Computational biology · Machine Learning. Artificial Intelligence

Kritika
Prasai

Ph.D. Student in Computer Science University of Missouri (Mizzou);Columbia

My research focuses on developing machine learning methods that learn from large-scale biological data and limited experimental observations to model protein interactions, viral evolution, and antigenic escape, with the goal of improving infectious disease prediction, surveillance, and intervention.

Kritika Prasai Mizzou · CS
Protein Interaction Modeling Viral Evolution Antigenic Escape Graph Neural Networks Protein Language Models Vaccine Effectiveness Infectious Disease Prediction Protein Interaction Modeling Viral Evolution Antigenic Escape Graph Neural Networks Protein Language Models Vaccine Effectiveness Infectious Disease Prediction

01 — About

Interpretable Machine Learning for Biological Systems

I'm a Ph.D. student in Computer Science at the University of Missouri-Columbia. My work sits at the intersection of artificial intelligence and computational biology, where I develop AI systems that learn from complex, sparse, and large-scale biological data.

My research focuses on protein interaction prediction, viral evolution, antigenic escape, vaccine effectiveness, and infectious disease modeling. I use deep learning, protein language models, foundational models, and scalable bioinformatics pipelines to transform biological data into predictive insight for infectious disease surveillance and intervention.

02 — Research

Highlighted work

A selection of research projects that showcase my work at the intersection of AI and computational biology.

Protein Binding Prediction Model

Deep Learning · PLMs

Compat Net: Interaction aware protein compatibility prediction model

A multi-task deep learning framework using protein language models and transformer-based gated cross-attention to model viral–host protein interactions.

Epistasis aware neutralization titer prediction model

Graph Neural Networks

Epistasis aware neutralization titer prediction model

Graph neural network models that capture epistatic mutation effects underlying influenza antigenic evolution and immune escape.

Diversity and antigenic evolution of H3N2 influenza viruses

Genomics · Vaccines

Viral Genomics & Vaccines

Analysis of viral sequence diversity, polymorphisms, entropy, and antigenic evolution to support improved influenza vaccine design.

03 — Publications

Selected publications

  1. P5

    Prediction models for COVID-19 disease outcomes.

    Tang, C.Y., Gao, C., Prasai, K., Li, T., Dash, S., McElroy, J.A., Hang, J., Wan, X.-F.

    Emerging Microbes & Infections · 2024

  2. P4

    Intrahost HA polymorphisms and culture adaptation shape antigenic profiles of H3N2 influenza viruses.

    Prasai, K., Yang, Z., Guan, M., Li, T., Ware, D., Hang, J., Wan, X.-F.

    Journal of Virology · 2026

  3. P3

    Epitope-spanning antigenic variation reprograms immunodominance and broadens immunity in sequential influenza vaccination.

    Wan, X.-F., Guan, M., Balamalaliyage, P., Chen, H., Prasai, K., et al.

    Nature Communications · 2026

  4. P2

    Species- and variant-specific ACE2 compatibility shapes SARS-CoV-2 spillover potential in North American cervids.

    Espada, C., Ye, K., Long, Y., Prasai, K., DeLiberto, T., Heale, J., Wiese, R., Yang, Q., Zhou, M., Streich, S., Tao, Y., Chandler, J.

    Nature Communications · 2026

  5. P1

    Interaction-aware multitask deep learning reveals cross-species receptor compatibility landscapes across sarbecoviruses.

    Prasai, K., Chandler, J.C., Espada, C., Wiese, R., Long, Y., Streich, S.P., Heale, J., Roberts, N., Tao, Y.J., DeLiberto, T.J., Wan, X.-F.

    Nature Communications Biology · under review

04 — Path

Experience & education

Professional Experience

Aug 2022 – Present

Research Assistant

University of Missouri-Columbia

Developing AI and bioinformatics methods for protein binding prediction, antigenic escape modeling, viral genomic analysis, vaccine effectiveness, and differential gene expression analysis.

Feb 2020 – May 2022

AI Engineer

PSP Corporation — South Asia Liaison Office, Nepal

Built deep learning models for medical image segmentation: multi-organ segmentation, pseudo-labeling for cost-efficient annotation, and lung fissure detection from real patient imaging.

Education

Aug 2022 – Present

Ph.D. in Computer Science

University of Missouri-Columbia

2015 – 2020

B.E. in Computer Engineering

Tribhuvan University, Nepal

05 — Service

Leadership & service

2025

President, EECS Graduate Student Association

University of Missouri · 2025–2026

Led graduate student engagement initiatives, including the Mizzou Tiger Internship Seminar, ShowMe Course Project Showcase, and a college-wide ARC-AGI hackathon.

2024

Director of Communication, EECS GSA

University of Missouri · 2024–2025

2025

Vice President, MUNSA

Missouri University Nepali Student Association · 2025–2026

2024

EECSDepartment Representative, Graduate Professional Council (GPC)

University of Missouri · 2025–2026

Represented the EECS department and contributed to student affairs initiatives supporting graduate student mental health and well-being.

06 — Recognition

Awards & honors

2026

Outstanding PhD Student Award

EECS Department, University of Missouri

April 2026

2024

Interdisciplinary Case Competition Winner

Graduate Professional Council, University of Missouri

2024

2026

Rising Stars in Computational & Data Sciences

Santa Fe Institute

Selected Research Talk

07 — Talks

Conference presentations

2025

Epidemics 2025 — Poster Presentation

Epidemics Conference 2025

Intrahost polymorphisms and antigenic adaptation in H3N2 influenza viruses.

2024

ASV 2024 — Oral Presentation

American Society of Virology (ASV 2024)

Differential expression analysis of innate immune responses.

08 — News

Recent updates

Add your own moments here — talks, papers, travel, lab events.

2026

Outstanding PhD Student Award

Recognized by the EECS department at the University of Missouri.

2026

Rising Stars Workshop

Selected for a research talk at the Santa Fe Institute.

2025

Epidemics 2025

Presented a poster on H3N2 intrahost polymorphisms and antigenic adaptation.

09 — Contact

Let's build something at the edge of AI and biology.

Open to collaborations, talks, and conversations about computational biology, viral evolution, and machine learning for the life sciences.