About me

Automated assessment of interstitial lung disease (ILD) in chest CT imaging @ Brainomix (Oxford, 2022-2026)

I developed and productized an explainable machine-learning respiratory phase classifier using features derived from shape analysis of deep learning-based trachea and lung segmentation in chest CT scans to improve the reliability of in-house ILD biomarkers. I also conducted survival analysis (Cox proportional hazards models, random survival forests) to gain insights on biomarker reliability and improve CI/ML testing infrastructure. Regarding classical image processing, I worked on advanced denoising (e.g., guided filtering) and level-set-based postprocessing to enhance airways and lung segmentation.

Deep generative modeling and NLP for finance @ Sony Computer Science Labs (Tokyo, 2022)

I developed a method to compute time-dependent loadings in security factor models based on the optical-flow algorithm and trained a VAE to learn and visualize low-dimensional fund-month exposure embeddings, to support GPIF activities. I also researched NLP algorithms (including approaches using transformer-based language models such as BERT and MPNet) for various problems, such as fake news detection and greenwashing evaluation, to help Nomura Asset management assess corporate sustainability.

• Project Webpage

Cine-MR image forecasting with RNNs and transformers for latency compensation in radiotherapy @ The University of Tokyo (2017-2021, independent work until 2026)

I designed an algorithm to forecast future frames in dynamic chest and liver MR images, based on motion modeling with PCA and temporal dynamics prediction with RNNs trained online and transformer encoders, for safe MR-guided radiotherapy. The proposed approach is modular, interpretable, and can help overcome data scarcity, motion irregularity, and distributional shift between the training and test datasets for population models.

• YouTube (English sub, 450k views)

• ArXiv
• Code

External marker position forecasting using RNNs trained online for safer radiotherapy @ The University of Tokyo (2020-2021, independent work until 2025)

I proposed implementations of online training algorithms for RNNs (UORO, SnAp-1, and DNI) with closed-form simplifications as well as improved accuracy and reduced time/memory complexity. I assessed their ability to forecast the positions of markers on the chest and abdomen for latency compensation and robotic control in radiotherapy for the first time. In our experiments, modern online learning algorithms enabled RNNs to adapt to irregular breathing patterns with limited data and achieve competitive accuracy compared to prior deep learning approaches trained on larger datasets, while keeping inference time relatively low.

UORO article:

• Publication
• ArXiv
• Code

Follow-up article (covering also SnAp-1 and DNI):

• Publication
• ArXiv
• Code

Simultaneous segmentation and recognition of characters @ Idemia (Île-de-France, 2016)

I prototyped an OCR pipeline in C for degraded text images, consisting of three steps: over-segmentation, character recognition, and shortest-path graph decoding using Viterbi's algorithm to select the most likely string (see the figure above). It achieved 13.4% higher accuracy than the open-source OCR engine Tesseract 3.04. The first step of the pipeline (candidate cut generation) relied on Dijkstra's algorithm, as each pixel was assigned to a node in the graph representation of the image and edge weights were based on pixel intensities and heuristics; I optimized speed using a binary-search-style strategy. Regarding the second step, I implemented from scratch a character-level neural network classifier, trained it on a dataset of ~20k character images, and improved recognition accuracy by ~5% by incorporating a class-conditional Bayesian aspect-ratio prior.

Graph-structured data classification for computer-aided diagnosis in neurology @ Fresnel Institute (Marseille, 2016)

I first derived a generalized likelihood ratio test for binary classification of graph-structured data via Fourier graph transforms, based on prior work from Hu et al. I applied this method to Alzheimer's disease detection, by modeling brain regions in PET images as nodes in a graph with edge weights defined by Gaussian RBF kernels over regional imaging features, and obtained a leave-one-out test F1 score of 0.78 on a dataset of 122 brain scans. Last, I explored optimization of region-independent weights corresponding to each image property to maximize classification accuracy.

Participation in the French robotics cup (2015)

I built and integrated two autonomous robots that execute actions on a table to score as many points as possible during 90-second matches, as part of a team of 6 students. Specifically, I focused on programming the Arduino microcontroller in C/C++, which includes motion control (proportional control), obstacle avoidance, and action sequencing. I also took part in project planning, communications, fundraising/partnerships, and mentoring junior students.

Mechanical modelling of a periodic bundle of fibers @ IEMN institute/LIMMS (Hauts-de-France, 2015)

I modeled a periodic inhomogeneous fiber-bundle structure using coupled ordinary differential equations (ODEs) to study effective stiffness under different conditions. This supports our understanding of DNA degradation mechanisms due to irradiation, as part of the SMMIL-E project, which promotes the development and use of BioMEMS against cancer. I estimated numerically the effective Young's modulus for chains of n repeated unit cells with a transfer-matrix formulation using MATLAB, and analyzed sensitivity to structural parameters. I also derived closed-form expressions for the effective modulus in simplified configurations, and characterized asymptotic regimes (weak/strong coupling and large-n behavior).

Fluid flow modeling for the development of a non-contact borehole vibration sensor @ Schlumberger (Kanagawa, 2014)

I developed a fluid-flow model (based on Navier-Stokes equations) to assess the feasibility of a non-contact borehole vibration sensor for geological formation analysis, requiring less time and energy to operate than conventional contact-based devices. I considered different geometry configurations and fluid types (e.g., non-Newtonian Bingham fluids) and estimated key physical parameters numerically with MATLAB. I derived and interpreted analytical pressure and velocity expressions as functions of wellbore vibration parameters using partial differential equations, Fourier analysis, and asymptotic expansions, to further validate sensor feasibility and back up design recommendations.