I am Md Tarikul Islam Papon, a PhD candidate at the Computer Science Department of Boston University advised by Professor Manos Athanassoulis. I am a member of the BU Data-intensive Systems and Computing (DiSC) lab, and part of the MiDAS group@BU. My research interests broadly include Data Systems, specifically focusing on hardware-aware data management challenges, stemming from the evolution of storage and memory devices. During last summer, I worked as a Research Scientist Intern with the Parallel Computing Lab at Intel.
Prior to joining Boston University, I was a lecturer at the Department of Computer Science & Engineering (CSE) in Bangladesh University of Engineering & Technology (BUET) for 4 years. I completed my MSc and BSc degree from the same department under the supervision of Dr. Ashikur Rahman. There I worked on medical informatics using various machine learning and embedded system techniques for health monitoring.
Outside of the office I enjoy traveling and playing Pool (mostly 8 Ball and 9 Ball) and Table Tennis. I also love watching movie and tv series.
I am currently developing a new design paradigm for interacting with durable storage that takes into account performance asymmetry between read and write operations, as well as the variable access concurrency that different devices may support. In addition, I am designing new abstractions for data systems architectures supporting efficient hybrid transactional/analytical processing (HTAP). I am also working on designing modern data systems that can support efficient persistent deletes. Prior to joining BU, I worked on medical informatics including software/hardware co-design approaches using various machine learning and embedded system techniques for tele-health monitoring.
We propose a simple yet expressive I/O model that considers asymmetry and concurrency of contemporary storage devices.Learn more
Lethe provides persistence guarantees for delete operations within bounded time and enables efficient secondary range deletes in LSM-based storage engines.Learn more
We introduce a new type of near-memory computation to transform between row-oriented data to column-oriented data on the fly.
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