About
I am a PhD Candidate in Statistics at Rutgers University, specializing in statistical machine learning, high-dimensional inference, and deep generative models. I have 3+ years of quantitative finance experience at Rabobank (New York), building forecasting and risk tools for derivatives portfolios.
My current interests include dynamic factor models and sparse state-space inference; VAEs, flows, and diffusion models; causal inference in finance and healthcare; and machine-learning–based stochastic weather generators for renewable energy.
Research
Dynamic Factor Models
Identifiable DFMs with episodic factor activation and efficient MAP-EM/Kalman filtering.
Time Series Kalman Spike-&-SlabDeep Generative Models
VAEs, normalizing flows, and diffusion for complex temporal structure and stochastic volatility.
VAE DiffusionCausal ML
Combining causal inference with deep learning for order book dynamics and clinical EEG.
Causal Inference FinanceStochastic Weather Generators
Minute-level wind vector generation trained on multi-decade atmospheric datasets.
Renewables WindPublications (Selected)
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Enhanced classification of tinnitus patients using EEG microstates and deep learning techniques.Raeisi Z, Sodagartojgi A, Sharafkhani F, Roshanzamir A, Najafzadeh H, Bashiri O, Golkarieh A. Scientific Reports (2025).
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EEG microstate analysis in trigeminal neuralgia: identifying potential biomarkers for enhanced diagnostic accuracy.Lashaki RA, Raeisi Z, Sodagartojgi A, Abedi Lomer F, Aghdaei E, Najafzadeh H. Acta Neurologica Belgica (2025).
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Modeling and optimization of efficient removal of diclofenac and naproxen based on chemometric approaches.Abdollahi SA, Pourabadeh A, Alishiri M, Sodagartojgi A, Ranjbar SF, Ehghaghi MB, Talati F. Water Resources and Industry 31 (2024).
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Deep learning-based retinal abnormality detection from OCT images with limited data.Talebzadeh M, Sodagartojgi A, Moslemi Z, Sedighi S, Kazemi B, Akbari F. World Journal of Advanced Research and Reviews 21(3) (2024).
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Exploring the impact of supply chain integration and agility on commodity supply chain performance.Dokhanian S, Sodagartojgi A, Tehranian K, Ahmadirad Z, Khorashadi Moghaddam P, Mohsenibeigzadeh M. WJARR 22(1) (2024).
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Honors & Awards
- Full 5‑Year PhD Fellowship, Rutgers University (2022–2027)
- Best Teaching Assistant Award, Rutgers Statistics (2024)
- Best Qualifying Exam Performance, Rutgers Statistics (2023)
- JSM Travel Grant, American Statistical Association (2024)
- AI for Statistics Workshop Travel Award, NSF (2024)
- Graduate Student Research Award, Rutgers Statistics (2023)
- College of Business Valedictorian, JMU (2019) — Summa Cum Laude, Phi Beta Kappa
Teaching
Teaching Assistant, Rutgers (2022–Present)
Statistical Inference · Linear Regression · Time Series Analysis · Machine Learning. Best Teaching Assistant Award (2024)
Curriculum Vitae
Download my CV (PDF) · Last updated: 2025‑09
Education
- PhD in Statistics, Rutgers University (2022–Present) — GPA 3.9/4.0 · Advisors: Prof. Gemma Moran & Prof. Han Xiao
- MS in Statistics, Rutgers University (2022–2024)
- BS in Mathematics & Economics, James Madison University (2016–2019) — Valedictorian, GPA 3.95/4.0
Experience
- Commodity Derivatives Trading Analyst, Rabobank, New York (2020–2022)
- Corporate Derivatives Quantitative Analyst, Rabobank, New York (2019–2021)
Contact
sodagaax@gmail.com · Highland Park, NJ