Tommi Bimbato
MSc Data Science, Machine Learning and Statistics — Uppsala University, Sweden
GitHub: github.com/tbimbatoORCID: 0009-0003-0667-3230
LinkedIn: linkedin.com/in/tommi-bimbato
About Me
MSc 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.
Education
| 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)
Experience
| 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.
Projects
| 2026 SAPFA: Spectral Audio Partials Frequency Annotator.
Finds the partials of a recording and labels each one with its frequency, nearest note and deviation
in cents. Blackman-Harris STFT at four window lengths;
peak picking above a median-filtered noise floor with parabolic interpolation; frame-to-frame partial
tracking in cents, with gap tolerance and splitting at note changes. Outputs an annotated spectrogram
(PNG) and a table of partials (CSV).
| [Repository]
| 2026 Acoustic Space Classification: Classical ML vs Neural Baselines —
Room-type recognition from impulse responses (IRs), trained on simulated rooms only (pyroomacoustics)
and tested on 17 real rooms never seen in training, from BUT ReverbDB, AIR and ACE. Six ISO 3382
parameters (RT60, EDT, C80, D50, Ts, DRR) with four classical classifiers, against four CNNs from 24k
to 11M parameters. Grouped by acoustic type, the parameters place 15 of the 17 rooms correctly
(majority baseline 10) and every classical model scores above all 20 neural runs.
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]
| 2024 Rock-Paper-Scissors FSMD: RTL and Gate-Level Hardware Design.
Finite state machine with datapath for the Rock-Paper-Scissors game, written in SystemVerilog with a full
testbench and synthesised to gate level with SiS from BLIF, with optimised and non-optimised netlists.
Computer Architecture course project at the University of Verona, with A. Iovine.
| [Repository]
Awards
| Nov 2025 — 1st place, AWS Immersion Day Hackathon — Stockholm
| Nov 2025 — 3rd place, Lovable x UUAIS Hackathon Night — Uppsala
Research Output
| 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]
Writing
| 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]
Earlier Work
| 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.