[PDL/SDI/ISTC] Derek Murray (Google)

Speaker:

Derek Murray

TensorFlow is an open-source machine learning system, originally developed by the Google Brain team, which operates at large scale and in heterogeneous environments. TensorFlow trains and executes a variety of machine learning models at Google, including deep neural networks for image recognition and machine translation. The system uses dataflow graphs to represent stateful computations, and achieves high performance by mapping... Read More

[DB Seminar] Spring 2017: Prashanth Menon

Speaker:

Prashanth Menon

In-memory database management systems (DBMSs) are a key component of modern on-line analytic processing (OLAP) applications, since they provide low-latency access to large volumes of data. Because disk accesses are no longer the principle bottleneck in such systems, the focus in designing query execution engines has shifted to optimizing CPU performance.  Recent systems have revived an older technique of using... Read More

[DB Seminar] Spring 2017: Viktor Leis

Speaker:

Viktor Leis

Managing data sets that are larger than RAM has always been one of the most important tasks for database systems. Traditional systems cache fixed-size pages in an in-memory buffer pool that has complete knowledge of all page accesses and transparently loads/evicts pages from/to disk. While this approach is effective at minimizing the number of I/O operations, it is also one... Read More

Dan Ports (University of Washington)

Speaker:

Dan Ports

Today's most popular applications are deployed as massive-scale distributed systems in the datacenter. Keeping data consistent and available despite server failures and concurrent updates is a formidable challenge. Two well-known abstractions, strongly consistent replication and serializable transactions, can free developers from these challenges by transparently masking failures and treating complex updates as atomic units. Yet the conventional wisdom is that... Read More

[MLD Seminar] Jure Leskovec (Stanford University)

Speaker:

Jure Leskovec

Evaluating whether machines improve on human performance is one of the central questions of machine learning. However, there are many domains where the data is selectively labeled in the sense that the observed outcomes are themselves a consequence of the existing choices of the human decision-makers. For instance, in the context of judicial bail decisions, the outcome of whether a... Read More

[HCII Seminar] Michael Franklin (University of Chicago)

Speaker:

Mike Franklin

The “P“ in AMPLab stands for "People" and an important research thrust in the lab was on integrating human processing into analytics pipelines. Starting with the CrowdDB project on human-powered query answering and continuing into the more recent SampleClean and AMPCrowd/Clamshell projects, we have been investigating ways to maximize the benefit that can be obtained through involving people in data... Read More

[DB Seminar] Spring 2017: Alex Poms

Speaker:

Alex Poms

A growing number of visual computing applications depend on the analysis of large video collections. The challenge is that scaling applications to operate on these datasets requires highly efficient systems for pixel data access and parallel processing. Few programmers have the capability to operate efficiently at these scales, limiting the field's ability to explore new applications that analyze large video... Read More

[DB Seminar] Spring 2017: Xiangyao Yu

Speaker:

Xiangyao Yu

Strong consistency in parallel systems provides high programmability, but requires expensive coordination and scales poorly. This challenge exists in multiple layers of abstraction across the whole hardware and software stack. Examples include multicore processors, parallel transaction processing, and distributed systems. In this talk, I will introduce a simple primitive called logical leases to achieve strong consistency while maintaining good scalability... Read More

[DB Seminar] Spring 2017: Huanchen Zhang

Speaker:

Huanchen Zhang

Succinct data structures are those that require, asymptotically, only the minimum number of bits required by information theory, while still answering queries efficiently. Despite the importance of space efficiency, particularly for today’s massive-scale data services, succinct data structures remain primarily of theoretical interest outside of a few application areas. Our goal in this paper is to make succinct tries practical... Read More

[DB Seminar] Spring 2017: Round table discussion

We will have a round table discussion. Read More