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.
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:
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 , because:
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:
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.
 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].
 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].
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.
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 . 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).
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.
 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.
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.
Without generality, the minimization problem is considered here and a DMOP  can be mathematically formulated as follows:
Evolutionary algorithms for solving DMOPs are called dynamic multi-objective evolutionary algorithms (DMOEAs) . In the development of DMO, a mature framework of DMOEAs  has been proposed by researchers, as shown in Fig. 1.
The detailed steps of the framework of DMOEAs are as follows:
Most existing work in DMO mainly focus on how to improve the effectiveness of response mechanisms.
. M. Farina, K. Deb, P. Amato, Dynamic multiobjective optimization problems: test cases, approximations, and applications, IEEE Trans. Evolut. Comput. 8 (5) (2004) 425–442.
. S. Yang and X. Yao, Evolutionary Computation for Dynamic Optimization Problems. Springer, 2013, vol. 490.
. 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.
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! 🙂
The adaptive synthetic (ADASYN) sampling technique is a method that aims to adaptively generate minority samples according to their distributions . 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.
. He, H. and Garcia, E.A., 2009. Learning from imbalanced data. IEEE Transactions on knowledge and data engineering, 21(9), pp.1263-1284
. Ganganwar, V., 2012. An overview of classification algorithms for imbalanced datasets. International Journal of Emerging Technology and Advanced Engineering, 2(4), pp.42-47.
. 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 magaziNe, 13(4), pp.59-76.
. Chawla, N.V., Bowyer, K.W., Hall, L.O. and Kegelmeyer, W.P., 2002. SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, pp.321-357.
. 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.
 Lütkepohl, Helmut. New introduction to multiple time series analysis. Springer Science & Business Media, 2005.
 LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. “Deep learning.” nature 521.7553 (2015): 436-444.
 Williams, Ronald J., and David Zipser. “A learning algorithm for continually running fully recurrent neural networks.” Neural computation 1.2 (1989): 270-280.
 Rangapuram, Syama Sundar, et al. “Deep state space models for time series forecasting.” Advances in Neural Information Processing Systems. 2018.
 Qiu, Jinwen, S. Rao Jammalamadaka, and Ning Ning. “Multivariate Bayesian Structural Time Series Model.” Journal of Machine Learning Research 19.68 (2018): 1-33.
 Harutyunyan, Hrayr, et al. “Multitask learning and benchmarking with clinical time series data.” arXiv preprint arXiv:1703.07771 (2017).
 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.
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