Mr. Rupam K. Karmakar
Computer Science Researcher & Machine Learning Specialist
Welcome to my academic portfolio. I am a computer science researcher specializing in machine learning and distributed systems. My work focuses on developing innovative algorithms and systems that push the boundaries of large-scale computation and artificial intelligence.
Institution: Department of Computer Science, University Name
GitHub: github.com/johndoe
LinkedIn: linkedin.com/in/johndoe
Google Scholar: View Profile
About Me
I am a doctoral candidate in Computer Science at University Name, where I work under the supervision of Professor Jane Smith. My research focuses on the intersection of machine learning and distributed systems, particularly on developing efficient algorithms for large-scale data processing and neural network training.
My work has been published in top-tier conferences including ICML, NeurIPS, and ICLR. I am passionate about bridging the gap between theoretical research and practical applications, with a focus on making advanced machine learning techniques accessible and efficient.
Research Interests
Education
Ph.D. in Computer Science
Dissertation: "Efficient Training Algorithms for Large-Scale Neural Networks in Distributed Environments"
Advisor: Professor Jane Smith
M.S. in Computer Science
Thesis: "Neural Network Optimization Techniques for Resource-Constrained Devices"
GPA: 3.9/4.0
B.E. in Computer Engineering
Honors: Summa Cum Laude, Dean's List (all semesters)
GPA: 3.95/4.0
Awards & Honors
- Best Paper Award - ICML 2024
- Graduate Research Fellowship - National Science Foundation, 2021-2024
- Outstanding Graduate Student Award - Another University, 2020
- Dean's List - All semesters (2014-2018)
Research Projects
Developed a novel distributed training framework that reduces training time for large neural networks by 40% through innovative gradient aggregation techniques. The framework supports heterogeneous computing clusters and provides fault tolerance mechanisms, making it suitable for both academic research and industrial applications.
Technologies:
Implemented and evaluated various graph partitioning strategies for distributed graph processing systems. Achieved 2x speedup on benchmark datasets compared to existing approaches. The research addresses fundamental challenges in load balancing and communication overhead in distributed graph computations.
Technologies:
Designed an automated neural architecture search system using evolutionary algorithms and reinforcement learning. The system discovers optimal network architectures for specific tasks while maintaining computational efficiency. Successfully deployed in production environments for image classification and natural language processing tasks.
Technologies:
Researched and implemented memory-efficient training techniques for deep learning models, enabling the training of larger models on resource-constrained hardware. Developed gradient checkpointing strategies that reduce memory consumption by 60% with minimal impact on training speed.
Technologies:
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Publications
This paper presents a novel approach to gradient aggregation in distributed training environments. Our method reduces communication overhead by 35% while maintaining convergence properties. The technique has been successfully deployed in production systems handling petabyte-scale datasets.
We analyze different graph partitioning strategies and propose a hybrid approach that adapts to graph topology characteristics, resulting in improved load balancing. Our method shows consistent improvements across various real-world graph datasets including social networks and web graphs.
We present a comprehensive infrastructure solution for training foundation models at scale. Our system handles model parallelism, pipeline parallelism, and data parallelism efficiently, enabling the training of models with over 100 billion parameters on clusters with thousands of GPUs.
This work introduces an evolutionary algorithm framework for automated neural architecture search that discovers architectures competitive with hand-designed networks. Our approach reduces the computational cost of architecture search by an order of magnitude compared to previous methods.
We propose a memory optimization technique that enables training of deep networks with limited GPU memory through gradient checkpointing and dynamic memory allocation. Our approach achieves a 60% reduction in memory usage while maintaining training efficiency within 5% of the baseline.
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Notebooks
This paper presents a novel approach to gradient aggregation in distributed training environments. Our method reduces communication overhead by 35% while maintaining convergence properties. The technique has been successfully deployed in production systems handling petabyte-scale datasets.
We analyze different graph partitioning strategies and propose a hybrid approach that adapts to graph topology characteristics, resulting in improved load balancing. Our method shows consistent improvements across various real-world graph datasets including social networks and web graphs.
We present a comprehensive infrastructure solution for training foundation models at scale. Our system handles model parallelism, pipeline parallelism, and data parallelism efficiently, enabling the training of models with over 100 billion parameters on clusters with thousands of GPUs.
This work introduces an evolutionary algorithm framework for automated neural architecture search that discovers architectures competitive with hand-designed networks. Our approach reduces the computational cost of architecture search by an order of magnitude compared to previous methods.
We propose a memory optimization technique that enables training of deep networks with limited GPU memory through gradient checkpointing and dynamic memory allocation. Our approach achieves a 60% reduction in memory usage while maintaining training efficiency within 5% of the baseline.
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