Hi i am Nasir Mahmood

Accomplished Data Scientist from Canada


A small introduction about my self

Nasir Mahmood, PhD

Data Scientist with 10+ years experience in machine learning, AI and statistics.

Nasir is a highly accomplished and likeable data scientist with 10+ years experience in machine learning, statistics, NLP, and big data. His strong interdisciplinary background has provided him with a unique perspective to successfully tackle new projects in a diverse range of areas. In addition, his extensive involvement with interdisciplinary teams, in both industry and academia, has been a great source of pride and inspiration that has helped him to expand his knowledge and develop his skills further.

Nasir is what you’d call a lifelong learner. He’s always reflecting on the lessons learned from the past as a tool to help him grow. This has led to a strong belief that mistakes are simply opportunities to learn and grow. It’s these failed attempts in life that have helped to mould him into the successful entrepreneur that he is today.

His mind is always turning over new ideas and some of his best thoughts come to him in the middle of the night while most people are winding down for the evening. It’s a never give up, progressive, and forward thinking attitude that has made him the success he is today. Nasir lives with his wife, two sons, and daughter and they are the inspiration and motivation that drive him every day to push the limits of what he can achieve.

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Data Science

Linear regression, logistic regression, deep learning

Machine Learning

Nueral networks, deep learning, convolutional networks, optimization

Advanced Analytics

SQL, Hive, Imapala

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Sheridan College

Predictive Analytics, Machine Learning & Big Data

  • Supervised Learning: linear regression, logistic regression, decision trees, decision tree ensembles, K-nearest neighbors
  • Unsupervised Learning: k-means clustering, Bayesian classification and hierarchical clustering.
  • Model Validation: bias, overfitting, cross-validation, feature importance, ethical AI.
  • Natural Language Processing (NLP): text preprocessing, feature representation, similarity measures (TF, TF-IDF, cosine similarity, word2vec)



Bell Network Big Data

Data Science and Machine Learning

  • Anomaly Detection: using descriptive statistics and linear regression
  • Network Health Prediction: Multivariate Guassian models of network metrics.



American Express Canada

Data Science and Analytics

  • Recommender Systems: using regression methods and Bayesian classification techniques
  • Exploratory Data Analysis: analyzing and interpreting key data characteristics
  • Text Analytics: converting words into variables to build predictive models
  • Regression Analysis: modeling and analyzing relationship output and independent variables



Hamburg Center for Bioinformatics

Research Scientist

  • Protein struction prediction: extension of existing algorithm by incorporating building blocks feature
  • Selecting discriminatory features of 3D protein building blocks
  • Unsupervised classification of protein building blocks using Bayesian statistics
  • Extending statistical model and Monte Carlo simulations for blind protein structure predictions



Technical University Berlin

Postdoctoral Fellow

  • Extension and improvement of model based search prediction algorithm
  • Developed and managed blind prediction framework for CASP biannual competition
  • Developed model assessment framework for CASP biannual competition
  • Proposed concept of protein building blocks: dynamically adapting to an appropriate resolution of structural representation, hence making statistical modeling and conformational search space manageable
  • Developed algorithm for extraction of building blocks by applying advanced data mining techniques
  • Model validation framework to see, whether 1) the anticipated building blocks exist at all, and 2) if they exist, could the representative set of building blocks be used to build models of protein structures



Hamburg Center for Bioinformatics

Research Scientist

  • Protein structure prediction using Bayesian classifications with Monte Carlo simulated annealing (MCSA)
  • Implemented interplay between Cartesian coordinates and dihedral angles of protein structures
  • Extracted water-molecule interaction features from known protein structures and performed unsupervised Bayesian re-classification of constituent structures along with existing statistical models
  • Incorporated water interaction (with protien structures) into existing Bayesian classification models
  • Implemented alogrithm to calculate hydrogen bonding energies in protein structures




Data Science Specialization

A 9-course specialization by Johns Hopkins University on Coursera. Specialization Certificate earned on December 22, 2014

15.071x: The Analytics Edge

A course of study offered by MITx, an online learning initiative of The Massachusetts Institute of Technology through edX.

Statistics in Medicine

A course of study offered by Stanford Online, an online learning initiative of Stanford University, through OpenEdX, the leading open source online learning platform.

Matrix Algebra and Linear Models

A course of study offered by HarvardX, an online learning initiative of Harvard University through edX.

Statistics and R for the Life Sciences

A course of study offered by HarvardX, an online learning initiative of Harvard University through edX.



University of Hamburg, Germany

PhD, Computational Biology

During PhD research I worked on protein structure prediction, a classical probelm from computational biology. I developed low-resolution coarse grain force fields, which do not involve physical model or Boltzmann statistics. They rather use a mixture of Bayesian probabilities from normal and discrete distributions of features of known proteins. Consequently, a ratio of probabilities provides acceptance criterion for Monte Carlo method in prediction simulations. For more details, please check out my doctoral dissertation and presentation slides.



Cologne University Bioinformatics Center, Germany

Postgrad. Diploma, Applied Bioinformatics

During 1-year instensive program, I worked on a mandatory research project (in addition of course work) with focus on protein structural alignment, an NP-hard problem. I analyzed performed of existing algorithm and then modified by advancing optimization step. My contribution led to a significant increase in overall algorithm performance. Also, I benchmarked new algorithm against existing methods. For more details, please check out my project report and presentation slides.



Otto-Von-Guericke University, Magdeburg, Germany

M.Sc. Computational Visualistics

During research thesis, I worked on implementation and benchmarking of a document retrieval system for a handwriting device - PC Notes Taker (PCNT). PCNT device is believed to be competitive compared to other device which we had already tested and benchmarked. I extended features of the document retrieval system by implementing a subtype of triangular grid features and compared its results with those of existing features. It was found that new features were slightly poorer but closer to square grid features. For more details, please check out my master thesis and presentation slides.



University of Central Punjab, Lahore, Pakistan

MS Computer Science



Bahauddin Zakaria University, Multan, Pakistan

B.Sc. (Hons) Agri. Entomology

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