Boris Goldengorin | Computer Science | Best Researcher Award

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Prof. Boris Goldengorin | Computer Science | Best Researcher Award

Optimal Management of Tools in Computer Science at Ohio University, United States

Prof. Boris Goldengorin is a globally recognized expert in combinatorial optimization, applied mathematics, and operations research, with a career spanning over five decades. Holding multiple PhDs and a Doctor of Science, he pioneered groundbreaking data correcting algorithms that revolutionized the solving of complex optimization problems such as the Quadratic Cost Partition, Max-Cut, and Traveling Salesman Problems. With over 100 publications in leading international journals and numerous books and monographs, his research has significantly advanced quantitative logistics, supply chain management, and industrial engineering. His algorithms have consistently outperformed global benchmarks, holding world records in solving large-scale combinatorial problems. Prof. Goldengorin has also served as an associate editor for several prestigious journals and has mentored generations of top-performing researchers and students. Honored internationally for his scientific contributions, he continues to influence both theoretical research and practical applications across disciplines, making him a leading figure in modern combinatorial optimization and applied mathematics.

Professional Profile

Education

Prof. Boris Goldengorin possesses an extensive and diverse educational background, reflecting his deep expertise across engineering, applied mathematics, and optimization. He earned his first MSc in Electrical Engineering from Ryazan Radio-Engineering Institute, Russia, in 1967, followed by a second MSc in Applied Mathematics from the Moscow Institute of Electronics and Mathematics in 1973. He completed his PhD in Engineering Sciences at the prestigious VNIINMASH, part of the USSR Ministry of Standardization, in 1975. Further demonstrating his commitment to advanced research, he earned a Doctor of Science (ScD) in Engineering Sciences from the Institute for System Analysis at the USSR Academy of Sciences in 1989. His academic journey continued internationally, obtaining a PhD in Combinatorial Optimization from the University of Groningen, The Netherlands, in 200

Professional Experience

Prof. Boris Goldengorin has built a distinguished career as a researcher, professor, and global leader in combinatorial optimization and operations research. He has held prominent academic and research positions at top institutions, including the University of Groningen (Netherlands), Ohio University (USA), and Khmelnitsky National University (Ukraine), contributing extensively to the fields of mathematical programming, quantitative logistics, and industrial engineering. His pioneering work on data correcting algorithms has shaped modern approaches to solving large-scale optimization problems. Prof. Goldengorin also serves as an associate editor for leading journals such as the Journal of Global Optimization, Journal of Combinatorial Optimization, and Journal of Computational and Applied Mathematics, showcasing his influence in global scientific discourse. Alongside his research, he has mentored generations of students, many of whom have become world-class researchers. His career reflects a rare blend of theoretical innovation, practical application, and global academic leadership, making him a pivotal figure in applied mathematics and operations research.

Research Interest

Prof. Boris Goldengorin’s research interests lie at the intersection of combinatorial optimization, operations research, applied mathematics, and quantitative logistics, where he has made pioneering contributions for over five decades. His primary focus is on developing data correcting algorithms (DCA) and tolerance-based approaches, which have significantly advanced the efficient solving of large-scale optimization problems. His work spans supply chain management, industrial engineering, network analysis, and scheduling problems, with a particular emphasis on benchmark instances such as the Quadratic Cost Partition Problem, Max-Cut Problem, Traveling Salesman Problem, and Simple Plant Location Problem. Beyond classical optimization, Prof. Goldengorin explores the mathematical foundations of algorithmic efficiency and robustness, contributing to big data analysis, game theory, and image processing. His research combines theoretical rigor with computational innovation, enabling faster and more accurate solutions to some of the most computationally challenging problems across disciplines, ensuring long-term impact on both academia and industry applications.

Awards and Honors

Prof. Boris Goldengorin has received numerous awards and honors throughout his illustrious career, recognizing his extraordinary contributions to combinatorial optimization, applied mathematics, and operations research. In 2015, he was named C. Paul Stocker Honorary Professor in Industrial and Systems Engineering at Ohio University, USA. In 2013, the United States Citizenship and Immigration Services (USCIS) granted him Honorable Recognition as an Alien with Extraordinary Ability in Science, Technology, and Education. In 2008, he was recognized as the Best Scientist in Applied Mathematics and Informatics by the Municipality of Khmelnitsky Region, Ukraine. His contributions were further acknowledged in 2005 when Khmelnitsky National University awarded him an Honorary Doctorate in Applied Mathematics and Computer Technologies. Earlier, in 2003, he was named a Fellow in Quantitative Logistics by the Royal Netherlands Academy of Arts and Sciences. These prestigious honors reflect Prof. Goldengorin’s global impact and pioneering role in advancing applied mathematics and optimization research.

Research Skills

Prof. Boris Goldengorin possesses exceptional research skills that span theoretical development, algorithm design, computational experimentation, and interdisciplinary application. His ability to formulate complex combinatorial optimization problems, develop innovative algorithms such as Data Correcting Algorithms (DCA), and rigorously validate their performance through extensive computational benchmarking sets him apart as a world-class researcher. His expertise includes algorithmic design for large-scale optimization problems, quantitative logistics modeling, and supply chain optimization, showcasing his ability to translate mathematical theory into practical solutions. Prof. Goldengorin also excels in analyzing computational complexity, ensuring his algorithms not only produce optimal solutions but do so with unmatched speed and efficiency, often outperforming the leading methods globally. His collaborative research style, combining mentorship, teamwork, and interdisciplinary thinking, has produced high-impact publications across applied mathematics, operations research, game theory, and industrial engineering, making him a highly versatile and innovative researcher with profound analytical and computational skills.

Conclusion

Dr. Boris Goldengorin is highly suitable for the Best Researcher Award.

His exceptional track record in combinatorial optimization, algorithmic innovations, world-record computational achievements, and long-term research leadership position him as a top contender for such a prestigious award.

His global impact, cross-disciplinary contributions, and ability to outperform top research teams in algorithmic efficiency make him a standout figure in applied mathematics, optimization, and industrial engineering.

Publications Top Noted

  • Proceedings of the 11th International Conference on Integer Programming and Combinatorial Optimization
    M. Jünger, V. Kaibel
    Springer-Verlag
    2005 — 233 citations

  • Branch and peg algorithms for the simple plant location problem
    B. Goldengorin, D. Ghosh, G. Sierksma
    Computers & Operations Research 30 (7), 967-981
    2003 — 112 citations

  • The data-correcting algorithm for the minimization of supermodular functions
    B. Goldengorin, G. Sierksma, G.A. Tijssen, M. Tso
    Management Science 45 (11), 1539-1551
    1999 — 76 citations

  • Improvements to MCS algorithm for the maximum clique problem
    M. Batsyn, B. Goldengorin, E. Maslov, P.M. Pardalos
    Journal of Combinatorial Optimization 27, 397-416
    2014 — 65 citations

  • Network approach for the Russian stock market
    A. Vizgunov, B. Goldengorin, V. Kalyagin, A. Koldanov, P. Koldanov, etc.
    Computational Management Science 11, 45-55
    2014 — 65 citations

  • A hybrid method of 2-TSP and novel learning-based GA for job sequencing and tool switching problem
    E. Ahmadi, B. Goldengorin, G.A. Süer, H. Mosadegh
    Applied Soft Computing 65, 214-229
    2018 — 60 citations

  • Tolerance-based branch and bound algorithms for the ATSP
    M. Turkensteen, D. Ghosh, B. Goldengorin, G. Sierksma
    European Journal of Operational Research 189 (3), 775-788
    2008 — 54 citations

  • Lower tolerance-based branch and bound algorithms for the ATSP
    R. Germs, B. Goldengorin, M. Turkensteen
    Computers & Operations Research 39 (2), 291-298
    2012 — 47 citations

  • Tolerances applied in combinatorial optimization
    B. Goldengorin, G. Jäger, P. Molitor
    Journal of Computational Science 2 (9), 716-734
    2006 — 47 citations

  • Cell formation in industrial engineering: Theory, Algorithms and Experiments
    B. Goldengorin, D. Krushinsky, P.M. Pardalos
    Springer
    2013 — 45 citations

  • Solving the simple plant location problem using a data correcting approach
    B. Goldengorin, G.A. Tijssen, D. Ghosh, G. Sierksma
    Journal of Global Optimization 25, 377-406
    2003 — 38 citations

  • Requirements of standards: optimization models and algorithms
    B. Goldengorin
    (No specific journal listed)
    1995 — 35 citations

  • Worst case analysis of max-regret, greedy, and other heuristics for multidimensional assignment and traveling salesman problems
    G. Gutin, B. Goldengorin, H.J.
    Journal of Heuristics, 169-181
    2008 — 34 citations

  • Complexity evaluation of benchmark instances for the p-median problem
    B. Goldengorin, D. Krushinsky
    Mathematical and Computer Modelling 53 (9-10), 1719-1736
    2011 — 32 citations

  • Flexible PMP approach for large-size cell formation
    B. Goldengorin, D. Krushinsky, J. Slomp
    Operations Research 60 (5), 1157-1166
    2012 — 31 citations

Dengtian Yang | Computer Science | Best Researcher Award

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Mr. Dengtian Yang | Computer Science | Best Researcher Award

Student at Institute of Microelectronics of the Chinese Academy of Sciences, China

Yang Dengtian is a promising researcher in the field of Circuit and System, currently pursuing his Ph.D. at the Institute of Microelectronics of the Chinese Academy of Sciences. His research interests focus on hardware-software co-optimization, object detection, and hardware acceleration, with key contributions in developing post-processing accelerators for object detection and improving micro-architecture design for GPGPU. Yang’s project experience spans from UAV object detection to the design of System on Chip (SoC) and the deployment of deep learning models on specialized hardware like NVDLA IP. His dedication to advancing technology is reflected in his published works in renowned journals. Yang is a proactive learner, often sharing his findings on blogs, contributing to the academic community’s growth. His work is poised to have a significant impact in fields such as artificial intelligence, hardware design, and computer vision.

Professional Profile 

Education

Yang Dengtian began his academic journey at Xi’an Jiaotong University, where he earned his Bachelor’s degree in Electronic Science and Technology in 2020. His strong foundational knowledge in electronics laid the groundwork for his current research. In 2020, he began his Ph.D. at the Institute of Microelectronics of the Chinese Academy of Sciences, specializing in Circuit and System. His doctoral research has primarily focused on hardware-software co-optimization and advanced object detection systems, areas that combine his deep understanding of both electronics and cutting-edge computing techniques. Yang’s education has been integral in shaping his research pursuits, allowing him to contribute valuable insights into hardware acceleration and the optimization of machine learning systems. His academic journey is ongoing, with an expected completion of his Ph.D. in 2025.

Professional Experience

Yang has worked on several innovative projects throughout his academic career. His recent project, “Learn and Improve Vortex GPGPU,” focuses on understanding GPGPU micro-architecture design and developing improvements for performance optimization. Another notable project was the “Post-Processing Accelerator for Object Detection,” where he investigated hardware-software co-optimization methods, contributing to the development of a unified accelerator system for object detection. In 2023, Yang worked on the “SoC Building and Yolox-Nano Network Deployment Based on NVDLA IP,” where he built an SoC with NVDLA IP and deployed a Yolox-Nano model on a specialized hardware platform. Yang has also worked on solutions to reduce off-chip memory accesses for CNN inference and deployed deep learning models using Vitis-AI. These experiences, along with his publications in renowned journals, highlight his advanced technical expertise and problem-solving abilities in cutting-edge electronics and AI research.

Research Interest

Yang Dengtian’s primary research interest lies in the intersection of Circuit and System design, hardware-software co-optimization, and artificial intelligence (AI). His work focuses on developing hardware accelerators for deep learning applications, particularly in object detection and micro-architecture optimization. He is passionate about creating more efficient systems for processing large-scale data, especially in environments that require real-time processing, such as unmanned aerial vehicles (UAVs) and embedded systems. Yang’s research includes developing GPGPU micro-architectures, improving System on Chip (SoC) designs, and enhancing the deployment of deep learning models on specialized hardware, such as NVDLA IP. His research aims to bridge the gap between hardware capabilities and software needs, making AI applications more accessible and efficient. He is particularly interested in creating unified frameworks for hardware-software co-design, which could significantly advance machine learning and computer vision technologies.

Awards and Honors

Yang Dengtian’s outstanding contributions to research have been recognized through various accolades. His publication in reputable journals, such as Information and IEICE Transactions on Information and Systems, demonstrates the impact of his work in the field of hardware and software co-optimization. While still early in his career, Yang’s commitment to research excellence has already led to numerous recognitions in his academic community. He has also been acknowledged for his innovative projects in hardware acceleration for AI applications, particularly in the development of post-processing accelerators for object detection. Yang’s work is a testament to his technical expertise and his potential for future awards as his research continues to make significant strides in the fields of electronics, AI, and machine learning. Given his promising trajectory, Yang is likely to receive further honors as his doctoral studies progress and his body of work grows.

Conclusion

Yang Dengtian is undoubtedly a strong contender for the Best Researcher Award due to his innovative approach to research, technical expertise, and significant contributions to the field of hardware-software co-design and optimization. His passion for learning, combined with his publications and project experience, highlights his potential to make substantial advancements in his area of study. However, expanding his collaborations and enhancing the practical impact of his research could further solidify his status as a leading researcher in the field.

Recommendation: Yang Dengtian is highly deserving of the Best Researcher Award, with his strengths outweighing areas for improvement. His future contributions are expected to have a lasting impact in the fields of object detection, hardware acceleration, and micro-architecture design.

Publications Top Noted

  • Title: Nano-carriers of combination tumor physical stimuli-responsive therapies
    Authors: W Jin, C Dong, D Yang, R Zhang, T Jiang, D Wu
    Journal: Current Drug Delivery
    Volume & Issue: 17 (7), 577-587
    Year: 2020
    Cited by: 7
  • Title: Object Detection Post Processing Accelerator Based on Co-Design of Hardware and Software
    Authors: D Yang, L Chen, X Hao, Y Zhang
    Journal: Information
    Volume & Issue: 16 (1), 63
    Year: 2025
    Cited by: Not yet cited (as of 2025)

 

Nunzio Alberto Borghese | Computer Science | Best Researcher Award

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Prof. Nunzio Alberto Borghese | Computer Science | Best Researcher Award

Full professor at Università degli Studi di MIlano, Italy

Professor N. Alberto Borghese is a renowned researcher in computational intelligence and its application to real-world problems. He graduated magna cum laude in Electrical Engineering from Politecnico di Milan and has held significant academic positions, including Full Professor at the University of Milan. His research focuses on innovative methods such as multi-scale hierarchical neural networks, adaptive clustering, and statistical data processing, with particular emphasis on limited processing time. He has made notable contributions to e-Health and robotics, integrating AI, service robots, virtual communities, and smart objects to improve healthcare and welfare systems. With over 90 journal papers, 140+ conference papers, and 16 international patents, he has a strong academic and industrial impact. He has led several high-profile projects funded by the European Commission and Italian government, including REWIRE, MOVECARE, and AIRCA. His work continues to advance the intersection of AI, robotics, and healthcare, addressing critical societal needs.

Professional Profile 

Education

Professor N. Alberto Borghese received his education in Electrical Engineering, graduating magna cum laude in 1986 from Politecnico di Milan, one of Italy’s leading institutions. This strong academic foundation laid the groundwork for his extensive research career. His academic journey furthered through his role as a tenured researcher at the National Research Council (CNR) from 1987 to 2000, where he began developing his expertise in computational intelligence. This led to his appointment as an Associate and later Full Professor at the Department of Computer Science, University of Milan (UNIMI). At UNIMI, he also directs the Laboratory of Applied Intelligent Systems, where he has mentored students and led cutting-edge research projects. Professor Borghese’s education and professional development have been marked by continuous innovation, research leadership, and a commitment to applying his knowledge to real-world challenges, particularly in e-Health, robotics, and AI.

Professional Experience

Professor N. Alberto Borghese has had a distinguished professional career, beginning as a tenured researcher at the National Research Council (CNR) from 1987 to 2000. During this time, he built a strong foundation in computational intelligence. He then transitioned to the University of Milan (UNIMI), where he became an Associate Professor and later a Full Professor in the Department of Computer Science. At UNIMI, he also directs the Laboratory of Applied Intelligent Systems, where he leads innovative research projects focused on AI, robotics, and e-Health. Throughout his career, he has contributed to over 90 journal papers, more than 140 conference papers, and holds 16 international patents. Professor Borghese has led several major research projects funded by the European Commission, including REWIRE, MOVECARE, and FITREHAB, and has been involved in multiple Italian government-funded initiatives. His work bridges academia and industry, addressing pressing societal needs in healthcare and welfare through technological advancements.

Research Interest

Professor N. Alberto Borghese’s research interests lie primarily in the field of computational intelligence, focusing on the development and application of advanced algorithms to solve real-world problems. He specializes in multi-scale hierarchical neural networks, adaptive clustering, and statistical data processing, with an emphasis on optimizing solutions for limited processing time. His work extends to the integration of Artificial Intelligence (AI) and robotics, particularly in the domains of e-Health and e-Welfare. Professor Borghese has pioneered the use of service robots, virtual communities, and smart objects, creating innovative platforms that enhance healthcare and welfare systems. His research also explores the intersection of AI with healthcare technologies such as exer-games, aiming to improve accessibility and promote well-being. Additionally, he has a strong focus on interdisciplinary collaboration, leading several European and Italian research projects that combine AI, robotics, and human-centered design to address societal challenges in health, aging, and rehabilitation.

Award and Honor

Professor N. Alberto Borghese has received numerous awards and honors throughout his distinguished academic and research career. His recognition stems from his innovative contributions to computational intelligence, AI, and robotics, particularly in the fields of e-Health and e-Welfare. With over 90 journal papers and 140+ conference papers, his research has garnered widespread acclaim, reflected in his h-index of 42. He has also been honored for his extensive intellectual property contributions, holding 16 international patents. His leadership in research has been recognized through his involvement in high-profile projects funded by the European Commission and Italian government, such as REWIRE (FP7), MOVECARE (H2020), and AIRCA (2023-2025). These honors not only underline his academic excellence but also highlight his impact on advancing technology in healthcare and welfare systems. His continued success in securing major funding and his role in shaping interdisciplinary research make him a highly respected figure in his field.

Conclusion

Based on his exceptional academic qualifications, pioneering research in computational intelligence and e-Health, leadership in high-profile projects, and impressive publication and patent record, N. Alberto Borghese is a highly suitable candidate for the Best Researcher Award. Addressing minor improvements in public engagement and cross-disciplinary impact could further strengthen his candidacy. Nonetheless, his proven expertise and contributions make him a deserving nominee.

Publications Top Noted

  • Kinematic determinants of human locomotion
    • Authors: N. Alberto Borghese, L. Bianchi, F. Lacquaniti
    • Year: 1996
    • Citations: 553
  • Different brain correlates for watching real and virtual hand actions
    • Authors: D. Perani, F. Fazio, N. A. Borghese, M. Tettamanti, S. Ferrari, J. Decety, …
    • Year: 2001
    • Citations: 402
  • Autocalibration of MEMS accelerometers
    • Authors: I. Frosio, F. Pedersini, N. A. Borghese
    • Year: 2008
    • Citations: 261
  • Time-varying mechanical behavior of multijointed arm in man
    • Authors: F. Lacquaniti, M. Carrozzo, N. A. Borghese
    • Year: 1993
    • Citations: 202
  • Internal models of limb geometry in the control of hand compliance
    • Authors: F. Lacquaniti, N. A. Borghese, M. Carrozzo
    • Year: 1992
    • Citations: 197
  • Reading the reading brain: a new meta-analysis of functional imaging data on reading
    • Authors: I. Cattinelli, N. A. Borghese, M. Gallucci, E. Paulesu
    • Year: 2013
    • Citations: 188
  • A functional-anatomical model for lipreading
    • Authors: E. Paulesu, D. Perani, V. Blasi, G. Silani, N. A. Borghese, U. De Giovanni, …
    • Year: 2003
    • Citations: 163
  • The role of vision in tuning anticipatory motor responses of the limbs
    • Authors: F. Lacquaniti
    • Year: 1993
    • Citations: 151
  • Exergaming and rehabilitation: A methodology for the design of effective and safe therapeutic exergames
    • Authors: M. Pirovano, E. Surer, R. Mainetti, P. L. Lanzi, N. A. Borghese
    • Year: 2016
    • Citations: 148
  • Self-adaptive games for rehabilitation at home
    • Authors: M. Pirovano, R. Mainetti, G. Baud-Bovy, P. L. Lanzi, N. A. Borghese
    • Year: 2012
    • Citations: 146
  • Transient reversal of the stretch reflex in human arm muscles
    • Authors: F. Lacquaniti, N. A. Borghese, M. Carrozzo
    • Year: 1991
    • Citations: 144
  • Computational intelligence and game design for effective at-home stroke rehabilitation
    • Authors: N. A. Borghese, M. Pirovano, P. L. Lanzi, S. Wüest, E. D. de Bruin
    • Year: 2013
    • Citations: 139
  • Automatic detection of powdery mildew on grapevine leaves by image analysis: Optimal view-angle range to increase the sensitivity
    • Authors: R. Oberti, M. Marchi, P. Tirelli, A. Calcante, M. Iriti, A. N. Borghese
    • Year: 2014
    • Citations: 128
  • Usability and effects of an exergame-based balance training program
    • Authors: S. Wüest, N. A. Borghese, M. Pirovano, R. Mainetti, R. van de Langenberg, …
    • Year: 2014
    • Citations: 121
  • Pattern recognition in 3D automatic human motion analysis
    • Authors: G. Ferrigno, N. A. Borghese, A. Pedotti
    • Year: 1990
    • Citations: 121

Sarra Jebri | Computer Science | Best Researcher Award

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Dr. Sarra Jebri | Computer Science | Best Researcher Award

Assistant professor of National Engineering School of Gabes, Tunisia

Sarra Jebri is an accomplished researcher and educator with a robust background in telecommunications. She earned her PhD from Ecole Nationale d’Ingénieurs de Tunis, specializing in IoT security and privacy, and has a comprehensive educational foundation that includes a National Diploma of Engineering and a Master’s in Instrumentation and Communication. Her professional experience includes significant teaching roles at the National School of Engineers in Gabès, reflecting her expertise in the field. Jebri’s research focuses on critical areas such as security, mutual authentication, and IoT, with several influential publications presented at international conferences. 🌟🌿🔬

Professional profile

Education📚

Sarra Jebri holds a PhD in Telecommunications from Ecole Nationale d’Ingénieurs de Tunis (ENIT) with a focus on Internet of Things (IoT) security and privacy. Her academic journey includes a National Diploma of Engineering in Communications and Networks from Ecole Nationale d’Ingénieurs de Gabès (ENIG), where she studied IPTV services, and a Master’s degree in Instrumentation and Communication from the Faculty of Sciences of Sfax, focusing on information system design. She also has a Bachelor’s Degree in Mathematics. Her diverse educational background underscores her strong foundation in telecommunications and related fields, preparing her well for research excellence.

Professional Experience🏛️

Sarra Jebri has extensive teaching experience in telecommunications, having served as a Contractual Teacher at the National School of Engineers in Gabès from 2020 to 2024 and as a Vacant Teacher from 2014 to 2019. This teaching experience, combined with her practical knowledge in the field, demonstrates her capability in both academic and applied research environments.

Research Interest🌐

Sarra Jebri’s research focuses on security, mutual authentication, privacy, and IoT. Her recent publications include notable works such as “Local Learning-based Collaborative Authentication In Edge-Fog Network” and “Light Automatic Authentication of Data Transmission in 6G/IoT Healthcare System,” presented at prestigious international conferences. Her research contributions are recognized through multiple conference papers, indicating a strong engagement with current challenges in her field.

Certifications🏆

Jebri has obtained several certifications, including Cisco and Huawei networking and security qualifications, and has strong programming skills in C, C++, Java, and Matlab. Her certifications reflect her ongoing commitment to professional development and her ability to apply theoretical knowledge in practical scenarios.

Publications top noted📜

Anup Burange | Computer Science | Best Researcher Award

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Dr. Anup Burange | Computer Science | Best Researcher Award

Assistant Professor of Prof. Ram Meghe Institute of Technology & Research, Badnera, India

Dr. Anup W. Burange is an esteemed academic based in Amravati, Maharashtra, India. He serves as an Assistant Professor in the IT department at Prof Ram Meghe Institute of Technology & Research 👩‍🏫. With a Ph.D. in Computer Science & Engineering from SGB Amravati University 🎓, he excels in teaching core IT subjects and has published around 20 articles 📚. As a departmental Training & Placement coordinator, he has engaged over 50 companies for campus placements 🎓. Dr. Burange’s dedication is reflected in consistently high student evaluations 📈 and his extensive technical expertise 🤖📊.

Professional profile
Education📚

Dr. Anup W. Burange has an impressive educational background in the field of Information Technology. He earned his Ph.D. in Computer Science & Engineering from SGB Amravati University in February 2024 🎓. Prior to that, he completed his Master of Engineering in Information Technology at Prof. Ram Meghe Institute of Technology & Research, Amravati, Maharashtra, in June 2014, achieving an aggregate pointer of 8.38 🎓. He also holds a Bachelor of Engineering degree in Information Technology from Sipna’s College of Engineering & Technology, Amravati University, Maharashtra, which he completed in June 2011 with an aggregate of 69.14% 🎓. Dr. Burange’s academic journey began with his Higher Secondary Certificate (HSC) from the Maharashtra State Board, where he scored 73% 📜, followed by his Secondary School Certificate (SSC) from the same board, with a score of 76.66% 📜

Professional Experience🏛️

Dr. Anup W. Burange has extensive professional experience as an Assistant Professor in the IT department at Prof Ram Meghe Institute of Technology & Research in Badnera-Amravati, Maharashtra, India, a position he has held since November 2011 👩‍🏫. He teaches core IT subjects such as Computer Architecture & Organization, Operating Systems, and various programming languages (C, C++, Java, Python) 📚. As the departmental Training & Placement coordinator since 2014, Dr. Burange has successfully engaged over 50 companies for campus placements 🎓. He consistently receives high student evaluations, with scores exceeding 85% over the past four years 📈. Additionally, he has published about 20 articles in reputed journals and guided more than 50 students in their final year projects 👨‍💻. He also served as a masking officer for end semester examinations 📝.

Research Interest🌐

Dr. Anup W. Burange’s research interests lie in the realms of Information Technology and Computer Science, with a strong focus on emerging technologies. He is deeply engaged in exploring advancements in Artificial Intelligence 🤖, Machine Learning 📈, and Data Science 📊. His work also delves into the development and optimization of programming languages like C, C++, Java, and Python 🐍. Dr. Burange is passionate about enhancing educational methodologies and integrating innovative technological solutions in IT education 🎓. His commitment to research is reflected in his numerous publications and contributions to reputable journals 📚.

Awards and Honors🏆

Dr. Anup W. Burange has been recognized for his exemplary contributions to academia and research with several awards and honors 🏆. He has consistently achieved student evaluations exceeding 85% over the past four years, highlighting his dedication to teaching excellence 📈. As a testament to his research prowess, Dr. Burange has published approximately 20 articles in well-reputed journals 📚. His efforts in facilitating campus placements have also been commendable, successfully engaging over 50 companies for Prof Ram Meghe Institute of Technology & Research 🎓. Dr. Burange’s accolades reflect his commitment to advancing the field of Information Technology and education.

Research skill🔬

Dr. Anup W. Burange possesses a robust set of research skills in the field of Information Technology and Computer Science. He is proficient in Artificial Intelligence 🤖 and Machine Learning 📈, with a keen ability to apply these technologies to real-world problems. Dr. Burange excels in programming languages such as C, C++, Java, and Python 🐍, leveraging these skills for data analysis and algorithm development. His expertise extends to Data Science 📊, where he employs statistical methods and data visualization techniques. Dr. Burange is also adept at academic writing and publishing, with around 20 articles in reputed journals 📚, showcasing his ability to conduct and disseminate impactful research.

Achievements🏅
  • 🏆 High Student Evaluation Scores: Consistently received student evaluations exceeding 85% over the past four years 📈.
  • 🎓 Successful Campus Placements: Engaged over 50 companies for campus placements at Prof Ram Meghe Institute of Technology & Research.
  • 📚 Research Publications: Published around 20 articles in well-reputed journals, contributing significantly to the field of Information Technology.
  • 👨‍💻 Guided Final Year Projects: Supervised more than 50 student final year projects, focusing on innovative IT solutions and technologies.
  • 📝 Academic Leadership: Served as a masking officer for end semester examinations, demonstrating leadership and organizational skills.
Projects🛠️
  • 🧑‍💻 Student Final Year Projects: Guided over 50 student projects on topics such as Artificial Intelligence 🤖, Machine Learning 📈, and Data Science 📊.
  • 💻 Programming Solutions Development: Worked on projects involving optimization and development using programming languages like C, C++, Java, and Python 🐍.
  • 📊 Data Visualization Tools: Developed tools for effective data visualization and analysis.
  • 🕵️‍♂️ Real-Time Detection Systems: Contributed to projects involving real-time detection and monitoring systems.
  • 📚 Educational Methodologies: Implemented innovative approaches to enhance IT education and practical learning experiences.
Publications📜
  • Article
    Title: Safeguarding the Internet of Things: Elevating IoT routing security through trust management excellence
    Authors: Burange, A.W., Deshmukh, V.M., Thakare, Y.A., Shelke, N.A.
    Journal: Computer Standards and Interfaces
    Year: 2025
    Citations: 0 🔍
  • Book Chapter
    Title: Different Security Breaches in Patients’ Data and Prevailing Ways to Counter Them
    Authors: Burange, A.W., Deshmukh, V.M.
    Book: Machine Learning in Healthcare and Security: Advances, Obstacles, and Solutions
    Year: 2024
    Pages: 149–159
    Citations: 0 🔍
  • Article
    Title: Securing IoT Attacks: A Machine Learning Approach for Developing Lightweight Trust-Based Intrusion Detection System
    Authors: Burange, A.W., Deshmukh, V.M.
    Journal: International Journal on Recent and Innovation Trends in Computing and Communication
    Year: 2023
    Volume: 11(7), pp. 14–22
    Citations: 0 🔍
  • Article
    Title: Trust based secured Routing system for low powered networks
    Authors: Burange, A.W., Deshmukh, V.M.
    Journal: Journal of Integrated Science and Technology
    Year: 2023
    Volume: 11(1), 431
    Citations: 2 🔍
  • Conference Paper
    Title: Detection of Rank, Sybil and Wormhole Attacks on RPL Based Network Using Trust Mechanism
    Authors: Burange, A.W., Deshmukh, V.M.
    Conference: CEUR Workshop Proceedings
    Year: 2021
    Volume: 3283, pp. 152–162
    Citations: 1 🔍
  • Conference Paper
    Title: Secured Routing System for Low Energy Networks
    Authors: Burange, A.W., Deshmukh, V.M.
    Conference: Lecture Notes in Networks and Systems
    Year: 2021
    Volume: 164, pp. 165–173
    Citations: 0 🔍
  • Conference Paper
    Title: Implementation of security algorithm and achieving energy efficiency for increasing lifetime of wireless sensor network
    Authors: Misalkar, H., Nikam, U., Burange, A.
    Conference: Communications in Computer and Information Science
    Year: 2019
    Volume: 839, pp. 298–307
    Citations: 0 🔍
  • Conference Paper
    Title: Security in MQTT and CoAP Protocols of IoT’s application layer
    Authors: Burange, A., Misalkar, H., Nikam, U.
    Conference: Communications in Computer and Information Science
    Year: 2019
    Volume: 839, pp. 273–285
    Citations: 2 🔍
  • Conference Paper
    Title: Increasing lifespan and achieving energy efficiency of wireless sensor network
    Authors: Misalkar, H.D., Burange, A.W., Nikam, U.V.
    Conference: 2016 International Conference on Information Communication and Embedded Systems (ICICES 2016)
    Year: 2016
    Citations: 3 🔍
  • Conference Paper
    Title: Review of Internet of Things in development of smart cities with data management & privacy
    Authors: Burange, A.W., Misalkar, H.D.
    Conference: 2015 International Conference on Advances in Computer Engineering and Applications (ICACEA 2015)
    Year: 2015
    Pages: 189–195
    Citations: 48 🔍