Umesh Khaniya Postdoc | Antibody Engineering | Computational Biology

Postdoctoral Researcher at NIH

Hi, I'm Umesh Khaniya

I am a postdoctoral researcher at NIH working on antibody engineering, CAR-T cell modeling, and computational immunology. My focus includes structure-based analysis of Ig folds, machine learning-based topology labeling, and predicting domain-domain interactions.

2022-Present Postdoctoral research at NIH
3 Focus Areas Antibody engineering, CAR-T modeling, computational immunology
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Umesh Khaniya

Education

Academic training

  • Ph.D. in Physics CUNY Graduate Center, 2016-2022
  • Master in Physics Tribhuvan University, Nepal, 2013-2015

Bio

Research profile

I am a postdoctoral researcher at NIH focused on antibody engineering, CAR-T modeling, and machine learning-based structural analysis.

Key Skills

Technical strengths

Core technical areas grouped by domain for quicker scanning across computational biology, machine learning, and genomics.

Computational Biology

  • MD & Docking: NAMD, GROMACS, OpenMM, CHARMM-GUI, MM-PBSA, FEP, Schrödinger BioLuminate, AutoDock, PIPER
  • Protein Modeling: AlphaFold (AF2/AF3), ESMFold, RoseTTAFold, Modeller, Chai Discovery
  • Protein-Ligand Docking: Schrödinger BioLuminate, PIPER, AutoDock
  • Cheminformatics: RDKit, PaDEL
  • Visualization & Tools: VMD, PyMol, UCSF Chimera, Jupyter Notebook

Machine Learning

  • ML: Graph Neural Networks, Transformer Models, Diffusion Models, Hugging Face, Fine-Tuning
  • Frameworks: PyTorch, TensorFlow, scikit-learn, PySpark
  • Programming: Python, SQL, Bash, R
  • Cloud & DevOps: AWS (EC2, S3, Redshift, Lambda), HPC environments, Docker, Git, Airflow

Genomics

  • Genomics: Variant analysis, RNA-seq, single-cell genomics, genome annotation, NGS pipelines

Credentials

Certifications & Training

Selected certifications and training areas relevant to computational biology, machine learning, and scientific software.

Designing Quality Ligand Libraries Introduction to Computational Antibody Engineering Data Engineering Machine Learning

View all certifications

Experience

Current work and research direction

Current Role

Postdoctoral Researcher, NIH

Working at the intersection of antibody engineering, CAR-T cell modeling, and computational immunology with a strong emphasis on structural analysis and predictive modeling.

2022-Present Machine learning Structural biology

Experience

  • Postdoc, NIH (2022-Present)
  • Graduate Researcher, CUNY (2016-2022)

Papers

Selected publications and citation history are available on Google Scholar.

Google Scholar

Projects

Selected work

Representative projects combining structural biology, simulation workflows, and computational modeling.

gene-ig-identify

Structural classification of Ig domains across proteomes using TM-align and AF2 models. This can predict immunoglobulin (Ig) and Ig-like domains in protein structures. The main goal is to quantify Ig domains in the human genome at both the domain and chain levels.

gene-ig-identify

CAR-T Modeling

Developed pipelines to simulate and evaluate synthetic CAR-T constructs using MD simulations and structural prediction tools.