About
I am a **Research Engineer** operating at the gap between elegant modeling and production-grade systems. I build scalable, multi-scale learning architectures that turn complex biological signals into reliable discovery infrastructure. My work emphasizes **evaluation realism**, stress-testing models under real-world heterogeneity—including cohort shifts and site effects—to ensure biological representations earn their value through predictive utility and scientific interrogation.
I operate fluently across deep learning systems, single-cell biology, and translational research, connecting efficient computation with biomedical domain insight through close collaboration with experimentalists and mathematicians.
Technical Skills
Programming
AI & Machine Learning
Bioinformatics
Cloud & Infrastructure
Research Experience
PhD Researcher
DKFZ & EMBL Heidelberg • Heidelberg, DE • Aug 2022 – Present
- Led end-to-end ML pipelines, scalable graph learning, and distributed multi-GPU systems for multimodal spatial omics at 10M+ scale.
- Focus on multi-agent systems and automation.
- Delivered production-grade segmentation and spatial inference systems underpinning multiple large-scale analyses and publications.
- Enabled routine processing of datasets previously considered computationally intractable.
Research Trainee
Cancer Research UK & EMBL-EBI • Cambridge, UK • Apr 2021 – Apr 2022
- Developed SageNet: supervised representation learning framework using graph attention and transformers for spatial inference from single-cell data.
- Marioni Lab.
Research Assistant
University of Zurich • Zurich, CH • Sep 2019 – Apr 2022
- Engineered single-cell analysis pipelines (QC, integration, cell typing).
- Developed immune repertoire analysis tools (Robinson Lab).
Research Trainee
EMBL Heidelberg • Heidelberg, DE • Jul 2018 – Sep 2018
- Developed graphical representation learning methods for single-cell data (Huber Group).
Research Assistant
Sharif University of Technology • Tehran, IR • 2017 – 2018
- Developed a population-scale graphical modeling toolkit for high-dimensional mixed-type multivariate data.
Education
Heidelberg University
PhD, Faculty of Biosciences (2022 – 2026)
Structured Representation Learning for Large-Scale Spatial Omics Data
ETH Zurich
MSc, Computational Biology & Bioinformatics (2019 – 2022)
GPA: 5.76/6.0 (Top 3 in cohort). Received ETH Medal for outstanding thesis.
Sharif University of Technology
BSc, Computer Engineering & Applied Mathematics (2014 – 2019)
Founder of **Sharif DataDays**. Head Teaching Assistant for Advanced Programming and Probability & Statistics.
Awards
ETH Medal
2023
Outstanding Master’s thesis, ETH Zurich (top <0.1%)
EMBL-EBI Research Fellowship
2021 – 2022
Competitive 12-month traineeship
EMBL Research Fellowship
2018
Undergraduate research traineeship
Talks & Posters
Invited Talks
Segger: Fast and accurate cell segmentation in spatial transcriptomics
ISMB 2025 (Liverpool)ESSB 2025 (Heidelberg)scverse 2024 (Munich)MOPITAS 2024 (Copenhagen)Supervised spatial inference... with SageNet
CSHL Genome Informatics 2021 (online)scGCN: A geometric deep learning framework...
EuroBioC 2020 (online)
Posters
Segger: Single-Cell Genomics 2025 (Stockholm), ESSB 2024 (Berlin)
SageNet: Single Cell Genomics 2023 (Bern)
scGCN: ISMB/ECCB 2019 (Basel)