Graduate

The 2018 Robert Hofstadter Memorial Lecture: The Dawn of Gravitational-Wave Astrophysics

Topic: 
The Dawn of Gravitational-Wave Astrophysics
Abstract / Description: 

In the past two years the gravitational-wave detections enabled by the LIGO detectors have launched a new field in observational astronomy allowing us to study compact object mergers involving pairs of black holes and neutron stars. I will discuss what current results reveal about compact object astrophysics, from binary black hole formation to short gamma-ray bursts and nuclear matter physics. I will also highlight what we can expect in the near future as detectors' sensitivity improves and multi-messenger astronomy further advances.

Date and Time: 
Tuesday, April 3, 2018 - 4:30pm
Venue: 
Hewlett 201

The 2018 Robert Hofstadter Memorial Lecture: Cosmic Collisions Reveal Einstein's Gravitational-Wave Universe

Topic: 
Cosmic Collisions Reveal Einstein's Gravitational-Wave Universe
Abstract / Description: 

For the first time, scientists have observed ripples in the fabric of spacetime called gravitational waves, arriving at the earth from a cataclysmic event in the distant universe. This confirms a major prediction of Albert Einstein's 1915 general theory of relativity and opens an unprecedented new window onto the cosmos. Gravitational waves carry unique information about their dramatic origins and about the nature of gravity that cannot otherwise be obtained. Detected gravitational waves were produced during the final fraction of a second of the mergers of two black holes but also during the last hundred seconds of the collision of two neutron stars. The latter is the first ever cosmic event to be observed both in gravitational waves and in electromagnetic waves, shedding light on several long-standing puzzles, like the production of gold in nature and the physics origins of brief gamma-ray flashes. I will review the beginnings of this exciting field of cosmic exploration and the unprecedented technology and engineering that made it possible.

Date and Time: 
Monday, April 2, 2018 - 7:30pm
Venue: 
Hewlett 200

Applied Physics/Physics Colloquium: Topological Quantum Chemistry

Topic: 
Topological Quantum Chemistry
Abstract / Description: 

The past decade has seen tremendous success in predicting and experimentally discovering distinct classes of topological insulators (TIs) and semimetals. We review the field and we propose an electronic band theory that highlights the link between topology and local chemical bonding, and combines this with the conventional band theory of electrons. Topological Quantum Chemistry is a description of the universal global properties of all possible band structures and materials, comprised of a graph theoretical description of momentum space and a dual group theoretical description in real space. We classify the possible band structures for all 230 crystal symmetry groups that arise from local atomic orbitals, and show which are topologically nontrivial. We show how our topological band theory sheds new light on known TIs, and demonstrate the power of our method to predict a plethora of new TIs.

Date and Time: 
Tuesday, February 27, 2018 - 4:15pm
Venue: 
Hewlett 201

IEEE IT Society, Santa Clara Valley presents From Differential Privacy to Generative Adversarial Privacy

Topic: 
From Differential Privacy to Generative Adversarial Privacy
Abstract / Description: 

6:00PM Refreshments and Conversation

6:30PM The explosive growth in connectivity and data collection is accelerating the use of machine learning to guide consumers through a myriad of choices and decisions. While this vision is expected to generate many disruptive businesses and social opportunities, it presents one of the biggest threats to privacy in recent history. In response to this threat, differential privacy (DP) has recently surfaced as a context-free, robust, and mathematically rigorous notion of privacy.
The first part of my talk will focus on understanding the fundamental tradeoff between DP and utility for a variety of unsupervised learning applications. Surprisingly, our results show the universal optimality of a family of extremal privacy mechanisms called staircase mechanisms. While the vast majority of works on DP have focused on using the Laplace mechanism, our results indicate that it is strictly suboptimal and can be replaced by a staircase mechanism to improve utility. Our results also show that the strong privacy guarantees of DP often come at a significant loss in utility.
The second part of my talk is motivated by the following question: can we exploit data statistics to achieve a better privacy-utility tradeoff? To address this question, I will present a novel context-aware notion of privacy called generative adversarial privacy (GAP). GAP leverages recent advancements in generative adversarial networks (GANs) to arrive to a unified framework for data-driven privacy that has deep game-theoretic and information-theoretic roots. I will conclude my talk by showcasing the performance of GAP on real life datasets.

Date and Time: 
Wednesday, February 28, 2018 - 6:00pm
Venue: 
Packard 202

ISL Colloquium: Deep Exploration via Randomized Value Functions

Topic: 
Deep Exploration via Randomized Value Functions
Abstract / Description: 

An important challenge in reinforcement learning concerns how an agent can simultaneously explore and generalize in a reliably efficient manner. It is difficult to claim that one can produce a robust artificial intelligence without tackling this fundamental issue. This talk will present a systematic approach to exploration that induces judicious probing through randomization of value function estimates and operates effectively in tandem with common reinforcement learning algorithms, such as least-squares value iteration and temporal-difference learning, that generalize via parameterized representations of the value function. Theoretical results offer assurances with tabular representations of the value function, and computational results suggest that the approach remains effective with generalizing representations.

Date and Time: 
Thursday, February 22, 2018 - 4:15pm
Venue: 
Packard 101

EE380 Computer Systems Colloquium: Graph Analysis of Russian Twitter Trolls using Neo4j

Topic: 
Graph Analysis of Russian Twitter Trolls using Neo4j
Abstract / Description: 

As part of the US House Intelligence Committee investigation into how Russia may have influenced the 2016 US election, Twitter released the screen names of nearly 3000 Twitter accounts tied to Russia's Internet Research Agency. These accounts were immediately suspended, removing the data from Twitter.com and Twitter's developer API. In this talk, we show how we can reconstruct a subset of the Twitter network of these Russian troll accounts and apply graph analytics to the data using the Neo4j graph database to uncover how these accounts were spreading fake news.

This case study style presentation will show how we collected and munged the data, taking advantage of the flexibility of the property graph. We'll dive into how NLP and graph algorithms like PageRank and community detection can be applied in the context of social media to make sense of the data. We'll show how Cypher, the query language for graphs is used to work with graph data. And we'll show how visualization is used in combination with these algorithms to interpret results of the analysis and to help share the story of the data. No familiarity with graphs or Neo4j is necessary as we'll start with a brief overview of graph databases and Neo4j.

Date and Time: 
Wednesday, February 21, 2018 - 4:30pm
Venue: 
Gates B03

ISL Special Seminar: Computational structure in large-scale neural population recordings: how to find it, and when to believe it

Topic: 
Computational structure in large-scale neural population recordings: how to find it, and when to believe it
Abstract / Description: 

One central challenge in neuroscience is to understand how neural populations represent and produce the remarkable computational abilities of our brains. Indeed, neuroscientists increasingly form scientific hypotheses that can only be studied at the level of the neural population, and exciting new large-scale datasets have followed. Capitalizing on this trend, however, requires two major efforts from applied statistical and machine learning researchers: (i) methods for finding structure in this data, and (ii) methods for statistically validating that structure. First, I will review our work that has used factor modeling and dynamical systems to advance understanding of the computational structure in the motor cortex of primates and rodents. Second, while these methods and the broader class of such methods are promising, they are also perilous: novel analysis techniques do not always consider the possibility that their results are an expected consequence of some simpler, already-known feature of the data. I will present two works that address this growing problem, the first of which derives a tensor-variate maximum entropy distribution with user-specified moment constraints along each mode. This distribution forms the basis of a statistical hypothesis test, and I will use this test to answer two active debates in the neuroscience community over the triviality of structure in the motor and prefrontal cortices. I will then discuss how to extend this maximum entropy formulation to arbitrary constraints using deep neural network architectures in the flavor of implicit generative modeling.

Date and Time: 
Thursday, February 15, 2018 - 10:00am
Venue: 
Munzer Auditorium

SystemX BONUS! Seminar: Soft Switching Inverters with Wide-Bandgap Devices

Topic: 
Soft Switching Inverters with Wide-Bandgap Devices
Abstract / Description: 

Soft switching has been successfully applied in switching supplies, single-phase inverter for induction heating etc. However, applications of soft switching to three-phase inverters or converters are not so common up to now. Three-phase converters/inverters are widely used in Data Center, UPS, fast EV chargers, PV/Wind power inverter, and drives. In this presentation soft switching inverters with Zero-Voltage-Switching SVM scheme(ZVS-SVM) is introduced. The ZVS-SVM can be used either three-Phase AC/DC converters or inverters and realize zero voltage switching for all switches including both inverter bridges switches and the auxiliary switch for three-phase inverters. Then impact of SiC device on soft switching inverters is investigated with respect to the power density and conversion efficiency. Finally experimental results of a soft-switching 20 kW SiC MOSFET grid inverter with 300kHz switching frequency is introduced.

Date and Time: 
Tuesday, February 20, 2018 - 4:30pm
Venue: 
Packard 204

KIPAC Public Lecture: The Universe Continues to Reveal Surprises

Topic: 
The Universe Continues to Reveal Surprises
Abstract / Description: 

Over the past few decades, astronomers have for the first time identified the major constituents of the universe. Unexpectedly, the universe hardly resembles what we thought only a couple of decades ago. The universe is filled with dark matter more abundant than ordinary matter and dark energy that is causing a runaway acceleration. We do not yet have a complete picture of this unexpected universe. Some discrepancies may be hinting at new discoveries to come. New giant telescopes planned for the next decade are likely to reveal more surprises. In her lecture, Professor Freedman will describe these recent advances.

Date and Time: 
Tuesday, February 13, 2018 - 7:30pm
Venue: 
Hewlett 201

CERC Lecture Series: Mihaela van der Schaar

Topic: 
AutoPrognosis: Automating the design of predictive models for clinical risk and prognosis
Abstract / Description: 

CERC Lecture Series Guest:
Mihaela van der Schaar, PhD
MAN Professor, University of Oxford
Faculty Fellow, Alan Turing Institute, London, UK

HER LECTURE TOPIC: "AutoPrognosis: Automating the design of predictive models for clinical risk and prognosis"

Date and Time: 
Wednesday, February 21, 2018 - 9:30am
Venue: 
AllenX 101 Auditorium

Pages

Subscribe to RSS - Graduate