnetic functionality is intended to enable the use of a magnetic probe to fill small areas of MEP arrays, such as those utilized in demonstrating near-field optical signatures. Assembly of these chip inclusions into our MEPs is currently ongoing. Overall, our observations demonstrate that MEP-based metasurfaces can function as reconfigurable diffraction-limited devices in the visible range. Our demonstration of MEPs enables tunable and nonvolatile beam steering, which has significant applications in imaging, spectroscopy, and other laser-based technologies.
Zoom Link: https://upenn.zoom.us/j/98737258070?pwd=[REDACTED]
Meeting ID: 987 3725 8070
Password: [REDACTED]

MEAM Ph.D. Thesis Defense: "Mechanical Robust Biocompatible Polymeric Networks for Repetitive Loading"
2025-07-02 13:00 - 14:00 | Towne 319, 220 S. 33rd Street, Philadelphia, 19104, United States
Crosslinked biocompatible polymer networks offer unique potential for biomedical applications that demand high resilience under repetitive load-bearing conditions. However, conventional hydrogels often exhibit poor mechanical strength and irreversible damage under cyclic deformation. To address these challenges, this work presents a class of engineered polymer network designed for enhanced mechanical robustness: cryogel-based double-network (DN) hydrogels.
In this system, collagen cryogels were formed through glutaraldehyde (GA) crosslinking, producing hyperelastic and macroporous scaffolds with shape-memory behavior. These cryogels served as the first network of DN hydrogels, further reinforced by an ionically crosslinked alginate network. Mechanical testing, including uniaxial compression, cyclic loading, and hyperelastic modeling, revealed excellent mechanical resilience (recovery after 90% compression), compressive modulus tunability (10-200 kPa), and peak stresses among 0.2~15 MPa. These hydrogels also demonstrated cytocompatibility, making them suitable for dynamic applications in soft robotics, tissue engineering, and mechanobiology research.
Together, these findings establish a framework for designing polymer networks that balance biocompatibility, resilience, and functionality under repetitive loading situation. The DN hydrogel systems exhibit reversible deformation, offering new opportunities for load-bearing tissue regeneration, minimally invasive implants, and wearable devices. By combining network architecture with functional chemistry, this research advances the next generation of durable, adaptive biomaterials for clinical and translational use.

CBE Doctoral Dissertation Defense: "Computational Strategies for Efficiently Sampling Conformational Changes in Solvated Macromolecules" (Akash Pallath)
2025-07-03 13:00 - 15:00 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Abstract:
Solvated macromolecules such as proteins and polymers undergo conformational changes in response to stimuli such as pressure, temperature, pH, ligand binding, and post-translational modification. These transitions are fundamental to biological function, from cellular signaling to misfolding and aggregation, and are increasingly harnessed in applications such as drug delivery, biosensing, and biomaterials design. Understanding how such stimuli shape macromolecular conformation at atomic resolution is critical, but experimentally challenging. Molecular simulations offer a powerful route to probe these transitions, yet conventional approaches often struggle to capture them, as the relevant timescales exceed those accessible to standard simulations.
In this work, we develop strategies to overcome these challenges for a small but challenging subset of stimuli and systems. We begin with a simple stimulus: hydrostatic pressure. Proteins are known to denature under elevated pressures in the kilobar range. Studying their pressure response can shed light on the molecular basis of extremophilic adaptation and help uncover latent functional sites, particularly those exposed upon allosteric activation. However, simulating pressure-induced responses remains difficult due to slow, solvent-coupled kinetics. We introduce a hydration-based biasing strategy that mimics the thermodynamic effects of pressure while bypassing its kinetic bottlenecks. Applying this approach to proteins such as Ubiquitin, we efficiently sample pressure-denatured ensembles and benchmark predictions against high-pressure NMR data. We then demonstrate how our strategy can identify a functionally relevant allosterically-gated interface in the signaling protein CheY and map pressure-temperature stability landscapes of homologs from mesophilic and thermophilic organisms.
We then briefly turn to more complex or coupled stimuli, which require us to fully resolve the underlying free energy landscape. To do so in a controlled setting, we study linear hydrophobic polymers as a model system. We evaluate a range of collective variables for capturing their collapse transitions, show that sampling becomes increasingly challenging at longer chain lengths, and uncover hidden orthogonal barriers that hinder exploration even in these simple systems.
Together, this work presents a framework for simulating conformational responses to environmental stimuli, enabling mechanistic insight across proteins, polymers, and other solvated macromolecules.

CBE Doctoral Dissertation Defense: "Computational Analysis of Colloidal Self-Assembly with Interaction Heterogeneity" (Po-Ting Wu)
2025-07-03 14:00 - 16:00 | PICS Conference Room 534 – A Wing , 5th Floor, 3401 Walnut Street, Philadelphia, PA, 19104, United States
Abstract:
Micron-scale colloidal particles with short-ranged attractions, e.g., colloids functionalized with single-stranded DNA oligomers, have emerged as a powerful platform for studying colloidal self-assembly phenomena with the long-term goal of identifying routes for metamaterial fabrication. Although these systems have been investigated extensively both experimentally and computationally, the role of ‘real world’ features that may impact self-assembly in unexpected ways have been largely ignored. One such example of an important, yet underappreciated, feature is interaction heterogeneity (IH), i.e., variations in interparticle interaction strengths across a population of particles, which can arise from variability in the DNA strand areal density on particle surfaces during fabrication. In this thesis, we systematically investigate the impact of IH on equilibrium and non-equilibrium self-assembly processes in colloidal systems.
First, we refine a physics-based interparticle interaction model for DNA-functionalized colloids. Rather than recalculating interactions for varying DNA strand areal densities while implementing IH, we propose a simplified approach by assigning a scalar binding modulator to each particle to scale interactions. This approach is shown to accurately and efficiently represent variations in DNA strand areal density. In the remainder of this thesis, we use this interaction model to investigate the effects of IH on equilibrium and non-equilibrium self-assembly behaviors. Using a multicomponent coexistence tracing approach originally developed for size polydispersity, we compute phase diagrams for both Gaussian and bidisperse IH distributions. Our results reveal that IH shifts the fluid-side coexistence boundaries outward, promoting crystallization at lower particle volume fractions while also resulting in crystals that are enhanced in the stronger binding species. Both Gaussian and bidisperse IH show qualitatively similar effects, suggesting that even relatively simple IH distributions produce the observed effects.
Under non-equilibrium conditions, we study colloidal gelation induced by thermal quenching. In these non-equilibrium simulations, crystallization is inhibited with size polydispersity so that gelation can be studied under a wide range of IH and quenching conditions. We find that IH spreads out the gelation processes in time whereby particles with higher binding modulators initiate gelation and weaker particles subsequently decorate the gel backbone. Although the influence of IH on macroscopic gel structures, e.g., structure factor, appears to be subtle, significant differences are observed at the local structural level, notably captured by the coordination number distribution. Overall, our results emphasize that IH profoundly impacts both equilibrium and non-equilibrium colloidal self-assembly behaviors, providing a new perspective on IH as a new control parameter for colloidal self-assembly.
Zoom Meeting ID: 94650952409

ESE Ph.D. Thesis Defense: "Towards General Microscopic Robots"
2025-07-08 15:00 | Towne 337
This defense presents my contributions towards general robotics at the microscopic scale. Namely, through the introduction of fully programmable, autonomous microscopic robots free to explore the microscopic world. The robots complete simple, but essential milestones for microscopic robots. The machines we build are small enough to experience the same physics as their biological counterparts, allowing us to draw comparisons. Yet because our devices are built and understood by humans; we can control and comprehend their behavior. Thus, the lessons learned through deploying these small machines will fill in our understanding of the physics, living matter, and the relationship between the two.
This platform lays the foundation for general robotics at the microscopic scale. This is made possible through several constituent parts, including design decisions needed during circuitry layout of the robot array, fabrication steps that transform the custom microprocessors into robots, and the user-centered experimental setup needed to communicate with and control these robots. Through decades-long and tremendous efforts in global semiconductor fabrication, we can readily make computers with massive parallelization. My aim is to go beyond this reality – Can we build on top of complex circuitry to create fundamentally new systems? can it be done without harsh chemical waste?

ESE Ph.D. Thesis Defense: "Energy-efficient Wavelength-Division-Multiplexing Systems for the Next Generation of Optical Transceivers"
2025-07-11 09:00 | Towne 337
The rapid growth of data traffic in data centers and AI applications demands faster, more energy-efficient communication solutions to scale up parallel computing capabilities. This dissertation explores integrated photonic-electronic systems designed to significantly enhance data transfer rates and reduce energy consumption. By simultaneously using multiple wavelengths of light, these systems achieve data rates reaching terabit-per-second (Tb/s). Key contributions include an ultra-efficient, zero-static-power optical demultiplexer, as well as two single-chip optical receivers capable of processing Tb/s-level data streams at minimal power consumption. These developments surpass current technologies, laying the groundwork for more compact, energy-efficient, and reliable optical transceivers critical to next-generation computing platforms.

MEAM Seminar: "Modularity Strategies for Pneumatic Control in Soft Robotic Systems"
2025-07-15 10:15 - 11:15 | Room 337, Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Soft robotic systems, defined as both compliant robotic platforms and mechanically adaptive structures, offer unique advantages such as safe human-machine interaction, structural flexibility, and environment-driven reconfigurability. By relying on deformable materials and embedded physical intelligence, these systems can achieve complex motions and responsive behaviors that are difficult for conventional rigid robots. Pneumatic control, including actuation, sensing, and logic, has emerged as a powerful strategy in soft robotics due to its simplicity, rapid response, and compatibility with soft materials. However, once fabricated, traditional pneumatic systems are typically tailored to perform specific tasks and lack the reconfigurability needed for broader adaptability.
This seminar introduces modularity strategies for pneumatic control in soft robotic systems. I will present three representative projects: stimuli-responsive pneumatic valves for autonomous environmental interaction, a modular actuator framework for task-specific motion and sensing, and a pneumatically tunable lattice for structural adaptation. Together, these works highlight how modularity enables pneumatic control across diverse functional domains, supporting reconfigurability, environmental adaptability, and stimulus responsiveness. The talk will also outline ongoing research in small-scale actuator fabrication, passive timing control, and the design of simplified logic valves within modular pneumatic systems.

MEAM Ph.D. Thesis: "Geometric Methods for Efficient and Explainable Control of Underactuated Robotic Systems"
2025-07-21 14:00 - 15:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Robots are complex, high-dimensional systems, governed by nonlinear, underactuated dynamics and evolving on non-Euclidean manifolds, posing numerous challenges for control synthesis and analysis. While optimization-based methods of control can flexibly accommodate diverse dynamics, costs, and constraints, they often demand coarse approximations or powerful onboard processors (infeasible for many aerial and space systems) due to their relatively poor computational efficiency. Although learned controllers can generally cope with more moderate onboard resources, the computational burden of offline training is heavy, and both the training pipeline and the policy obtained are often brittle. Conversely, explicit control laws designed analytically often have miniscule computational overhead and perform robustly, but they are typically only applicable to individual systems or a narrow class, limiting their broader usefulness.
Nonetheless, robots are not black-box nonlinear control systems—rather, their dynamics enjoy powerful properties (e.g., symmetry and mechanical structure) that can be leveraged to gain traction on control design problems. In this thesis, we explore the role of geometric methods in mitigating many of the above drawbacks, across both analytical and data-driven methods. We study the role of symmetry in identifying effective abstractions for trajectory planning in underactuated mechanical systems (in particular, “flat outputs”) and explore applications to task space planning for aerial manipulation. We also develop methods for synthesizing tracking controllers for mechanical systems evolving on the general class of homogeneous Riemannian manifolds, and give certificates for the almost global asymptotic stability of cascades, which often appear in the closed-loop dynamics of hierarchical controllers for underactuated systems. Lastly, we leverage symmetry to accelerate training of tracking controllers via reinforcement learning (by constructing “continuous MDP homomorphisms”), also improving converged performance.
In all these methods, a geometric perspective enables us to explainably construct abstractions that reduce dimensionality, enforce structure, and capture essential properties, all the while representing the system or problem in a form more convenient for analysis or design. In contrast to ad hoc methods, such reduced representations typically improve computational efficiency, while also encouraging generality over a broader class of systems and affording insight into why prior handcrafted approaches were successful for particular cases. Sometimes, such realizations also guide mechanical design, closing the control-morphology feedback loop and leading to synergies between a robot’s embodiment and its controller. By combining explainable abstractions with scalable computation, such methods build towards a future in which robotic systems move through their surroundings as capably and dynamically as their counterparts in Nature.

MEAM Seminar: "Exploring Jet-Propelled Soft Robots: Design, Experiments, and Theory"
2025-07-22 10:15 - 11:15 | Room 337, Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Understanding how marine animals migrate is critical for assessing the impacts of climate change on ocean ecosystems—and yet current Autonomous Underwater Vehicles (AUVs), with their noisy propellers and rigid hulls, are ill-suited to operate alongside sensitive species. Bio-inspired robots offer a promising alternative by emulating the natural locomotion strategies of fish, cephalopods, and other marine organisms; however, most existing prototypes still fall short of their biological counterparts in speed, and energy efficiency—highlighting a significant performance gap.
In this talk, I will focus on one specific locomotion—jet propulsion—and present our efforts to narrow that gap. First, I will introduce our squid-inspired underwater robotic system and its evolution over the past few years, discussing experiments and theoretical models that show how design and control parameters influence its performance. Next, building on the squid-inspired robot platform, we developed a salp-inspired robot with an additional frontal nozzle; experiments are conducted between the two robots to compare their performances and theoretical explanations are proposed to address the differences. Finally, I will talk about our recently developed swivel-nozzle steering mechanism and describe our plan for achieving controlled two-dimensional navigation.

ESE Guest Seminar: "On-Device Probabilistic AI: From Gaussian Transistors to Light-Driven Spike Encoding"
2025-07-29 11:30 - 12:30 | Room 221, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
Emerging edge AI systems call for device-level approaches that are inherently low-power, secure, and capable of managing uncertainty. In this talk, I will share our recent exploratory efforts toward realizing on-device probabilistic intelligence using custom-designed semiconductor devices. I will introduce Gaussian transistors that support analog Gaussian activation and probabilistic inference by harnessing device-level variability. These devices offer a potential path for implementing Bayesian operations directly at the transistor level. In parallel, we have been developing photo-spike photodetectors that convert light fluctuations into asynchronous spike trains, functioning as both neuromorphic input interfaces and entropy sources for physical randomness. While still in early stages, the combination of these platforms suggests a promising direction for hardware-embedded probabilistic learning, secure classification, and physical random number generation. This work aims to show how tuning the physics of emerging devices may open up new opportunities for edge AI systems.
