Sujaya Maiyya

As a Computer Science Ph.D. candidate at UC Santa Barbara, I design, prototype and evaluate protocols for managing large-scale data in a performance efficient and secure manner. The research topics I am interested in are Large Scale Data Management, Distributed Systems, and Cryptography. Today, much of the data that is produced - including data that is private and sensitive - is stored on third party cloud providers. When the data is outsourced, there exists a fundamental tradeoff between security and efficiency while accessing the outsourced data. My research focuses on designing, prototyping and evaluating protocols that strike a balance between efficiency and security in both trusted and untrusted systems. The increasing popularity of blockchains and the use of third-party cloud -- scenarios in which the servers storing and processing the data are not trusted but the data owners nonetheless need low data access latencies -- makes my research interests highly relevant to today's problems. I am co-advised by Prof.Amr El Abbadi and Prof.Divy Agrawal.


PhD in Computer Science University of California, Santa Barbara (Jan 2018 - present)
MSc in Computer Science University of California, Santa Barbara (Sept 2016 - Dec 2017)
BE in Information Science PESIT, Bangalore (Aug 2010 - May 2014)


Sujaya Maiyya, Ishtiyaque Ahmad, Divyakant Agrawal, Amr El Abbadi "Samya: Geo-Distributed Data System for High Contention Data Aggregates" ICDE 2021 [pdf].

Fuheng Zhao, Sujaya Maiyya, Ryan Wiener, Divyakant Agrawal, Amr El Abbadi "KLL±: Approximate Quantile Sketches over Dynamic Datasets" VLDB 2021 [pdf].

Sujaya Maiyya, Danny HB Cho, Divyakant Agrawal, Amr El Abbadi "Fides: Managing Data on Untrusted Infrastructure" ICDCS 2020 [pdf].

Mohammad Javad Amiri, Sujaya Maiyya, Divyakant Agrawal, Amr El Abbadi "SeeMoRe: A Fault-Tolerant Protocol for Hybrid Cloud Environments" ICDE 2020 [pdf].

Sujaya Maiyya, Faisal Nawab, Divyakant Agrawal, Amr El Abbadi "Unifying Consensus and Atomic Commitment for Effective Cloud Data Management" VLDB 2019 [pdf][ppt].

Victor Zakhary, Mohammad Javad Amiri, Sujaya Maiyya, Divy Agrawal, Amr El Abbadi "Towards Global Asset Management in Blockchain Systems" BCDL co-located with VLDB 2019 [pdf].

Sujaya Maiyya, Victor Zakhary, Mohammad Javad Amiri, Divy Agrawal, Amr El Abbadi "Database and Distributed Computing Foundations of Blockchains" SIGMOD 2019 (tutorial) [pdf].

Sujaya Maiyya, Victor Zakhary, Divy Agrawal, Amr El Abbadi "Database and Distributed Computing Fundamentals for Scalable, Fault-tolerant, and Consistent Maintenance of Blockchains" VLDB 2018 (tutorial) [pdf] [slides].

Vaibhav Arora, RKS Babu, Sujaya Maiyya, Divyakant Agrawal, Amr El Abbadi, Xun Xue, Yanan Zhi and Jianfeng Zhu "Dynamic Timestamp Allocation for Reducing Transaction Aborts" IEEE Cloud 2018 [pdf].


  • Received IBM PhD Fellowship 2020 award.
  • Received Google PhD Fellowship 2020 award (declined).
  • Received Outstanding Graduate Student 2018-19 award in the CS department at UCSB.
  • Received travel awards to attend CRA-W grad cohort 2019 and GHC - 2017.
  • Elected as a Graduate Student Representative for 3 consecutive years at UCSB.
  • Recipient of VLDB Travel Grant in 2018.
  • Secured Spot Bonus incentive for leadership qualities and timely delivery of a critical product in Citrix.
  • Four times a recipient of SAP Labs Scholarship for securing the highest CGPA in the department at PESIT.
  • Received Summer Research Fellowship from Ministry of Human Resource Development, India.

  • Ongoing Research

    Building a fault-tolerant ORAM system

    Oblivious RAM or ORAM is a well known technique that preserves data access privacy by hiding access patterns. While there exists many variants of ORAM solutions, most are not fault-tolerant and will loose the application data in the presence of crash failures. This project proposes a fault-tolerant ORAM system using replication, which allows the system to tolerate bounded number of crash failures, while preserving privacy

    Privacy Preserving Transactions

    The project proposes a solution to execute transactions privately on data hosted on untrusted third party cloud providers. The access frequencies of data items are hidden from the adversary to mitigate any inference attacks based on access patterns.

    Distributed transactions in a serverless setting

    IBM KAR is a runtime that significantly simplifies developing stateful, microservices-based applications. KAR employs an actor based model where individual actors manage their own state. This projects provides distributed transactional semantics within KAR. Transactions can be concurrent and span multiple actors, who may fail at any point in time; the transactional framework guarantees ACID properties in spite of failures.

    Teaching Experience

    I have served as a Teaching Assistant in the Computer Science department at UCSB for two years. The courses I have taught are:
  • CS130A: Data Structures and Algorithms using C++ - Fall 2018, Winter 2018
  • CS171: Distributed Systems - Spring 2018
  • CS32: Object Oriented Design - Fall 2017
  • CS16: Problem Solving using C++ - Spring 2017
  • CS56: Advanced Application Programming - Winter 2017

  • Industrial Experience

    PhD Research Intern - Hybrid Cloud - IBM Research (June 2021 - September 2021)

    As part of the IBM PhD Fellowship program, I interned at the Hybrid Cloud division on IBM Research, working on KAR runtime, a system similar in concept to aserverless platform. In this project, I added distributed transactional semantics within KAR to guarantee ACID properties. I implemented TPC-C benchmarkingto evaluate the proposed transactional framework and to highlight the expansiveness of KAR.

    PhD Software Engineer Intern - Cloud Infra - Google (June 2018 - September 2018)

    I worked in Quotaserver – a distributed bin counting - team. During my internship, I identified the performance bottleneck of Quotaserver which was due to the backend database, Spanner. I optimized the performance of Quotaserver by sharding a single key in Spanner across different cells. The optimization resulted in 2.3x performance gain and 51% reduction in latency of the system.

    Software Engineer Intern - Ads - Google (June 2017 - September 2017)

    I worked in Google Ads group to introduce a new type of scheduler for Ads report generation that produced timely reports for customers across the globe. The project went in production and impacted many ad publishers, redefining the existing client SLAs.

    Software Developer - NetScaler - Citrix R&D (Feb 2014 - July 2016)

    I worked with Cloud Orchestration team of NetScaler in developing various features for NetScaler Control Center (NCC). I successfully drove the project of adding a new feature in NCC which enabled major CloudStack tenants to use load-balancing features of NetScaler. I have also committed fixes into OpenSource Apache CloudStack. I took ownership of extending admin partition feature of NetScaler to various cloud users, thus eliminating hardware constraints required for loadbalancing.