ALI HAIDER RIZVI

ALI HAIDER RIZVI

Class of 2022
BS Computer Science
Minor: Electrical and Computer Engineering

Aspiration Statement

My career aspiration is to be a Software Engineer and an Al Developer. To do this, I have worked hard on my problem solving and competitive programming skills and also developed a deep understanding of Al, Computer Vision, and Deep Learning.

Core Skills

  • Python
  • Deep Learning/Tensorflow
  • SQL (Structured Query Language)
  • Flask
  • API Development

Academic Awards / Achievements

  • 2nd place, International Collegiate Programming Contest (ICPC) 2021
  • Dean's List, Spring 2019
  • HU-TOPS Scholar (Habib University's Talent Outreach, Promotion and Support Program)

Experience

Leadership / Meta-curricular

  • 2nd Place, Google Hash Code 2020
  • Finance Chair, Student Travel Grant Committee —Habib University
  • General Secretary, ACM Chapter — Habib University

Internship / Volunteer Work

  • Computer Vision Research Engineer - Motive
  • Associate Software Development Engineer - Securiti
  • Empathic Computing Lab - Research Intern (January 2022 — March 2022)
  • Folio3 - Software Engineering Intern (July 2021 — September 2021)
  • Analogue - Software Engineering Intern (May 2020 — August 2020)
  • Empathic Computing Lab - Virtual Research Internship

Publications / Creative Projects

  • Lead Habib University RoboCup Soccer Simulation 3D team for the Robocup competition

Final Year Project

Project Title

Compression Based Perceiver

Description

Biological systems perceive the world by processing high-dimensional inputs from modalities as diverse as vision, audio, touch, etc. However, the perception models in Deep Learning are designed for individual modalities, often relying on domain-specific assumptions. These assumptions introduce helpful biases, but also lock models to individual modalities. The Perceiver, developed by Google DeepMind,is a multi-modal architecture that has low biases, allowing it to scale to multi-modal inputs. However, the architecture is extremely expensive in computational resources, meaning that it cannot be used in most practical applications. The Compression-Based Perceiver is a novel architecture for Computer Vision problems and building up from Google and DeepMind's Perceiver. Our research question evaluates and investigates this architecture and our hypothesis is that if a compression block can compute efficient lower-dimensional data representations, the architecture will be more resource optimized and will remain independent of the input modality, and still maintain comparable accuracy metrics.