Edited by Suparerk Angkawattanawit and Raazesh Sainudiin.
Peer-reviewed by project authors according to these instructions.
A total of 22 PhD Student Groups did Projects of their choosing in Scalable Data Science and Distributed Machine Learning, a mandatory course of The WASP Graduate School AI-track in 2020-2021. See ScaDaMaLe Course Pathways to appreciate the pre-requisite modules 000_1 through 000_9 for the union of all 23 projects, including a voluntary one from Masters thesis students.
Best Group Project: The Group Project named MixUp and Generalization by by Olof Zetterqvist, Jimmy Aronsson and Fredrik Hellström of Chalmers University won the Best Group-Project Prize on the basis of peer-review. The prize was donated kindly by the Databricks University Alliance under Rob Reed.
- The Two Cultures by Daniel Ahlsén, Martin Andersson, Niklas Gunnarsson and Jonathan Styrud.
- Exploring the GQA Scene Graph Dataset Structure and Properties by Adam Dahlgren, Pavlo Melnyk and Emanuel Sanchez Aimar.
- Signed Triads in Social Media by Guangyi Zhang.
- Distributed Linear Algebra by Måns Williamson and Jonatan Vallin.
- Wikipedia analysis using Latent Dirichlet Allocation (LDA) by Axel Berg, Johan Grönqvist and Jens Gulin.
- Unsupervised clustering of particle physics data with distributed training by Karl Bengtsson Bernander, Colin Desmarais, Daniel Gedon and Olga Sunneborn Gudnadottir.
- Motif Finding by Adam Lindhe, Petter Restadh and Francesca Tombari.
- Distributed Ensemble by Amanda Olmin, Amirhossein Ahmadian and Jakob Lindqvist.
- Topic Modeling with SARS-Cov-2 Genome by Hugo Werner and Gizem Çaylak.
- Twitter Streaming Using Geolocation and Emoji Based Sentiment Analysis by Georg Bökman and Rasmus Kjær Høier.
- Anomaly Detection with Iterative Quantile Estimation and T-digest by Alexander Karlsson, Alvin Jin and George Osipov.
- Analysis and Prediction of COVID-19 Data by Chi Zhang, Shuangshuang Chen and Magnus Tarle.
- Genomics Analysis with Glow and Spark by Karin Stacke and Milda Pocoviciute.
- Distributed Combinatorial Bandits by Niklas Åkerblom, Jonas Nordlöf and Emilio Jorge.
- Reinforcement Learning for Intraday Trading by Fabian Sinzinger, Karl Bäckström and Rita Laezza.
- Intrusion Detection by MohamedReza Faridghasemnia, Javad Forough, Quantao Yang and Arman Rahbar.
- Density Estimation via Voronoi Diagrams in High Dimensions by Robert Gieselmann and Vladislav Polianskii.
- Recommender System by Ines De Miranda De Matos Lourenço, Yassir Jedra and Filippo Vannella.
- Fundamental Matrix by Linn Öström, Patrik Persson, Johan Oxenstierna and Alexander Dürr.
- MixUp and Generalization by Olof Zetterqvist, Jimmy Aronsson and Fredrik Hellström.
- Graph Spectral Analysis by Ciwan Ceylan and Hanna Hultin.
- SWAP With DDP by Christos Matsoukas, Emir Konuk, Johan Fredin Haslum and Miquel Marti.
- Distributed Deep Learning by William Anzén and Christian von Koch.