Marco Cirillo | Interactive Analytics | Best Researcher Award

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Dr. Marco Cirillo | Interactive Analytics | Best Researcher Award

Non-Profit Polyambulance Foundation | Italy

Dr. Marco Cirillo is a distinguished cardiac surgeon whose career seamlessly bridges clinical excellence and experimental research. After completing classical studies, he graduated in Medicine and Surgery with highest honors  from the University of Bologna and specialized in Cardiac and Great Vessels Surgery. His career has encompassed leadership roles as Head of the Cardiac Surgery Unit in Bologna for six years and the Heart Failure Surgery Unit in Brescia for twelve years. With expertise spanning the full range of traditional cardiac surgery-including mitral repair, coronary bypass, valve replacement, and aortic arch procedures-Dr. Cirillo has performed over 8,000 surgeries as first surgeon. His innovative contributions include the KISS procedure for physiological left ventricular reconstruction, the “Arterial-source No-touch Aorta” technique for off-pump coronary revascularization, and the NINFEA method for annular stabilization in endocarditis. Renowned for his commitment to quality and risk management (JCI accreditation), he continues to advance the field through research on bioprostheses and ventricular assist systems, recognized internationally through publications and awards. In 2025, he earned a Master’s degree in Echocardiography from the University of Verona. Beyond medicine, his intellectual pursuits extend to writing, photography, and cosmology, where he recently published a paper exploring Darwinian natural evolution as applied to the development of the Universe.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

"Achilles and the Tortoise: Rethinking Evidence Generation in Cardiovascular Surgery and Interventional Cardiology", Marco Cirillo, Hearts, 2025.

"Abiotrophia defectiva and Granulicatella: A Literature Review on Prosthetic Joint Infection and a Case Report on defectiva PJI and Concurrent Native Valve Endocarditis", MCristina Seguiti; Edda Piacentini; Angelica Fraghì; Mattia Zappa; Elia Croce; Angelo Meloni; Marco Cirillo; Clarissa Ferrari; Chiara Zani; David Belli et al., Microorganisms, 2025.

"Finger ischemia in a young lady: an unusual presentation of papillary fibroelastoma with intraventricular location", Matteo Pernigo; Elisabetta Dinatolo; Marco Cirillo; Zean Mhagna; Alida Filippini; Fabiana Cozza; Marco Berti; Roberto Bazzani; Tony Sabatini; Claudio Cuccia et al., Monaldi Archives for Chest Disease, 2023.

"Exploring Personal Protection During High-Risk PCI in a COVID-19 Patient", Marco Cirillo, JACC: Case Reports, 2020.

Farhad Hossain Sojib | Data Science | Best Researcher Award

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Mr. Farhad Hossain Sojib | Data Science | Best Researcher Award 

University of Hull | Bangladesh

Mr. Farhad Hossain Sojib is an engineer with a strong foundation in electronics and communication engineering and a growing specialization in data science and artificial intelligence. He is currently pursuing his M.Sc. in Artificial Intelligence and Data Science at the University of Hull, United Kingdom, following the completion of his B.Sc. in Engineering from Hajee Mohammad Danesh Science and Technology University, Bangladesh, where he conducted notable research on explainable AI in educational data mining and machine learning applications in 5G antenna optimization. With professional experience as an IELTS Instructor at Lexicon Plus, he has trained over 50 students, developed course materials, and mentored junior instructors. His leadership and organizational skills were further demonstrated through his role as a Program Committee Member at the IEEE Student Branch, HSTU, where he managed events, seminars, and competitions. Additionally, his internship at BRACNet Limited provided hands-on experience in ISP operations, ICT technologies, and server management. Farhad combines his technical expertise, research acumen, and collaborative mindset to contribute meaningfully to the fields of machine learning and data-driven innovation.

Profiles: Orcid 

Featured Publications

"The integration of explainable AI in Educational Data Mining for student academic performance prediction and support systems", Md. Mahmudul Islam; Farhad Hossain Sojib; Md. Fazle Hasan Mihad; Mahmudul Hasan; Mahfujur Rahman, Telematics and Informatics Reports, 2025.

"A Bioinformatics Approach to Uncover Hub Genes and Potential Drug Targets of Stroke, Heart-Disease, Hyperglycemia, and Hypertension", Md. Emran Biswas; M D. Fazle Hasan Mihad; Farhad Hossain Sojib; Mohammad Jubair Ahmmed; M D Galib Hasan; Md. Jobare Hossain; Md. Abul Basar; Md. Mehedi Islam; Md. Delowar Hossain; Md. Selim Hossain et al., 27th International Conference on Computer and Information Technology (ICCIT), 2024.

"An Explainable Educational Data Mining System for Predicting Student Academic Performance", Md. Mahmudul Islam; Farhad Hossain Sojib; Md. Fazle Hasan Mihad; Mahmudul Hasan; Mahfujur Rahman; FARHAD HOSSAIN SOJIB, 2024 IEEE International Conference on Signal Processing, Information, Communication and Systems, 2024.

OJO Olufisayo Emmanuel | Graph Data Structures | Best Researcher Award

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Mr. OJO Olufisayo Emmanuel | Graph Data Structures | Best Researcher Award

Durban University of Technology | South Africa

Engr. OJO Olufisayo Emmanuel, R.Engr., IEng (UK), MSc, B.Eng (Hons), M.I.E.T, MNSE, is an accomplished Electromechanical and Water Engineer, as well as a seasoned Project Manager, with extensive experience in institutional infrastructural development projects. He has held key roles in several international initiatives funded by organizations such as the World Bank, AFD, EBRD, USAID/E-WASH/RTI, and atmosfair gGmbH’s Carbon Mitigation projects in Nigeria. Currently serving with CKW Environment Limited, an engineering consultancy specializing in Water and Sanitary Engineering, he is deeply involved in the design of water treatment facilities, civil and electromechanical infrastructures, hydraulic design and optimization of water distribution systems, environmental impact assessments, and construction supervision. With strong technical and commercial expertise, Engr. OJO combines strategic project management skills with deep engineering insight, providing consultancy services across all project phases-from design and procurement to supervision, monitoring, evaluation, and project delivery-ensuring quality, compliance, and cost efficiency.

Profiles: Orcid

Featured Publications

"Innovative Recovery Methods for Metals and Salts from Rejected Brine and Advanced Extraction Processes-A Pathway to Commercial Viability and Sustainability in Seawater Reverse Osmosis Desalination", Olufisayo E. Ojo; Olanrewaju A. Oludolapo, Water, 2025.

"Cost–Benefit and Market Viability Analysis of Metals and Salts Recovery from SWRO Brine Compared with Terrestrial Mining and Traditional Chemical Production Methods", Olufisayo E. Ojo; Olanrewaju A. Oludolapo, Water, 2025.

"Modeling A Reverse Osmosis Desalination Plant: A Practical Framework Using Wave Software", Olufisayo Emmanuel Ojo; Olanrewaju Akanni Oludolapo, Science, Engineering and Technology, 2025.

"A Review of Renewable Energy Powered Seawater Desalination Treatment Process for Zero Waste", Olufisayo Emmanuel Ojo; Olanrewaju Akanni Oludolapo, Water, 2024.

Kumail Abbas | Technological Networks | Best Researcher Award

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Dr. Kumail Abbas | Technological Networks | Best Researcher Award

Chulalongkorn University | Thailand

Dr. Kumail Abbas is a veterinary scientist and researcher specializing in precision livestock farming, animal health, and welfare technologies. He is currently pursuing his Doctor of Philosophy in Veterinary Science and Technology at Chulalongkorn University, Thailand, where his doctoral research focuses on employing artificial intelligence to track and monitor dairy cow behaviour during the transition period to predict postpartum disorders. As a Visiting Researcher at the Bristol Vet School, University of Bristol, and University of Salford (UK), Dr. Abbas contributes to the British Dairy Cattle Welfare Strategy (2023–2028) through AI-based behavioural monitoring and data-driven welfare benchmarking. He holds a Master’s in Bioscience Technology from Chung Yuan Christian University, Taiwan, where he investigated the neurophysiological and toxicological effects of ractopamine in zebrafish models, and a Doctor of Veterinary Medicine (DVM) from the University of Veterinary and Animal Sciences, Lahore, Pakistan. His professional experience spans academia, industry, and farm management, including roles as Graduate Research Assistant at Chung Yuan Christian University, Product Information Officer at Prix Pharmaceutica (Pvt.) Ltd., and Assistant Farm Manager at Kasur Dairies Pvt. Ltd., where he developed strong expertise in animal reproduction, health management, and sustainable dairy operations. Dr. Abbas’s research integrates AI, deep learning, and animal behaviour analysis to promote sustainable livestock production. He has received multiple awards and grants, including the Second Century Fund (C2F) Doctoral Fellowship, the 90th Anniversary Ratchadaphisek Somphot Endowment Fund, and the Biotech Excellent Award for academic excellence. His work has been published in international journals and presented at global conferences, reflecting his commitment to advancing innovation in animal science, digital agriculture, and welfare-oriented livestock technologies.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

"Impacts of early postpartum behavioral patterns on the fertility and milk production of tropical dairy cows"

"Behavioral Adaptations in Tropical Dairy Cows: Insights into Calving Day Predictions"

"Ractopamine at the Center of Decades-Long Scientific and Legal Disputes: A Lesson on Benefits, Safety Issues, and Conflicts"

"Evaluation of Effects of Ractopamine on Cardiovascular, Respiratory, and Locomotory Physiology in Animal Model Zebrafish Larvae"

"Sex determination by CHD (Chromo helicase DNA binding) gene in local rock pigeons (Columba livia) from Lahore, Pakistan"

Jiyoon Lee | Graph Data | Best Researcher Award

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Ms. Jiyoon Lee | Graph Data | Best Researcher Award

Ewha Womans University | South Korea

Ms. Jiyoon Lee is a doctoral researcher in Big Data Analytics at Ewha Womans University, Seoul, Korea, with expertise in graph data structures, algorithms, and GeoAI applications. Her research explores urban mobility, safety, and congestion management through advanced spatiotemporal modeling. She has presented her work at major conferences, including SIGSPATIAL 2025, where she introduced a novel GraphLSTM-Attn framework for modeling stopped vehicle dynamics on urban backstreets, and Asiacarto 2024, where she proposed a CCTV-based trajectory algorithm for road congestion and alleyway safety. Her scholarly contributions include multiple publications in the ISPRS International Journal of Geo-Information, such as studies on pedestrian congestion and safety in urban alleyways and the development of PGTFT, a lightweight graph-attention temporal fusion transformer for predicting pedestrian congestion in shadow areas. Through her innovative research at the intersection of graph data and GeoAI, she continues to advance data-driven solutions for safer and more efficient urban environments.

Profiles: Orcid ID

Featured Publications

"PGTFT: A Lightweight Graph-Attention Temporal Fusion Transformer for Predicting Pedestrian Congestion in Shadow Areas"

"Correction: Lee, J.; Kang, Y. A Dynamic Algorithm for Measuring Pedestrian Congestion and Safety in Urban Alleyways. ISPRS Int. J. Geo-Inf. 2024, 13, 434"

"A Dynamic Algorithm for Measuring Pedestrian Congestion and Safety in Urban Alleyways"

Dongfang Zhao | Machine Learning | Best Researcher Award

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Prof. Dongfang Zhao | Machine Learning | Best Researcher Award

Prof. Dongfang Zhao at University of Washington, United States

🌟 Dongfang Zhao, Ph.D., is a Tenure-Track Assistant Professor at the University of Washington Tacoma and a Data Science Affiliate at the eScience Institute. With a Ph.D. in Computer Science from Illinois Institute of Technology (2015) and PostDoc from the University of Washington, Seattle (2017), Dr. Zhao’s career spans academic excellence and groundbreaking research in distributed systems, blockchain, and machine learning. His work, recognized with federal grants and best paper awards, has significantly impacted cloud computing, HPC systems, and AI-driven blockchain solutions. Dr. Zhao is an influential editor, reviewer, and committee member in prestigious venues. 📚💻✨

Professional Profile:

Google Scholar

Orcid

Education and Experience 

🎓 Education:

  • Postdoctoral Fellowship, Computer Science, University of Washington, Seattle (2017)
  • Ph.D., Computer Science, Illinois Institute of Technology, Chicago (2015)
  • M.S., Computer Science, Emory University, Atlanta (2008)
  • Diploma in Statistics, Katholieke Universiteit Leuven, Belgium (2005)

💼 Experience:

  • Tenure-Track Assistant Professor, University of Washington Tacoma (2023–Present)
  • Visiting Professor, University of California, Davis (2018–2023)
  • Assistant Professor, University of Nevada, Reno (2017–2023)
  • Visiting Scholar, University of California, Berkeley (2016)
  • Research Intern, IBM Almaden Research Center (2015), Argonne National Laboratory (2014), Pacific Northwest National Laboratory (2013)

Professional Development

📊 Dr. Dongfang Zhao is a leading voice in distributed systems, blockchain technologies, and scalable machine learning. He contributes to academia as an Associate Editor for the Journal of Big Data and serves on the editorial board of IEEE Transactions on Distributed and Parallel Systems. A sought-after reviewer and conference organizer, Dr. Zhao actively shapes the future of AI and cloud computing. With a deep commitment to mentorship, he has guided doctoral students to successful careers in academia and industry. His collaborative initiatives reflect a passion for addressing real-world challenges through computational innovation. 🌐✨📖

Research Focus

🔬 Dr. Zhao’s research emphasizes cutting-edge developments in distributed systems, blockchain, machine learning, and HPC (high-performance computing). His work delves into creating energy-efficient, scalable blockchain platforms like HPChain and developing frameworks for efficient scientific data handling. His contributions include lightweight blockchain solutions for reproducible computing and innovations in AI-driven systems like HDK for deep-learning-based analyses. Dr. Zhao’s interdisciplinary approach fosters impactful collaborations, addressing pressing technological needs in cloud computing, scientific simulations, and data analytics. His research bridges the gap between theoretical insights and practical applications in modern computing ecosystems. 🚀📊🧠

Awards and Honors 

  • 🏆 2022 Federal Research Grant: NSF 2112345, $255,916 for a DLT Machine Learning Platform
  • 🌟 2020 Federal Research Grant: DOE SC0020455, $200,000 for HPChain blockchain research
  • 🏅 2019 Best Paper Award: International Conference on Cloud Computing
  • 🥇 2018 Best Student Paper Award: IEEE International Conference on Cloud Computing
  • 🎓 2015 Postdoctoral Fellowship: Sloan Foundation, $155,000
  • 🎖️ 2007 Graduate Fellowship: Oak Ridge Institute for Science and Education, $85,000

Publication Top Notes:

1. Regulated Charging of Plug-In Hybrid Electric Vehicles for Minimizing Load Variance in Household Smart Microgrid

  • Authors: L. Jian, H. Xue, G. Xu, X. Zhu, D. Zhao, Z.Y. Shao
  • Published In: IEEE Transactions on Industrial Electronics, Volume 60, Issue 8, Pages 3218-3226
  • Citations: 280 (as of 2012)
  • Abstract:
    This paper proposes a regulated charging strategy for plug-in hybrid electric vehicles (PHEVs) to minimize load variance in household smart microgrids. The method ensures that the charging process aligns with household power demand patterns, improving grid stability and efficiency.

2. ZHT: A Lightweight, Reliable, Persistent, Dynamic, Scalable Zero-Hop Distributed Hash Table

  • Authors: T. Li, X. Zhou, K. Brandstatter, D. Zhao, K. Wang, A. Rajendran, Z. Zhang, …
  • Published In: IEEE International Symposium on Parallel & Distributed Processing (IPDPS)
  • Citations: 212 (as of 2013)
  • Abstract:
    This paper introduces ZHT, a zero-hop distributed hash table designed for high-performance computing systems. It is lightweight, scalable, and reliable, making it suitable for persistent data storage in distributed environments.

3. Optimizing Load Balancing and Data-Locality with Data-Aware Scheduling

  • Authors: K. Wang, X. Zhou, T. Li, D. Zhao, M. Lang, I. Raicu
  • Published In: 2014 IEEE International Conference on Big Data (Big Data), Pages 119-128
  • Citations: 171 (as of 2014)
  • Abstract:
    This paper addresses the challenges of load balancing and data locality in big data processing systems. A novel data-aware scheduling algorithm is proposed to improve efficiency and performance in high-performance computing environments.

4. FusionFS: Toward Supporting Data-Intensive Scientific Applications on Extreme-Scale High-Performance Computing Systems

  • Authors: D. Zhao, Z. Zhang, X. Zhou, T. Li, K. Wang, D. Kimpe, P. Carns, R. Ross, …
  • Published In: 2014 IEEE International Conference on Big Data (Big Data), Pages 61-70
  • Citations: 154 (as of 2014)
  • Abstract:
    FusionFS is a distributed file system tailored for extreme-scale high-performance computing systems. It provides efficient data storage and retrieval, supporting data-intensive scientific applications and overcoming the bottlenecks in traditional storage systems.

5. Enhanced Data-Driven Fault Diagnosis for Machines with Small and Unbalanced Data Based on Variational Auto-Encoder

  • Authors: D. Zhao, S. Liu, D. Gu, X. Sun, L. Wang, Y. Wei, H. Zhang
  • Published In: Measurement Science and Technology, Volume 31, Issue 3, Article 035004
  • Citations: 105 (as of 2019)
  • Abstract:
    This study enhances fault diagnosis for machines using a data-driven approach. By leveraging variational auto-encoders (VAEs), the method effectively handles small and unbalanced datasets, achieving high diagnostic accuracy for industrial applications.

Myrto Limnios | Outlier Detection | Best Researcher Award

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Mrs. Myrto Limnios | Outlier Detection | Best Researcher Award

Bernoulli Instructor at Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland📖

Myrto Limnios is a French-Greek researcher specializing in statistical learning theory, causal inference, and machine learning. She currently serves as a Bernoulli Instructor at the Ecole Polytechnique Fédérale de Lausanne (EPFL), focusing on hypothesis testing and causal modeling. Myrto’s research spans nonparametric hypothesis testing, high-dimensional data analysis, and biomedical applications. Her innovative methodologies, which include modern machine learning algorithms, are available as open-access tools to support reproducible research.

Profile

Google Scholar Profile

Education Background🎓

  • Ph.D. in Nonparametric Statistics and Statistical Learning Theory
    Université Paris-Saclay, France (2018–2022)
    Thesis: Rank Processes and Statistical Applications in High Dimension
    Supervisors: Prof. Nicolas Vayatis, Dr. Ioannis Bargiotas
  • M.Sc. in Random Modeling, Finance, and Data Science (M2MO)
    Université Paris 1 Panthéon-Sorbonne and Université Paris Diderot, France (2016–2017)
    Thesis: Random Modeling in Electronic Market Making with Numerical Applications
  • Engineering Program (French Grande École)
    Ecole des Mines de Nancy, France (2014–2017)
    Major: Industrial Engineering and Applied Mathematics

Professional Experience🌱

  • Bernoulli Instructor (2024–2026)
    EPFL, Lausanne, Switzerland
    Research focus: Hypothesis testing, causal inference, and ranking-based methods with applications to statistical learning theory.
  • Postdoctoral Fellow (2022–2024)
    University of Copenhagen, Denmark
    Research on causal learning and conditional independence testing for dynamic systems under the mentorship of Prof. Niels R. Hansen.
  • Research Associate (2017–2018)
    ENS Paris-Saclay, France
    Investigated high-dimensional statistical testing and machine learning methodologies.
Research Interests🔬

Myrto’s primary research interests include:

  • Development of nonparametric hypothesis tests for complex data structures.
  • Sparse modeling and penalized loss function solutions (e.g., LASSO) with theoretical guarantees.
  • Causal inference and conditional independence testing for continuous-time systems.
  • Applications of statistical and machine learning methodologies in biomedical research.

Author Metrics

Myrto Limnios has an h-index of 4, reflecting her impactful contributions to the fields of statistical learning and machine learning. She has authored several peer-reviewed articles published in renowned journals, including Machine Learning (Springer), Electronic Journal of Statistics, PLOS ONE, and IEEE Transactions on Neural Systems and Rehabilitation Engineering. Her research encompasses diverse areas such as nonparametric hypothesis testing, causal inference, and biomedical applications. Additionally, she has contributed book chapters, conference proceedings, and preprints, showcasing her dedication to advancing scientific knowledge. Myrto actively collaborates with leading experts, including Prof. Nicolas Vayatis and Prof. Niels R. Hansen, and regularly serves as a reviewer for esteemed journals and conferences

Publications Top Notes 📄

1. Revealing Posturographic Profile of Patients with Parkinsonian Syndromes Through a Novel Hypothesis Testing Framework Based on Machine Learning

  • Authors: I. Bargiotas, A. Kalogeratos, M. Limnios, P.-P. Vidal, D. Ricard, N. Vayatis
  • Published in: PLOS ONE
  • Volume and Issue: 16(2)
  • DOI: 10.1371/journal.pone.0246790
  • Abstract: This paper proposes a novel machine learning-based hypothesis testing framework to analyze posturographic data. The study focuses on Parkinsonian syndromes, identifying key features linked to the risk of falling. The methodology combines modern hypothesis testing with machine learning algorithms for biomedical applications.
  • Citations: 14

2. A Langevin-Based Model with Moving Posturographic Target to Quantify Postural Control

  • Authors: A. Nicolaï, M. Limnios, A. Trouvé, J. Audiffren
  • Published in: IEEE Transactions on Neural Systems and Rehabilitation Engineering
  • Volume and Pages: 29, 478–487
  • DOI: 10.1109/TNSRE.2021.3052395
  • Abstract: This work introduces a Langevin-based model that uses dynamic targets to evaluate postural control. The study integrates stochastic modeling and rehabilitation engineering for a quantitative assessment of postural stability.
  • Citations: 7

3. Concentration Inequalities for Two-Sample Rank Processes with Application to Bipartite Ranking

  • Authors: S. Clémençon, M. Limnios, N. Vayatis
  • Published in: Electronic Journal of Statistics
  • Volume and Pages: 15, 4659–4717
  • DOI: 10.1214/21-EJS1901
  • Abstract: The paper investigates concentration inequalities for rank processes in high-dimensional settings, focusing on bipartite ranking. The authors provide theoretical guarantees and applications to machine learning tasks.
  • Citations: 6

4. Epidemic Models for COVID-19 During the First Wave from February to May 2020: A Methodological Review

  • Authors: M. Garin, M. Limnios, A. Nicolaï, I. Bargiotas, O. Boulant, S. Chick, A. Dib, et al.
  • Published in: arXiv Preprint
  • ArXiv ID: 2109.01450
  • Abstract: This comprehensive review examines epidemic models developed during the early phase of the COVID-19 pandemic. The paper highlights methodological approaches, their advantages, and limitations for modeling and forecasting outbreaks.
  • Citations: 4

5. Multivariate Two-Sample Hypothesis Testing Through AUC Maximization for Biomedical Applications

  • Authors: I. Bargiotas, A. Kalogeratos, M. Limnios, P.-P. Vidal, D. Ricard, N. Vayatis
  • Published in: 11th Hellenic Conference on Artificial Intelligence
  • Pages: 56–59
  • Abstract: This conference paper introduces a new multivariate hypothesis testing framework using AUC maximization. It is specifically tailored for biomedical applications, providing robust statistical analysis tools.
  • Citations: 4

Conclusion

Myrto Limnios is an exceptional candidate for the Best Researcher Award. Her innovative methodologies, impactful publications, and dedication to interdisciplinary research make her a standout in her field. While opportunities exist to expand her engagement with broader audiences and applied research domains, her achievements thus far establish her as a leading figure in statistical learning and machine learning. Awarding her this recognition would not only celebrate her accomplishments but also inspire continued excellence in research and collaboration