I'm a Computer Science professional with a deep focus on AI, Machine Learning, and Natural Language Processing — currently pursuing a research-based Master’s at Ontario Tech University, where my work centers on improving the fairness, trust, and efficiency of LLMs and multilingual NLP systems.
🚀 I’ve led and published impactful research (NAACL, ICST) in areas like:
- Multilingual model alignment and performance benchmarking (ALIGNFREEZE)
- Flaky test detection using few-shot learning and Siamese networks (FlakyXbert)
- Data leakage mitigation in LLM benchmarks (HumanEval analysis)
My mission? To build scalable, trustworthy AI solutions — especially for resource-constrained environments — that bridge the gap between cutting-edge research and real-world applications.
🧠 Key Strengths: • LLMOps & MLOps expertise • Proficient in PyTorch, Hugging Face, BERT, Transformers • Strong in NLP, few-shot learning, and evaluation frameworks • End-to-end experience from research to deployment • Cross-functional collaboration, teaching & mentoring (500+ students taught)
🌎 Previously, I’ve worked with Google-Talentsprint, Major League Hacking, and open-source communities, contributing to both academic and applied AI problems.
✅ Currently open to:
- ML/NLP/LLM Engineer roles
- AI Research positions (industry or labs)
- Internships or part-time roles in MLOps/LLMOps
Let’s connect if you're looking for someone who brings deep technical expertise, a researcher's curiosity, and a builder's mindset.
A lightweight, few-shot learning model for flaky test detection
🔬 What it does:
Uses fine-tuned LLMs and Siamese networks to identify flaky software tests with minimal data.
📌 Key Features:
- +20% accuracy over baselines
- 80% reduced compute cost
- Built with PyTorch and HuggingFace Transformers
- Published at IEEE ICST 2025
📄 Publication:
An Analysis of LLM Fine-Tuning and Few-Shot Learning for Flaky Test Detection
📘 ALIGNFREEZE
NAACL 2025, New Mexico
Enhanced XLM-RoBERTa for cross-lingual tasks using layer freezing. Achieved 15% boost in multilingual accuracy.
🧪 FlakyXbert
IEEE ICST 2025, Naples
Few-shot fine-tuned LLM model improving flaky test detection accuracy while reducing resource use.
🔍 HumanEval_T
IEEE ICST 2025
A benchmark to combat data leakage in LLM evaluations using combinatorial test design.
⚖️ Bias in Data Augmentation
Fairware Workshop, 2025
Explored augmented data's impact on fairness in flaky test classification.
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🧠 Graduate Research Assistant, Ontario Tech (2023–Now)
Grants awarded for NLP in software testing, LLM optimization for resource-constrained settings, and AI fairness. -
👩🏫 Teaching Assistant, Ontario Tech
Guided 500+ students in Software QA and Data Analysis. -
🎮 MITACS Research Intern
Created Threaded Paws, an educational Unity game teaching parallel programming pitfalls. -
🤖 Google + TalentSprint Fellow
Completed a rigorous two-year program involving intensive bootcamps, collaborative projects, and mentorship, enhancing skills in software development, problem-solving, and professional growth. -
🌍 Business Analyst, X-Culture
Led market research for US-based drone expansion strategies.
- ✅ IBM ML Professional Certificate
- ✅ Applied Scrum for Agile Project Management
I'm always open to research collaboration, innovative AI projects, and tech-for-good initiatives.
- 📬 Email: riddhi.more1@ontariotechu.net
- 🌐 GitHub
- 📚 Google Scholar
"Research isn't about answers—it's about asking better questions." 🚀