Graduate

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Negative capacitance has been proposed to overcome the fundamental limits of power dissipation in nanoscale transistors by using ferroelectric materials. This idea was based on the Landau theory of phase transitions, which was first applied to ferroelectrics in 1945. Landau theory predicts an S-shaped relationship between the polarization and the electric field in a ferroelectric, which was thought to be inaccessible by experiment until now. In this talk, it will be shown how the intrinsic S-shaped polarization-electric field curve can be electrically measured in hafnium zirconium oxide, which is the most promising ferroelectric material for prospective applications in computation.

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One of the main obstacles in the development of effective algorithms for inference and learning from discrete time series data, is the difficulty encountered in the identification of useful temporal structure. We will discuss a class of novel methodological tools for effective Bayesian inference and model selection for general discrete time series, which offer promising results on both small and big data. Our starting point is the development of a rich class of Bayesian hierarchical models for variable-memory Markov chains. The particular prior structure we adopt makes it possible to design effective, linear-time algorithms that can compute most of the important features of the resulting posterior and predictive distributions without resorting to MCMC. We have applied the resulting tools to numerous application-specific tasks, including on-line prediction, segmentation, classification, anomaly detection, entropy estimation, and causality testing, on data sets from different areas of application, including data compression, neuroscience, finance, genetics, and animal communication. Results on both simulated and real data will be presented.

Privacy is the problem of ensuring limited leakage of information about sensitive features while sharing information (utility) about non-private features to legitimate data users. Even as differential privacy has emerged as a strong desideratum for privacy, there is a need for varied yet rigorous approaches for applications with different requirements. This talk presents an information-theoretic approach and takes a holistic view focusing on leakage measures, design of privacy mechanisms, and verifiable implementations using generative adversarial models. Specifically, we introduce maximal alpha leakage as a new class of adversarially motivated tunable leakage measures that quantifies the maximal gain of an adversary in refining a tilted belief of any (potentially random) function of a dataset conditioned on a disclosed dataset. The choice of alpha determines the specific adversarial action ranging from refining a belief for alpha = 1 to guessing the best posterior for alpha = ∞, and for these extremal values this measure simplifies to mutual information (MI) and maximal leakage (MaxL), respectively. The problem of guaranteeing privacy can then be viewed as one of designing a randomizing mechanism that minimizes alpha leakage subject to utility constraints. We then present bounds on the robustness of privacy guarantees that can be made when designing mechanisms from a finite number of samples. Finally, we focus on a data-driven approach, generative adversarial privacy (GAP), to design privacy mechanisms using neural networks. GAP is modeled as a constrained minimax game between a privatizer (intent on publishing a utility-guaranteeing learning representation that limits leakage of the sensitive features) and an adversary (intent on learning the sensitive features). We demonstrate the performance of GAP on multi-dimensional Gaussian mixture models and the GENKI dataset. Time permitting, we will briefly discuss the learning-theoretic underpinnings of GAP as well as connections to the problem of algorithmic fairness.

This work is a result of multiple collaborations: (a) maximal alpha leakage with J. Liao (ASU), O. Kosut (ASU), and F. P. Calmon (Harvard); (b) robust mechanism design with M. Diaz (ASU), H. Wang (Harvard), and F. P. Calmon (Harvard); and (c) GAP with C. Huang (ASU), P. Kairouz (Google), X. Chen (Stanford), and R. Rajagopal (Stanford).

The distance between the real and the digital is clearest at the interface layer. The ways that our bodies interact with the physical world are rich and elaborate while digital interactions are far more limited. Through an increased level of direct and intuitive interaction, my work aims to raise computing devices from external systems that require deliberate usage to those that are truly an extension of us, advancing both the state of research and human ability. My approach is to use the entire body for input and output, to allow for implicit and natural interactions. I call my concept "perceptual engineering," i.e., a method to alter the user's perception (or more specifically the input signals to their perception) and manipulate it in subtle ways. For example, modifying a user's sense of space, place, balance and orientation or manipulating their visual attention, all without the user's explicit input, and in order to assist or guide their interactive experience in an effortless way.

I build devices and immersive systems that explore the use of cognitive illusions to manage attention, physiological signals for interaction, deep learning for automatic VR generation, embodiment for remote collaborative learning, tangible interaction for augmenting play, haptics for enhancing immersion, and vestibular stimulation to mitigate motion sickness in VR. My "perceptual engineering" approach has been shown to, (1) support implicit and natural interactions with haptic feedback, (2) induce believable physical sensations of motion in VR, (3) provide a novel way to communicate with the user through proprioception and kinesthesia, and (4) serve as a platform to question the boundaries of our sense of agency and trust. For decades, interaction design has been driven to answer the question: how can new technologies allow users to interact with digital content in the most natural way? If we look at the evolution of computing over the last 50 years, interaction has gone from punch cards to mouse and keyboard to touch and voice. Similarly, devices have become smaller and closer to the user's body. With every transition, the things people can do have become more personal. The main question that drives my research is: what is the next logical step?

Modern technological developments have enabled the acquisition and storage of increasingly large-scale, high-resolution, and high-dimensional data in many fields. Yet in domains such as biomedical data, the complexity of these datasets and the unavailability of ground truth pose significant challenges for data analysis and modeling. In this talk, I present new unsupervised spectral approaches for extracting structure from large-scale high-dimensional data. By looking deep within the spectrum of the graph-Laplacian, we define a new robust measure, the Spectral Embedding Norm, to separate clusters from background, and demonstrate its application to both outlier detection and data visualization. This measure further motivates a new greedy clustering approach based on Local Spectral Viewpoints for identifying high-dimensional overlapping clusters while disregarding noisy clutter. We demonstrate our approach on two-photon calcium imaging data, successfully extracting hundreds of individual cells. Finally, to address the computational complexity of applying spectral approaches to large-scale data, we present a new randomized near-neighbor graph construction. Compared to the traditional k-nearest-neighbors graph, using our near-neighbor graph for spectral clustering on datasets of a few million points is two orders of magnitude faster, while achieving similar clustering accuracy.

In many practical parameter estimation problems, such as medical experiments and cognitive radio communications, parameter selection is performed prior to estimation. The selection process has a major impact on subsequent estimation by introducing a selection bias and creating coupling between decoupled parameters. As a result, classical estimation theory may be inappropriate and inaccurate and a new methodology is needed. In this study, the problem of estimating a preselected unknown deterministic parameter, chosen from a parameter set based on a predetermined data-based selection rule, Ψ, is considered. In this talk, I will present a general non-Bayesian estimation theory for estimation after parameter selection, includes estimation methods, performance analysis, and adaptive sampling strategies. The new theory is based on the post-selection mean-square-error (PSMSE) criterion as a performance measure instead of the commonly used mean-square-error (MSE). We derive the corresponding Cramér-Rao-type bound on the PSMSE of any Ψ-unbiased estimator, where the Ψ -unbiasedness is in the Lehmann-unbiasedness sense. Then, the post-selection maximum-likelihood (PSML) estimator is presented and its Ψ–efficiency properties are demonstrated. Practical implementations of the PSML estimator are proposed as well. As time permits, I will discuss the similar ideas that can be applied to estimation after model selection and to estimation in Good-Turing models.

In this talk I will present planning and decision-making techniques for safely and efficiently maneuvering autonomous aerospace vehicles during proximity operations, manipulation tasks, and surface locomotion. I will first address the "spacecraft motion planning problem," by discussing its unique aspects and presenting recent results on planning under uncertainty via Monte Carlo sampling. I will then turn the discussion to higher-level decision making; in particular, I will discuss an axiomatic theory of risk and how one can leverage such a theory for a principled and tractable inclusion of risk-awareness in robotic decision making, in the context of Markov decision processes and reinforcement learning. Throughout the talk, I will highlight a variety of space-robotic applications my research group is contributing to (including the Mars 2020 and Hedgehog rovers, and the Astrobee free-flying robot), as well as applications to the automotive and UAV domains.

This work is in collaboration with NASA JPL, NASA Ames, NASA Goddard, and MIT.

Leela is a semantic artificially intelligent agent modeled on the theories of Jean Piaget. She builds increasingly abstract semantic models of the world from her experiences of exploration, play, and experimentation. As an agent she is able to formulate, execute, and explain her own plans.

This talk will provide an introduction to Leela's background and design and will show her in action.

About the event:
The Stanford Compression Workshop 2019 will be held on 15 February 2019 at Bechtel Conference Center, Stanford. The Workshop is a gathering of people from academia and industry interested in new and improved ways to model, represent, store, compress, query, process, communicate and protect the data the world is amassing. Please register.

Talks:
The Workshop will include talks in diverse areas, including:

1. Multimedia compression
2. Genomic compression & Privacy
3. Information theory & Learning
4. Humans & Compression
5. Machine learning & Compression
6. Hardware & Compression
7. Entertainment & Compression

Panel:
The workshop will include a panel discussion on "Compression via and for Machine Learning". The panel will include discussions on the emerging connections and interplay between machine learning and data compression with pioneers from this area.

Posters:
The Stanford Compression Workshop 2019 will include a poster session, showcasing recent research in the broad area of data compression and its applications.

More Details:
compression.stanford.edu/2019-stanford-compression-workshop

This talk will provide a general overview and several examples of powerful robots that can kill people – unintentionally. It will then focus on human-transporting vehicles (self-driving cars). There are numerous significant limitations that will restrict your ability to own a self-driving car. Some of these limitations will be discussed in detail. The areas of discussion will include dynamics, customer needs, failure modes, and legal concerns.