MSc Data Science, Machine Learning and Statistics — Uppsala University, Sweden
GitHub: github.com/tbimbatoMSc in Data Science at Uppsala. Architecture degree at IUAV Venice, five years of practice before switching fields.
Interested in how data science can model systems that have a physical reality, medical and health data, acoustic spaces, complex environments. Currently working on acoustic scene classification and machine learning and signal processing. Self-taught in DSP: Max/MSP, gen~, audio modelling.
Still figuring out where these things connect.
Outside: trail running, hiking, gardening.
| Uppsala University, Sweden —
MSc Data Science, Machine Learning and Statistics (2025 - 2027)
Machine learning · statistics · time series analysis · deep learning · data engineering (Hadoop, Spark) ·
combinatorial optimization (MiniZinc) · software engineering · scientific computing
| University of Verona, Italy —
Coursework in Computer Science and Data Science (2023 - 2025)
Statistical learning · probability · databases · mathematical logic ·
computer architecture & assembly · C · SystemVerilog · FPGA prototyping · algorithms and data structures
| IUAV University of Venice, Italy — BA Architecture (2020)
| STEM teacher, FabSchool (2024 - 2025)
Teaching in STEM subjects, within outreach initiatives dedicated to
STEM education and public science communication.
| Architect (2018 - 2023)
Architectural practice, across more than twenty projects.
| 2026 Acoustic Space Classification: Classical ML vs Neural Baselines —
Controlled sim-to-real study on room-type recognition from impulse responses (IRs). Six interpretable
acoustic parameters (RT60, EDT, C80, D50, Ts, DRR) with classical classifiers, benchmarked against
CNNs and a pretrained ResNet18 on the identical protocol: trained on simulated rooms
(pyroomacoustics), tested on real held-out rooms from BUT ReverbDB, AIR and ACE.
The physical features transfer more robustly than the learned representation
(accuracy drop from simulation to real rooms: 0.37 vs 0.52), and the gap widens as the simulator is
made more physically realistic. Includes a published 5,000-RIR synthetic dataset with a datasheet.
| [Repository]
| [Dataset DOI]
| 2026 MCP RIR: Room Impulse Responses from Natural Language —
Model Context Protocol server that turns a spoken description of a space into a simulated room
impulse response. The calling model maps free text in any language onto room geometry, absorption
coefficients and mic/source placement; the server runs the image-source simulation
(pyroomacoustics) and writes a stereo 48 kHz WAV ready for any convolution reverb. Unspecified
parameters are randomised per call, so repeated requests give different takes of the same room.
Intended for matching a location no longer accessible when no IR was captured, and for building
reverbs of rooms that could not be built.
| [Repository]
| 2026 Helsinki Urban Noise Analysis: Hourly LAeq Time-Series Modelling —
Comprehensive SARIMA analysis of hourly equivalent sound pressure level (LAeq) measurements from
a Helsinki urban sensor. Stationarity testing (ADF/KPSS), seasonal differencing,
ACF/PACF-guided model identification. Performed model diagnostics (residual analysis, Ljung-Box test) and
forecasting evaluation.
| [Repository]
| 2025 Diabetes Classifier: EDA, Benchmarking &
Interactive Prediction —
End-to-end ML pipeline on a clinical diabetes dataset (blood markers:
HbA1c, BMI, lipid panel, renal indicators).
Physiologically-motivated outlier detection, unit inconsistency discovery
(VLDL mg/dL vs mmol/L), benchmark of four classifiers on balanced and
imbalanced splits. Streamlit dashboard with interactive patient input,
confidence scoring, and cross-model comparison.
| [Repository]
| Nov 2025 — 1st place, AWS Immersion Day Hackathon — Stockholm
| Nov 2025 — 3rd place, Lovable x UUAIS Hackathon Night — Uppsala
| 2026 Bimbato, T. ASC-26: A Synthetic Room Impulse Response Dataset for
Room-Type Classification [Dataset]. Zenodo.
5,000 labelled room impulse responses across ten room types, simulated with pyroomacoustics
and documented with a datasheet (taxonomy, generation procedure, per-class acoustic statistics,
stated limitations). CC BY 4.0.
| [10.5281/zenodo.21771185]
| 2026 Deep Learning — Informal Notes (PDF) —
Informal notes on deep learning.
| [PDF Link]
| 2024 Probability Theory Handbook & Formula Sheet —
Reference handbook and formula sheets.
| [Repository]
| [Handbook PDF]
| 2018 — Mentioned in IQD n. 53 (Singolarità) and cited in
Lampedusa: La Cattedrale di Solomon — Vol. II
as student-contributor to a project with Arch. Renato Rizzi.
| [ResearchGate]
| 2017 — Credited as student contributor in F. Cacciatore, Rubble or ruins? (Università Iuav di Venezia, series Syria. The making of the future), ISBN 8899243255.
| 2015 — Co-designed Nothing Personal — exhibition at IUAV's Ex Cotonificio combining spatial layout and immersive audiovisual experience. Extended beyond its initial run due to public and academic reception. Team of six.