
Markus Dablander
Member of the Oxford Protein Informatics Group (OPIG)
University of Oxford
Andrew Wiles Building
Radcliffe Observatory Quarter
Woodstock Road
Oxford
OX2 6GG
Siamese Neural Networks Work for Activity Cliff Prediction
M. Dablander, G. M. Morris, R. Lambiotte, T. Hanser
Conference Poster at 4th RSC-BMCS / RSC-CICAG Artificial Intelligence in Chemistry Symposium (Virtual, 2021)
Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning
J. Berner, M. Dablander, P. Grohs
In: Advances in Neural Information Processing Systems 33 (NeurIPS 2020 Proceedings). arXiv
Multivariate Network Analysis of Cerebral and Systemic Variables for Assessement of Injury Following Hypoxic Ischaemic Encephalopathy
M. Dablander, S. Mitra, G. Bale, M. Dinan, C. Uria-Avellanal, D. Price, M. Sokolska, A. Bainbridge, G. Kendall, J. Meek, I. Tachtsidis, N. Robertson
Talk given at Summer Meeting of The Neonatal Society (Brighton, UK, 2017)
Direct and Iterative Implementations of the Spectral Chebyshev Method for the 2D Poisson Equation
M. Dablander, I. Vrubleuski, V. Volkov
Conference Poster at International Scientific and Practical Conference (Brest, Belarus, 2015)
C6.5 Theories of Deep Learning: Teaching Assistant, MT 2021
C5.4 Networks: Teaching Assistant, HT 2021
2022:
2016/2017: Most Outstanding MRes Research Student at the UCL Center of Mathematics and Physics in the Life Sciences and Experimental Biology (UCL CoMPLEX)
Partial Differential Equations via Deep Learning
- Machine learning and deep learning
- Graphs and Networks
- Cheminformatics and computational drug-discovery