Milena Živković | Artificial Intelligence in Medicine | Best Researcher Award

Posted on by sciencefather

Ms. Milena Živković | Artificial Intelligence in Medicine | Best Researcher Award

Research Associate| University of Kragujevac, Faculty of Science, Serbia

Milena Živković is a Research Associate at the University of Kragujevac, Faculty of Science, Serbia, with a background in physics and a research focus on the integration of artificial intelligence into medical physics and science education. Her expertise lies in AI-supported educational systems, Monte Carlo simulations in radiotherapy, and environmental radioactivity. With over 38 published papers, her work bridges physics, machine learning, and curriculum innovation. Milena is recognized for her mentorship of gifted students, contribution to interdisciplinary AI-based learning models, and international collaborations with researchers in Europe and the Middle East. She has co-authored dosimetric simulation software for cancer treatment optimization and earned accolades such as Best Oral Presentation Awards at international conferences. As an active member of the Serbian and German Physical Societies, she fosters science communication through national outreach projects and educational initiatives. Her contributions span both academic excellence and impactful community-based science promotion efforts.

Profile

🎓 Education

Milena Živković earned her formal education in physics, culminating in specialized research focused on medical physics, radiation dosimetry, and educational technology. She has completed advanced academic training in English for Academic Communication and Python programming, including Stanford’s “Code in Place.” She holds a Cambridge English Certificate and multiple certificates from the University of Kragujevac in academic writing and pedagogy. Her achievements during her student years include receiving the Annual Award for Best Student from 2015 to 2019, reflecting both academic excellence and extracurricular engagement. Additionally, she has participated in numerous interdisciplinary workshops, competitions, and science communication events, contributing to both her intellectual and pedagogical growth. With a strong foundation in applied physics, her academic journey has been characterized by the seamless integration of theoretical knowledge and practical research, which she continues to expand through post-academic training, conference participation, and interdisciplinary research collaboration with clinical and educational institutions.

🧪 Experience

Milena Živković has significant experience as a Research Associate at the University of Kragujevac, where she combines artificial intelligence with physics education and medical applications. Her research includes machine learning models for radiation dosimetry, classification systems in physics education, and anomaly detection in environmental radioactivity. She serves as a section editor and reviewer for journals like Imaging and Radiation Research and Radiation Science and Technology. Milena is also a contributor to national gifted education programs, curriculum development initiatives, and AI-assisted learning models. She has collaborated with international institutions, including projects with the Clinical Center Kragujevac and partners from Iraq, enhancing the practical application of her research. She has guided STEM projects for youth and mentored students in high school competitions. Her book on Monte Carlo simulations is used in academic and clinical contexts. Her scientific outreach projects further amplify her impact across the academic, educational, and public spheres.

🏅 Awards and Honors

Milena Živković has been the recipient of numerous awards recognizing both academic and community contributions. She received the Best Researcher Award at the University of Kragujevac in 2023 and multiple Best Oral Presentation Awards at international conferences in gynecology, women’s health, and ophthalmology. She also won the Bridge of Mathematics First Place Projects (2023, 2024), highlighting innovative physics education. From 2015 to 2019, she was honored with the Annual Best Student Award and continues to receive high praise for promoting science through projects funded by Serbia’s Center for the Promotion of Science. These include thematic campaigns like Brian May and the Queen of Physics, Our Air = Our Health, and Work + Active = Radioactive. Additionally, she holds advanced training certifications in pedagogy, communication, academic writing, and programming. Her dedication to science communication, youth mentorship, and educational innovation has made her a strong contender for the Young Scientist or Best Researcher Award.

🔬 Research Focus

Milena Živković’s research sits at the intersection of artificial intelligence, medical physics, and education technology. She focuses on developing machine learning-based models for radiation dose analysis, anomaly detection in environmental radioactivity, and AI-assisted problem classification in physics education. Her contributions to the FOTELP-VOX Monte Carlo platform enable precision 3D dose distribution modeling, now applied in clinical settings. She also investigates the ecological effects of radionuclide transfer and food safety. Milena’s interdisciplinary work includes collaborations with philosophers, clinicians, educators, and AI developers to improve curriculum delivery and treatment outcomes. She actively integrates AI into educational strategies to support gifted students and has co-authored software tools used in radiotherapy optimization. Her studies are not only technical but are aimed at real-world impact—ensuring safer radiation practices, informed public health strategies, and accessible science education. Her research promotes knowledge translation, making complex physics applicable to both education and healthcare.

✅ Conclusion

Milena Živković exemplifies a new generation of researchers merging artificial intelligence with applied physics to transform education, healthcare, and science communication. Through interdisciplinary projects, academic excellence, and outreach initiatives, she continues to redefine how science serves society while mentoring future innovators and advancing clinical safety and educational equity.

Publications

  • FOTELP-VOX-OA: Enhancing radiotherapy planning precision with particle transport simulations and Optimization Algorithms

    Computer Methods and Programs in Biomedicine
    2025-08 | Journal article
    DOI: 10.1016/j.cmpb.2025.108838
    CONTRIBUTORS: Milena Zivkovic; Filip Andric; Marina Svicevic; Dragana Krstic; Lazar Krstic; Bogdan Pirkovic; Tatjana Miladinovic; Mohamed El Amin Aichouche

  • FOTELP-VOX 2024: Comprehensive overview of its capabilities and applications

    Nuclear Technology and Radiation Protection
    2024 | Journal article
    DOI: 10.2298/NTRP2403212Z
    CONTRIBUTORS: Milena Zivkovic, P.; Tatjana Miladinovic, B.; Zeljko Cimbaljevic, M.; Mohamed Aichouche, E.A.; Bogdan Pirkovic, A.; Dragana Krstic, Z.

  • Radionuclide contamination in agricultural and urban ecosystems: A study of soil, plant, and milk samples

    Kragujevac Journal of Science
    2024 | Journal article
    DOI: 10.5937/KgJSci2400011A
    CONTRIBUTORS: Mohamed Aichouche, E.A.; Mihajlo Petrović, V.; Milena Živković, P.; Dragana Krstić, Ž.; Snežana Branković, R.

  • Development of DynamicMC for PHITS Monte Carlo package

    Radiation Protection Dosimetry
    2023-11-13 | Journal article
    DOI: 10.1093/rpd/ncad278
    Part of ISSN: 0144-8420
    Part of ISSN: 1742-3406
    CONTRIBUTORS: Hiroshi Watabe; Tatsuhiko Sato; Kwan Ngok Yu; Milena Zivkovic; Dragana Krstic; Dragoslav Nikezic; Kyeong Min Kim; Taiga Yamaya; Naoki Kawachi; Hiroki Tanaka et al.

Chongyuan Wang | Deep learning | Best Researcher Award

Posted on by sciencefather

Dr. Chongyuan Wang | Deep learning | Best Researcher Award

Dr. Chongyuan Wang, a Ph.D. researcher at Hohai University, specializes in artificial intelligence 🤖 and neural computation 🧠. He completed his B.S. at Jiangsu University 🇨🇳 and M.S. in Energy and Power from Warwick University 🇬🇧. His research journey is centered around biologically inspired learning algorithms, with notable contributions to dendritic neuron modeling and evolutionary optimization. Through innovative algorithms like Reinforced Dynamic-grouping Differential Evolution (RDE), Dr. Wang advances the understanding of synaptic plasticity in AI systems. His patent filings and international publications reflect a strong commitment to academic innovation and impact 🌍.

Profile

Education 🎓

🎓 B.S. in Engineering – Jiangsu University, China 🇨🇳
🎓 M.S. in Energy and Power – University of Warwick, UK 🇬🇧 (2018)
🎓 Ph.D. Candidate – Hohai University, majoring in Artificial Intelligence 🤖
Dr. Wang’s educational path bridges engineering and intelligent systems. His strong technical foundation and global exposure foster advanced thinking in machine learning and neuroscience. His current doctoral research integrates deep learning, dendritic neuron models, and biologically plausible architectures for improved learning accuracy and model efficiency. 📘🧠

Experience 👨‍🏫

Dr. Wang is currently pursuing his Ph.D. at Hohai University, where he investigates dendritic learning algorithms and synaptic modeling. 🧬 He proposed the RDE algorithm, enhancing dynamic learning in artificial neurons. His hands-on experience includes research design, algorithm optimization, patent writing, and international publication. He has contributed to projects such as “Toward Next-Generation Biologically Plausible Single Neuron Modeling” and “RADE for Lightweight Dendritic Learning.” 📊 His work balances theoretical depth and applied research, particularly in neural computation, classification systems, and resource-efficient AI. 🔬💡

Awards & Recognitions 🏅

🏅 Patent Holder (CN202410790312.0, CN202410646306.8, CN201510661212.9)
📄 Published in SCI-indexed journal Mathematics (MDPI)
🌐 Recognized on ORCID (0009-0002-6844-1446)
🧠 Nominee for Best Researcher Award 2025
His inventive research has earned him national patents and global visibility. His SCI publications in computational modeling reflect both novelty and academic rigor. His continued innovation in biologically inspired AI learning systems has established his position as an emerging researcher in intelligent systems. 🚀📘

Research Interests 🔬

Dr. Wang’s research fuses deep learning 🤖 and dendritic modeling 🧠 to create biologically plausible AI. He developed the RDE algorithm to mimic synaptic plasticity, improving convergence and adaptability in neural networks. His research areas include evolutionary optimization, adaptive grouping, resource-efficient models, and dendritic learning. He explores how artificial neurons can reflect real-brain behavior, leading to faster, more accurate AI systems. Current projects like RADE aim to make AI lightweight and biologically relevant. 🌱📊 His vision is to bridge the gap between neuroscience and AI through interpretable, high-performance algorithms. 🧠💡

Publications

  • Toward Next-Generation Biologically Plausible Single Neuron Modeling: An Evolutionary Dendritic Neuron Model

    Mathematics
    2025-04-29 | Journal article
    DOI: 10.3390/math13091465
    CONTRIBUTORS: Chongyuan Wang; Huiyi Liu

Farshad Sadeghpour | Data prediction | Best Researcher Award

Posted on by sciencefather

Dr. Farshad Sadeghpour | Data prediction | Best Researcher Award

Farshad Sadeghpour (b. 1996) 🇮🇷 is a Petroleum Engineer and Data Scientist 💻🛢️ with expertise in reservoir engineering, petrophysics, and AI applications in the energy sector. Based in Tehran, Iran 📍, he holds a Master’s and Bachelor’s in Petroleum Exploration. With extensive experience in EOR, SCAL/RCAL analysis, and machine learning, Farshad has contributed to both academic and industrial R&D at RIPI, NISOC, and PVP. He has published multiple research articles 📚, won international awards 🏆, and participated in key petroleum projects. He served in the military 🪖 and actively collaborates with academia and industry on AI-driven energy solutions.

Profile

Education 🎓

🧑‍🎓 Master’s in Petroleum Engineering (Petroleum Exploration), Petroleum University of Technology, Abadan 🇮🇷 (2019–2022) | GPA: 18.82/20
🎓 Bachelor’s in Petroleum Engineering, Islamic Azad University (Science & Research Branch), Tehran 🇮🇷 (2015–2019) | GPA: 19.14/20
📚 Courses covered include reservoir engineering, geomechanics, well-logging, and advanced data analytics.
🛠️ Projects include CO₂ storage modeling, permeability prediction via AI, and LWD-based mud loss forecasting.
📊 Developed key industry collaborations with NISOC, RIPI, and OEID through thesis, internships, and military service projects.
💡 Honed computational and simulation skills using MATLAB, Python, COMSOL, Petrel, and ECLIPSE.
🏛️ Academic mentors: Dr. Seyed Reza Shadizadeh, Dr. Bijan Biranvand, Dr. Majid Akbari.

Experience 👨‍🏫


🔬 Computer Aided Process Engineering (CAPE) – Petroleum Reservoir Engineer (Nov 2024–Present)
🛢️ Petro Vision Pasargad – Reservoir Engineer & Lab Operator (Sep 2023–May 2024)
🧠 Research Institute of Petroleum Industry (RIPI) – Petroleum Engineer, Data Scientist (Mar 2023–Apr 2024)
🏭 National Iranian South Oil Company (NISOC) – Petroleum Engineer, Petrophysicist (Mar 2021–Nov 2024)
🧪 Internships: NIOC – Exploration Management, Oil & Energy Industries Development (OEID)
📊 Key contributions include EOR analysis, SCAL/RCAL lab testing, permeability modeling, machine learning pipelines, and field data analysis.
🧾 Delivered reports, simulations, and AI models supporting production optimization and reservoir characterization.

Awards & Recognitions 🏅

🥉 3rd Prize Winner – EAGE Laurie Dake Challenge 2022 (Madrid, Spain) 🌍
🎖️ Recognized for thesis excellence in AI-driven mud loss prediction with NISOC collaboration
📌 Acknowledged during military service project with RIPI for developing ANN-based well log models
🏅 Published in high-impact journals such as Energy, Geoenergy Science and Engineering, and JRMGE
✍️ Co-author of multiple peer-reviewed papers and under-review articles across petroleum engineering disciplines
🔬 Worked alongside top researchers including Dr. Ostadhassan, Dr. Gao, and Dr. Hemmati-Sarapardeh
🛠️ Actively participated in multidisciplinary teams combining AI, geomechanics, and petrophysics
📢 Regular presenter and contributor at petroleum conferences and AI-in-energy seminars.

Research Interests 🔬

📌 AI & ML applications in petroleum engineering 🧠🛢️ – including ANN, genetic algorithms, and deep learning
📊 Mud loss zone prediction, formation permeability modeling, CO₂ storage feasibility using ML
🧪 Experimental rock mechanics: nanoindentation, geomechanical upscaling, SCAL/RCAL testing
📈 Petrophysical property estimation in carbonate and unconventional reservoirs
🌍 Reservoir simulation, LWD analysis, and smart data integration using Python, Petrel, COMSOL
📖 Notable studies include: elastic modulus upscaling, kerogen behavior under pyrolysis, RQI/FZI modeling
🔬 Interdisciplinary projects bridging data science with geoscience and reservoir engineering
🤝 Collaboration with academic and industry leaders to develop practical, AI-driven solutions for energy challenges.

Publications 

  • Elastic Properties of Anisotropic Rocks Using an Stepwise Loading Framework in a True Triaxial Testing Apparatus

    Geoenergy Science and Engineering
    2025-04 | Journal article
    DOI: 10.1016/j.geoen.2025.213883
    CONTRIBUTORS: Farshad Sadeghpour; Hem Bahadur Motra; Chinmay Sethi; Sandra Wind; Bodhisatwa Hazra; Ghasem Aghli; Mehdi Ostadhassan

  • Storage Efficiency Prediction for Feasibility Assessment of Underground CO2 Storage: Novel Machine Learning Approaches

    Energy
    2025-04 | Journal article
    DOI: 10.1016/j.energy.2025.136040
    CONTRIBUTORS: Farshad Sadeghpour

  • A new petrophysical-mathematical approach to estimate RQI and FZI parameters in carbonate reservoirs

    Journal of Petroleum Exploration and Production Technology
    2025-03 | Journal article
    DOI: 10.1007/s13202-025-01952-6
    CONTRIBUTORS: Farshad Sadeghpour; Kamran Jahangiri; Javad Honarmand

  • Effect of stress on fracture development in the Asmari reservoir in the Zagros Thrust Belt

    Journal of Rock Mechanics and Geotechnical Engineering
    2024-11 | Journal article
    DOI: 10.1016/j.jrmge.2023.12.027
    CONTRIBUTORS: Ghasem Aghli; Babak Aminshahidy; Hem Bahadur Motra; Ardavan Darkhal; Farshad Sadeghpour; Mehdi Ostadhassan

  • Comparison of geomechanical upscaling methods for prediction of elastic modulus of heterogeneous media

    Geoenergy Science and Engineering
    2024-08 | Journal article
    DOI: 10.1016/j.geoen.2024.212915
    CONTRIBUTORS: Farshad Sadeghpour; Ardavan Darkhal; Yifei Gao; Hem B. Motra; Ghasem Aghli; Mehdi Ostadhassan

Mansoor Ali Darazi | Artificial Intelligence | Best Researcher Award

Posted on by sciencefather

Dr. Mansoor Ali Darazi | Artificial Intelligence | Best Researcher Award

Dr. Mansoor Ali Darazi is an accomplished English language educator and researcher with extensive experience in ELT, curriculum development, and student mentorship. Passionate about modern pedagogical techniques, he fosters an inclusive learning environment while actively contributing to academic research. His expertise in language teaching, academic writing, and leadership roles has earned him recognition in the field. Committed to continuous professional growth, he participates in conferences and research projects. His dynamic teaching approach and strong managerial skills enhance students’ academic success and institutional development.

Profile

Education 🎓

Dr. Darazi is pursuing a Ph.D. in English Linguistics at the University of Sindh (2023–2026). He holds a Ph.D. in Education (ELT) (2022) and an M.Phil. in Education (ELT) (2014) from Iqra University, Karachi. He completed his Bachelor of Arts at Shah Abdul Latif University, Khairpur (1997). His academic journey reflects his dedication to English language teaching, research, and linguistic studies.

Experience 👨‍🏫

Dr. Darazi is an Assistant Professor at Benazir Bhutto Shaheed University, Lyari (2022–present). He has served as a Lecturer (2015–2022), ELT Coordinator, and English Lecturer at various institutions, including Army Public School, Pakistan Marine Academy, and Bahria Foundation College. With over two decades in academia, he has contributed to curriculum development, language instruction, and educational leadership, shaping student success through innovative teaching methodologies.

Awards & Recognitions 🏅

Dr. Darazi has received recognition for his contributions to education and research. His accolades include academic excellence awards, research grants, and honors from national and international organizations. His active participation in TESOL, IELTA, and Linguistic Society of America highlights his commitment to advancing English language education and pedagogy.

Research Interests 🔬

Dr. Darazi’s research explores English language proficiency, ELT methodologies, academic motivation, and student engagement. His publications address linguistic pedagogy, transformational leadership in education, and the role of feedback in language learning. His work contributes to developing innovative teaching strategies that enhance students’ academic performance and career prospects.

Publications 

Yuanming Zhang | Intelligent data processing and analysis | Best Researcher Award

Posted on by sciencefather

Dr. Yuanming Zhang | Intelligent data processing and analysis | Best Researcher Award

Yuanming Zhang is an Associate Professor at the College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China. He earned his Ph.D. in Information Science from Utsunomiya University, Japan, in 2010. His research focuses on data processing, graph neural networks, knowledge graphs, prognostics, health management, and condition monitoring. With expertise in deep learning and artificial intelligence, he has contributed significantly to neural network advancements. His work integrates cutting-edge technologies for intelligent data analysis and predictive maintenance. 📊🧠🔍

Profile

Education 🎓

Yuanming Zhang obtained his Ph.D. in Information Science from Utsunomiya University, Japan, in 2010. His academic journey emphasized computational intelligence, machine learning, and advanced data analytics. He developed expertise in deep learning models, including convolutional and graph neural networks. His education laid a strong foundation for interdisciplinary research, integrating artificial intelligence with real-world applications. 📚🧑‍🎓📈

Experience 👨‍🏫

Yuanming Zhang has been an Associate Professor at Zhejiang University of Technology since completing his Ph.D. in 2010. His professional journey spans over a decade in academia, focusing on AI, neural networks, and knowledge graphs. He has supervised research projects, collaborated on industry applications, and contributed to advancements in predictive analytics and condition monitoring. His expertise extends to teaching, mentoring, and interdisciplinary AI applications. 🏫🤖📡

Research Interests 🔬

Yuanming Zhang specializes in deep learning, attention mechanisms, graph neural networks, and AI-driven predictive analytics. His research explores neural architectures for data processing, knowledge representation, and condition monitoring. His expertise spans convolutional networks, LSTMs, GRUs, and deep belief networks. His work contributes to advancements in AI-driven diagnostics, intelligent systems, and real-time health monitoring applications. 🧠📊🖥️

Awards & Recognitions 🏅

Yuanming Zhang has received recognition for his contributions to AI, machine learning, and data analytics. His work in deep learning and knowledge graphs has earned him accolades from research institutions and conferences. His papers in neural networks and predictive maintenance have been highly cited, solidifying his impact in the field. His research excellence has been acknowledged through grants and academic distinctions. 🎖️📜🔬

Publications 

 

Chunyu Liu | Cognitive Computing | Best Researcher Award

Posted on by sciencefather

Dr. Chunyu Liu | Cognitive Computing | Best Researcher Award

Chunyu Liu is a Lecturer at North China Electric Power University, specializing in machine learning, neural decoding, and visual attention. 📚 She earned her B.S. in Mathematics and Applied Mathematics from Henan Normal University, an M.S. in Applied Mathematics from Northwest A&F University, and a Ph.D. in Computer Application Technology from Beijing Normal University. 🎓 She completed postdoctoral training at Peking University. 🔬 Her research integrates AI methodologies with cognitive neuroscience, focusing on neural encoding, decoding, and attention mechanisms. 🧠 She has published over 10 research papers, including six SCI-indexed publications as the first author. 📝 Her work aims to bridge artificial intelligence with human cognitive function understanding, contributing significantly to computational neuroscience. 🌍 Liu has also been involved in several major research projects, furthering advancements in neural signal analysis and cognitive computing. 🚀

Profile

Education 🎓

Chunyu Liu holds a strong academic background in mathematics and computational sciences. She obtained her B.S. degree in Mathematics and Applied Mathematics from Henan Normal University. ➕ She pursued her M.S. in Applied Mathematics at Northwest A&F University, where she deepened her expertise in mathematical modeling. 🔢 Continuing her academic journey, she earned a Ph.D. in Computer Application Technology from Beijing Normal University. 🖥️ Her doctoral research explored advanced AI techniques applied to neural decoding and cognitive processing. 🧠 To further refine her skills, she completed postdoctoral training at Peking University, focusing on integrating artificial intelligence with neural mechanisms. 🔬 Her academic pathway reflects a multidisciplinary approach, merging mathematics, computer science, and cognitive neuroscience to address complex challenges in brain science and AI. 📊 Liu’s education laid the foundation for her contributions to machine learning, visual attention studies, and neural encoding research.

Experience 👨‍🏫

Dr. Chunyu Liu is currently a Lecturer at North China Electric Power University, where she teaches and conducts research in cognitive computing and machine learning. 🎓 She has led and collaborated on multiple projects related to neural encoding and decoding, investigating how the brain processes object recognition, emotions, and attention. 🧠 Prior to her current role, she completed postdoctoral research at Peking University, where she worked on advanced AI-driven models for neural signal analysis. 🔍 Over the years, Liu has gained extensive experience in analyzing multimodal neural signals, including magnetoencephalography (MEG) and functional MRI (fMRI). 📡 She has also served as a reviewer for esteemed scientific journals and collaborated with interdisciplinary research teams on AI and brain science projects. 🔬 Her expertise extends to both academia and industry, where she has contributed to the development of novel computational models for decoding brain activity. 🚀

Research Interests 🔬

Dr. Chunyu Liu’s research integrates artificial intelligence and brain science to understand cognitive functions through neural decoding. 🧠 She employs multi-modal neural signals such as magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) to analyze brain activity. 📡 Her work explores neural encoding and decoding, focusing on object recognition, emotion processing, and multiple-object attention. 🎯 She develops AI-based models to extract human brain features and gain insights into cognitive mechanisms. 🤖 By integrating psychological experimental paradigms with AI, Liu aims to advance computational neuroscience. 🏆 Her research also inspires the development of new AI theories and algorithms based on principles of brain function. 📊 She has led major projects in cognitive computing, contributing significantly to both theoretical advancements and practical applications in neural signal processing. 🚀 Through her work, she bridges the gap between human cognition and artificial intelligence, driving innovations in brain-computer interface research. 🏅

 

Awards & Recognitions 🏅

Dr. Chunyu Liu has received recognition for her outstanding contributions to cognitive computing and AI-driven neuroscience research. 🏅 She has been nominated for the prestigious International Cognitive Scientist Award for her pioneering work in neural decoding and visual attention mechanisms. 🎖️ Liu’s research publications have been featured in high-impact journals, earning her accolades from the scientific community. 📜 Her first-author papers in IEEE Transactions on Neural Systems and Rehabilitation Engineering, Science China Life Sciences, and IEEE Journal of Biomedical and Health Informatics have been widely cited. 📝 She has also been honored with research grants and funding for AI-driven cognitive studies. 🔬 Her innovative work in decoding brain signals has been recognized in international AI and neuroscience conferences. 🌍 Liu’s academic excellence and contributions continue to shape the field of computational neuroscience and machine learning applications in cognitive science. 🚀

Publications 📚

Mudassar Raza | Machine Learning | Best Researcher Award

Posted on by sciencefather

Prof. Mudassar Raza | Machine Learning | Best Researcher Award

Prof. Dr. Mudassar Raza is a leading AI researcher and academician, serving as a Professor at Namal University, Mianwali, Pakistan. He is a Senior IEEE Member, Chair Publications of IEEE Islamabad Section, and an Academic Editor for PLOS ONE. With 20+ years of teaching and research experience, he has worked at HITEC University Taxila and COMSATS University Islamabad. His research spans AI, deep learning, image processing, and cybersecurity. He has published 135+ research papers with a cumulative impact factor of 215+, 6066+ citations, an H-index of 44, and an I-10 index of 93. He was listed in Elsevier’s World’s Top 2% Scientists (2023) and ranked #11 in Computer Science in Pakistan. Dr. Raza has supervised 3 PhDs, co-supervising 6 more, and mentored 100+ undergraduate R&D projects. He actively contributes to academia, industry collaborations, and curriculum development while serving as a reviewer for prestigious journals. 🌍📖

Profile

Education 🎓

  • Ph.D. in Control Science & Engineering (2014-2017) – University of Science & Technology of China (USTC), China 🇨🇳
    • Specialization: Pattern Recognition & Intelligent Systems
  • MS (Computer Science) (2009-2010) – Iqra University, Islamabad, Pakistan 🇵🇰
    • CGPA: 3.64 | Specialization: Image Processing
  • MCS (Master of Computer Science) (2004-2006) – COMSATS Institute of Information Technology, Pakistan
    • CGPA: 3.24 | 80% Marks
  • BCS (Bachelor in Computer Science) (1999-2003) – Punjab University, Lahore, Pakistan
    • CGPA: 3.28 | 64.25% Marks
  • Higher Secondary (Pre-Engineering) – Islamabad College for Boys
  • Matriculation (Science) – Islamabad College for Boys
    Dr. Raza’s academic journey is marked by top-tier universities and a strong focus on AI, pattern recognition, and cybersecurity. 🎓📚

Experience 👨‍🏫

  • Professor (2024-Present) – Namal University, Mianwali
    • Teaching AI, Cybersecurity, and Research Supervision
  • Associate Professor/Head AI & Cybersecurity Program (2023-2024) – HITEC University, Taxila
    • Led AI & Cybersecurity programs, supervised PhDs, and organized industry-academic collaborations
  • Associate Professor (2023) – COMSATS University, Islamabad
  • Assistant Professor (2012-2023) – COMSATS University, Islamabad
  • Lecturer (2008-2012) – COMSATS University, Islamabad
  • Research Associate (2006-2008) – COMSATS University, Islamabad
    Dr. Raza has 20+ years of experience in academia, R&D, and industry collaborations, contributing significantly to AI, deep learning, and cybersecurity. 🏫📊

Research Interests 🔬

Prof. Dr. Mudassar Raza’s research revolves around Artificial Intelligence, Deep Learning, Computer Vision, Image Processing, Cybersecurity, and Parallel Programming. His work includes pattern recognition, intelligent systems, visual robotics, and AI-driven cybersecurity solutions. With 135+ international publications, he has significantly contributed to AI’s real-world applications. His research impact includes 6066+ citations, an H-index of 44, and an I-10 index of 93. He leads multiple AI research groups, supervises PhD/MS students, and actively collaborates with industry and academia. His work is frequently cited, placing him among the top AI researchers globally. As an IEEE Senior Member and a PLOS ONE Academic Editor, he is a key figure in AI-driven innovations and technology advancements. 🧠📊

  • National Youth Award 2008 by the Prime Minister of Pakistan for contributions to Computer Science 🎖️
  • Listed in World’s Top 2% Scientists (2023) by Elsevier 🌍
  • Ranked #11 in Computer Science in Pakistan by AD Scientific Index 📊
  • Senior IEEE Member (ID: 91289691) 🔬
  • HEC Approved PhD Supervisor 🎓
  • Best Research Productivity Awardee at COMSATS University multiple times 🏆
  • Recognized by ResearchGate with a Research Interest Score higher than 97% of members 📈
  • Reviewer & Editor for prestigious journals including PLOS ONE 📝
    Dr. Raza has received numerous accolades for his contributions to AI, research excellence, and academia. 🌟

Publications 📚

Xin SU | Multi-temporal remote sensing information extraction | Best Researcher Award

Posted on by sciencefather

Prof Dr. Xin SU | Multi-temporal remote sensing information extraction | Best Researcher Award

Xin Su, PhD, is an Associate Professor at the School of Artificial Intelligence, Wuhan University. He supervises both master’s and PhD students. He earned his doctorate in Signal and Image Processing from Telecom ParisTech in 2015. He then worked as a postdoctoral researcher at INRIA, France, from 2015 to 2018. His research focuses on intelligent analysis of time-series images, spatiotemporal target recognition, and large-scale remote sensing models. He has led and participated in multiple national research projects, publishing extensively in top-tier journals such as IEEE TIP, IEEE TGRS, ISPRS, and JAG. 📚🎓

Profile

Education 🎓

  • PhD (2015): Telecom ParisTech, France – Signal and Image Processing 🎓
  • Master’s Studies (2008-2011, uncompleted): Wuhan University – Signal and Information Processing 🏫
  • Bachelor’s (2004-2008): Wuhan University – Electronic Science and Technology (Engineering) 🎓

Experience 👨‍🏫

  • 2015-2018: Postdoctoral Researcher, INRIA, France 🇫🇷
  • 2015: Postdoctoral Researcher, Telecom ParisTech, France 🎓

Research Interests 🔬

Xin Su specializes in intelligent analysis of time-series remote sensing images, spatiotemporal object recognition, and large-scale AI models for remote sensing. His work spans geospatial applications, UAV-based surveillance, and hyperspectral data processing. He actively contributes to developing advanced AI techniques for satellite video analysis and infrastructure monitoring. 🚀🌍

Awards & Recognitions 🏅

Xin Su has been recognized for his contributions to remote sensing and AI, receiving multiple national research grants and awards for excellence in scientific research and innovation. He has secured funding from National Natural Science Foundation projects and defense-related initiatives. His research has been featured in top IEEE and ISPRS journals, reinforcing his position as a leading researcher in the field. 🌟🏅

Publications 📚

Grazia Ragone | Human-Computer Interaction | Best Researcher Award

Posted on by sciencefather

Dr. Grazia Ragone | Human-Computer Interaction | Best Researcher Award

🔬 Grazia Ragone is a researcher in Human-Computer Interaction (HCI) with a focus on autism and interactive systems. 🏫 She earned her PhD from the University of Sussex, UK, where she investigated social motor synchrony in autistic children through motion capture and sonification. 🎼 With a background in psychology, developmental science, and music therapy, she integrates interdisciplinary methods into assistive technology. 💻 She has extensive teaching experience in research methods, cognitive science, and HCI at the University of Sussex. 🏆 Her research has been recognized with multiple international awards, including Microsoft Research’s Best Student Research Competition. 🌍 She actively contributes as a reviewer and associate chair for HCI conferences and journals. 📖 Her work bridges psychology, technology, and education, aiming to enhance accessibility and interaction for neurodiverse individuals.

Profile

Education 🎓

She completed her PhD in 2023 at the University of Sussex, UK, where her research focused on autism, motion capture, and social motor synchrony. Prior to this, she earned an MSc in Psychological Methods from the University of Sussex in 2018, with a focus on autism and interactional features. She also holds an MPhil in Developmental Psychology from London Metropolitan University (2015), specializing in child development and interaction. In 2014, she completed her BSc in Developmental Psychology at London Metropolitan University, studying early cognitive and social development. She further enriched her expertise with a Master’s in Music & Art Therapy from Tor Vergata University in Rome (2006), where she focused on therapeutic interventions for individuals with special needs. Her academic journey began with a BA in Humanities from the University of Pavia, Italy (2004), where she studied philosophy, linguistics, and cultural studies.

Experience 👨‍🏫

From 2019 to 2023, she worked as a Teaching Assistant at the University of Sussex, UK, where she taught Human-Computer Interaction (HCI), research methods, and professional skills. Prior to this, she served as a Research Assistant at the University of Sussex (2016-2018), focusing on technology designed for neurodiverse children. From 2014 to 2016, she conducted research on autism and interactive environments at London Metropolitan University. Earlier in her career, she was a Research Assistant at CNR-ISTI Pisa, Italy (2008-2014), where she contributed to the development of assistive software for autistic children. Her experience also includes working as a Music Therapist for the Rome City Council (2005-2010), providing therapeutic interventions for autistic children. Additionally, from 2010 to 2019, she worked as a Trainer and Consultant, conducting workshops and training programs for professionals in the field of autism.

Research Interests 🔬

Her research focuses on Human-Computer Interaction (HCI) and autism, developing interactive systems to support neurodiverse individuals. She explores the role of music and sonification in enhancing motor and social skills through auditory feedback. Her work also includes investigating social motor synchrony using motion capture technology. She designs AI-powered assistive technology to support autistic children and applies user-centered design principles to create accessible interfaces for individuals with special needs.

Awards & Recognitions 🏅

She has received several prestigious awards and honors for her contributions to autism research and assistive technology. In 2021, she was awarded the Best Student Research Award by Microsoft Research at the ASSETS Conference. Her work was also recognized with the Best Work in Progress Award at the IDC Conference on autism research in 2020. In 2013, she received the Horizon Research Award from London Metropolitan University for outstanding research. Her contributions to autism research earned her a Massachusetts Senate Citation in 2012, and in 2011, she was honored with the Rotary Club Research Award from CNR Pisa for excellence in autism studies.

Publications 📚

  •  Supporting and understanding autistic children’s non-verbal interactions through OSMoSIS, a motion-based sonic system
    International Journal of Child-Computer Interaction
    2025-02 | Journal article
    DOI: 10.1016/j.ijcci.2025.100726
    CONTRIBUTORS: Grazia Ragone; Judith Good; Kate Howland

  • Child-Centered AI for Empowering Creative and Inclusive Learning Experiences

    Proceedings of ACM Interaction Design and Children Conference: Inclusive Happiness, IDC 2024
    2024 | Conference paper
    DOI: 10.1145/3628516.3661157

    EID:

    2-s2.0-85197894406

    Part ofISBN: 9798400704420
    CONTRIBUTORS: Ragone, G.; Ali, S.A.; Esposito, A.; Good, J.; Howland, K.; Presicce, C.

  • Designing Safe and Engaging AI Experiences for Children: Towards the Definition of Best Practices in UI/UX Design

    arXiv
    2024 | Other
    DOI: 10.48550/arXiv.2404.14218

    EID:

    2-s2.0-85192517180

    Part of ISSN: 23318422
    CONTRIBUTORS: Ragone, G.; Buono, P.; Lanzilotti,

Radhika Subramanian | Speech Processing | Women Researcher Award

Posted on by sciencefather

Dr. Radhika Subramanian | Speech Processing | Women Researcher Award

 

Profile

Education

She is currently pursuing a PhD at Anna University, Chennai, with an expected completion in 2025. She obtained her Master of Engineering in Communication Systems from B.S. Abdur Rahman Crescent Engineering College, Chennai, achieving 82.3% in the academic years 2007-2009. Prior to that, she completed her Bachelor of Engineering in Electronics and Communication Engineering from Kanchi Pallavan Engineering College, Kanchipuram, affiliated with Anna University, securing 84% from 2003 to 2007. She completed her Higher Secondary education at S.S.K.V Higher Secondary School, Kanchipuram, with 88% marks from 2001 to 2003, and her Secondary School Leaving Certificate from the same institution, scoring 84% in the year 2000-2001.

Work experience

As of January 31, 2025, she has a total academic experience of 14 years, 7 months, and 15 days. She has been serving as an Assistant Professor Grade-II at Sri Venkateswara College of Engineering, Sriperumpudur, since June 11, 2010. Prior to this, she worked as a Lecturer at Arulmigu Meenakshi Amman College of Engineering, Kanchipuram, from July 1, 2009, to May 7, 2010, gaining 10 months of experience. Her cumulative teaching experience amounts to 14 years, 17 months, and 15 days.

AREA OF INTEREST

  • Data Communication and Networking
  • Satellite communication
  • Signal Processing
  • Machine Learning

Publication

  • Radhika, S & Prasanth, A 2024, „An Effective Speech Emotion Recognition Model for Multi-Regional Languages Using Threshold-based Feature Selection Algorithm‟, Circuits, Systems, and Signal Processing, vol. 43, pp. 2477–2506, ISSN: 1531-5878,DOI: 10.1007/s00034-023-02571-4, Impact Factor: 2.3.
  • Radhika, S & Prasanth, A 2024, „An Effective Speech Emotion Recognition Model for Multi-Regional Languages Using Threshold-based Feature Selection Algorithm‟, Circuits, Systems, and Signal Processing, vol. 43, pp. 2477–2506, ISSN: 1531-5878,DOI: 10.1007/s00034-023-02571-4, Impact Factor: 2.3.
  • A Survey of Human Emotion Recognition Using Speech Signals: Current Trends and Future Perspectives
    R Subramanian, P Aruchamy
    Micro-Electronics and Telecommunication Engineering: Proceedings of 6th