Graduate

SCIEN & EE 292E: Pushing the Limits of Fluorescence Microscopy with adaptive imaging and machine learning

Topic: 
Pushing the Limits of Fluorescence Microscopy with adaptive imaging and machine learning
Abstract / Description: 

Fluorescence microscopy lets biologist see and understand the intricate machinery at the heart of living systems and has led to numerous discoveries. Any technological progress towards improving image quality would extend the range of possible observations and would consequently open up the path to new findings. I will show how modern machine learning and smart robotic microscopes can push the boundaries of observability. One fundamental obstacle in microscopy takes the form of a trade-of between imaging speed, spatial resolution, light exposure, and imaging depth. We have shown that deep learning can circumvent these physical limitations: microscopy images can be restored even if 60-fold fewer photons are used during acquisition, isotropic resolution can be achieved even with a 10-fold under-sampling along the axial direction, and diffraction-limited structures can be resolved at 20-times higher frame-rates compared to state-of-the-art methods. Moreover, I will demonstrate how smart microscopy techniques can achieve the full optical resolution of light-sheet microscopes — instruments capable of capturing the entire developmental arch of an embryo from a single cell to a fully formed motile organism. Our instrument improves spatial resolution and signal strength two to five-fold, recovers cellular and sub-cellular structures in many regions otherwise not resolved, adapts to spatiotemporal dynamics of genetically encoded fluorescent markers and robustly optimises imaging performance during large-scale morphogenetic changes in living organisms.

Date and Time: 
Wednesday, May 16, 2018 - 4:30pm
Venue: 
Packard 101

SCIEN & EE 292E: Advances in automotive image sensors

Topic: 
Advances in automotive image sensors
Abstract / Description: 

In this talk I present recent advances in 2D and 3D image sensors for automotive applications such as rear view cameras, surround view cameras, ADAS cameras and in cabin driver monitoring cameras. This includes developments in high dynamic range image capture, LED flicker mitigation, high frame rate capture, global shutter, near infrared sensitivity and range imaging. I will also describe sensor developments for short range and long range LIDAR systems.

Date and Time: 
Wednesday, May 9, 2018 - 4:30pm
Venue: 
Packard 101

SCIEN & EE 292E: 3D single-molecule super-resolution microscopy using a tilted light sheet

Topic: 
3D single-molecule super-resolution microscopy using a tilted light sheet
Abstract / Description: 

To obtain a complete picture of subcellular structures, cells must be imaged with high resolution in all three dimensions (3D). In this talk, I will present tilted light sheet microscopy with 3D point spread functions (TILT3D), an imaging platform that combines a novel, tilted light sheet illumination strategy with engineered long axial range point spread functions (PSFs) for low-background, 3D super localization of single molecules as well as 3D super-resolution imaging in thick cells. Here the axial positions of the single molecules are encoded in the shape of the PSF rather than in the position or thickness of the light sheet. TILT3D is built upon a standard inverted microscope and has minimal custom parts. The result is simple and flexible 3D super-resolution imaging with tens of nm localization precision throughout thick mammalian cells. We validated TILT3D for 3D super-resolution imaging in mammalian cells by imaging mitochondria and the full nuclear lamina using the double-helix PSF for single-molecule detection and the recently developed Tetrapod PSFs for fiducial bead tracking and live axial drift correction. We think that TILT3D in the future will become an important tool not only for 3D super-resolution imaging, but also for live whole-cell single-particle and single-molecule tracking.

Date and Time: 
Wednesday, May 2, 2018 - 4:30pm
Venue: 
Packard 101

Applied Physics/Physics Colloquium: Large Volume 3D Imaging by FIBSEM and Cryo-Fluorescence for Cell Biology and Neural Circuits

Topic: 
Large Volume 3D Imaging by FIBSEM and Cryo-Fluorescence for Cell Biology and Neural Circuits
Abstract / Description: 

3D Electron microscopy volume data can be acquired by a variety of approaches. Focused Ion beam – scanning electron microscopy, FIBSEM, offers no limitation on section thickness, so that isotropic voxels with 8 nm or less sampling in x,y,z dimensions can be acquired. The FIBSEM, which is normally limited to a couple days of continuous operation, was refined to enable year-long reliable data acquisition needed for the large volumes of neural imaging and the fly brain connectome. Concurrently, this capability opens a new regime where entire cells can be imaged with 4 nm voxel sampling, thereby surpassing partial cell or section limitations to complete cell data. The heavy metal staining for EM contrast gives spatially detailed but generic black and white rendering of protein and membrane defined structures. On the other hand, fluorescence microscopy is highly protein specific, by labeling only a tiny subset (1-3) of the thousands of constituent proteins of the cell. Most 99.9% of the cell remains dark. Correlated cryogenic fluorescence microscopy offers a way to combine both without compromising the quality of either EM or fluorescence image. Fluorescent properties at low temperatures (down to 10K) include new regimes of stable fluorescence with highly reduced bleaching, new blinking regimes, good contrast ratios useable for PALM, nonlinearity to excitation power, and photo-reactivation. Multicolor 3D structured illumination SIM images can be acquired on such samples and 2 color, 3D PALM images offer even higher resolution. Examples of such correlative Cryo SIM/PALM and FIBSEM images will be presented on cultured cells.

Date and Time: 
Tuesday, May 1, 2018 - 4:30pm
Venue: 
Hewlett 201

SCIEN & EE292E seminar: Video Coding before and beyond HEVC

Topic: 
Video Coding before and beyond HEVC
Abstract / Description: 

We are enjoying video contents in various situations. Though they are already compressed down to 1/10 - 1/1000 from its original size, it has been reported that video traffic over the internet is increasing 31% per year, within which the video traffic will occupy 82% by 2020. This is why development of better compression technology is eagerly demanded. ITU-T/ISO/IEC jointly developed the latest video coding standard, High Efficiency Video Coding (HEVC), in 2013. They are about to start next generation standard. Corresponding proposals will be evaluated at April 2018 meeting in San Diego, just a week before this talk.

In this talk, we will first overview the advances of video coding technology in the last several decades, latest topics including the report of the San Diego meeting, some new approaches including deep learning technique etc. will be presented.

Date and Time: 
Wednesday, April 25, 2018 - 4:30pm
Venue: 
Packard 101

ISL Seminar: Inventing Algorithms via Deep Learning

Topic: 
Inventing Algorithms via Deep Learning
Abstract / Description: 

Deep learning is a part of daily life, owing to its successes in computer vision and natural language processing. In these applications, the success of the model-free deep learning approach can be attributed to a lack of (mathematical) generative model. In yet other applications, the data is generated by a simple model and performance criterion mathematically precise and training/test samples infinitely abundant, but the space of algorithmic choices is enormous (example: chess). Deep learning has recently shown strong promise in these problems too (example: alphazero). In this talk, we study two canonical problems of great scientific and engineering interest through the lens of deep learning.

The first is reliable communication over noisy media where we successfully revisit classical open problems in information theory; we show that creatively trained and architected neural networks can beat state of the art on the AWGN channel with noisy feedback by a 100 fold improvement in bit error rate.

The second is optimization and classification problems on graphs, where the key algorithmic challenge is scalable performance to arbitrary sized graphs. Representing graphs as randomized nonlinear dynamical systems via recurrent neural networks, we show that creative adversarial training allows one to train on small size graphs and test on much larger sized graphs (100~1000x) with approximation ratios that rival state of the art on a variety of optimization problems across the complexity theoretic hardness spectrum.

Apart from the obvious practical value, this study of mathematically precise problems sheds light on the mysteries of deep learning methods: training example choices, architectural design decisions and loss function/learning methodologies. Our (mostly) empirical research is conducted under the backdrop of a theoretical research program of understanding gated neural networks (eg: attention networks, GRU, LSTM); we show the first provably (globally) consistent algorithms to recover the parameters of a classical gated neural network architecture: mixture of experts (MoE).

Date and Time: 
Thursday, April 26, 2018 - 4:15pm
Venue: 
Packard 101

Special Seminar: Secure Speculative Execution Processors

Topic: 
Secure Speculative Execution Processors
Abstract / Description: 

Software side channel attacks have become a serious concern with the recent rash of attacks on speculative processor architectures. Most attacks that have been demonstrated exploit the cache tag state as their exfiltration channel. While many existing defense mechanisms that can be implemented solely in software have been proposed, these mechanisms appear to patch specific attacks, and can be circumvented. We propose minimal modifications to hardware to defend against a broad class of attacks, including those based on speculation, with the goal of eliminating the entire attack surface associated with the cache state covert channel.

These modifications are layered on top of the Sanctum secure processor architecture that offers strong provable isolation of software modules running concurrently and sharing resources.

Joint work with Ilia Lebedev, Vladimir Kiriansky, Saman Amarasinghe and Joel Emer.

Date and Time: 
Friday, April 27, 2018 - 4:00pm
Venue: 
Gates 463A

VR for Everyone

Topic: 
Stanford Immersive Media Conference 2018
Abstract / Description: 

Stanford University has an amazing talent pool of student engineers, designers, storytellers, and entrepreneurs. Our theme focuses on inclusion and adoption, both in the market and in the industry, during a year of transition from early adopters to the consumer market. The goal of "VR for Everyone" is to increase exposure, awareness, and involvement in VR/AR for Stanford students and enthusiasts in the Bay Area, while promoting accessibility and diversity in industry. Read more

Date and Time: 
Saturday, May 12, 2018 (All day)
Venue: 
Packard 101

ISL Colloquium: Reinforcement Learning without Reinforcement

Topic: 
Reinforcement Learning without Reinforcement
Abstract / Description: 

Reinforcement Learning (RL) is concerned with solving sequential decision-making problems in the presence of uncertainty. RL is really about two problems together. The first is the 'Bellman problem': Finding the optimal policy given the model, which may involve large state spaces. Various approximate dynamic programming and RL schemes have been developed, but either there are no guarantees, or not universal, or rather slow. In fact, most RL algorithms have become synonymous with stochastic approximation (SA) schemes that are known to be rather slow. This is an even more difficult problem for MDPs with continuous state (and action) spaces. We present a class of non-SA algorithms for reinforcement learning in continuous state space MDP problems based on 'empirical' ideas, which are simple, effective and yet universal with probabilistic guarantees. The idea involves randomized Kernel-based function fitting combined with 'empirical' updates. The key is the first known "probabilistic contraction analysis" method we have developed for analysis of fairly general stochastic iterative algorithms, wherein we show convergence to a probabilistic fixed point of a sequence of random operators via a stochastic dominance argument.

The second RL problem is the 'online learning (or the Lai-Robbins) problem' when the model itself is unknown. We propose a simple posterior sampling-based regret-minimization reinforcement learning algorithm for MDPs. It achieves O(sqrt{T})-regret which is order-optimal. It not only optimally manages the "exploration versus exploitation tradeoff" but also obviates the need for expensive computation for exploration. The algorithm differs from classical adaptive control in its focus on non-asymptotic regret optimality as opposed to asymptotic stability. This seems to resolve a long standing open problem in Reinforcement Learning.

Date and Time: 
Tuesday, April 24, 2018 - 4:00pm
Venue: 
Packard 101

Applied Physics/Physics Colloquium: Ultracold Atom Quantum Simulations: From Exploring Low Temperature Fermi-Hubbard Phases to Many-body Localization

Topic: 
Ultracold Atom Quantum Simulations: From Exploring Low Temperature Fermi-Hubbard Phases to Many-body Localization
Abstract / Description: 

Ultracold-atom model-systems offer a unique way to investigate a wide range of many-body quantum physics in uncharted regimes. Quantum gas microscopy enables us to "zoom in" both, in space and time, on a single particle level. We can explore many-body quantum physics in regimes that are not computationally accessible. In my talk I will present an overview of recent experiments, including the first observation of an anti-ferromagnetic phase of Fermions in an optical lattice, and the observation of many-body localization.

Date and Time: 
Tuesday, April 24, 2018 - 4:30pm
Venue: 
Hewlett 201

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