Below is the preliminary program for the 2007 Workshop on ML, which is
co-located with ICFP 2007 and will take place on Friday, October 5.
The early registration deadline is this Friday, September 7!
We are pleased to have Didier R=E9my as our invited speaker. In
addition, we are planning to conclude the workshop with a "5-Minute
Madness" session, in which attendees can give brief impromptu talks on
ML-related topics. The proposed theme for this year's madness session
is "Design Ideas for Next-Generation ML", but talks on other topics
are welcome as well.
If you are interested in giving a 5-Minute Madness talk, please send
mail (with a brief mention of what you would like to talk about) to
the program chair at dr...@tt.... I hope to see you at the
workshop!
Derek Dreyer
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ML 2007 Website: http://research.microsoft.com/~crusso/ml2007/
Registration Website:
http://proglang.informatik.uni-freiburg.de/ICFP2007/registration.shtml
Preliminary Program:
8:50-9:00
Welcome/Opening remarks
Derek Dreyer, Program Chair
9:00-10:00
Invited talk: MLF for Everyone (Users, Implementers, and Designers)
Didier R=E9my
10:00-10:30
Coffee Break
10:30-12:30
Efficient ML Type Inference Using Ranked Type Variables
George Kuan and David MacQueen
Status Report: The Manticore Project
Matthew Fluet, Nic Ford, Mike Rainey, John Reppy, Adam Shaw and Yingqi =
Xiao
Status Report: HOT Pickles, and How to Serve Them
Andreas Rossberg, Guido Tack and Leif Kornstaedt
A Persistent Union-Find Data Structure
Sylvain Conchon and Jean-Christophe Filliatre
12:30-14:00
Lunch Break
14:00-15:30
Status Report: Specifying JavaScript with ML
David Herman and Cormac Flanagan
Status Report: Layered Streaming XML Processing with Modules
Tyng-Ruey Chuang and Max Schaefer
Status Report: Marionnet -- How to Implement a Virtual Network
Laboratory in Six Months and Be Happy
Jean-Vincent Loddo and Luca Saiu
15:30-16:00
Coffee Break
16:00-17:00
Generics for the Working ML'er
Vesa Karvonen
Practical Generic Programming in OCaml
Jeremy Yallop
17:00-18:00
5-Minute Madness: Design Ideas for Next-Generation ML
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