Sajad Abavisani
Final-year PhD Candidate in Electrical & Computer Engineering
With seven years of experience in machine learning, causal inference, and time series analysis, I am actively seeking full-time positions starting in May 2026.
Specializing in causal learning, deep learning, and advanced time series analysis.
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 →- 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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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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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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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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Piecing Together the Causal Puzzle
Answer Set Programming approach to recover true causal structure from undersampled, noisy neuroimaging time series data with robust fMRI performance.
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)