Dr. Raghvendra Mall


Dr. Raghvendra Mall


Data Analytics


Dr. Raghvendra Mall is a Research Scientist at Qatar Computing Research Institute. He works on developing and utilizing data driven modeling techniques for computational biology with a primary focus on network biology and structural bioinformatics. He is primarily interested in problems like differential network analysis, gene regulatory network inference, master regulator analysis and disease module identification in biological networks.

From a structural bioinformatics point of view, he is particularly interested in designing sequence-based approaches for protein solubility, protein crystallization and ultimately protein function prediction. He is also intent on predicting viral protein neutralization and protein structure prediction e.g. secondary structure and residue-residue contact prediction.

He is always on the lookout for new opportunities and collaborating with his peers.

Jul-Oct, 2013: Research Associate, Qatar Computing Research Institute, Hamad Bin Khalifa University — Qatar
Developed primal-dual framework for feature extraction using least squares support vector machines. Developed sparse reductions to kernel spectral clustering for microarray datasets.
References: Dr. Mohammed El Anbari & Dr. Halima Bensmail

Aug-Jan, 2011-12: Research Intern, Microsoft, R & D — India
Formulated large scale micro-markets based on search queries using the BING Keyword-Advertiser graph on a distributed platform. Implemented a parallel version of power iteration clustering in combination with hierarchical clustering.

Aug-Jan, 2010-11: Research Intern, INRIA — France
Developed algorithms for incremental clustering based on variants of growing neural gas algorithms. Applied the same to textual and biological datasets.
References: Prof. Jean-Charles Lamirel

2012-2015: Doctorate at KU Leuven, Belgium - Summa Cum Laude
Specialization: Large Scale Kernel Methods, Large Scale Social Network Analysis and Sparsity in Kernel based Models

2006-2012: Bachelors + Masters by Research in CSD at IIIT-Hyderabad, India
Specialization: Associative Rule Mining and Incremental Clustering

  • Mall, R.,Ehsan, U., Rawi, R., Moustaid, N.M., Butt, A.A. and Bensmail, H., 2018. Harnessing Qatar Biobank to understand type 2 diabetes and obesity in adult Qataris from the First Qatar Biobank Project. Journal of translational medicine, 16(1), p.99.

  • Khurana, S., Rawi, R., Kunji, K., Chuang, G.Y., Bensmail, H. and Mall, R., 2018. DeepSol: a deep learning framework for sequence-based protein solubility prediction. Bioinformatics.

  • Mall, R., Cerulo, L., Garofano, L., Frattini, V., Kunji, K., Bensmail, H., Sabedot, T.S., Noushmehr, H., Lasorella, A., Iavarone, A. and Ceccarelli, M., 2018. RGBM: regularized gradient boosting machines for identification of the transcriptional regulators of discrete glioma subtypes. Nucleic Acids Research.

  • Frattini, V., Pagnotta, S.M., ... Mall, R., Sanson, H. and Frederick, V., 2018. A metabolic function of FGFR3-TACC3 gene fusions in cancer. Nature.

  • Rawi, R., Mall, R., Kunji, K., Shen, C.H., Kwong, P.D. and Chuang, G.Y., 2017. PaRSnIP: sequence-based protein solubility prediction using gradient boosting machine. Bioinformatics.

  • Mall, R., Cerulo, L., Bensmail, H., Iavarone, A. and Ceccarelli, M., 2017. Detection of statistically significant network changes in complex biological networks. BMC systems biology, 11(1), p.32.

  • Mall, R., Langone, R. and Suykens, J.A., 2015. Netgram: Visualizing Communities in Evolving Networks. PloS one, 10(9), p.e0137502.

  • Mehrkanoon, S., Alzate, C., Mall, R., Langone, R. and Suykens, J.A., 2015. Multiclass semisupervised learning based upon kernel spectral clustering. IEEE transactions on neural networks and learning systems, 26(4), pp.720-733.

  • Mall, R. and Suykens, J.A., 2015. Very sparse LSSVM reductions for large-scale data. IEEE transactions on neural networks and learning systems, 26(5), pp.1086-1097.

  • Mall, R., Jumutc, V., Langone, R. and Suykens, J.A., 2014, October. Representative subsets for big data learning using k-NN graphs. In Big Data (Big Data), 2014 IEEE International Conference on (pp. 37-42). IEEE.

  • Mall, R., Langone, R. and Suykens, J.A., 2013, October. Self-tuned kernel spectral clustering for large scale networks. In Big Data, 2013 IEEE International Conference on (pp. 385-393). IEEE.

  • Langone, R., Mall, R., Alzate, C. and Suykens, J.A., 2016. Kernel spectral clustering and applications. In Unsupervised Learning Algorithms (pp. 135-161). Springer, Cham.

  • Mall, R. and Suykens, J.A., 2013, April. Sparse reductions for fixed-size least squares support vector machines on large scale data. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 161-173). Springer, Berlin, Heidelberg.

For other research see his Google Scholar page

Professional Activities: Journal Reviewer for TNNLS, Bioinformatics, Neural Computing and Applications.


  • 2012-15: Doctorate Funded by European Research Council
  • 2010: Undergraduate Research Award
  • 2006: Secured Rank 70 (out of ~100000) in WBJEE
  • 2006: Ranked 1772 (out of ~600000) in AIEEE

For more information about Dr. Raghvendra, visit his website