Paras Memon

Paras Memon

PhD Student,
Colorado State University


About Me

Software engineer working at the intersection of machine learning, deep learning and artificial intelligence techniques to solve complex and real world problems for the advancement of technology. I am also passionate about learning the latest tools and techniques to solve the real world problems. Such as Keras, tensor flow, pytorch in brain vision, EEG, reservoir engineering and related domains.


Current PhD Project - Language Mapping of Brain Vision using Deep Neural Network

Brain vision data is recorded from left and right hemispheres using conventional and tripolar electrodes of size 10mm, 6mmm ,and 4mm. Subjects were shown pictures for 3500 ms each, with 2500 ms inter-stimulus interval during which the computer monitor was grey. There were two conditions: overt and covert. In the overt condition, participants were instructed to verbalize aloud each presented image. In the covert condition, participants silently named the image without verbalizing aloud. Deep Learning techniques are applied on this data to find which part of the brain is giving good result for overt and covert. Also deep learning is used to find out which electrodes are performing best during subjects' recording.

Masters' Project - Implementation of Artificial Neural Network for Multiphase Flow Simulation

Reservoir simulation model contains large number of different data, number of injection/production wells and millions of grid-blocks with complex geological structure. This complexity leads recovery process changes from : 1) natural depletion, 2) followed by water- flooding ,and finally 3) enhanced oil recovery. Numerical models takes large number of simulation runs and time to calculate the final response. Therefore, machine learning techniques such as neural networks were used as surrogate reservoir models to estimate flow characteristics of oil, gas and water in the reservoir in very less amount of time.

Target and Non-Target classification of P300 Event Related Potentials

The P300 wave data is collected from Brain Computer Interface (BCI), Colorado State University (CSU) website. In which the data is collected from unimpaired subjects. Data consists of single letter display on the screen, not the grid data. The conventional EEG was used to record the signals from these channels: 'F3', 'F4', 'C3', 'C4', 'P3', 'P4', 'O1', 'O2' according to 10-20 system. Machine Learning techniques and Convolutional Neural Network (CNN) was used to predict the target and non-target Event Related Potentials.

Predicting Pediatric Bone Age from X-rays Using Convolutional Neural Network

Deep neural networks were used for the prediction of bone age from X-rays.

Hyperlink-Induced Topic Search (HITS) over Wikipedia Articles using Apache Spark

HITS algorithm was designed to find the key pages for specific web communities.

Detecting Phishing Websites using MachineLearning Techniques

Machine Learning was used to differentiate between the legitimate and phishing websites.

Yelp Review Star Rating Prediction

state-of-the-art machine learning algorithms were used to find the useful reviews based on the number of stars that are given to reviews.

Work Experience

Fulbright PhD Student - Colorado State University, USA (2018 - Present)

Work on Brain Computer Interface (BCI) using deep learning techniques.

Assistant Professor - Sukkur IBA University, Pakistan (2013 - 2014)

Currently on sabbatical leave to complete PhD degree.

Lecturer - Sukkur IBA University, Pakistan (2015 - 2018)

Involved in teaching courses

Graduate Research Assistant - Universiti Teknologi PETRONAS, Malaysia (2015 - 2018)

Involved in research predicting flow characteristics of oil, gas and water in the reservoir using machine learning techniques.


  1. Paras Q. Memon, Suet-Peng Yong, William Pao, "Surrogate Reservoir Model for Average Reservoir Pressure", IEEE Technically Sponsored SAI Intelligence System Conference 2016, September/2016, London, UK.

  2. Paras Q. Memon, Suet-Peng Yong, William Pao, "Dynamic Surrogate Reservoir Model with Well Constraints" , The 3rd, publisher UTM Press, indexed in SCOPUS,.

  3. Book Chapter - Pau J. Sean, William Pao, Suet-Peng Yong and Paras Q. Memon, "Evaluation of Relative Permeability Models in CO2/Brine System Using Mixed and Hybrid Finite Element Method", Applied Mechanics and Materials. Vol. 819. Trans Tech Publications Ltd, 2016.

  4. Book Chapter - Paras Q. Memon, Suet-Peng Yong, William Pao, “Dynamic Well Bottom-Hole Flowing Pressure Prediction based on Radial Basis Neural Network”, Springer Book Series- Studies in Computational Intelligence, Indexed by BLP, Ulrichs, SCOPUS, MathSciNet, Current Mathematical Publications, Zentralblatt Math: MetaPress and Springerlink.

  5. Paras Q. Memon , Suet-Peng Yong, William Pao, “Surrogate Reservoir Modeling – Prediction of Bottom-Hole Flowing Pressure using Radial Basis Neural Network", Science and Information Conference 2014, August/2014, , London, UK.

  6. Pau J. Sean, William Pao, Suet-Peng Yong and Paras Q. Memon, "Effects of Capillary Pressure on Multiphase Flow During CO2 Injection in Saline Aquifer",The 4th International Conference on Production, Energy and Reliability, June/2014, Malaysia

  7. Paras Q. Memon, Suet-Peng Yong, William Pao, and Pau J. Sean, "Prediction of bottom-hole flowing pressure using general regression neural network”,The 2nd International Conference on Computer and Information and Sciences, June/2014, Malaysia

  8. Paras Q. Memon, Suet-Peng Yong, William Pao, “A Preliminary Study on Well-Based Surrogate Reservoir Model”, 2013 IEEE Student Conference on Research & Development (SCOReD), December/2013, Malaysia