CV

Hamed Karimi — Curriculum Vitae

Last Updated: January 2026

Affiliation

• Trustworthy AI Laboratory (TAILab)
• Department of Electrical, Computer, and Biomedical Engineering
• Faculty of Engineering and Architectural Science (FEAS)

Highlights

• I am currently a Ph.D. candidate in Computer Engineering and a Research Assistant at Toronto Metropolitan University (TMU), Toronto, ON, Canada. I received my B.Sc. degree from Shahid Beheshti University (2016) and M.Sc. degree from the University of Tehran (2019) in Computer Engineering.
• My current research focuses on confidence and uncertainty quantification in deep neural networks, employing techniques such as conformal prediction and risk control, evidential and Bayesian approaches, and information-theoretic methods to enhance reliability in AI systems.
• I have published peer-reviewed research articles in refereed journals and conferences including Expert Systems with Applications (Q1, IF: 8.5), AAAI Human AI Symposium, Conformal and Probabilistic Prediction with Applications (COPA), and Medical Imaging with Deep Learning (MIDL).
• Other research interests include uncertainty in large language models (LLMs), adversarial robustness, model calibration, federated learning, and interpretability in machine learning.

Education

Toronto Metropolitan University (TMU), Toronto, Ontario, Canada

• Research Area: Trustworthy AI and Machine Learning
• Supervisor: Dr. Reza Samavi (Vector Institute Faculty Affiliate)

University of Tehran (UT), Tehran, Iran

• Thesis Topic: Ontology Mapping Using Inductive Logic Programming and Word Embedding Approaches
• Supervisors: Dr. Ali Kamandi and Dr. Abdorreza Torabi
  

Shahid Beheshti University (SBU), Tehran, Iran

• Specialty: Software Engineering
• Final Project Topic: A Reinforcement Learning Approach in Multi-Agent Systems Based on Cooperation
• Supervisor: Dr. Monireh Abdoos

Publications

SACP: Spatially-Adaptive Conformal Prediction in Uncertainty Quantification of Medical Image Segmentation

Bereska, J. I., Karimi, H., & Samavi, R. (2025). “SACP: Spatially-Adaptive Conformal Prediction in Uncertainty Quantification of Medical Image Segmentation.” In Medical Imaging with Deep Learning (MIDL 2025). Published in Proceedings of Machine Learning Research (PMLR).

Evidential Uncertainty Sets in Deep Classifiers Using Conformal Prediction

Karimi, H., & Samavi, R. (2024). “Evidential Uncertainty Sets in Deep Classifiers Using Conformal Prediction.” In Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications. Proceedings of Machine Learning Research (PMLR), vol. 230, 466–489.

DENL: Diverse Ensemble and Noisy Logits for Improved Robustness of Neural Networks

Yazdani, M., Karimi, H., & Samavi, R. (2024). “DENL: Diverse Ensemble and Noisy Logits for Improved Robustness of Neural Networks.” In Proceedings of the 15th Asian Conference on Machine Learning. Proceedings of Machine Learning Research (PMLR), vol. 222, 1574–1589.

Quantifying Deep Learning Model Uncertainty in Conformal Prediction

Karimi, H., & Samavi, R. (2023). “Quantifying Deep Learning Model Uncertainty in Conformal Prediction.” Proceedings of the AAAI Symposium Series, 1(1), 142–148.

A Learning-based Ontology Alignment Approach Using Inductive Logic Programming

Karimi, H., & Kamandi, A. (2019). “A Learning-based Ontology Alignment Approach Using Inductive Logic Programming.” Journal of Expert Systems with Applications (ESWA), Elsevier, 125(C), 412–424.

Application-Based Review of Recent Advances of Data Mining in Healthcare

Shirazi, S., Baziyad, H., & Karimi, H. (2019). “An Application-Based Review of Recent Advances of Data Mining in Healthcare.” Journal of Biostatistics and Epidemiology, 5(4).

YAD: A Learning-based Inductive Logic Programming Tool

Kamandi, A., & Karimi, H. (2018). “YAD: A Learning-based Inductive Logic Programming Tool.” Journal of Open Source Software (JOSS), 3(30), 892.

Ontology Alignment Using Inductive Logic Programming

Karimi, H., & Kamandi, A. (2018). “Ontology Alignment Using Inductive Logic Programming.” In 4th International Conference on Web Research (ICWR), 118–127. IEEE.

Research Experience

Full details are listed in the Research tab.

Teaching Experience

Full details are listed in the Teaching tab.

Conferences and Presentations

Full details are listed in the Talks tab.

Professional Skills and Academic Projects

Programming Languages

Python / ML Frameworks

Selected Academic Projects

• Implemented machine learning projects using PyTorch and Keras.
• Implemented machine learning projects for ontology alignment using Python and NLP frameworks (NLTK, spaCy).
• Implemented ML course projects: Reinforcement Learning (maze agents), Case-Based Reasoning (house pricing), Weka (classification tasks).
• Implemented course projects using C/C++, C#, HTML & CSS, JavaScript, Verilog, and Assembly (Intel x86 and MIPS32).
• Implemented course projects with ARM 1768 and AVR ATMEGA32 microcontrollers.
• Implemented course projects with XNA (C#), Qt (C++), Lucene (Java).
• Implemented course projects using ModelSim and Xilinx ISE.
• Implemented database projects with MySQL and Redis (NoSQL).

Certifications and Related Skills

Network+ and CISCO CCNA (Route & Switch)

Familiar with passive networking

Work Experience and Extra-Curricular Activities

Teacher (Tutor) — High Schools and Private Institutions

Mathematics, Physics, and Social Studies

High School and College Educational Consultancy

Course consultancy, study planning, program selection for national annual university entrance exam

IT Technical Support — Hamayesh Afarinan Javid Company

Internship

Java SE Developer