Amirabbas Asadi

Education

Sharif University of Technology Sept 2023 – Sept 2026

M.Sc. in Applied Mathematics (Stochastic Mathematics and Data Science)

Bu-Ali Sina University Sept 2017 – March 2022

B.Sc. in Computer Engineering

Retail Time Series Forecasting Using An Automated Deep MetaLearning Framework
E. Joshaghani, S. Barak, A. Asadi, A. Mirafzali.
Preprint
Short-Term Forecasting of Uncertain Parameters for Virtual Power Plants
M. Abbasi, A. Asadi, M. E. BenElghali, R. Zerrougui.
IEEE CAMAD 2021
Satisfiability Checking of Clinical Practice Guidelines Using an Analyzer
Y. Sanati, A. Asadi.
Modern Care Journal, 2019

Probabilistic Machine Learning with Julia — Kharazmi University, Faculty of Finance (2025). [YouTube Videos]
Probabilistic Programming for Machine Learning — Sharif University of Technology (2024). [Slides] [Video]
Deep Probabilistic Modeling with Normalizing Flows — Sharif University of Technology (2023). [Slides]
Bayesian Machine Learning — Online lecture (2022). [Slides]
Probabilistic Programming for AI — Online lecture (2021). [Slides]
Time Series Forecasting Using RNNs — Online lecture. [Slides]
Teaching Assistant: Deep Generative Models (Sharif University of Technology).
Teaching Assistant: Data Mining, Data Structures, Advanced C++ Programming (Bu-Ali Sina University).

1st Place: Iran Annual AI Award 2024 (Brain Abnormality MRI Image Classification).
National Rank 4: Graduate Entrance Exam in Mathematics (4th Data Science, 5th Cryptography).
Silver Medal: Optimizer 2022 (Sharif University of Technology), Multi-Manifold Clustering via random walks.
2nd Place: The Second National fNIRS Data Analysis Competition (focusing on brain computer interface systems to help patients with movement disorders) held by National Brain Mapping Lab 2022
1st Place: West Iran Collegiate Programming Contest (WICPC) 2018.

Particle-based and nonparametric Variational Inference framework in Julia.

An optimization framework for HPC clusters leveraging C++, MPI, TBB, and a custom DSL (Dena).

Meta-learning for time-series forecasting and time-series augmentation using deep generative models.

Generating fractals using complex-valued neural networks.