Vahid Jahangiri | Machin Learning | Best Researcher Award

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Assist. Prof. Dr. Vahid Jahangiri | Machin Learning | Best Researcher Award

Assistant Professor at University of Mohaghegh Ardabili, Iran

Assist. Prof. Dr. Vahid Jahangiri is an Assistant Professor at the University of Mohaghegh Ardabili, Iran, specializing in Civil (Earthquake) Engineering. He holds a Ph.D. in Earthquake Engineering from Tarbiat Modares University (2016), an M.Sc. from Sharif University of Technology (2009), and a B.A. in Civil Engineering from the University of Tabriz (2007). Dr. Jahangiri has extensive teaching experience in areas such as seismic design, risk assessment, structural dynamics, and earthquake engineering. Additionally, he worked as a Civil Engineer Consultant at Arte Tarrahan (2011-2014). His research focuses on seismic risk assessment, structural resilience, and earthquake-induced infrastructure damage, with key publications in prestigious journals such as the Bulletin of Earthquake Engineering and Structures. Dr. Jahangiri’s work has made significant contributions to the safety of infrastructure under seismic events, particularly in buried pipelines and structural collapse scenarios.

Profile:

Education:

Assist. Prof. Dr. Vahid Jahangiri has a strong academic foundation in civil and earthquake engineering. He completed his Ph.D. in Civil (Earthquake) Engineering at Tarbiat Modares University, Tehran, Iran, in 2016. Prior to this, he earned his M.Sc. in Civil (Earthquake) Engineering from Sharif University of Technology in 2009 and his B.A. in Civil Engineering from the University of Tabriz in 2007. His academic journey underscores his dedication to mastering seismic engineering, equipping him with advanced expertise to contribute significantly to the field.

Professional Experience:

Dr. Jahangiri’s career includes valuable consulting experience and a robust academic role. Between 2011 and 2014, he worked as a Civil Engineer Consultant at Arte Tarrahan in Tehran, where he applied engineering knowledge to practical projects. In 2018, he joined the Faculty of Engineering at the University of Mohaghegh Ardabili as an Assistant Professor. Here, he teaches various specialized courses to M.Sc. and Ph.D. students, including Performance-Based Seismic Design and Dynamics of Structures, and fundamental subjects like Mechanics of Materials to undergraduate students, blending theory with practical application.

Research Interests:

Assist. Prof. Dr. Vahid Jahangiri’s research interests primarily lie in the fields of earthquake engineering, seismic risk assessment, and structural resilience. His work focuses on evaluating and enhancing the seismic performance of critical infrastructure, including buried pipelines and bridges. Dr. Jahangiri is particularly interested in the development of performance-based seismic design methods, fragility analysis, and the dynamic behavior of structures under earthquake loading. He has conducted extensive research on the seismic response of buried steel pipelines and the impact of seismic wave propagation on gas pipeline networks. Additionally, his research extends to the assessment of progressive collapse in buildings, with a particular focus on fire-induced damage. His work aims to improve the safety and reliability of structures in seismically active regions, contributing to the development of more resilient infrastructure systems. Through his research, Dr. Jahangiri strives to bridge the gap between theoretical models and practical applications in earthquake engineering.

Skills:

Assist. Prof. Dr. Vahid Jahangiri possesses a diverse set of skills in earthquake engineering, structural dynamics, and seismic risk assessment. He is proficient in advanced seismic analysis techniques, including performance-based seismic design, fragility analysis, and the evaluation of structural responses to seismic events. Dr. Jahangiri has expertise in modeling the dynamic behavior of structures, particularly in the context of buried infrastructure such as pipelines and bridges, using both numerical and experimental methods. He is skilled in the application of various intensity measures to assess the seismic response of infrastructure systems. His experience also includes the development and application of engineering tools for evaluating the resilience of buildings under extreme events like earthquakes and fires. Additionally, Dr. Jahangiri is highly knowledgeable in soil-structure interaction and the dynamics of soil during seismic activity. His teaching and consulting roles have further honed his abilities in conveying complex engineering concepts and providing practical solutions to real-world challenges in earthquake engineering. These skills, combined with his strong research capabilities, make him a leading expert in his field.

Publication Top Noted:

  • Title: Intensity measures for the assessment of the seismic response of buried steel pipelines
    • Authors: H. Shakib, V. Jahangiri
    • Journal: Bulletin of Earthquake Engineering
    • Pages: 1265-1284
    • Cited by: 53
    • Year: 2016
  • Title: Seismic risk assessment of buried steel gas pipelines under seismic wave propagation based on fragility analysis
    • Authors: V. Jahangiri, H. Shakib
    • Journal: Bulletin of Earthquake Engineering
    • Pages: 1571-1605
    • Cited by: 50
    • Year: 2018
  • Title: Evaluation of Plasco Building fire-induced progressive collapse
    • Authors: H. Shakib, M. Zakersalehi, V. Jahangiri, R. Zamanian
    • Journal: Structures
    • Pages: 205-224
    • Cited by: 38
    • Year: 2020
  • Title: Intensity measures for the seismic response assessment of plain concrete arch bridges
    • Authors: V. Jahangiri, M. Yazdani, M.S. Marefat
    • Journal: Bulletin of Earthquake Engineering
    • Pages: 4225-4248
    • Cited by: 38
    • Year: 2018
  • Title: Seismic performance assessment of plain concrete arch bridges under near-field earthquakes using incremental dynamic analysis
    • Authors: M. Yazdani, V. Jahangiri, M.S. Marefat
    • Journal: Engineering Failure Analysis
    • Page: 104170
    • Cited by: 37
    • Year: 2019
  • Title: Seismic reliability and limit state risk evaluation of plain concrete arch bridges
    • Authors: V. Jahangiri, M. Yazdani
    • Journal: Structure and Infrastructure Engineering
    • Volume: 17, Issue 2, Pages: 170-190
    • Cited by: 32
    • Year: 2021
  • Title: Appropriate intensity measures for probabilistic seismic demand estimation of steel diagrid systems
    • Authors: M. Heshmati, V. Jahangiri
    • Journal: Engineering Structures
    • Page: 113260
    • Cited by: 14
    • Year: 2021
  • Title: Intensity measure-based probabilistic seismic evaluation and vulnerability assessment of ageing bridges
    • Authors: M. Yazdani, V. Jahangiri
    • Journal: Earthquakes and Structures
    • Volume: 19, Issue 5, Pages: 379-393
    • Cited by: 13
    • Year: 2020

Conclusion:

Dr. Jahangiri’s extensive research, combined with his teaching and practical experience, makes him an outstanding choice for the Best Researcher Award, underscoring his commitment to advancing seismic safety and structural engineering.

Kiran Ravulakollu | Machine Learning | Best Researcher Award

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Prof Dr. Kiran Ravulakollu | Machine Learning | Best Researcher Award

Dean at Woxsen University, India.

Prof. Dr. Kiran Ravulakollu is currently the Dean and Professor at the School of Technology at Woxsen University, Kamkole, Telangana, where he has played a pivotal role in establishing the school since 2021. He also serves as the Director of Research and Development at the same institution. With a strong academic background, Dr. Ravulakollu holds a Ph.D. in Computer Science from the University of Sunderland, UK, and has extensive teaching and research experience in areas such as artificial intelligence, image processing, and hybrid intelligent systems. Throughout his career, he has held various academic positions, including Assistant Dean of Research at the University of Petroleum and Energy Studies and has been instrumental in developing research ecosystems that foster collaboration and innovation. His contributions to the field are evident in his impressive publication record, with over 879 publications and numerous patents. An active participant in research advisory committees and academic councils, Dr. Ravulakollu is dedicated to advancing technology and education through strategic leadership and innovative research initiatives.

Profile:

Education:

Prof. Dr. Kiran Ravulakollu has a solid academic foundation, beginning with a Bachelor’s degree in Computer Science and Engineering from Jawaharlal Nehru Technological University, Hyderabad, India, awarded in 2004. He further advanced his expertise by earning a Postgraduate Certificate in Artificial Intelligence from City, University of London, United Kingdom, in 2006. Dr. Ravulakollu’s academic journey culminated in a Ph.D. in Computer Science from the University of Sunderland, United Kingdom, in 2012. His Ph.D. research focused on developing a computational architecture inspired by the Superior Colliculus of the mid-brain, utilizing neural networks for the efficient integration of audio and visual stimuli. Additionally, he completed a Post-Graduate Certificate in Academic Practices from the University of Petroleum and Energy Studies, Dehradun, India, in 2019, further enhancing his teaching and research credentials.

Professional Experience:

Prof. Dr. Kiran Ravulakollu has an extensive professional experience in academia and research, currently serving as the Dean and Professor at the School of Technology at Woxsen University, Kamkole, Telangana, since August 2021. In this role, he successfully established the school, significantly increasing student enrollment from 31 to 1,500, faculty and staff from 2 to 75, and generating revenue of ₹80 lakhs. He has also served as the Director of Research and Development at Woxsen University since August 2022, where he developed a robust R&D ecosystem, resulting in a remarkable rise in publications from 10 to 879, citations from 428 to over 7,400, and the acquisition of 36 patents. Prior to his current roles, Dr. Ravulakollu held various positions at the University of Petroleum and Energy Studies, Dehradun, including Assistant Dean of Research and Senior Associate Professor. His academic journey began as an Assistant Professor at Sharda University, where he contributed to curriculum development and student mentoring. His research background includes a tenure as a Research Associate at the HIS Research Group at the University of Sunderland, UK, where he focused on sensory data integration for artificial agents. Throughout his career, Dr. Ravulakollu has demonstrated exceptional leadership, strategic thinking, and a commitment to fostering research excellence in technology and engineering.

Research Interests:

Prof. Dr. Kiran Ravulakollu’s research interests lie at the intersection of artificial intelligence, computer science, and intelligent systems. His Ph.D. research focused on creating computational architectures inspired by the mid-brain’s Superior Colliculus, utilizing artificial neural networks to integrate audio and visual stimuli for enhanced localization capabilities. This foundational work has propelled his ongoing investigations into various advanced topics, including image processing methodologies, artificial agents, ambient intelligence, and hybrid intelligent systems. Additionally, Dr. Ravulakollu is keenly interested in the Internet of Things (IoT) and its applications, exploring how interconnected devices can contribute to smarter solutions in urban environments. His dedication to innovation is reflected in his ability to translate complex theoretical concepts into practical applications, fostering knowledge transfer that shapes products and enhances research outcomes. Overall, his diverse research portfolio showcases a commitment to pushing the boundaries of technology and developing cutting-edge solutions for real-world challenges.

Skills:

Prof. Dr. Kiran Ravulakollu possesses a robust skill set that encompasses analytical expertise, strong research methodologies, and innovative development capabilities. His analytical skills enable him to design and implement significant projects, effectively identifying problems and conducting feasibility studies to ensure research viability. With a solid foundation in project management, he excels at coordinating research and development activities, fostering collaboration among diverse teams. Dr. Ravulakollu is adept at data analysis, utilizing advanced techniques to derive meaningful insights and inform decision-making processes. His proficiency in knowledge transfer allows him to bridge the gap between theoretical research and practical application, shaping products that meet industry demands. Additionally, his experience in developing policies and frameworks for effective administration reflects his strategic thinking ability and commitment to continuous improvement within academic and research environments. Overall, Dr. Ravulakollu’s diverse skill set positions him as a leader in his field, driving impactful research initiatives and fostering innovation.

Conclusion:

Prof. Dr. Kiran Ravulakollu’s exceptional track record in academia, research, and administration make him a highly suitable candidate for the Best Researcher Award. His ability to blend innovative research with practical applications, along with his leadership in establishing a robust R&D environment, highlights his suitability for this prestigious recognition. His career demonstrates a consistent commitment to advancing knowledge and technology in the fields of artificial intelligence, machine learning, and hybrid intelligent systems, positioning him as a leader in contemporary research.

Publication Top Noted:

SIGN LANGUAGE RECOGNITION: STATE OF THE ART

  • Authors: AK Sahoo, GS Mishra, KK Ravulakollu
  • Year: 2013
  • Cited by: 105

Improving automated latent fingerprint detection and segmentation using deep convolutional neural network

  • Authors: M Chhabra, KK Ravulakollu, M Kumar, A Sharma, A Nayyar
  • Journal: Neural Computing and Applications
  • Year: 2023
  • Cited by: 33

Naïve Bayes Classifier with LU Factorization for Recognition of Handwritten Odia Numerals

  • Authors: KKR, Pradeepta K. Sarangi, P. Ahmed
  • Journal: Indian Journal of Science and Technology
  • Year: 2014
  • Cited by: 32

HANDWRITTEN DEVNAGARI DIGIT RECOGNITION: BENCHMARKING ON NEW DATASET

  • Authors: R Kumar, KK Ravulakollu
  • Journal: Journal of Theoretical & Applied Information Technology
  • Year: 2014
  • Cited by: 27

A Hybrid Intrusion Detection System: Integrating Hybrid Feature Selection Approach with Heterogeneous Ensemble of Intelligent Classifiers

  • Authors: KKR, Amrita
  • Journal: International Journal of Network Security
  • Year: 2018
  • Cited by: 25

Fuzzy-membership based writer identification from handwritten devnagari script

  • Authors: R Kumar, KK Ravulakollu, R Bhat
  • Journal: Journal of Information Processing Systems
  • Year: 2017
  • Cited by: 23

Indian sign language recognition using skin color detection

  • Authors: AK Sahoo, KK Ravulakollu
  • Journal: International Journal of Applied Engineering Research
  • Year: 2014
  • Cited by: 19

VISION BASED INDIAN SIGN LANGUAGE CHARACTER RECOGNITION

  • Authors: AK Sahoo, KK Ravulakollu
  • Journal: Journal of Theoretical & Applied Information Technology
  • Year: 2014
  • Cited by: 18

A review on artificial intelligence in orthopaedics

  • Authors: T Hamid, M Chhabra, K Ravulakollu, P Singh, S Dalal, R Dewan
  • Conference: 2022 9th International Conference on Computing for Sustainable Global Development
  • Year: 2022
  • Cited by: 17

WORD BASED STATISTICAL MACHINE TRANSLATION FROM ENGLISH TEXT TO INDIAN SIGN LANGUAGE

  • Authors: GS Mishra, AK Sahoo, KK Ravulakollu
  • Journal: ARPN Journal of Engineering & Applied Sciences
  • Year: 2017
  • Cited by: 17

State-of-the-Art: A Systematic Literature Review of Image Segmentation in Latent Fingerprint Forensics

  • Authors: KKR, Megha Chhabra, Manoj Kumar Shukla
  • Journal: Recent Patents on Computer Science
  • Year: 2019
  • Cited by: 16

Surface Corrosion Grade Classification using Convolution Neural Network

  • Authors: KKR, Sanjay Kumar Ahuja, Manoj Kumar Shukla
  • Journal: International Journal of Recent Technology and Engineering
  • Year: 2019
  • Cited by: 15