About me

• Open to collaboration and position on Bioinformatics, Deep Learning, Language Deep models,and DevOPs.
• Candidate word: If i have applied for your open position then i am surely interested
• Read this: Immediate joining and relocation, Past collaborator and employers, Mentality Note, Professionalism
• Application package: Covering Letter, Curriculum Vitae, Scopus, ORCID, Web of Science. Employers references.
• Approachable at gauravcodepro@gmail.com
• All code and ideas concieved and written by me unless specified.
• Voracious reader of language model implementations language-models
• Continous learning, and latest feature addition in the priority languages and areas of interest
• Prior to 2022, all the code I wrote was internally hosted by the research institutions/universities.

Work Attitude and Professionalism

• Decisions and approaches/attempts not made in time have no value and importance.
• If someone is understanding without speaking it is a disordered personality trait and supporting such is a matter of your loss.
• I read scientific aptitude to increase my work potential for continous development.
• Communicative, approachable, motivated, task and solution oriented, time responsive, and initiative taker.
• Pro-active in making effective communication as communication leads to result oriented approaches.
• Active in solving limitations and applicative person, proficient in developing new approaches for code development.
• Plan and code well ahead as to have a planned approach of problem solving.
• I think very precisely before speaking as what has spoken cant be fetched back.
• I belive in effectively working with those, who talk to me and not other way around.
• I belive in not taking and appreciating indirect approaches as it never generate results.
• Extremely collaborative and open to supporting those who support me.
• I dont work with the past and this keeps me result oriented and give importance to the people i am working with.
• I pick the programming langauge to get the work done and can drop anytime and pick another.

Areas of Interest

• Bioinformatics,Deep Learning,RAG,Language Models and Graphs.

  • Priority Language: Python, R, Ruby, Julia, Javascript, Shell, Awk, Nextflow
  • Machine/Deep Learning:PyTorch, Tensorflow, CatBoost, XGBoost, Sklearn, Keras, LightGBM, Fastai.
  • Package Development: Python, Julia, Javascript, Ruby.
  • API Development using FASTAPI/Javascript
  • Documentation: Markdown, ReadTheDocs, MkDocs, Jekyll, Github Pages, Quatro

• DevOPs

  • Priority Language: Shell, NuShell, Awk, Python
  • Code Management: Git, GitKraken.
  • Application deployment: Docker
  • Orchestration:Terraform, Kubernetes.
  • Database Management: SQlite3, MySQL, MongoDB.
  • PBS and SLURM as job scheduler

• Web Develpoment

  • Priority Language: Ruby on Rails, Streamlit, Python Shiny/ShinyExpress, Django, BootStrap, Julia, HTML, Javascript
  • JSON Format: jinja and jinja2, jq, yq

• Application Development

  • Learning Electron, Slint, Tauri, PyQT, PyGTK

Streamlit: Streamlit Applications and Streamlit Community

• Metabolic-analyzer:API to BIGG database models
• Metabolic-json: analyzing BIGG metabolic models
• Ontology-graphs: analyzing arabidopsis ontologies and plant obo
• Pacbiohifi: pacbiohifi sequencing information
• DevOPS Application Slurm Generator

Ruby Gems: Ruby gems, Ruby app and Ruby Community: released and development version

• Devopsutils: devops system configuration
• Ruby-template: ruby templating
• Fasta-tokenizer: fasta tokenizer and motif indexer
• Panacheextract: snp extractor
• Protalign: protein alignment analyzer and proteinmultialign
• Pacbiohifi-analyzer: pacbio hifi data from reads to graphs

Python Package: Python Packages and Python Community: building development release version

• Tairaccession: python package for interacting with tair
• Graphanalyzer: python package for graph alignment tools
• RNAprocessor: python package for sequence based machine learning
• ProteinAnnotator: python package for genome-protein alignments
• Ontologyanalyzer: python package ontologies using the semantic web