Short Bio
- Ph.D., Stony Brook CS
- B.Eng., Jadavpur University EE
- Software Engineer III (L4), Google
- (Former) Intern at Berkeley Lab
- (Former) Intern at Google
- (Former) Intern at Nokia Bell Labs
Research Interest
- External memory algorithm
- Parallel algorithm
- Machine learning (ML) advice
- Computer networks
- Dynamic graph algorithm
- Filesystem aging
Technical Skills
Programming & Databases
- C/C++
- Python
- Bash
- GoogleSQL
- Matlab
- HTML/CSS/JS
Machine Learning
- PyTorch
- Keras
Scientific Writing
- LaTeX
- Markdown
Summary
- I have joined as a Software Engineer III (L4) at Google Cloud after my Ph.D. since September 2024. I engineer Google Compute Engine solutions to develop, maintain, and operationalize Bare Metal cloud solutions for demanding applications.
- I graduated with a Ph.D. in Computer Science in August 2024. My doctoral thesis shed light on designing cache-friendly algorithms that breed theoretical and practical advantages. These algorithms are helpful for shared memory and cloud systems. My thesis also showed how to design ML-advised algorithms for traditional online decision-making problems.
- Research Interest: Broadly, I am interested in algorithm engineering and distributed networked systems design. I primarily focus on algorithms, data structures, probability, and machine learning (ML). More specifically, I study the performance of cache-efficient external-memory algorithms, parallel (multi-threaded) algorithms, ML-advised online algorithms and data structures, dynamic graph algorithms, and filesystem aging.
- Technical Skills: My programming skills include C/C++ (including Multithreading tools: std::thread, posix thread, OpenMP), Bash (Memory management: control group; Virtualization: QEMU, KVM; Network: iperf, netcat, FTP, Ethernet/InfiniBand networks, TCP/IP stack, RDMA, RoCE, OFED, and peer-to-peer data transfer systems), Python (Libraries: NumPy, Pandas, Scipy; ML: Keras, PyTorch, Scikit-learn, XGBoost, Docker Cvxpy; Data visualization: Matplotlib, Seaborn, Plotly; Web development: Streamlit, Flask), GitHub (Version control), GoogleSQL, Matlab, Latex, Markdown, HTML, CSS, JavaScript.
Education
Sep 2018 — Aug 2024
- Doctoral Thesis: Going Beyond Worst-Case: A Study on Cache Adaptivity and Machine Learning Advice.
- I was fortunate that Prof. Michael A. Bender (John L. Hennessy Chaired Professor of Computer Science) advised my doctoral thesis.
- I am also elated that Prof. Rezaul A. Chowdhury (Associate Professor of Computer Science)) served as the chair of my thesis committee. I am also honored to have Prof. Joseph S. B. Mitchell (SUNY Distinguished Professor and Chair of Applied Mathematics and Statistics) on my committee. Finally, Prof. Helen Xu (Assistant Professor at Georgia Tech CS) was the external member.
- Graduate Coursework: Analysis of Algorithms, Data Science Fundamentals, Discrete Mathematics, Fundamentals of Computer Networks, Introduction to Computer Vision, Medical Imaging, Theory of Database Systems.
- Academic Services: I subreviewed papers and articles for ESA’23, SPAA’22, SPAA’23, SPAA’24, IPDPS’23, SEA’23, APOCS’23 and Reviewer for Soft Computing, Springer and Neural Computing and Applications, Springer.
- Notable Class projects:
- Optimizing network congestion window using Ricci Curvature
- Semantic segmentation using U-Net and instance segmentation of nuclei using Mask R-CNN.
- Identifying fundraising donors with Logistic Regression, Decision Tree, Random Forest, and LightGBM.
Jul 2012 — May 2016
- Advisor: Prof. Debangshu Dey
- Related Coursework: Advanced Instrumentation (Special Paper), Reliability Engineering, Circuit Theory, Computer Fundamentals, Control System Engineering, Digital Signal Processing, Signals and Systems, Engineering Economics, Engineering Mathematics, Microprocessor and Microcontroller, Numerical Analysis, Principles of Communications, Process Instrumentation.
- I was awarded the Jagadis Bose National Science Talent Search (JBNSTS) Senior Scholarship 2012.