Graduate

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

MSE Colloquium: van der Waals Integration of Everything: Pushing the Limit of 2D Transistors and Diodes

Topic: 
van der Waals Integration of Everything: Pushing the Limit of 2D Transistors and Diodes
Abstract / Description: 

Semiconductor heterostructures are central for all modern electronic and optoelectronic devices. Traditional semiconductor heterostructures are typically created through a "chemical integration" approach with covalent bonds, and generally limited to the materials with highly similar lattice symmetry and lattice constants (and thus similar electronic structures) due to lattice/processing compatibility requirement. Materials with substantially different structure or lattice parameters can hardly be epitaxially grown together without generating too much defects that could seriously alter their electronic properties. In contrast, van der Waals integration, where pre-formed materials are "physically assembled" together through van der Waals interactions, offers an alternative "low-energy" material integration approach (vs. the more aggressive "chemical integration" strategy). The flexible "physical assembly" approach is not limited to materials that have similar lattice structures or require similar synthetic conditions. It can thus open up vast possibilities for damage-free integration of highly distinct materials beyond the traditional limits posed by lattice matching or process compatibility requirements, as exemplified by the recent blossom in van der Waals integration of a broad range of 2D heterostructures. Here I will discuss van der Waals integration as a general material integration approach beyond 2D materials for creating diverse heterostructures (e.g., dielectric/semiconductor and metal/semiconductor) with minimum integration-induced damage and interface states, enabling high-performing devices (including high speed transistors, diodes, plasmonic devices) difficult to achieve with conventional "chemical integration" approach. A particular highlight is the creation of van der Waals metal/semiconductor contacts free of interfacial disorder and Fermi level pinning, thus for the first time enabling experimental validation of the Schottky-Mott rule first proposed in 1930s.

Date and Time: 
Friday, April 20, 2018 - 3:00pm
Venue: 
McCullough 115

ISL Colloquium: Recent Developments in Compressed Sensing

Topic: 
Recent Developments in Compressed Sensing
Abstract / Description: 

Compressed sensing refers to the reconstruction of high-dimensional but low-complexity objects from a limited number of measurements. Examples include the recovery of high-dimensional but sparse vectors, and the recovery of high-dimensional but low-rank matrices, which includes the so-called partial realization problem in linear control theory. Much of the work to date focuses on probabilistic methods, which are CPU-intensive and have high computational complexity. In contrast, deterministic methods are far faster in execution and more efficient in terms of storage. Moreover, deterministic methods draw from many branches of mathematics, including graph theory and algebraic coding theory. In this talk a brief overview will be given of such recent developments.

Date and Time: 
Thursday, April 19, 2018 - 4:15pm
Venue: 
Packard 101

SmartGrid Seminar: Renewable Scenario Generation Using Adversarial Networks

Topic: 
Renewable Scenario Generation Using Adversarial Networks
Abstract / Description: 

Scenario generation is an important step in the operation and planning of power systems. In this talk, we present a data-driven approach for scenario generation using the popular generative adversarial networks, where to deep neural networks are used in tandem. Compared with existing methods that are often hard to scale or sample from, our method is easy to train, robust, and captures both spatial and temporal patterns in renewable generation. In addition, we show that different conditional information can be embedded in the framework. Because of the feedforward nature of the neural networks, scenarios can be generated extremely efficiently.

Date and Time: 
Thursday, April 19, 2018 - 1:30pm
Venue: 
Y2E2 111

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