Batuhan Koyuncu
Batuhan Koyuncu

PhD Candidate & AI Scientist

Saarland University
ELLIS PhD Program
Intor AI

About Me

I build reliable, interpretable, and scalable AI systems for real-world deployment. I am a Ph.D. candidate in Computer Science at Saarland University, part of the ELLIS PhD program, co-advised by Prof. Isabel Valera and Prof. Ole Winther.

My research focuses on probabilistic and generative models for temporal and heterogeneous data, with applications spanning macroeconomic forecasting, healthcare, and audio. I am particularly interested in combining foundation models with agentic AI frameworks to enable robust reasoning, forecasting, and decision-making under uncertainty.

Previously, I was a visiting researcher at the Bank for International Settlements where I built macroeconomic time-series foundation models for central-bank-grade policy simulations, and a visiting researcher at the Technical University of Denmark working on time-continuous generator functions for sequential data. I also work as an AI Scientist at Intor AI, building RAG systems and AI-powered applications.

Download CV
Interests
  • Time-Series Foundation Models
  • Generative AI
  • Probabilistic Modeling
  • Agentic AI Systems
  • LLM Applications & RAG
Education
  • PhD Computer Science

    Saarland University

  • MSc Computer Engineering

    Boğaziçi University

  • BSc Physics

    Boğaziçi University

Recent Publications
(2026). BISTRO: A Foundational Model for Unconditional and Conditional Forecasting of Macroeconomic Time Series. BIS Working Papers, No 1337.
(2026). Temporal Variational Implicit Neural Representations. In Transactions on Machine Learning Research (TMLR), 2026..
(2025). A Foundational Model For Conditional Forecasting Macroeconomic Variables. In Conference on Nontraditional Data, Machine Learning and Natural Language Processing in Macroeconomics (ECONDAT) 2025.
(2025). Hyper-Transforming Latent Diffusion Models. In International Conference on Machine Learning (ICML) 2025.
(2024). E-ProTran: Efficient Probabilistic Transformers for Forecasting. In Workshop on Structured Probabilistic Inference & Generative Modeling. ICML'24.