I am an informatics master student at the University of Lugano, Switzerland, currently working as visiting researcher at the UCL Machine Reading Group where I am advised by Sebastian Riedel working on information retrieval and knowledge graphs. Before that, I did two research internship at Microsoft, where I worked on how automatic, personalized knowledge graphs for speech recognition and on memory optimizations for deep learning. Before that, I did another research internship with the UCL Machine Reading Group, where I focused on focused on neural link prediction in knowledge graphs, that is to infer knowledge between concepts, people, organizations, which can be inferred from the overall structure of the knowledge graph.
I started out in research by building my own GPU cluster and developing algorithms to speed up deep learning on GPU clusters.
I am passionate about large-scale deep learning and unsupervised learning which I think can be achieved by hierarchical associative memory akin to psychological schemas. I am fond of neuroscience and I see my work as a symbiosis of neuroscience and deep learning — the more I know about the brain and its behavior the more I know about AI; the more I know about AI the more I know about the brain.
I am also passionate about computational efficiency. I try to write algorithms and software which scales efficiently to practical and large-scale workloads.
In the past, I also took part in Kaggle competitions where I have reached world rank 63.
I obtained a degree in applied mathematics at the Open University and did a dual apprenticeship as Mathematical and Technical Software Developer where I worked in the automation industry.
Besides deep learning, I am also very interested in understanding the human brain, human nature, the human condition and their evolution. In my spare time, I like to study and think about fields aligned to these topics.
Feel free to contact me at email@example.com; if you have questions regarding deep learning, I prefer that you post your questions as comments on one of my blog posts (or on this page if it does not fit to any blog post); this way all people can profit from your questions and my answers.
Convolutional 2D Knowledge Graph Embeddings, Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel. Proceedings of the 32th Conference on Artificial Intelligence (AAAI), 2018 (acceptance rate 25%).
8-Bit Approximations for Parallelism in Deep Learning, Tim Dettmers, 4th International Conference on Learning Representations (ICLR), 2016 (conference track, acceptance rate 25%).
Computational and Parallel Deep Learning Performance Benchmarks for the Xeon Phi, Tim Dettmers, Hanieh Soleimani, technical report, 2016.
2017-09 – Now UCL Machine Reading Group, London
2017-06 – 2017-09 Microsoft Research, Redmond
2017-01 – 2017-06 UCL Machine Reading Group, London
2016-06 – 2016-09 Microsoft Research, Redmond
2015-06 Hamburg Hackathon 2; 1st place (19 teams). Analyzing Twitter stream to find viral content in real-time.
2014-11 Media Hack Day – Video, Berlin; 1st place (13 teams). Finding the most viral youtube videos using twitter and youtube data.
2014-06 Hamburg Hackathon; 1st place in Technology (17 teams). Using deep neural networks to predict personality on Twitter.
Awards & Honors
2018/2019 Paul G. Allen School First-Year PhD Fellowship
2016/2017 Google Scholarship for Students with Disabilities
2016 ICRL16 Travel Award
2011/2012 Best regional graduate in apprenticeship “Mathematical-technical Software Developer”