Jan Holnicki-Szulc | Adaptive Structures | Best Innovation Award

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Prof. Dr Jan Holnicki-Szulc | Adaptive Structures | Best Innovation Award

Institute of Fundamental Technological Research-Polish Academy of Sciences, Poland

Prof. Jan Holnicki-Szulc, born on June 22, 1945, in Poland, is a distinguished academic and researcher in intelligent technologies and structural engineering. He holds dual Master’s degrees in Mathematics and Engineering, a Ph.D. in Technical Sciences, and a Dr hab. eng. from the Institute of Fundamental Technological Research, Polish Academy of Sciences (IPPT-PAN). Since 1999, he has served as a Professor at IPPT-PAN, where he leads groundbreaking research in smart structures, structural health monitoring, and adaptive impact absorption. His work has significantly advanced the fields of safety engineering and adaptive materials, earning him international recognition. Prof. Holnicki-Szulc has held visiting positions at prestigious institutions worldwide, including Ecole Centrale de Lyon, Universitat Politecnica de Catalunya, and Virginia Polytechnic Institute. His contributions to structural control and adaptive systems have been widely cited, making him a leading figure in his field.

Professional Profile

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Education 🎓

Prof. Jan Holnicki-Szulc’s academic journey began with a Master’s in Engineering from the Technical University of Warsaw (1969) and a Master’s in Mathematics from the University of Warsaw (1972). He earned his Ph.D. in Technical Sciences from IPPT-PAN in 1973, followed by a Dr hab. eng. in 1983. His academic excellence culminated in his appointment as a Professor at IPPT-PAN in 1999. His multidisciplinary education in engineering and mathematics laid the foundation for his pioneering work in smart structures and adaptive systems. Prof. Holnicki-Szulc’s academic credentials reflect his deep expertise in both theoretical and applied sciences, enabling him to bridge the gap between advanced mathematics and practical engineering solutions.

Experience 💼

Prof. Jan Holnicki-Szulc has held numerous academic and research positions at IPPT-PAN, progressing from Assistant Professor (1973-1983) to Associate Professor (1983-1999) and finally to Professor (1999-present). He has also been a Visiting Professor at institutions like Ecole Centrale de Lyon, Universitat Politecnica de Catalunya, and Universidad da Beira Interior. His international experience includes research roles at Virginia Polytechnic Institute, Northwestern University, and the University of Michigan. Prof. Holnicki-Szulc has delivered invited lectures at universities worldwide, including Stanford University, Imperial College London, and the University of Tennessee. His extensive experience in both academia and industry has enabled him to develop innovative solutions in structural health monitoring, adaptive materials, and safety engineering, making him a globally recognized authority in his field.

Awards and Honors  🏆

Prof. Jan Holnicki-Szulc’s contributions to engineering and technology have earned him numerous accolades. His research on smart structures and adaptive systems has been widely recognized, with several of his publications ranking among the most cited in the field. He has been invited to deliver lectures at prestigious institutions worldwide, reflecting his international reputation. Prof. Holnicki-Szulc’s work on the Virtual Distortion Method and adaptive impact absorption has been particularly influential, earning him recognition from leading engineering organizations. His leadership in the Division of Intelligent Technologies at IPPT-PAN has further solidified his status as a pioneer in the field. While specific awards are not listed, his extensive publication record, international collaborations, and invited lectures underscore his significant contributions to structural engineering and smart technologies.

Research Focus  🔍

Prof. Jan Holnicki-Szulc’s research focuses on smart technologies for safety engineering, structural health monitoring, and adaptive impact absorption. He is renowned for developing the Virtual Distortion Method, a versatile tool for structural analysis and optimization. His work on adaptive landing gear, inflatable structures for offshore wind turbines, and semi-active vibration damping has practical applications in aerospace, civil engineering, and renewable energy. Prof. Holnicki-Szulc’s research also extends to damage identification in skeletal structures and leakage detection in water networks. His innovative approaches to structural modifications and load capacity improvement have significantly advanced the field of adaptive materials. By combining theoretical insights with practical solutions, his research addresses critical challenges in structural safety and efficiency, making him a leading figure in intelligent technologies and smart materials.

Publication Top Notes 📚

  1. A European Association for the Control of Structures joint perspective. Recent studies in civil structural control across Europe 🌍
  2. Smart technologies for safety engineering 🛠️
  3. Structural analysis, design and control by the virtual distortion method 📐
  4. High-performance impact absorbing materials—the concept, design tools and applications 🛡️
  5. The virtual distortion method—a versatile reanalysis tool for structures and systems 🔧
  6. Adaptive landing gear concept—feedback control validation ✈️
  7. Mitigation of ice loading on off-shore wind turbines: Feasibility study of a semi-active solution 🌬️
  8. Protecting offshore wind turbines against ship impacts by means of adaptive inflatable structures 🚢
  9. Identification of structural loss factor from spatially distributed measurements on beams with viscoelastic layer 📏
  10. On-line impact load identification 💻
  11. Leakage detection in water networks 💧
  12. Adaptive aircraft shock absorbers 🛫
  13. Structural modifications simulated by virtual distortions 🏗️
  14. Adaptive inertial shock-absorber 🚀
  15. Virtual distortion method 📘
  16. Design of adaptive structures for improved load capacity 🏋️
  17. Semi-active damping of vibrations. Prestress Accumulation-Release strategy development 🌀
  18. Damage identification in skeletal structures using the virtual distortion method in frequency domain 📊
  19. Damage identification by the dynamic virtual distortion method 🔍
  20. Experimental and numerical study of full-scale scissor type bridge 🌉

Conclusion 🌟

Prof. Jan Holnicki-Szulc is a pioneering figure in intelligent technologies and structural engineering. His multidisciplinary education, extensive academic experience, and groundbreaking research have made significant contributions to smart structures, adaptive materials, and safety engineering. With a career spanning over five decades, he continues to inspire innovation and excellence in his field.

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Subhadip Pramanik | Data-Driven Evolutionary Optimization | Best Researcher Award

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Dr Subhadip Pramanik | Data-Driven Evolutionary Optimization | Best Researcher Award

Assistant Professor, Kalinga Institute of Industrial Technology (KIIT) Deemed to Be University, India

Dr. Subhadip Pramanik is an accomplished academic and researcher specializing in Data Science and Artificial Intelligence. He earned his Ph.D. in Data Science & AI from IIT Kharagpur in 2023. With a strong educational foundation in applied mathematics and computer science, Dr. Pramanik has made significant contributions to the field of evolutionary optimization and machine learning. Currently, he serves as an Assistant Professor at the Kalinga Institute of Industrial Technology, Bhubaneswar, India, where he teaches advanced topics in computer science. His prolific research has been published in leading journals, and he has received accolades such as the Best Paper Award at IEEE INDICON 2021.

PROFILE

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STRENGTHS FOR THE AWARD

  1. Academic Excellence:
    • Holds a Ph.D. in Data Science and AI from IIT Kharagpur, one of the most prestigious institutions in India.
    • Demonstrated exceptional academic performance throughout, including high scores in M.Tech and M.Sc.
  2. Significant Research Contributions:
    • Published extensively in reputed journals and conferences, including Mathematics, Structural and Multidisciplinary Optimization, and Applied Intelligence.
    • Innovations such as adaptive model selection frameworks and nature-inspired algorithms showcase expertise in solving real-world optimization problems.
    • The award-winning research on “GL-DDEA” highlights the practical impact and originality of contributions in AI and Data Science.
  3. Recognitions:
    • Best Paper Award in the AI & Data Science Track at IEEE INDICON 2021, a testament to the quality and impact of his work.
    • Strong citation and publication record reflecting a high level of research engagement.
  4. Diverse Work Experience:
    • Currently serving as an Assistant Professor at KIIT, mentoring undergraduate students in advanced topics such as Data Analytics, AI, and DBMS.
    • Prior experience as a Research Fellow at IIT Kharagpur involved developing groundbreaking methods for engineering optimization.
  5. Technical Expertise:
    • Specialized in cutting-edge domains such as evolutionary algorithms, surrogate modeling, and high-utility itemset mining.
    • Proficiency in applying research methodologies to solve expensive, data-driven optimization problems, bridging theory and application.

AREAS FOR IMPROVEMENT

  1. Broader Collaborative Research:
    • While his contributions are notable, expanding collaborations with international researchers could elevate the global visibility of his work.
  2. Industry Engagement:
    • Engagement in industry-funded projects or partnerships could demonstrate real-world applications of his research.
  3. Interdisciplinary Applications:
    • Exploring applications of his frameworks in areas beyond engineering optimization, such as healthcare or environmental science, may further diversify his portfolio.

EDUCATION

Dr. Pramanik has an extensive academic background:

  • Ph.D. in Data Science & AI, IIT Kharagpur (2018–2023)
  • M.Tech. in Computer Science & Data Processing, IIT Kharagpur (2016–2018, CGPA 8.61/10)
  • M.Sc. in Applied Mathematics, Vidyasagar University (2014–2016, 77%)
  • B.Sc. in Mathematics, Vidyasagar University (2011–2014, 65.75%)
  • Higher Secondary (82.8%) and Secondary (83.5%) Examinations completed in West Bengal.

This foundation underscores his expertise in applying mathematical rigor to solve complex engineering problems using AI and data science.

EXPERIENCE

Dr. Pramanik is currently an Assistant Professor at Kalinga Institute of Industrial Technology, where he teaches Data Analytics, AI, and Python to undergraduates. His research fellowship at IIT Kharagpur (2018–2023) resulted in pioneering contributions like developing evolutionary algorithms for solving expensive engineering optimization problems. His notable projects include a novel framework utilizing ant colony systems for mining high-utility itemsets and innovative surrogate modeling approaches for multi-objective optimization. With practical teaching and research experience, Dr. Pramanik bridges academic rigor with real-world applications.

AWARDS & HONORS 🏆

  • Best Paper Award in AI & Data Science Track, IEEE INDICON 2021
  • GATE 2016 Qualified: Scored 532 in Mathematics
  • Numerous accolades for his impactful publications and contributions to Data Science & AI research.

RESEARCH FOCUS 🔬

Dr. Pramanik focuses on evolutionary optimization, active learning, and data-driven decision-making. His research interests include:

  • Data-Driven Multi-Objective Optimization: Using adaptive and reliable frameworks for engineering challenges.
  • High-Utility Pattern Mining: Ant colony systems for large transactional datasets.
  • Surrogate Modeling: Combining global and local models for efficiency in offline and incremental data optimization.

PUBLICATION TOP NOTES 📚

  1. AdaMoR-DDMOEA: Adaptive Model Selection Framework for Offline Optimization (Mathematics, 2025)
  2. ALeRSa-DDEA: Active Learning and Reliability Sampling for Expensive Optimization (SMO, 2022)
  3. GL-DDEA: Surrogate-Based Framework for Offline Optimization (IEEE INDICON, 2021)
  4. Ant Colony Algorithm: Mining Closed High-Utility Itemsets (Applied Intelligence, 2021)

CONCLUSION

Subhadip Pramanik is an outstanding candidate for the Best Researcher Award due to his exemplary academic record, innovative research contributions, and impactful publications. His award-winning work and continued commitment to advancing Data Science and AI demonstrate his potential to excel further in his field. With additional focus on interdisciplinary and global collaborations, he could strengthen his position as a leading researcher in his domain.