Data Science Seminar by Joao Paulo Carvalho – University of Lisbon

You are cordially invited to the Data Science Seminar that will take place on Friday, 28th October 2016 (TU/e AUD2).

Participation is free of charge, but please register by sending an email to is@tue.nl before 26th October 2016.

Speaker: Joao Paulo Carvalho, INESC ID’s Spoken Language Systems Laboratory
Title: Fuzzy Fingerprints: Identification and classification in Big data using top-k values

Information about the event is as follows:

Abstract: Fuzzy Fingerprints are a recently introduced technique inspired by the fact that many types of data studied in the physical and social sciences can be approximated with a Zipfian distribution. I.e., the frequency of an item is inversely proportional to its rank in the frequency table. Fuzzy Fingerprints efficiently use the implicit information contained in top-k most frequent data values to perform identification in large datasets. The term “fingerprint” is used in the sense that fingerprints are unique, and are usually left unintentionally, allowing us to identify their “owners”. The fingerprint concept can be extended from single users to categories, topics or classes, allowing us to perform tasks such as classification and recommendation.

In this talk I will approach the ideas behind Fuzzy Fingerprints and show case studies and applications involving: identification of anonymous users based on their phone and web usage habits; text author identification based on their writing habits; classification and identification in social data (e.g. detecting tweets related to a given trending topic); classification based on medical text data; movie recommendation; etc.

This event is technically co-sponsored by the Benelux Chapter of IEEE
Computational Intelligence Society.

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New Employee Juntao Gao

JuntaoI am a visiting scholar in the Information Systems group at Eindhoven University of Technology and an associate professor of School of Computer and Information Technology, Northeast Petroleum University, Heilongjiang Province, China.

I graduated from Beihang University in 2009 with Doctor of Science Degree. I have been working in Northeast Petroleum University for 6 years. I have done more than 11 projects of Business Process Modeling, Data Integration and Quality Management.
My current research and work focuses on (1) Business Process Intelligence (2)Business Process Management (3)Big Data.

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New employee: Jason Rhuggenaath

foto TUe v3_smallMy name is Jason and I will be joining the Information Systems group in September. I grew up in Curaçao and when I was 18 years old I left Curaçao to go study in Rotterdam. In 2012 I obtained my Bachelor’s diploma in Economics and in the following years I also obtained Master’s degrees in Economics (2014) and Econometrics and Management Science (2015). For the last two years I worked at CPB Netherlands Bureau for Economic Policy Analysis as an academic researcher.

The PhD project which I will be working on will aim to develop a decision-support tool for revenue optimization for online companies who provide content or physical goods by combining state-of-the-art techniques from the world of big data and from the world of optimization. Furthermore, the project will involve both the OPAC and IS groups. I am looking forward to getting to know and collaborating with the rest of both groups!

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New employee Peipei Chen

peipeiMy hometown is Henan Province, China. The Shaolin Temple in my hometown is famous around the world because of the Chinese Kung fu.

My Bachelor’s Degree was received from Zhengzhou University, China after four years (2010-2014) study majored in Biomedical Engineering. Then I was admitted into Zhejiang University as a Direct Ph.D. student in September, 2014. From March 2015 to December 2015, my work mainly focused on the standardization and mapping of Chinese clinical terminology and related Medical Terminology Services, which aimed to promote the medical information exchange and sharing. The technologies used included text similarity algorithm and Chinese words segmentation. In the end, I developed a web-based middleware software including terminology mapping and management services, which will be evaluated in a real hospital.

From the end of December of 2015, I was selected to take part in the Brain Bridge Project” Risk assessment to improve patient outcomes for cardiovascular diseases”. The goal of this project is to predict the risk of cardiovascular patients of different conditions using data mining technique so that practitioners can adopt suitable therapies.

New Employee: Yingqian Zhang

zhang-websiteI received my Ph.D. degree in Computer Science from University of Manchester in UK. Before joining TU Eindhoven, I worked as an assistant professor in the Department of Econometrics at Erasmus University Rotterdam.  Earlier, I worked in the Algorithmics group at TU Delft, in the Computational Intelligence group at TU Clausthal (Germany), and in the Institute for Advanced Computer Studies at University of Maryland, College Park, USA.

I consider myself an “Artificial Intelligence” researcher. During my Ph.D and post-doc period, I developed algorithms and mechanisms to solve multi-agent problems, i.e., task allocation, planning and scheduling problems that involve cooperative or self-interested players. Over the past a few years, I have been also investigating how data can help to improve decision making and optimization. For example, I designed machine learning methods to learn bidders’ behaviour models, and used the learned models to optimize auction design.

At TUE, I will continue my research on data driven decision making,  algorithm design, (computational) game theory, and multi-agent systems.  In addition, I am looking forward to learning new knowledge, tackling new application domains, and establishing new cooperations!

New Employee: Saskia van Loon

In March 2015 I got my Master’s degree in Biomedical Engeneering at the Eindhoven University of Technology (TU/e) in the field of Regenerative Medicine and Tissue Engineering. I also studied at Leiden University where I obtained my Master’s degree in Medicine in 2008. With this background I am able to bridge the gap between technology and patient. It inspires me to combine multiple disciplines and work with experts from different professional backgrounds.Saskia van Loon

Currently, I work as PhD student at the department of Clinical Chemistry and Laboratory Medicine at the Catharina Hospital in Eindhoven. My PhD project is part of the Data Science Flagship, a collaboration between TU/e and Philips Research (IMPULS 2.0). The aim of the project is to improve the outcome of cardiac resynchronization therapy (CRT) using hospital data and/or personal health devices by discriminating beforehand between responders and non-responders of CRT.

Presentation by Dr. Zhengxing Huang: Predictive monitoring of clinical pathways

Dr. Zhengxing Huang of Zhejiang University (Hangzhou, China)

Title:
Predictive monitoring of clinical pathways.

Abstract:
Accurate and timely monitoring, as a key aspect of clinical pathway (CP) management, provides crucial information to medical staff and hospital managers for determining the efficient medical service delivered to individual patients, and for promptly handling unusual treatment behaviors in CPs. In many applications, CP monitoring is performed in a reactive manner, e.g., variant treatment events are detected only after they have occurred in CPs. Alternatively, this study systematically presents a learning framework for predictive monitoring of CPs. The proposed framework is composed of both offline analysis and online monitoring phases. In the offline phase, a particular probabilistic topic model, i.e., treatment pattern model (TPM), is generated from electronic medical records to describe essential/critical medical behaviors of CPs. Using TPM-based measures as a descriptive vocabulary, online monitoring of CPs can be provided for ongoing patient-care journeys. Specifically, two typical predictive monitoring services, i.e., unusual treatment event prediction and clinical outcome prediction, are presented to illustrate how the potential of the proposed framework can be exploited to provide online monitoring services from both internal and external perspectives of CPs. Extensive evaluation on a real clinical data-set, typically missing from other work, demonstrates the efficacy and generality of the proposed framework for surveillance-based CP management in a predictive manner.

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New Employee: Zahra Valizadeh

Zahra1Zahra Valizadeh-Gh received her BSc. degree in applied mathematics from Kharazmi University, Tehran, Iran, in 2005, the MSc. degree in the same major from Sharif University of Technology, Tehran, Iran, in 2007, and the PhD. degree in numerical optimization from the Islamic Azad University (IAU), Iran, in 2012. Since 2012, she has been Assistant Professor at IAU. Now, she has joint to Information Systems Group as a visiting researcher for six mounths. Her research interests include the fuzzy logic, systems of fuzzy equations and systems of fuzzy relational equations/inequalities as well as the multi-criteria optimization theory.

Journal articles:

Z . Valizadeh-Gh, E . Khorram (2015) Linear Fractional Multi-Objective Optimization Problems Subject to Fuzzy Relational Equations with the Max-Average Composition, Applied and Computational Mathematics. Special Issue: New Advances in Fuzzy Mathematics: Theory, Algorithms, and Applications, 4(1-2), 20-30.

E. Khorram, R. Ezzati, Z. Valizadeh (2014) Linear fractional multi-objective optimization problems subject to fuzzy relational equations with a continuous Archimedean triangular norm, Information Sciences, 267, 225-239.

R. Ezzati, S. Khezerloo, N. Mahdavi-Amiri, Z. Valizadeh (2014) Approximate Non-negative Symmetric Solution of Fully Fuzzy Systems Using Median IntervalDefuzzi_cation, Fuzzy Information and Engineering, 6, 1-28.

E. Khorram, R. Ezzati, Z. Valizadeh (2012) Solving nonlinear multi-objective optimization problems with fuzzy relation inequality constraints regarding Archimedean triangular norm compositions, Fuzzy Optimization and Decision Making, 11(3), 299-335.

R. Ezzati, S. Khezerloo, N. Mahdavi-Amiri, Z. Valizadeh (2012) New Models and Algorithms for Approximate Solutions of Single-Signed Fully Fuzzy LR Linear Systems, Iranian Journal of Fuzzy Systems, 10(3), 1-26.

Z. Valizadeh, R. Ezzati, S. Khezerloo (2012) Approximate Symmetric Solution of Dual Fuzzy Systems Regarding Two Di_erent Fuzzy Multiplications, Indian Journal of Science and Technology, 5 (2), 2100-2112.

S. Khezerloo, M. Montazeri, Z. Valizadeh (2010) A New Method for Solving Fuzzy Linear System, International Journal of Industrial Mathematics, 2 (2), 97-104.

New Employee Lonneke Vermeulen

LVI studied Industrial Engineering (bachelor) and Operations, Management and Logistics (master) at the Eindhoven University of Technology, The Netherlands. In 2013 I graduated on my research towards ‘A process modelling method for Care Pathways’, which was conducted in participation with the Heart centre of the Catharina Hospital Eindhoven and supervised by Uzay Kaymak, Pieter van Gorp, Hui Yan and Erik Korsten. Since then I’ve been working on the development of the Clinical Decision Support System Tracebook, developed within the Brainbridge project of Shan Nan, from within the Catharina hospital. For the upcoming months I will continue my work on the Tracebook system and clinical trials, as well as write a paper of my master thesis research and preparing a conference on Fuzzy sets.

Colloquium IE&IS/IS Sept 5, 2014

Speaker:
Anna Wilbik

Title:
Decision support via linguistic summaries

Abstract:
The business environment (climate) is constantly changing, and it is becoming more and more complex. Making good decisions may require considerable amounts of relevant data, information, and knowledge. With the advancement in information technology, more and more data are stored and analyzed. The amount of data is beyond human cognitive capabilities and comprehension skills. There is an urgent need to process those data into knowledge. To meet those needs, the fields of data mining and knowledge discovery are developing rapidly. Following this trend, also the methods to summarize the data and to analyze these summaries are getting more and more important.

In this presentation we will present briefly the decision support framework, as well how the linguistic summaries fit there. We will conclude with some examples.