PhD studentships in programming languages, provenance, data management

I am currently advertising two PhD studentships, on topics spanning programming languages, provenance, and data management, to start in fall 2016. 

Both of these studentships are fully funded for applicants of any nationality (in contrast to many UK PhD studentships which are only funded up to the UK/EU tuition level, and do not cover full tuition fees for students from outside the European Union.)
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PhD studentship in data-centric programming at LFCS, University of Edinburgh

A fully-funded 3-year PhD studentship has become available in LFCS in data-centric programming.  Applications and expressions of interest are welcome now, with a closing date of April 28, 2014.

This studentship is partly funded by a EU FP7 project (DIACHRON) and partly by a Google Research Award.  The funding includes UK/EU tuition and fees, and a non-taxable stipend of approximately £13,800 per year for 3 years; a small amount of additional funding is available that may be used flexibly for equipment, travel or additional stipend support. 

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PhD opportunities in data-centric programming at LFCS, University of Edinburgh

And now for a word from our sponsors...

I would like to recruit 1-2 PhD students, working on any of the following topics (with further details given at the associated links). 

• Language-based provenance security: Provenance-based security and audit; applications to slicing, failure analysis for system configuration languages; information-flow and provenance-tracking for multi-tier programs; designing new [functional/declarative] languages or dynamic information flow analyses for secure, high-reliability datacenter programming
• Data-centric programming and provenance: Types and language design for integrating multiple data-centric programming models; language-integrated query; extending bidirectional programming for synchronizing data across data models;  language-based techniques for data curation and preservation, provenance tracking, or archiving; query and update techniques for longitudinal or provenance-aware queries.

One position is funded by a Microsoft Research PhD studentship, joint with Paul Anderson and Dimitrios Vytiniotis (MSR-Cambridge).  The funding includes a laptop for the student and there may be additional opportunities for collaboration or internships (at the discretion of MSR).  A second funded position may be available.

Applicants should have a good honors degree or equivalent in computer science, or a related discipline, and ideally familiarity with foundations of programming languages, security, or databases and interests in applications of these foundations to real systems.

Other PhD studentships are also available, including on topics related to types and programming languages.  Please contact me (jcheney@inf.ed.ac.uk) or others in LFCS to discuss alternative project ideas.

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MTCProv: A practical provenance query framework for many-task scientific computing

MTCProv: A practical provenance query framework for many-task scientific computing
L. M. R. Gadelha Jr., M. Wilde, M. Mattoso, I. Foster
Distrib. Parallel Databases (2012) 30:351-370

This article presents a system for storing provenance of runs of scientific computations.  It is fairly high-level, but presents two technical contributions:

1. a detailed description of the schema / data model used, which allows for repeated tasks, annotations describing relevant content/configuration file parameters, linkage between the program graph and run graph, and nesting of tasks (analogous to our hierarchical provenance model
2. a higher-level query language (SPQL) that allows for more direct statements of typical queries over the results

I would have liked to see more about the computational model (Swift scripts/programs) and how these are mapped to the provenance records.  Most workflow systems/languages lack a formal specification of their semantics (with or without provenance).  Similarly, detailed comparison with other proposed (e.g. the Harvard systems group's PQL) would be interesting.