Alireza Nazemi | Neural network | Best Researcher Award

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Prof. Alireza Nazemi | Neural network | Best Researcher Award

Prof at Shahrood University of Technology, Iran

Dr. Alireza Nazemi is a Professor of Applied Mathematics at Shahrood University of Technology, specializing in control and optimization. He holds a Ph.D. in Applied Mathematics from Ferdowsi University of Mashhad. His research interests include optimal control, nonlinear and convex optimization, portfolio optimization, and neural network theory. Dr. Nazemi has published extensively, with recent works addressing neural network applications in optimization and control. He teaches courses such as Optimal Control, Nonlinear Optimization, and Neural Networks & Optimization. Dr. Nazemi is dedicated to advancing mathematical methods to solve complex engineering and financial problems.

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šŸŽ“ Education

B. Sc. in Applied Mathematics Sharif University of Technology, Tehran, Iran (1997-2001). M. Sc. in Applied Mathematics (Field: Control & Optimization)Ā  Hakim Sabzevari University, Sabzevar, Iran (2001-2003). Dissertation: ā€œTo solve some nonlinear programming problems by using measure theory and neural network modelsā€ Supervisor: Prof. Sohrab Effati. Ph.D. in Applied Mathematics (Field: Control & Optimization)Ā  Ferdowsi University of Mashhad, Mashhad, Iran (2005-2009). Dissertation: ā€œTo solve some optimal shape design problems with free boundaryā€ Supervisor: Prof. Mohammad Hadi Farahi Advisor: Prof. Ali Vahidian Kamyad

šŸ” Research Interests

  • Optimal Control
  • Nonlinear Optimization
  • Convex Optimization
  • Portfolio Optimization
  • Optimization of PDEā€™s
  • Neural Network Theory

Publication:šŸ“

  • Title: Neural network models and its application for solving linear and quadratic programming problems
    Authors: S. Effati, A.R. Nazemi
    Journal: Applied Mathematics and Computation
    Year: 2006
    Volume: 172
    Issue: 1
    Pages: 305-331
    Citations: 84

 

  • Title: A dynamic system model for solving convex nonlinear optimization problems
    Author: A.R. Nazemi
    Journal: Communications in Nonlinear Science and Numerical Simulation
    Year: 2012
    Volume: 17
    Issue: 4
    Pages: 1696-1705
    Citations: 73

 

  • Title: A gradient-based neural network method for solving strictly convex quadratic programming problems
    Authors: A. Nazemi, M. Nazemi
    Journal: Cognitive Computation
    Year: 2014
    Volume: 6
    Pages: 484-495
    Citations: 72

 

  • Title: Analytical solution for the Fokkerā€“Planck equation by differential transform method
    Authors: S. Hesam, A.R. Nazemi, A. Haghbin
    Journal: Scientia Iranica
    Year: 2012
    Volume: 19
    Issue: 4
    Pages: 1140-1145
    Citations: 62

 

  • Title: MĆ¼ntzā€“Legendre spectral collocation method for solving delay fractional optimal control problems
    Authors: S. Hosseinpour, A. Nazemi, E. Tohidi
    Journal: Journal of Computational and Applied Mathematics
    Year: 2019
    Volume: 351
    Pages: 344-363
    Citations: 59

 

  • Title: A neural network model for solving convex quadratic programming problems with some applications
    Author: A. Nazemi
    Journal: Engineering Applications of Artificial Intelligence
    Year: 2014
    Volume: 32
    Pages: 54-62
    Citations: 59

 

  • Title: An efficient dynamic model for solving the shortest path problem
    Authors: A. Nazemi, F. Omidi
    Journal: Transportation Research Part C: Emerging Technologies
    Year: 2013
    Volume: 26
    Pages: 1-19
    Citations: 59

 

  • Title: Application of projection neural network in solving convex programming problems
    Authors: S. Effati, A. Ghomashi, A.R. Nazemi
    Journal: Applied Mathematics and Computation
    Year: 2007
    Volume: 188
    Issue: 2
    Pages: 1103-1114
    Citations: 52

 

  • Title: A dynamical model for solving degenerate quadratic minimax problems with constraints
    Author: A.R. Nazemi
    Journal: Journal of Computational and Applied Mathematics
    Year: 2011
    Volume: 236
    Issue: 6
    Pages: 1282-1295
    Citations: 49

 

  • Title: Solving general convex nonlinear optimization problems by an efficient neurodynamic model
    Author: A. Nazemi
    Journal: Engineering Applications of Artificial Intelligence
    Year: 2013
    Volume: 26
    Issue: 2
    Pages: 685-696
    Citations: 45

 

  • Title: A capable neural network model for solving the maximum flow problem
    Authors: A. Nazemi, F. Omidi
    Journal: Journal of Computational and Applied Mathematics
    Year: 2012
    Volume: 236
    Issue: 14
    Pages: 3498-3513
    Citations: 44

sofia aftab | Neural network | Best Researcher Award

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Ā Ms. sofia aftab | Neural network | Best Researcher Award

Ms. Norges Teknisk-Naturvitenskapelige Universitet,Ā Norway

šŸ‘©ā€šŸ”¬ Highly experienced Data Scientist with expertise in research, data science, machine learning, advanced analytics, and Generative AI. Proficient in Python, SQL, SAS, Teradata, R, and QlikView. Possesses strong skills in business analysis, model building, and communication. Excels in statistical techniques, machine learning algorithms, deep learning frameworks, NLP, data engineering, and Generative AI. Experienced in agile project management and collaborative team environments.šŸŒŸ

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šŸŽ“šŸ“šAcademics

  • MS (IT) specialization in Data Mining/Analytics from NUST
  • Ph.D (Machine Learning/Data Science) from NTNU

 

šŸ’¼šŸ“ŠCareer Skills/Knowledge

Over 12 years of experience as an enthusiastic Data Scientist Strong business analysis and data mining skills Expertise in model building and execution Proficient in segmentation and customer value management analytics Skilled in statistical techniques including regression, A/B testing, and statistical significance of ML models Experienced in machine learning algorithms such as NN, SVM, Naive Bayes, ensemble modeling, and deep learning (DNN)Knowledgeable in NLP techniques, data engineering, MLOps, and cloud deployments Experienced in Generative AI techniques including prompt engineering, Lang chain, and Semantic search Research-focused with experience in improving evaluation metrics and developing recommendation algorithms Proficient in agile project management methodologies

šŸ¢šŸ’¼Experience

Accenture-Norway (Aug 2022 – Present)Data Science Consultant/Team Lead Built and maintained data and ML pipelines Developed ML models and CI/CD workflows Collaborated with cross-functional teams Led agile projects and worked on Generative AI Telenor-Norway (Dec 2020 – Aug 2022)Data Scientist Evaluated and improved ML projects Transformed business questions into analysis Led agile projects and presented to management NTNU (Apr 2018 – 2020)Research Scientist Worked on Recommendation systems using deep learning Improved evaluation metrics for recommender systems HPE (May 2016 – 2018)Data Scientist (Consultant)Identified cross-sell and upsell opportunities Developed and maintained next best action engine Telenor (Aug 2015 – May 2016)Specialist Advance Analytics and Consumer Insight Conducted subscriber analysis and developed churn/retention framework Telenor (Aug 2013 – Aug 2015)Executive Advance Analytics and Consumer Insight Developed behavioral segmentation and churn prediction models ProtĆ©gĆ© Global (Aug 2012 – 2013)Team Lead Data Miner/Data Scientist Managed a team for data mining projects and conducted exploratory data analysis Muhammad Ali Jinnah University (MAJU) (2012 – 2013)Lecturer, Trainer National University of Science and Technology (NUST) (2011 – 2012)Research Assistant

Publications Top Notes šŸ“

  1. Title: Data Mining in Insurance Claims (DMICS) Two-way mining for extreme values
    • Authors: S Aftab, W Abbas, MM Bilal, T Hussain, M Shoaib, SH Mehmood
    • Conference: Eighth International Conference on Digital Information Management (ICDIM)
    • Year: 2013
    • Citations: 6

 

  1. Title: Evaluating top-n recommendations using ranked error approach: An empirical analysis
    • Authors: S Aftab, H Ramampiaro
    • Journal: IEEE Access
    • Volume: 10
    • Pages: 30832-30845
    • Year: 2022
    • Citations: 4

 

  1. Title: Improving top-N recommendations using batch approximation for weighted pair-wise loss
    • Authors: S Aftab, H Ramampiaro
    • Journal: Machine Learning with Applications
    • Volume: 15
    • Pages: 100520
    • Year: 2024

 

  1. Title: Deep Contextual Grid Triplet Network for Context-Aware Recommendation
    • Authors: S Aftab, H Ramampiaro, H Langseth, M Ruocco
    • Journal: IEEE Access
    • Year: 2023

 

  1. Title: Data Mining in Insurance Claims (DMICS)
    • Authors: S Aftab, W Abbass, MM Bilal, T Hussain, M Shoaib, SH Mehmood