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Alex Vakanski

Learning From Demonstration Vision-Based Control Intelligent Systems For Robotics Biomedical Informatics
The University of Idaho Computer Science

About

Dr. Alex Vakanski is an Associate Professor in the Department of Computer Science with a Ph.D. in Mechanical and Industrial Engineering from Toronto Metropolitan University (2013). He specializes in machine learning, computer vision, robotics, and biomedical informatics, focusing on learning from demonstration, vision-based control, and intelligent systems for robotic and biomedical applications.

Research Performance Summary

54
Total Papers
2478
Total Citations
22
H-Index
2010-2025
Active Research Span

First Recorded Paper

Trajectory learning based on conditional random fields for robot programming by demonstration

Year: 2010

Citations: 13

Venue: Proceedings of the IASTED International Conference on Robotics and

Latest Recorded Paper

GCSAM: Gradient Centralized Sharpness Aware Minimization

Year: 2025

Citations: 0

Venue: IEEE Access 13

Last 10 Years Publication Activity

This timeline shows the professor's yearly publication activity.

2025
12
2024
6
2023
7
2022
5
2021
3
2020
5
2019
3
2018
3
2017
4
2016
1
2014
1

Publication Venues and Collaboration

Journal, Conference, and Book Publication Breakdown

Conference 15
Journal 7
Book 1

Top Coauthors

Vakanski Xian Janabi-Sharifi

Research Impact by Period

2025-2026

Period Stats
Papers 12
Citations 57
Avg. Citations / Paper 4.8
H-Index 5

2020-2024

Period Stats
Papers 26
Citations 1652
Avg. Citations / Paper 63.5
H-Index 17

2015-2019

Period Stats
Papers 11
Citations 544
Avg. Citations / Paper 49.5
H-Index 9

2010-2014

Period Stats
Papers 5
Citations 225
Avg. Citations / Paper 45
H-Index 5

Contact and Professional Links

Contact Information

vakanski@uidaho.edu
2087575422

Detected Research Keywords

Robot Programming Demonstration Network Breast Ultrasound Breast Ultrasound Image Histopathology Image Synthesis Physical Rehabilitation Exercises Tumor Aware Network Aware Network Breast Ultrasound Image Segmentation Bending Loss Regularized Network Nuclei Segmentation