Computer Science Internships
for International Students

Process Simulation and Log Generation

Gena is a simple tool that allows to generate event logs by executing process models. It is useful in the testing and evaluation of process mining algorithms. Gena supports two important notations used in business process modelling and process mining: Petri nets and basic BPMN models. However, many improvements can be made in Gena to support more sophisticated simulation scenarios. Interns have an opportunity to join this project.

Process Mining Using Neural Networks

Process mining [1] is a modern research discipline that aims at improving (mostly business) processes on the base of so-called event logs. The event logs are created by information system supporting processes (process-aware information systems, PAIS) as a technical by-product of their life. With that along, event logs are a source of valuable information about the real lifeline of processes. Thus, event logs can be used to extract process models (the discovery task), to check the real behaviour of a process against its prescribed model (the conformance checking task), and, ultimately, to improve processes (the process enhancement task).

By now, there are a large number of algorithms, methods and techniques for extracting and analyzing process models. They are mostly based on pure algorithmic or mixed statistical approaches. However, only a very few works use neural networks-based approaches to process event data. For example, see [2].

The use of neural networks and deep machine learning for solving process mining problems seem to be very productive, especially from the point of view of working with large event logs that one has to deal with in practice. The project is dedicated to elaborate existing process mining algorithms and develop new ones, as well as methods and tools using neural networks.

[1] Wil van der Aalst. Process Mining: Data Science in Action. 2nd ed. Springer, 2016.

[2] T. Shunin, N. Zubkova, and S. Shershakov. Neural Approach to the Discovery Problem in Process Mining. In Proceedings: AIST 2018 Conference, LNCS 11179, pp. 261–273, Springer, 2018.

Universality Classes in Sandpiles

The project deals with self-organized critical systems and the prediction of rare events generated by them. Self-organized criticality (SOC) is prescribed to various observed systems and processes related to physics and economics. The first model with SOC was defined by Bak, Tang, and Wiesenfeld in 1987. They introduced a mechanism (the BTW mechanism) which balances two multi-scale processes: a constant slow loading and a quick stress-release. This mechanism results in power-law probability distributions that characterize the critical state but lacks parameters to fit the model regularities to observations.

The planned contribution of this project is twofold. First, we intend to establish that the BTW mechanism can be designed on a square lattice in such a way that the power-law exponent becomes adjustable. This contribution will extend the current picture of sand-pile models with two universality classes realized in various settings. The extension is based on the idea that several processes of stress propagation should be united into a single event if they occur next to one another in space and time. The second direction of this project pursues the prediction of extremes. We plan to address the prediction problem in sand-piles, exposing the predictability of events that are so large that they occur only in the supercritical state. In other words, extra system loading is required to make the system generate extremes. This natural property underlies predictability. The research has to reconcile general claims regarding unpredictability in sand-piles, which may describe typical events and some examples of efficient predictions related to large events.

Reasoning with Neural Differentiable Machines

Question Answering (QA) and Machine Reasoning (MR) have become a crucial application problem in evaluating the progress of AI systems in the realm of natural language processing and understanding, and to measure the progress of machine intelligence in general. However, most of the advances have focused on “shallow” QA tasks that can be tackled very effectively by existing retrieval-based techniques. Deep learning-based methods achieve human-like performance on benchmark datasets; however, it is suspected that these methods merely learn to match answers to questions or focus attention on specific words and pieces of text and they do not perform real reasoning or cognition.

There are several research projects related to this topic on BSc, MSc, PhD and postdoc levels ranging from testing a scrutinizing recent methods to developing your own method for machine reasoning using augmented machines (RNNs with external memory which is learned to be used by data, also known as neural Turing machines).

All projects are conducted in English, so it’s a good opportunity to improve your communication & presentation skills in English. All projects involve programming.

Learning for Mass Spectrometry Data Identification (Bioinformatics)

Mass spectrometry has become the de facto method to identify molecules in complex mixtures (e.g. blood, cell, food) in many areas. A mass spectrometer analyses a complex mixture of biological or chemical samples and produces tens of thousands of spectra from the input sample. These spectrum data can be considered as fingerprints of the samples and the main computational challenge is to identify the original materials (reverse engineering). Mass spectrometry is used in e.g.: (a) Proteomics to identify proteins in biological samples (e.g. blood), (b) Clinical applications to identify proteins related to cancer or other diseases, (c) Pharmaceutical analysis to determine the effects of new drugs, (d) Environmental contamination analysis to ensure that the air, drinking water, soils, and food are safe to consume and does not contain pollution, heavy metals, hormone, pesticides, and herbicides, (e) Forensic analysis to trace of evidence in arson investigation, drug abuse, and (f) Metabolomics to identify small molecules used by bacteria for communication in the microbiome, etc.

There are several research projects available on BSc, MSc, PhD, and postdoc levels, which focus on the development of deep and machine learning methods to identify mass spectrometry data. You will obtain a good understanding of the aspects and the challenges of computational mass spectrometry and deep learning with non-human readable data.

All projects are conducted in English, so it’s a good opportunity to improve your communication & presentation skills in English. All projects involve programming.

Project 1. Coordinates and singularities of misorientation spaces

A misorientation is a measure of displacement of a crystal lattice relative to another lattice. The space of all misorientations is a two-sided identification space G1\SO(3)/G2 where G1,G2 are point crystallography groups of lattices. It can be seen that misorientation space is a 3-dimensional orbifold. The underlying topological space of this orbifold depends on the crystallography groups. However, surprisingly often this space is homeomorphic to the 3-sphere according to Poincare conjecture. During the internship, you can do one of the two tasks (or both). (1) For particular choices of crystallography groups, construct the convenient coordinates on the corresponding misorientation spaces (in particular, you can try to prove the homeomorphism to 3-sphere, avoiding the reference to Poincare conjecture). (2) As a 3-orbifold each misorientation space has a 3-valent weighted graph as its orbifold locus or singularity. The task is to describe the orbifold singularities for all pairs of point crystallography groups. This problem can be approached either by visualizing things in Python or by careful examination of some known papers in invariant theory and low-dimensional topology.

Project 2. TDA and FCA in experiments with neurons

There is a famous experiment confirming the existence of the so-called place cells in mammals hippocampus. In this experiment, a mouse moves freely in a labyrinth, while the activity of some set of its neurons is recorded. The general goal is to understand neurons' activation patterns and their relation to the shape of the labyrinth, the trajectory of the rodent, and so on. Generally, one can reconstruct the topology of the labyrinth from neural activity, based on Nerve lemma. Instead of taking the nerves of the covers, one can try to find implications between different neurons, by constructing the lattice of formal concepts, relating the neural activity with the physical position of a mouse. Then one will be able to study the topology of this lattice instead of the nerve. During the internship, you will join our research team in this subject to work on the data obtained in real experiments. Your particular task will be to understand the theoretical basics of formal concept analysis and make a review of the packages which can be combined with the existing packages in TDA in order to proceed in the task described above. As a minimum, we plan that you can describe some basic implications between the activities of different groups of neurons.

Geometry of J

The first goal of this project is to further understand the geometric properties of this metric. For instance, one objective would be to study whether the set P(R^d), endowed with J, is a geodesic metric space, characterize its shortest paths and eventually its curvature properties. Detailed description.

Barycenters

A related field of investigation is to study barycenters relative to metric J. One natural question is whether such barycenters exist, are unique and if one can estimate them in a consistent way. These questions could be studied in the light of the recent paper by Le Gouic and Loubes (2017). Detailed description.

Variance inequalities

Given the recent work by Ahidar-Coutrix et al. (2018), another interesting question is whether one can establish so-called variance inequalities for the metric J. Such an inequality was shown to guarantee fast convergence rates for certain estimators of barycenters in Ahidar-Coutrix et al. (2018) and would be of great interest from a statistical point of view. Detailed description.