Shengyu Lu | Artificial Intelligence | Young Scientist Award

Dr. Shengyu Lu | Artificial Intelligence | Young Scientist Award

Tenure-Track Associate Professor, Harbin Engineering University

Shengyu Lu is a Tenure-Track Associate Professor at Harbin Engineering University, specializing in deep learning and computer vision. He obtained his Ph.D. in Computer Science from the University of Southampton (2020-2024) and has a strong background in software engineering and information security. With experience as a lecturer and teaching assistant, he has mentored students in machine learning, image processing, and programming. His research focuses on medical image analysis, particularly using deep learning for fracture discrimination and volumetric texture classification. He has collaborated with leading clinical experts and published extensively in high-impact journals. Shengyu has also served as a reviewer for prestigious journals, including Pattern Recognition and IEEE Transactions on Industrial Informatics. Recognized for his contributions, he has received multiple scholarships and awards, such as the Best Abstract Award in Rheumatology and the Best Oral Presentation Award at the WCSE Conference.

Profile

Scholar

Orcid

🎓 Education 

Ph.D. in Computer Science (2020-2024) – University of Southampton, specializing in deep learning and medical image analysis under the supervision of Sasan Mahmoodi and Mahesan Niranjan. Master’s in Software Engineering (2017-2020) – Xiamen University, focusing on machine learning, AI, and software development. Bachelor’s in Information Security (2013-2017) – Nanchang University, with expertise in cybersecurity and data protection. Throughout his academic journey, Shengyu Lu has developed advanced AI-driven solutions for medical imaging, object detection, and computational intelligence. His research integrates deep learning with real-world applications, particularly in healthcare and security. He has actively participated in conferences and received multiple scholarships, including the National Merit Scholarship and the China Scholarship Council (CSC) Scholarship. His educational background reflects a strong foundation in AI, computer vision, and software engineering, preparing him for impactful research and teaching.

💼 Work Experience 

Lecturer (2024-Present) – Harbin Engineering University, delivering lectures, mentoring students, and conducting AI research. Teaching Assistant (2020-2024) – University of Southampton, guiding master’s students in machine learning, image processing, and Python programming. Mentor (2021-2023) – University of Southampton, assisting master’s students in academic and career development. Teaching Assistant (2018-2019) – Xiamen University, instructing undergraduate students in software engineering and C programming. Shengyu Lu has a strong teaching portfolio, mentoring students in AI-related disciplines and conducting impactful research. His role as a lecturer involves fostering academic excellence while advancing research in deep learning and medical image analysis. With experience in both undergraduate and postgraduate education, he has developed expertise in curriculum development, student mentorship, and academic service.

🏆 Awards and Honors 

2022 – Best Abstract Award, Rheumatology 2020 – Best Oral Presentation Award, WCSE Conference 2020 – China Scholarship Council (CSC) Scholarship 2020 – Outstanding Graduate, Xiamen University m2019 – National Merit Scholarship 2019 – “Xuangong Wu” Research Scholarship 2018 – “Huawei” Scholarship 2017 – Outstanding Graduate, Nanchang University 2016-2014 – Multiple Excellent Student and Cadre Awards Recognized for his academic excellence and research contributions, Shengyu Lu has received numerous awards and scholarships. His outstanding performance in AI and deep learning has earned him national and international accolades. His achievements highlight his dedication to cutting-edge research and academic leadership.

🔬 Research Focus 

Shengyu Lu’s research centers on deep learning and computer vision, with a particular emphasis on medical image analysis. His work includes designing deep neural networks for fracture discrimination using high-resolution peripheral quantitative computed tomography (HR-pQCT) images. He develops AI-driven volumetric texture analysis techniques to assess cortical and trabecular compartments in bone structure. Collaborating with clinical experts from the MRC Lifecourse Epidemiology Centre and University Hospital, he integrates AI with medical diagnostics. His research extends to object detection, machine learning for protein-protein interactions, and real-time AI applications. He has published extensively in SCI-indexed journals such as Bone, IEEE Access, and Computers & Electrical Engineering. His innovative contributions improve automated medical diagnostics, reducing human error in clinical assessments. His work advances AI’s role in healthcare, bridging the gap between technology and medicine to enhance early disease detection and predictive analytics.

🔍 Conclusion

Shengyu Lu is a strong candidate for the Young Scientist Award, given his impressive academic achievements, impactful research contributions in deep learning for medical imaging, and international recognition. To further strengthen his candidacy, he could focus on securing independent research funding, leading high-impact clinical studies, and increasing first-author publications in top-tier journals.

Publication

Author: S Lu, B Wang, H Wang, L Chen, M Linjian, X Zhang
Title: A real-time object detection algorithm for video
Year: 2019
Citations: 145

Author: S Lu, B Wang, H Wang, Q Hong
Title: A hybrid collaborative filtering algorithm based on KNN and gradient boosting
Year: 2018
Citations: 20

Author: S Lu, H Chen, XZ Zhou, B Wang, H Wang, Q Hong
Title: Graph‐Based Collaborative Filtering with MLP
Year: 2018
Citations: 19

Author: L Chen, B Xu, J Chen, K Bi, C Li, S Lu, G Hu, Y Lin
Title: Ensemble-machine-learning-based correlation analysis of internal and band characteristics of thermoelectric materials
Year: 2020
Citations: 18

Author: S Lu, NR Fuggle, LD Westbury, MÓ Breasail, G Bevilacqua, KA Ward, …
Title: Machine learning applied to HR-pQCT images improves fracture discrimination provided by DXA and clinical risk factors
Year: 2023
Citations: 16

Author: S Lu, Q Hong, B Wang, H Wang
Title: Efficient resnet model to predict protein-protein interactions with GPU computing
Year: 2020
Citations: 15

Author: S Lu, B Wang
Title: An image retrieval algorithm based on improved color histogram
Year: 2019
Citations: 8

Author: NR Fuggle, S Lu, MÓ Breasail, LD Westbury, KA Ward, E Dennison, …
Title: OA22 Machine learning and computer vision of bone microarchitecture can improve the fracture risk prediction provided by DXA and clinical risk factors
Year: 2022
Citations: 5

Author: S Lu, S Mahmoodi, M Niranjan
Title: Robust 3D rotation invariant local binary pattern for volumetric texture classification
Year: 2022
Citations: 4

Author: S Lu, H Chen, L Peng, B Wang, H Wang, X Zhou
Title: A compression algorithm of FASTQ file based on distribution characteristics analysis
Year: 2018
Citations: 1

 

Manasvi Aggarwal | Artificial Intelligence | Young Scientist Award

Ms. Manasvi Aggarwal | Artificial Intelligence | Young Scientist Award

Senior Data Scientist, Mastercard

Name: Manasvi Aggarwal  Gender: Female Designation: Senior Data Scientist Department: AI Garage Organization: Mastercard Specialization: Artificial IntelligenceExpertise: Graph Neural Networks (GNNs), Spatio-Temporal Forecasting, Neural Algorithmic Reasoning Industry Experience: Microsoft, Myntra, Mastercard Notable Work: Developed MRP-GNN, identifying $17M in fraud and increasing fraud detection rates by 20% Research Collaborations: Oxford, Cambridge (INAR project) Publications: ICPR, IEEE BigData, ICML workshops, RecSys, Springer book Awards: Business Excellence Award (Mastercard), Google-sponsored EEML travel grant Professional Contributions: ICLR, IEEE BigData, and NeurIPS program committee member Vision: Advancing scalable AI solutions for real-world impact

profile:

Scholar

🎓 Education 

B.Tech: Computer Science, University of Delhi (Ranked 4th in cohort) Competitive Exams: GATE (99.47 percentile), JEST (All India Rank 35) M.Tech (Research): Computer Science, IISc Bengaluru Thesis: “Embedding Networks: Node and Edge Representations” (Graph-based learning, NLP, Computer Vision) PhD Offers: Fully funded PhD admissions from Canada and the USA (deferred due to financial reasons) Key Research Areas: Graph Neural Networks, Spatio-Temporal Forecasting, Neural Algorithmic Reasoning Notable Academic Achievements: IISc Research Contributions in GNNs and self-supervised learning

💼 Experience 

Microsoft: Developed multi-label categorization for Azure offers using BERT embeddings and web scraping  Myntra: Worked on pricing automation, demand forecasting, and user cohort modeling with graph-based approaches Mastercard: Leading AI-driven fraud detection initiatives, including MRP-GNN (detected $17M fraud, 20% improved detection rates) Industry Collaborations: Worked with teams in Israel, USA, and India on AI-driven solutions Mentorship: Guides junior AI researchers, participates in hiring interviews

🏆 Awards & Honors 

Business Excellence Award – Mastercard Priceless Mentoring and Guidance Award – Mastercard Google-sponsored travel grant – EEML 2024 1st Runner-up – Myntra HackerRamp Hackathon Invited Program Committee Member – ICLR, IEEE BigData, NeurIPS

🔬 Research Focus 

Key Areas: Graph Neural Networks (GNNs), Spatio-Temporal Forecasting, Neural Algorithmic Reasoning Mastercard Research: Developed MRP-GNN, detecting high-risk merchants and preventing financial fraud  AI for Security: Fraud detection, risk prediction, secure transactions Spatio-Temporal AI: Developed GNN for terror activity forecasting in Jammu & Kashmir Academic Collaborations: Works with Oxford & Cambridge on INAR, optimizing CLRS-30 benchmarks Publications: Published in ICPR, IEEE BigData, ICML workshops, RecSys Community Contributions: Co-organized Southeast Asian Learning on Graphs (LoG) 2024 Meetup

✅ Conclusion

Manasvi Aggarwal is a highly competitive candidate for the Best Researcher Award, with a strong blend of academic excellence, industry impact, publications, and global collaborations. If she strengthens her patent portfolio, citation metrics, and leadership in independent research projects, she would be an even stronger contender for distinguished research awards.

publication

Deep Learning – M. Aggarwal, M.N. Murty (2021) – 21 citations

 

Machine Learning in Social Networks: Embedding Nodes, Edges, Communities, and Graphs – M. Aggarwal, M.N. Murty (2021) – 18 citations

 

Self-supervised Hierarchical Graph Neural Network for Graph Representation – S. Bandyopadhyay, M. Aggarwal, M.N. Murty (2020) – 4 citations

 

Robust Hierarchical Graph Classification with Subgraph Attention – S. Bandyopadhyay, M. Aggarwal, M.N. Murty (2020) – 4 citations

 

Unsupervised Graph Representation by Periphery and Hierarchical Information Maximization – S. Bandyopadhyay, M. Aggarwal, M.N. Murty (2020) – 3 citations

 

A Deep Hybrid Pooling Architecture for Graph Classification with Hierarchical Attention – S. Bandyopadhyay, M. Aggarwal, M.N. Murty (2021) – 2 citations

 

Region and Relations Based Multi Attention Network for Graph Classification – M. Aggarwal, M.N. Murty (2021) – 2 citations

 

Using Relational Graph Convolutional Networks to Assign Fashion Communities to Users – A. Budhiraja, M. Sukhwani, M. Aggarwal, S. Shevade, G. Sathyanarayana (2022) – 1 citation

 

Node Representations – M. Aggarwal, M.N. Murty (2021) – 1 citation

 

Hierarchically Attentive Graph Pooling with Subgraph Attention – S. Bandyopadhyay, M. Aggarwal, M.N. Murty (2020) – 1 citation

 

Embedding Graphs – M. Aggarwal, M.N. Murty (2021)

 

Representations of Networks – M. Aggarwal, M.N. Murty (2021)

 

Embedding Networks: Node and Graph Level Representations – M. Aggarwal

Narayan Vyas | Artificial Intelligence | Young Scientist Award

Mr Narayan Vyas | Artificial Intelligence | Young Scientist Award

Mr Narayan Vyas , Vivekananda Global University, India

Dr. Narayan Vyas is an Assistant Professor at Vivekananda Global University, Jaipur, specializing in Internet of Things (IoT) and application development. With a Ph.D. in Computer Science and multiple publications in peer-reviewed journals, he is recognized for his significant contributions to IoT, machine learning, and remote sensing. Dr. Vyas has a proven track record in academia and industry, including roles as a Technical Trainer and Research Consultant. His research includes developing frameworks for agricultural changes and innovations in mobile app development. He is also an active keynote speaker and workshop organizer, dedicated to advancing technological education.

Publication Profile

Orcid

Strengths for the Award

  1. Extensive Research Contributions: Narayan has a robust research profile with numerous publications in peer-reviewed international journals and conferences. His work spans diverse and cutting-edge fields, including IoT, remote sensing, machine learning, and computer vision. This breadth and depth of research indicate a high level of expertise and a significant contribution to the field.
  2. Active Engagement in Academia: As an Assistant Professor and Research Coordinator, Narayan plays a key role in shaping academic programs and mentoring students. His leadership in developing syllabi and organizing workshops demonstrates a commitment to advancing both education and research.
  3. Industry Experience and Practical Impact: His practical experience in mobile application development and work with clients worldwide show his ability to bridge the gap between academic research and real-world applications. This experience is valuable for translating theoretical research into practical solutions.
  4. Editorial and Consultancy Roles: Editing several books and working as a research consultant highlight his expertise and recognition in the field. These roles suggest a high level of respect and credibility among peers.
  5. National Recognition: Passing the NTA UGC NET & JRF on his first attempt underscores his strong foundational knowledge and commitment to research excellence.

Areas for Improvement

  1. Research Continuity and Focus: While Narayan’s work is extensive, he could benefit from focusing more deeply on a narrower set of research areas. This could lead to more impactful and cohesive contributions in specific domains.
  2. Increased Research Collaboration: Although Narayan has collaborated with others, increasing his participation in interdisciplinary research projects could further enhance his impact and visibility in diverse fields.
  3. Awards and Grants: While Narayan has significant achievements, actively seeking and obtaining prestigious awards and grants could bolster his profile and provide additional validation of his research impact.
  4. Public Engagement: Enhancing efforts to communicate research findings to a broader audience, including the general public, could improve the societal impact of his work.

Education

Dr. Narayan Vyas is pursuing a Ph.D. in Computer Science at Punjabi University, Patiala, focusing on developing an image fusion framework for agricultural detection using remote sensing data. He cleared the NTA UGC NET & JRF in Computer Science on his first attempt. Dr. Vyas holds a Master’s in Computer Application from Maharshi Dayanand Saraswati University, where he achieved the first rank. He completed his Bachelor’s in Computer Applications with a specialization in IoT, where he developed a self-balancing robot as his major project.

Experience

Dr. Narayan Vyas currently serves as an Assistant Professor and Research Coordinator at Vivekananda Global University, Jaipur. Previously, he was a Technical Trainer at Chandigarh University, where he founded the Student Research Cell. His role as Principal Research Consultant at AVN Innovations Pvt. Ltd. involved leading research projects and mentoring junior researchers. As a Senior Mobile App Developer at Flexxited, he developed applications for clients globally. His experience spans from academic roles to hands-on technical work, including freelance and training positions.

Awards and Honors

Dr. Narayan Vyas has been recognized for his contributions to technology and research. He served as a Session Chair at the 1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI-2023). He has reviewed papers for prestigious conferences such as IEEE NMITCON-2023 and ICDSNS-2023. He was a proctor for the IEEEXtreme 17.0 programming competition and conducted notable workshops on mobile app development and IoT at leading universities.

Research Focus

Dr. Narayan Vyas’s research focuses on integrating advanced technologies like IoT, machine learning, and remote sensing to solve complex problems in agriculture, healthcare, and environmental monitoring. His work includes developing frameworks for detecting agricultural changes, improving mobile app development methodologies, and enhancing remote sensing data analysis. He is passionate about applying these technologies to real-world problems and advancing the academic understanding of their applications.

Publication Top Notes

“Applying Machine Learning Techniques to Bioinformatics” 📚

“Innovations in Machine Learning and IoT for Water Management” 💧

“Quantum Innovations at the Nexus of Biomedical Intelligence” 🧬

“AI-Driven Alzheimer’s Disease Detection and Prediction” 🧠

“Secure Energy Optimization: Leveraging IoT and AI for Enhanced Efficiency” ⚡

“Internet of Medicine for Smart Healthcare” 🏥

“Multimodal Data Fusion for Bioinformatics” 🌐

“Elevating IoT Sensor Data Management and Security Through Blockchain Solutions” 🔒

“A Machine Learning Framework for Accurate Prediction of Parkinson’s Disease from Speech Data” 🗣️

“Advancing Precision Agriculture: Leveraging YOLOv8 for Robust Deep Learning Enabled Crop Diseases Detection” 🌾

Conclusion

Narayan Vyas is a compelling candidate for the Research for Young Scientist Award due to his significant contributions to research, his active role in academia, and his practical experience. His extensive publication record and leadership in academia reflect a high level of expertise and dedication. Addressing the areas for improvement, such as focusing research efforts and seeking additional recognition, could further strengthen his candidacy. Overall, his achievements and ongoing contributions make him a strong contender for this award.

 

 

Parijata Majumdar | Artificial Intelligence | Women Researcher Award

Dr. Parijata Majumdar | Artificial Intelligence | Women Researcher Award

Dr at Techno College of Engineering Agartala India

Dr. Parijata Majumdar is an accomplished academic and researcher in Computer Science and Engineering, currently serving as Associate Professor at Techno College of Engineering Agartala (TCEA). She holds a Ph.D. from NIT Agartala, specializing in AI for Precision Agriculture. With expertise in Machine Learning, Optimization Techniques, and IoT, her research focuses on applications in Precision Agriculture, Image Processing, and Blockchain. Dr. Majumdar has authored numerous SCI and SCOPUS indexed publications, holds multiple patents, and has received several awards and recognitions for her contributions to research and development. She actively participates as a reviewer and speaker in international conferences and journals.

profile:

Scopus

Scholar

 

🎓 Educational Qualification Details:

Madhyamik (Secondary Education): Board: Tripura Board of Secondary Education Year: 2010 Percentage: 70.29% Division: First Class. Diploma in Computer Science: Institution: Women’s Polytechnic, Hapania Affiliated: Tripura University Year: 2013 CGPA: 8.27 (A Grade). B.E. in Computer Science and Engineering: Institution: TIT, Narsingarh Affiliated: Tripura University Year: 2016 CGPA: 7.92 (B Grade). M.Tech. in Computer Science and Engineering: Institution: Tripura University Year: 2018 CGPA: 8.97 (Gold Medalist). Ph.D. in Computer Science and Engineering: Institution: NIT Agartala Year: 2023 (October) Thesis: “Design and Development of AI Approaches for Precision Agriculture Applications” Supervisor: Dr. Diptendu Bhattacharya, Associate Professor, NIT Agartala

📚 MOOCs/SWAYAM/FDP/Online Courses:

Computational Intelligence (AICTE Training And Learning, NIT Agartala), Joy of Computing using Python (NPTEL, IIT Madras), Internet of Things and Sensor Networks (TEQIP-III, Tripura Institute of Technology), Recent Trends and Research Opportunities in Engineering and Technology (TCEA), Contrast Enhancement in Poor Visibility (Global Initiative of Academic Networks, Tripura University), Image and Video Forensics (Global Initiative of Academic Networks, Tripura University), Entrepreneurship Skills Development Using Open Access Resources (TEQIP-II, Tripura Institute of Technology), Security aspects of IoT based Eco System (AICTE Training And Learning, ATAL Academy)

👩‍🏫 Working Experience Profile:

Assistant Professor (Regular): Techno College of Engineering Agartala (TCEA) since February 2018., Associate Professor: Techno College of Engineering Agartala (TCEA) since November 2023.

Publication:📝

  • Title: IoT for promoting agriculture 4.0: a review from the perspective of weather monitoring, yield prediction, security of WSN protocols, and hardware cost analysis
    Authors: P. Majumdar, S. Mitra, D. Bhattacharya
    Journal: Journal of Biosystems Engineering
    Year: 2021
    Volume: 46
    Issue: 4
    Pages: 440-461
    Citations: 21

 

  • Title: Application of green IoT in agriculture 4.0 and beyond: Requirements, challenges and research trends in the era of 5G, LPWANs and Internet of UAV Things
    Authors: P. Majumdar, D. Bhattacharya, S. Mitra, B. Bhushan
    Journal: Wireless Personal Communications
    Year: 2023
    Volume: 131
    Issue: 3
    Pages: 1767-1816
    Citations: 13

 

  • Title: IoT and machine learning‐based approaches for real-time environment parameters monitoring in agriculture: an empirical review
    Authors: P. Majumdar, S. Mitra
    Book Chapter: Agricultural Informatics: Automation Using the IoT and Machine Learning
    Year: 2021
    Pages: 89-115
    Citations: 9

 

  • Title: Detection of Inflammation from temperature profile using Arthritis knee joint Datasets
    Authors: P. Majumdar, K. Das, N. Nath, M.K. Bhowmik
    Conference: 2018 IEEE International Conference on Healthcare Informatics (ICHI)
    Year: 2018
    Pages: 409-411
    Citations: 7

 

  • Title: Prediction of evapotranspiration and soil moisture in different rice growth stages through improved salp swarm based feature optimization and ensembled machine learning algorithm
    Authors: P. Majumdar, D. Bhattacharya, S. Mitra
    Journal: Theoretical and Applied Climatology
    Year: 2023
    Volume: 153
    Issue: 1
    Pages: 649-673
    Citations: 4

 

  • Title: Demand prediction of rice growth stage-wise irrigation water requirement and fertilizer using Bayesian genetic algorithm and random forest for yield enhancement
    Authors: P. Majumdar, D. Bhattacharya, S. Mitra, R. Solgi, D. Oliva, B. Bhusan
    Journal: Paddy and Water Environment
    Year: 2023
    Volume: 21
    Issue: 2
    Pages: 275-293
    Citations: 4

 

  • Title: Honey badger algorithm using lens opposition based learning and local search algorithm
    Authors: P. Majumdar, S. Mitra, D. Bhattacharya
    Journal: Evolving Systems
    Year: 2024
    Volume: 15
    Issue: 2
    Pages: 335-360
    Citations: 2