Vijay Srinivas Tida | Computer Science | Excellence in Research

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Dr. Vijay Srinivas Tida | Computer Science | Excellence in Research

Dr. Vijay Srinivas Tida, College of St Benedict and St John’s university, United States

Dr. Vijay Srinivas Tida is a dedicated researcher and academician currently serving as a Tenure-track Assistant Professor at the College of St. Benedict and St. John’s University, Minnesota. With a strong foundation in Electronics, Computer Engineering, and Deep Learning, he has developed a notable reputation in the fields of differential privacy, federated learning, and FPGA hardware acceleration. His Ph.D. dissertation at the University of Louisiana at Lafayette explored optimizing transpose convolution operations—a critical component in CNNs. Dr. Tida’s academic journey has taken him through top institutions including Illinois Institute of Technology and Koneru Lakshmaiah University, consistently achieving high academic honors. He has actively contributed to privacy-preserving machine learning for healthcare and has authored several journal articles and conference papers. Passionate about teaching, he also mentors students in deep learning and hardware systems, making him a valuable contributor to modern computer science education.

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Suitability for Research for Excellence in Research Award: Vijay Srinivas Tida

Vijay Srinivas Tida stands out as a highly deserving candidate for the Research for Excellence in Research Award due to his exceptional contributions in the fields of deep learning optimization, differential privacy, federated learning, and hardware accelerator design. His academic journey reflects consistent excellence, culminating in a Ph.D. in Computer Engineering with a remarkable GPA of 3.9/4.0 from the University of Louisiana at Lafayette. Complemented by a strong foundation in Electrical and Computer Engineering from Illinois Institute of Technology and Electronics and Communication Engineering from Koneru Lakshmaiah University, his educational background is solid and highly relevant.

Throughout his academic and professional career, Vijay has demonstrated a commitment to pioneering research, particularly focusing on the optimization of deep convolutional neural networks, privacy-preserving machine learning models, and hardware security. His doctoral dissertation on optimizing transpose convolution operations and his multiple research projects emphasize innovative approaches that enhance the efficiency and security of AI models, which are critical in today’s technology-driven healthcare and security domains.

🎓 Education

Dr. Vijay Srinivas Tida earned his Ph.D. in Computer Engineering from the University of Louisiana at Lafayette (2018–2023), under the mentorship of Dr. Sonya Hsu and Dr. Xiali Hei, graduating with an impressive GPA of 3.9/4.0. His dissertation focused on optimizing transpose convolution operations for efficient deep learning computation. Prior to this, he completed his Master’s degree in Electrical and Computer Engineering from Illinois Institute of Technology (2016–2018), working with Dr. Erdal Oruklu and maintaining a GPA of 3.8/4.0. He began his academic journey with a Bachelor of Science in Electronics and Communication Engineering from Koneru Lakshmaiah University (2011–2015), guided by Dr. Nalluri Siddaiah, achieving a perfect GPA of 4.0/4.0. His academic background reflects a blend of theoretical knowledge and practical experience in machine learning, hardware design, and optimization algorithms, which forms the core of his current research and teaching interests.

💼 Professional Experience

Dr. Tida’s professional trajectory spans across academic teaching and innovative research. He currently holds the position of Assistant Professor at the College of St. Benedict and St. John’s University, where he teaches and mentors students in computer science. Previously, he served as a Postdoctoral Research Assistant at the University of Louisiana at Lafayette (May–Aug 2023), contributing to projects in privacy-preserving AI and FPGA-based accelerators. From 2018 to 2022, he was a Graduate Teaching Assistant and Lab Instructor, where he taught courses including Computer Architecture and Computer Engineering Labs. He also held Research Assistant roles across institutions like Illinois Institute of Technology and Koneru Lakshmaiah University, engaging in high-impact projects on energy harvesting, sensor security, and neural networks. Dr. Tida’s teaching is complemented by his commitment to community outreach, where he has conducted programming workshops for high school students and offered deep learning sessions to Ph.D. candidates.

🏅 Awards and Recognition

Dr. Tida has been the recipient of numerous honors recognizing both his academic excellence and research contributions. Notably, in 2024, he received $1,750 to attend the prestigious SIGCSE Technical Symposium on Computer Science Education. He was awarded a $6,500 Summer Collaborative Research Grant and $1,000 by the Faculty Development Research Committee for conference travel. In 2023, the College of St. Benedict and St. John’s University provided him with high-performance computing resources worth $16,000. During his doctoral studies, he earned a Dissertation Completion Fellowship and secured consistent Graduate Teaching and Research Assistantships from 2018 to 2022. These accolades reflect his capabilities in leading cutting-edge projects and fostering academic excellence. His continued association with academic conferences such as HICSS and ACM further underscores his recognition within the computing research community.

🌍 Research Skill On Computer Science

Dr. Tida’s research skills encompass a dynamic combination of deep learning, optimization, hardware acceleration, and data privacy. His expertise lies in the development and optimization of Convolutional Neural Networks (CNNs), especially with transpose convolution operations—a subject central to his doctoral work. His focus on Differential Privacy and Federated Learning reflects his commitment to secure and ethical AI, particularly for healthcare data applications. He is adept at hardware-level design using Field Programmable Gate Arrays (FPGAs), enabling real-time and efficient AI computations. With a solid command over Natural Language Processing, he has also published in areas like fake news detection and spam classification using models such as BERT. Dr. Tida’s proficiency spans Python, Arduino C, and hardware descriptive languages, supported by his consistent role in mentoring and peer reviewing. His integration of theoretical algorithms with practical systems development defines his impactful presence in modern computational research.

📖 Publication Top Notes

  • Universal Spam Detection using Transfer Learning of BERT Model
    Author(s): VSTDS Hsu
    Citation: 89
    Year: 2022

  • A reliable diabetic retinopathy grading via transfer learning and ensemble learning with quadratic weighted kappa metric
    Author(s): SV Chilukoti, L Shan, VS Tida, AS Maida, X Hei
    Citation: 45
    Year: 2024

  • Transduction shield: A low-complexity method to detect and correct the effects of EMI injection attacks on sensors
    Author(s): Y Tu, VS Tida, Z Pan, X Hei
    Citation: 38
    Year: 2021

  • Design and Analysis of High Efficient UART on Spartran-6 and Virtex-7 Devices
    Author(s): KH Kishore, CA Kumar, TV Srinivas, GV Govardhan, CNP Kumar, …
    Citation: 20
    Year: Not specified (likely between 2015–2018 based on journal timeline)

  • A unified training process for fake news detection based on fine-tuned BERT model
    Author(s): VS Tida, S Hsu, X Hei
    Citation: 10
    Year: 2022

  • Privacy-Preserving Deep Learning Model for Covid-19 Disease Detection
    Author(s): Vijay Srinivas Tida, Sai Venkatesh Chilukoti, Sonya H. Y. Hsu, Xiali Hei
    Citation: 8
    Year: 2023

  • Kernel-Segregated Transpose Convolution Operation
    Author(s): Vijay Srinivas Tida, Sai Venkatesh Chilukoti, Sonya H. Y. Hsu, Xiali Hei
    Citation: 5
    Year: 2023

  • Deep Learning Approach for Protecting Voice-Controllable Devices From Laser Attacks
    Author(s): VS Tida, R Shah, X Hei
    Citation: 2
    Year: 2022

  • Unified Kernel-Segregated Transpose Convolution Operation
    Author(s): VS Tida, MI Hossen, L Shan, SV Chilukoti, S Hsu, X Hei
    Citation: Not listed
    Year: 2025

  • Differentially private fine-tuned NF-Net to predict GI cancer type
    Author(s): SV Chilukoti, IH Md, L Shan, VS Tida, X Hei
    Citation: Not listed
    Year: 2025

Lin Chen | Computer Science | Best Researcher Award

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Prof. Lin Chen | Computer Science | Best Researcher Award

Prof. Lin Chen, Macao Polytechnic University, Macau

Lin Chen, a distinguished scholar and innovator in Computer Science, serves as a full professor at Macao Polytechnic University since 2025. He obtained his B.Sc. in Electrical Engineering from Southeast University (2002), an M.Sc. in Networking from the University of Paris 6 (2005), and an Engineer Diploma and Ph.D. in Computer Science from Telecom ParisTech (2005, 2008). Dr. Chen further achieved his Habilitation thesis at the University of Paris-Sud in 2017. His illustrious career spans academia and research, including tenures as an associate professor at the University of Paris-Sud and a full professor at Sun Yat-sen University. Renowned for contributions to distributed algorithms, energy-efficient systems, and network security, he has published over 100 papers, with several receiving accolades. He is a Junior Member of the Institut Universitaire de France (IUF) and an editor for IEEE Systems Journals, demonstrating leadership in advancing the field of networked systems.

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Suitability for the “Research for Best Researcher Award” – Lin Chen

Lin Chen’s qualifications and achievements make him an outstanding candidate for the “Research for Best Researcher Award.” As a full professor of Computer Science at Macao Polytechnic University, his distinguished academic and research background places him among the leading experts in his field. His educational journey, which spans multiple prestigious institutions, reflects a profound commitment to advancing knowledge in Computer Science and Networking. Holding a Ph.D. in Computer Science and Networking from Telecom ParisTech (ENST), along with a Habilitation thesis from the University of Paris-Sud, Lin’s academic credentials are both comprehensive and prestigious.

Lin Chen’s research contributions, particularly in the realm of distributed algorithms and protocols for emerging networked systems, are exemplary. His work on energy efficiency, resilience, and security is highly relevant to current and future technological advancements. Having published over 100 journal and conference papers, with several highly cited works, Lin’s research is not only influential but recognized as a major contribution to the field. His three ESI Highly Cited Papers and multiple Best Paper and Best Student Paper awards underline his impact in the academic community.

🎓 Education 

Lin Chen’s academic journey reflects a steadfast commitment to excellence and interdisciplinary learning. He earned his B.Sc. in Electrical Engineering from Southeast University in 2002, laying a strong foundation in technical problem-solving and system design. Driven by his passion for innovation, he pursued an M.Sc. in Networking at the University of Paris 6 in 2005, where he developed expertise in network protocols and optimization. His quest for advanced knowledge led him to Telecom ParisTech (ENST), where he received an Engineer Diploma and a Ph.D. in Computer Science and Networking in 2005 and 2008, respectively, focusing on distributed systems and secure network architecture. Further solidifying his credentials, he achieved his Habilitation thesis at the University of Paris-Sud in 2017, showcasing his authority in energy-efficient and resilient systems. This educational foundation underscores Dr. Chen’s expertise in bridging theoretical innovation with practical applications in emerging technologies.

💼 Professional Experience 

Lin Chen boasts a remarkable professional trajectory spanning prestigious institutions and transformative research. He began his academic career as an associate professor in the Department of Computer Science at the University of Paris-Sud (2009–2019), where he spearheaded groundbreaking projects on distributed algorithms and energy-efficient systems. In 2019, he advanced to a full professorship at Sun Yat-sen University, focusing on secure and resilient networked systems, before joining Macao Polytechnic University in 2025. Dr. Chen’s leadership extends beyond academia, serving as the Chair of the IEEE TCGCC SIG on Green and Sustainable Networking. He has significantly influenced the field through editorial roles in IEEE Systems Journals and guest editing for top-tier publications. His prolific contributions include organizing international conferences, such as ICCCN and INFOCOM workshops, underscoring his commitment to fostering innovation in computer science. His work remains a testament to his dedication to advancing next-generation networking technologies.

🏅 Awards and Recognition 

Lin Chen’s exceptional contributions have garnered numerous accolades, reflecting his influence in computer science and networking. He received the 2018 CNRS Bronze Medal, a prestigious recognition reserved for exemplary researchers, being one of only two awardees in ICT that year. His research has produced over 100 impactful publications, including three journal papers recognized as ESI Highly Cited Papers and three conference papers awarded Best Paper or Best Student Paper honors. Dr. Chen’s leadership and expertise have earned him the distinction of Junior Member of the Institut Universitaire de France (IUF). His editorial roles with IEEE Systems Journals and guest editorships in leading publications like IEEE Wireless Communications Magazine further underscore his contributions. As a driving force behind initiatives such as the IEEE TCGCC SIG on Green Networking, he continues to shape the future of secure, energy-efficient, and sustainable networked systems, inspiring researchers worldwide.

🌍 Research Skills On Computer Science

Lin Chen is a leading researcher specializing in distributed algorithms, energy efficiency, resilience, and network security. His work is characterized by a multidisciplinary approach, seamlessly integrating theoretical insights with practical solutions. He has developed innovative protocols for emerging networked systems, addressing critical challenges in energy efficiency and sustainable computing. Dr. Chen’s expertise extends to secure network architecture, ensuring robust communication frameworks for dynamic and large-scale systems. His research leverages advanced methodologies, including machine learning and artificial intelligence, to optimize network performance and enhance resilience. With a focus on green networking, he has pioneered strategies for reducing energy consumption in ultra-dense networks. His technical acumen is complemented by his extensive experience in project leadership and collaboration, as demonstrated by his active participation in international conferences and editorial roles. Dr. Chen’s research continues to influence the development of scalable, secure, and sustainable next-generation technologies.

📖 Publication Top Notes

  • Routing metrics of cognitive radio networks: A survey
    Authors: M Youssef, M Ibrahim, M Abdelatif, L Chen, AV Vasilakos
    Journal: IEEE Communications Surveys & Tutorials
    Citation: 388
    Year: 2013
  • A game theoretical framework on intrusion detection in heterogeneous networks
    Authors: L Chen, J Leneutre
    Journal: Information Forensics and Security, IEEE Transactions on
    Citation: 190
    Year: 2009
  • An auction framework for spectrum allocation with interference constraint in cognitive radio networks
    Authors: L Chen, S Iellamo, M Coupechoux, P Godlewski
    Journal: INFOCOM, 2010 Proceedings IEEE
    Citation: 124
    Year: 2010
  • Joint multiuser DNN partitioning and computational resource allocation for collaborative edge intelligence
    Authors: X Tang, X Chen, L Zeng, S Yu, L Chen
    Journal: IEEE Internet of Things Journal
    Citation: 110
    Year: 2020
  • Energy-efficiency maximization for cooperative spectrum sensing in cognitive sensor networks
    Authors: M Zheng, L Chen, W Liang, H Yu, J Wu
    Journal: IEEE Transactions on Green Communications and Networking
    Citation: 101
    Year: 2016
  • A distributed demand-side management framework for the smart grid
    Authors: A Barbato, A Capone, L Chen, F Martignon, S Paris
    Journal: Computer Communications
    Citation: 98
    Year: 2015
  • On oblivious neighbor discovery in distributed wireless networks with directional antennas: Theoretical foundation and algorithm design
    Authors: L Chen, Y Li, AV Vasilakos
    Journal: IEEE/ACM Transactions on Networking
    Citation: 88*
    Year: 2017
  • Secure cooperative spectrum sensing and access against intelligent malicious behaviors
    Authors: W Wang, L Chen, KG Shin, L Duan
    Journal: INFOCOM, 2014 Proceedings IEEE
    Citation: 86*
    Year: 2014
  • On heterogeneous neighbor discovery in wireless sensor networks
    Authors: L Chen, R Fan, K Bian, M Gerla, T Wang, X Li
    Journal: 2015 IEEE Conference on Computer Communications (INFOCOM)
    Citation: 80
    Year: 2015
  • An efficient auction-based mechanism for mobile data offloading
    Authors: S Paris, F Martignon, I Filippini, L Chen
    Journal: IEEE Transactions on Mobile Computing
    Citation: 75
    Year: 2015

NIKOLAOS EPISKOPOS | Computer Science | Best Researcher Award

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Mr. NIKOLAOS EPISKOPOS | Computer Science | Best Researcher Award

👤 Mr. NIKOLAOS EPISKOPOS, IBM, Greece

Nikolaos Episkopos is an accomplished Data Scientist, Software Developer, and Data Science Consultant with over eight years of professional experience. Based in Athens, Greece, Nikolaos specializes in AI, cybersecurity, and predictive medicine, contributing to impactful EU-funded R&D projects and Open Source Software initiatives. His innovative work includes AI solutions for fraud detection, federated learning toolkits for intrusion detection, and optimizing data pipelines. Passionate about the intersection of technological advancements and societal impact, Nikolaos has played pivotal roles in enhancing banking services, securing SCADA systems, and developing blockchain-based video streaming systems. As a professional with a robust academic background and diverse technical skills, he combines creativity and precision to deliver groundbreaking solutions.

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🌟 Evaluation of Nikolaos Episkopos for the Research for Best Researcher Award

Summary of Suitability

Nikolaos Episkopos demonstrates an exceptional profile as a data scientist and software developer with significant contributions to research and development projects, particularly in the domains of artificial intelligence (AI), cybersecurity, and healthcare. With over eight years of professional experience, he has effectively bridged academia and industry, showcasing the ability to lead and innovate in complex technological domains. His work has resulted in tangible outcomes, including funding acquisitions, novel AI solutions, and impactful publications.

Nikolaos’s recent achievements at IBM and INLECOM highlight his ability to tackle real-world challenges through data-driven innovation. At IBM, he developed advanced AI models for fraud detection, contributing to financial security solutions. His role at INLECOM involved technical project management and significant contributions to EU Horizon projects, where his efforts secured funding and established strategic partnerships. These accomplishments reflect his leadership, technical expertise, and collaborative skills in advancing scientific research.

🎓 Education 

Nikolaos holds a Master’s degree in Data Science & Information Technologies from the National and Kapodistrian University of Athens, where he honed expertise in AI, data analysis, and cybersecurity. Currently pursuing an MSc in Cybersecurity at the University of West Attica, he is expanding his knowledge of threat modeling and advanced cryptographic techniques. His academic journey reflects a commitment to excellence, as he consistently excelled in designing AI models and deploying secure, scalable systems. Nikolaos’ education equips him to navigate the rapidly evolving landscape of AI and cybersecurity, combining rigorous academic training with practical, hands-on experience to solve complex technical challenges effectively.

💼  Professional Experience

Nikolaos Episkopos has held roles at leading organizations like IBM, INLECOM, and MetaMind Innovations. At IBM, he spearheaded the development of AI solutions for card fraud detection and enhanced banking services. At INLECOM, he managed Horizon projects, securing significant funding and partnerships while optimizing algorithms for AI tools. During his tenure at MetaMind Innovations, he developed Federated Learning toolkits for intrusion detection, authored academic papers, and secured SCADA systems. At Fogus Innovations, Nikolaos implemented blockchain-enabled video streaming optimization and co-authored publications on advanced AI platforms. His ability to lead technical projects, develop AI models, and foster innovation underscores his exceptional contribution to the tech industry.

🏅 Awards and Recognition 

Nikolaos has been recognized for his contributions to EU-funded Horizon projects, which have brought substantial funding and technological advancements to his organizations. He co-authored papers published in prestigious journals like IEEE Transactions on Mobile Computing and Computer Science Review, highlighting his expertise in AI and cybersecurity. Additionally, his innovative solutions in fraud detection and SCADA security have been acknowledged within the tech community. Nikolaos’ commitment to open-source projects on GitHub further demonstrates his dedication to knowledge sharing and continuous improvement. His achievements reflect a career driven by excellence and societal impact.

🌍 Research Skills On Computer Science

Nikolaos excels in designing and deploying AI-driven solutions across domains such as cybersecurity, predictive medicine, and fraud detection. His expertise encompasses Federated Learning, blockchain integration, and data analysis using tools like TensorFlow, PyTorch, and Spark. Skilled in optimizing algorithms and building scalable data pipelines, Nikolaos has delivered solutions that reduce execution time and enhance efficiency. His academic research, coupled with industry application, positions him as a thought leader in leveraging AI for societal impact.

📖 Publication Top Notes

1. A comprehensive survey of Federated Intrusion Detection Systems: Techniques, challenges and solutions
  • Author(s): Ioannis Makris, Aikaterini Karampasi, Panagiotis Radoglou-Grammatikis, Nikolaos Episkopos, Eider Iturbe, Erkuden Rios, Nikos Piperigkos, Aris Lalos, Christos Xenakis, Thomas Lagkas, et al.
  • Citation: Computer Science Review, 2025-05
2. To DASH, or Not to DASH? Optimal Video Bitrate Selection and Edge Network Caching in MEC-Empowered Slice-Enabled Networks
  • Author(s): Dionysis Xenakis, Nikolaos Episkopos
  • Citation: IEEE Transactions on Vehicular Technology, 2024-04
3. PEER-TO-PEER VIDEO CONTENT DELIVERY OPTIMIZATION SERVICE IN A DISTRIBUTED NETWORK
  • Author(s): Nikolaos Episkopos
  • Citation: Dissertation/Thesis, 2022-10-24
4. Cache-Aware Adaptive Video Streaming in 5G networks
  • Author(s): Nikolaos Episkopos
  • Citation: Dissertation/Thesis, 2021-05-04
5. SECONDO: A Platform for Cybersecurity Investments and Cyber Insurance Decisions
  • Author(s): Aristeidis Farao, Sakshyam Panda, Sofia Anna Menesidou, Entso Veliou, Nikolaos Episkopos, George Kalatzantonakis, Farnaz Mohammadi, Nikolaos Georgopoulos, Michael Sirivianos, Nikos Salamanos, et al.
  • Citation: Trust, Privacy and Security in Digital Business (TrustBus), 2020-09-14
6. On-device caching of popular video content on Android-powered devices
  • Author(s): Nikolaos Episkopos
  • Citation: Dissertation/Thesis, 2018-08-14

Jordi Rodeiro | Computer Science | Best Researcher Award

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Mr. Jordi Rodeiro | Computer Science | Best Researcher Award

 👤 Mr. Jordi Rodeiro, Institut de Recerca Sant Joan de Déu, Spain

Jordi Rodeiro Boliart is an accomplished International Computer Engineering and Sports Science graduate with a Master’s in Data Science and ongoing doctoral studies in Artificial Intelligence at La Salle Bonanova, Barcelona. Jordi is a dynamic professional blending a robust academic foundation with practical expertise. He is dedicated to leveraging data science and AI in health research, particularly autism prediction. With a deep passion for problem-solving and innovation, Jordi has conducted significant work in basketball analytics, biomedical data analysis, and medical imaging. His projects have included building Python tools, web applications, and dashboards that streamline decision-making. Jordi’s multilingual fluency in Catalan, Spanish, and English (C1) and his adaptability, critical thinking, and leadership skills underscore his commitment to excellence. As a mental health researcher, programming professor, and basketball coach, Jordi excels at interdisciplinary collaboration, fostering innovation, and making meaningful contributions to both academia and real-world applications.

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🌟  Summary of Suitability for the Award

Jordi Rodeiro Boliart demonstrates an exceptional combination of academic excellence, multidisciplinary expertise, and impactful research, making him a strong candidate for the Research for Best Researcher Award. His academic journey spans multiple disciplines, including International Computer Engineering, Sports Science, and Data Science, culminating in a PhD in Artificial Intelligence and Autism Prediction. His diverse background equips him with a unique perspective in integrating technology, data science, and health research to address complex societal challenges.

Jordi’s research outputs reflect significant contributions to both applied and theoretical domains. Notably, his final master’s thesis focused on analyzing basketball data to enhance coaching strategies, while his degree project in the biomedical field led to a published scientific paper.

🎓 Education

Jordi Rodeiro Boliart boasts an impressive academic journey beginning with a dual degree in International Computer Engineering (La Salle, UPC) and Sports Science (INEFC Barcelona, UB). He further honed his expertise with a Master’s in Data Science (La Salle Bonanova, Barcelona), culminating in award-winning academic recognition. Currently pursuing a Ph.D. in Artificial Intelligence with a focus on autism prediction, Jordi demonstrates a commitment to cutting-edge research. His educational highlights include a final master’s thesis analyzing basketball data to enhance coaching strategies and a degree project in metabolomics published in a peer-reviewed journal. Jordi has also contributed to projects in medical imaging, such as using x-rays for illness detection. His academic journey is enriched by attending technology seminars at globally renowned institutions like Harvard and MIT, along with specialized training in leadership and organizational behavior. Jordi’s blend of technical and interdisciplinary studies defines his innovative, research-oriented career trajectory.

💼   Professional Experience

Jordi Rodeiro Boliart has a versatile professional background spanning research, teaching, and leadership. As a mental health researcher at Parc Sanitari Sant Joan de Déu, Jordi applies statistics and data science to critical health data, contributing to global assemblies and conferences. He serves as a university professor at La Salle Barcelona, teaching programming, mathematics, and IT software. As a data science intern at Sener, Jordi specialized in Power BI dashboards and analyzing corporate metrics. His engineering research internship included creating biomedical tools for metabolomic analysis, leading to a published paper. Jordi’s sports background complements his tech expertise, with roles as a basketball coach and coordinator, focusing on player development and team strategy. His earlier internships at Alfred Smart Systems and other engineering roles solidified his Python and gateway programming skills. Jordi’s diverse experiences exemplify his ability to integrate technology, data science, and education for impactful contributions.

🏅Awards and Recognitions

Jordi Rodeiro Boliart’s contributions have been widely recognized through various awards and honors. He received the prestigious Malaspina Award as part of the Empower consortium in 2023 and was a HackB finalist in the same year. Jordi was acknowledged with an academic excellence certificate for the best master’s record in Data Science (2023) and emerged as the LS Future Lab – Impact Challenge Hackathon winner in 2022. He represented his university as a National Model United Nations delegate in New York (2022) and participated in an international cooperation project in Perú. Jordi’s outstanding research on metabolomics earned him the opportunity to present at the Metabolomics 2022 conference. Beyond academia, Jordi is a certified Level II basketball coach, an FCBQ leadership trainee, and a master-certified Gannon Baker basketball coach. These accolades reflect his exceptional abilities in technical innovation, leadership, and interdisciplinary collaboration.

🌍  Research Skills On Computer Science

Jordi Rodeiro Boliart excels in applying advanced research methodologies to interdisciplinary challenges. His expertise includes data science, artificial intelligence, and object-oriented programming. Jordi has developed sophisticated tools for biomedical research, basketball analytics, and mental health studies. His doctoral research focuses on autism prediction through AI, combining statistical analysis and data visualization techniques. Jordi’s proficiency spans Python, MATLAB, MySQL, and Power BI, with skills in machine learning and medical image processing. He has designed Python programs to predict basketball outcomes, web apps for metabolomics, and diagnostic tools for x-rays. Jordi’s critical thinking, decision-making, and integrity define his research approach. His ability to present findings, such as at the Metabolomics 2022 conference, underscores his communication and analytical skills. Jordi’s research bridges academia and practical applications, demonstrating a commitment to addressing complex problems in health and technology.

📖 Publication Top Notes

1. The longitudinal relationship among physical activity, loneliness, and mental health in middle-aged and older adults: Results from the Edad con Salud cohort
  • Authors: Jordi Rodeiro, Beatriz Olaya, Josep Maria Haro, Aina Gabarrell-Pascuet, José Luis Ayuso-Mateos, Lea Francia, Cristina Rodríguez-Prada, Blanca Dolz-del-Castellar, Joan Domènech-Abella
  • Year: 2024
  • Citation: DOI: 10.1016/j.mhpa.2024.100667
2. The association of material deprivation with major depressive disorder and the role of loneliness and social support: A cross-sectional study
  • Authors: Joan Domènech-Abella, Carles Muntaner, Jordi Rodeiro, Aina Gabarrell-Pascuet, Josep Maria Haro, José Luis Ayuso-Mateos, Marta Miret, Beatriz Olaya
  • Year: 2024
  • Citation: DOI: 10.1016/j.jad.2024.09.071
3. Feasibility of an occupational e-mental health intervention for enhancing workplace mental health (EMPOWER RCT): Effectiveness and lessons learned (Preprint)
  • Authors: Carlota de Miquel, Christina M. Van der Feltz-Cornelis, Leona Hakkaart-van Roijen, Dorota Merecz-Kot, Marjo Sinokki, Jordi Rodeiro, Jennifer Sweetman, Kaja Staszewska, Ellen Vorstenbosch, Daniele Porricelli et al.
  • Year: 2024
  • Citation: DOI: 10.2196/preprints.66041
4. Trends of use of drugs with suggested shortages and their alternatives across 52 real-world data sources and 18 countries in Europe and North America
  • Authors: Marta Pineda-Moncusí, Alexandros Rekkas, Álvaro Martínez Pérez, Angela Leis, Carlos Lopez Gomez, Eric Fey, Erwin Bruninx, Filip Maljković, Francisco Sánchez-Sáez, Jordi Rodeiro et al.
  • Year: 2024
  • Citation: DOI: 10.1101/2024.08.28.24312695
5. CloMet: A Novel Open-Source and Modular Software Platform That Connects Established Metabolomics Repositories and Data Analysis Resources
  • Authors: Jordi Rodeiro, Ester Vidaña-Vila, Joan Navarro, Roger Mallol
  • Year: 2023

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Sheeja Rani S | Computer Science Award | Best Researcher Award

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Dr. Sheeja Rani S | Computer Science Award | Best Researcher Award

👤 Dr. Sheeja Rani S, American University of Sharjah, United Arab Emirates

Dr. Sheeja Rani S is a visionary researcher and academician specializing in Computer Science and Engineering, with a strong focus on Wireless Sensor Networks, IoT, and Smart Grids. She earned her Ph.D. from Noorul Islam Centre for Higher Education in 2023, where her thesis emphasized energy-efficient clustering algorithms for wireless sensor networks. Her academic journey is complemented by over a decade of teaching and research experience, where she worked on innovative solutions in cybersecurity, cloud computing, and machine learning. Currently serving as a Postdoctoral Research Assistant at the American University of Sharjah, Dr. Sheeja collaborates with leading experts on cutting-edge projects. With over 20 journal papers, numerous conference contributions, and a passion for impactful research, she strives to advance technology and foster intellectual growth. Her mission is to combine her expertise and mentorship skills to inspire future innovators while contributing to meaningful explorations in academia and beyond.

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🌟 Evaluation of Dr. Sheeja Rani S for the Research for Best Researcher Award

Summary of Suitability

Dr. Sheeja Rani S stands out as a highly qualified candidate for the “Research for Best Researcher Award,” showcasing an exceptional academic trajectory, prolific research output, and impactful contributions to multiple interdisciplinary domains. With a Ph.D. in Computer Science and Engineering focusing on improving energy efficiency in wireless sensor networks (WSNs), her research has addressed critical challenges in IoT, cloud computing, and smart grid technologies. These fields are not only contemporary but also pivotal for sustainable and secure technological advancements.

🎓 Education 

  • Ph.D. in Computer Science and Engineering (2023)
    Noorul Islam Centre for Higher Education
    Thesis: Improving Energy Efficiency Based on Clustering Algorithms for Wireless Sensor Networks.
  • M.E. in Computer Science and Engineering (2012)
    Noorul Islam Centre for Higher Education
  • M.Sc. Integrated Software Engineering (2009)
    Anna University, Chennai

Dr. Sheeja’s academic pursuits are rooted in innovation, particularly in optimizing computational techniques for energy efficiency and data security. Her Ph.D. research laid a foundation for creating advanced clustering mechanisms in wireless sensor networks, while her postgraduate and undergraduate studies focused on mastering computer science fundamentals and software engineering. She remains committed to lifelong learning and applying her knowledge to address emerging technological challenges.

💼  Professional Experience 

  • Postdoctoral Research Assistant (2023-Present)
    American University of Sharjah

    • Research on cybersecurity, smart grids, and cloud computing.
    • Published 12 journal papers in high-impact areas like IoT and machine learning.
  • Research Assistant (2022-2023)
    University of Sharjah

    • Focused on IoT, WSNs, and cloud computing.
    • Published 11 journal papers on financial distress prediction and IoT advancements.
  • Assistant Professor (2012-2021)
    John Cox Memorial CSI Institute of Technology

    • Taught advanced programming and database systems.
    • Managed academic coordination and examination processes.

Dr. Sheeja’s professional journey showcases a blend of teaching, research, and academic leadership, reflecting her dedication to advancing the field of computer science.

🏅 Awards and Recognitions 

  • Best Researcher Award (2023) – Recognized for impactful research in IoT and WSN.
  • Academic Excellence Award (2021) – Awarded for outstanding teaching and mentorship.
  • Research Grant Award (2022) – Funded for innovative studies on machine learning and cybersecurity.
  • Publication Excellence Award (2023) – Honored for prolific contributions to reputed journals.

Dr. Sheeja has consistently received accolades for her exceptional academic and research contributions. Her achievements reflect her dedication to excellence and her ability to produce innovative solutions that address global challenges.

🌍  Research Skills On Computer Science Award 

Dr. Sheeja’s research expertise spans:

  • Wireless Sensor Networks (WSN): Energy-efficient routing and clustering.
  • IoT: Developing secure and scalable architectures for smart environments.
  • Machine Learning: Applying predictive models for financial and cybersecurity domains.
  • Smart Grids: Integration of AI for optimal energy distribution.
  • Cloud Computing: Enhancing reliability and fault tolerance in virtualized environments.

📖 Publication Top Notes

Improved buffalo optimized deep feed forward neural learning based multipath routing for energy-efficient data aggregation in WSN
    • Authors: SS Rani, KS Sankar
    • Citation: Measurement: Sensors 27, 100662
    • Cited by: 8
    • Year: 2023
Optimized deep learning for Congestion-Aware continuous target tracking and boundary detection in IoT-Assisted WSN
    • Authors: AM Khedr, SS Rani, M Saad
    • Citation: IEEE Sensors Journal 23 (7), 7938-7948
    • Cited by: 8
    • Year: 2023
Enhancing Supply Chain Management with Deep Learning and Machine Learning Techniques: A Review
    • Authors: SSR Khedr, Ahmed M
    • Citation: Journal of Open Innovation: Technology, Market, and Complexity, 100379
    • Cited by: 5
    • Year: 2024
Hybridized Dragonfly and Jaya algorithm for optimal sensor node location identification in mobile wireless sensor networks
    • Authors: AM Khedr, SS Rani, M Saad
    • Citation: The Journal of Supercomputing 79 (15), 16940-16962
    • Cited by: 4
    • Year: 2023
Enhancing financial distress prediction through integrated Chinese Whisper clustering and federated learning
    • Authors: AI Al Ali, AM S S Rani Khedr
    • Citation: Journal of Open Innovation: Technology, Market, and Complexity 10 (3), 100344
    • Cited by: 2
    • Year: 2024