Michael is an Assistant Professor of Computer Science and leader of the group. His research has two aims: to make data analysis efficient end-to-end (from data management and processing to machine learning and inference); and to harness computing for social applications (e.g., for data-based policy design or web analysis). Previously, he taught at INSA Lyon for one semester (2017) and spent four years (2013-2017) as a postdoctoral researcher at Aalto University. He completed his doctoral studies at the University of Toronto (2013) and undergraduate studies at the National Technical University of Athens.

Yanhao is postdoctoral researcher at the Department of Computer Science. His research interests include learned indexes, data summarization, and stream processing. He completed his doctoral studies at the National University of Singapore in 2020. His research has appeared in top-tier venues for data management (PVLDB, ICDE, EDBT, and IEEE TKDE) and data mining (KDD, CIKM, ICDM, and ACM TOIS).

Arpit is a doctoral student in Computer Science. His research develops algorithms for network analysis using low-dimensional embeddings. In more detail, Arpit uses network embeddings for node classification, network summarization, and network query processing. The premise of this research is that embedding-based algorithms for such tasks may be expressed in terms of relational operations, which in turn may benefit from highly optimized existing technology for processing relational data and potentially lead to improved performance.

Ananth is a doctoral student in Computer Science. His research focuses on developing efficient end-to-end data science systems. Ananth's research is based on the premise that computational efforts can often be shared across different data science tasks. For example, when the data are updated incrementally, it is usually beneficial to update previously trained models incrementally, too, while possibly re-using previous training efforts; or, when multiple users perform similar data science tasks on similar parts of the data, some computations may be performed once and shared across tasks, thus saving computational efforts. Automating such optimizations will lead to data science systems that are not only efficient, but more easily manageable and user-friendly.

Sachith is a doctoral student in Computer Science. His research focuses on developing learned index structures. In more detail, Sachith develops algorithms to learn patterns in the available data and harness them for optimized data retrieval and query processing for a given data science task. Specifically, in his current work, Sachith builds indexes for multi-dimensional data that are optimized to adapt to an underlying density model for the data.  Such indexes have the potential to be more efficient than traditional, model-agnostic index structures, and thus increase the efficiency of many data science tasks.

Madhav Narendran, MSc Student in Computer Science, works on "News Network Analysis".

Eeva-Maria Laiho, MSc Student in Computer Science, works on "Bias in Natural Language Generation systems".

Laura Huuskonen, Doctoral student in Social Sciences.

Francesco Fabbri, Doctoral student at UPF Barcelona, intern.

Andrei Comanescu, Computer Science, MSc thesis on “Analysing Controversy on Twitter via Graph Embeddings”. 2020.

Corinna Hertweck, Computer Science, MSc thesis on “Affirmative Action Policies for University Admissions“. 2019-20.

Riku Laine, Social Sciences, internship, worked on “Evaluating Decision Makers over Selectively Labelled Data”. 2019-20.

Antti Karikoski, MSc thesis on “Compression on Column-oriented Database Systems“. 2019.

Nikola Mandic, MSc thesis on “Semantic text similarity using autoencoders”. 2018.