In a nutshell
I was born in Mexico City, where I grew up and lived until the age of 25. I practiced rowing for about 9 years, of which about 5 were at international competitive level for Mexico's national team. During that time I started studying Physics at the Faculty of Sciences at UNAM, but later on I decided to change to Computer Science. My bachelor thesis was supervised by Prof. Dr. Jorge Urrutia. I obtained a master degree in Computer Science from Saarland University, in Saarbrücken, Germany, where my thesis was supervised by Prof. Dr. Raimund Seidel, who remained as my Ph.D supervisor. I am currently a Research Associate at the Information Systems Group headed by Prof. Dr. Jens Dittrich at Saarland University.
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In the academics
Education
20072012:Ph.D in Computer Science (Dr.Ing.) at Saarland University under supervision of Prof. Dr. Raimund Seidel. 20062007:Master in Computer Science (M. Sc.) at Saarland University and International MaxPlanck Research School For Computer Science (IMPRS) under supervision of Prof. Dr. Raimund Seidel. 20012005:Bachelor of Computer Science (B. Sc.) at National Autonomous University of Mexico (UNAM) under supervision of Prof. Dr. Jorge Urrutia.Research interests
My current research is mainly focused on developing and engineering (new) algorithms and data structures for (1) indexing, and (2) tuple reconstruction; both for mainmemory databases on multicore systems. This development process ought to, for example, make the algorithms (or implementations) NUMAaware when applicable — rather relevant nowadays, and to profile these in order to detect bottlenecks in performance scalability. Previously, nonetheless, I mostly worked in Combinatorial and Algorithmic Geometry, Algorithm Engineering, Combinatorics, Data Structures, and Parameterized Complexity. In general, I am highly interested in areas having algorithmic flavor.
Publications
 Conferences:
[C9]Victor Alvarez, Felix Martin Schuhknecht, Jens Dittrich, Stefan Richter. Main Memory Adaptive Indexing for Multicore Systems. In Proc. of the 10th International Workshop on Data Management on New Hardware (DaMoN '14), Snowbird, USA, 2014.
Abstract: Adaptive indexing is a concept that considers index creation in databases as a byproduct of query processing; as opposed to traditional full index creation where the indexing effort is performed up front before answering any queries. Adaptive indexing has received a considerable amount of attention, and several algorithms have been proposed over the past few years; including a recent experimental study comparing a large number of existing methods. Until now, however, most adaptive indexing algorithms have been designed singlethreaded, yet with multicore systems already well established, the idea of designing parallel algorithms for adaptive indexing is very natural. In this regard, and to the best of our knowledge, only one parallel algorithm for adaptive indexing has recently appeared in the literature: The parallel version of standard cracking. In this paper we describe three alternative parallel algorithms for adaptive indexing, including a second variant of a parallel standard cracking algorithm. Additionally, we describe a hybrid parallel sorting algorithm, and a NUMAaware method based on sorting. We then thoroughly compare all these algorithms experimentally. Parallel sorting algorithms serve as a realistic baseline for multithreaded adaptive indexing techniques. In total we experimentally compare seven parallel algorithms. The initial set of experiments considered in this paper indicates that our parallel algorithms significantly improve over previously known ones. Our results also suggest that, although adaptive indexing algorithms are a good design choice in singlethreaded environments, the rules change considerably in the parallel case. That is, in future highlyparallel environments, sorting algorithms could be serious alternatives to adaptive indexing.Abstract: We consider the problem of counting straightedge triangulations of a given set $P$ of $n$ points in the plane. Until very recently it was not known whether the exact number of triangulations of $P$ can be computed asymptotically faster than by enumerating all triangulations. We now know that the number of triangulations of $P$ can be computed in $O^{*}(2^{n})$ time, which is less than the lower bound of $\Omega(2.43^{n})$ on the number of triangulations of any point set. In this paper we address the question of whether one can approximately count triangulations in subexponential time. We present an algorithm with subexponential running time and subexponential approximation ratio, that is, if we denote by~$\Lambda$ the output of our algorithm, and by $c^{n}$ the exact number of triangulations of $P$, for some positive constant $c$, we prove that $c^{n}\leq\Lambda\leq c^{n}\cdot 2^{o(n)}$. This is the first algorithm that in subexponential time computes a $(1+o(1))$approximation of the base of the number of triangulations, more precisely, $c\leq\Lambda^{\frac{1}{n}}\leq(1 + o(1))c$. Our algorithm can be adapted to approximately count other crossingfree structures on~$P$, keeping the quality of approximation and running time intact. Our algorithm may be useful in guessing, through experiments, the right constants $c_1$ and $c_2$ such that the number of triangulations of any set of $n$ points is between $c_1^n$ and $c_2^n$. Currently there is a large gap between $c_1$ and $c_2$. We know that $c_1 \geq 2.43$ and $c_2 \leq 30$.
Abstract: We study the following algorithmic problem: given $n$ points within a finite $d$dimensional box, what is the smallest number of extra points that need to be added to ensure that every $d$dimensional unit box is either empty, or contains at least $k$ points. We motivate the problem through an application to data privacy, namely $k$anonymity. We show that minimizing the number of extra points to be added is strongly NPcomplete, but admits a Polynomial Time Approximation Scheme (PTAS). In some sense, this is the best we can hope for, since a Fully Polynomial Time Approximation Scheme (FPTAS) is not possible, unless P=NP.
Abstract: We give an algorithm that determines the number $\mbox{tr}(S)$ of straight line triangulations of a set $S$ of $n$ points in the plane in worst case time $O(n^2 2^n)$. This is the the first algorithm that is provably faster than enumeration, since $\mbox{tr}(S)$ is known to be $\Omega(2.43^n)$ for any set $S$ of $n$ points. Our algorithm requires exponential space. The algorithm generalizes to counting all triangulations of $S$ that are constrained to contain a given set of edges. It can also be used to compute an optimal triangulation of $S$ (unconstrained or constrained) for a reasonably wide class of optimality criteria (that includes e.g. minimum weight triangulations). Finally, the approach can also be used for the random generation of triangulations of $S$ according to the perfect uniform distribution. The algorithm has been implement and is substantially faster than existing methods on a variety of inputs.
Abstract: Let $P$ be a set of $n$ points in the plane. A crossingfree structure on $P$ is a straightedge planar graph with vertex set in $P$. Examples of crossingfree structures include triangulations of $P$, and spanning cycles of $P$, also known as polygonalizations of $P$, among others. There has been a large amount of research trying to bound the number of such structures. In particular, bounding the number of triangulations spanned by $P$ has received considerable attention. It is currently known that every set of $n$ points has at most $O(30^{n})$ and at least $\Omega(2.43^{n})$ triangulations. However, much less is known about the algorithmic problem of counting crossingfree structures of a given set $P$. For example, no algorithm for counting triangulations is known that, on all instances, performs faster than enumerating all triangulations. In this paper we develop a general technique for computing the number of crossingfree structures of an input set $P$. We apply the technique to obtain algorithms for computing the number of triangulations and spanning cycles of $P$. The running time of our algorithms is upper bounded by $n^{O(k)}$, where $k$ is the number of onion layers of $P$. In particular, we show that our algorithm for counting triangulations is not slower than $O(3.1414^{n})$. Given that there are several wellstudied configurations of points with at least $\Omega(3.464^{n})$ triangulations, and some even with $\Omega(8^{n})$ triangulations, our algorithm is the first to asymptotically outperform any enumeration algorithm for such instances. In fact, it is widely believed that any set of $n$ points must have at least $\Omega(3.464^{n})$ triangulations. If this is true, then our algorithm is strictly sublinear in the number of triangulations counted. We also show that our techniques are general enough to solve the restricted triangulation counting problem, which we prove to be $W[2]$hard in the parameter $k$. This implies a "no free lunch" result: In order to be fixedparameter tractable, our general algorithm must rely on additional properties that are specific to the considered class of structures.
Abstract: Let $P\subset\mathbb{R}^{2}$ be a $k$colored set of $n$ points in general position, where $k\geq 2$. A $k$colored quadrangulation of $P$ is a properly colored straightedge plane graph $G$ with vertex set $P$ such that the boundary of the unbounded face of $G$ coincides with the convex hull of $P$ and that each bounded face of $G$ is quadrilateral. It is easy to check that in general not every $k$colored $P$ admits a $k$colored quadrangulation, and hence the use of extra points, for which we can choose the color among the $k$ available colors, is required in order to obtain one. These extra points are known in the literature as Steiner points. In this paper, we show that if $P$ satisfies some condition for the colors of the points in the convex hull, then a $k$colored quadrangulation of $P$ can always be constructed using less than $ \frac{(16 k2) n+7 k2}{39 k6}$ Steiner points. Our upper bound improves the previous known upper bound for $k=3$, and represents the first bounds for $k\geq 4$.
Abstract: Nearest Neighbor Searching, i.e. determining from a set $S$ of $n$ sites in the plane the one that is closest to a given query point $q$, is a classical problem in computational geometry. Fast theoretical solutions are known, e.g. point location in the Voronoi Diagram of $S$, or specialized structures such as socalled Delaunay hierarchies. However, practitioners tend to deem these solutions as too complicated or computationally too costly to be actually useful. Recently in ALENEX 2010 Birn et al. proposed a simple and practical randomized solution. They reported encouraging experimental results and presented a partial performance analysis. They argued that in many cases their method achieves logarithmic expected query time but they also noted that in some cases linear expected query time is incurred. They raised the question whether some variant of their approach can achieve logarithmic expected query time in all cases. The approach of Birn et al. derives its simplicity mostly from the fact that it applies only one simple type of geometric predicate: which one of two sites in $S$ is closer to the query point $q$. In this paper we show that any method for planar nearest neighbor searching that relies just on this one type of geometric predicate can be forced to make at least $n1$ such predicate evaluations during a worst case query.
Abstract: Let $P\subset\mathbb{R}^{2}$ be a set of $n$ points of which $k$ are interior points. Let us call a triangulation $T$ of $P$ even if all its vertices have even degree, and pseudoeven if at least the $k$ interior vertices have even degree. (Pseudo)Even triangulations have one nice property; their vertices can be $3$colored, see here for example. Since one can easily check that for some sets of points, such triangulation do not exist, we show an algorithm that constructs a set $S$ of at most $\left\lfloor\frac{k + 2}{3}\right\rfloor$ Steiner points (extra points) along with a pseudoeven triangulation $T$ of $P\cup S = V(T)$.
Abstract: We study the problem of approximating $\mbox{MST}(P)$, the Euclidean minimum spanning tree of a set $P$ of $n$ points in $[0,1]^d$, by a spanning tree of some subset $Q\subset P$. We show that if the weight of $(P)$ is to be approximated, then in general $Q$ must be large. If the shape of $\mbox{MST}(P)$ is to be approximated, then this is always possible with a small $Q$. More specifically, for any $0<\varepsilon<1$ we prove:
 There are sets $P\subset [0, 1]^{d}$ of arbitrarily large size $n$ with the property that any subset $Q'\subset P$ that admits a spanning tree $T'$ with $\bigl \leftT'\right\left\mbox{MST}(P)\right\bigr < \varepsilon\cdot\left\mbox{MST}(P)\right$ must have size at least $\Omega\left({n}^{1  1/d}\right)$. Here $T$ denotes the weight, i.e. the sum of the edge lengths of tree $T$.
 For any $P\subset [0,1]^d$ of size $n$ there exists a subset $Q\subseteq P$ of size $O\left(1/\varepsilon^{d}\right)$ that admits a spanning tree $T$ that is $\varepsilon$close to $\mbox{MST}(P)$ in terms of Hausdorff distance (which measures shape dissimilarity).
 This set $Q$ and this spanning tree $T$ can be computed in time $O\left(\tau_d(n) + 1/\varepsilon^d\log\left(1/\varepsilon^d\right)\right)$ for any fixed dimension $d$. Here $\tau_d(n)$ denotes the time necessary to compute the minimum spanning tree of $n$ points in $\mathbb{R}^d$, which is known to be $O(n\log n)$ for $d=2$, $O\left((n\log n)^{4/3}\right)$ for $d=3$, and $O\left(n^{22/\left(\lceil d/2\rceil+1\right)+\phi}\right)$, with $\phi>0$ arbitrarily small, for $d>3$, see here.
 Journals:
[J4]Victor Alvarez, Karl Bringmann, Saurabh Ray, Raimund Seidel. Counting Triangulations and other CrossingFree Structures Approximately. Computational Geometry, Theory and Applications. To appear 2014. Special Issue on the 25th Canadian Conference on Computational Geometry (CCCG '13).
Abstract:We consider the problem of counting straightedge triangulations of a given set~$P$ of $n$ points in the plane. Until very recently it was not known whether the exact number of triangulations of $P$ can be computed asymptotically faster than by enumerating all triangulations. We now know that the number of triangulations of $P$ can be computed in $O^{*}(2^{n})$ time, see here, which is less than the lower bound of $\Omega(2.43^{n})$ on the number of triangulations of any point set. In this paper we address the question of whether one can approximately count triangulations in subexponential time. We present an algorithm with subexponential running time and subexponential approximation ratio, that is, denoting by $\Lambda$ the output of our algorithm and by $c^{n}$ the exact number of triangulations of $P$, for some positive constant $c$, we prove that $c^{n}\leq\Lambda\leq c^{n}\cdot 2^{o(n)}$. This is the first algorithm that in subexponential time computes a $(1+o(1))$approximation of the base of the number of triangulations, more precisely, $c\leq\Lambda^{\frac{1}{n}}\leq(1 + o(1))c$. Our algorithm can be adapted to approximately count other crossingfree structures on $P$, keeping the quality of approximation and running time intact. In this paper we show how to do this for matchings and spanning trees.
Abstract: Let $P\subset\mathbb{R}^{2}$ be a set of $n$ points, of which $k$ lie in the interior of the convex hull $\text{CH}(P)$ of $P$. Let us call a triangulation $T$ of $P$ even (odd) if and only if all its vertices have even (odd) degree, and pseudoeven (pseudoodd) if at least the $k$ interior vertices have even (odd) degree. On the one hand, triangulations having all its interior vertices of even degree have one nice property; their vertices can be 3colored, see here for example. On the other hand, odd triangulations have recently found an application in the colored version of the classic "Happy Ending Problem" of Erdős and Szekeres, see here.
In this paper we show that there are sets of points that admit neither pseudoeven nor pseudoodd triangulations. Nevertheless, we show how to construct a set of Steiner points $S = S(P)$ of size at most $\frac{k}{3} + c$, where $c$ is a positive constant, such that a pseudoeven (pseudoodd) triangulation can be constructed on $P\cup S$. Moreover, we also show that even (odd) triangulations can always be constructed using at most $\frac{n}{3} + c$ Steiner points, where again $c$ is a positive constant. Our constructions have the property that every Steiner point lies in the interior of $\text{CH}(P)$.
Abstract: We study the problem of approximating $\mbox{MST}(P)$, the Euclidean minimum spanning tree of a set $P$ of $n$ points in $[0,1]^d$, by a spanning tree of some subset $Q\subset P$. We show that if the weight of $(P)$ is to be approximated, then in general $Q$ must be large. If the shape of $\mbox{MST}(P)$ is to be approximated, then this is always possible with a small $Q$. More specifically, for any $0<\varepsilon<1$ we prove:
 There are sets $P\subset [0, 1]^{d}$ of arbitrarily large size $n$ with the property that any subset $Q'\subset P$ that admits a spanning tree $T'$ with $\bigl \leftT'\right\left\mbox{MST}(P)\right\bigr < \varepsilon\cdot\left\mbox{MST}(P)\right$ must have size at least $\Omega\left({n}^{1  1/d}\right)$. Here $\leftT\right$ denotes the weight, i.e. the sum of the edge lengths of tree $T$.
 For any $P\subset [0,1]^d$ of size $n$ there exists a subset $Q\subseteq P$ of size $O\left(1/\varepsilon^{d}\right)$ that admits a spanning tree $T$ that is $\varepsilon$close to $\mbox{MST}(P)$ in terms of Hausdorff distance (which measures shape dissimilarity).
 This set $Q$ and this spanning tree $T$ can be computed in time $O\left(\tau_{d,p}(n) + 1/\varepsilon^d\log\left(1/\varepsilon^d\right)\right)$ for any fixed dimension $d$. Here $\tau_{d,p}(n)$ denotes the time necessary to compute the minimum weight spanning tree of $n$ points in $\mathbb{R}^d$ under any fixed metric $L_p,\ 1\leq p\leq\infty$, where $\tau_{2,p}(n) = O(n\log n)$, see here, $\tau_{3,2}(n) = O\left((n\log n)^{4/3}\right)$, and $\tau_{d, 2}(n) = O\left(n^{22/\left(\lceil d/2\rceil+1\right)+\phi}\right)$, with $\phi>0$ arbitrarily small, for $d>3$, see here. Also $\tau_{3,1}(n)$ and $\tau_{3,\infty}(n)$ is known to be $O(n\log n)$, see here.
Abstract: Let $P$ be a $k$colored point set in general position, $k\geq 2$. A family of quadrilaterals with disjoint interiors $Q_{1},\ldots, Q_{m}$ is called a quadrangulation of $P$ if $V(Q_{1})\cup\cdots V(Q_{m}) = P$, the edges of all $Q_{i}$ join points with different colors, and $Q_{1}\cup\cdots\cup Q_{m} = Conv(P)$. In general it is easy to see that not all $k$colored point sets admit a quadrangulation; when they do, we call them quadrangulatable. For a point set to be quadrangulatable it must satisfy that its convex hull $Conv(P)$ has an even number of points and that consecutive vertices of $Conv(P)$ receive different colors. This will be assumed from now on. In this paper, we study the following type of questions: Let $P$ be a $k$colored point set. How many Steiner points in the interior of $Conv(P)$ do we need to add to $P$ to make it quadrangulatable? When $k$ = 2, we usually call $P$ a bichromatic point set, and its color classes are usually denoted by $R$ and $B$, i.e. the red and blue elements of $P$. In this paper, we prove that any bichromatic point set $P = R\cup B$ where $R = B = n$ can be made quadrangulatable by adding at most $\left\lfloor\frac{n  1}{3}\right\rfloor + \left\lfloor\frac{n}{2}\right\rfloor + 1$ Steiner points, and that $\frac{m}{3}$ Steiner points are occasionally necessary. To prove the latter, we also show that the convex hull of any monochromatic point set $P$ of $n$ elements can be always partitioned into a set $S = \{S_{1}, \ldots, S_{t}\}$ of starshaped polygons with disjoint interiors, where $V(S_{1})\cup\cdots V(S_{t}) = P$, and $t\leq\left\lfloor\frac{n−1}{3}\right\rfloor + 1$. For $n = 3k$ this bound is tight. Finally, we prove that there are 3colored point sets that cannot be completed to 3quadrangulatable point sets.
 Other manuscripts:
[M4]Victor Alvarez, Felix Martin Schuhknecht, Jens Dittrich, Stefan Richter. Main Memory Adaptive Indexing for Multicore Systems. Computing Research Repository (CoRR), 2014. abs/1404.2034. This is the extended version of [C9].
Abstract: Adaptive indexing is a concept that considers index creation in databases as a byproduct of query processing; as opposed to traditional full index creation where the indexing effort is performed up front before answering any queries. Adaptive indexing has received a considerable amount of attention, and several algorithms have been proposed over the past few years; including a recent experimental study comparing a large number of existing methods. Until now, however, most adaptive indexing algorithms have been designed singlethreaded, yet with multicore systems already well established, the idea of designing parallel algorithms for adaptive indexing is very natural. In this regard only one parallel algorithm for adaptive indexing has recently appeared in the literature: The parallel version of standard cracking. In this paper we describe three alternative parallel algorithms for adaptive indexing, including a second variant of a parallel standard cracking algorithm. Additionally, we describe a hybrid parallel sorting algorithm, and a NUMAaware method based on sorting. We then thoroughly compare all these algorithms experimentally; along a variant of a recently published parallel version of radix sort. Parallel sorting algorithms serve as a realistic baseline for multithreaded adaptive indexing techniques. In total we experimentally compare seven parallel algorithms. Additionally, we extensively profile all considered algorithms. The initial set of experiments considered in this paper indicates that our parallel algorithms significantly improve over previously known ones. Our results suggest that, although adaptive indexing algorithms are a good design choice in singlethreaded environments, the rules change considerably in the parallel case. That is, in future highlyparallel environments, sorting algorithms could be serious alternatives to adaptive indexing.
Abstract: We consider the problem of counting straightedge triangulations of a given set $P$ of $n$ points in the plane. Until very recently it was not known whether the exact number of triangulations of $P$ can be computed asymptotically faster than by enumerating all triangulations. We now know that the number of triangulations of $P$ can be computed in $O^{*}(2^{n})$ time AS13, which is less than the lower bound of $\Omega(2.43^{n})$ on the number of triangulations of any point set SSW11. In this paper we address the question of whether one can approximately count triangulations in subexponential time. We present an algorithm with subexponential running time and subexponential approximation ratio, that is, denoting by $\Lambda$ the output of our algorithm, and by $c^{n}$ the exact number of triangulations of $P$, for some positive constant $c$, we prove that $c^{n}\leq\Lambda\leq c^{n}\cdot 2^{o(n)}$. This is the first algorithm that in subexponential time computes a $(1+o(1))$approximation of the base of the number of triangulations, more precisely, $c\leq\Lambda^{\frac{1}{n}}\leq(1 + o(1))c$. Our algorithm can be adapted to approximately count other crossingfree structures on $P$, keeping the quality of approximation and running time intact. In this paper we show how to do this for matchings and spanning trees.
Abstract: Let $P$ be a set of $n$ points in the plane. A crossingfree structure on $P$ is a straightedge planar graph with vertex set in $P$. Examples of crossingfree structures include triangulations of $P$, and spanning cycles of $P$, also known as polygonalizations of $P$, among others. There has been a large amount of research trying to bound the number of such structures. In particular, bounding the number of triangulations spanned by $P$ has received considerable attention. It is currently known that every set of $n$ points has at most $O(30^{n})$ and at least $\Omega(2.43^{n})$ triangulations. However, much less is known about the algorithmic problem of counting crossingfree structures of a given set $P$. For example, no algorithm for counting triangulations is known that, on all instances, performs faster than enumerating all triangulations.
In this paper we develop a general technique for computing the number of crossingfree structures of an input set $P$. We apply the technique to obtain algorithms for computing the number of triangulations and spanning cycles of $P$. The running time of our algorithms is upper bounded by $n^{O(k)}$, where $k$ is the number of onion layers of $P$. In particular, we show that our algorithm for counting triangulations is not slower than $O(3.1414^{n})$. Given that there are several wellstudied configurations of points with at least $\Omega(3.464^{n})$ triangulations, and some even with $\Omega(8^{n})$ triangulations, our algorithm is the first to asymptotically outperform any enumeration algorithm for such instances. In fact, it is widely believed that any set of $n$ points must have at least $\Omega(3.464^{n})$ triangulations. If this is true, then our algorithm is strictly sublinear in the number of triangulations counted. We show experiments comparing our algorithm for counting triangulations with the algorithm presented here, which is supposed to be very fast in practice.
We also show that our techniques are general enough to solve the restricted triangulation counting problem, which we prove to be $W[2]$hard in the parameter $k$. This implies a "no free lunch" result: In order to be fixedparameter tractable, our general algorithm must rely on additional properties that are specific to the considered class of structures.
Abstract: Let $P\subset\mathbb{R}^{2}$ be a set of $n$ points. In A99 and ARSS03 an algorithm for counting triangulations and pseudotriangulations of $P$, respectively, is shown. Both algorithms are based on the divideandconquer paradigm, and both work by finding substructures on triangulations and pseudotriangulations that allow the problems to be split. These substructures are called triangulation paths for triangulations, or Tpaths for short, and zigzag paths for pseudotriangulations, or PTpaths for short. Those two algorithms have turned out to be very difficult to analyze, to the point that no good analysis of their running time has been presented so far. The interesting thing about those algorithms, besides their simplicity, is that they experimentally indicate that counting can be done significantly faster than enumeration.
In this paper we show two new algorithms, one to compute the number of triangulations of $P$, and one to compute the number of pseudotriangulations of $P$. They are also based on Tpaths and PTpaths respectively, but use the sweep line paradigm and not divideandconquer. The important thing about our algorithms is that they admit a good analysis of their running times. We will show that our algorithms run in time $O^{*}(t(P))$ and $O^{*}(pt(P))$ respectively, where $t(P)$ and $pt(P)$ is the largest number of Tpaths and PTpaths, respectively, that the algorithms encounter during their execution. Moreover, we show that $t(P) = O^{*}(9^{n})$, which is the first nontrivial bound on $t(P)$ to be known.
While the algorithm for counting triangulations of ABCR12 is faster in the worst case, $O^{*}\left(3.1414^{n}\right)$, than our algorithm, $O^{*}\left(9^{n}\right)$, there are sets of points where the number of Tpaths is $O(2^{n})$. In such cases our algorithm may be faster. Furthermore, it is not clear whether the algorithm presented in ABCR12 can be modified to count pseudotriangulations so that its running time remains $O^{*}(c^n)$ for some small constant $c\in\mathbb{R}$. Therefore, for counting pseudotriangulations (and possibly other similar structures) our approach seems better.
Awards
 Best paper award at the 29th Symposium on Computational Geometry (SoCG '13), Rio de Janeiro, Brazil.
Recent teaching support activities
 Current Topics in Big Data Management given by Prof. Dr. Jens Dittrich at Saarland University in winter semester 2013/2014.
 Algorithms and Data Structures given by Prof. Dr. Raimund Seidel at Saarland University in winter semester 2012/2013.
 Das Buch der Beweise (in german) given by Prof. Dr. Raimund Seidel at Saarland University in winter semester 2012/2013.
 Perlen der Theoretischen Informatik (in german) given by Prof. Dr. Raimund Seidel at Saarland University in summer semester 2012.
 Algorithms and Data Structures (Intensive Summer Course) given by Prof. Dr. Raimund Seidel and Dr. Jiong Guo at Saarland University in summer semester 2010.
 Allerlei Algorithmen (in german) given by Prof. Dr. Raimund Seidel at Saarland University in summer semester 2010.
 Probleme in der diskreten kombinatorischen Geometrie (in german) given by Prof. Dr. Raimund Seidel at Saarland University in summer semester 2009.
 Algorithms and Data Structures (Intensive Summer Course) given by Prof. Dr. Raimund Seidel and Prof. Dr. Kurt Mehlhorn at Saarland University in summer semester 2008.
Other professional activities
I have been a reviewer for the following conferences and journals: CCCG, SoCG, VLDB, ICDE, IJCGA.
The rest of the time
I (hopelessly try to) keep a blog.
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