Resume

Education

Ph.D. in Psychology, Université de Montréal, present
- Specialization: Cognitive Science and Neuropsychology
- Courses: IFT 6135 (Representation Learning), PSY 6983 (Neural Data Science Project)
- Supervisor: Shahab Bakhtiari
M.Sc. in Computer Science, Heinrich-Heine-University Düsseldorf, January 2023
- Specialization: Machine Learning and Artificial Intelligence
- Courses: Deep Learning, Reinforcement Learning, Causality, Generative Models and Sampling Methods, Methods of Artificial Intelligence in Life Sciences, Advanced Topics in Bayesian Data Science
B.Sc. in Computer Science, Heinrich-Heine-University Düsseldorf, December 2018
- Minor: Modern Japanese Studies
- Courses: Machine Learning, Overview of Artificial Intelligence, Calculus I and II, Linear Algebra, Stochastics

Title: Visual Enhancement of Whole Brain Slide Images from Z-Scanning Microscopes with Deep Style Learning
Supervisors: Timo Dickscheid & Stefan Harmeling
Description: Developed the Z-Stack Enhancement (ZSE) pipeline, a framework for improving the visual quality and reducing blurring of z-stack brain images from whole slide microscopy scanners. The deep style learning-based method was trained on a large dataset of microscopic images to transfer the style of a selected in-focus reference image to out-of-focus microscopic images, resulting in improved visual appearance and aiding researchers in identifying individual cells and other structures.

Title: Playing Go with Recurrent Neural Networks
Supervisors: Stefan Harmeling & Michael Leuschel
Description: Investigated the effectiveness of recurrent neural networks (RNNs) in playing the game of Go using imitation learning. Compared results with DeepMind’s AlphaGo to analyze how different architectures influence performance.

Winter 2023: Research Assistant
- Research Center Jülich - Institute of Neurosciences and Medicine (INM-1)
- Responsibilities:
  - Continued developing and improving the Z-Stack Enhancement pipeline, which was initially developed during my master’s thesis.
December 2020 – June 2021: Medical Biometry
- Coordination Center for Clinical Trials at University Hospital Düsseldorf
- Responsibilities:
  - Created statistical analysis for clinical trials using Tidyverse.
  - Helped maintain the R packages bamdit and jarbes.
2018 – 2021: Teaching Assistant
- Heinrich-Heine-University Düsseldorf
- Courses:
  - Deep Learning (Summer 2021 & Summer 2019)
  - AI in Life Sciences (Summer 2020)
  - Machine Learning (Winter 2019/2020)
  - Algorithms and Data Structures (Winter 2018/2019)
  - Theoretical Computer Science (Summer 2018)
- Responsibilities:
  - Conducted weekly exercise classes.
  - Created and graded assignments, weekly exercises, and final exams.
  - Supported student learning through hands-on tutorials and practical demonstrations.

November 2024: Presentation at UNIQUE Fellows Get-Together
- A comparative, multi-species study of visual systems: How do distinct movement repertoires shape the visual systems of different species?
October 2023: Poster at UNIQUE Center Scientific Retreat
- Learn to see like a Treeshrew: How the animal’s movements shape the representational properties of its visual system

2025-2026: UNIQUE Excellence Scholarship
- Project: Cross-Species Analysis of Visual Systems: Understanding Visual System Development via Species-Specific Movements
- Supervisors: Shahab Bakhtiari & Blake Richards
- Scholarship declined due to a change in supervision.
Fall 2023: Vision Health Research Network (VHRN) - Recruitment Scholarship

Fall 2024 – present: Technical Lead, UNIQUE Student Affairs Committee (SAC)
- Maintaining the UNIQUE students website
- Setting up the tech for events
Winter 2025: Treasurer, UNIQUE Student Symposium (USS) 2025
Fall 2024: Student Volunteer for MAIN 2024 conference
- Managed shifts at the registration desk, assisting attendees with check-in and inquiries
- Coordinated poster session setup
- Assisted with logistical tasks, such as directing attendees and supporting the conference party setup
Winter 2024: Program Committee, UNIQUE Student Symposium (USS) 2024
- Defining the academic program of the conference
- Selecting speakers (one professor and two students) for each of the four academic sessions

Python: Expertise in PyTorch, PyTorch Lightning, and TensorFlow for neural network implementation.
Compute clusters: Experienced in implementing and training distributed neural network training on multiple GPUs on Compute Canada and the Jülich compute cluster.
R: Experienced in statistical modeling and package development (bamdit, jarbes).
Other: Proficient in LaTeX, Prolog, and data visualization techniques.