Sajad Abavisani
PhD Candidate · Georgia Tech · Causal AI Researcher
Building the causal layer of AI — algorithms that are not just accurate, but trustworthy enough to deploy in neuroscience, finance, and healthcare. ICLR 2023 Oral · 1,000× speedup in causal discovery · validated on real fMRI for schizophrenia detection.
Actively seeking Research Scientist, Quantitative Researcher, and Applied ML roles starting May 2026. Green Card pending — no sponsorship required.
Education
View Full Resume →Ph.D. in Electrical and Computer Engineering
Aug 2020 – May 2026Georgia Institute of Technology
GPA: 3.8/4.0
Select coursework: Mathematical Foundations of ML, ML with Limited Supervision, Conversational AI, Convex Optimization, Statistical ML, Information Theory, Random Processes
M.S. in Electrical and Computer Engineering
Aug 2020 – Aug 2023Georgia Institute of Technology
GPA: 3.9/4.0
B.S. in Electrical Engineering
Sep 2014 – Sep 2019Ferdowsi University of Mashhad
GPA: 4.0/4.0
Research & Publications
Google Scholar →- RnR: A Meta-Solver for Causal Discovery in Undersampled Time Series – Under review, ICML 2026 (45% F1 improvement over SOTA)
- GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints – ICLR 2023 (Oral Presentation, Top 25%)
- Causal Learning through Deliberate Undersampling – Proceedings of the Second Conference on Causal Learning and Reasoning (2023)
- Reducing Causal Illusions through Deliberate Undersampling – NeurIPS 2022 Workshop (2022)
- Greedy AutoAugment – Pattern Recognition Letters Journal (Elsevier, 2020)
- Radiologist-Level COVID-19 Detection Using CT scans – (2020)
- Causal Graph Recovery in Neuroimaging through Answer Set Programming – Preprint
- ION-C: Integration of Overlapping Networks via Constraints – Preprint
Featured Research
GRACE-C: Generalized Rate Agnostic Causal Estimation
A breakthrough method for causal discovery in undersampled time series data, achieving up to 3 orders of magnitude speed improvement. Presented at ICLR 2023 (Oral, Top 25%).
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RnR: Meta-Solver for Causal Discovery
An ASP-based meta-solver that refines outputs from any causal discovery algorithm (PCMCI, FASK, MVGC, GIMME), delivering 45% F1 improvement over SOTA. Under review at ICML 2026.
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Causal Learning through Deliberate Undersampling
Groundbreaking work showing that measuring data less frequently can actually provide more information about causality by reducing ambiguity in causal relationships.
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Solution-Set Transformer for Brain Classification
A Graph Transformer + Set Transformer architecture that learns diagnostic biomarkers from multi-graph RASL output, classifying healthy vs. schizophrenia subjects directly from fMRI connectivity.
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Radiologist-Level COVID-19 Detection
Novel DECAPS architecture using detail-oriented capsule networks for automated COVID-19 diagnosis from CT scans, achieving radiologist-level accuracy.
Learn More →Technical Projects
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LLM-Guided Causal Graph Selection
LoRA/QLoRA fine-tuning of Mistral-7B on synthetic gunfolds data to select ground-truth DAGs from RASL equivalence classes — combining chain-of-thought reasoning with learned domain priors.
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Quantfolio: Portfolio Signal Engine
A three-tier attention system (morning brief → hourly digest → push alert) with a 4-axis scoring pipeline: materiality, portfolio-weight impact, SHA-256 novelty dedup, and urgency. Claude API synthesis, SQLite persistence, ntfy.sh phone push, and a curated sector theme graph — all deployable via cron/launchd.
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Greedy AutoAugment
Efficient image data augmentation using greedy search strategy, achieving comparable accuracy on CIFAR-10, CIFAR-100, and SVHN while using 360x fewer computational resources.
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ION-C: Integration of Overlapping Networks
Computationally efficient algorithm that builds unified causal models from separate, partially overlapping datasets, scaling to graphs with up to 25 nodes with 99.55% accuracy.
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RnR: Causal Discovery Meta-Solver
Answer Set Programming meta-solver that recovers true causal structure from undersampled, noisy neuroimaging time series — validated on real fMRI for schizophrenia detection. Under review at ICML 2026.
View Details →Skills & Expertise
View Full Resume & Skills →Languages
Python, C++, JavaScript, R, Java, SQL, Clingo/ASP
ML & Deep Learning
PyTorch (CUDA), TensorFlow, Hugging Face (Transformers, PEFT, Datasets), LLM fine-tuning, RAG pipelines (LangChain, LlamaIndex), Graph Neural Networks (PyTorch Geometric), Transformers, GANs, Diffusion Models, Capsule Networks, Scikit-learn
Data & Infrastructure
Pandas, NumPy, SciPy, NetworkX, Statsmodels, Docker, AWS, Azure ML, W&B, MLflow, Git, GitHub Actions CI/CD, Linux, SLURM clusters
Specializations
Causal inference, constraint programming (ASP/Clingo), graph algorithms, time series analysis & forecasting, statistical modeling, Bayesian inference, optimization, open-source development (PyPI), agentic coding (Claude Code, Cursor)