Janibul Bashir

About

I am Dr. Janibul Bashir, a researcher dedicated to advancing the frontiers of Artificial Intelligence (AI) and Computer Architecture. I lead Gaash, a research group at NIT Srinagar focused on designing and optimizing innovative computer architectures that push the boundaries of AI applications. Our mission is to develop efficient, scalable, and adaptable systems that meet the dynamic demands of modern AI.

At Gaash, we are pioneering research in several cutting-edge areas:

AI-Driven Computer Architectures: We focus on creating high-performance architectures specifically optimized for complex AI applications, with an emphasis on achieving new levels of efficiency and adaptability.

Federated Learning and Transformers: By fusing federated learning with transformer architectures, we are exploring ways to enhance privacy-preserving AI that generalizes effectively across diverse datasets while safeguarding user data.

Transformers for Large Language Models (LLMs): Our work with transformers extends to large language models, with a particular emphasis on zero-shot and in-context learning, enabling these models to perform well on new tasks with minimal or no additional training.

Vision Models: CNN and Transformer Synergies: In computer vision, we are combining convolutional neural networks (CNNs) with transformers to develop hybrid models that surpass current benchmarks in image recognition and classification.

Employment

• National Institute of Technology Srinagar Senior Assistant Professor
  2022 - Present
• National Institute of Technology Srinagar Assistant Professor
  2020 - 2022
• National Institute of Technology Srinagar Trainee Teaher
  2016 - 2020
• Samsung Reserach India Software Engineer
  2014 - 2015

Education

Education history is unavailable.

Projects & Funding

Projects & funding information is unavailable.

Publications (17)

• MedMask: A Self-supervised Vision Foundation Model for Breast Cancer Detection Using Mammograms
  2026 DOI: 10.1007/978-3-032-05559-0_34
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• OpSAVE: Eviction Based Scheme for Efficient Optical Network-on-Chip
  Microprocessors and Microsystems 2024 DOI: 10.1016/j.micpro.2024.105061
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• An Analysis of Various Design Pathways Towards Multi-Terabit Photonic On-Interposer Interconnects
  ACM Journal on Emerging Technologies in Computing Systems 2024 DOI: 10.1145/3635031
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• TBCELF: Temporal Budget-Aware Influence Maximization
  2024 DOI: 10.1145/3632410.3632485
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• GPUOPT
  ACM Journal on Emerging Technologies in Computing Systems 2020 DOI: 10.1145/3416850
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• SecSched
  Proceedings of the ACM International Conference on Parallel Architectures and Compilation Techniques 2020 DOI: 10.1145/3410463.3414631
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• SecONet: A Security Framework for a Photonic Network-on-Chip
  2020 14th IEEE/ACM International Symposium on Networks-on-Chip (NOCS) 2020 DOI: 10.1109/nocs50636.2020.9241713
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• Predict, Share, and Recycle Your Way to Low-power Nanophotonic Networks
  ACM Journal on Emerging Technologies in Computing Systems 2019 DOI: 10.1145/3356585
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• Power efficient photonic network-on-chip for a scalable GPU
  Proceedings of the 13th IEEE/ACM International Symposium on Networks-on-Chip - NOCS '19 2019 DOI: 10.1145/3313231.3352370
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• BigBus
  ACM Journal on Emerging Technologies in Computing Systems 2019 DOI: 10.1145/3289391
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