Vikas Palakonda

Vikas Palakonda, Ph.D.

Postdoctoral Researcher

Kumoh National Institute of Technology, South Korea

Hi! 👋

I am a Postdoctoral Researcher in Geospatial Artificial Intelligence (GeoAI) at the Convergence Geospatial Intelligence Lab, Kumoh National Institute of Technology, South Korea. My research focuses on Artificial Intelligence, with particular emphasis on Neural Network Optimization, Edge AI, Evolutionary AI, Multi-objective Optimization, Federated Learning, and Computer Vision. I develop efficient and interpretable AI systems that address complex real-world problems.

Previously, I worked as a Postdoctoral Researcher at Kyungpook National University, South Korea (2021–2025), where I also earned my Ph.D. from the School of Electronics and Electrical Engineering in 2021, with my doctoral research focused on multi-objective evolutionary algorithms. I also hold an M.Sc. from the same university.

Updates

  • Mar 2026: Serving as Guest Editor for Special Issue on Data Mining in Graph Neural Networks, Big Data and Cognitive Computing (MDPI)
  • Dec 2025: Paper on multi-objective federated learning published in CAAI Transactions on Intelligence Technology
  • Aug 2025: Paper on deep compression for split computing published in IEEE Transactions on Vehicular Technology
  • Apr 2025: Paper on metaheuristics for CNN pruning published in Expert Systems with Applications
  • Nov 2024: Paper on radial-grid multi-objective differential evolution published in Scientific Reports
  • Aug 2024: Paper on OCR using diffusion models published in IEEE Internet of Things Journal
  • Jun 2024: Paper on evolutionary hybrid deep learning for energy theft detection published in Applied Energy
Google Scholar GitHub Email

Research Interests

Artificial Intelligence

Efficient and interpretable AI systems for real-world problems

Edge AI

Deploying efficient AI models on resource-constrained edge devices for real-time inference

Neural Network Optimization

Neural network pruning, model compression, and efficient inference techniques

Evolutionary AI

Hybrid intelligent systems combining evolutionary algorithms with deep learning

Multi-objective Optimization

Multi/many-objective evolutionary algorithms and ensemble optimization methods

Federated Learning

Hybrid intelligent systems with evolutionary computation

Computer Vision

OCR systems, defect detection, and data augmentation techniques

Background

Work Experience

February 2026 - Present

Postdoctoral Researcher

Convergence Geospatial Intelligence Lab, Kumoh National Institute of Technology, South Korea

Research Focus: Geospatial Artificial Intelligence (GeoAI), neural network optimization, Edge AI, evolutionary AI, and multi-objective optimization

April 2024 - January 2026

Postdoctoral Researcher

Department of Mathematics, Kyungpook National University, South Korea

Research Focus: Mathematical optimization frameworks, hybrid intelligent systems, symbolic regression, and federated learning with evolutionary computation

April 2021 - February 2024

Postdoctoral Researcher

Department of Artificial Intelligence, Kyungpook National University, South Korea

Research Focus: Multi/many-objective evolutionary algorithms, ensemble optimization methods, neural network pruning, and computer vision applications (OCR, defect detection, data augmentation)

Education

Ph.D. in Electronics and Electrical Engineering

Kyungpook National University, South Korea (2017-2021)

Thesis: Multi-objective Evolutionary Algorithms with Application to Community Network Detection

GPA: 3.80/4.30

M.Sc. in Electronic Engineering

Kyungpook National University, South Korea (2015-2017)

Thesis: Sorting-based Techniques for Pareto-dominance based Multi-objective Optimization

GPA: 4.10/4.30

B.Tech in Electronics and Communication Engineering

GMR Institute of Technology, India (2011-2015)

Project: Anti-Symmetric Biorthogonal Wavelet Based Image Edge Detection

Aggregate: 83.07%

Publications

17
Journal Articles
11
Conference Papers
28
Total Publications

Journal Articles (17)

Metaheuristics for pruning convolutional neural networks: A comparative study
Expert Systems with Applications, vol. 262, p. 126326
2025 • V. Palakonda, J. Tursunboev, J.-M. Kang, S. MoonQ1IF: 7.5
Multi-objective optimisation framework for heterogeneous federated learning
CAAI Transactions on Intelligence Technology
2025 • J. Tursunboev, V. Palakonda, I.-M. Kim, S. Moon, J.-M. KangQ1IF: 7.3
DeCo-MeSC: Deep compression-based memory-constrained split computing framework
IEEE Transactions on Vehicular Technology
2025 • M. Sung, V. Palakonda, I.-M. Kim, S. Yun, J.-M. KangQ1IF: 7.1
External archive guided radial-grid multi objective differential evolution
Scientific Reports, vol. 14, no. 1, p. 29006
2024 • V. Palakonda, S. Ghorbanpour, J.-M. Kang, H. JungQ1IF: 3.9
Clustering-aided grid-based one-to-one selection-driven evolutionary algorithm
IEEE Access, vol. 12, pp. 120612–120623
2024 • V. Palakonda, J.-M. Kang, H. JungQ2IF: 3.6
OCR-diff: A two-stage deep learning framework for OCR using diffusion model in IIoT
IEEE Internet of Things Journal, vol. 11, no. 15, pp. 25997–26000
2024 • C.-W. Park, V. Palakonda, S. Yun, I.-M. Kim, J.-M. KangQ1IF: 8.9
Multi-objective evolutionary hybrid deep learning for energy theft detection
Applied Energy, vol. 363, p. 122847
2024 • J. Tursunboev, V. Palakonda, J.-M. KangQ1IF: 11.0
Benchmarking real-world many-objective problems: A problem suite with baseline results
IEEE Access
2024 • V. Palakonda, J.-M. Kang, H. JungQ2IF: 3.6
Enhanced non-maximum suppression for the detection of steel surface defects
Mathematics, vol. 11, no. 18, p. 3898
2023 • S.-H. Kang, V. Palakonda, I.-M. Kim, J.-M. Kang, S. YunQ2IF: 2.2
RandMixAugment: A novel unified technique for region-and image-level data augmentations
IEEE Access
2023 • Y. Shin, V. Palakonda, et al.Q2IF: 3.6
Many-objective real-world engineering problems: A comparative study
IEEE Access
2023 • V. Palakonda, J.-M. KangQ2IF: 3.6
Pre-DEMO: Preference-inspired differential evolution for multi/many-objective optimization
IEEE Transactions on Systems, Man, and Cybernetics: Systems
2023 • V. Palakonda, J.-M. KangQ1IF: 8.7
An effective ensemble framework for many-objective optimization based on AdaBoost and K-means
Expert Systems with Applications, vol. 227, p. 120278
2023 • V. Palakonda, J.-M. Kang, H. JungQ1IF: 7.5
An adaptive neighborhood based evolutionary algorithm with pivot-solution based selection
Information Sciences, vol. 607, pp. 126–152
2022 • V. Palakonda, J.-M. Kang, H. JungQ1IF: 6.8
An ensemble approach with external archive for multi-and many-objective optimization
Information Sciences, vol. 555, pp. 164–197
2021 • V. Palakonda, R. Mallipeddi, P. N. SuganthanQ1IF: 6.8
An evolutionary algorithm for multi and many-objective optimization with adaptive mating
IEEE Access, vol. 8, pp. 82781–82796
2020 • V. Palakonda, R. MallipeddiQ2IF: 3.6
Pareto dominance-based algorithms with ranking methods for many-objective optimization
IEEE Access, vol. 5, pp. 11043–11053
2017 • V. Palakonda, R. MallipeddiQ2IF: 3.6

Conference Papers (11)

An generational SDE based indicator for multi and many-objective optimization
ICAIIC, IEEE, pp. 203–209
2021 • J. Yusupov, V. Palakonda, S. Ghorbanpour, R. Mallipeddi, K. C. Veluvolu
Multi-objective evolutionary algorithm based on ensemble of initializations for community detection
ICEIC, IEEE, pp. 1–7
2021 • J. Yusupov, V. Palakonda, R. Mallipeddi, K. C. Veluvolu
KnEA with ensemble approach for parameter selection for many-objective optimization
BIC-TA, Springer, pp. 703–713
2020 • V. Palakonda, R. Mallipeddi
MOEA with approximate nondominated sorting based on sum of normalized objectives
SEMCCO, Springer, pp. 70–78
2020 • V. Palakonda, R. Mallipeddi
Differential evolutionary (DE) based interactive recoloring based on YUV based edge detection
ICTC, IEEE, pp. 597–601
2019 • F. W. Ramlan, V. Palakonda, R. Mallipeddi
Ensemble of Pareto-based selections for many-objective optimization
IEEE SSCI, pp. 981–988
2018 • S. Ghorbanpour, V. Palakonda, R. Mallipeddi
Differential evolution with stochastic selection for uncertain environments: A smart grid application
IEEE CEC, pp. 1–7
2018 • V. Palakonda, N. H. Awad, R. Mallipeddi, M. Z. Ali, K. C. Veluvolu, P. N. Suganthan
Pareto dominance-based MOEA with multiple ranking methods for many-objective optimization
IEEE SSCI, pp. 958–964
2018 • V. Palakonda, S. Ghorbanpour, R. Mallipeddi
Iterative sorting-based non-dominated sorting algorithm for bi-objective optimization
SCI, Springer, pp. 133–141
2018 • V. Palakonda, R. Mallipeddi
Nondominated sorting based on sum of objectives
IEEE SSCI, pp. 1–8
2017 • V. Palakonda, T. Pamulapati, R. Mallipeddi, P. P. Biswas, K. C. Veluvolu
Rank-based nondomination set identification with preprocessing
ICSI, Springer, pp. 150–157
2016 • V. Palakonda, R. Mallipeddi

Skills & Activities

Technical Skills

Programming

PythonMATLABC/C++Java

AI/ML Tools

TensorFlowPyTorchscikit-learnOpenCV

Other Tools

GitLaTeXLinux/UnixHPC

Soft Skills

Research LeadershipTechnical WritingMentoringPresentation

Professional Activities

Editorial Activities

  • 2026: Guest Editor, Special Issue on Data Mining in Graph Neural Networks, Big Data and Cognitive Computing (MDPI) — co-edited with Samira Ghorbanpour
  • 2023-2025: Guest Editor, Special Issue on Machine Learning and AI with Applications, Information Journal
  • 2023-2024: Guest Editor, Special Issue on AI-Based Mathematical Modeling Optimization, AIMS Mathematics Journal

Reviewer for Journals (22+)

IEEE Trans. Evolutionary Computation IEEE Trans. Systems, Man, and Cybernetics IEEE Trans. Neural Networks and Learning Systems IEEE Computational Intelligence Magazine IEEE Trans. Emerging Topics in Computing IEEE Access Swarm and Evolutionary Computation Information Sciences Applied Soft Computing Expert Systems with Applications Neural Networks Neurocomputing Artificial Intelligence Reviews Journal of Machine Learning and Cybernetics Journal of Big Data Scientific Reports Cluster Computing Evolutionary Intelligence The Journal of Supercomputing Memetic Computing

Teaching Experience

Signals and Systems

Teaching Assistant

March 2018 - June 2018 | Kyungpook National University

Conducted tutorial sessions, graded assignments, and assisted students with MATLAB implementations

Logic Circuits

Teaching Assistant

March 2017 - June 2017 | Kyungpook National University

Assisted in lab sessions, evaluated circuit designs, and provided guidance to students

How to Write Research Papers

Teaching Assistant

September 2015 - January 2016 | Kyungpook National University

Provided feedback on draft papers, taught LaTeX typesetting, guided students on scientific writing

Contact

Vikas.11475@gmail.com Google Scholar GitHub LinkedIn
+8210-9956-0475