Aakanksha Mathuria

Software Engineer

Seattle, Washington, United States7 yrs 11 mos experience

Most Likely To SwitchHighly Stable

Key Highlights

Expert in Java and Elasticsearch for scalable solutions.
Proven track record in optimizing data processing.
Strong background in test-driven development and microservices.

Stackforce AI infers this person is a SaaS Backend Engineer specializing in search and data processing solutions.

Contact

aakanksha.mathuria@gmail.com LinkedIn

Skills

Core Skills

JavaElasticsearch

Other Skills

MicronautJUnitMockitoMicroservicesTerraformOpenSearchApache AirflowDockerOCIReact.jsSearchBitbucketPostman APIAPI DevelopmentOracle Database

About

I have a Master of Science in Electrical and Computer Engineering from Portland State University and a Master of Technology in Information Technology (Specialization in Robotics) from the Indian Institute of Information Technology. I am currently a Member of Technical Staff at Oracle, where I am part of the Search Team in Oracle Content Management (OCM). My core competencies include Java, ElasticSearch, SQL, Docker, Jenkins, GitLab, JUnit, Mockito, and REST API. In my role at Oracle, I work with a diverse and collaborative team of engineers, product managers, and designers to deliver high-quality search features and solutions for OCM customers. I leverage my Java, ElasticSearch, and SQL skills to design, develop, test, and deploy scalable and reliable search features. I am passionate about creating user-centric and data-driven products that enhance the user experience and satisfaction of users. I am also eager to learn new skills and technologies and to share my knowledge and insights with my team and the wider Oracle community.

Experience

7 yrs 11 mos

Total Experience

2 yrs 7 mos

Average Tenure

5 yrs 8 mos

Current Experience

Oracle

2 roles

Software Engineer

Sep 2023 – Present · 2 yrs 8 mos

OCI Network Availability team

JavaMicronaut

Software Engineer

Aug 2020 – Aug 2023 · 3 yrs

Search Team in Oracle Content Management (OCM)
Optimized Data Processing & Indexing: Developed a two-step partial indexing approach for CRUD operations, improving batch processing and reducing processing time by up to 100 times for large datasets.
Asynchronous Text Extraction & Indexing: Designed and implemented an asynchronous process for text extraction, reducing payload size and memory usage by eliminating the need for large binary blobs in Elasticsearch.
Search Engine Optimization: Enhanced data indexing processes and specialized search functionalities, making them accessible via REST APIs.
Improved Resiliency & Error Handling: Strengthened system resiliency and error handling in batch processing, ensuring smoother and more reliable operations.
Test-Driven Development: Applied TDD principles by developing comprehensive unit and integration tests, ensuring the robustness and reliability of our microservices architecture.

JavaElasticsearch

Portland state university

2 roles

Graduate Research Assistant

Sep 2018 – Jul 2020 · 1 yr 10 mos · Portland, Oregon

Performed research under the Center for Brain-inspired Computing Enabling Autonomous Intelligence (C-BRIC) program funded by the Semiconductor Research Corp. (SRC).
Advisor: Dr. Dan Hammerstrom (BICL | Biologically Inspired Computing Lab)

Research Intern

Jan 2018 – May 2018 · 4 mos · Portland, Oregon, USA

Approximate Pattern Matching using Hierarchical Graph Construction and Sparse Distributed Representation (Master's Thesis - IIITA)
Advisors:
Dr. Dan Hammerstrom, Portland State University USA (BICL | Biologically Inspired Computing Lab)
Dr. Uma Shanker Tiwary, IIITA India (SILP LAB | SPEECH IMAGE AND LANGUAGE PROCESSING LAB)
Created a hierarchical graph structure for an image, which makes the computation faster and reduces the memory usage, and combined it with SDR (Sparse Distributed Representation) to do the approximate heuristic pattern matching. It is done by taking advantage of hierarchy as the pattern matching is done at image level by comparing two image graph’s SDR and object matching is done at the object level by comparing lower level SDRs. The comparison of SDRs is done by using the 'union' property, a single fixed-size SDR vector can store a dynamic set of elements, and by determining the similarity between them i.e. overlap. Continuation of summer internship (2016).

Indian institute of information technology

Teaching Assistant, Information Retrieval

Jul 2017 – Dec 2017 · 5 mos · Allahabad, India

Helped students with assignments and other doubts
Grade students' exams

Portland state university

Research Intern

May 2016 – Jul 2016 · 2 mos · Portland, Oregon, USA

Project - Application of Neuromorphic Computing in Object Recognition
Advisor: Dr. Dan Hammerstrom (BICL | Biologically Inspired Computing Lab)
In this project, I proposed a new algorithm to generate SDR (Sparse Distributed Representation) for 2D planar object graphs and used it in graph isomorphism problem. And this can be used further in object recognition. This approach is believed to address the key challenges in Deep Learning.