Email: KisanThapa33 [AT] gmail.com
Email: Kisan [DOT] Thapa001 [AT] umb.edu
I am currently on the journey to attain a PhD in Computer Science at the esteemed University of Massachusetts Boston. My research passions revolve around the captivating domains of Computational Biology, Network Analysis, and Pathway Analysis. Guiding me in this pursuit is the distinguished Professor Ozgun Babur, under whose mentorship I thrive within the dynamic Network Biology Laboratory.
Analysis of Algorithms | Object-Oriented Software Design | Machine Learning | Artificial Intelligence | Mobile Applications Development | Computer Vision | Neural Networks | Neural Network | User Interface Design | Algorithms in Bioinformatics
Completed my Bachelor of Computer Engineering from Pokhara University, Nepal. I graduated with a 3.8 GPA out of 4, standing as the third-highest achievement within the university.
"Achieved excellence by securing a spot on the Dean's List with an impressive 3.8 GPA out of 4, standing as the third-highest achievement within the university."
Data Structure and Algorithms | Programming in C, C++, Java, Python | Database Management System | Computer Networks | Operating System | Software Engineering | Computer Architecture | Web Technologies | Computer Graphics | Microprocessor | Computer Organization and Architecture | Theory of Computation | Numerical Methods | Probability and Statistics | Discrete Mathematics
Leveraged Knowledge in Python, Numpy, Pandas, Java, HTML, Tailwind CSS, Node JS, React JS, Django, Rest API, Debugging
Leveraged Knowledge in Java, Kotlin, Android SDK, JUnit, Espresso, DI, Rest API, AWS S3, Firebase, Caching, Jira, C++, Google Map
Leveraged Knowledge in Java, Android SDK, Node JS webserver, Javascript, Firebase, Rest API, Project Management
CausalPath works on a set of molecular profiles and predicts the causal mechanisms that acts on the data. It integrates prior knowledge from biological pathway databases, and uses them to explain new observations. While the main focus is phosphoproteomics, CausalPath can also work on global proteomic, transcriptomic, acetylomic and methylomic profiles, and combinations of them.
Utilized: HTML, JavaScript, CSS, Node JS, Git, Chrome Dev Tools
https://github.com/PathwayAndDataAnalysis/causalpath-newt-webserverI've developed a comprehensive solution for
single-cell pathway analysis:
1. Client: I built a user-friendly web app with React JS for generating, visualizing, and modifying
cell
trajectories.
2. Server: A robust Django-based server handles communication with real-time responses and data
storage.
3. Database: I use MongoDB for data storage, ensuring data integrity.
I've also implemented a Python pipeline for efficient multi-step analysis, making pathway analysis
more
accessible to
researchers. My goal is to advance bioinformatics and simplify complex analyses for a better
understanding of cellular
behavior.
Utilized: React JS, Django, MongoDB, HTML, Tailwind CSS, JavaScript, Rest API, Graph JS, Python, Git
Server GitHub: https://github.com/PathwayAndDataAnalysis/single-cell-pathway-analysis
Client GitHub: https://github.com/PathwayAndDataAnalysis/single-cell-pathway-analysis-client
Developed a new transcription factor activity prediction tool using Python, Numpy, and Pandas. I implemented parallelization in Python to work on large CSV files(more than 600 MB) and to run the analysis for each cell. This project is based on rank-based analysis.
Utilized: Python, Numpy, Pandas, Git, Jupyter Notebook, Google Colab, Vectorization, Parallelization
https://github.com/PathwayAndDataAnalysis/TF-Analysis
I built a machine-learning model to classify whether a gene is mutated or not based on its read count. As genes mutate so frequently, some mutations are harmful some are not. As we have many datasets already available about the gene mutations. So if we can detect gene is mutated or not of a patient using machine learning models, we can infer other information about the patient like suggesting a similar type of treatment to other patients who also have the same mutation. I used a dataset from The Cancer Genome Atlas (TCGA), a landmark cancer genomics program, molecularly characterized over 20,000 primary cancer and matched normal samples spanning 33 cancer types.
Utilized: Python, Tensorflow, Keras, Pandas, Numpy, Git, Jupyter Notebook, Google Colab
Source Code: https://github.com/KisanThapa/GeneMutationClassification
Project Report: https://sites.google.com/view/ml-project-report/home
"Facebook Video Downloader and Player" allows you to browse and navigate through your Facebook account and profile by using an easy to use interface, you can browse your news feed, your friends timelines, your pages and the groups you liked using a built-in browser that enables you to download any video from Facebook simply by clicking on the video you want to download or watch and save them on your device so you can watch it later offline or share it with your friends via different apps. I publised it to Google Play Store, but it is not live now.