Welcome! I'm building interpretable & explainable AI.
Hello! I’m Akshat Dubey, a Research Associate at the Robert Koch Institute and a PhD candidate at Freie Universität Berlin, working under the supervision of Dr. habil. Georges Hattab.
My research lies at the intersection of Explainable Artificial Intelligence (XAI), NLP, and human-centered AI. I focus on building interpretable, transparent, and efficient ML systems for critical domains like healthcare. My work draws from ensemble tree ML models, probabilistic modeling, optimization, statistics, and LLMs, with current themes including:
- 🔍 Uncertainty Quantification in XAI
Designing principled methods to estimate and communicate explanation confidence—especially in high-stakes domains.
- 🏥 XAI for Healthcare
Embedding explainable and uncertainty-aware models into real-world clinical workflows to support informed decision-making.
- 🧠 Persona-Adaptable LLM Strategies
Tailoring large language models to align with users’ cognitive styles and mental models—enabling intuitive and adaptive interaction.
- ⚖️ Regulatory-Compliant AI Design
Implementing the “Nested Model for AI Design & Validation” to meet the EU AI Act’s transparency and safety requirements.
- 🎲 Distributed Gaussian Process Learning
Developing decentralized, trust-aware GP frameworks for collaborative and privacy-preserving learning.
- 🧠 LLMs with LoRA/QLoRA & Quantization
Enabling efficient, on-device large-model inference using quantized, low-rank-adapted LLMs for constrained environments.
- 🐳 Reproducible ML via Docker & Orchestration
Building automated, scalable pipelines for training, deploying, and monitoring ML models with full reproducibility.
- 📈 MILP for Interpretable Graph Design
Using mixed-integer linear programming to optimize surrogate graph structures from decision forests for maximum interpretability.
- 🤝 Human-in-the-Loop AI: GNNs, NLP, and Visual Analytics
Fusing graph learning, language understanding, and interaction design to make AI decisions transparent, explorable, and trustworthy.
Outside of research, I’m a lifelong learner, “math nerd,” and “algorithm enthusiast” passionate about systems that empower humans through explainability. I enjoy building bridges between theory and practice.
Let’s connect and collaborate—reach out on LinkedIn.
