👨💻 Research Associate @ Robert Koch Institute, Berlin
🎓 PhD Student in Computer Science @ Freie Universität Berlin
🇨🇦 Former Research Assistant at Toronto Metropolitan University, Toronto, Canada (Formerly Ryerson University)
🥇 National Winner of EXL-EQ 2022 Competition by EXL Analytics
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Explainable AI (XAI): Build interpretable, trustworthy models—especially for healthcare—using both model-agnostic and model-specific methods, paired with textual justifications to enhance expert confidence.
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Human-Computer Interaction (HCI): Design intuitive interfaces and interaction frameworks that make AI systems more accessible, usable, and aligned with user needs.
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Visualization-Driven XAI: Develop storytelling frameworks that combine multi-task distillation and interpretability techniques to communicate model behavior effectively to healthcare professionals and ML practitioners.
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AI Regulations & Ethics: Create responsible AI frameworks, including a five-layer nested model for AI design and validation, to align AI development with fairness, safety, and regulatory standards.
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Current Focus: Advancing domain-centric XAI in healthcare to boost adoption and trust among clinicians and data scientists alike.
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ML Frameworks: TensorFlow, PyTorch, PyTorch Lightning, and JAX for building scalable and efficient models.
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Deployment & Workflow: Docker, Kubernetes, and Flyte for containerization, orchestration, and workflow automation.
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LLM Techniques:
- RAG: Boost LLM performance with external knowledge integration.
- LoRA: Fine-tune large models efficiently with minimal compute overhead.
- MCP: Use Anthropic’s open standard to securely connect LLMs to external systems and data sources.
I'm open to collaborations, discussions, and exploring new frontiers in AI, XAI, HCI, and related fields. Feel free to reach out—let's build something amazing together!