Aasish Kumar Sharma
Biography
Aasish is a scientific employee under Julian Kunkel and is interested in performance optimization, big data analysis, data science and computer vision using advanced technologies such as parallel and high-performance computing, as well as, quantum computing.
ResearchGate
Research Interests
- Optimization
- High-Performance Computing
- Data Science
Teaching
Open Thesis Topics
Constraint Programming for Workload Optimization in HPCApply
Constraint programming offers a robust approach to optimize workload scheduling in HPC systems. This project explores formulating resource allocation and task scheduling as constraint satisfaction problems to minimize makespan and maximize resource utilization.
Integration of Constraint Programming and Machine Learning for HPC SchedulingApply
Machine learning can enhance constraint programming by predicting constraints or guiding solution search. This project investigates hybrid approaches to improve scheduling performance in HPC systems.
Quantum Constraint Programming for Workload Optimization in HPCApply
Constraint programming offers a robust approach to optimize workload scheduling in HPC systems. This project explores formulating resource allocation and task scheduling as constraint satisfaction problems to minimize makespan and maximize resource utilization.
Theses
- Performance Analysis of Convolutional Neural Network Applying Quantum Annealing, Aasish Kumar Sharma (Master's Thesis), Advisors: Sanjeeb Prasad Pandey, 2020-12-30, BibTeX URL
Publications
2024
- State-of-the-art artificial intelligence techniques in healthcare publications, and their correlation with disease and data: A data driven analysis (Sadegh Keshtkar, Dagmar Krefting, Anne-Christin Hauschild, Zully Maritza Ritter, Narges Lux, Aasish Kumar Sharma, Pavan Kumar Siligam, Julian Kunkel), In Journal of Artificial Intelligence and Robotics (1), 2024-11-26 BibTeX URL DOI
- Poster: Optimizing Heterogeneous HPC Workflows With Data Transfer Time (Aasish Kumar Sharma, Christian Boehme, Patrick Gelß, Julian Kunkel), NHR Conference 2024, Darmstadt, Darmstadt, Germany, 2024-09-09 BibTeX URL PDF
- HOSHMAND: Accelerated AI-Driven Scheduler Emulating Conventional Task Distribution Techniques for Cloud Workloads (Michael Bidollahkhani, Aasish Kumar Sharma, Julian Kunkel), IEEE Computers, Software, and Applications Conference, pp. 1-8, IEEE, IEEE, COMPSAC 2024, 2024-07 BibTeX
All publications as BibTex