Arghya Bhattacharya
	Short Bio Ph.D. Candidate, Stony Brook CS Visiting Research Student Intern, Berkeley Lab Software Engineering Ph.D. Intern, Google Cloud and Networking Intern, Nokia Bell Labs Mentor, HS-WISE Senior Scholar'12, JBNSTS Administrative Member, JBSPDF	Research Interest External memory algorithm Parallel algorithm Machine learning (ML) advice Computer networks Dynamic graph algorithm Filesystem aging	Technical Skills Programming Language: C/C++, Bash, Python Database: MySQL Machine Learning: Keras, PyTorch Numeric Computing: Matlab Scientific Writing: Latex

Summary

I am a final-year doctoral candidate with experience in academic research and industry. Broadly, I am interested in algorithm engineering and experimental framework design. My thesis sheds light on designing cache-friendly algorithms that breed theoretical and practical advantages. These algorithms are helpful for shared memory and cloud systems. I also design machine-learning-advised algorithms for traditional online decision-making problems.
I am actively seeking post-doctoral positions in applied sciences, specifically software engineering, machine learning, and data science.

Research Interest

Work Experience

• Lawrence Berkeley National Laboratory, Berkeley, CA, USA
  
  • Visiting Research Student Intern, PASSION Lab, Performance and Algorithms Research Group, Host: Dr. Helen Xu, Group Lead: Dr. Leonid Oliker [Sep'23 -- Dec'23]
    
    • Dynamic GAP: Designed an experimental framework to study the performance of external-memory algorithms (APIs) for large sparse dynamic graphs.
• Google, San Bruno, CA, USA
  
  • Software Engineering Ph.D. Intern, YouTube, Host: Dr. Jeremy Zhou, Dr. Kevin Chen, Team Manager: Dr. Jon Varsanik [May'23 -- Aug'23]
    
    • Designed an empirical framework and programmed an experimental pipeline to test several experimental learning models to understand the subjective perception of the video quality of YouTube users from the context (content type, device type, geolocation, and other streaming objectives). [Under NDA]
    • Intern Learning: Personal Branding, Fundamentals of Innovation, Impactful Performance Writing.
    • Volunteered 3.5 hours for Job Hackers - Non Client Facing Resume Reviews and Climb Hire Virtual Mock Interviews.
• Nokia Bell Labs, Murray Hill, NJ, USA
  
  • Cloud and Networking Intern, Network Systems and Security Research Lab, Bell Labs Core Research, Host: Dr. Edward Grinshpun, Chuck Payette, Manager: Dr. T. V. Lakshman [Jun'22 -- Aug'22]
    
    • Designed out-of-band ML-based prediction augmented congestion control algorithms for low-latency high-volume variable-bitrate applications (such as live video streaming) for 5G wireless network systems. [Under NDA]
    • Completed Harvard ManageMentor Leading People by Harvard Business Publishing.
• Stony Brook University, Stony Brook, NY, USA
  
  • Graduate Teaching Assistant, Dept. of Computer Science [Jan'23 -- Apr'23]
    
    • Graded papers and homework assignments, proctored exams, and took weekly office hours to teach theory of database systems (CSE 532 Course page, Instructor: Prof. Fusheng Wang), concepts on query optimization, indexing, spatial queries, and using MapReduce/Hadoop and Spark.
  • Research Project Assistant, Algorithms Lab, Dept. of Computer Science, Advisor: Prof. Michael A. Bender [Jun'19 -- Dec'22]
    
    • Cache-efficient algorithms: Designed an empirical framework to evaluate when (a, b, c)-regular algorithms (a class of recursive and cache-oblivious algorithms) are cache-adaptive for external-memory applications, i.e., do not degrade in the face of memory fluctuations [1]. These dynamic memory fluctuations are common in most modern shared-memory and cloud systems. This work closes the logarithmic I/O-performance gap between (a,b,c)-regularity and cache-adaptivity previously shown in theory. [code] The journal version of the work builds an empirical framework to extend the study of the cache-adaptivity of external-memory algorithms in a multi-threading ecosystem [Manuscript in preparation]. [code]
    • Machine-learning augmentation: Explored a recently developed algorithm-design framework that brings out the advantage of both ML and traditional online algorithms. Redesigned ML-advised online algorithms for rent-or-buy problems with augmentation by single and multiple ML oracles [2] [code]; the journal version deals with a more generalized version of the problem with an arbitrarily fluctuating discount on the rent of the resource [3]. Designed algorithms for non-clairvoyant non-preemmptive job scheduling problem (e.g., query scheduling on clusters) and carried out an empirical study on two real-world datasets (Google and Alibaba) [code]. The follow-up work revisits conventional online decision-making problems, such as list-update problem, secretary selection problem, green paging and parallel paging problem, and designed ML-advised algorithms with improved performance [Manuscript in preparation].
    • Filesystem aging: Wrote Bash scripts to test synthetic micro- and macro-benchmark and realistic application-level fragmentation benchmark workloads to measure slowdown in the file system performance for several production filesystems (ext4, btrfs, xfs, zfs, and f2fs) as well as a B^ε-tree based write-optimized in-kernel filesystem BetrFS [4]. [code]
  • Mentor, High School Women in Science and Engineering (HS-WISE), Manager: Jacquelyn Harper [Sep'19 -- Present]
    
    • Taught high-school students a lesson named as Basics of Probability and Thinking Algorithmically.
    • Shoreham-Wading River High School (2019-20), Comsewogue High School (2020-21), Hauppauge High School (2021-22), South Country Bellport High School (2022-23).
  • Teaching Assistant, Dept. of Computer Science [Sep'18 -- May'19]
    
    • Graded programming assignments using Python scripts and took weekly office hours to help students program in C for System Fundamentals - II (CSE 320 course page, Instructor: Prof. Eugene Stark)
    • Graded final and midterm exams and took weekly office hours for the course Fundamentals of Information Technology (ISE 218, Instructor: Prof. Kevin McDonnell)
• National University of Singapore, Singapore
  
  • Research Engineer, Energy Systems and Microgrid lab, Dept. of Electrical and Computer Engineering, Advisor: Prof. Dipti Srinivasan [May'18 -- Aug'18]
    
    • Reviewed the performance of Multi Objective Evolutionary Algorithm based on Decomposition (MOEAD) techniques experimentally using Matlab.
• Pricewaterhouse Coopers India Pvt. Ltd, Kolkata, WB, India
  
  • Consultant, Technology Consulting, Manager: Sudipto Sarkar [Jul'16 -- Sep'17]
    
    • Implemented the Microsoft Navision enterprise resource planning software (ERP) 2016 for finance, procurement, and inventory management as well as performed a client-facing onsite role to gather reporting requirements for an international client in the power sector. [Under NDA]
    • Built a payroll automation system using DotNet technologies, programmed using C# following MVC architecture. [Under NDA]
    • Activities: Performed the roles of a speaker and a reviewer (public speaking) in the Toastmasters International Club (PwC Kolkata chapter) .
• Jadavpur University, Kolkata, WB, India
  
  • Undergraduate Research Assistant, Measurement and Instrumentation Lab, Dept. of Electrical Engg., Advisor: Prof. Debangshu Dey [Jun'15 -- May'16]
    
    • Designed algorithms for affective computing and programmed a methodology to detect emption from images and videos in Matlab. Studied feature extration (using Bi-dimensional Empirical Mode Decomposition (BEMD)), dimensionality reduction (using PCA and LDA), feature elimination (using GLCM, HOG, and LTP), and classification (using M-SVM, k-NN) [6, 7].
    • Designed algorithms for biomedical image processing and cancer detection using Optical Colonoscopy videos by cross-wavelet feature extraction [8].
• Indian Institute of Technology (IIT) Kharagpur, Kharagpur, WB, India
  
  • Undergraduate Summer Intern, Telemedicine Lab, Dept. of Computer Science, Advisor: Prof. Jayanta Mukhopadhyay [Jun'14 -- Aug'14]
    
    • Designed a portable electronic device for non-invasive continuous measurement of blood pressure by bio-impedance measurement for a scalable cardiac-health assessment model.

Education

Hackathons

Test Scores and Awards

Articles for fun (Flavors of my life)

About Me

I stay in the beautiful Long Island, NY, USA. I grew up in the village Mourigram, Howrah, WB, India. Connect with me on social media.  
Read my blog on Medium. In this blog, I share my thoughts and opinions on several social, technical, and political issues.

Collaborators/Co-authors/References

• Prof. Michael A. Bender (Stony Brook), Ph.D. (Harvard)
• Prof. Rezaul A. Chowdhury (Stony Brook), Ph.D. (UT Austin)
• Prof. Helen Xu (Georgia Tech. + Berkeley Lab), Ph.D. (MIT)
• Prof. Rathish Das (U Houston + U Liverpool), Ph.D. (Stony Brook)
• Prof. Shahin Kamali (York), Ph.D. (U Waterloo)
• Prof. Bill Jannen (Williams), Ph.D. (Stony Brook)
• Dr. Edward Grinshpun (Nokia Bell Labs), Ph.D.
• Chuck Payette (Nokia Bell Labs)
• Prof. Debangshu Dey (Jadavpur), Ph.D. (Jadavpur)
• Prof. Amitava Chatterjee (Jadavpur), Ph.D. (Jadavpur)
• Prof. Sugata Munshi (Jadavpur), Ph.D. (Jadavpur)
• Sudipto Sarkar (PwC India)