[Haskell] Postdoc position in deep probabilistic programming

[Fritz Henglein]

We have an open postdoc position in deep probabilistic programming (DPP) at
DIKU, University of Copenhagen, with a focus on programming language
technology. The position is for one year and can be extended to two years.
It is open immediately.

To inquire about the position or apply for it, please send your CV, your
research interests, how you believe you can contribute to and benefit from
the research program below, and one relevant research paper you have
authored to Fritz Henglein, henglein at diku.dk.

About DPP

Deep probabilistic programming combines programming with probability
distributions and parameters that can be optimized to match observed
outcomes by automatic differentiation (AD); it is essentially Bayesian
inference plus deep learning in an expressive programming language.

About AD

AD is of particular interest to us in this project. We are exploring
DSL-based linear operator representations for derivatives that share the
benefits of matrices (expressive algebra, easy parallelism-preserving
adjoints for running ‘in reverse’, hardware-supported high-performance
execution on GPUs, etc),  but avoid their prohibitive storage and execution
costs at high dimension, without using so-called ‘jvp’ and ‘vjp’
representations of derivatives expressed in a general-purpose programming
language, which is inherently tricky to analyze and optimize.  This is
where we’ll need your help: Our hypothesis is that AD by aggressive
almost-symbolic differentiation, clever symbolic representation (e.g.
tensor product representation for low-rank matrices) of derivatives,
followed by parallelism-preserving transformation and optimization, and
eventual compilation to a functional high-performance array programming
language such as Futhark can provide execution performance advantages in a
suitably constructed DSL.

About PP

Subsequently we’d like to add probability distributions/random variables as
data types with exploitable algebraic properties to the DSL to implement
efficient (Hamiltonian and Newtonian) probabilistic inference techniques
that minimize the need for sampling and maximize computational performance.


About applications

The present project is driven by multiple applications in protein structure
prediction, where computational efficiency is (still) a major bottleneck.
This is the targeted problem domain for this postdoc position.   The
overall multilinear algebraic programming approach has broader
applications, though, ranging from database query processing (‘vector
spaces’ — actually modules — over Z as generalized relations) via machine
learning (vector spaces over R) to quantum computation (vector spaces over
C).

Required background

Ideally you should have both


   - a good mathematical grounding, specifically in (multi)linear algebra
   and in common vector spaces such as Hilbert spaces, and ideally also in
   probability theory for the probabilistic programming aspects, and
   - practical mastery of functional programming language technology for
   generating high-performance code.

The reason is that the former is not a goal by itself —we aim for very high
performance in practice, the generated code really has to run very fast —
and the latter by itself will make the optimization ideas seem mysterious
since they exploit the former in nonobvious ways.

No particular background in bioinformatics, proteins or such is required.
If you have some, wonderful; but we have plenty of expertise in the team
that you can draw on.

About employment terms

The position can be started as soon as the paperwork is done after an
employment offer has been made and accepted, which may be weeks to months,
depending on your citizenship.  The salary is according to the standard
Danish union contract for academics.  Including mandatory contribution to
your own pension account, it can be expected to be between DKK 40,000 and
DKK 50,000 /month before income taxes.  The actual amount depends on formal
qualifications and seniority.

About the project team, DIKU, UCPH and Copenhagen

The project, deep probabilistic programming for protein structure
prediction, is funded by the Danish Research Council and hosted by the
Programming Language and Theory of Computation (PLTC) section at the
Department of Computer Science (DIKU) at the University of Copenhagen
(UCPH).  It is led by Thomas Hamelryck (Department of Biology and DIKU) and
Fritz Henglein (DIKU) and is performed in collaboration with and in close
proximity to the Futhark high-performance functional programming language
research group.

DIKU is located on the North Campus of UCPH, next to the largest park in
Copenhagen.

Copenhagen is routinely ranked as one of the most livable cities in the
world.

You’ll have the opportunity to gather teaching qualifications during the
second postdoc year and UCPH is usually ranked as one of the better or even
best universities in Europe, both of which can contribute effectively to
your academic career.

Fritz Henglein
fritz at henglein.com (private, fastest), henglein at diku.dk (office, DIKU)
+45-30589576
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