Mujeeb Abiola Abdulrazaq | engineering | Young Scientist Award

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Mr. Mujeeb Abiola Abdulrazaq l engineering
| Young Scientist Award

University of North Carolina at Charlotte | United States

Mr. Mujeeb Abiola’s research focuses on advancing transportation safety and efficiency through data-driven methodologies and emerging technologies. His work extensively employs large-scale traffic and crash data, including millions of federal highway administration records, to investigate the spatiotemporal dynamics of pedestrian crashes and the evolution of crash hotspots. Utilizing advanced statistical and machine learning models, he has developed predictive frameworks that outperform traditional Highway Safety Manual standards, providing robust insights into risk factors and injury severity in both human-driven and autonomous vehicle contexts. His research on connected and autonomous vehicles (CAVs) has led to the development of traffic control algorithms that significantly enhance safety, operational efficiency, and environmental sustainability in freeway work zones. Furthermore, his studies integrate GPU-accelerated data processing, simulation-based optimization, and multi-level heterogeneity modeling to evaluate vulnerable road user behavior and assess dynamic collision risks. Through simulation platforms such as VISSIM and SUMO, combined with Python-based data analysis and GIS applications, his work systematically addresses complex traffic scenarios, including merging, diverging, and weaving segments, while also accounting for seasonal variations and temporal constraints in crash determinants. His contributions include empirical analyses of autonomous vehicle incidents, methodological advancements in microsimulation accuracy, and development of actionable strategies for real-world traffic management, ultimately aiming to improve roadway safety, inform policy, and guide evidence-based planning in modern transportation systems.

Profile:  Google Scholar 

Featured Publications

  • Abdulrazaq, M. A., & Fan, W. D. (2024). Temporal dynamics of pedestrian injury severity: A seasonally constrained random parameters approach. International Journal of Transportation Science and Technology, 9.

  • Abdulrazaq, M. A., & Fan, W. (2025). A priority based multi-level heterogeneity modelling framework for vulnerable road users. Transportmetrica A: Transport Science, 1–34. https://doi.org/10.1080/23249935.2025.2516817

  • Abdulrazaq, M. A., & Fan, W. (2025). Seasonal instability in crash determinants: A partially temporally constrained modeling analysis. SSRN 5341417. https://doi.org/10.2139/ssrn.5341417

Sheharyar Khan | Engineering | Young Scientist Award

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Dr. Sheharyar Khan l Engineering
| Young Scientist Award

Shandong University | Pakistan

Dr. Sheharyar Khan is a distinguished computer scientist and software engineer with extensive expertise in software engineering, artificial intelligence, and cybersecurity, specializing in IoMT edge-cloud frameworks and network intrusion detection systems. Currently a Postdoctoral Research Fellow at Shandong University, he leads independent and collaborative research initiatives, designing experiments, analyzing data, and publishing findings in high-impact journals. His doctoral research at Northwestern Polytechnical University focused on optimization-based hybrid offloading frameworks for IoMT in edge-cloud healthcare systems, demonstrating the integration of advanced computing techniques with practical healthcare applications. Dr. Khan has made significant contributions to explainable AI and hybrid ensemble machine learning, as seen in publications such as “HCIVAD: Explainable hybrid voting classifier for network intrusion detection systems” and “Consensus hybrid ensemble machine learning for intrusion detection with explainable AI”. With prior experience as a lecturer and IT specialist, he combines academic rigor with practical software development expertise. Dr. Khan has 104 citations across 10 documents, an h-index of 6, an i10-index of 5, is indexed under Scopus Author ID 57221647889, and holds ORCID 0000-0002-0089-0168, reflecting his impact on the field. Recognized for his analytical skills, innovation, and interdisciplinary research, he continues to advance secure, intelligent, and explainable computing systems for both academic and real-world applications.

Profile: Scopus | Google Scholar | Orcid | Researchgate 

Featured Publications

Khan, S., Liu, S., Pan, L., & Mei, G. (2025). Optimization-based hybrid offloading framework for IoMT in edge-cloud healthcare systems. Future Generation Computer Systems, 108163. https://doi.org/

Ahmed, S. K. M. T. S., Jiangbin, Z., & Khan, S. (2025). HCIVAD: Explainable hybrid voting classifier for network intrusion detection systems. Cluster Computing, 28(343). https://doi.org/

Ahmed, M. T. S., Jiangbin, Z., & Khan, S. (2024). Consensus hybrid ensemble machine learning for intrusion detection with explainable AI. Journal of Network and Computer Applications, 5*. https://doi.org/

Khan, S., Jiangbin, Z., & Ali, H. (2024). Soft computing approaches for dynamic multi-objective evaluation of computational offloading: A literature review. Cluster Computing, 27(9), 12459–12481. https://doi.org/

Heyu Peng | Engineering | Best Researcher Award

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Mr. Heyu Peng | Engineering | Best Researcher Award

Xi’an Jiaotong University | China

Heyu Peng is an emerging researcher in the field of nuclear science and technology, currently pursuing his doctoral studies at the School of Nuclear Science and Technology, Xi’an Jiaotong University, China, since March . His research primarily focuses on the development and application of advanced computational methods in nuclear engineering, particularly Monte Carlo particle-transport simulations and coupled deterministic–stochastic modeling approaches. He has contributed to significant advancements in the refinement of nuclear simulation tools, demonstrating his expertise in improving accuracy, efficiency, and applicability for nuclear reactor analysis and radiation transport problems. he co-authored a paper published in IEEE Transactions on Nuclear Science that presented a coupled deterministic and Monte Carlo method for modeling and simulating self-powered neutron detectors, a study that addressed critical aspects of detector response modeling and its implications for nuclear instrumentation and monitoring. More recently, a cutting-edge computational tool designed to enhance nuclear reactor physics simulations and broaden its utility in research and practical applications. Through these publications, Peng has established himself as a promising researcher contributing to the advancement of computational nuclear science. His work reflects a strong commitment to bridging theoretical development with real-world applications, offering tools and methodologies that can improve safety, efficiency, and innovation in nuclear energy systems. As a doctoral candidate, Peng continues to expand his research profile, collaborating with experts in the field and contributing to interdisciplinary efforts in nuclear engineering. His growing academic contributions highlight his potential to become a leading researcher in nuclear science, with a focus on computational methods that can shape the future of nuclear technology and its safe, sustainable applications.

Profile: Orcid

Featured Publications

  • He, Q., Zheng, Q., Li, J., Huang, Z., Huang, J., Qin, S., Shu, H., Peng, H., Yang, X., Shen, J., et al. (2024). Overview of the new capabilities in the Monte-Carlo particle-transport code NECP-MCX V2.0. EPJ Nuclear Sciences & Technologies.

  • Zhou, Y., Cao, L., He, Q., Feng, Z., & Peng, H. (2022). A coupled deterministic and Monte-Carlo method for modeling and simulation of self-powered neutron detector. IEEE Transactions on Nuclear Science.

 

Yogesh Thakare | Engineering | Best Researcher Award

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Yogesh Thakare | Engineering | Best Researcher Award

Dr Yogesh Thakare, Ramdeobaba University, Nagpur, India

Dr. Yogesh Thakare 🎓 is an accomplished researcher and educator in Electronics and Communication Engineering. He earned his Ph.D. (2020) from SGB Amravati University, specializing in DRAM design using submicron technology 💾. Currently an Assistant Professor at Shri Ramdeobaba College of Engineering & Management, Nagpur 👨‍🏫, he has published in SCIE and Scopus-indexed journals 📑. His research spans FPGA architectures, AI, IoT, and biomedical systems 🤖. A GATE qualifier (94.92%), he has led government-funded projects 💰 and organized AI & IoT workshops 🏗️. Passionate about innovation, he contributes to cutting-edge electronics and computing technologies ⚡.

Publication Profile

Google Scholar

Academic Excellence

Dr. Yogesh Thakare earned his Ph.D. in Electronics Engineering from SGB Amravati University in 2020, focusing on Dynamic Random Access Memory (DRAM) design using submicron technology ⚡🔬. His academic journey reflects excellence, having completed his M.Tech with Distinction (85.10%) 🎓🏆 and his B.E. with First-Class (72.62%) 📚✨. With a strong foundation in electronics and a passion for advanced semiconductor technologies, Dr. Thakare has made significant contributions to memory design and innovation. His expertise in microelectronics and circuit design continues to drive advancements in the field, shaping the future of high-performance computing and digital storage solutions 💡🔍.

Funded Research & Grants

Dr. Yogesh Thakare has demonstrated exceptional research leadership by securing ₹24.6 Lakhs from CSIR for developing an automated water distribution system 💧🔬. His innovative approach aims to enhance water management efficiency through automation, contributing to sustainable resource utilization 🌱💡. This significant funding underscores his expertise in engineering solutions that address real-world challenges 🏗️⚙️. With a strong commitment to technological advancement, Dr. Thakare continues to drive impactful research that promotes water conservation and smart distribution systems 🌍📊. His work not only fosters scientific progress but also supports community welfare by ensuring efficient and equitable water access 🚰✅.

Experience

Dr. Yogesh Thakare is an experienced educator with over 14 years of teaching in top engineering institutes 🏫, including Shri Ramdeobaba College of Engineering and Management, Nagpur. As an Assistant Professor, he has played a key role in shaping technical education 📚. His passion for emerging technologies has led him to organize numerous workshops on Artificial Intelligence 🤖, the Internet of Things 🌐, and Machine Learning 📊, empowering students with cutting-edge knowledge. Through his dedication to academic excellence and innovation, Dr. Thakare continues to inspire the next generation of engineers and researchers 🚀.

Research Focus

Dr. Yogesh Thakare’s research spans electronics, artificial intelligence, IoT, and machine learning 🤖📡. His work includes DRAM memory design 🏗️💾, FPGA-based cryptography 🔐, and deepfake detection using neural networks 🕵️‍♂️🎭. He has contributed to environmental intelligence systems 🌱📊, weather prediction for agriculture 🌦️🚜, and smart monitoring technologies 📡🏠. Additionally, he has explored cortisol detection for stress monitoring 🧪⚕️ and crime reporting frameworks 🚔📜. His interdisciplinary research integrates hardware and AI-driven solutions, making impactful advancements in computing, security, and human well-being 🔬💡. His innovative approach bridges technology and real-world applications, enhancing automation, safety, and intelligence. 🚀

Publication Top Notes

Intelligent Life Saver System for People Living in Earthquake Zone.

An Effect of Process Variation on 3T-1D DRAM

Analysis of power dissipation in design of capacitorless embedded DRAM

IoT-Enabled Environmental Intelligence: A Smart Monitoring System

Detection of Deepfake Video Using Residual Neural Network and Long Short-Term Memory.

A Read-out Scheme of 1T-1D DRAM Design with Transistor Assisted Decoupled Sensing Amplifier in 7 nm Technology

Enhancing weather prediction and forecasting for agricultural applications using machine learning

FPGA Implementation of Compact Architecture for Lightweight Hash Algorithm for Resource Constrained Devices

Crafting visual art from text: A generative approach

Cortisol Detection Methods for Stress Monitoring: Current Insight and Future Prospect: A Review

An Ensemble Learning with Deep Feature Extraction Approach for Recognition of Traffic Signs in Advanced Driving Assistance Systems

Development and design approach of an sEMG-based Eye movement control system for paralyzed individuals

 

Desen Özkan | Engineering | Best Researcher Award

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Desen Özkan | Engineering | Best Researcher Award

Dr Desen Özkan, University of Connecticut, United States

Dr. Desen Özkan is an Assistant Professor of Chemical and Biomolecular Engineering at the University of Connecticut, with an affiliate position in the Neag School of Education. He is also the Graduate Program Director for the Engineering Education Ph.D. program. Dr. Özkan’s research focuses on sociotechnical identity development, equity in engineering education, and offshore wind energy. He holds a Ph.D. in Engineering Education from Virginia Tech and has held postdoctoral roles at Tufts University. Dr. Özkan’s work bridges engineering, education, and social justice, emphasizing interdisciplinary collaboration and inclusive curricula. 🌍⚙️📚💡🌱

Publication Profile

google scholar

Education

Dr. Desen Özkan holds a Ph.D. in Engineering Education from Virginia Polytechnic Institute and State University (2020), where she focused on transdisciplinary approaches in interdisciplinary faculty teaching. She has an extensive academic background with courses from prestigious institutions. Dr. Özkan completed projects on offshore wind energy economics at the University of Massachusetts and structural engineering at Tufts University. She also studied environmental chemistry, microbiology, and mathematical modeling at the University of Tennessee. Her B.S. in Chemical and Biological Engineering was earned at Tufts University in 2013. Dr. Özkan’s work merges engineering, education, and sustainability. 🌍⚙️🎓📚

Experience

Dr. Desen Özkan has diverse research experience in both engineering and social sciences. As a Postdoctoral Researcher at Tufts University, she analyzed job development in Maine’s offshore wind industry, producing the report Floating to the Top (2021), and contributed to a study on equity in offshore wind job development, invited by Connecticut State Legislators (2022). At Virginia Tech, she worked on the NSF-funded Revolutionizing Engineering and Computer Engineering Departments project (2018-2019) and contributed to the Science, Technology, and Society department’s undergraduate degree proposal (2019). Additionally, Dr. Özkan conducted water quality research at the University of Tennessee, focusing on wastewater reclamation. 🌊💡🔬

Awards and Recognition

Dr. Desen Özkan has received multiple nominations for Tufts University’s Significant Impact Awards, recognizing her outstanding contributions to STEM education. Her dedication to mentoring and promoting diversity within the field has been a hallmark of her career. Additionally, Dr. Özkan was selected to participate in the prestigious New Energy Summer Summit at Dartmouth, further highlighting her commitment to advancing innovation and sustainability. These accolades underscore her impactful work in fostering inclusive environments and pushing boundaries in science and technology. Her achievements inspire future generations of diverse STEM leaders. 🏆👩‍🔬🌍💡

Conference Activity

Dr. Desen Özkan has presented at numerous conferences, focusing on sociotechnical engineering education and diversity in the field. Notable presentations include “Positionality, Empathy, and Subjectivity in Research” at the 2024 Compassion and Global Citizenship Conference, and “What is a Job? Deconstructing Offshore Wind Jobs” at the 2024 Petrocultures Conference. Additionally, Dr. Özkan co-presented papers on worker safety in offshore wind at the ASEE Annual Conference and explored environmental racism in engineering courses. Her work also includes teaching design through sociotechnical perspectives, with a focus on student experiences in first-year engineering courses. 🎤🌍📚

Research Focus

Dr. Desen Özkan’s research primarily focuses on the intersection of engineering education, diversity, and sociotechnical systems. Her work explores how contextualization and cultural considerations can enhance learning experiences in engineering education. She investigates methods like persona-based curricular design and emphasizes the importance of addressing reality gaps in senior design projects. Additionally, Dr. Özkan examines the positionality of researchers in engineering education and the teacher-learner dynamic. Her research aims to make engineering education more inclusive, effective, and adaptable, particularly for minoritized groups. 🛠️📚💡🎓

Publication Top Notes

Positionality statements in engineering education research: A look at the hand that guides the methodological tools

Contextualization as virtue in engineering education

Using personas as curricular design tools: Engaging the boundaries of engineering culture

Contextualization in engineering education: A scoping literature review

Teacher learner, learner teacher: parallels and dissonance in an interdisciplinary design education minor

Reality gaps in industrial engineering senior design or capstone projects

Perspectives of Seven Minoritized Students in a First-Year Course Redesign toward Sociotechnical Engineering Education