Machine Learning Engineer
I build software at the intersection of intelligence and the physical world — with a focus on machine learning, medical AI, and tools that make a real difference. My background as a professional climber for over 12 years taught me to push limits and think creatively under pressure; I bring that same drive to every engineering challenge.
Universitätsklinik Balgrist / ETH Research Group, Spine Biomechanics — Zürich
Developed and optimised client-server communication pipelines, increasing CT scan analysis efficiency by 30%. Enhanced medical imaging processes by developing machine learning algorithms for automatic segmentation of CT scans. Built full-stack solutions leveraging Python, Kotlin, Docker, AWS, REST APIs, and SQL databases.
Universitätsklinik Balgrist / ETH Research Group, Spine Biomechanics — Zürich
Supported the development and enhancement of a CNN-based pipeline for automated segmentation of spinal CT imagery. Managed the end-to-end ML workflow from data acquisition and preprocessing to model training, evaluation, and performance tuning. Employed Python, SimpleITK, Slicer, and MedPy for robust, accurate model predictions.
Global
Professional climber and alpinist with expertise in rock, ice, and high-altitude ascents. Successfully summited challenging global peaks including Gasherbrum II (8,035m). Twice nominated for Brazil's prestigious Golden Carabiner Award for Best Alpinist. Organised logistics for extensive expeditions across the Himalayas and Patagonia, and secured sponsorships from prominent global brands.
Vertical Horizon — Shanghai, China
Founded and operated an innovative educational enterprise blending outdoor adventure with impactful teaching. Designed outdoor programmes that measurably improved teamwork and problem-solving, and integrated community-driven humanitarian projects to enhance student engagement in local communities.
Where blockchain meets the mountains. MNT Token is a community-driven crypto project built on the Base Network that merges a passion for outdoor sports — climbing, alpinism, and high-altitude adventure — with decentralised finance. The token aims to build a global community of athletes and adventurers, rewarding real-world exploration through Web3 technology.
A free Blender add-on that automates the customisation of 3D-printed prosthetic hands. Uses landmark-based alignment, anisotropic scaling, and shrinkwrap socket conformation to reduce fitting time from 30–60 minutes to under 5 minutes — with sub-millimeter precision across all 8 Kinetic Hand variants.
Bachelor thesis investigating deep learning architectures for metal artifact reduction (MAR) in CT scans. Compared dual-domain networks (InDuDoNet, InDuDoNet+) and image-domain methods (DICDNet) against state-of-the-art baselines. Explored supervised, unsupervised, and semi-supervised approaches to reduce streak artifacts while preserving anatomical detail.
A Streamlit app to build your own Chinese number hand-sign classifier (数字 0–9) from scratch. Collect training data via webcam, train an MLP on MediaPipe hand landmarks, and run live inference — all in the same browser UI.
Interactive browser demo visualising five path planning algorithms — A*, Dijkstra, BFS, Greedy Best-First, and RRT — on a drawable obstacle grid. Watch each algorithm explore in real-time, compare stats, and adjust speed on the fly.
Live interactive dashboard scraping real accident data from WSL SLF — Switzerland's official avalanche authority. Four map views (scatter, heatmap, hexagon grid, 3D columns), analytics charts, and live webcam feeds for Andermatt, Engelberg, and Klewenalp.
Applied three progressively improved Deep Q-Network algorithms (DQN, Double DQN, Dueling Double DQN) to OpenAI Gymnasium's CarRacing-v3 environment. Each variant builds on the last — fixing Q-value overestimation and separating state value from action advantage — with the Dueling Double DQN achieving the highest and most stable rewards.
Trained a Deep Q-Network agent to play Flappy Bird, documenting a real training plateau caused by Q-value overestimation in vanilla DQN. Double DQN resolves it by decoupling action selection from evaluation, pushing past the score-62 ceiling and achieving significantly higher performance.
London, UK
Feb 2025
Built Base Super Assistant, an AI-powered trading assistant that automates on-chain trading through natural language processing and ML-driven decision-making. Leveraged OpenAI's API and LangChain for voice and text-based trade execution via Telegram, integrated FastAPI and the Base SDK for real-time blockchain transactions, and applied AI-based risk assessment for automated trade execution.
Jump Trading × EPFL Trading Blitz — Lausanne, Switzerland
Oct 2025
Won 1st place as a team (4th individually) at a high-pressure quantitative trading hackathon with 75 participants and live, rapidly shifting market simulations. Collaborated in a cross-functional team to design and execute disciplined trading strategies under macro-driven volatility over a six-hour session.
ZHAW — Zürich University of Applied Sciences
Dec 2026
University of Colorado Boulder
July 2027
Lucerne University of Applied Sciences and Arts (HSLU)
Sept 2022 – Jul 2025
Thesis: Effectiveness of deep learning architectures for metal artifact reduction (MAR) in CT imaging. Supervisor: Javier Montoya.
University of California San Diego (UC San Diego / edX)
Feb 2023 – Feb 2024
Loyola Marymount University — Los Angeles
Sept 2002 – Sept 2004
Long before writing my first line of code, I was climbing mountains. As a professional alpinist for over 12 years I've led expeditions across the Himalayas and Patagonia, summited Gasherbrum II (8,035m), and been twice nominated for Brazil's Golden Carabiner Award for Best Alpinist. The same focus and resilience I developed on high-altitude walls is what I bring to every engineering challenge.
I'm also the founder of MNT Token — a blockchain project born from this passion, building a Web3 community around outdoor adventure and exploration.