Four PhD positions in the Data Mining group
Four PhD positions in the Data Mining group
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Job description
The Data Mining group at Eindhoven University of Technology (TU/e) is searching for candidates for four fully-funded 4-years PhD positions in the data science projects with the corresponding focuses on deep learning, exceptional model mining and interpretable predictive analytics over evolving data,
TU/e is a dynamic, research-intensive university in the heart of Europe. TU/e is consistently ranked within the top-100 positions in several world rankings for its research and quality of education. The Data Mining group consists of 5 professors and 17 PhD students and postdocs. The research results of the group appear in top-tier conferences and journals including but not limited to IJCAI, AAAI, ICDM, SDM, ECMLPKDD, DAMI, MACH, and Nature Communications. The group has close collaboration with several leading companies, organizations and hospitals. The advertised positions are in collaboration with Philips Research (Positions A and B), Rabobank (Position C) and National Police (Position D)
All 4 successful candidates will be based in the Data Mining group, where are opportunities to collaborate with other PhDs and to co-supervise MSc students relevant to PhD their research. The successful candidates are expected to be willing to spend some of their time at Philips Research, Rabobank and National Police correspondingly.
Position A. EDIC-DeepLearning. Generative (deep) Artificial Neural Networks models have shown to be very successful in encoding various data distributions. We aim to investigate how the generative models can be used to 1) encode well the data being received as the stream and 2) detect events in these time evolving data in an unsupervised or semi-supervised online manner making use just of their learned encoded knowledge (i.e. being mostly memory-free) and some expert knowledge.
Position B. EDIC-EMM. Exceptional model mining (EMM) is an emerging research topic in the area of local pattern mining, aiming to identify subsets of data where something unusual is going on. In order to qualify as interesting, a subset must satisfy two requirements. First, it must be interpretable: EMM only considers subsets that can be described in terms of a few conditions on a few attributes of the dataset at hand (also called subgroups). This ensures that the resulting subgroups can be understood by domain experts, and the subgroups are actionable, i.e., we can base a policy on them. Second, a subgroup must be exceptional: this is measured in terms of unusual interaction (correlation, regression, preference relations, etcetera) between several target attributes. Hence, the best subgroups represent situations in which key indicators display unusual interplay.
Positions A and B are within the larger EDIC project funded by the NWO Commit2Data program. It aims to develop a novel data-driven artificial coaching platform for chronically ill patients. We focus on studying two novel methodologies: (1) informed and interpretable complex event detection and model adaptation on heterogeneous and noisy data streams capturing patient's medical condition, lifestyle and behavior, and (2) data-driven personalized coaching based on modeling and causal inference from these data streams.
Position C. Explainable and accountable KYC Predictive Analytics. We aim to address the challenges of explainable and accountable predictive analytics on evolving heterogeneous data streams for Know Your Customer (KYC) process automation that should comply with local and international regulations. Possible directions include, but are not limited to predictive model output interpretability with the focus on domain expert decision-support, detection and quantification predictive model biases, and compliance of predictive modeling to ethical and legal constraints. Two more PhD students in the group collaborate with Rabobank in the area of KYC. Their corresponding focuses are on interactive interpretable predictive modeling and deep learning on text data.
Position D. Exceptional behavior profiling / EMM in operational field labs. Recent research into EMM shows the potential of developing effective approaches for subjectively interesting subgroup discovery on binary, ordered and real-valued targets. There is an ongoing research investigating exceptionality in human mobility. However, significant amount of application-inspired research still remains to be done from the point of view of handling heterogeneous streaming data collected from different sources, handling temporal dynamics, and defining relevant model classes and techniques for efficient mining of actionable patterns. We aim to develop such novel EMM-based methods for detecting and profiling anomalous behavior.
TU/e is a dynamic, research-intensive university in the heart of Europe. TU/e is consistently ranked within the top-100 positions in several world rankings for its research and quality of education. The Data Mining group consists of 5 professors and 17 PhD students and postdocs. The research results of the group appear in top-tier conferences and journals including but not limited to IJCAI, AAAI, ICDM, SDM, ECMLPKDD, DAMI, MACH, and Nature Communications. The group has close collaboration with several leading companies, organizations and hospitals. The advertised positions are in collaboration with Philips Research (Positions A and B), Rabobank (Position C) and National Police (Position D)
All 4 successful candidates will be based in the Data Mining group, where are opportunities to collaborate with other PhDs and to co-supervise MSc students relevant to PhD their research. The successful candidates are expected to be willing to spend some of their time at Philips Research, Rabobank and National Police correspondingly.
Position A. EDIC-DeepLearning. Generative (deep) Artificial Neural Networks models have shown to be very successful in encoding various data distributions. We aim to investigate how the generative models can be used to 1) encode well the data being received as the stream and 2) detect events in these time evolving data in an unsupervised or semi-supervised online manner making use just of their learned encoded knowledge (i.e. being mostly memory-free) and some expert knowledge.
Position B. EDIC-EMM. Exceptional model mining (EMM) is an emerging research topic in the area of local pattern mining, aiming to identify subsets of data where something unusual is going on. In order to qualify as interesting, a subset must satisfy two requirements. First, it must be interpretable: EMM only considers subsets that can be described in terms of a few conditions on a few attributes of the dataset at hand (also called subgroups). This ensures that the resulting subgroups can be understood by domain experts, and the subgroups are actionable, i.e., we can base a policy on them. Second, a subgroup must be exceptional: this is measured in terms of unusual interaction (correlation, regression, preference relations, etcetera) between several target attributes. Hence, the best subgroups represent situations in which key indicators display unusual interplay.
Positions A and B are within the larger EDIC project funded by the NWO Commit2Data program. It aims to develop a novel data-driven artificial coaching platform for chronically ill patients. We focus on studying two novel methodologies: (1) informed and interpretable complex event detection and model adaptation on heterogeneous and noisy data streams capturing patient's medical condition, lifestyle and behavior, and (2) data-driven personalized coaching based on modeling and causal inference from these data streams.
Position C. Explainable and accountable KYC Predictive Analytics. We aim to address the challenges of explainable and accountable predictive analytics on evolving heterogeneous data streams for Know Your Customer (KYC) process automation that should comply with local and international regulations. Possible directions include, but are not limited to predictive model output interpretability with the focus on domain expert decision-support, detection and quantification predictive model biases, and compliance of predictive modeling to ethical and legal constraints. Two more PhD students in the group collaborate with Rabobank in the area of KYC. Their corresponding focuses are on interactive interpretable predictive modeling and deep learning on text data.
Position D. Exceptional behavior profiling / EMM in operational field labs. Recent research into EMM shows the potential of developing effective approaches for subjectively interesting subgroup discovery on binary, ordered and real-valued targets. There is an ongoing research investigating exceptionality in human mobility. However, significant amount of application-inspired research still remains to be done from the point of view of handling heterogeneous streaming data collected from different sources, handling temporal dynamics, and defining relevant model classes and techniques for efficient mining of actionable patterns. We aim to develop such novel EMM-based methods for detecting and profiling anomalous behavior.
Specifications
- max. 38 hours per week
- Eindhoven View on Google Maps
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Eindhoven University of Technology (TU/e)
Requirements
We are looking for candidates that meet the following requirements:a solid background in Computer Science with specialization in data mining, machine learning, deep leaning; a strong interest in data science research with focus in predictive analytics, exceptional model mining or deep learning over evolving data; data mining software development skills at least in one language, e.g. R, Python, Java; good communication skills in English, both in speaking and in writing; capability and willingness to work both independently and in a team of data scientists and interact with domain experts; being highly motivated, rigorous, and disciplined; being enthusiastic about working on the changing use cases; experience in research and a publication record will be considered additional advantages.
Conditions of employment
Conditions of employment:A full time temporary appointment for a period of 4 years, with an intermediate evaluation after 9 months; A gross salary of €2.266 per month in the first year increasing up to €2.897 in the fourth year; Tight collaboration of academia with industry with access to real data and domain expertise. Strong collaboration ties with several research groups in Europe and world-wide. Healthy travel funding for presenting your work at the leading conferences and going on visiting research. Support for your personal development and career planning including courses, summer schools, conference visits etc.; A broad package of fringe benefits, e.g. excellent technical infrastructure, child daycare and excellent sports facilities, extra holiday allowance (8%, May), and end-of-year bonus (8.3%, December).
