Manikya Swathi Vallabhajosyula

Computer Science · The Ohio State University

Early career · publishing since 2018Rising activity

Distributed and Parallel Computing Systems Parallel Computing and Optimization Techniques Cloud Computing and Resource Management Natural Language Processing Techniques Scientific Computing and Data Management

Publications

Citations

Est. group size

Recurring co-author estimate

Active years

Publishing since 2018

Research summary

AI-generated

This researcher works on making high-performance computing (HPC) systems smarter, focusing on AI-driven tools that predict how much computing resources (like processing time and memory) a scientific application will need and then schedule jobs more efficiently on large computing clusters. Earlier work also touched on natural language processing, specifically automatically discovering word relationships (hypernyms) from text. The more recent research centers on building practical machine-learning pipelines and infrastructure (schedulers, gateways, MLOps frameworks) to support scientific computing workflows.

AI-driven scheduling for high-performance computingPredicting resource needs for scientific applicationsMLOps and infrastructure for AI workflowsDistributed and parallel computing systemsNatural language processing (hypernym discovery)

Publication output was minimal or absent for several years in the middle of the decade but has grown steadily since 2022, reaching its highest recent activity in 2025-2026.

Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026

Publication cadence

Publications per year over the last 10 years — averaging 2.2/year recently

Recent publications

Configuring, Training, and Deploying AI Applications Through Integrated Gateways and Frameworks for Scientific Field Research
SN Computer Science · 2026
Beyond Automation: Integrating Agentic Capabilities into MLOps with ICICLE Infrastructure
2025
Orchestrating a DNN training job using an iScheduler Framework: a use case
2024
Reference Implementation of Smart Scheduler: A CI-Aware, AI-Driven Scheduling Framework for HPC Workloads
2024
Insights from the HARP Framework: Using an AI-Driven Approach for Efficient Resource Allocation in HPC Scientific Workflows
Practice and Experience in Advanced Research Computing · 2023
Towards Characterizing DNNs to Estimate Training Time using HARP (HPC Application Resource (runtime) Predictor
Practice and Experience in Advanced Research Computing · 2023
Towards Practical, Generalizable Machine-Learning Training Pipelines to build Regression Models for Predicting Application Resource Needs on HPC Systems
Practice and Experience in Advanced Research Computing · 2022
UMDuluth-CS8761 at SemEval-2018 Task9: Hypernym Discovery using Hearst Patterns, Co-occurrence frequencies and Word Embeddings
2018
Hypernym Discovery over WordNet and English Corpora - using Hearst Patterns and Word Embeddings
University of Minnesota Digital Conservancy (University of Minnesota) · 2018
UMDuluth-CS8761 at SemEval-2018 Task 9: Hypernym Discovery using Hearst\n Patterns, Co-occurrence frequencies and Word Embeddings
arXiv (Cornell University) · 2018

Publishes in

Practice and Experience in Advanced Research Computing×3
University of Minnesota Digital Conservancy (University of Minnesota)×1
arXiv (Cornell University)×1
Open MIND×1
Zenodo (CERN European Organization for Nuclear Research)×1

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This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.

Last updated Jul 19, 2026.

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