Visiting Researcher @ Stanford · Incoming Quantitative Researcher @ WorldQuant
Hello, I'm Iliass Sijelmassi
Researcher & Engineer
Currently researching AI for cardiology at Stanford University, joining WorldQuant in October. Passionate about statistics, machine learning, and markets.
About Me
I'm a Visiting Researcher at Stanford University, passionate about statistics, machine learning, and quantitative research.
Currently researching AI for cardiology at Stanford under Dr. Louise Sun. With experience at Crédit Agricole CIB and Infosys (Renault), I combine research depth with industry expertise to deliver impactful solutions.
Education
MSc Data Science & AI
Polytechnique & HEC Paris
Visiting Student @ Stanford
Location
Stanford, CA / Paris, France
Status
Visiting Researcher @ Stanford
Projects
Featured Work
Dollar-neutral long-short strategy across 411 crypto perpetual futures. Ranked #1 of 13 teams on out-of-sample Sharpe (ML for Financial Markets, HEC Paris). 379 candidate features filtered via Spearman IC and correlation pruning to 316, feeding a Ridge + XGBoost ensemble under a rolling 12-month walk-forward.
Quantified pass decision quality on FIFA World Cup 2022 tracking data (~5.4 GB): Expected Threat model via Markov-chain value iteration on 126k possession actions across all 64 matches, NumPy-vectorised pitch-control engine, Streamlit dashboard.
Survival analysis for leukemia patients. Finished top 12% in the national QRT-ENS data challenge. 75.45% IPCW C-index.
Real-time options market-making simulation: quoting and inventory management, served via FastAPI.
Experience
Where I've Worked
Quantitative Researcher Intern (Incoming)
WorldQuant
Incoming 6-month internship in systematic alpha signal research and backtesting.
Visiting Student Researcher
Stanford University
Conducting research in AI for cardiology under the supervision of Dr. Louise Sun, building deep learning models to predict cardiovascular risks. Processing and extracting predictive signals from large-scale, noisy longitudinal clinical datasets, applying rigorous statistical validation to ensure robustness.
Data Scientist Intern
Crédit Agricole CIB
Built audit risk prediction models (XGBoost) to forecast overdue internal audit actions on 200k+ historical records. Applied time-aware validation and SHAP for explainability.
Java Consultant Intern
Infosys – Renault
Developed backend modules for supply-chain platforms (R3, EPO). Java, PostgreSQL, Oracle.
Software Developer Intern
Kuyper's Auto
Built web interface and online reservation system. Direct client collaboration.
Technical
Skills & Tools
Languages
ML & Data
Tools
Research
Papers & Reports
Predicting Severe Pericardial Tamponade After Cardiac Surgery
2026MSc research paper in collaboration with Stanford University School of Medicine, supervised by Prof. Louise Sun. Benchmarks four model families on a Stanford surgical cohort (95 severe incident events, 6,761 controls); a calibrated pre-operative model reaches AUROC 0.74, and later ICU and bedside-waveform data add no established increment once monitoring-intensity confounds and selection optimism are controlled. Includes a methodological framework for trustworthy evaluation on small, rare-event waveform cohorts.
GMSK Modulation: Analysis and Implementation
2024Technical report on Gaussian Minimum Shift Keying modulation: theoretical foundations, spectral efficiency analysis, and practical implementation considerations for modern communication systems.
Contact
Let's Connect
I'm joining WorldQuant as a Quantitative Researcher Intern in October 2026, and open to full-time quantitative research roles from Spring 2027. Feel free to reach out.