Robotics · Localization · Experimental Systems
Follow Me
- Role
- Bachelor's Thesis Research Project
- Date
- Feb 2026 – Jun 2026
ESP32DW3000 UWBROS 2Gazebo
UC Berkeley · EPFL
Thibault Verny
Statistical Learning, Reliable AI & Real-World Systems
M.A. student in Statistics & Data Science at UC Berkeley, following a B.Sc. in Mechanical Engineering at EPFL.
My interests lie at the intersection of statistical learning, uncertainty, and sensing systems, with a focus on methods that remain reliable on imperfect real-world data.
Available for full-time roles from May 2027. Open to conversations before then.
Berkeley, California
Areas of focus
The technical areas connecting my projects and experience.
My work spans statistical learning, uncertainty, intelligent systems, and technical decision tools, with a focus on methods that remain useful and reliable on imperfect real-world data.
Statistical Learning & Uncertainty
Probabilistic modeling, robust inference, calibration, validation, and uncertainty quantification.
Robust Machine Learning & Evaluation
Model reliability, data quality, distribution shift, reproducible evaluation, and performance under imperfect real-world conditions.
Sensing, Localization & Control
Signal processing, sensor fusion, localization, simulation-to-hardware validation, and feedback control.
Featured project
Follow Me — Robust UWB Localization and Control for a Mobile Robot
EPFL Automatic Control LaboratoryBachelor's Thesis Research ProjectThree-person teamGrade: 6.0 / 6.0 — highest possible grade
Experimental development and validation of a UWB-based robotic tracking system combining real-world sensor characterization, empirical noise modeling, localization, filtering, feedback control, ROS 2 simulation, and transfer to physical hardware.
Project scope
- ESP32 and DW3000 UWB sensing
- Measurement bias, noise, multipath, and dropout analysis
- Empirical noise models derived from physical experiments
- ROS 2 and Gazebo localization and control pipeline
- Gyroscope-assisted ambiguity resolution
- Simulation-to-hardware integration
My contributions
- This was a three-person bachelor's thesis research project; the points below describe the team's work rather than claiming sole individual ownership.
- Physical UWB experiments and characterization of bias, noise, multipath, and dropout behavior.
- Empirical noise models derived from the experiments and integrated into the localization and filtering stages.
- ROS 2 / Gazebo localization and feedback-control pipeline, including gyroscope-assisted disambiguation, and transfer to physical rover hardware.
Pipeline
- 01UWB sensing
- 02Raw distance data
- 03Filtering
- 04Localization
- 05Control
- 06Physical rover
Projects
Selected projects
Financial Data · Risk · Applied AI
Market Whisperer
- Role
- HackEurope 2026 project
- Date
- 2026
PythonMarket dataSentiment analysisNews & prediction-market data
Conceptual diagram
Probabilistic NLP · Responsible AI
DPULSE.AI
- Role
- Co-founder & Founding Engineer
- Date
- Sep 2025 – Jan 2026
PythonProbabilistic topic modelingNLPUncertainty calibration
Computer Vision · Experimental Measurement
Computer Vision System
- Role
- Image-processing and calibration pipeline within a four-person engineering team
PythonOpenCVNumPyHSV segmentation
Background
Education and experience
Two separate tracks: academic education, and professional, research, and leadership experience.
Education
University of California, Berkeley
Aug 2026 – May 2027 (expected)M.A. Statistics & Data Science
Berkeley, CaliforniaCurrent graduate student
École Polytechnique Fédérale de Lausanne (EPFL)
Sep 2023 – Jul 2026B.Sc. Mechanical Engineering
Lausanne, Switzerland
Activities & leadership
Vice-President, Junior Enterprise EPFLWindsurfing InstructorHead of Sailing, SPI LausanneFirst-Year Student Coach
Selected quantitative coursework
- Calculus III5.75 / 6.0
- Calculus IV5.50 / 6.0
- Probability and Statistics5.50 / 6.0
- Programming for Engineers5.50 / 6.0
- Dynamical Systems5.50 / 6.0
- Optimization and Operations Research5.00 / 6.0
- Control Systems + Lab5.00 / 6.0
EPFL grading scale: 6.0 is the highest possible grade; 4.0 is the passing grade.
Experience
Wakam
Jun 2026 – Aug 2026AI Engineer Intern
IndustryBuilt the governance and analytics layer for an internal LLM agent platform: ingestion workflows into PostgreSQL, monthly snapshots, and agent lifecycle, ownership and freshness signals feeding review recommendations rather than automated deletions. Also delivered credit-consumption analytics by period, user and agent, and prototyped a semantic similarity pipeline for agent configurations using deterministic fingerprinting and vector retrieval.
EPFL Automatic Control Laboratory
Feb 2026 – Jun 2026Bachelor's Thesis Research Project
Research ProjectDeveloped and experimentally validated a UWB-based localization and control system for robotic tracking, combining physical sensing, empirical noise modeling, ROS 2 simulation, and hardware integration.
EPFL
Sep 2025 – Dec 2025Teaching Assistant — Calculus III & Programming for Engineers
TeachingLed problem-solving sessions and supported undergraduate students in advanced calculus, scientific computing, Python, C, and MATLAB.
DPULSE.AI
Sep 2025 – Jan 2026Co-founder & Founding Engineer
EntrepreneurshipDeveloped probabilistic NLP pipelines for organizational narrative analysis, with a focus on uncertainty and responsible interpretation.
Junior Enterprise EPFL
Sep 2024 – May 2026Vice-President
LeadershipScoped and delivered a CHF 150k+ portfolio of ML/AI and software mandates directly with external clients, from first scoping conversation through delivery, in a year when Junior Enterprise EPFL reached a record CHF 425k and was named Best Junior Enterprise in Switzerland 2026.
Organizational recognition: Best Junior Enterprise in Switzerland 2026 — Junior Enterprises Switzerland
Capabilities
Technical breadth
Tools and methods I work with across statistics, software, and real-world systems.
Methods
- Statistical inference
- Probabilistic modeling
- Optimization
- Signal processing
- Experimental analysis
Programming
- Python
- C / C++
- SQL
- MATLAB
- TypeScript
Systems
- ROS 2 / Gazebo
- Embedded systems
- Real-time data pipelines
- Data acquisition
- API integration
Evaluation
- Experimental design
- Calibration
- Uncertainty analysis
- Out-of-sample validation
- Reproducible analysis
Contact
Let’s build reliable systems for real-world data.
Open to technical conversations and opportunities involving data science, applied machine learning, quantitative modeling, reliable AI, and real-world systems.