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High level experience in the development of statistical and machine learning models for better decision-making processes.
M Campi, G Peters, N Azzaoui, T Matsui
IEEE Access 9, 136831 - 136860
M Campi
UCL (University College London)
M Campi, G Peters, D Toczydlowska
PLOS One 18 (4), e0284667
S Wiener-Vacherm, M Campi, P Boizeau, H Thai-Van
Frontiers in Neurology, Section Neuro-Otology 14
M Campi, G Staerman, GW Peters, T Matsui
arXiv preprint arXiv:2403.04405
AISTAT 2025
SR Wiener-Vacher, M Campi, S Caldani, H Thai-Van
JAMA Network Open 7 (5), e2412846-e2412846
M Campi, GW Peters, KA Richards
Franklin Open, 100113
M Campi, GW Peters, P Morvan, M Buhl, H Thai-Van
M Campi, C Gaultier, G Gerenton, P Avan
P Morvan, M Campi, GW Peters, H Thai-Van
P Morvan, M Campi et al.
M Buhl, E Kludt, L Schell-Majoor, M Campi, P Avan
MH Sehatpour, M Campi, CS Nikitopoulos, GW Peters, KA Richards
M Campi, GW Peters, P Morvan, M Buhl, H Thai-Van
I Katz, GW Peters, M Campi
MH Sehatpour, M Campi, CS Nikitopoulos, GW Peters, KA Richards
Presentation at OMAI 2026.
Seminar at he Medical Physics Section, Carl von Ossietzky University of Oldenburg in Germany.
Podium Presentation at the VCCA 2026.
Invited Speaker at the 6th French-Brazilian Symposium in Belo Horizonte.
Seminar at the Computational Linguistic Group at University of Zurich.
Presentation at the Blunch Series.
Invited Keynote Speaker, together with Prof. Gareth W. Peters, at the Climate & Finance Risk 2025 Conference.
Invited speaker for the Seminar of the Centre for Climate Risk and Resilience, UTS Business School, University Technology Sydney.
Invited speaker for the seminar at the Department of Statistics and Applied Probability, University California Santa Barbara.
Invited speaker and chair at WCA.
Invited speaker at internal workshop for audiologists.
Poster presentation.
Internal Seminar at Télécom Paris.
International Workshop on Green Finance and UK Decarbonization Processes at the Business School of Heriot-Watt University.
International Workshop on spatio-temporal statistics methods at ISM in Tokyo.
The 10th International Conference of the ERCIM WG on Computational and Methodological Statistics.
Seminar at the Business School of Essex.
University College London (2016-2021)
Teaching Assistant in the Statistical Science and Computer Science departments,
instructing courses in Probability, Statistics, and Programming Methods.
I received recognition as the Best Tutor within the Computer Science Department (2019) and was
awarded
the UCL Faculty Education
Award. I was also shortlisted for the prestigious UCL Provost's Education Award, competing against
more
experienced faculty members.
Student Supervision
I supervise/ed graduate and doctoral students across multiple institutions:
PhD students at University of California Santa Barbara and University of Technology Sydney focusing on cross-curve interest rate stress testing and green bond yield analysis.
Master's students at the Hearing Institute from various French Schools such as Sorbonne University (Machine Learning), ECE Paris (Acoustics), and AIX-Marseille Université in different audiological applications.
Philosophy
My approach to teaching is grounded in the belief that theoretical concepts must be connected to real-world applications. I emphasize the development of both technical proficiency and critical thinking skills, encouraging students to move beyond formula memorization to deeper understanding of statistical principles.
I value creating an inclusive learning environment where students from diverse backgrounds can thrive. Drawing from my experience as a professional athlete, I incorporate collaborative problem-solving and team-based learning that prepares students for careers in both academic and industry settings.
I believe in adaptive teaching methods that respond to individual learning styles, using a combination of traditional instruction, interactive demonstrations, and hands-on project work to engage students with different strengths and interests.
I maintain active research collaborations across disciplines and institutions
worldwide.
Along my academic journey, I have had the pleasure of working on several interesting problems
with talented researchers.
Duncan Endowed Chair Actuarial & Professor of Statistics for Risk & Insurance.
Professor of physiology, University of Lyon 1.
Institute of Statistical Mathematics.
Software Engineer, NVIDIA, Warsaw.
LMBP - Laboratoire de Mathématiques Blaise Pascal.
PostDoc, (CERIAH), Institut de l'Audition, Institut Pasteur, Paris.
French National Center for Scientific Research (CNRS).
Postdoc at INRIA (Parietal).
University of Technology Sydney (UTS).
Institut Pasteur, CERIAH.
Project Manager, Institut Pasteur, CERIAH.
Associate Professor, UTS Business School.
Senior Lecturer, UTS Sydney.
Head of Investment Research,
Director, Future Fund.
Prof. University of Zurich.
Lead of Deep Hearing Lab.
I am a Marie Skłodowska-Curie Postdoctoral Fellow at the Deep Hearing Lab, University of Zurich
and University Hospital Zurich, with a second affiliation at CERIAH, IHU reConnect (Institut
Pasteur), Paris. My work sits at the interface of statistics, signal processing, and machine
learning, and centres on a single question: what makes hearing outcomes individual, and how far
they can be predicted.
Within my current project REHEAR (Marie Skłodowska-Curie Actions grant no. 101275781), I develop
methods that connect the underlying mechanisms of hearing to the outcomes people experience in
practice, with a particular focus on speech understanding in cochlear-implant users and on
auditory neuropathy. I work extensively with large-scale audiological data, drawing on
statistical signal processing, state-space and time-series modelling, and tools from optimal
transport and machine learning to build models that are both accurate and interpretable.
These methods are chosen for a reason. Clinical hearing measures are typically aggregated into
single scores, discarding the structure that distinguishes one listener from another, and I work
on measures and models that preserve it under the constraints of real diagnostic data: few
trials, imperfect labels, heterogeneous cohorts.
The same methodology transfers beyond hearing, and I have applied it in medical diagnostics,
speaker verification and cybersecurity, and the analysis of financial and sustainability data.
This is a consistent thread in my work: turning advanced statistical methodology into solutions
for complex, structured, real-world data.
Trained in Statistical Science and Signal Processing at University College London, I bring a
multidisciplinary approach and a track record of international collaboration. Drawing on my
background as a professional athlete, I also bring a strategic, results-driven mindset, with an
emphasis on adaptability and problem-solving.
