Muhammad Ammad-ud-din

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.

Publications

• Eemeli Leppäaho, Muhammad Ammad-ud-din, Samuel Kaski (2016). GFA: Exploratory Analysis of Multiple Data Sources with Group Factor Analysis. [Preprint]


• 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. [Link] [Code]


• 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. [Preprint]


• 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

Challenges

• We (TeamFin) won the NCI-DREAM Drug Sensitivity Prediction Challenge (Dream7)! published in Nautre Biotechnology.

• We (FIN&AL) participated in the NCI-DREAM NIEHS-NCATS-UNC DREAM Toxicogenetics Challenge (Dream8)! published in Nautre Biotechnology.
  • listed under "NIEHS-NCATS-UNC DREAM Toxicogenetics Collaboration"

• We (TeamMI) won upto the Consortium Phase of Rheumatoid Arthritis Responder Challenge (Dream8.5) in press at Nature Communications.

Refereed International Workshop Papers and Conference Posters

• 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.



Education

Erasmus Mundus Master's in Systems Biology (euSYSBIO)
• 2010-2011 Aalto University (Finland)
• 2011-2012 KTH - Royal Institute of Information Technology (Sweden)
  • Thesis: Predicting drug responses in Cancer cells by combining multiple views cell and drug views
  • Supervisor: Samuel Kaski and Erik Aurell
  • Instructor: Elisabeth Georgii
  
  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)
  • Thesis: Analyzing Gene Expression of Saccharomyces cerevisiae using Markov Clustering Algorithm.
  • Supervisor: Dr. Nayyer Masood and Dr. Sahar Fazal and Dr. Alberto de la Fuente
  
  

  Contact

  Office:

  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

  Telephone:

  Cell+358-50-4302599
  Fax:+358-9470-23277

  Email:

  firstname.lastname@aalto.fi