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ECOLE blog

Tackling our PhD Tasks in Home Office

Thiago Rios

During the PhD we are constantly challenged to find solutions for technical problems and push the knowledge frontier in our fields a little further ahead. As a measure to support controlling the current COVID-19 epidemic, many of us started working in home office a couple of weeks ago and, therefore, we face new sorts of problems, such as staying healthy, connected and focused on our work [1,2]. Hence, we wrote down a few pros and cons of working from home in our notepads, as well as some tips to “survive” the home office season, which we are sharing with you in this post.

The Pros

Working from home can be very pleasant and fruitful. Apart from tasks that require special hardware, for example, running chemistry experiments in a lab, we usually can do a lot with a notebook, access to the internet and remote access to our workstation in the office.  Furthermore:

  • We save everyday the time dedicated to preparing ourselves and driving back and forth to the office. We can use this time either to accelerate our work and meet some tight deadlines, catch up with the news or read a little more of that book we are trying to finish. More importantly, it gives us some flexibility to adjust our schedule without the pressure to go or be somewhere.
  • The working space can be more personalized. We are free to choose and set our working environment, which might be quite motivating. It could be a good opportunity to read some papers in the garden while enjoying a good day of sun, or even to change the view from time to time. Nevertheless, remember to keep the distractions away! Even though the space might not be an office, it should allow us to focus on work.
  • The working environment might be less stressful at home. Since we can adapt the work to our rhythm more easily and we are familiar with the environment, we enjoy better both working hours and breaks. We can also choose different activities to do in the break, such as watch tv or take a short nap after lunch.

The Cons

Working at home can be very convenient, however, it has some drawbacks, especially in the long term. In such cases, it’s common to feel isolated and less productive [1], because:

  • With the freedom to define our schedules comes the difficulty to balance personal and professional tasks. From daily chores to free time activities we like, which are now 24/7 just a decision away from us, keeping the usual among of time dedicated to the PhD might be hard and even stressful. This lack of balance can lead to frustration, decreasing our productivity.
  • We miss being with our colleagues and friends. And it goes further than social contact, having a few (or many) coffee breaks during the week, discussing about different topics we like or having something to eat together. They contribute to our work, as well as we contribute to theirs, and we learn from each other, which is very motivating; and, unfortunately, the feeling of teamwork is very hard to achieve working at home on our own.
  • At home there are too many distractions around. Family, flatmates, pets and noise are a few of the items in the list that can disrupt our lines of thought while doing home office. Since the interaction with the home environment is inevitable, we usually take longer to finish usual tasks, due to interruptions we would not have in an office environment.

Surviving the home office

Staying healthy is now the priority, so going back to the office is not yet an option, so all we can do is focus on improving our home-office experience by minimizing the cons in our list. Here is a short list of tips we came up with for tackling the drawbacks of working during the quarantine:

  • Stablish a routine and try to stick to it. Since it’s more flexible to set a schedule in home office, pin some periodical activities in the time slots that suit you better, combining tasks that are very demanding to activities that can easy you mind for a while. It does not have to be “carved in stone”, but make sure it helps you to get your day started and everything done at the end.
  • Work on your long-term schedule. Some of the tasks might take longer to be finished, due to multiple reasons, so make sure to give priority to the things you can do from home. Simplify things to fit your available hardware, check with your supervisors how the processes are being handled and discuss with them about what could be tackled from home.
  • If possible, your office and free time rooms should be separated. There are several articles available on how to set an ergonomic home office depending on your tasks and availability of materials, but in summary, the recommendations are: make sure you have what is necessary to work at your disposition, keep the distractions away and remember to take short breaks from time to time.
  • Keep the channels of communication with your supervisors, colleagues and family open. There are multiple communication tools available (from phone calls to web meetings) and most of them do not require any fancy hardware. This is important to keep them aware of your progress, have support while working on experiments and manuscripts, as well as to have some social interaction with different people.
  • Keep your mind and body fit! It does not mean that you have to become an athlete, but dedicate some time to work out—outdoors, as long as it is allowed and does not put you or anyone else at risk—or to do some activity that keeps you body moving and your mind out of the work. It also applies to the food! Since we might have to handle the preparation of most of our meals, make sure to balanced meals, with everything you need to stay healthy.

Finally, we hope our impressions and tips help you to go through this exceptional working conditions. It is not ideal for many of us, but it is temporary and very important to contain the epidemic. Let’s do our part and help keeping our communities safe.

Reference

[1] B. Lufkin, “Coronavirus: How to work from home, the right way”, bbc.com, 2020. [Online]. Available: https://www.bbc.com/worklife/article/20200312-coronavirus-covid-19-update-work-from-home-in-a-pandemic. [Accessed: 30- Mar- 2020].
[2] S. Philipp and J. Bexten, “Home Office: Das sind die wichtigsten Vor- und Nachteile – ingenieur.de”, ingenieur.de – Jobbörse und Nachrichtenportal für Ingenieure, 2020. [Online]. Available: https://www.ingenieur.de/karriere/arbeitsleben/alltag/home-office-das-wichtigsten-vorteile-nachteile/. [Accessed: 30- Mar- 2020].

First Year for Being an ESR in ECOLE

Giuseppe Serra

How everything has started

It has been during my Master’s thesis internship when I first heard about the Early Stage Researcher (ESR) figure. PhD student and industry researcher at the same time, I thought it was the perfect mix for my future development. Thus, after been graduated, I started looking for a job and finally, I found the one I was interested in: Machine Learning ESR. I applied for this position, did the interview and got hired… The beginning of a new phase of my life, within the ECOLE project.

My role in ECOLE

Nowadays, we are living in the digital era and the amount of user-generated data is exponentially growing every day. In this context, users usually explain what they like, dislike or think in the form of textual comments (e.g. tweets, social media posts, reviews). Leveraging the rich latent information contained in the user-generated data available can be crucial for many purposes. For example, an automobile company can launch a face-lift car that would satisfy customers more than before, by mining history order and users’ feedback [1]. Recently, research in the manufacturing industry focuses on developing advanced text mining approaches to discover hidden patterns, to predict market trends and to learn customer preferences and unknown relations, for improving their competitiveness and productivity. Keeping this in mind, my objective within ECOLE is on developing statistical machine learning and probabilistic models for preference learning. The focus will be not only on obtaining good results but also on getting interpretability of the outputs and on providing a measure of confidence about them. ECOLE aims at solving a series of related optimisation problems, instead of treating each problem instance in isolation, thus the learned information could be integrated to include preference constraints in multi-criteria optimization frameworks (e.g. product design, where structural, aerodynamics and aesthetic constraints have to be considered simultaneously).

ECOLE: one year later

Now, it has been more than a year since I have been involved in ECOLE.  During this period, I had the possibility to know and to work with the other ESRs, to learn a lot from the experienced supervisors (from academia and industry), and to travel and visit several countries for attending summer schools, workshops, conferences. The mixture of different cultures, backgrounds and the collaboration between academia and industry has created an inspiring and motivating environment to improve either soft-skills, technical skills and grow as a researcher.

Reference

[1] Ray Y Zhong, Xun Xu, Eberhard Klotz, and Stephen T Newman. Intelligent manufacturing in the context of industry 4.0: a review. Engineering, 3(5):616–630, 2017.

Brief Introduction to Evolutionary Dynamic Multi-objective Optimization

Gan Ruan

Dynamic multi-objective optimization problems (DMOPs) involve multiple conflicting and time-varying objectives, which widely exist in real-world problems. Evolutionary algorithms are broadly applied to solve DMOPs due to their competent ability in handling highly complex and non-linear problems and most importantly solving those problems that cannot be addressed by traditional optimization methods.

  1. Basic Definitions in Dynamic Multi-objective Optimization (DMO)

Without generality, the minimization problem is considered here and a DMOP [1] can be mathematically formulated as follows:

  1. Dynamic Multi-objective Evolutionary Algorithm

Evolutionary algorithms for solving DMOPs are called dynamic multi-objective evolutionary algorithms (DMOEAs) [2]. In the development of DMO, a mature framework of DMOEAs [3] has been proposed by researchers, as shown in Fig. 1.

BLOG7 algorithm description

Fig. 1. Flowchart of DMOEAs

The detailed steps of the framework of DMOEAs are as follows:

  • Initialize a population, set the initial parameters;
  • Change detection: if the environmental changes have been detected, the mechanism of change reaction would be evoked to respond to the changes; if not, static optimization algorithms would be continued;
  • Optimization algorithm: select a specific evolutionary algorithm to optimize the population;
  • The mechanism of change reaction: the mechanism is designed to respond to environmental changes.
  • Termination: if the stopping conditions are satisfied, then the optimization is terminated.

Most existing work in DMO mainly focus on how to improve the effectiveness of response mechanisms.

References

[1]. M. Farina, K. Deb, P. Amato, Dynamic multiobjective optimization problems: test cases, approximations, and applications, IEEE Trans. Evolut. Comput. 8 (5) (2004) 425–442.
[2]. S. Yang and X. Yao, Evolutionary Computation for Dynamic Optimization Problems. Springer, 2013, vol. 490.
[3]. G. Ruan, G. Yu, J. Zheng, J. Zou, and S. Yang, “The effect of diversity maintenance on prediction in dynamic multi-objective optimization,” Applied Soft Computing, vol. 58, pp. 631–647, 2017.

First Year of ECOLE – A Review

Stephen Friess

Time flies fast. It has been almost over one year since I joined ECOLE. And it has been an exciting year. Travelling between Birmingham, Warsaw, Leiden, Berlin, Coimbra and Xiamen. Listening to talks in state of the art on research in Evolutionary Computation and Machine Learning. Attending workshops in the research institutions of two world-known tech companies. The opportunities promised and given by the Innovative Training Network have not fallen short. But aside from all the excitement and activities, who am I, what do I do and what is that drove me to ECOLE in the first place?

Well, my prior academic pursuits were in Theoretical Nuclear Physics at the Technical University Darmstadt. And while I of course enjoyed studying the field and gaining understanding into the inner subnuclear workings of the world, I felt at times the research self was missing out in some way on my initial fascinations which took me too it. So, where to go else? Stumbling upon ECOLE was to be honest a bit of a lucky coincidence. I was working prior at software company while looking for PhD and residency programs, when a friend of mine was recommending me the posting for an Early Stage Researcher position he found at one of the partner institutions. I applied, was interviewed and subsequentially have been offered the position. It has been a bit unusual for me to travel this much for the Marie Curie Fellowship, but I am happy to have found a new home. And with Computational Intelligence research being situated at the intersection between Natural Computation and Artificial Intelligence, there is still plenty of space for me to philosophize about nature, while keeping in touch with reality through real-world problems.

ECOLE is especially great in regards of opportunities to learn, grow and try out new things. I particularly had a lot of fun (but also stress) with preparing and giving a short keynote-style public talk on nature-inspired artificial intelligence for the popular science segment of the Z2X festival in Berlin last August. The talk was luckily to be held lightweight and in front of a small audience. I decided to talk about the history and inspirations behind current popular methods and highlight evolutionary approaches as a means for creative problem-solving. While part of the audience seemed to have got lost with some of the bare technicalities, the main ideas sticked. Which was the most important thing for me. I was thus more than happy to have received and interesting questions in return. By the way: I am planning to put my practice slides online soon, if you are interested, feel free to take a look. The actual talk was given completely in oral form, as I was informed one day prior that the beamer was missing, but it worked out.

So, what’s up for the future? Currently, I work on model-based approaches as a means to tackle the transfer learning problem in continuous optimisation. A bit of a complicated idea, but I find this notion to be intuive and interesting to explore. In any way, I will keep you updated about my work in a future blog post. Until then, everyone keep up with the good work! 🙂

MIP-EGO4ML: A python Hyperparameter optimisation library for machine learning

Duc Anh Nguyen

Introduction

Machine learning techniques become essential for the research on big data as well as applications on real-world problems. For instance, many machine learning algorithms are effective in areas, e.g., handwriting recognition, time series forecasting, self-driving etc. Despite its high applicability, most of the machine learning system has various hyperparameters, which affect the performance of those algorithms significantly. Thus, it is crucial to investigate how good configurations of hyperparameters can be obtained in an automatic manner for real-world applications. This consideration naturally leads to the well-established Automatic Configuration methodology.

A general random forest architecture
Fig 1. A general random forest architecture [1].

Hyperparameter optimisation

There are two common techniques for hyperparameter optimisation are Grid-search and Random-search [2, 3]. Grid-search is a traditional method, which will be studying every point in the search space. However, it considerably expensive due to the number of search points needed to achieve. Instead of searching on full search points, Random-search will choose random points, that will reduce the studying cost. However, the effect of this approach is completely random. Bayesian optimisation [2, 3, 4] is more modern techniques, which aims to predict and suggest new promising search points for the optimizer. In this blog post, we will focus on one of our implementation of Bayesian optimization, a Python hyperparameter optimisation library called MIP-EGO4ML.

MIP-EGO4ML: A python hyperparameter optimisation library for machine learning

The Hyperparameter Optimisation (HPO) library “MIP-EGO4ML” is a python library based on the MIP-EGO library (available at http://hyperparameter.ml). In principle, MIP-EGO4ML is an extended version of the MIP-EGO library with conditional search space added. Thus, if your search space does not contain any conditional parameters, that means the MIP-EGO library is processing your HPO process.

MIP-EGO4ML is very simple and easy to use. To run the library, we only need three steps:

  1. Objective function: the function must return a real value.

  2. Configuration space: hyperparameters for the objective function

  3. Configuration of MIP-EGO4ML.

Firstly, we must have an objective function, which must return a single real-value number to minimize or maximize. In this article, we will use a simple classification problem that will describe how to run an optimize for a classification problem by MIP-EGO4ML.

to introduce the python library
Fig 2. MIP-EGO4ML supported four types of search space: Continuous Space, Nominal Space, Ordinal Space, and Conditional Space
to introduce python library
Fig 3. An example of the objective function, this objective function will use the classic Iris data set and do two supervised machines learning: Support Vector Machine (SVM) and Random forest (RF).

And then, we need define a Configuration space or hyperparameter space for this objective function, which we want to optimize.

to introduce python library
Fig 4. An Example of Configuration Space. This configuration space consists of two sub-search space for SVM and RF with their hyperparameters. The algorithms and their search spaces controlled by ConditionalSpace.

At the final step, we need to set the configuration for MIP-EGO4ML.

to introduce the python library
Fig 5. Example of configuration for MIP-EGO4ML
to introduce python library

The code for this article is available in a Jupyter Notebook on GitHub.

This article only provides the basic introduction about our novel hyperparameter optimisation library; we will update more article about hyperparameter optimisation techniques in the future posts.

References

[1]. Gelzinis, A. Verikas, E. Vaiciukynas, M. Bacauskiene, J. Minelga, M. Hållander, V. Uloza and E. Padervinskis, “Exploring sustained phonation recorded with acoustic and contact microphones to screen for laryngeal disorders,” in Conference: 2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE), 2014.

[2]. M. Feurer and F. Hutter, “Hyperparameter Optimization,” in AutoML: Methods, Sytems, Challenges, Springer, 2018, pp. 3-37.

[3]. H. H. Hoos, “Automated Algorithm Configuration and Parameter Tuning,” in Hamadi Y., Monfroy E., Saubion F. (eds) Autonomous Search, Berlin, Heidelberg, Springer, 2011, pp. 37-71.

[4]. H. Wang, B. v. Stein, M. Emmerich and T. Bäck, “A new acquisition function for Bayesian optimization based on the moment-generating function,” 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 507-512, 2017.

Experience of My First Paper and Conference

Sneha Saha

Experience of Writing My First Conference Paper:

It is now more than a year since I started working for the ECOLE project. It has completely changed my concept of pursuing a Ph. D, i.e., from understanding the perspective of subjects of industrial use cases to approach a technical problem in gradual steps. Initially, I had spent a lot of time in trying to understand the problem by studying related literature and research papers while performing initial experiments. After nine months since the start of the project, I decided to write my first conference paper. The most difficult part that I faced while writing my first paper was structuring the paper. Initially, my focus was on carrying out a number of experiments, gathering results and plotting graphs instead of starting to write the paper in parallel.  If I look back and think about my experience, I realize I had made several mistakes. These include the delay in starting to write the manuscript and circulating the draft several times. In the end, I was confused with the large number of items to include in a single paper and in which section to insert each one of them. Also, it was a collaborative paper with another researcher. It gave me the pleasure of working together for a project. Still in the end, we ended up working till the last moment on the last date of the submission. Fortunately, our work paid off and the paper was accepted and I got the chance to attend my first conference.

LMID Workshop (IEE ICDM 2019) November 8-11 2019 in China National Convention Center (CNCC) Beijing

Experience of Attending the First Conference:

It was my first experience of attending a conference and also my first visit to China.  We submitted our paper in the Learning Mining with Industrial Data (LMID 2019) held in conjunction with the International Conference on Data Mining (2019 IEEE) from 8th November to 11th November in Beijing, China.  The workshop was related to machine learning and data mining of industrial data. On the first day of the conference, there were talks by Prof Bodgan Gabyrs on automation as a predictive system and from Prof Xin Yao on ensemble approaches to class imbalance problems. There were eight other interesting presentations from different researchers. There were also several other keynote talks on the application of AI related to Industrial applications like in transportation, social networking, medical diagnosis, etc.  Other than attending this workshop, there were a number of keynote speakers and tutorials on the four days’ long conference. Listening to all these talks has actually helped me to think out of the box of daily research. Also, it gave me an opportunity to meet other researchers, to learn networking and enjoy some really good food.  Personally, I enjoyed the four-day long conference in Beijing.

Short Introduction about the Class Imbalance Classification Problem

Jiawen (Fay) Kong

What is a class imbalance classification problem?

Strictly speaking, any dataset that contains an unequal class distribution between its classes can be considered as imbalanced [1]. However, in the community, only the datasets that contain significant or extreme imbalance are regarded as imbalances datasets. An illustration of class imbalance problem is shown in Figure 1. Even if the classifier predicts all the samples as majority class, the accuracy is still 95%, which makes the classifier seems extremely efficient but actually it neglects the minority class.

explain the class-imbalance problem
Fig 1. An illustration of class imbalance problems.

Several steps to deal with class imbalance classification problem.

From Figure 1, we know that the accuracy does not reflect the actual effectiveness of an algorithm in imbalanced domains. Hence, the first thing needs to be done to deal with imbalanced dataset is to change the performance evaluation metrics. The Area Under the ROC curve (AUC), F-measure, geometric mean are three commonly used performance metrics (detailed information for the metrics can be found in [1]). Over years of development, many techniques have proven to be efficient in handling imbalanced datasets. These methods can be divided into data-level approaches and algorithmic-level approaches [2, 3], where the data-level approaches aim to produce balanced datasets and the algorithmic-level approaches aim to adjust classical classification algorithms in order to make them appropriate for handling imbalanced datasets.

Two popular data-level approaches.

Data-level approaches, as known as resampling techniques, can be divided into undersampling (under sample majority class samples) and oversampling techniques (oversample the minority class samples). Here we only introduce two popular oversampling techniques.

The synthetic minority over-sampling technique (SMOTE), proposed in 2002, is the most popular resampling technique [4]. SMOTE produces balanced data through creating artificial data based on the randomly chosen minority samples and their K-nearest neighbors. The procedure to generate a new synthetic sample is shown in Figure 2.

explain SMOTE algorithm mentioned in the blog
Fig 2. SMOTE working procedure.

The adaptive synthetic (ADASYN) sampling technique is a method that aims to adaptively generate minority samples according to their distributions [5]. The main improvement compared to SMOTE is the samples which are harder to learn are given higher importance and will be oversampled more often in ADASYN. The general idea of ADASYN is shown in Figure 3.

explain the ADASYN algorithm mentioned in the blog

We only include the most basic knowledge of class imbalance in this blog and more tutorial about class imbalance will be posted later.

References

[1]. He, H. and Garcia, E.A., 2009. Learning from imbalanced data. IEEE Transactions on knowledge and data engineering21(9), pp.1263-1284
[2]. Ganganwar, V., 2012. An overview of classification algorithms for imbalanced datasets. International Journal of Emerging Technology and Advanced Engineering2(4), pp.42-47.
[3]. Santos, M.S., Soares, J.P., Abreu, P.H., Araujo, H. and Santos, J., 2018. Cross-validation for imbalanced datasets: Avoiding overoptimistic and overfitting approaches [research frontier]. ieee ComputatioNal iNtelligeNCe magaziNe13(4), pp.59-76.
[4]. Chawla, N.V., Bowyer, K.W., Hall, L.O. and Kegelmeyer, W.P., 2002. SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research16, pp.321-357.
[5]. He, H., Bai, Y., Garcia, E.A. and Li, S., 2008, June. ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence) (pp. 1322-1328). IEEE.

Crucial Facets of Modern Time Series Analysis

Sibghat Ullah

Time series analysis is an important motif in the domains of “Macroeconomics”, “Financial Portfolio Management”, “Weather Forecasting”, “Disaster Management” and “Health Care” to name a few. With the age of digitalization, the industrial and ubiquitous systems such as smart phones, health care units and economic transaction processing systems produce huge volume of time ordered data. Understanding the complex and dynamic processes that produce these time ordered data can lead to significant benefits for the society and businesses. This require a holistic analysis of the time series generated by these processes including feature selection, model selection, validation and prediction of future time related events. In this blog post, I try to summarize some of the major challenges in time series analysis that require the attention of scientific community. Two example signals, respectively in Figure 1 and 2 are plotted for this blog. Figure 1 shows the plot of daily minimum temperature in Celsius as measured by the Australian bureau of Meteorology for the city of Melbourne from 1981 to 1991. Figure 2 shows the plots concerning the power consumption of all households in Germany, and the solar and wind power generation in the country from 2006-2018. The unit of measurement for Fig 2 is Giga watthours (GWh).

Give an example in the blog.
Fig 1. Minimum Daily Temperature of Melbourne, Australia as recorded by Australian bureau of Meteorology from 1981-1991.

The classical analysis of time series relies heavily on “Auto Regressive (AR)” and “Exponential Smoothing” models [1] which are typically linear and are suited for univariate time series. These state space methods involve human expertise in the loop and require explicit specifications of trend, seasonality, cyclical effects and shocks when modeling time series. As a result, these methods are interpretable and the prediction is ready to use. However, this interpretability comes at the sacrifice of prediction accuracy since such methods lack the dynamic and complex nature of modern industrial and ubiquitous processes with multivariate time series e.g., stock changes, energy consumption in power plants etc. Recently, with the advent of empirical models such as deep neural networks [2], there has been a growing interest in time series modeling using deep learning. This usually involves modeling time series by a sequential neural network architecture such as Recurrent Neural Network and its variants. The predictions for future observations can be made once the parameters of the neural network are optimized using any methodology to compute gradients in recurrent structures e.g., Back Propagation Through Time (BPTT), Real Time Recurrent Learning (RTRL) [3] etc. Although the neural networks enjoy higher accuracy than linear state space models in the context of time series, their prediction is often not interpretable since they behave as black box models. Additionally, the computational complexity and choice of architecture make things much more complicated. Hence, we’re left with the dilemma of simplicity vs complexity, both of which have their own pros and cons.

explain the time series definition in the blog
Fig 2. Daily Open Power System Data for electricity consumption and generation in Germany from 2006-2018.

Recently, hybrid models such as deep state space models [4] have been proposed in the context of time series analysis which combine the best of both worlds to produce robust predictions. Hybrid models have the advantage of working with both simple and complex time series. This further means that size of time series does not affect the learning itself since for small data, state space models make full use of the structural assumptions in the data while for large data sets, deep neural networks can capture the complex and nonlinear relationship and hence, the resulting model usually perform better than a stand-alone model. It is important to understand that despite the advent in hybrid models, there’re still major issues in time series analysis that need the attention of scientific community. This involve feature selection, model selection, cross validation and interpretation in time series analysis. To this end, Bayesian time series frameworks [5] have been proposed that take care of feature and model selection however such frameworks have the drawback of computational complexity since estimating the posterior using MCMC models is computationally expensive for higher dimensions.

Added to these are the issues of missing data imputation, irregular & asynchronous time series [6], non-causal and non-invertible processes, categorical/qualitative processes, class imbalance learning, and mechanisms for early outliers and changepoints detection [7] i.e., structural breaks. Not addressing these issues may have grave consequences in the context of learning and the predictions might be misleading. Finally, there’s lack of cohesive literature in Multivariate Time Series Analysis and consequently there is no consensus on the selection of multiple related time series based on the information from the application domain e.g., disease diagnostics in the case of electronic health records in ICU etc. which could be useful for inference.

References

[1] Lütkepohl, Helmut. New introduction to multiple time series analysis. Springer Science & Business Media, 2005.
[2] LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. “Deep learning.” nature 521.7553 (2015): 436-444.
[3] Williams, Ronald J., and David Zipser. “A learning algorithm for continually running fully recurrent neural networks.” Neural computation 1.2 (1989): 270-280.
[4] Rangapuram, Syama Sundar, et al. “Deep state space models for time series forecasting.” Advances in Neural Information Processing Systems. 2018.
[5] Qiu, Jinwen, S. Rao Jammalamadaka, and Ning Ning. “Multivariate Bayesian Structural Time Series Model.” Journal of Machine Learning Research 19.68 (2018): 1-33.
[6] Harutyunyan, Hrayr, et al. “Multitask learning and benchmarking with clinical time series data.” arXiv preprint arXiv:1703.07771 (2017).
[7] Guo, Tian, et al. “Robust online time series prediction with recurrent neural networks.” 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA). Ieee, 2016.

Sharing Experience of First Year in ECOLE

Thiago Rios

Personal Background:

Not much longer than one year ago, at the end of my Master’s degree and after 8 years in the Mechanical Engineering department at UFSC (Brazil), starting a PhD was not exactly what I had in mind. I have seen many friends in the post-graduate program doing pure academic research, sometimes without perspective of any real-world industrial application for their developments. Since I experienced several extra-curricular activities in parallel to my studies – 3 one-year internships and 6 years dedicated to projects in student racing teams – I did not see myself working on an exclusively academic environment and enduring for additional three or four years of lectures and publications, even though the doctorate program there was really good. On top of that, I was afraid that what I learned during practical assignments would slowly vanish away in order to leave space for new, but highly specialized, knowledge.

Marie Skłodowska-Curie Actions (ITN)

Research Networks

Innovative Training Networks (ITN) drive scientific excellence and innovation. They bring together universities, research institutes and other sectors from across the world to train researchers to doctorate level.

Motivation to apply for ECOLE:

The proposal of the ECOLE project and the concept of the Innovative Training Networks (ITN) challenged me with the idea that pursuing the PhD degree could be very different to what I expected. Spending three years among industry and academia; living in two different countries; and being able to finish a dissertation, papers and training modules sounded very intense even before the job interview. However, the aspect of bringing the fundamental research together to real-world industrial applications was exactly what I was looking for; and the particularity of the ECOLE being applied to the automotive engineering, which is deeply related to my educational background, really pushed me to join the team.

University of Birmingham, Universiteit Leiden - Academic Institutes, Honda Research Institute, NEC - Industrial Partners

Experience in ECOLE:

Now, almost one year after my start in the project, it is satisfying to realize how much this interesting mix between academia and industry, computer science and engineering, improved my technical and professional skills. The difficulties to fit the research into engineering applications still exist – several years of practice with mechanical engineering tasks help me tough – but it has become easier to face them as part of the research and take it as a motivation to move forward with the PhD. The support of the supervisors has been essential: through constructive feedbacks and fruitful scientific discussions, we already achieved great results (nine accepted publications distributed among the ESRs in the project!) and put great ideas together for future research.

ECOLE blog

Sharing technical concepts, experience, applications in industry.

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