Asif's Profile

• 0: Problem StatementSimulate Cell Cycle with boolean model of protein interactions in mammalian and yeast cells.
• 1: Current workDeveloping an automation that analyzes the simulation results for all perturbations of the model and identify the perturbations that hinder or improve the cell cycle. Current manuscript is under review by PLOS One journal.
• 2: OptimizationAchieved about 5x speedup with the automated perturbation analysis on ARC@VT supercomputer using parallel processing and algorithm optimization for exponentially growing (16 million +) interaction data.
• 3: Validity of perturbationsAutomatically identifying and verifying valid perturbations using SIGNOR 3.0 database, which contains curated information about cell interactions. These information were used to filter out invalid perturbations for optimized processing.
• 4: Future plansImplementing a better scoring mechanism, optimizing the method to even run larger models, automating the whole process from beginning till the end, and applying the method on models of cancerous cells to identify potential drug targets.
• Publication: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0306523
• GitHub Repo: https://github.com/asif256000/boolean_cellcycle_analysis
• SIGNOR 3.0 Database: https://signor.uniroma2.it/

• 0: Cloud automationDeveloped DevOps automation that was able to deploy complete security product from Seclore for each client in less than an hour in a fully managed and scalable AWS architecture. This decreased customer onboarding time from several days when done manually to few hours with the automated approach.
• 1: Customer Infra Stack CodificationImplemented individual customer infrastructure as a AWS Cloudformation stack via aws-cdk and pynamodb modules in Python. This helped create infra stacks for each customer in Cloudformation and store its data in DynamoDB with encryption at rest. This also helped in easy management of customer infra stacks and data.
• 2: CollaborationTeam of 3 worked with Subversion in an agile environment to develop the cloud automation in under 4 months.

• 0: Surface Automation FrameworkDeveloped custom RPA framework that helped automate various network management tools by identifying elements with OpenCV module and performing actions on them with win32 and/or selenium. The reason of this approach as opposed to using window handlers to directly access window elements was, for additional security the tools usually ran on virtual machines where the tool was an interactive video stream with no access to any window on a system level.
• 1: Rule-based Data Analysis EngineImplemented a rule-based recommendation and data analysis engine that calculates lists of high-impact network cells for telcos from various parameters and formulas given by network engineers. The data analysis engine worked on large network data (10M rows and 5K cols, approximately 30GB parquet file daily) regularly for about 20 clients in less than 4 hours with parallel processing. The process was automated using Apache Airflow that automatically triggered data preprocessing, which eventually ran the rule engine separately on individual machines on each client's data.
• 2: ImpactWith the rule-based recommendation & root cause analysis system automation gain reached 36% per month, and the RPA framework automation gain was also over 35%. Here automation gain is defined as the percentage of human effort (billable hours) saved by using the automation framework as opposed to performing all the tasks manually.
• 3: Other projectsIntegrated the custom RPA framework with existing automation platform in the company, BotStore. Also developed a system to fetch large amount of network data from datalake to local system via API, cleaned the data and stored them as parquet files individually for each client.

• 0: LearningsLearned about network towers and how the base stations and cells work in them. Also learned about Optical Fibers in detail.
• 1: ExperiencesHad exposure to National Optical Fiber Network (NOFN) project, which was a nationwide optical fiber operation. Also learned about various network monitoring tools used in telecom companies.