EE380 Computer Systems Colloquium

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

EE380 Computer Systems Colloquium: Tiny functions for codecs, compilation, and (maybe) soon everything

Topic: 
Tiny functions for codecs, compilation, and (maybe) soon everything
Abstract / Description: 

Networks, applications, and media codecs frequently treat one another as strangers. By expressing large systems as compositions of small, pure functions, we've found it's possible to achieve tighter couplings between these components, improving performance without giving up modularity or the ability to debug. I'll discuss our experience with systems that demonstrate this basic idea: ExCamera (NSDI 2017) parallelizes video encoding into thousands of tiny tasks, each handling a fraction of a second of video, much shorter than the interval between key frames, and executing in parallel on AWS Lambda. This was the first system to demonstrate "burst-parallel" thousands-way computation on functions-as-a-service infrastructure. Salsify (NSDI 2018) is a low-latency network video system that uses a purely functional video codec to explore execution paths of the encoder without committing to them, allowing it to closely match the capacity estimates from a video-aware transport protocol. This architecture outperforms more loosely-coupled applications -- Skype, Facetime, Hangouts, WebRTC -- in delay and visual quality, and suggests that while improvements in video codecs may have reached the point of diminishing returns, video systems still have low-hanging fruit. Lepton (NSDI 2017) uses a purely functional JPEG/VP8 transcoder to compress images in parallel across a distributed network filesystem with arbitrary block boundaries. This free-software system is in production at Dropbox and has compressed, by 23%, more than 200 petabytes of user JPEGs.

Based on our experience, we propose a general abstraction for outsourced morsels of computation, called cloud "thunks" -- stateless closures that describe their data dependencies by content-hash. We have created a tool that uses this abstraction to capture off-the-shelf Makefiles and other build systems, letting the user treat a FaaS service like an outsourced build farm with global memoization of results. The bottom line: expressing systems and protocols as compositions of small, pure functions will lead to a new wave of "general-purpose" lambda computing, permitting us to transform many time-consuming operations into large numbers of functions executing with massive parallelism for short durations in the cloud.

Date and Time: 
Wednesday, February 7, 2018 - 4:30pm
Venue: 
Gates 403

EE380 Computer Systems Colloquium: Computational Memory

Topic: 
Computational Memory
Abstract / Description: 

Description TBA


The Stanford EE Computer Systems Colloquium (EE380) meets on Wednesdays 4:30-5:45 throughout the academic year. Talks are given before a live audience in Room B03 in the basement of the Gates Computer Science Building on the Stanford Campus. The live talks (and the videos hosted at Stanford and on YouTube) are open to the public.

Date and Time: 
Wednesday, March 7, 2018 - 4:30pm
Venue: 
Gates B03

EE380 Computer Systems Colloquium: Stopping grinding attacks in proofs of space

Topic: 
Stopping grinding attacks in proofs of space
Abstract / Description: 

The reduced power requirements of proofs of space, which is one of its core features, opens it up to grinding attacks, in which an attacker tries many different possible histories at once and selects the most advantageous one. I'll explain how through extensive use of canonical primitives, the addition of verifiable delay functions, and careful hooking of everything together, it's possible to get grinding attacks under control.


The Stanford EE Computer Systems Colloquium (EE380) meets on Wednesdays 4:30-5:45 throughout the academic year. Talks are given before a live audience in Room B03 in the basement of the Gates Computer Science Building on the Stanford Campus. The live talks (and the videos hosted at Stanford and on YouTube) are open to the public.

Date and Time: 
Wednesday, February 14, 2018 - 4:30pm
Venue: 
Gates B03

EE380 Computer Systems Colloquium: Exploiting modern microarchitectures: Meltdown, Spectre, and other hardware attacks

Topic: 
Exploiting modern microarchitectures: Meltdown, Spectre, and other hardware attacks
Abstract / Description: 

Recently disclosed vulnerabilities against modern high performance computer microarchitectures known as 'Meltdown' and 'Spectre' are among an emerging wave of hardware focused attacks. These include cache side channel exploits against underlying shared resources, which arise as a result of common industry-wide performance optimizations. More broadly, attacks against hardware are entering a new phase of sophistication that will see more in the months ahead. This talk will describe several of these attacks, how they can be mitigated, and generally what we can do as an industry to bring performance without trading security.


The Stanford EE Computer Systems Colloquium (EE380) meets on Wednesdays 4:30-5:45 throughout the academic year. Talks are given before a live audience in Room B03 in the basement of the Gates Computer Science Building on the Stanford Campus. The live talks (and the videos hosted at Stanford and on YouTube) are open to the public.

Date and Time: 
Wednesday, January 31, 2018 - 4:30pm
Venue: 
Gates B03

EE380 Computer Systems Colloquium: Personal BioHacking

Topic: 
Personal BioHacking
Abstract / Description: 

"Cells Are Not Computers and DNA is Not a Programming Language and That's Ok"


 

The Stanford EE Computer Systems Colloquium (EE380) meets on Wednesdays 4:30-5:45 throughout the academic year. Talks are given before a live audience in Room B03 in the basement of the Gates Computer Science Building on the Stanford Campus. The live talks (and the videos hosted at Stanford and on YouTube) are open to the public.

Date and Time: 
Wednesday, January 24, 2018 - 4:30pm
Venue: 
Gates B03

EE380 Computer Systems Colloquium: Combining Physical and Statistical Models in Order to Narrow Uncertainty in Projected of Global Warming

Topic: 
Combining Physical and Statistical Models in Order to Narrow Uncertainty in Projected of Global Warming
Abstract / Description: 

A key question in climate science is How much global warming should we expect for a given increase in the atmospheric concentration of greenhouse gasses like carbon dioxide? One strategy for addressing this question is to run physical models of the global climate system but these models vary in their estimates of future warming by about a factor of two. Our research has attempted to narrow this range of uncertainty around model-projected future warming and to assess whether the upper or lower end of the model range is more likely. We showed that there are strong statistical relationships between how models simulate fundamental features of the Earth's energy budget over the recent past, and how much warming models simulate in the future. Importantly, we find that models that match observations the best over the recent past, tend to simulate more warming in the future than the average model. Thus, statistically combining information from physical models and observations tells us that we should expect more warming (with smaller uncertainty ranges) than we would expect if we were just looking at physical models in isolation and ignoring observations.

Date and Time: 
Wednesday, January 17, 2018 - 4:30pm
Venue: 
Gates B03

Special Seminar: Formal Methods meets Machine Learning: Explorations in Cyber-Physical Systems Design

Topic: 
Formal Methods meets Machine Learning: Explorations in Cyber-Physical Systems Design
Abstract / Description: 

Cyber-physical systems (CPS) are computational systems tightly integrated with physical processes. Examples include modern automobiles, fly-by-wire aircraft, software-controlled medical devices, robots, and many more. In recent times, these systems have exploded in complexity due to the growing amount of software and networking integrated into physical environments via real-time control loops, as well as the growing use of machine learning and artificial intelligence (AI) techniques. At the same time, these systems must be designed with strong verifiable guarantees.

In this talk, I will describe our research explorations at the intersection of machine learning and formal methods that address some of the challenges in CPS design. First, I will describe how machine learning techniques can be blended with formal methods to address challenges in specification, design, and verification of industrial CPS. In particular, I will discuss the use of formal inductive synthesis --- algorithmic synthesis from examples with formal guarantees — for CPS design. Next, I will discuss how formal methods can be used to improve the level of assurance in systems that rely heavily on machine learning, such as autonomous vehicles using deep learning for perception. Both theory and industrial case studies will be discussed, with a special focus on the automotive domain. I will conclude with a brief discussion of the major remaining challenges posed by the use of machine learning and AI in CPS.

Date and Time: 
Monday, December 4, 2017 - 4:00pm
Venue: 
Gates 463A

EE380 Computer Systems Colloquium: Enabling NLP, Machine Learning, and Few-Shot Learning using Associative Processing

Topic: 
Enabling NLP, Machine Learning, and Few-Shot Learning using Associative Processing
Abstract / Description: 

This presentation details a fully programmable, associative, content-based, compute in-memory architecture that changes the concept of computing from serial data processing--where data is moved back and forth between the processor and memory--to massive parallel data processing, compute, and search directly in-place.

This associative processing unit (APU) can be used in many machine learning applications, one-shot/few-shot learning, convolutional neural networks, recommender systems and data mining tasks such as prediction, classification, and clustering.

Additionally, the architecture is well-suited to processing large corpora and can be applied to Question Answering (QA) and various NLP tasks such as language translation. The architecture can embed long documents and compute in-place any type of memory network and answer complex questions in O(1).

Date and Time: 
Wednesday, November 8, 2017 - 4:30pm
Venue: 
Gates B03

EE380 Computer Systems Colloquium: Petascale Deep Learning on a Single Chips

Topic: 
Petascale Deep Learning on a Single Chips
Abstract / Description: 

Vathys.ai is a deep learning startup that has been developing a new deep learning processor architecture with the goal of massively improved energy efficiency and performance. The architecture is also designed to be highly scalable, amenable to next generation DL models. Although deep learning processors appear to be the "hot topic" of the day in computer architecture, the majority (we argue all) of such designs incorrectly identify the bottleneck as computation and thus neglect the true culprits in inefficiency; data movement and miscellaneous control flow processor overheads. This talk will cover many of the architectural strategies that the Vathys processor uses to reduce data movement and improve efficiency. The talk will also cover some circuit level innovations and will include a quantitative and qualitative comparison to many DL processor designs, including the Google TPU, demonstrating numerical evidence for massive improvements compared to the TPU and other such processors.

ABOUT THE COLLOQUIUM:

See the Colloquium website, http://ee380.stanford.edu, for scheduled speakers, FAQ, and additional information. Stanford and SCPD students can enroll in EE380 for one unit of credit. Anyone is welcome to attend; talks are webcast live and archived for on-demand viewing over the web.

Date and Time: 
Wednesday, December 6, 2017 - 4:30pm
Venue: 
Gates B03

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