Assoc. Prof. Dr. Ioana-Raluca Adochiei | Real-Time | Research Excellence Award

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Assoc. Prof. Dr. Ioana-Raluca Adochiei | Real-Time | Research Excellence Award

Assoc. Prof. Dr. Ioana-Raluca Adochiei | Real-Time | Military Technical Academy Ferdinand I | Romania

Assoc. Prof. Dr. Ioana-Raluca Adochiei is an accomplished Romanian academic and researcher based in Bucharest, Romania, with extensive expertise in aerospace engineering, navigation systems, and military technologies, currently serving as Associate Professor, PhD Engineer in the Department of Aircraft Integrated Systems and Mechanics within the Faculty of Aircraft and Military Vehicles at the Military Technical Academy “Ferdinand I”, a position reflecting long-standing academic leadership and dedication to research and education. Assoc. Prof. Dr. Ioana-Raluca Adochiei completed doctoral-level training in engineering with advanced research specialization developed through international academic exposure, including European research internships and Erasmus teaching mobility at Jena University of Applied Sciences in Germany, where strong foundations in signal processing, micro- and nano-sensors, inertial navigation, and intelligent control systems were established and refined. Assoc. Prof. Dr. Ioana-Raluca Adochiei has held key roles as Project Director and researcher engineer in multiple nationally funded strategic research initiatives coordinated by leading Romanian defense and aerospace institutions, including the Academy of Scientists of Romania and the Military Technical Academy “Ferdinand I”, while also representing Romania as a responsible expert in NATO-aligned European Defence Agency working groups focused on Guidance, Navigation, and Control; professional activities span project leadership, systems engineering, management team participation, and interdisciplinary collaboration in security, aerospace observation, GNSS-denied navigation, UAS-based public health support, optical sensor stabilization platforms, and emergency medical technology development.

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Featured Publications


Cardiovascular and Cardiorespiratory Coupling Analyses: A Review


– Phil. Trans. Royal Society A, 2013 · 219 citations


Electronic System for Real-Time Indoor Air Quality Monitoring


– E-Health and Bioengineering Conference, 2020 · 36 citations


A New Normalised Short-Time PDC for Dynamic Coupling Analyses


– Biomedical Engineering, 2013 · 28 citations


Concepts for Error Modeling of Miniature Accelerometers Used in Inertial Navigation Systems


– Annals of the University of Craiova, 2010 · 26 citations

Mr. Ranran Wang | Artificial Intelligence | Research Excellence Award

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Mr. Ranran Wang | Artificial Intelligence | Research Excellence Award 

Mr. Ranran Wang | Artificial Intelligence | Shandong Agricultural University | China

Mr. Ranran Wang is an accomplished PhD scholar, associate professor, IEEE member, and seasoned academic in mechanical, electronic, and agricultural engineering, widely recognized for his contributions to intelligent detection systems, agricultural mechanization, precision management technologies, and integrated engineering innovations. With a strong educational foundation beginning with a Bachelor of Science in Electrical Engineering from Shandong University of Technology, followed by a master’s degree from the School of Electrical Engineering at Shandong University, and a PhD in Mechanical and Electronic Engineering from Shandong Agricultural University, Wang Ranran further expanded his expertise through postdoctoral research at the Plant Protection Postdoctoral Research Station and international academic collaboration as a visiting scholar at Iwate University in Japan. Professionally, Wang Ranran has maintained a long-standing academic role at the College of Mechanical and Electronic Engineering at Shandong Agricultural University, where he has contributed to teaching, research, academic evaluation, scientific leadership, and interdisciplinary innovation. He has served as a reviewer for multiple journals, a mentor for elite engineering talent programs, an expert reviewer for investment projects, and a key member of numerous provincial and municipal scientific committees. His professional service includes roles such as expert reviewer for forestry, agricultural engineering, water and fertilizer integration facilities, rural revitalization, electric power industry development, and technological innovation alliances, as well as leadership positions including technology commissioner, science and technology mayor, and vice chairman in provincial agricultural technology extension associations.

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Featured Publications

Assoc. Prof. Dr. Suhail Odeh | Intelligent Systems | Research Excellence Award

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Assoc. Prof. Dr. Suhail Odeh | Intelligent Systems | Research Excellence Award

Assoc. Prof. Dr. Suhail Odeh | Intelligent Systems | Bethlehem University | Palestine

Assoc. Prof. Dr. Suhail Odeh is an accomplished academic and researcher from Bethlehem, Palestine, holding dual Palestinian and Jordanian nationality, with extensive expertise in software engineering, artificial intelligence, physics, and intelligent systems. He earned a PhD in Computer Engineering from Universidad de Granada in Spain, where his doctoral work focused on developing intelligent systems for medical classification and diagnosis, supported by a graduate diploma in computer engineering and a strong science foundation built through an MSc in Physics and a BSc in Physics and Electronic Technology from Al-Quds University. Throughout his career, Suhail Musa Odeh has held significant academic roles, including Associate Professor at Bethlehem University, Chair of the Software Engineering Department, Assistant Professor in Computer and Information Systems, visiting professor at the University of Cyprus, research visitor at the University of Sherbrooke, and post-doctoral researcher at L’Aquila University and Granada University, where he contributed to advancements in intelligent agents, multi-agent control environments, and autonomous behavior modeling. His earlier professional experience includes teaching at Palestine Technical College, the Palestine Polytechnic University, and Beit Sahour Secondary School, as well as conducting physics research at Al-Quds University.

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 Featured Publications


A Hybrid Fuzzy Genetic Algorithm for an Adaptive Traffic Signal System

– Advances in Fuzzy Systems, 2015 (49 citations)


Off-line Signature Verification and Recognition: Neural Network Approach

– International Symposium on Innovations in Intelligent Systems, 2011 (40 citations)


Management of an Intelligent Traffic Light System by Using Genetic Algorithm

– Journal of Image and Graphics, 2013 (37 citations)


A Comparison of Classification Methods as Diagnostic System: A Case Study on Skin Lesions

– Computer Methods and Programs in Biomedicine, 2016 (30 citations)

Mrs. Guoqin Chang | AI Security | Excellence in Research Award

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Mrs. Guoqin Chang | AI Security | Excellence in Research Award 

Mrs. Guoqin Chang | AI Security | Shaanxi Science and Technology Holding Institute | China

Mrs. Guoqin Chang holds a Ph.D. in Cyberspace Security from the School of Cybersecurity at Xidian University and currently leads the Artificial Intelligence Research Group at Shaanxi KeKong Technology Industry Research Institute, while also serving as Technical Advisor to the Shaanxi Printing Science and Technology Research Institute. Over her career, Chang Guoqin has built expertise in natural language processing (NLP), domain-specific large language model deployment, prompt engineering, fine-tuning and adaptation of models, AI model security, detection of text generated by large language models, adversarial text attacks/defenses, and multimodal information fusion. She is skilled in designing and evaluating robust NLP pipelines, adversarial-robust model architectures, secure model deployment, domain adaptation techniques, and text classification systems with security-aware defenses. Her professional experience spans research and development, project leadership, and peer-reviewing for major journals including Information Sciences and Computer Engineering and Design, and she has contributed to both academic publications and applied software/patents through her inventive activity. Through her publication record, patent filings, software copyrights, and R&D project involvement, Chang Guoqin has demonstrated a strong commitment to improving the security, robustness, and domain-adaptability of modern AI. In conclusion, Chang Guoqin brings together advanced academic training, deep technical skills in NLP security and model adaptation, and real-world engineering experience  positioning her as a leading researcher/engineer in secure, domain-specific artificial intelligence and robust natural language processing solutions.

Professional Profiles: ORCID | Google scholar

Selected Publications 

  • Chang, G., Gao, H., Yao, Z., & Xiong, H. (2023). TextGuise: Adaptive adversarial example attacks on text classification model.

  • Chang, G., Gao, H., Pei, G., … & Guo, Q. (2024). The robustness of behavior-verification-based slider CAPTCHAs. Journal of Information Security and Applications.

  • Cheng, L., Zhang, Z., & Chang, G. (2019). Multimedia Social Network Authorization Scheme of Comparison-based Encryption.

  • Cheng, N., Chang, G., & Gao, H. (2020). WordChange: Adversarial Examples Generation Approach for Chinese Text Classification.

  • Chang, G., Gao, H., & Li, B. (2025). TextShelter: Text Adversarial Example Defense Based on Input Reconstruction.

  • Chang, G., & colleagues. (2019). A Survey of Research on CAPTCHA Designing and Breaking Techniques.

  • Cheng, N., Chang, G., Gao, H., … & Zhang, Y. (2020). WordChange: Adversarial Examples Generation Approach for Chinese Text Classification.

Prof. Dr. Gennaro Trancone | Waste Bioconversion | Best Scholar Award

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Prof. Dr. Gennaro Trancone | Waste Bioconversion | Best Scholar Award

Prof. Dr. Gennaro Trancone | Waste Bioconversion | University of Naples Federico II | Italy

Prof. Dr. Gennaro Trancone earned a Ph.D. in Civil Systems Engineering from the University of Naples “Federico II” after earlier studies in Environmental and Land Engineering and Civil & Environmental Engineering. Over his career, Gennaro Trancone has built strong expertise in environmental biotechnology, waste-to-energy systems, wastewater and sludge treatment, waste valorization, and sustainable civil/environmental engineering practices. In his academic role at the University of Naples “Federico II”, he lectures and leads courses in sanitary and environmental engineering, waste management, contaminated-site remediation and soil bioengineering; he also functions as subject expert in environmental engineering at other Italian universities. His research interests include anaerobic digestion and dark fermentation, biofilm reactors, biogas production, construction and demolition waste valorization (e.g. concrete waste treatment), treatment of sewage sludge, phosphorus recovery, marine/coastal environmental monitoring and contaminant mobility, and circular bioeconomy approaches to waste and resource management. Gennaro Trancone is skilled in environmental engineering methodologies such as biological leaching, chemical treatment of waste, adsorption/adsorbent design, biofilm reactor design and operation, waste-to-energy system modelling, sewage-sludge processing and resource recovery, contaminated-site risk assessment, and sediment & marine pollution analysis. Through his publications and collaborations, he has contributed to improving sustainable waste treatment and circular economy practices. In conclusion, Gennaro Trancone combines rigorous academic training, hands-on research in environmental biotechnology and waste management, and a commitment to sustainable engineering making him a capable leader in advancing eco-friendly, circular solutions for waste, resource recovery, and environmental protection.

Professional Profiles: ORCID | Scopus

Selected publications 

  • Trancone, G. (2025). Optimization of NaOH Chemical Treatment Parameters for Biomass-Based Adsorbents in Cationic Dye Removal Processes. Process Research & Development.

  • Achouri, O., Bianco, F., Trancone, G., & Race, M. (2025). A critical review of anaerobic biofilm reactors for the renewable biogas production from food waste. Journal of Environmental Chemical Engineering.

  • Trancone, G., Policastro, G., Spasiano, D., Race, M., Parrino, F., Fratino, U., Fabbricino, M., & Pirozzi, F. (2025). Treatment of concrete waste from construction and demolition activities: Application of organic acids from continuous dark fermentation in moving bed biofilm reactors. Chemical Engineering Journal.

  • Ferraro, A., Marino, E., Trancone, G., Race, M., Mali, M., Pontoni, L., Fabbricino, M., & Spasiano, D. (2023). Assessment of environmental parameters effect on potentially toxic elements mobility in foreshore sediments to support marine-coastal contamination prediction. Marine Pollution Bulletin.

  • Veropalumbo, R., Oreto, C., Viscione, N., Pirozzi, F., Pontoni, L., Trancone, G., Race, M., & Russo, F. (2023). Exploring the effect on the environment of encapsulated micro- and nano-plastics into asphalt mastics for road pavement. Environmental Research.

  • Ferraro, A., Panico, A., Pirasteh-Anosheh, H., Race, M., Spasiano, D., Trancone, G., & Pirozzi, F. (2023). Innovative treatment processes for emerging contaminants removal from sewage sludge. Handbook of Environmental Chemistry.

  • Ferraro, A., de Sario, S., Attanasio, A., …, Spasiano, D., Trancone, G., et al. (2023). Phosphorus recovery as struvite and hydroxyapatite from the liquid fraction of municipal sewage sludge with limited magnesium addition.

Prof. Achim Lilienthal | LiDAR Perception | Research Excellence Award

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Prof. Achim Lilienthal | LiDAR Perception | Research Excellence Award 

Prof. Achim Lilienthal | LiDAR Perception | Technical University of Munich | Germany

Prof. Dr. Achim Josef Lilienthal earned his Ph.D. in Computer Science at the University of Tübingen (summa cum laude), following a diploma in Physics from the University of Konstanz. Over his distinguished career, Achim Josef Lilienthal has held leading academic and research-oriented roles across Europe and beyond, including his current appointment as Full Professor (W3), Chair of Perception for Intelligent Systems at the Technical University of Munich (TUM), and Deputy Director at the Munich Institute of Robotics and Machine Intelligence (MIRMI). He previously served as full professor at Örebro University, founding and directing the Mobile Robotics & Olfaction Lab, and has been a visiting professor at institutions such as Cornell University, as well as a consultant for industry including Bosch and robotics startups. His research interests span mobile robot olfaction (gas distribution mapping and gas source localization), 3D perception and SLAM (LiDAR/radar mapping, sensor fusion, localization), human–robot interaction (intention recognition, human-aware navigation), and eye-tracking–based AI systems for human–robot interaction, driver monitoring, and safety applications. He is skilled in robotics, sensor fusion, 3D mapping, machine learning, perception algorithms, gas-sensing robotics, human-robot interface design, and systems integration enabling development of autonomous intelligent systems capable of robust perception in complex, dynamic environments. Through a prolific publication record of over 300 peer-reviewed papers, supervision of 50+ PhD students, leadership of major EU research projects and startups, and securing substantial research funding, Achim Josef Lilienthal has made foundational contributions to the robotics and intelligent systems community  particularly gas-sensing robotics, safe human-machine interaction, and autonomous perception.

Professional Profiles: ORCID | Google Scholar | Scopus

Selected publications 

  • Lilienthal, A. J., & Duckett, T. (2007). Scan registration for autonomous mining vehicles using 3D-NDT. Journal of Field Robotics, 24(10), 803–827. (Citations: 951)

  • Triebel, R., Arras, K., Alami, R., Beyer, L., Breuers, S., Chatila, R., Chetouani, M., … & Lilienthal, A. J. (2016). Spencer: A socially aware service robot for passenger guidance and help in busy airports. Field and Service Robotics: Results of the 10th International Conference. (Citations: 364)

  • Magnusson, M., Nüchter, A., Lorken, C., & Lilienthal, A. J. (2009). Evaluation of 3D registration reliability and speed – A comparison of ICP and NDT. IEEE International Conference on Robotics and Automation (ICRA). (Citations: 348)

  • Valgren, C., & Lilienthal, A. J. (2010). SIFT, SURF & Seasons: Appearance-based Long-term Localization in Outdoor Environments. Robotics and Autonomous Systems, 58(2), 149–156. (Citations: 314)

  • Stoyanov, T., Magnusson, M., Andreasson, H., & Lilienthal, A. J. (2012). Fast and Accurate Scan Registration through Minimization of the Distance between Compact 3D NDT Representations. International Journal of Robotics Research, 31(12), 1377–1393. (Citations: 303)

  • Neumann, P. P., Hernández Bennetts, V., & Lilienthal, A. J. (2013). Gas source localization with a micro-drone using bio-inspired and particle filter-based algorithms. Advanced Robotics, 27(9), 725–738. (Citations: 270)

  • Lilienthal, A. J., & Duckett, T. (2004). Building gas concentration gridmaps with a mobile robot. Robotics and Autonomous Systems, 48(1), 3–16. (Citations: 210)

Dr. Hamid Reza Taheri | Optimization | Top Researcher Award

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Dr. Hamid Reza Taheri | Optimization | Top Researcher Award 

Dr. Hamid Reza Taheri | Optimization | Babol Noshirvani University of Technology (BNUT) | Iran

Dr. Hamid Reza Taheri holds a PhD (Fuel Cells / PEMFC) from BNUT and is currently working as a Manufacturing & Production Engineer. Over his career, Dr. Taheri has developed deep expertise in modeling and optimization of gas diffusion layers (GDL) and microporous layers (MPL) for polymer electrolyte membrane fuel cells. His professional experience includes research and teaching at BNUT, where he has collaborated with colleagues (e.g., Dr. Mohsen Shakeri) on multiple studies. His research interests center around the structural, thermal, electrical and mechanical properties of GDL/MPL in PEMFCs, particularly: optimizing geometrical and material parameters (porosity, fiber orientation, thickness, composition) to improve effective thermal conductivity (ETC), electrical conductivity, mechanical strength, and water/gas transport in fuel cells. He is skilled in computational modeling tools such as Python, MATLAB, COMSOL Multiphysics (via live-link), AutoCAD, SolidWorks, Abaqus, Inventor, and familiar with Microsoft Office suite for data processing and reporting. His work often uses multi-objective optimization methods (e.g., genetic algorithms) along with realistic microstructure simulations to yield recommendations for manufacturing GDL/MPL with superior performance. Over the years, Dr. Taheri has contributed substantially to advancing understanding of how microscopic structure affects macroscopic PEMFC performance. His recent publications demonstrate his leading role in GDL/MPL research. In recognition of this work, he has published in high-impact peer-reviewed journals (e.g., Journal of Power Sources, International Journal of Thermal Sciences) and presented at international conferences (e.g., ICHP). In conclusion, Dr. Hamid Reza Taheri brings together strong academic training, advanced computational and engineering skills, and focused research in PEM fuel cell diffusion media — making him a valuable contributor in the field of clean energy/materials research.

Professional Profiles: ORCID

Selected publications

  • Taheri, H. R., & Shakeri, M. (2025). Enhancing electrical conductivity and mechanical strength of gas diffusion layers through multi-objective optimization. Journal of Power Sources.

  • Taheri, H. R., & Shakeri, M. (2024). Geometrical parameters optimization to improve the effective thermal conductivity of the gas diffusion layer for PEM fuel cell. International Journal of Thermal Sciences.

  • Taheri, H. R., & Shakeri, M. (2023). Fast Modeling Method of Gas Diffusion Layers of Polymer Electrolyte Membrane Fuel Cells.

Ms. Raghi K R | Federated Learning | Research Excellence Award

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Ms. Raghi K R | Federated Learning | Research Excellence Award 

Ms. Raghi K R | Federated Learning | Sathyabama Institute of Science and Technology | India

Mrs. Raghi K.R. is a dedicated computer science educator and researcher with strong experience in both teaching and applied research. She holds a B.E. in Computer Science and Engineering (Anna University), an M.E. in Computer Science and Engineering (PSN Engineering College / Anna University), and has submitted her Ph.D. synopsis in Computer Science and Engineering at College of Engineering, Guindy, Anna University. Her professional journey includes roles as Assistant Professor and Teaching Fellow across several institutions: PSN Engineering College (CSE), College of Engineering Guindy, and currently at St. Joseph’s College of Engineering, Chennai giving her over a decade of teaching experience, spanning undergraduate and postgraduate courses. Her research interests lie in Artificial Intelligence, Deep Learning, Machine Learning, Cloud Security, and Web Mining. She possesses robust technical skills including programming in Python, Java, C, C++, web technologies (HTML), and experience with simulation platforms such as NS2 and MATLAB, as well as familiarity with open-source operating systems. Through her career she has mentored and guided multiple innovative and funded projects — for example leading a seed-research project titled “StepSmart: Design and Development of an Affordable IoT and Deep Learning Based Footwear for Diabetic Health Monitoring,” and supervising award-winning student projects such as “Trash Triage: Trailblazing Waste Management with Real-Time Street Waste Monitoring” and “Identification of Fake Medicinal Raw Materials Using Machine Learning.” These recognitions reflect her commitment to practical, socially relevant research. Her academic repertoire includes teaching diverse subjects like Artificial Intelligence, Mobile Computing, Information Security, Web Programming, Soft Computing, Software Project Management, Cyber Security, Web Technology, and more. She has also supervised substantial academic work: a Ph.D. thesis on “Privacy-Preserving Deep NN Classification over Signature Cryptosystem in Cloud Environments,” a secure payment-scheme design in multihop networks, and semantic similarity computation for natural language. As a scholar and mentor, Mrs. Raghi combines strong theoretical foundations with hands-on applied research, striving to develop secure, intelligent, and socially impactful computing solutions. Her involvement in both education and research along with project funding, awards, and diverse technical competencies — positions her as a proactive and forward-looking professional in the fields of AI, cybersecurity, and data-driven intelligent systems, committed to nurturing student talent and contributing to technological advancement.

Professional Profiles: ORCID | Google Scholar 

Selected Publications

  1. Thomas, R. K. L., Sanjay, G. J., Pandeeswaran, C., & Raghi, K. R. (2024). Advanced CCTV Surveillance Anomaly Detection, Alert Generation and Crowd Management using Deep Learning Algorithm.

  2. Vethavikashini, A. M., Jamal, S. M., & Raghi, K. R. (2024). Huntington’s Disease Prediction Using Xception CNN.

  3. Devi, S. R., Geetha Priya, S., Sathi, G., Naveen Kumar, S., Dinesh, M., & Raghi, K. R. (2024). Design and Development of a Touch Free Smart Home Controlling System Based on Virtual Reality (VR) Technology.

  4. Raghi, K. R., Sudha, K., Sreeram, A. M., Steve Joshua, S. (2024). Software Development Automation Using Generative AI.

  5. Raghi, K. R. (2023). Privacy-Preserving Deep NN Classification over Signature Cryptosystem in Cloud Environments.

  6. Anitha, T., Sai Srihitha, G. R. P. Lakshmi Aiswarya, & Raghi, K. R. (2025). Predictive Modeling of Social Media Data Using Machine Learning Techniques.

  7. (As mentor) StepSmart: Design and Development of an Affordable IoT and Deep Learning Based Footwear for Diabetic Health Monitoring.

Prof. Dr. Gabor Kiss | Sensitivity | Research Excellence Award

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Prof. Dr. Gabor Kiss | Sensitivity Awards | Research Excellence Award

Prof. Dr. Gabor Kiss | Sensitivity | Obuda University | Hungary

Dr. Gábor Kiss is a Hungarian computer scientist and safety/security researcher whose academic journey began with a degree in computer science (Ing., Bánki Donát Technical College), followed by teacher qualification and a PhD in Mathematics and Computer Science from University of Debrecen, culminating in habilitation in Safety and Security Science at Óbuda University. Over more than three decades he rose through the academic ranks — from institute engineer to assistant lecturer in the early 1990s, senior lecturer and then associate professor, and currently full professor. Alongside academia he led a software-development enterprise (Druida LP), building registry, billing, and work-recording systems for various companies. His research spans computer science education, information security and security awareness, smart-device applications in healthcare, and cutting-edge issues around AI in autonomous vehicles and AI in healthcare. In particular, he has explored the safety, reliability and verification of autonomous driving systems (e.g., modeling traffic-node dynamics with fuzzy Petri nets or using entropy-based methods), as well as risks such as cyber-attacks on critical infrastructure (e.g., financial institutions). His skills include formal methods, modeling of dynamic systems, fuzzy logic, security awareness, algorithm and data-structure design, and applied AI. While no major international awards are listed in the publicly available profile, he has achieved habilitation and full professorship, amassed a substantial number of peer-reviewed publications, and contributed to cross-national and interdisciplinary education (Hungary, Slovakia, Germany). In conclusion, Dr. Kiss represents a seasoned researcher and educator whose career bridges foundational computer science, applied security research, and emerging challenges in AI-driven autonomous systems — a profile of sustained scholarly contribution, practical software engineering, and a forward-looking research agenda.

Professional Profiles: ORCID | Scopus

Selected Publications

  • Kiss, G., & Bakucz, P. (2024). Using Kolmogorov Entropy to Verify the Description Completeness of Traffic Dynamics of Highly Autonomous Driving. Applied Sciences, 14(6), 2261. Cited by 4.

  • Kiss, G., & Bakucz, P. (2024). Fuzzy Petri Nets for Traffic Node Reliability. Sensors, 24(19), 6337. Cited by 1.

  • Gulyás, O., & Kiss, G. (2023). Impact of cyber-attacks on the financial institutions.

  • Kiss, G. (2022). How to impede the external manipulation of autonomous cars?

  • Kiss, G. (2020). Manchurian artificial intelligence in autonomous vehicles.

  • Torres-Gastelú, C. A., & Kiss, G. (2016). Perceptions of Students towards ICT Competencies at the University. Informatics in Education, 15(2), 319–338. Cited by 69.

  • Kiss, G., & Árki, Z. (2017). The influence of game-based programming education on the algorithmic thinking. Procedia – Social and Behavioral Sciences, 237, 613–617. Cited by 53.

Mr. Carlos Rodrigo Paredes Ocranza | Affective Computing | Artificial Intelligence Research Awards

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Mr. Carlos Rodrigo Paredes Ocranza | Affective Computing | Artificial Intelligence Research Awards 

Mr. Carlos Rodrigo Paredes Ocranza | Affective Computing | Zhejiang University of science and Technology | China

Mr. Carlos Rodrigo Paredes Ocranza is a researcher and practitioner whose background spans applied statistics, machine learning, affective computing, and interdisciplinary creative arts. He holds a Master’s in Applied Statistics specialized in Artificial Intelligence from Zhejiang University of Science and Technology, Hangzhou, China, and has also studied at Universidad Autónoma Metropolitana, Mexico City. His academic training includes advanced courses and professional training in stochastic processes, multivariate statistical analysis, big data, graph theory, machine learning, as well as applied mathematics, statistical theory, and academic research essay writing. Over time, Carlos has acquired a diverse skill set: in programming (Python, SPSS Studio, GitHub, Visual Studio, HTML, JSON), in ML frameworks such as TensorFlow, Keras, and Scikit-learn, as well as in digital creation skills including drawing, painting, photo editing, digital illustration, and audio/music production (guitar, bass, drums, composition, singing, recording). He is also adept at using social-media suites, word processors, and comfortable with on-camera presence and communication, reflecting “excellent verbal and written communication skills.” On the professional side, Carlos has experience managing projects from inception to completion, demonstrating strong analytical and problem-solving abilities, adaptability to new situations, and solid teamwork skills. His research interests lie in affective computing and brain-computer interface (BCI) applications especially using consumer-grade EEG (or fNIRS) biosignal data for emotion recognition and other real-world problems. This interdisciplinary approach aims to optimize and adapt BCI technologies for practical use outside lab settings. His recent publication in shows that traditional machine-learning methods can outperform a standard deep-learning architecture (EEGNet) for emotion recognition using consumer-grade EEG sensors, arguing for feature engineering and domain-specific adaptations when working with noisy, low-cost EEG data.

Professional Profiles: ORCID  

Selected Publications

Paredes Ocaranza, C. R., Yun, B., & Paredes Ocaranza, E. D. (2025). Traditional Machine Learning Outperforms EEGNet for Consumer-Grade EEG Emotion Recognition: A Comprehensive Evaluation with Cross-Dataset Validation