Tim Berti

About Me

I'm a scientist at the Institute for Information Management in Engineering from KIT, where I develop machine learning and optimization algorithms for additive manufacturing of metals.

I hold a Master's degree in Physics and have experience across startups, academia, and consulting. I co-founded StocksOnView, a platform offering statistical analysis for retail investors, worked as a research assistant at Forschungszentrum Informatik, and was an analyst in the Risk Advisory Group at Zanders.

I'm always open to discussing research, collaboration, or new opportunities in data science and engineering. Feel free to reach out!

LinkedIn | GitHub | Work

Projects

Identifying Color Vision Deficiency in EEG Data

Stimulating steady-state visually evoked potentials (SSVEP) in subjects with and without color vision deficiencies to investigate differences in brain activity using signal processing and machine learning techniques like RANSAC detrending, Butterworth filters, Canonical Correlation Analysis, Wavelet Transforms, Principal Component Analysis and Support Vector Machines.

Zeiss Computer Vision Hackathon 2024

We placed 2nd as a team of five people who had just met at the hackathon with the development of a camera, image processing and deep learning setup on a Raspberry Pi to recognize free seats in public places such as libraries.

Simulated Wind Power Generation

Simulating different regimes of wind power generation with Hidden Markov Models (HMM) and Generative Adversarial Networks (GAN).

Diffusion-Limited Aggregation

Using convolutional neural networks (CNN) and graph neural networks (GNN), to predict the growth patterns of diffusion-limited aggregation (DLA) clusters, which are fractal structures that form when particles aggregate in a random walk process.

Donut Augmentation

Introduction of a data augmentation technique for CNNs by wrapping images in torus form, which enables shift transformations without loss of information and thus promotes translation invariance in models trained on images with features near the edge.

Constructing Custom Kernels

A notebook that demonstrates employing domain knowledge to construct custom kernels for Support Vector Machines (SVM) to improve classification performance on a synthetic dataset.

Pharynx CV

Analysis of images acquired with a low-cost endoscope and a 3D-printed holder using transfer learning with a ResNet18 model, image segmentation and LIME for feature importance to investigate muscle activation in the mouth and throat during Frenzel equalization, a pressure equalization technique commonly used by scuba divers.

Magnetic Dipole Lattice

Simulation of magnetic dipoles on a 2D lattice, employing Verlet integration and a Nosé thermostat to explore behaviors like magnetic domain formation, phase transitions and hysteresis effects.

Deep Learning Cheat Sheet

A cheat sheet that provides an overview of deep learning concepts, covering some of the core aspects of modern architectures, optimization techniques and regularization methods.