I am a fourth year doctoral student in the Probabilistic Machine Learning group (earlier known as Statistical Machine Learning and Bioinformatics). The group also belongs to the Helsinki Institute for Information Technology and The Finnish Centre of Excellence in
Computational Inference Research (COIN). I am also a member of the Helsinki Doctoral Education Network in Information and Communications Technology (HICT). My research interests span probabilistic machine learning and its applications to high-dimensional data integration in cancer. Specifically, my doctoral thesis aims to develop probabilistic multi-source machine learning methods for predicting drug responses in cancer cells. Such predictions are valuable in personalised medicine to generate hypotheses for selecting therapies tailored to individual cancer patients.
Eemeli Leppäaho, Muhammad Ammad-ud-din, Samuel Kaski (2016).
GFA: Exploratory Analysis of Multiple Data Sources with Group Factor Analysis.
Muhammad Ammad-ud-din, Suleiman A.Khan, Disha Malani, Astrid
Murumägi, Olli Kallioniemi, Tero Aittokallio and Samuel Kaski (2016).
Drug response prediction by inferring pathway-response associations with
Kernelized Bayesian Matrix Factorization.
Bioinformatics. 32, 17, p. i455-i463.
Marta Soare, Muhammad Ammad-ud-din,, Samuel Kaski (2016).
Regression with n → 1 by Expert Knowledge Elicitation.
In the 15th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA'16), accepted for publication.
- Muhammad Ammad-ud-din; Georgii, Elisabeth; Gonen, Mehmet; Laitinen, Tuomo; Kallioniemi, Olli; Wennerberg, Krister; Poso, Antti; Kaski, Samuel Integrative and Personalized QSAR Analysis in Cancer by Kernelized Bayesian Matrix Factorization , Journal of Chemical Information and Modeling (JCIM), 2014 PDF , CODE
- J. C. Costello, L. M. Heiser, E. Georgii, M. Gonen, M. P. Menden, N. J. Wang, M. Bansal, Muhammad Ammad-ud-din, P. Hintsanen, Suleiman A. Khan, J.P. Mpindi, NCI Dream Community, O. Kallioniemi, A. Honkela, T. Aittokallio, K. Wennerberg, J. J. Collins, D. Gallahan, D. Singer, J. Saez-Rodriguez, S. Kaski, J. W. Gray, and G. Stolovitzky A Community Effort to Assess and Improve Drug Sensitivity Prediction Algorithms, Nature Biotechnology, 2014 PDF , CODE, Press Release
- Drug response prediction by inferring pathway-response associations with
Kernelized Bayesian Matrix Factorization
- NIPS 2016 Machine Learning in Computational Biology Workshop
- Probabilistic integration of multiple high-dimensional data sources in personalized cancer medicine
- UAI 2016 Machine Learning for Health Workshop
- Regression with n → 1 by expert knowledge elicitation
- ICML 2016 Data Efficient Machine Learning Workshop
- Kernelled Bayesian Matrix Factorization
- NIPS 2013 Machine Learning in Computational Biology Workshop
- Integrative and personalized QSAR analysis in cancer by kernelized Bayesian matrix factorization
- MASAMB 2015, Helsinki, Finland, 16-17 April.
- RECOMB/ISCB 2014, San Diego, California, USA, 09-14 November.
- Cancer Pharmacogenomics and Targeted Therapies 2013, Wellcome Trust Sanger Institute, Hinxton, Cambridge, United Kingdom, 15-17 September.
Erasmus Mundus Master's in Systems Biology (euSYSBIO)
- 2010-2011 Aalto University (Finland)
- 2011-2012 KTH - Royal Institute of Information Technology (Sweden)
Master of Science(Technology) in Computational biology and Biomedicine. University of Nice Sophia-Antipolis (France) (2009-2010)
- Thesis: Modeling antibody-antigen binding patches.
- Supervisor: Frederic Cazals
Bachelor of Science in Bioinformatics. Capital University of Science and Technology (earlier known as Mohammad Ali Jinnah University Islamabad, Pakistan, 2004-2008)
Room A341, Computer Science Building,
Konemiehentie 2, Otaniemi campus area, Espoo
- Postal Address:
Aalto University School of Science,
Department of Computer Science,
P.O. Box 15400, FI-00076 Aalto, Finland