Zhang Zhenqian | Neuroscience | Best Researcher Award

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Mr. Zhang Zhenqian | Neuroscience | Best Researcher Award

University of Toyama | Japan

Mr. Zhang Zhenqian is a dedicated researcher whose work bridges artificial intelligence, machine learning, and meteorology, with an emphasis on developing advanced neural network models for predictive analytics. His recent publication, “RD2: Reconstructing the Residual Sequence via Under Decomposing and Dendritic Learning for Generalized Time Series Predictions,” featured in Neurocomputing (October 2025), showcases his innovative approach to enhancing time series forecasting accuracy through the integration of dendritic learning mechanisms and residual sequence reconstruction. Collaborating with Houtian He, Zhenyu Lei, Zihang Zhang, and Shangce Gao, Mr. Zhang contributes to advancing the computational intelligence field by addressing challenges in dynamic data modeling and predictive reliability. His research explores the intersection of data-driven modeling and environmental systems, offering valuable insights for improving real-world forecasting, particularly in meteorological and environmental applications. With a growing scholarly presence and contributions recognized through peer-reviewed international publications, Mr. Zhang exemplifies a new generation of researchers committed to interdisciplinary innovation. His work not only strengthens the theoretical foundations of artificial intelligence but also demonstrates its transformative potential in understanding and managing complex natural and engineered systems.

Profile : Orcid

Featured Publication

Zhang, Z., He, H., Lei, Z., Zhang, Z., & Gao, S. (2025). RD2: Reconstructing the residual sequence via under decomposing and dendritic learning for generalized time series predictions. Neurocomputing, 131867.

Simy Baby | Engineering | Best Researcher Award

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Mrs. Simy Baby | Engineering | Best Researcher Award

National Institute of Technology | India

Mrs. Simy Baby is an emerging researcher whose scholarly contributions center on semantic communications, machine learning, and computer vision, with a strong emphasis on communication-efficient feature extraction for edge inference tasks. She has authored 2 documents, received 2 citations, and holds an h-index of 1, reflecting the growing impact of her research in advanced communication technologies. Her publications in SCI-indexed journals, including Elsevier’s Computers & Electrical Engineering and IEEE Transactions on Cognitive Communications and Networking, demonstrate her commitment to innovation and excellence. Her study, “Complex Chromatic Imaging for Enhanced Radar Face Recognition”, introduced a novel complex-valued representation preserving amplitude and phase information of mmWave radar signals, achieving 99.7% recognition accuracy. Another major contribution, “Complex-Valued Linear Discriminant Analysis on mmWave Radar Face Signatures for Task-Oriented Semantic Communication”, proposed a CLDA-based encoding framework that improved feature interpretability and robustness under varying channel conditions. Her ongoing projects explore Data Fusion Discriminant Analysis (DFDA) for multi-view activity recognition and Semantic Gaussian Process Regression (GPR) for vehicular pose estimation, advancing the integration of semantic communication and computer vision. Mrs. Simy Baby’s research represents a vital step toward the development of intelligent, efficient, and adaptive communication systems for next-generation technologies.

Profiles : Scopus | Orcid | Google Scholar

Featured Publications

Baby, S. M., & Gopi, E. S. (2025). Complex valued linear discriminant analysis on mmWave radar face signatures for task-oriented semantic communication. IEEE Transactions on Cognitive Communications and Networking.

Baby, S. M., & Gopi, E. S. (2025, April). Complex chromatic imaging for enhanced radar face recognition. Computers and Electrical Engineering.

Diogo Santiago | Computer Science | Best Researcher Award

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Mr. Diogo Santiago | Computer Science | Best Researcher Award

Oracle | Brazil

Mr. Diogo Santiago is a highly accomplished technology professional with extensive experience spanning software engineering, big data, and artificial intelligence. Beginning his career in 2009 as a software engineer developing major e-commerce platforms in Brazil, he transitioned into data engineering and science, mastering technologies like Hadoop, Spark, Hive, and Sqoop for large-scale data processing and migration. Since 2018, he has specialized in data science and AI, contributing to diverse projects in computer vision, anomaly detection, logistics optimization, and generative AI, including GAN and diffusion model applications for virtual try-on systems. As an AI Architect at Oracle for LATAM, he designs advanced AI architectures, supports clients with resource planning, and enhances model deployment efficiency through GPU optimization and large language model serving using vLLM and SGLang. His prior roles at Lambda3, Tivit, and Qintess involved developing ML models, data pipelines, and automation systems using cloud technologies such as GCP, AWS, and OCI. With multiple postgraduate qualifications in Big Data and Machine Learning for Finance, along with a Master’s in Medical Texture Imaging, he exemplifies innovation and leadership in merging AI research with scalable enterprise solutions.

Profile : Orcid

Featured Publication

Adorno, P. L. V., Jasenovski, I. M., Santiago, D. F. D. M., & Bergamasco, L. (2023, May 29). Automatic detection of people with reduced mobility using YOLOv5 and data reduction strategy. Conference paper.

 

Yaqin Wu | Computer Science | Excellence in Research Award

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Ms. Yaqin Wu | Computer Science | Excellence in Research Award

Shanxi Agricultural University | China

Ms. Yaqin Wu is an accomplished researcher and educator specializing in acoustic signal analysis, deep learning, and multimodal information fusion, with a research record reflecting 80 citations across 78 documents, 9 publications, and an h-index of 3. She holds a Master of Engineering in Electronic and Communication Engineering from Tianjin University and a Bachelor’s degree in Communication Engineering from Dalian Maritime University. Currently serving as a full-time faculty member at the School of Software, Shanxi Agricultural University, she teaches courses such as Speech Signal Processing, Natural Language Processing, and Human-Computer Interaction. Ms. Wu has led and contributed to several cutting-edge research projects, including pathological voice restoration, multimodal animal behavior monitoring, and AVS audio codec development. She has authored multiple SCI-indexed papers and holds several patents and software copyrights related to voice signal processing. Her technical proficiency spans Python, MATLAB, Linux systems, and MySQL databases. Notably, her master’s thesis earned the Outstanding Achievement Award of Engineering Master’s Practice from Tianjin University. Through her innovative contributions in signal processing and intelligent systems, Ms. Wu continues to advance the intersection of engineering and artificial intelligence research.

Profiles : Scopus | Orcid

Featured Publications

Zhang, J., Wu, Y., & Zhang, T. (2025). Fusing time-frequency heterogeneous features with cross-attention mechanism for pathological voice detection. Journal of Voice. Advance online publication.

Li, X., Wang, K., Chang, Y., Wu, Y., & Liu, J. (2025). Combining Kronecker-basis-representation tensor decomposition and total variational constraint for spectral computed tomography reconstruction. Photonics, 12(5), 492.

Michele Buzzicotti | Physics and Astronomy | Best Paper Award

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Dr. Michele Buzzicotti | Physics and Astronomy | Best Paper Award

University of Rome Tor Vergata | Italy

Dr. Michele Buzzicotti is an accomplished physicist whose research bridges turbulence modeling, data-driven fluid dynamics, and machine learning applications in complex systems. He has authored 39 scientific documents, accumulating 703 citations across 513 records with an h-index of 17, reflecting his consistent scientific influence in computational physics and atmospheric modeling. Holding a Ph.D. in Physics from the University of Rome Tor Vergata (2017), his doctoral work focused on the effects of Fourier mode reduction in turbulence. Currently serving as a tenure-track faculty member at the Department of Physics, University of Rome Tor Vergata, Dr. Buzzicotti has held visiting research appointments at the Technical University of Eindhoven and the University of Rochester. His projects, such as the €1.3 million Italian Ministry of Research–funded “Data-driven and Equation-based Tools for Complex Turbulent Flows,” showcase his leadership in advancing AI-integrated turbulence studies. His publications in Nature Machine Intelligence, Physical Review Letters, and Europhysics Letters highlight pioneering contributions to stochastic modeling and generative AI for fluid dynamics. A reviewer for top-tier journals and a member of EUROMECH and APS, Dr. Buzzicotti continues to shape the future of theoretical and applied turbulence research through innovative interdisciplinary approaches.

Profiles : Scopus | Orcid | Google Scholar

Featured Publications

Li, T., Biferale, L., Bonaccorso, F., Buzzicotti, M., & Centurioni, L. (2025). Stochastic reconstruction of gappy Lagrangian turbulent signals by conditional diffusion models. Communications Physics.

Freitas, A., Um, K., Desbrun, M., Buzzicotti, M., & Biferale, L. (2025). Solver-in-the-loop approach to closure of shell models of turbulence. Physical Review Fluids.

Martin, J., Lübke, J., Li, T., Buzzicotti, M., Grauer, R., & Biferale, L. (2025). Generation of cosmic-ray trajectories by a diffusion model trained on test particles in 3D magnetohydrodynamic turbulence. The Astrophysical Journal Supplement Series.

Li, T., Tommasi, S., Buzzicotti, M., Bonaccorso, F., & Biferale, L. (2024). Generative diffusion models for synthetic trajectories of heavy and light particles in turbulence. International Journal of Multiphase Flow.

Khatri, H., Griffies, S. M., Storer, B. A., Buzzicotti, M., Aluie, H., Sonnewald, M., Dussin, R., & Shao, A. (2024). A scale‐dependent analysis of the barotropic vorticity budget in a global ocean simulation. Journal of Advances in Modeling Earth Systems.

Li, T., Biferale, L., Bonaccorso, F., Scarpolini, M. A., & Buzzicotti, M. (2024). Synthetic Lagrangian turbulence by generative diffusion models. Nature Machine Intelligence.

Li, T., Lanotte, A. S., Buzzicotti, M., Bonaccorso, F., & Biferale, L. (2023). Multi-scale reconstruction of turbulent rotating flows with generative diffusion models. Atmosphere, 15(1), 60.

Storer, B. A., Buzzicotti, M., Khatri, H., Griffies, S. M., & Aluie, H. (2023). Global cascade of kinetic energy in the ocean and the atmospheric imprint. Science Advances.

Li, T., Buzzicotti, M., Biferale, L., Bonaccorso, F., Chen, S., & Wan, M. (2023). Multi-scale reconstruction of turbulent rotating flows with proper orthogonal decomposition and generative adversarial networks. Journal of Fluid Mechanics.

Buzzicotti, M., Storer, B. A., Khatri, H., Griffies, S. M., & Aluie, H. (2023). Spatio‐temporal coarse‐graining decomposition of the global ocean geostrophic kinetic energy. Journal of Advances in Modeling Earth Systems.

Seyedeh Azadeh Fallah Mortezanejad | Mathematics | Best Researcher Award

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Dr. Seyedeh Azadeh Fallah Mortezanejad | Mathematics | Best Researcher Award

Jiangsu University | China

Author Profile

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Early Academic Pursuits

Dr. Seyedeh Azadeh Fallah Mortezanejad began her academic journey with a strong foundation in statistics, completing her undergraduate and postgraduate studies at Guilan University, Iran. Her master’s research on semi-parametric estimation of conditional copula reflected her interest in statistical theory and dependence structures. She advanced her academic training with doctoral research at Ferdowsi University of Mashhad, focusing on applications of entropy in statistical quality control, which laid the groundwork for her later interdisciplinary research.

Professional Endeavors

Following her doctoral studies, Dr. Mortezanejad pursued postdoctoral research at Jiangsu University in China, under the guidance of Professor Ruochen Wang. Supported by the National Natural Science Foundation of China, her work explored advanced applications of statistical inference in engineering systems. Her professional engagements span teaching, collaborative research, and presenting at international conferences, reflecting her role as both a researcher and an academic contributor.

Contributions and Research Focus

Her research lies at the intersection of statistics, data science, and engineering. She has significantly contributed to areas such as time series analysis, dependence data, deep learning, and statistical quality control. Her expertise in copula functions and entropy has enabled novel methods for addressing challenges in multivariate data analysis and control charts. More recently, her work integrates machine learning and physics-informed neural networks for solving complex problems in multivariate time series and image processing.

Accolades and Recognition

Dr. Mortezanejad’s scholarly contributions have been recognized through numerous publications in leading journals, including Entropy, Sankhya B, and Physica A. She has been invited to present her findings at international workshops in Germany, France, Vietnam, and Spain, underscoring her recognition in global research communities. Her role as a reviewer for reputed journals and conferences further reflects her professional standing in the field.

Impact and Influence

Through her interdisciplinary research, Dr. Mortezanejad has bridged the gap between theoretical statistics and practical applications in fields such as healthcare, engineering, and financial modeling. Her contributions to statistical quality control, machine learning applications, and Bayesian inference have influenced both academic discourse and applied research, making her work relevant across diverse scientific domains.

Legacy and Future Contributions

With her strong background in both theoretical and applied statistics, Dr. Mortezanejad is poised to continue advancing research in modern statistical methods, particularly in integrating entropy-based approaches with machine learning. Her future work is expected to focus on enhancing predictive analytics, developing robust statistical tools for big data, and contributing to sustainable innovations in engineering and healthcare.

Publications

Article: Physics-Informed Neural Networks with Unknown Partial Differential Equations: An Application in Multivariate Time Series
Authors: Seyedeh Azadeh Fallah Mortezanejad, Ruochen Wang, Ali Mohammad-Djafari
Journal: Entropy
Year: 2025

Article: Variational Bayesian Approximation (VBA): Implementation and Comparison of Different Optimization Algorithms
Authors: Seyedeh Azadeh Fallah Mortezanejad, Ali Mohammad-Djafari
Journal: Entropy
Year: 2024

Article: Variational Bayesian Approximation (VBA) with Exponential Families and Covariance Estimation
Authors: Seyedeh Azadeh Fallah Mortezanejad, Ali Mohammad-Djafari
Journal: Physical Sciences Forum
Year: 2023

Article: Variational Bayesian Approximation (VBA): A Comparison between Three Optimization Algorithms
Authors: Seyedeh Azadeh Fallah Mortezanejad, Ali Mohammad-Djafari
Journal/Conference: MaxEnt 2022 (Conference Proceedings)
Year: 2023

Article: Evaluation of Anti-lice Topical Lotion of Ozonated Olive Oil and Comparison of its Effect with Permethrin Shampoo
Authors: Omid Rajabi, Atoosa Haghighizadeh, Seyedeh Azadeh Fallah Mortezanejad, Saba Dadpour
Journal: Reviews on Recent Clinical Trials
Year: 2022

Conclusion

Dr. Seyedeh Azadeh Fallah Mortezanejad’s career reflects a rare blend of statistical rigor, innovative application, and international recognition. Her early commitment to statistical theory, coupled with her interdisciplinary contributions, has positioned her as a rising figure in applied statistics and data science. With her expanding research footprint, she is set to leave a lasting impact on statistical research and its applications in science, technology, and industry.

Swathi Priyadarshini Tigulla | Computer Science | Best Researcher Award

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Dr. Swathi Priyadarshini Tigulla | Computer Science | Best Researcher Award

Osmania University | India

Author Profile

Scopus

Early Academic Pursuits

Dr. Swathi Priyadarshini Tigulla laid the foundation of her academic journey with a degree in Information Technology, followed by a master’s program in Information Technology with a specialization in network security. Her pursuit of advanced knowledge culminated in a doctoral degree in Computer Science and Engineering from Osmania University. From the beginning, she demonstrated a strong inclination toward solving computational problems and a keen interest in the emerging domains of artificial intelligence, machine learning, and network security.

Professional Endeavors

Her professional career reflects an extensive teaching and mentoring journey across reputed institutions. She began her career as an Assistant Professor in engineering colleges where she taught computer science, network security, and software engineering, and guided student projects. Over the years, she progressed to significant academic roles, including serving as Head of the Department, coordinating extracurricular activities, and contributing to student training and placement. Presently, she continues her academic engagement as an Assistant Professor specializing in artificial intelligence and machine learning, while also actively mentoring projects and participating in innovative academic initiatives such as GEN-AI teams and project schools.

Contributions and Research Focus

Dr. Tigulla’s research is strongly anchored in artificial intelligence, machine learning, and soft computing, with a particular focus on healthcare applications such as heart stroke prediction models. Her publications have proposed innovative approaches that integrate clustering, classification, and deep learning techniques to enhance medical predictions, combining accuracy with practical applicability. Beyond healthcare, her work also explores security strategies in cloud computing and data-driven approaches to protect systems from vulnerabilities. This blend of healthcare informatics and cyber security positions her research at the intersection of technology and community impact.

Accolades and Recognition

Her expertise has been recognized through publications in reputed international journals such as Measurement: Sensors and Journal of Positive School Psychology, along with contributions to international conferences under IEEE. She has served as a reviewer for scholarly journals and academic book chapters, demonstrating her standing as a trusted evaluator in her field. Her involvement as an organizer of technical workshops, hackathons, and project expos reflects her commitment to academic innovation and student skill development, further reinforcing her recognition as a versatile academic leader.

Impact and Influence

The impact of Dr. Tigulla’s work is evident in both her research outcomes and her teaching contributions. Her models for heart stroke prediction contribute significantly to community health by combining artificial intelligence with real-world medical applications. As an educator, she has influenced generations of students by equipping them with knowledge in machine learning, artificial intelligence, and advanced computational concepts. Her leadership in academic events has fostered a culture of innovation, creativity, and hands-on learning among students, thereby extending her influence beyond traditional teaching.

Legacy and Future Contributions

Dr. Tigulla’s legacy is one of blending research excellence with community benefit. By focusing on both healthcare prediction models and system security, she has addressed two domains of immense social importance—public health and digital trust. Looking forward, her future contributions are expected to further deepen the integration of artificial intelligence into real-world applications, enhance her role as a reviewer and academic guide, and continue her efforts to shape students into innovative researchers and industry-ready professionals.

Publications

Article: Developing Heart Stroke Prediction Model using Deep Learning with Combination of Fixed Row Initial Centroid Method with Naïve Bayes, Decision Tree, and Artificial Neural Network
Authors: T. Swathi Priyadarshini, Vuppala Sukanya, Mohd Abdul Hameed
Journal: Measurement: Sensors
Year: 2024

Article: Collaboration of Clustering and Classification Techniques for Better Prediction of Severity of Heart Stroke using Deep Learning
Authors: T. Swathi Priyadarshini, Vuppala Sukanya, Mohd Abdul Hameed
Journal: Measurement: Sensors
Year: 2025

Article: Deep Learning Prediction Model for Predicting Heart Stroke using the Combination Sequential Row Method Integrated with Artificial Neural Network
Authors: T. Swathi Priyadarshini, Mohd Abdul Hameed, Balagadde Ssali Robert
Journal: Journal of Positive School Psychology
Year: 2022

Article: Methods of Hidden Pattern Usage in Cloud Computing Security Strategies with K-means Clustering
Authors: T. Swathi Priyadarshini, Dr. S. Ramachandram
Journal: AIJREAS
Year: 2021

Article: A Review on Security Issue Solving Methods in Public and Private Cloud Computing
Authors: T. Swathi Priyadarshini, S. Ramachandram
Journal: IJMTST
Year: 2020

Conclusion

Dr. Swathi Priyadarshini Tigulla embodies the qualities of an academician and researcher who successfully bridges the gap between theoretical advancements and community impact. Her journey, marked by academic rigor, extensive teaching experience, and impactful research, showcases her dedication to advancing artificial intelligence and machine learning for practical applications. Recognized as both a researcher and a mentor, she continues to inspire through her contributions in education, healthcare, and cyber security. In conclusion, her career highlights a sustained commitment to knowledge, innovation, and community-oriented research, establishing her as a distinguished academic voice in the field of computer science and engineering.

 

Vaggelis Lamprou | Computer Science | Best Researcher Award

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Mr. Vaggelis Lamprou | Computer Science | Best Researcher Award

National Technical University of Athens | Greece

Author Profile

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Orcid

Google Scholar 

Early Academic Pursuits

Mr. Vaggelis Lamprou began his academic journey with a strong foundation in mathematics, earning his Bachelor’s degree from the National and Kapodistrian University of Athens, where he developed a deep interest in calculus, probability theory, and statistics. His passion for analytical reasoning and theoretical problem-solving led him to pursue a Master’s degree in Mathematics at the University of Bonn, Germany, where he focused on probability theory and its applications, culminating in a thesis on large deviations in mean field theory. This early academic phase not only honed his mathematical rigor but also laid the groundwork for his transition into the emerging domains of artificial intelligence and machine learning.

Professional Endeavors

Building upon his academic background, Mr. Lamprou advanced into roles that blended research with real-world applications. As a Data Analyst at Harbor Lab, he utilized statistical and computational tools to optimize platform usability and collaborated in developing innovative cost estimation tools for the maritime industry. His transition into machine learning engineering at Infili Technologies SA and later at the DSS Lab, EPU-NTUA, marked a shift toward high-impact AI-driven research and development, particularly within European-funded projects focusing on federated learning, generative AI, anomaly detection, and privacy-preserving technologies.

Contributions and Research Focus

Mr. Lamprou’s research is rooted in the intersection of mathematics, computer science, and artificial intelligence, with a strong emphasis on interpretable AI, deep learning, and probabilistic modeling. His work spans applications in medical imaging, cybersecurity, and large-scale distributed learning systems. In his Master’s thesis in Artificial Intelligence, he explored the evaluation of interpretability methods for deep learning models in medical imaging, underlining his dedication to developing transparent and trustworthy AI solutions. His contributions also extend to federated learning frameworks, enhancing data security and performance in next-generation communication networks.

Publications and Scholarly Engagement

His scholarly output reflects a commitment to both theoretical innovation and practical problem-solving. Notable works include a study on interpretability in deep learning for medical images published in Computer Methods and Programs in Biomedicine, and a comprehensive survey on federated learning for cybersecurity and trustworthiness in 5G and 6G networks in the IEEE Open Journal of the Communications Society. He actively participates in academic discourse, presenting at international conferences such as the International Conference on Information Intelligence Systems and Applications, further contributing to the global exchange of ideas in AI research.

Accolades and Recognition

Mr. Lamprou’s academic excellence is evident in his high academic distinctions throughout his studies, including top GPAs in his advanced degrees. His recognition extends beyond academic grades, with his selection to contribute to high-profile European R&D initiatives—a testament to his expertise and reliability in cutting-edge technological research. His invited participation in prestigious conferences and collaborations with leading research institutions reflects the respect he commands within the AI and machine learning community.

Impact and Influence

Through his research and professional activities, Mr. Lamprou has contributed to advancing AI methodologies in fields of societal importance, such as healthcare and cybersecurity. His work in interpretable AI has the potential to bridge the gap between complex machine learning models and human understanding, fostering trust in AI-assisted decision-making. In the realm of federated learning, his contributions support data sovereignty and privacy, addressing critical challenges in the deployment of AI at scale across sensitive domains.

Legacy and Future Contributions

As a PhD candidate at the National Technical University of Athens, Mr. Lamprou is poised to further deepen his contributions to the AI research landscape. His ongoing work aims to push the boundaries of interpretable and probabilistic AI models, with a vision to create transparent, reliable, and secure machine learning systems. His trajectory suggests a lasting influence on both the academic and industrial sectors, with the potential to inspire future researchers to prioritize ethical and explainable AI solutions.

Publications

Article: Federated Learning for Enhanced Cybersecurity and Trustworthiness in 5G and 6G Networks: A Comprehensive Survey
Authors: Afroditi Blika, Stefanos Palmos, George Doukas, Vangelis Lamprou, Sotiris Pelekis, Michael Kontoulis, Christos Ntanos, Dimitris Askounis
Journal: IEEE Open Journal of the Communications Society
Year: 2025

Article: On the trustworthiness of federated learning models for 5G network intrusion detection under heterogeneous data
Authors: Vangelis Lamprou, George Doukas, Christos Ntanos, Dimitris Askounis
Journal: Computer Networks
Year: 2025

Article: Data analytics for research on complex brain disorders
Authors: Michail Kontoulis, George Doukas, Theodosios Pountridis, Loukas Ilias, George Ladikos, Vaggelis Lamrpou, Kostantinos Alexakis, Dimitris Askounis, Christos Ntanos
Journal: Open Research Europe
Year: 2024

Article: On the evaluation of deep learning interpretability methods for medical images under the scope of faithfulness
Authors: Vangelis Lamprou, Athanasios Kallipolitis, Ilias Maglogiannis
Journal: Computer Methods and Programs in Biomedicine
Year: 2024

Article: Grad-CAM vs HiResCAM: A comparative study via quantitative evaluation metrics
Author: Vaggelis Lamprou
Institution: University of Piraeus
Year: 2023

Conclusion

With his blend of theoretical insight, technical skill, and a forward-looking research vision, Mr. Lamprou stands out as a promising researcher whose work is set to have a significant impact on the development of transparent and reliable AI technologies. His career embodies the bridge between rigorous academic inquiry and impactful, real-world AI solutions.

Ziang Liu | Engineering | Best Researcher Award

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Mr. Ziang Liu | Engineering | Best Researcher Award

Nanjing University | China

Author Profile

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Early Academic Pursuits

Mr. Ziang Liu began his academic journey with distinction at Tianjin University, where he earned his Bachelor of Science in Electronic Engineering. His strong foundation in engineering and mathematics laid the groundwork for advanced research and innovation. Continuing his academic trajectory, he pursued a Master of Science in Electronic Engineering at the prestigious Nanjing University, where he was recognized as an Outstanding Student and awarded the First-class Academic Scholarship.

Professional Endeavors

Ziang has accumulated valuable industry experience through impactful internships. At Meituan Shanghai, he served as an LLMs Evaluation Algorithm Intern, where he designed evaluation schemes and analyzed instruction-following capabilities across large language models such as Qwen, Doubao, ChatGPT 3.5/4, and Llama2-70B.  In another significant role at Alibaba DingTalk in Hangzhou, he worked on the back-end development of Chatmemo, an enterprise AI assistant. There, he implemented knowledge graph subgraph displays and integrated Retrieval-Augmented Generation (RAG), significantly boosting response speed and system performance.

Contributions and Research Focus

Mr. Liu’s core interests revolve around LLMs (Large Language Models), RAG (Retrieval-Augmented Generation), and knowledge graph technologies. He has contributed to the design and optimization of backend systems for intelligent applications in healthcare and enterprise settings. His work on deploying frameworks like Graph RAG and utilizing tools like Redis, MySQL, and Spring Boot has shown practical outcomes in real-world systems, particularly in performance optimization, load balancing, and cache management. His participation in the Nanjing University Intelligent Hospital Project resulted in a custom online medication purchasing system, complete with AI-powered Q&A capabilities and scalable backend infrastructure.

Accolades and Recognition

Ziang Liu’s academic excellence is evident through a remarkable series of accolades earned during both his undergraduate and postgraduate studies. He was honored as the Outstanding Student of Nanjing University in 2023 and received the First-class Academic Scholarship in 2022, recognizing his superior academic performance. His analytical and technical skills were demonstrated through competition achievements, including the Third Prize in the 19th Chinese Graduate Mathematical Modeling Competition (2022) and the Second Prize in the 18th Chinese Electronic Design Competition (2023). Earlier in his academic journey, he was named a Meritorious Winner in the Mathematical Contest in Modeling (MCM) in 2021 and was recognized as an Outstanding Graduate of Tianjin University in 2022. These accomplishments reflect his consistent dedication, innovation, and leadership in engineering and applied mathematics.

Impact and Influence

Ziang Liu’s work has made a tangible impact in both academia and industry. His efforts in improving instruction-following performance in LLMs and optimizing backend systems for enterprise AI applications have proven valuable for real-world implementation. His innovations in intelligent hospital systems demonstrate a commitment to applying advanced AI technologies to enhance societal well-being and operational efficiency.

Legacy and Future Contributions

Poised at the intersection of AI, backend engineering, and applied innovation, Mr. Ziang Liu is emerging as a key contributor to the next generation of AI infrastructure. His hands-on experience with cutting-edge technologies like gRPC, GraphRAG, JWT, and multi-threaded optimization positions him to drive future advancements in AI systems, enterprise platforms, and digital healthcare. With a strong academic record and robust technical expertise, he is well on his way to becoming a leading voice in intelligent systems development.

 

 

Publications

Channel-Dependent Multilayer EEG Time-Frequency Representations Combined with Transfer Learning-Based Deep CNN Framework for Few-Channel MI EEG Classification

Authors: Ziang Liu, Kang Fan, Qin Gu, Yaduan Ruan
Journal: Bioengineering
Year: 2025

Studying Multi-Frequency Multilayer Brain Network via Deep Learning for EEG-Based Epilepsy Detection

Authors: Weidong Dang, Dongmei Lv, Linge Rui, Ziang Liu, Guanrong Chen, Zhongke Gao
Journal: IEEE Sensors Journal
Year: 2021

Rishabh Kumar | Computer Science | Best Researcher Award

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Mr. Rishabh Kumar | Computer Science | Best Researcher Award

IIT Bombay | India

Author Profile

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🌱 Early Academic Pursuits

Mr. Rishabh Kumar’s journey in the field of Computer Science and Engineering began with academic brilliance and passion for innovation. He completed his Bachelor of Technology at IIT (ISM) Dhanbad in 2016, where he laid the foundation for his interest in language technologies under the mentorship of Prof. Sukomal Pal. His intellectual trajectory reached new heights when he joined IIT Bombay as a PhD scholar in 2019. There, under the guidance of Prof. Ganesh Ramakrishnan and Prof. Preethi Jyothi, he began pioneering work in Automatic Speech Recognition (ASR) for low-resource Indian languages, including Sanskrit and Hindi — an effort that merges linguistics with machine learning in service of cultural preservation and accessibility.

👨‍💼 Professional Endeavors

With roles spanning startups to tech giants, Mr. Kumar has applied his research to real-world systems. At Samsung R&D, Bangalore, during his 2023–2024 internship, he developed advanced ASR models for Hindi and spearheaded cross-lingual proper noun recognition using Large Language Models (LLMs). His early stints as Technical Head at Gartley618 Technologies and Lead Developer at Pocketin demonstrate his versatility across full-stack development, iOS applications, and blockchain-based platforms. Additionally, his research internship at Wrig Nanosystems involved Android-based biomedical device integration, showcasing his interdisciplinary reach.

🧠 Contributions and Research Focus

Mr. Kumar’s work is particularly focused on ASR for underrepresented languages, speech-text alignment, and LLM-based improvements in speech technologies. His innovations include:

🔹 Developing Vāgyojaka, a Sanskrit ASR annotation and post-editing tool
🔹 Creating a SpeechQC Agent, a natural language–driven framework for speech dataset validation
🔹 Building ASR pipelines for Indian Parliament (SansadTV)
🔹 Advancing BharatGen Hindi ASR systems

His contributions are documented across top-tier conferences and journals, including ACL, EMNLP, INTERSPEECH, and CSL, with several first-author papers that blend linguistic knowledge and computational innovation.

🏆 Accolades and Recognition

Mr. Kumar’s excellence has been widely recognized. He received the Microsoft Research India Travel Grant to attend INTERSPEECH 2022 and has won prestigious competitions like the State Android App Contest by Jharkhand Government and the National Multilingual Theater Competition. His early achievements include multiple Math Olympiad prizes, an NTSE Level II qualification, and distinctions in Macmillan Olympiads by the University of New South Wales. These accolades reflect his lifelong dedication to problem-solving, innovation, and excellence.

🌍 Impact and Influence

Rishabh Kumar’s research has directly impacted India’s speech technology ecosystem, contributing essential tools and models for building inclusive, vernacular AI systems. His work supports broader national initiatives such as Bhashini and aligns with global goals of linguistic equity in AI. By building publicly usable ASR tools, datasets, and systems for resource-poor languages, he is democratizing access to technology for millions of Indian users.

🔭 Legacy and Future Contributions

As Mr. Kumar approaches the culmination of his PhD, his trajectory signals an exciting future. His legacy lies in fusing computational prowess with cultural sensitivity — bringing Indian linguistic diversity to the forefront of AI innovation. Whether in academia, industry, or open-source collaborations, he is poised to continue shaping the next generation of multilingual ASR systems, LLM-based speech understanding, and resource-efficient AI tools. His work will inspire young researchers to explore the intersection of language, society, and technology.

Publications

📝 Linguistically Informed Automatic Speech Recognition in Sanskrit
 Author: Rishabh Kumar (assumed from your context)
 Journal: Computer Speech & Language (CSL)
 Year: 2025

📝 Beyond Common Words: Enhancing ASR Cross-Lingual Proper Noun Recognition Using LLMs
 Author: Rishabh Kumar (assumed)
 Conference: EMNLP (Findings of the 2024 Conference on Empirical Methods in Natural Language Processing)
 Year: 2024

📝 Linguistically Informed Post-processing for ASR Error Correction in Sanskrit
 Author: Rishabh Kumar
 Conference: INTERSPEECH
 Year: 2022

📝 Vāgyojaka: An Annotating and A Post-Editing Tool for Automatic Speech Recognition
 Author: Rishabh Kumar
 Conference: INTERSPEECH (Show and Tell)
 Year: 2022

📝 Automatic Speech Recognition in Sanskrit: A New Speech Corpus and Modelling Insights
 Author: Rishabh Kumar
 Conference: ACL (Findings of the Association for Computational Linguistics)
 Year: 2021