Akshay Gupta

Software Engineer

Kanpur, Uttar Pradesh, India2 yrs 9 mos experience

Most Likely To SwitchAI Enabled

Key Highlights

Developed scalable object storage solutions.
Published research on explainable AI algorithms.
Experience with AWS and secure data pipelines.

Stackforce AI infers this person is a SaaS-focused technical professional with expertise in machine learning and cloud infrastructure.

Contact

agupta@cohesity.com +919411459571 LinkedIn

Skills

Core Skills

AwsMachine Learning

Other Skills

C (Programming Language)C++Computer VisionDeep LearningDistributed SystemsExplainable AIGitGo (Programming Language)JavaScriptKerasObject DetectionOperating SystemsPyTorchPython (Programming Language)Rego

About

I am working as Member of Technical Staff in S3 team at Cohesity since July 2023, developing scalable object storage on top of Cohesity's proprietary distributed file system known as SpanFs. I graduated from IIT Kanpur with major in Computer Science in the fall of 2023. During my undergrad, I worked with Prof. Biplav Srivastava from University of South Carolina on topics such as Explainable Algorithms for DNN, Adversarial Attacks against DNN and ML for Healthcare. Our collaborative efforts resulted in the publication of two research papers, presented at AAAI and CODS-COMAD. I also have exposure in System Programming particularly on GemOS and Linux Kernel Programming.

Experience

Cohesity

2 roles

Member of Technical Staff II

Sep 2024 – Present · 1 yr 6 mos

Member of Technical Staff

Jul 2023 – Sep 2024 · 1 yr 2 mos

Devrev

Member Of Technical Staff

May 2022 – Jul 2022 · 2 mos · Bengaluru, Karnataka, India · On-site

Developed a secure email ingestion pipeline using AWS and parsing logic for use cases.
Used open-source components to create a one-way communication sync with the Discord app.
Implemented automaton flows and commands for product use cases using YAML and Rego.

AWSYAMLRego

University of south carolina

Research Intern

Jan 2021 – May 2022 · 1 yr 4 mos · Remote

The objective was to explore and implement explainable AI algorithms for object detection.
Proposed two generalized black-box input-perturbation-based algorithms for precise & robust visual explanations
Published and presented research papers at AAAI-W3PHIAI and CODS-COMAD 2022.
Open sourced a SOTA face-mask detection model on FaceMask dataset.

Explainable AIObject DetectionMachine Learning