Research

Concerns of Foreign Private Issuers on "The Enhancement and Standardization of Climate Related Disclosures for Investors"

Aarsh Shekhar

UT Austin LBJ School thesis, in progress, 2026

Analyzes foreign private issuer concerns around climate-related disclosure standardization, investor comparability, and cross-jurisdiction regulatory friction.

A Computational Pipeline for Glioblastoma Vaccine Development: Integrating Novel Omics-Driven OIP5 Target Discovery to Create a Deep Learning-Based Immunogenicity Framework for Personalized Immunotherapy

Aarsh Shekhar

bioRxiv, 2025

Integrates omics-driven OIP5 target discovery with a deep learning immunogenicity framework for personalized glioblastoma vaccine development.

TEIP: A Compact, Open-Source Framework for Predicting Tumor Epitope Immunogenicity in Glioblastoma Using Deep Learning and Multi-Modal Biological Features

Aarsh Shekhar

bioRxiv, 2025

Introduces a compact open-source deep learning framework for predicting tumor epitope immunogenicity using multi-modal biological features.

Neurodegenerative Disease Modeling

Aarsh Shekhar

ISEF finalist research; UT Austin TACC, 2025

Designed predictive frameworks for early detection and therapeutic screening using UT Austin supercomputers, integrating in-silico modeling with in-vitro trials and presenting at nine conferences.

Novel Development of LLM-Driven mCODE Data Model for Improved Clinical Trial Matching to Enable Standardization and Interoperability in Oncology Research

Aarsh Shekhar

arXiv, 2024

Develops an LLM-driven mCODE workflow for oncology clinical trial matching, emphasizing standardized data representation and interoperability across patient records.

The Carbon Risk Real Estate Monitor (CRREM) under Pressure: Decomposing GHG Pathways and Evaluating CRREM Results amid Asset- and Environment-Level Shifts

Aarsh Shekhar

Working manuscript, 2024

Evaluates CRREM pathway behavior under shifting asset- and environment-level assumptions, decomposing emissions pathways and interpreting climate-risk outputs.

Agentic AI and Misinformation Modeling Using Reinforcement Learning

Aarsh Shekhar

IEEE SSCI, 2024

First-authored work modeling agentic AI and misinformation dynamics through reinforcement learning systems, including Q-Learning, Nim-inspired environments, and multi-agent simulation.