I build both foundational and refine existing AI systems for finance, healthcare and agritech ML workflows, with a focus on multimodal learning, retrieval pipelines, and scalable model deployment.
- Currently: AI Research Engineer at STARlab Capital, working in finance and leading an agentic AI side project in agriculture field.
- I write blogs at imaad.bearblog.dev.
- Open to collaborating on impactful applied AI research projects.
Languages
Frameworks
Cloud and MLOps
Developer Tools
Data and Libraries
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STARlab Capital — AI Research Engineer (Jul 2025 - Present)
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STARlab Capital — AI Research Intern (Apr 2025 - Jun 2025)
Leading R&D for ViviSTAR, building self-supervised WSI pipelines, optimizing slide visualization workloads, and supporting graph-based biomedical retrieval for LLM pretraining. Backtested multiple trading strategies by transforming intuition-based hyperparameters into mathematically grounded simulations. -
Aavaaz.ai — AI Engineer, Part-time (Dec 2024 - Jan 2025, Remote)
Designed back-translation and data-filtering pipelines for Azure STT models and trained CNN-based speech emotion recognition models across 12 languages. -
Zenon Analytics — Predictive Software Intern (Jun 2024 - Aug 2024)
Improved feature engineering runtime by 15% and integrated model interpretability in an AutoML production pipeline.
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Aligarh Muslim University (ZHCET)
Bachelor of Technology (B.Tech), Artificial Intelligence
CPI: 8.44/10 (GPA: 3.38/4.0) -
Relevant Coursework
Machine Learning, Artificial Intelligence, Deep Learning, NLP, Reinforcement Learning, Recommender Systems, Data Visualization, Image and Video Processing, Linear Algebra, Calculus.
- AutoML, LLMs, Bayesian Optimization
Hasan, I., Tausif, M. (2025). Designing an Interpretable and Efficient AutoML Pipeline for Enhanced Data Analytics. 2025 3rd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), IEEE.
DOI: 10.1109/ICSSAS66150.2025.11081354 | IEEE Xplore
Combined deep feature synthesis, LLMs, and Bayesian optimization; achieved 10%+ performance gains across datasets belonging from healthcare, finance, telecom, and educational domains.