I am an informatics master student at the University of Lugano, Switzerland, currently doing a second research internship at Microsoft, where I will work on how automatic, personalized knowledge graphs can be leveraged to improve speech recognition. Before that, I did a research internship with the UCL Machine Reading Group where I was advised by Sebastian Riedel. My work at UCLMR 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.
In my work I focus on natural language understanding and more specifically, I work on deep learning for question answering and automatic knowledge base construction from raw text data. Before that, I build my own GPU cluster and developed algorithms to speed up deep learning on GPU clusters. During my internship at Microsoft Research, I worked on algorithms which make deep learning more memory efficient so that larger networks fit into GPU memory.
I also took part in Kaggle competitions where I have reached world rank 63, but currently research is more important to me than the application of machine learning and deep learning.
In the past, I studied applied mathematics at the Open University and did a dual apprenticeship as Mathematical and Technical Software Developer where I worked in 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 firstname.lastname@example.org; 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.
8-Bit Approximations for Parallelism in Deep Learning, Tim Dettmers, ICLR 2016.
2016-06 – Now 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
2016/2017 Google Scholarship
2011/2012 Best regional graduate in apprenticeship “Mathematical-technical Software Developer”