Researcher · Educator · Senior Software Engineer
Ubaid Ullah
Bridging rigorous research and production-grade engineering in AI and intelligent systems.
13+
Years Experience
800+
Students Trained
3
Research Projects
Stanford
Section Leader
Journey
Education, Teaching, Industry & Awards
2014 · Teaching
Founded Vision Institute of IT
Vision Institute of IT
Started training students in programming and IT fundamentals; has since trained 800+ students.
2018 · Award
Bronze Medal, MCS
MCS
Awarded Bronze Medal for academic excellence in the Master of Computer Science program.
2021 · Industry
Joined NITB as Senior Software Engineer
National Information Technology Board
Began leading government-scale software engineering initiatives at NITB, Pakistan.
2024 · Education
MS Computer Science, SZABIST
SZABIST University
Completed MS thesis on the Application of Deep Neural Network Models for Credit Risk Prediction.
2025 · Award
CS50x Puzzle Day 2025
Harvard CS50
Participated in CS50x Puzzle Day 2025, a global algorithmic problem-solving event.
2025 · Award
Meta Hacker Cup 2025 — Round 1 Qualified
Meta
Qualified for Round 1 of Meta Hacker Cup 2025, a global competitive programming contest.
2025 · Teaching
Stanford Code in Place Section Leader
Stanford University
Selected as a Section Leader for Stanford's Code in Place program, continuing into the 2026 cohort.
Technical Toolkit
Skills
Selected Work
Featured Research
Credit Risk Prediction using Deep Learning
Traditional credit scoring models struggle to capture complex, non-linear relationships in borrower data, leading to suboptimal default prediction and missed risk signals.
Read more →DASS-42 Mental Health Assessment using Machine Learning
Manual scoring and interpretation of the DASS-42 (Depression, Anxiety and Stress Scale) questionnaire is time-consuming and can benefit from automated, data-driven severity classification.
Read more →Bone Classification using Deep Learning
Manual classification of bone types and conditions from medical images is labor-intensive and subject to inter-observer variability.
Read more →Engineering
Featured Projects
Credit Risk Prediction using Deep Learning
Deep neural network models for predicting credit default risk, benchmarked against traditional ML baselines.
DASS-42 Mental Health Assessment
ML-based automated severity classification for the DASS-42 mental health questionnaire.
NITB Government Digital Services Platform
Backend architecture and engineering for government-scale digital service delivery.