Personal details

Erum M. - Remote back-end developer

Erum M.

Based in: 🇵🇰 Pakistan
Timezone: Islamabad (UTC+5)

Summary

Embark on a transformative coding journey with me, a visionary computer science expert and a passionate mentor, bringing over two decades of rich expertise in the dynamic realms of programming, data science, and software architecture.
My technical skills span across a broad spectrum, including mastery in programming languages like Python, Java, C++, C, and Scala, along with a profound understanding of algorithms, data structures, and the complex art of software design. My research interests include big data analysis, semi-stream join algorithms, and real-time data warehousing. As a mentor, I've illuminated the path for students globally, from prestigious universities in the USA, Australia, Canada, and the UK, in a vast array of subjects from Big Data Analytics to Cyber Security, and advanced programming languages. Currently, I am a lecturer of Computer Science at a Higher Education Institute. My academic prowess is underscored by a PhD in Computer Science, with my doctoral thesis titled: "Integrated Real-Time Distributed Stream-Disk Processing Architecture for Unstructured Big Data". Join me on codementor, where together, we'll navigate the intricacies of technology, unleashing the potential to innovate, solve real-world problems, and excel in your programming career.

Work Experience

Lecturer
Govt. Graduate College | Dec 2015 - Present
HTML/CSS
Python
Java
C++
C
Database
Data Science
JavaScript
Data structure
Data Warehouse
Lecturer in Computer Science, teaching programming, algorithms, data structures, web development and databases to undergrad students.

Education

UMT Pakistan
Doctor's degree・Computer Science
Nov 2018 - Jan 2024
NCBAE Pakistan
Master's degree・Computer Science
Oct 2014 - Feb 2017

Personal Projects

DHSDJArch: An Efficient Design of Distributed Heterogeneous Stream-Disk Join ArchitectureIconOpenNewWindows
2023
Python
Java
MongoDB
Apache Spark
Apache Kafka
In this work I developed a distributed heterogeneous stream-disk join architecture (DHSDJArch) which can prevent stream data loss as well as maintaining balance between heterogeneous distributed data sources and accuracy of stream-disk join. A four phased distributed architecture is proposed for the multi-objective optimization to transform heterogeneous incomplete stream. To prevent stream loss, configuration of log retention is proposed based on the characteristics of distributed event streaming platform (DESP) . Specifically, two transformations are designed to pre-process heterogeneous streams and to join pre-processed stream with distributed disk data by performing real-time disk access while compensating the differences between data sources and streaming application, respectively.
Data Wrangling and Neural Network Optimization
2023
Python
Machine Learning
Neural Networks
TensorFlow
Conducted comprehensive data wrangling and optimization for neural network performance, addressing missing data, converting textual information to numeric values, and implementing a single hidden layer neural network with 16 neurons to measure accuracy. Standardized data, removed highly correlated features, and experimented with various neural network models, hyper-parameters, and optimization techniques to enhance overall accuracy.