I am currently a freelance computer scientist teaching computer science teachers in northern Germany and developing software for different companies. See my CV for a summary of what I did previously.
I maintain Haskell libraries for
See my github page for a complete list of my open source projects.
Program Commitee member of the 22nd International Workshop on Functional and (Constraint) Logic Programming, WFLP 2013 in Kiel, Germany
Program Commitee member of the 21st International Workshop on Functional and (Constraint) Logic Programming, WFLP 2012 in Nagoya, Japan
Program Commitee member of the Eleventh International Symposium on Functional and Logic Programming, FLOPS 2012 in Kobe, Japan
Program Commitee member of the ACM SIGPLAN 2012 Workshop on Partial Evaluation and Program Manipulation, PEPM 2012 in Philadelphia, Pennsylvania, USA
Program Commitee member of the 20th International Workshop on Functional and (Constraint) Logic Programming, WFLP 2011 in Odense, Denmark
presented at MPC’15 in Königswinter, Germany (June 2015) [slides]
A lens is an optical device which refracts light. Properly adjusted, it can be used to project sharp images of objects onto a screen; a principle underlying photography as well as human vision. Striving for clarity, we shift our focus to lenses as abstractions for bidirectional programming. By means of standard mathematical terminology as well as intuitive properties of bidirectional programs, we observe different ways to characterize lenses and show exactly how their laws interact. Like proper adjustment of optical lenses is essential for taking clear pictures, proper organization of lens laws is essential for forming a clear picture of different lens classes. Incidentally, the process of understanding bidirectional lenses clearly is quite similar to the process of taking a good picture.
By showing that it is exactly the backward computation which defines lenses of a certain standard class, we provide an unusual perspective, as contemporary research tends to focus on the forward computation.
presented at University of Bonn, Germany (January 2013) [slides, programs]
I have presented the functional-logic programming language Curry to students at the University of Bonn. My slides are accompanied by Curry programs that we discussed (and partially wrote) during my lecture. The programs archive linked above contains the following Literate Curry files:
Mountains.lcurry solves a problem from a German computer science competition that the students have already been familiar with.
Evaluator.lcurry defines a type for arithmetic expressions which is used to demonstrate how to use Curry’s logic features to implement simplification in an elegant way.
Parser.lcurry defines parser combinators using Curry’s built-in nondeterminism.
ExprParser.lcurry uses the defined parser combinators to parse expressions from strings and generate strings for expressions that evaluate to a certain number.
Bridge.lcurry solves a logic puzzle that we discussed over lunch.
presented at National Institute of Informatics in Tokyo, Japan (November 2012) [slides]
Bidirectional Transformations (BX), programs that come with an automatic way to reconstruct their input based on their output, are gaining interest in the programming language community. This community has developed laws to accompany BX for programmers to reason about transformations without knowledge of the underlying automatisms. These laws capture intuitions about BX but lack theoretical background beyond such intuitions.
We aim at developing a systematic foundation for BX by relating it to existing mathematical concepts. We investigate the connections between existing laws for BX and standard mathematical concepts, such as injectivity and surjectivity. We find that a certain class of BX is uniquely determined by their backwards transformations which suggests to let programmers specify BX by defining backwards transformations and getting forward transformations for free.
presented at National Institute of Informatics in Tokyo, Japan (March 2012) [slides]
Divide-and-Conquer is an important algorithm design paradigm where a problem is divided into sub-problems that can be conquered individually. Recent opportunities to parallelize computations draw new interest to this paradigm as individual sub-problems can be solved in parallel. For example, the MapReduce framework allows to distribute massively parallel computations based on associative combining operations that leave the necessary freedom to be executed in parallel.
Unfortunately, associative combining operations are often non-trivial and there is little support helping programmers to define them. I will present a method that facilitates the implementation of parallel algorithms in divide-and-conquer style by helping programmers to define complex associative combining operations based on more intuitive specifications in generate-test-and-aggregate style. The method has firm algebraic roots and functional programming will be helpful in explaining it based on program calculation.
presented at University of Tsukuba, Japan (March 2012) [slides]
Functional Logic Programming (FLP) combines features from functional programming such as first-class functions and algebraic datatypes with features from logic programming such as unbound logic variables and nondeterministic search. I will introduce lazy (call-by-need) FLP using the Curry language and review different ways to encapsulate search, that is, to access different results of nondeterministic computations deterministically. The interaction of laziness and encapsulated search is an old problem with some new solutions in the FLP community. By talking about both the problem and some existing solutions, I hope to motivate discussion on the topic from the perspective of control operators in the presence of call-by-need.
presented at Chalmers University of Technology in Gothenburg, Sweden (November 2011) [slides]
MapReduce is a popular and successful framework to implement massively parallel algorithms. It can be used to distribute the execution of data intensive tasks and hide implementation details of parallelization and data distribution. However, it is often difficult to define MapReduce programs because realistic problems do not match its simple divide and conquer form. Although many case studies are being published that show non-trivial applications of MapReduce, no generalized theory has been developed that captures underlying common ideas.
I will present a framework for systematic parallel programming with MapReduce that generalizes existing implementation ideas for parallel algorithms and is applicable to a wide class of search problems. Parallel algorithms can be specified as generate-and-test problems combined with result aggregation and we provide two theorems that allow to implement such specifications efficiently using MapReduce. Our approach brings MapReduce programming, which is inspired by the map and reduce primitives available in many functional languages, back to its roots by providing a calculation-based framework for program development. It makes expert knowledge applicable for a broader group of programmers by automatically bridging the gap between intuitive specifications and efficient implementations.