About Me

Hi! I'm Alex Mara an AI researcher and entrepreneur based in Ghent, Belgium. Originally from Romania, I have spent most of my adult life in various European countries including Spain, Italy, Finland, Germany and Belgium. I am deeply passionate about AI and the myriad of opportunities it can bring us, so I built my entire career around it. To offset this nerdy part of my life, I also practice many sports such as callisthenics, bouldering and mountain biking. I am also a huge fan of videogames both modern and old-school (P.S.: try searching for a non-existent subdomain on my website, like this one :P). In what follows, you can learn more about myself, my interests, publications, projects, teaching... just keep reading!

Currently, I am one of the Co-Founders and CEO of Nobl.ai, a tech startup focused on delivering trustworthy AI for the labour market. We provide state-of-the-art recommender systems that match candidates and jobs, mapping (normalization) of job descriptions and courses into standard (ISCO, ESCO, O*Net, ...) and proprietary taxonomies, career coaching, and much more! If you are interested in learning more, drop me a message!

I also hold a part-time appointment as a Postdoctoral Researcher in the Artificial Intelligence and Data Analytics (AIDA) group at Ghent University led by Prof. Tijl De Bie. My research interests revolve around recommender systems and representation learning as well as their applications to the labour market. Over the past decade, I have built an international academic career in AI and data mining obtaining a PhD from Ghent University in Belgium, an MSc. (with honours) from Aalto University in Finland and a B.Eng. (first class honours) from the Technical University of Madrid in Spain. You can check out my full academic CV here.

Below you can find some of my main publications, but for an always up-to-date list, you can take a look at my Google Scholar profile. The code of my open-source projects can be found on GitHub here and here.

Research Interests

Representation Learning

Dimensionality Reduction

Recommender Systems

Graph Semi-supervised Learning

Big Data Analytics

Distributed Computing

Experimental Design & Evaluation

Extreme multi-label classification

AI for HR and the labour market



Mara, A. (2023). Accelerating Progress in Network Representation Learning: Systematic Evaluations and New Approaches., PhD Dissertation, Ghent University Press.
Mara, A. (2017). A Comparative Analysis of Graph Signal Recovery Methods for Big Data Networks. MSc. Dissertation, Aalto University Press, 67(24).

Journal Articles

Mara, A., Lijffijt, J., & De Bie, T. (2022). An Empirical Evaluation of Network Representation Learning Methods. Big Data.
Mara, A., Lijffijt, J., & De Bie, T. (2022). EvalNE: A Framework for Network Embedding Evaluation. SoftwareX, 17.
Jung, A., Hero, III, A. O., Mara, A., Jahromi, S., Heimowitz, A., & Eldar, Y. C. (2019). Semi-supervised Learning in Network-structured Data Via Total Variation Minimization. IEEE Transactions on Signal Processing, 67(24), 6256–6269.
Jung, A., Tran, N., & Mara, A. (2018). When is Network Lasso Accurate? Frontiers in Applied Mathematics and Statistics, 3, 28.

Conference Proceedings

Mara, A., Lijffijt, J., Günnemann, S., & De Bie, T. (2022). A Systematic Evaluation of Node Embedding Robustness. Proceedings of the First Learning on Graphs Conference, vol. 198, PMLR, 2022.
Adriaens, F., Mara, A., Lijffijt, J., & De Bie, T. (2020). Block-approximated Exponential Random Graphs. In 2020 IEEE 7th International Conference on Data Science and Advanced Analytics.
Mara, A., Lijffijt, J., & d. Bie, T. (2020). Benchmarking Network Embedding Models for Link Prediction: Are we Making Progress? In 2020 IEEE 7th International Conference on Data Science and Advanced Analytics.
Mara, A., Mashayekhi, Y., Lijffijt, J., & De Bie, T. (2020). CSNE: Conditional Signed Network Embedding. In Proceedings of the 29th ACM International Conference on Information Knowledge Management.
Mara, A., Lijffijt, J. De Bie, T. (2019). EvalNE : A Framework for Evaluating Network Embeddings on Link Prediction. Procceedings of EDML, (SDM).
Mara, A. & Jung, A. (2017). Recovery Conditions and Sampling Strategies for Network Lasso. In 51st Asilomar Conference on Signals, Systems and Computers. Finalist of the Asilomar 2017 best student paper contest.
Zhu, B., Mara, A., & Mozo, A. (2015). Clus: Parallel Subspace Clustering Algorithm on Spark. In T. Morzy, P. Valduriez, & L. Bellatreche (Eds.), New Trends in Databases and Information Systems.

Teaching Experience

  • Teaching Assistant: AI Research Seminar, Ghent University, 2021/2022
  • Teaching Assistant: Big Data Science, Ghent University, 2018/2019/2020
  • Teaching Assistant: Machine Learning Basic Principles, Aalto University, 2016
  • Teaching Assistant: Convex Optimization for Big Data, Aalto University, 2015
  • Teaching Assistant: Mathematical Analysis, Technical University of Madrid, 2011/2012/2013

Other Activities

  • Virtual conference chair: ECMLPKDD'20
  • Area chair: ICMD'23, ICMD'24
  • Program committee member: ICDM, ECML-PKDD
  • Reviewer: Springer Nature, IEEE TNNLS, IEEE TSP, Information Processing and Management, Big Data, SoftwareX, MDPI Applied Sciences, MDPI Mathematics
  • Group Lead: Slush Music construction team, 2017
  • Volunteer: Slush event help team, 2016
  • Volunteer: ESN Aalto, 2016-2017