SCIEN Talk

Forensic DNA analysis has been critical in prosecuting crimes and overturning wrongful convictions. At the same time, other physical and digital forensic identification techniques, used to link a suspect to a crime scene, are plagued with problems of accuracy, reliability, and reproducibility. Flawed forensic science can have devastating consequences: the National Registry of Exonerations identified that flawed forensic techniques contribute to almost a quarter of wrongful convictions in the United States. I will describe our recent efforts to examine the reliability of two such photographic forensic identification techniques: (1) identification based on purported distinct patterns in clothing; and (2) identification based on measurements of height and weight.

A new virtual panel discussion series about groundbreaking mixed reality technology and innovation in medicine and how it impacts patients, clinicians, and the healthcare industry.

The event will start with a one-hour panel discussion featuring Dr. Bruce Daniel of Stanford Radiology and the Stanford IMMERS Lab; Dr. Thomas Grégory of Orthopedic Surgery at the Université Sorbonne Paris Nord; Christoffer Hamilton of Brainlab, a surgical software and hardware leader in Germany; and Dr. Jennifer Silva of Pediatric Cardiology at Washington University in St. Louis, who founded the medical mixed reality company SentiAR. This panel will be moderated by Dr. Christoph Leuze of Stanford University and the Stanford Medical Mixed Reality (SMMR) program. Immediately following the panel discussion, you are also invited to a 30-minute interactive session with the panelists where questions and ideas can be explored in real time.

Stanford Medical Mixed Reality (SMMR) is dedicated to building community across Stanford Medicine together with global industry leaders and academia focusing on medical virtual and augmented reality technology. Founded by the Incubator for Medical Mixed and Extended Reality at Stanford (IMMERS) Lab, we aim to bring together leading researchers, clinicians and engineers to share work, exchange ideas, collaborate and discuss opportunities and challenges. The SMMR Panel Discussion Series highlights labs around Stanford working in this area and focuses on particular topics such as surgical guidance, VR-based therapy, and training & education.

You will receive invitations to upcoming events if you are on the SMMR distribution list. You can add or remove yourself from this list by completing the information here.

Unsupervised learning with generative models has the potential of discovering rich representations of 3D scenes. Such Neural Scene Representations may subsequently support a wide variety of downstream tasks, ranging from robotics to computer graphics to medical imaging. However, existing methods ignore one of the most fundamental properties of scenes: their three-dimensional structure. In this talk, I will make the case for equipping Neural Scene Representations with an inductive bias for 3D structure, enabling self-supervised discovery of shape and appearance from few observations. By embedding an implicit scene representation in a neural rendering framework and learning a prior over these representations, I will show how we can enable 3D reconstruction from only a single posed 2D image. I will show how the features we learn in this process are already useful to the downstream task of semantic segmentation. I will then show how gradient-based meta-learning can enable fast inference of implicit representations.

We developed photoacoustic tomography to peer deep into biological tissue. Photoacoustic tomography (PAT) provides in vivo omniscale functional, metabolic, molecular, and histologic imaging across the scales of organelles through organisms. We also developed compressed ultrafast photography (CUP) to record 10 trillion frames per second in real time, orders of magnitude faster than commercially available camera technologies. CUP can capture the fastest phenomenon in the universe, namely, light propagation, at light speed and can be slowed down for slower phenomena such as combustion.

Registration is required to attend. The talks will be presented via Zoom, and you will receive a Zoom meeting URL when you register for the presentation.

 The lens has long been a central element of cameras, since its early use in the mid-nineteenth century by Niepce, Talbot, and Daguerre. The role of the lens, from the Daguerrotype to modern digital cameras, is to refract light to achieve a one-to-one mapping between a point in the scene and a point on the sensor. This effect enables the sensor to compute a particular two-dimensional (2D) integral of theincident 4D light-field. We propose a radical departure from this practice and the many limitations it imposes. In the talk we focus on two inter-related research projects that attempt to go beyond lens-based imaging.
First, we discuss our lab's recent efforts to build flat, extremely thin imaging devices by replacing the lens in a conventional camera with an amplitude mask and computational reconstruction algorithms. These lensless cameras, called FlatCams can be less than a millimeter in thickness and enable applications where size, weight, thickness or cost are the driving factors. Second, we discuss high-resolution, long-distance imaging using Fourier Ptychography, where the need for a large aperture aberration corrected lens is replaced by a camera array and associated phase retrieval algorithms resulting again in order of magnitude reductions in size, weight and cost. Finally, I will spend a few minutes discussing how the wholistic computational imaging approach can be used to create ultra-high-resolution wavefront sensors.

Registration is required to attend. The talks will be presented via Zoom, and you will receive a Zoom meeting URL when you register for the presentation.

Optical computational imaging seeks enhanced performance and new functionality by the joint design of illumination, unconventional optics, detectors, and reconstruction algorithms. Among the emergent approaches in this field, two remarkable examples enable overcoming the diffraction limit and imaging through complex media.
Abbe's resolution limit has been overcome enabling unprecedented opportunities for optical imaging at the nanoscale. Fluorescence imaging using photoactivatable or photoswitchable molecules within computational optical systems offers single molecule sensitivity within a wide field of view. The advent of three-dimensional point spread function engineering associated with optimal reconstruction algorithms provides a unique approach to further increase resolution in three dimensions.
Focusing and imaging through strongly scattering media has also been accomplished recently in the optical regime. By using a feedback system and optical modulation, the resulting wavefronts overcome the effects of multiple scattering upon propagation through the medium. Phase-control holographic techniques help characterize scattering media at high-speed using micro-electro-mechanical technology, allowing focusing through a temporally dynamic, strongly scattering sample, or a multimode fiber. In this talk we will further discuss implications for ultrathin optical endoscopy and adaptive nonlinear wavefront shaping.

Registration is required to attend. The talks will be presented via Zoom, and you will receive a Zoom meeting URL when you register for the presentation.

Skydio is the leading US drone company and the world leader in autonomous flight. Our drones are used for everything from capturing amazing video, to inspecting bridges, to tracking progress on construction sites.

At the core of our products is a vision-based autonomy system with seven years of development at Skydio, drawing on decades of academic research. This system pushes the state of the art in deep learning, geometric computer vision, motion planning, and control with a particular focus on real-world robustness.

Drones encounter extreme visual scenarios not typically considered by academia nor encountered by cars, ground robots, or AR applications. They are commonly flown in scenes with few or no semantic priors and must deftly navigate thin objects, extreme lighting, camera artifacts, motion blur, textureless surfaces, and water. These challenges are daunting for classical vision because photometric signals are simply not consistent, and for learning-based methods because there is no ground truth for direct supervision of deep networks. In this talk we'll take a detailed look at our approaches to these problems.

We will also discuss new capabilities on top of our core navigation engine to autonomously map complex scenes and build high quality digital twins, by performing real-time 3D reconstruction across multiple flights. Our vision-based 3D Scan approach allows anyone to build millimeter-scale maps of the world.

Registration is required to attend. The talks will be presented via Zoom, and you will receive a Zoom meeting URL when you register for the presentation.

Imagine a futuristic version of Google Street View that could dial up any possible place in the world, at any possible time. Effectively, such a service would be a recording of the plenoptic function—the hypothetical function described by Adelson and Bergen that captures all light rays passing through space at all times. While the plenoptic function is completely impractical to capture in its totality, every photo ever taken represents a sample of this function. I will present recent methods we've developed to reconstruct the plenoptic function from sparse space-time samples of photos—including Street View itself, as well as tourist photos of famous landmarks. The results of this work include the ability to take a single photo and synthesize a full dawn-to-dusk timelapse video, as well as compelling 4D view synthesis capabilities where a scene can simultaneously be explored in space and time.

Registration is required to attend. The talks will be presented via Zoom, and you will receive a Zoom meeting URL when you register for the presentation.

Holographic displays promise unprecedented capabilities for direct-view displays as well as virtual and augmented reality applications. However, one of the biggest challenges for computer-generated holography (CGH) is the fundamental tradeoff between algorithm runtime and achieved image quality. Moreover, the image quality achieved by most holographic displays is low, due to the mismatch between the optical wave propagation of the display and its simulated model. We develop an algorithmic CGH framework that achieves unprecedented image fidelity and real-time framerates. Our framework comprises several parts, including a novel camera-in-the-loop optimization strategy that allows us to either optimize a hologram directly or train an interpretable model of the optical wave propagation and a neural network architecture that represents the first CGH algorithm capable of generating full-color high-quality holographic images at FHD resolution in real-time. Based on this framework, we further propose a holographic display architecture using two SLMs, where the camera-in-the-loop optimization with an automated calibration procedure is applied. As such, both diffracted and undiffracted light on the target plane are acquired to update hologram patterns on SLMs simultaneously. The experimental demonstration delivers higher contrast and less noisy holographic images without the need for extra filtering, compared to conventional single SLM-based systems. In summary, we envision that bringing artificial intelligence advances into conventional optics/photonics research opens many opportunities to both communities and is promising to enable high fidelity imaging and display solutions.

Registration is required to attend. The talks will be presented via Zoom, and you will receive a Zoom meeting URL when you register for the presentation.

There is a growing need in biological, medical, and materials imaging research to recover information lost during data acquisition. There are currently two distinct viewpoints on addressing such information loss: model-based and learning-based. Model-based methods leverage analytical signal properties (such as sparsity) and often come with theoretical guarantees and insights. Learning-based methods leverage flexible representations (such as convolutional neural nets) for best empirical performance through training on big datasets. The goal of this talk is to introduce a Regularization by Artifact Removal (RARE) framework that reconciles both viewpoints by providing the "deep learning prior" counterpart of the classical regularized inversion. This is achieved by specifying "artifact-removing deep neural nets" as a mechanism to infuse learned priors into recovery problems, while maintaining a clear separation between the prior and physics-based acquisition models. Our methodology can fully leverage the flexibility offered by deep learning by designing learned prior to be used within our new family of fast iterative algorithms. Yet, our results indicate that the such algorithms can achieve state-of-the-art performance in different computational imaging tasks, while also being amenable to rigorous theoretical analysis. We will focus on the application of the methodology to the problem to various biomedical imaging modalities, such as magnetic resonance imaging and intensity diffraction tomography.

Registration is required to attend. The talks will be presented via Zoom, and you will receive a Zoom meeting URL when you register for the presentation.