Markus Dablander

MRes, MSc, BSc

Status

Postgraduate Student

Member of the Oxford Protein Informatics Group (OPIG)

+44 1865 615219

Contact form

Research groups

Address

Mathematical Institute
University of Oxford
Andrew Wiles Building
Radcliffe Observatory Quarter
Woodstock Road
Oxford
OX2 6GG

Major / recent publications

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)

Teaching

C6.5 Theories of Deep Learning: Teaching Assistant, MT 2021

C5.4 Networks: Teaching Assistant, HT 2021

Prizes, awards, and scholarships

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)

Recent publications

Exploring QSAR models for activity-cliff prediction

Dablander, M Hanser, T Lambiotte, R Morris, G Journal of Cheminformatics volume 15 issue 1 (17 Apr 2023)

Exploring QSAR models for activity-cliff prediction

Dablander, M Hanser, T Lambiotte, R Morris, G (31 Jan 2023)

Numerically solving parametric families of high-dimensional Kolmogorov partial differential equations via deep learning

Berner, J Dablander, M Grohs, P Advances in Neural Information Processing Systems 33 (NeurIPS 2020) 1-13 (10 Dec 2020)

Numerically Solving Parametric Families of High-Dimensional Kolmogorov
Partial Differential Equations via Deep Learning

Berner, J Dablander, M Grohs, P (09 Nov 2020) http://arxiv.org/abs/2011.04602v1

Research interests

Machine learning and deep learning
Graphs and Networks
Cheminformatics and computational drug-discovery