irisdania.dev

Hi, I'm Iris

Master’s student in Statistics & Data Science (LMU) with a deep passion for Machine Learning and AI.

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Munich, Germany · Open to collaborations
Iris Dania Jimenez

About

I enjoy building data-driven models and ML pipelines that solve real problems. I’ve worked on explainable AI, computer vision, and NLP model merging. My background spans Python, SQL/NoSQL, statistical modelling, and visualization tools. In my free time I like to read. A lot.

Education

Ludwig Maximilian University of Munich

Master’s in Statistics & Data Science · Apr 2023 – present

Minor in Machine Learning.

University of Milan‑Bicocca

BSc in Statistics & Information Management · Oct 2019 – Mar 2023

Coursework: Python, SQL/NoSQL, R, SAS; supervised & unsupervised learning; statistical modelling; demography; medical/health statistics; Tableau/Orange/RapidMiner; parallel DBs & queries.

Experience

Research Assistant · Chair of Computer Vision & AI @ TUM

Nov 2024 – Present · Munich

  • Collaborated with a PhD candidate on Computer Vision research.
  • Implemented algorithms and simulation models.
  • Conducted literature research in XAI and CV.
  • Skills: Python, Cluster configuration, Git

Research Assistant · Bavarian AI Chair for Mathematical Foundations of AI

Jun 2023 – Feb 2025

  • Advanced research in explainable AI (XAI).
  • Implemented algorithms and simulation models.
  • Comprehensive literature research in XAI and CV.
  • Skills: Python, Cluster configuration, Git

Machine Learning Intern · EOMAP GmbH (Germany)

Oct 2022 – Dec 2022

  • Analyzed satellite images using unsupervised ML techniques.
  • Developed a classification algorithm to detect cloud shadows.
  • Skills: Python, QGIS, AWS, k‑means

Selected Projects

VI-MIDAS: Variational inference for microbiome survey data

Extended VI-MIDAS for microbial abundance modeling with latent ecological factors; applied to a new marine dataset. Built a variant enabling unified latent representations of environmental drivers. Project page

Model Merging for NLP

Combined TinyBERT and BERT-Base to balance accuracy and efficiency. Evaluated on IMDb & SNLI (GLUE), comparing accuracy and FLOPs against standard fine-tuning. Project page

Publications & Thesis

Languages

Contact

Reach out for collaborations, research, or ML projects.