Fares Kallel - Research Assistant – AI & Computer Vision

Berlin, Germany

Experience

Jan 2023 - Jul 2025

2 years 7 months

Berlin, Germany

Designed and implemented a real-time perception pipeline using YOLOv7 on Time-of-Flight (ToF) sensor data, enabling live streaming, inference, and on-frame visualization for passenger detection.
Fine-tuned and evaluated multiple state-of-the-art monocular depth estimation models for Automatic Passenger Counting (APC), and developed a custom hybrid depth model that improved depth accuracy in challenging scene regions.
Demonstrated that model-generated depth maps outperform raw sensor depth for APC tasks across several datasets, contributing to measurable reductions in counting error.

Jan 2022 - Dec 2022

1 year

Berlin, Germany

Investigated anomaly detection methods for biomedical sensor signals and evaluated early-stage model-based detection approaches.
Prototyped signal-processing and analysis workflows in Python using PyTorch and NumPy to support internal research experiments.

Mar 2021 - Dec 2021

10 months

Berlin, Germany

Extracted and engineered a wide range of lexical, semantic, and syntactic features for German text complexity assessment using spaCy-based NLP pipelines.
Built regression-based readability prediction models and contributed to feature selection, model evaluation, and dataset analysis.
Co-authored a peer-reviewed paper published at LREC 2022 (Subjective Text Complexity Assessment for German), contributing to feature design, modeling experiments, and interpretation of results.

May 2019 - Mar 2021

1 year 11 months

Karlsruhe, Germany

Contributed to early-phase software research projects, including UI components, backend logic, and security-related modules using Java and model-based development tools.

AI Engineer with a broad background in applied machine learning, combining research experience with real-time deployment.
Experienced across depth estimation, sensor-based perception, and NLP/medical AI, supported by strong academic performance (two theses graded 1.0).
Track record of fine-tuning state-of-the-art models and translating research ideas into practical AI systems and proofs of concept in research and industry settings.

Programming: Python, C++, Bash, Sql, Git, Linux
Deep Learning: Pytorch, Tensorflow, Huggingface (Transformers, Diffusers), Scikit-learn, Numpy, Pandas, Opencv, Pytorch3d
Ai Domains & Methods: Computer Vision (Monocular Depth Estimation, 3d Vision, Object Detection), Generative Modeling (Diffusion Models, Flow Matching Models, Vaes, Gans), Nlp (Feature Extraction, Readability Modeling), Image Processing, Real-time Perception, Transfer Learning, Fine-tuning, Prompt Engineering
Mlops & Tools: Mlflow, Wandb, Docker, Fastapi, Pytest, Vscode
Cloud & Infrastructure: Aws (Ec2, S3, Iam, Lambda, Ecr), Kubernetes, Postgresql, Gstreamer