Angelos Athanasiadis | Neural Networks | Research Excellence Award

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Mr. Angelos Athanasiadis | Neural Networks | Research Excellence Award

Aristotle University of Thessaloniki | Greece

Angelos Athanasiadis is a Ph.D. candidate in Electrical and Computer Engineering at Aristotle University of Thessaloniki (AUTH), specializing in FPGA-based acceleration of Convolutional Neural Networks. With expertise spanning embedded system development, heterogeneous computing, and cyber-physical systems, he has contributed to both academic and industrial innovation through participation in EU research initiatives—including the ADVISER and REDESIGN projects—and through consultancy and R&D roles at EXAPSYS and SEEMS PC. His work focuses on advancing energy-efficient hardware acceleration, leading to the development of a parameterizable HLS matrix multiplication library for AMD FPGAs that enables full-precision CNN inference for accuracy-critical domains such as aerial monitoring and autonomous embedded systems. He further expanded the field with FUSION, an open-source high-fidelity distributed emulation framework integrating QEMU with OMNeT++ via HLA/CERTI synchronization to support deterministic, timing-aware multi-node execution and realistic prototyping of heterogeneous systems. Complementing his strong technical background, he holds an MBA awarded with high distinction and an M.Eng. in electronics and computer systems, supported by internships at Cadence Design Systems in Munich.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

"Pose Analysis in Free-Swimming Adult Zebrafish, Danio rerio: "fishy" Origins of Movement Design", Jagmeet S. Kanwal; Bhavjeet Sanghera; Riya Dabbi; Eric Glasgow, Preprint, 2025.

"Complex Sound Discrimination in Zebrafish: Auditory Learning Within a Novel “Go/Go” Decision-Making Paradigm", Anna Patel; Sai Mattapalli; Jagmeet S. Kanwal, Animals, 2025.

"From Information to Knowledge: A Role for Knowledge Networks in Decision Making and Action Selection", Jagmeet S. Kanwal, Information, 2024.

"NemoTrainer: Automated Conditioning for Stimulus-Directed Navigation and Decision Making in Free-Swimming Zebrafish", Bishen J. Singh; Luciano Zu; Jacqueline Summers; Saman Asdjodi; Eric Glasgow; Jagmeet S. Kanwal, Animals, 2022.

"NemoTrainer: Apparatus and Software for Automated Conditioning of Stimulus-directed Navigation and Decision Making in Freely Behaving Animals", Bishen Singh; Luciano Zu; Jacqueline Summers; Saman Asdjodi; Eric Glasgow; Jagmeet S. Kanwal, Preprint, 2022.

Delphine Vandame | Link Prediction | Best Researcher Award

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Ms. Delphine Vandame | Link Prediction | Best Researcher Award

Biocodex | France

Delphine Vandame, PhD, is an accomplished Global Medical Affairs leader with a strong scientific foundation in neuropharmacology and more than 15 years of progressive experience across the pharmaceutical and biotechnology sectors. Driven by a deep passion for science, resilience, and adaptability within the evolving healthcare environment, she currently serves as Global Medical Affairs Head at Biocodex, where she directs worldwide medical strategy for Diacomit in rare pediatric epilepsy, oversees a team of seven Medical Advisors, accelerates digital transformation through AI-driven insight and analytics, and strengthens strategic capabilities across international affiliates. Prior to this, she held key leadership roles at UCB Pharma, including Ecosystem Lead-where she designed and executed immunology strategies for Cimzia and Bimzelx, created a new dermatology business unit, and led a multifunctional medical–commercial team recognized with the 2019 Best MSL Team in EU award-and Medical Manager, guiding EU and French medical strategy across rheumatology and dermatology indications. Earlier roles include Medical Affairs Project Manager at Boehringer Ingelheim, Senior Project Manager at WPP supporting top global pharma organizations, Business Developer at Universal Biotech, Project Manager at CNRS, and Researcher at INSERM and the University of Texas, during which she secured grant funding, contributed to R&D programs, and presented internationally. Dr. Vandame holds a PhD in Neuropharmacology from the Pharmaceutical University of Montpellier, dual Master’s degrees in Biochemistry and Chemistry, and executive leadership training from HEC Paris and Krauthammer; recognized for her work ethic, strategic drive, and leadership excellence, she continues to thrive in fast-paced, cross-functional environments where innovation, scientific rigor, and patient-centric impact intersect.

Profiles: Scopus | Orcid

Featured Publications

"Comparative efficacy and safety of stiripentol, cannabidiol and fenfluramine as first-line add-on therapies for seizures in Dravet syndrome: A network meta-analysis", Delphine Vandame, Epilepsia, 2024.

An Zeng | Machine Learning | Best Researcher Award

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Prof. An Zeng | Machine Learning | Best Researcher Award

Professor at Guangdong University of Technology, China📖

Professor Zeng An is a distinguished researcher with extensive expertise in machine learning, data mining technologies, and their applications in medicine. Her work has significantly contributed to the advancement of deep learning, neural networks, probabilistic models, rough set theory, genetic algorithms, and other optimization methods. Since her postdoctoral research at the National Research Council of Canada and Dalhousie University (2008–2011) under the guidance of Professor Kenneth Rockwood, Professor Xiaowei Song, and Professor Arnold Mitnitski, she has been dedicated to applying these computational techniques to clinical research on Alzheimer’s Disease (AD).

Profile

Scopus Profile

Education Background🎓

Professor Zeng An completed her postdoctoral research at the National Research Council of Canada, collaborating with leading experts in medical AI applications. She holds a Ph.D. in Computer Science with a focus on machine learning and data mining techniques for medical applications. Her academic journey also includes a master’s and a bachelor’s degree in computer science or related fields (specific institutions and years can be added if available).

Professional Experience🌱

With a career spanning academia and research, Professor Zeng An has held key positions in leading universities and research institutions. During her postdoctoral tenure (2008–2011), she worked at Dalhousie University’s Faculty of Computer Science and Faculty of Medicine, contributing to AI-driven clinical research on neurodegenerative diseases. She has since continued her work in academia, conducting research on advanced machine learning techniques, medical data analysis, and clinical decision support systems.

Research Interests🔬

Professor Zeng An’s research focuses on developing intelligent algorithms for medical applications, particularly in Alzheimer’s Disease diagnostics and prediction. She specializes in deep learning, neural networks, probabilistic models, genetic algorithms, and optimization techniques. Her work extends to clinical data mining, patient risk assessment, and AI-driven medical decision-making, significantly impacting precision medicine.

Author Metrics

Professor Zeng An has a strong publication record in high-impact journals and conferences related to machine learning, AI in healthcare, and medical informatics. Her work has received substantial citations, reflecting her influence in the field. Key metrics such as H-index, i10-index, and total citations further highlight her academic contributions (specific numbers can be added if available).

Awards & Honors

Throughout her career, Professor Zeng An has received prestigious awards and recognitions for her contributions to AI and medical research. Her collaborations with renowned scientists in AI-driven healthcare innovations have led to groundbreaking advancements in the field. She continues to be a leading figure in interdisciplinary research, bridging computer science and medicine for improved healthcare outcomes.

Publications Top Notes 📄

1. Reinforcement Learning-Based Method for Type B Aortic Dissection Localization

  • Authors: Zeng An, Xianyang Lin, Jingliang Zhao, Baoyao Yang, Xin Liu
  • Journal: Journal of Biomedical Engineering (Shengwu Yixue Gongchengxue Zazhi), 2024
  • Citations: 0
  • Summary: This study presents a reinforcement learning-based approach for accurately localizing Type B aortic dissection, improving diagnostic precision in medical imaging.

2. Progressive Deep Snake for Instance Boundary Extraction in Medical Images (Open Access)

  • Authors: Zixuan Tang, Bin Chen, Zeng An, Mengyuan Liu, Shen Zhao
  • Journal: Expert Systems with Applications, 2024
  • Citations: 2
  • Summary: The research introduces a progressive deep snake model to enhance boundary extraction in medical images, facilitating precise segmentation for clinical applications.

3. Multi-Scale Quaternion CNN and BiGRU with Cross Self-Attention Feature Fusion for Fault Diagnosis of Bearing

  • Authors: Huanbai Liu, Fanlong Zhang, Yin Tan, Shenghong Luo, Zeng An
  • Journal: Measurement Science and Technology, 2024
  • Citations: 1
  • Summary: This paper develops a multi-scale quaternion CNN and BiGRU model integrating cross self-attention feature fusion to enhance the accuracy of bearing fault diagnosis in industrial applications.

4. An Ensemble Model for Assisting Early Alzheimer’s Disease Diagnosis Based on Structural Magnetic Resonance Imaging with Dual-Time-Point Fusion

  • Authors: Zeng An, Jianbin Wang, Dan Pan, Wenge Chen, Juhua Wu
  • Journal: Journal of Biomedical Engineering (Shengwu Yixue Gongchengxue Zazhi), 2024
  • Citations: 0
  • Summary: The study proposes an ensemble model utilizing dual-time-point fusion of MRI scans to improve early detection and diagnosis of Alzheimer’s Disease.

5. FedDUS: Lung Tumor Segmentation on CT Images Through Federated Semi-Supervised Learning with Dynamic Update Strategy

  • Authors: Dan Wang, Chu Han, Zhen Zhang, Zhenwei Shi, Zaiyi Liu
  • Journal: Computer Methods and Programs in Biomedicine, 2024
  • Summary: This research introduces a federated semi-supervised learning framework with a dynamic update strategy for effective lung tumor segmentation in CT imaging.

Conclusion

Professor An Zeng is a highly qualified candidate for the Best Researcher Award, given her outstanding contributions to AI in medicine, deep learning, and computational diagnostics. Her strong publication record, international research experience, and interdisciplinary approach make her an excellent nominee. While expanding clinical collaborations and citation impact would further enhance her profile, her cutting-edge research already positions her as a leader in medical AI applications.

Graph Analytics Mastermind Award

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Introduction of Graph Analytics Mastermind Award

Welcome to the forefront of innovation and excellence in the realm of Network Science and Graph Analytics. The Graph Analytics Mastermind Award celebrates pioneers who have demonstrated exceptional insight, leadership, and groundbreaking contributions in this dynamic field.

Eligibility:

This award is open to individuals of all ages who have significantly impacted the domain of Graph Analytics through their outstanding achievements. There are no age limits, and participants may come from diverse academic, industry, or research backgrounds.

Qualifications:

Candidates must possess a proven track record of achievements in Graph Analytics, showcasing their expertise and mastery in the field. Qualifications may include advanced degrees, relevant certifications, and a history of notable contributions.

Publications and Requirements:

A strong publication history in reputable journals and conferences is desirable. Candidates are expected to have demonstrated their commitment to advancing knowledge in Graph Analytics through high-quality, impactful publications.

Evaluation Criteria:

Entries will be evaluated based on innovation, influence, and the depth of impact in the realm of Graph Analytics. Judges will consider the candidate's overall contribution to the field, the significance of their work, and its potential for future developments.

Submission Guidelines:

Please submit a comprehensive biography, an abstract of your work, and supporting files that provide evidence of your contributions to Graph Analytics. Ensure that your submission adheres to the specified format guidelines for a thorough evaluation.

Recognition:

The Graph Analytics Mastermind Award offers unparalleled recognition within the global network science community. Winners will receive a prestigious accolade and gain exposure in relevant industry publications and conferences.

Community Impact:

Recipients of this award are expected to have made a substantial impact on the Graph Analytics community. Whether through mentorship, collaboration, or the development of tools and methodologies, community engagement is a crucial aspect of consideration.

Biography:

Include a detailed biography outlining your journey in the field of Graph Analytics, emphasizing key milestones, projects, and contributions. Showcase how your work has shaped the landscape of network science.

Abstract and Supporting Files:

Craft a concise abstract summarizing your most impactful contributions. Include supporting files that substantiate your achievements, such as research papers, project documentation, or testimonials from peers and collaborators.

Innovation Excellence Award in Network Science and Graph Analytics
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Outstanding Research Achievement Award in Network Science and Graph Analytics
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Academic Excellence Award in Network Science and Graph Analytics
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Industry Impact Award in Network Science and Graph Analytics
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Leadership in Business Applications Award in Network Science and Graph Analytics
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Collaborative Achievement Award in Network Science and Graph Analytics
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Emerging Talent Award in Network Science and Graph Analytics
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Strategic Implementation Award in Network Science and Graph Analytics
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Pioneering Contribution Award in Network Science and Graph Analytics
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Outstanding Contribution to Graph Analytics in Business Award
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