Chongyuan Wang | Deep learning | Best Researcher Award

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Dr. Chongyuan Wang | Deep learning | Best Researcher Award

Dr. Chongyuan Wang, a Ph.D. researcher at Hohai University, specializes in artificial intelligence šŸ¤– and neural computation šŸ§ . He completed his B.S. at Jiangsu University šŸ‡ØšŸ‡³ and M.S. in Energy and Power from Warwick University šŸ‡¬šŸ‡§. 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 šŸŒ.

Profile

Education šŸŽ“

šŸŽ“ B.S. in Engineering ā€“ Jiangsu University, China šŸ‡ØšŸ‡³
šŸŽ“ M.S. in Energy and Power ā€“ University of Warwick, UK šŸ‡¬šŸ‡§ (2018)
šŸŽ“ Ph.D. Candidate ā€“ Hohai University, majoring in Artificial Intelligence šŸ¤–
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. šŸ“˜šŸ§ 

Experience šŸ‘Øā€šŸ«

Dr. Wang is currently pursuing his Ph.D. at Hohai University, where he investigates dendritic learning algorithms and synaptic modeling. šŸ§¬ 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.” šŸ“Š His work balances theoretical depth and applied research, particularly in neural computation, classification systems, and resource-efficient AI. šŸ”¬šŸ’”

Awards & Recognitions šŸ…

šŸ… Patent Holder (CN202410790312.0, CN202410646306.8, CN201510661212.9)
šŸ“„ Published in SCI-indexed journal Mathematics (MDPI)
šŸŒ Recognized on ORCID (0009-0002-6844-1446)
šŸ§  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. šŸš€šŸ“˜

Research Interests šŸ”¬

Dr. Wangā€™s research fuses deep learning šŸ¤– and dendritic modeling šŸ§  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. šŸŒ±šŸ“Š His vision is to bridge the gap between neuroscience and AI through interpretable, high-performance algorithms. šŸ§ šŸ’”

Publications

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

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

Amar Salehi | Reinforcement Learning | Best Researcher Award

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Dr. Amar Salehi | Reinforcement Learning | Best Researcher Award

Dr. Amar Salehi is a postdoctoral researcher at South China University of Technology šŸ‡ØšŸ‡³, specializing in microrobotics šŸ¤–, AI šŸ§ , and biosystems engineering šŸŒ±. With a Ph.D. in Mechanical Engineering of Biosystems šŸŽ“ from the University of Tehran šŸ‡®šŸ‡·, he developed intelligent and independent control systems for magnetic microrobots. His work integrates machine learning, deep learning, and bio-inspired design for environmental and biomedical applications šŸŒšŸ§¬. Passionate about innovation, he has contributed to several peer-reviewed journals šŸ“š, international conferences šŸŒ, and interdisciplinary projects. He also served as a teaching assistant and reviewer and held leadership roles in scientific societies šŸ‘Øā€šŸ«. A top-ranked scholar in national entrance exams šŸ†, Dr. Salehi actively collaborates across borders for research and development in cutting-edge AI and robotics šŸ”¬.

Profile

Education šŸŽ“

Dr. Salehi earned his Ph.D. in Mechanical Engineering of Biosystems šŸŽ“ from the University of Tehran (2019ā€“2024), focusing on intelligent magnetic microrobot control šŸ¤–. He completed his M.S. at Isfahan University of Technology (2013ā€“2015) šŸ§Ŗ, where he explored fluid heat transfer using CFD methods and mechanical behavior modeling with neural networks. His B.S. was from Razi University (2008ā€“2012) in Biosystems Mechanical Engineering šŸ”§šŸŒ¾. A consistent top performer, he ranked 2nd in the Ph.D. entrance exam and 90th in the M.S. exam among thousands šŸ…. His academic record features exceptional GPAs and thesis scores šŸŒŸ. Dr. Salehi’s interdisciplinary education blends mechanical systems, AI, and biology, building a strong foundation for his current microrobotics and biosensor research šŸ”¬šŸ“Š.

Experience šŸ‘Øā€šŸ«

Experience (150 words): Dr. Salehi is currently a Postdoctoral Fellow at the Shien-Ming Wu School of Intelligent Engineering, South China University of Technology šŸ‡ØšŸ‡³ (2024ā€“present), working on intelligent agents and microrobotics šŸ¤–. Previously, he was a teaching assistant at the University of Tehran, supporting physics and mechanical engineering courses šŸ‘Øā€šŸ«. He also taught part-time at Azad University, Iran (2016ā€“2019) šŸ“˜. As a research assistant at the AIAX Lab, he contributed to AI and advanced control systems. He led several interdisciplinary projects, including a joint Iran-Turkey research on microfluidic biochips šŸ§«. A reviewer for ā€œThe Innovationā€ journal, he is proficient in tools like COMSOL, SolidWorks, Python, and statistical analysis šŸ“ŠšŸ–„ļø. He also chaired a student startup ā€œGreen Daal Mechanicsā€ and served in university and parliamentary scientific committees šŸš€šŸ“ˆ.

Awards & Recognitions šŸ…

Awards and Honors (150 words): Dr. Salehi received the Best Oral Presentation Award šŸ„‡ at IRAC 2024 for his work on deep learning and microrobots šŸ¤–. Ranked 2nd in the national Ph.D. entrance exam and 90th in the M.S. exam, he also achieved excellent scores in his thesis evaluations (Ph.D.: 19.65/20, M.S.: 19.49/20) šŸ†. His academic and research excellence has earned him recognition in national and international forums šŸ“œ. He has been an active member of the Scientific Association of Biosystems Engineering and the Interdisciplinary Scientific Student Association at the University of Tehran šŸ§ . He also served as Editor-in-Chief of the New Green Industry Journal šŸŒ±. With strong leadership in university-industry interaction, he contributes to Iranā€™s agricultural, food, and energy research panels and policy discussions šŸ§‘ā€šŸ”¬šŸ“¢.

Research Interests šŸ”¬

.Research Focus (150 words): Dr. Salehiā€™s research lies at the intersection of microrobotics šŸ¤–, artificial intelligence šŸ§ , and biosystems šŸŒ±. His Ph.D. work focused on intelligent, model-free control of magnetic microrobots using deep reinforcement learning in real-world environments šŸ”. He explores biosensor optimization using genetic algorithms šŸ§¬, natural language interfaces for microrobot control šŸ—£ļø, and micro/nano-systems for biomedical and environmental applications šŸŒ. He integrates fuzzy logic, ANN, and reinforcement learning in his predictive modeling. Ongoing research includes yield prediction in intercropping systems šŸŒ¾ and AI-driven environmental cleanup technologies. Dr. Salehiā€™s goal is to create autonomous, intelligent microsystems that can navigate, sense, and interact with biological and physical environments, with potential applications in diagnostics, therapy, and sustainability šŸ§Ŗā™»ļø.

PublicationsĀ 

 

 

 

Alvaro Garcia | Computer vision | Best Researcher Award

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Dr. Alvaro Garcia | Computer vision | Best Researcher Award

Ɓlvaro GarcĆ­a MartĆ­n es Profesor Titular en la Universidad AutĆ³noma de Madrid, especializado en visiĆ³n por computadora y anĆ”lisis de video. šŸŽ“ Obtuvo su tĆ­tulo de Ingeniero de TelecomunicaciĆ³n en 2007, su MĆ”ster en IngenierĆ­a InformĆ”tica y Telecomunicaciones en 2009 y su Doctorado en 2013, todos en la Universidad AutĆ³noma de Madrid. šŸ« Ha trabajado en detecciĆ³n de personas, seguimiento de objetos y reconocimiento de eventos, con mĆ”s de 22 artĆ­culos en revistas indexadas y 28 en congresos. šŸ“ Ha realizado estancias en Carnegie Mellon University, Queen Mary University y Technical University of Berlin. šŸŒ Su investigaciĆ³n ha contribuido al desarrollo de sistemas de videovigilancia inteligentes, anĆ”lisis de secuencias de video y procesamiento de seƱales multimedia. šŸ“¹ Ha sido reconocido con prestigiosos premios y ha participado en mĆŗltiples proyectos europeos de innovaciĆ³n tecnolĆ³gica. šŸš€

Profile

Education šŸŽ“

šŸŽ“ Ingeniero de TelecomunicaciĆ³n por la Universidad AutĆ³noma de Madrid (2007). šŸŽ“ MĆ”ster en IngenierĆ­a InformĆ”tica y Telecomunicaciones con especializaciĆ³n en Tratamiento de SeƱales Multimedia en la Universidad AutĆ³noma de Madrid (2009). šŸŽ“ Doctor en IngenierĆ­a InformĆ”tica y TelecomunicaciĆ³n por la Universidad AutĆ³noma de Madrid (2013). Su formaciĆ³n ha sido complementada con estancias en reconocidas universidades internacionales, incluyendo Carnegie Mellon University (EE.UU.), Queen Mary University (Reino Unido) y la Technical University of Berlin (Alemania). šŸŒ Durante su doctorado, recibiĆ³ la beca FPI-UAM para la realizaciĆ³n de su investigaciĆ³n. Su sĆ³lida formaciĆ³n acadĆ©mica le ha permitido contribuir significativamente al campo del anĆ”lisis de video y visiĆ³n por computadora, consolidĆ”ndose como un experto en la detecciĆ³n, seguimiento y reconocimiento de eventos en secuencias de video. šŸ“¹

Experience šŸ‘Øā€šŸ«

šŸ”¬ Se uniĆ³ al grupo VPU-Lab en la Universidad AutĆ³noma de Madrid en 2007. šŸ“” De 2008 a 2012, fue becario de investigaciĆ³n (FPI-UAM). šŸŽ“ Entre 2012 y 2014, trabajĆ³ como Profesor Ayudante. šŸ‘Øā€šŸ« De 2014 a 2019, fue Profesor Ayudante Doctor. šŸ“š De 2019 a 2023, ocupĆ³ el cargo de Profesor Contratado Doctor. šŸ›ļø Desde septiembre de 2023, es Profesor Titular en la Universidad AutĆ³noma de Madrid. šŸ† Ha participado en mĆŗltiples proyectos europeos sobre videovigilancia, transmisiĆ³n de contenido multimedia y reconocimiento de eventos, incluyendo PROMULTIDIS, ATI@SHIVA, EVENTVIDEO y MobiNetVideo. šŸš€ Ha realizado estancias de investigaciĆ³n en Carnegie Mellon University, Queen Mary University y Technical University of Berlin. šŸŒ Su experiencia docente abarca asignaturas en IngenierĆ­a de Telecomunicaciones, IngenierĆ­a InformĆ”tica e IngenierĆ­a BiomĆ©dica.

Research Interests šŸ”¬

šŸŽÆ Su investigaciĆ³n se centra en la visiĆ³n por computadora, el anĆ”lisis de secuencias de video y la inteligencia artificial aplicada a entornos de videovigilancia. šŸ“¹ Especialista en detecciĆ³n de personas, seguimiento de objetos y reconocimiento de eventos en video. šŸ§  Desarrolla algoritmos de aprendizaje profundo y visiĆ³n artificial para mejorar la seguridad y automatizaciĆ³n en ciudades inteligentes. šŸ™ļø Ha trabajado en proyectos sobre videovigilancia, transmisiĆ³n multimedia y detecciĆ³n de anomalĆ­as en video. šŸ”¬ Su investigaciĆ³n incluye procesamiento de imĆ”genes, anĆ”lisis semĆ”ntico y redes neuronales profundas. šŸš€ Participa activamente en proyectos internacionales y colabora con universidades como Carnegie Mellon, Queen Mary y TU Berlin. šŸŒ Ha publicado en IEEE Transactions on Intelligent Transportation Systems, Sensors y Pattern Recognition, consolidĆ”ndose como un referente en el campo de la visiĆ³n por computadora. šŸ“œ

Awards & Recognitions šŸ…

šŸ„‡ Medalla “Juan LĆ³pez de PeƱalver” 2017, otorgada por la Real Academia de IngenierĆ­a. šŸ“œ Reconocimiento por su contribuciĆ³n a la ingenierĆ­a espaƱola en el campo de la visiĆ³n por computadora y anĆ”lisis de video. šŸ›ļø Ha recibido financiaciĆ³n para mĆŗltiples proyectos de investigaciĆ³n europeos y nacionales. šŸ”¬ Ha participado en iniciativas de innovaciĆ³n en videovigilancia y anĆ”lisis de video para seguridad. šŸš€ Sus contribuciones han sido publicadas en las principales conferencias y revistas cientĆ­ficas del Ć”rea. šŸ“š Su trabajo ha sido citado mĆ”s de 4500 veces y cuenta con un Ć­ndice h de 16 en Google Scholar. šŸ“Š

PublicationsĀ 

1. Rafael Martƭn-Nieto, Ɓlvaro Garcƭa-Martƭn, Alexander G. Hauptmann, and Jose. M.
MartĆ­nez: ā€œAutomatic vacant parking places management system using multicamera
vehicle detectionā€. IEEE Transactions on Intelligent Transportation Systems, Volume 20,
Issue 3, pp. 1069-1080, ISSN 1524-9050, March 2019.

2. Rafael Martƭn-Nieto, Ɓlvaro Garcƭa-Martƭn, Jose. M. Martƭnez, and Juan C. SanMiguel:
ā€œEnhancing multi-camera people detection by online automatic parametrization using
detection transfer and self-correlation maximizationā€. Sensors, Volume 18, Issue 12, ISSN
1424-8220, December 2018.

3. Ɓlvaro GarcĆ­a-MartĆ­n, Juan C. SanMiguel and Jose. M. MartĆ­nez: ā€œCoarse-to-fine adaptive
people detection for video sequences by maximizing mutual informationā€. Sensors,
Volume 19, Issue 4, ISSN 1424-8220, January 2019.

4. Alejandro LĆ³pez-Cifuentes, Marcos Escudero-ViƱolo, JesĆŗs BescĆ³s and Ɓlvaro GarcĆ­aMartĆ­n: ā€œSemantic-Aware Scene Recognitionā€. Pattern Recognition. Accepted February
2020.

5. Paula Moral, Ɓlvaro Garcƭa-Martƭn, Marcos Escudero ViƱolo, Jose M. Martinez, Jesus
BescĆ³s, Jesus PeƱuela, Juan Carlos Martinez, Gonzalo Alvis: ā€œTowards automatic waste
containers management in cities via computer vision: containers localization and geopositioning in city mapsā€. Waste Management, June 2022.

6. Javier Montalvo, Ɓlvaro GarcĆ­a-MartĆ­n, Jesus BescĆ³s: ā€œExploiting Semantic Segmentation
to Boost Reinforcement Learning in Video Game Environmentsā€. Multimedia Tools and
Applications. September 2022.

7. Paula Moral, Ɓlvaro GarcĆ­a-MartĆ­n, Jose M. Martinez, Jesus BescĆ³s: ā€œEnhancing Vehicle
Re-Identification Via Synthetic Training Datasets and Re-ranking Based on Video-Clips
Informationā€. Multimedia Tools and Applications. February 2023.

8. Roberto Alcover-Couso, Juan C. SanMiguel, Marcos Escudero-ViƱolo and Alvaro GarciaMartin: ā€œOn exploring weakly supervised domain adaptation strategies for semantic
segmentation using synthetic dataā€. Multimedia Tools and Applications. February 2023.

9. Juan Ignacio Bravo PĆ©rez-Villar, Ɓlvaro GarcĆ­a-MartĆ­n, JesĆŗs BescĆ³s, Marcos EscuderoViƱolo: ā€œSpacecraft Pose Estimation: Robust 2D and 3D-Structural Losses and
Unsupervised Domain Adaptation by Inter-Model Consensusā€. IEEE Transactions on
Aerospace and Electronic Systems. August 2023.

10. Javier Montalvo, Ɓlvaro GarcĆ­a-MartĆ­n, JosĆ© M. Martinez. “An Image-Processing Toolkit
for Remote Photoplethysmography”, Multimedia Tools and Applications. July 2024.

11. Juan Ignacio Bravo PĆ©rez-Villar, Ɓlvaro GarcĆ­a-MartĆ­n, JesĆŗs BescĆ³s, Juan C. SanMiguel:
ā€œTest-Time Adaptation for Keypoint-Based Spacecraft Pose Estimation Based on
Predicted-View Synthesisā€. IEEE Transactions on Aerospace and Electronic Systems.
May 2024.

12. Kirill Sirotkin, Marcos Escudero-ViƱolo, Pablo Carballeira, Ɓlvaro Garcƭa-Martƭn:
ā€œImproved Transferability of Self-Supervised Learning Models Through Batch
Normalization Finetuningā€. Applied Intelligence. Aug 2024.

13. Javier GalƔn, Miguel GonzƔlez, Paula Moral, Ɓlvaro Garcƭa-Martƭn, Jose M. Martinez:
ā€œTransforming Urban Waste Collection Inventory: AI-Based Container Classification and
Re-Identificationā€. Waste Management, Feb 2025.

Chunyu Liu | Cognitive Computing | Best Researcher Award

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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. šŸ“š 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. šŸŽ“ She completed postdoctoral training at Peking University. šŸ”¬ Her research integrates AI methodologies with cognitive neuroscience, focusing on neural encoding, decoding, and attention mechanisms. šŸ§  She has published over 10 research papers, including six SCI-indexed publications as the first author. šŸ“ Her work aims to bridge artificial intelligence with human cognitive function understanding, contributing significantly to computational neuroscience. šŸŒ Liu has also been involved in several major research projects, furthering advancements in neural signal analysis and cognitive computing. šŸš€

Profile

Education šŸŽ“

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. āž• She pursued her M.S. in Applied Mathematics at Northwest A&F University, where she deepened her expertise in mathematical modeling. šŸ”¢ Continuing her academic journey, she earned a Ph.D. in Computer Application Technology from Beijing Normal University. šŸ–„ļø Her doctoral research explored advanced AI techniques applied to neural decoding and cognitive processing. šŸ§  To further refine her skills, she completed postdoctoral training at Peking University, focusing on integrating artificial intelligence with neural mechanisms. šŸ”¬ Her academic pathway reflects a multidisciplinary approach, merging mathematics, computer science, and cognitive neuroscience to address complex challenges in brain science and AI. šŸ“Š Liuā€™s education laid the foundation for her contributions to machine learning, visual attention studies, and neural encoding research.

Experience šŸ‘Øā€šŸ«

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. šŸŽ“ She has led and collaborated on multiple projects related to neural encoding and decoding, investigating how the brain processes object recognition, emotions, and attention. šŸ§  Prior to her current role, she completed postdoctoral research at Peking University, where she worked on advanced AI-driven models for neural signal analysis. šŸ” Over the years, Liu has gained extensive experience in analyzing multimodal neural signals, including magnetoencephalography (MEG) and functional MRI (fMRI). šŸ“” She has also served as a reviewer for esteemed scientific journals and collaborated with interdisciplinary research teams on AI and brain science projects. šŸ”¬ Her expertise extends to both academia and industry, where she has contributed to the development of novel computational models for decoding brain activity. šŸš€

Research Interests šŸ”¬

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

 

Awards & Recognitions šŸ…

Dr. Chunyu Liu has received recognition for her outstanding contributions to cognitive computing and AI-driven neuroscience research. šŸ… She has been nominated for the prestigious International Cognitive Scientist Award for her pioneering work in neural decoding and visual attention mechanisms. šŸŽ–ļø Liu’s research publications have been featured in high-impact journals, earning her accolades from the scientific community. šŸ“œ 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. šŸ“ She has also been honored with research grants and funding for AI-driven cognitive studies. šŸ”¬ Her innovative work in decoding brain signals has been recognized in international AI and neuroscience conferences. šŸŒ Liu’s academic excellence and contributions continue to shape the field of computational neuroscience and machine learning applications in cognitive science. šŸš€

Publications šŸ“š

Mudassar Raza | Machine Learning | Best Researcher Award

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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 Elsevierā€™s Worldā€™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. šŸŒšŸ“–

Profile

Education šŸŽ“

  • Ph.D. in Control Science & Engineering (2014-2017) ā€“ University of Science & Technology of China (USTC), China šŸ‡ØšŸ‡³
    • Specialization: Pattern Recognition & Intelligent Systems
  • MS (Computer Science) (2009-2010) ā€“ Iqra University, Islamabad, Pakistan šŸ‡µšŸ‡°
    • CGPA: 3.64 | Specialization: Image Processing
  • MCS (Master of Computer Science) (2004-2006) ā€“ COMSATS Institute of Information Technology, Pakistan
    • CGPA: 3.24 | 80% Marks
  • BCS (Bachelor in Computer Science) (1999-2003) ā€“ Punjab University, Lahore, Pakistan
    • CGPA: 3.28 | 64.25% Marks
  • Higher Secondary (Pre-Engineering) ā€“ Islamabad College for Boys
  • Matriculation (Science) ā€“ Islamabad College for Boys
    Dr. Razaā€™s academic journey is marked by top-tier universities and a strong focus on AI, pattern recognition, and cybersecurity. šŸŽ“šŸ“š

Experience šŸ‘Øā€šŸ«

  • Professor (2024-Present) ā€“ Namal University, Mianwali
    • Teaching AI, Cybersecurity, and Research Supervision
  • Associate Professor/Head AI & Cybersecurity Program (2023-2024) ā€“ HITEC University, Taxila
    • Led AI & Cybersecurity programs, supervised PhDs, and organized industry-academic collaborations
  • Associate Professor (2023) ā€“ COMSATS University, Islamabad
  • Assistant Professor (2012-2023) ā€“ COMSATS University, Islamabad
  • Lecturer (2008-2012) ā€“ COMSATS University, Islamabad
  • Research Associate (2006-2008) ā€“ 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. šŸ«šŸ“Š

Research Interests šŸ”¬

Prof. Dr. Mudassar Razaā€™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 AIā€™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. šŸ§ šŸ“Š

  • National Youth Award 2008 by the Prime Minister of Pakistan for contributions to Computer Science šŸŽ–ļø
  • Listed in Worldā€™s Top 2% Scientists (2023) by Elsevier šŸŒ
  • Ranked #11 in Computer Science in Pakistan by AD Scientific Index šŸ“Š
  • Senior IEEE Member (ID: 91289691) šŸ”¬
  • HEC Approved PhD Supervisor šŸŽ“
  • Best Research Productivity Awardee at COMSATS University multiple times šŸ†
  • Recognized by ResearchGate with a Research Interest Score higher than 97% of members šŸ“ˆ
  • Reviewer & Editor for prestigious journals including PLOS ONE šŸ“
    Dr. Raza has received numerous accolades for his contributions to AI, research excellence, and academia. šŸŒŸ

Publications šŸ“š

Yangyang Huang | Object detection | Excellence in Innovation

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Dr. Yangyang Huang | Object detection | Excellence in Innovation

Yangyang Huang is a Ph.D. student at the School of Computer Science and Engineering, South China University of Technology (SCUT), Guangzhou, China. His research focuses on artificial intelligence, computer vision, and large models. He previously graduated from Wuhan University, where he developed a strong foundation in AI and computational sciences. Yangyang has contributed to significant research projects, including the Collaborative Innovation Major Project for Industry, University, and Research. His work, “LVMUM: Toward Open-World Object Detection with Large Vision Models and Unsupervised Modeling,” has gained notable citations. Passionate about AI advancements, he actively participates in academic collaborations and professional memberships, contributing to AI-driven innovations.

Profile

Education šŸŽ“

Yangyang Huang completed his undergraduate studies at Wuhan University, where he gained expertise in artificial intelligence and computational sciences. Currently, he is pursuing his Ph.D. at the School of Computer Science and Engineering, South China University of Technology (SCUT), Guangzhou, China. His doctoral research focuses on large vision models, unsupervised modeling, and object detection. He has been involved in cutting-edge AI research, particularly in deep learning and computer vision. His academic journey has been marked by significant contributions to AI-driven innovations, leading to multiple publications in high-impact journals. Yangyang actively collaborates with researchers in academia and industry, further strengthening his expertise in AI and machine learning applications.

Experience šŸ‘Øā€šŸ«

Yangyang Huang has extensive research experience in artificial intelligence, computer vision, and large models. As a Ph.D. student at SCUT, he has been involved in the Collaborative Innovation Major Project for Industry, University, and Research. His research contributions include developing large vision models for open-world object detection, leading to highly cited publications. Yangyang has also participated in consultancy and industry projects, applying AI techniques to real-world problems. He has authored several journal articles indexed in SCI and Scopus and has contributed to the academic community through editorial roles. His collaborative research efforts have led to impactful AI advancements, making him a rising scholar in the field of AI and machine learning.

Research Interests šŸ”¬

Yangyang Huang’s research primarily focuses on artificial intelligence, computer vision, and large models. His recent work, “LVMUM: Toward Open-World Object Detection with Large Vision Models and Unsupervised Modeling,” explores novel AI techniques for enhancing object detection capabilities. He specializes in deep learning, unsupervised learning, and AI-driven automation. His research interests include developing robust AI models for real-world applications, advancing AI ethics, and improving AI interpretability. Yangyang actively collaborates with academia and industry to bridge the gap between theoretical AI research and practical applications. His contributions extend to consultancy projects, AI innovation, and scholarly publications, making him a key contributor to AI advancements. šŸš€

Awards & Recognitions šŸ…

Yangyang Huang has received recognition for his outstanding contributions to artificial intelligence and computer vision. His research on large vision models and open-world object detection has been widely cited, earning him academic recognition. He has been nominated for prestigious research awards, including Best Researcher Award and Excellence in Research. His work in AI has been acknowledged through various grants and funding for industry-academic collaborative projects. Yangyang’s active participation in international conferences has led to best paper nominations and accolades for his innovative contributions. He is a member of esteemed professional organizations, further cementing his reputation as an emerging AI researcher.

Publications šŸ“š

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

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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 masterā€™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. šŸ“ššŸŽ“

Profile

Education šŸŽ“

  • PhD (2015): Telecom ParisTech, France ā€“ Signal and Image Processing šŸŽ“
  • Masterā€™s Studies (2008-2011, uncompleted): Wuhan University ā€“ Signal and Information Processing šŸ«
  • Bachelorā€™s (2004-2008): Wuhan University ā€“ Electronic Science and Technology (Engineering) šŸŽ“

Experience šŸ‘Øā€šŸ«

  • 2015-2018: Postdoctoral Researcher, INRIA, France šŸ‡«šŸ‡·
  • 2015: Postdoctoral Researcher, Telecom ParisTech, France šŸŽ“

Research Interests šŸ”¬

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. šŸš€šŸŒ

Awards & Recognitions šŸ…

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. šŸŒŸšŸ…

Publications šŸ“š

Vikas Palekar | Machine Leaning | Best Researcher Award

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Mr. Vikas Palekar | Machine Leaning | Best Researcher Award

 

Profile

Education

He is currently pursuing a Ph.D. in Computer Science and Engineering at Vellore Institute of Technology, Bhopal, Madhya Pradesh, since December 2018. His research focuses on developing an Adaptive Optimized Residual Convolutional Image Annotation Model with a Bionic Feature Selection Strategy. He holds a Master of Engineering (M.E.) in Information Technology from Prof. Ram Meghe College of Engineering Technology and Research, Badnera (SGBAU Amravati), which he completed in December 2012 with an impressive 88.00%, securing the first merit position in the university for the summer 2012 examination. Prior to that, he earned a Bachelor of Engineering (B.E.) in Computer Science and Engineering from Shri Guru Gobind Singhji Institute of Engineering Technology and Research, Nanded (SRTMNU, Nanded), in June 2007, achieving a commendable 74.40%.

Work experience

He is currently working as an Assistant Professor in the Department of Computer Engineering at Bajaj Institute of Technology, Wardha, since July 31, 2023. In addition to his teaching responsibilities, he serves as the Academic Coordinator of the department and has worked as a Senior Supervisor for the DBATY Winter-23 Exam at Government College of Engineering, Yavatmal.

Previously, he worked as an Assistant Professor (UGC Approved, RTMNU, Nagpur) in the Department of Computer Science and Engineering at Datta Meghe Institute of Engineering, Technology & Research, Wardha, from June 14, 2011, to June 30, 2023. During this tenure, he held the position of Head of the Department from April 21, 2016, to June 30, 2023. He taught various subjects, including Distributed Operating Systems, TCP/IP, System Programming, Data Warehousing and Mining, Artificial Intelligence, and Computer Architecture and Organization. Additionally, he contributed to university examinations as the Chief Supervisor in the Winter-2015 Examination and a committee member for the Summer-2013, Summer-2015, and Summer-2018 Examinations. He also played a key role in institutional development as a member of the Admission Committee, NBA & NAAC core committees at the department level, and as the convener of the National Level Technical Symposium “POCKET 16” organized by the CSE Department on March 16, 2016.

Earlier in his career, he served as an Assistant Professor in the Department of Computer Engineering at Bapurao Deshmukh College of Engineering, Wardha, from November 26, 2008, to April 30, 2011. He taught subjects such as Unix and Shell Programming, Object-Oriented Programming, and Operating Systems while also serving as a Department Exam Committee Member.

Achievement

He was the first university topper (merit) in M.Tech (Information Technology) and received the Best Paper Award at the 2021 International Conference on Computational Performance Evaluation (ComPE), organized by the Department of Biomedical Engineering, North Eastern Hill University (NEHU), Shillong, Meghalaya, India, from December 1st to 3rd, 2023. He has actively participated in various conferences, including presenting the paper “Label Dependency Classifier using Multi-Feature Graph Convolution Networks for Automatic Image Annotation” at ComPE 2021 in Shillong, India. He also presented his research on “Visual-Based Page Segmentation for Deep Web Data Extraction” at the International Conference on Soft Computing for Problem Solving (SocProS 2011) held from December 20-22, 2011. Additionally, he contributed to the Computer Science & Engineering Department at Sardar Vallabhbhai National Institute of Technology, Surat, by presenting “A Critical Analysis of Learning Approaches for Image Annotation Based on Semantic Correlation” from December 13-15, 2022. His work on “A Survey on Assisting Document Annotation” was featured at the 19th International Conference on Hybrid Intelligent Systems (HIS) at VIT Bhopal University, India, from December 10-12, 2022. Furthermore, he co-authored a study titled “Review on Improving Lifetime of Network Using Energy and Density Control Cluster Algorithm,” which was presented at the 2018 IEEE International Students’ Conference on Electrical, Electronics, and Computer Science (SCEECS) in Bhopal, India.

 

Publication

Mahmoud Alimoradi | Machine Learning | Best Researcher Award

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Mr. Mahmoud Alimoradi | Machine Learning | Best Researcher Award

Lahijan Azad ,Iran

He understands the growing need for Machine Learning and has a keen interest in the field, which he considers a blessing. Recognizing the importance of managing large and complex computations to control various aspects of the human environment has led him into this vast world. He is particularly fascinated by machine learning, especially reinforcement learning, supervised learning, semi-supervised learning, outliers, and basic data challenges. Furthermore, optimization, an area of artificial intelligence that requires fundamental studies and a change in approach, is another of his key research interests.

Profile

Education

He holds a Masterā€™s degree in Artificial Intelligence Engineering from the University of Shafagh, completed in 2020. His thesis was titled “Trees Social Relations Optimization Algorithm: A New Swarm-Based Metaheuristic Technique to Solve Continuous and Discrete Optimization Problems.” He also earned a Bachelorā€™s degree in Software Engineering from Azad Lahijan University, which he attended from 2007 to 2011.

Research Interests

Theory: Reinforcement Learning (high-dimensional problems, regularized algorithms, model
learning,
representation learning and deep RL, learning from demonstration, inverse optimal control, deep
Reinforcement Learning); Machine Learning (statistical learning theory, nonparametric
algorithms, time series. processes, manifold learning, online learning); Large-scale Optimization;
Evolutionary Computation, Metaheuristic Algorithm, Deep Learning, Healthcare Machine
learning, Big Data, Data Problems (Imbalanced), Signal Analysis
Applications: Automated control, space affairs, robotic control, medicine and health, asymmetric
data, data science, scheduling, proposing systems, self-enhancing systems

Work Experience

He is a freelance programmer with expertise in various operating systems, including Microsoft Windows and Linux (Arch, Ubuntu, Fedora). He is proficient in software tools such as Microsoft Office, Anaconda, Jupyter, PyCharm, Visual Studio, Tableau, RapidMiner, MATLAB, and Visual Studio. His programming skills include Matlab, Python, C++, Scala, Java, and Julia, with a focus on data mining, data science, computer vision, and machine learning. He is experienced with Python libraries like Pandas, Numpy, Matplotlib, Seaborn, PyCV, TensorFlow, Time Series Analysis, Spark, Hadoop, and Cassandra. Additionally, he is skilled in using Github, Docker, and MySQL. His expertise spans machine learning, deep learning, imbalanced data, missing data, semi-supervised learning, healthcare machine learning, algorithm design, and metaheuristic algorithms. He is fluent in English and Persian.

Publications