I am a graduate student in Computer Science with interests spanning machine learning, distributed systems, and data-intensive infrastructure. My work focuses on building reliable, scalable systems informed by empirical evaluation and research-driven design.
- Machine learning and deep learning (NLP, multimodal systems)
- Distributed and stream processing systems
- Cloud-native system design and deployment
- Data-driven experimentation and evaluation
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Distributed Stream Processing Engine
Designed a Flink-inspired system featuring task scheduling, state management, and fault tolerance. -
EEG-Based Cognitive State Analysis
Built deep learning pipelines for time-series brain signal classification using CNN-based architectures. -
Text-to-SQL for Healthcare Data
Evaluated open-source language models for structured query generation; published IEEE work.
Languages: Python, Java, C/C++, JavaScript, SQL
Machine Learning: PyTorch, TensorFlow, Scikit-learn, Hugging Face
Systems & Backend: Docker, Kubernetes, FastAPI, Spring Boot, Kafka, Spark
Cloud & Infrastructure: Google Cloud Platform, Linux, CI/CD
I prioritize clarity, correctness, and measurable impact. I value systems that are interpretable, scalable, and well-evaluated, and I enjoy collaborating on technically rigorous problems.
- Portfolio: https://ishneet42.github.io
- GitHub: https://github.com/ishneet42
- linkedin: https://www.linkedin.com/in/ishneet-kaur-chadha/