Yerassyl Saparbekov | Machine Learning | Research Excellence Award

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Mr. Yerassyl Saparbekov | Machine Learning | Research Excellence Award

Nazarbayev University | Kazakhstan

Mr. Yerassyl Saparbekov is a researcher in computer science specializing in deep learning, natural language processing, and computer vision. His work focuses on embedding models, clustering techniques, and retrieval-augmented generation systems for evidence-based analysis. He has contributed to developing advanced AI solutions for student feedback interpretation and decision-support systems. His research also spans medical visual question answering, scene text super-resolution, and multimodal learning approaches. He is actively engaged in advancing applied artificial intelligence for real-world problem solving and intelligent data-driven insights.


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Featured Publications

Vaibhav Tummalapalli | Machine learning | Excellence in Innovation Award

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Mr. Vaibhav Tummalapalli l Machine learning
| Excellence in Innovation Award

Epsilon Data Management, LLC | United States

Mr. Vaibhav Tummalapalli’s research focuses on the advancement of applied machine learning methodologies, predictive modeling, and data-driven optimization across large-scale industrial domains, particularly automotive and telecommunications. His work emphasizes the integration of artificial intelligence in lifecycle analytics, customer engagement, and personalization strategies to enhance business intelligence and operational efficiency. His studies explore innovative modeling frameworks such as EV Conquest modeling, VIN-level mileage prediction, and vehicle recommendation systems, which apply behavioral, telematics, and demographic data to drive precision marketing and service optimization. Additionally, his contributions to outlier detection, cohort-based stratified sampling, and KNN imputation distance metrics extend theoretical and applied understanding in data preprocessing and imbalanced learning. His research also addresses model monitoring and drift management using SAS Viya and PySpark-based architectures, ensuring robust model performance in production environments. Through the development of scalable ML pipelines, channel propensity models, and retention-focused predictive systems, his work demonstrates the transformative potential of AI in driving measurable business outcomes, customer retention, and ethical personalization. His scholarly and technical pursuits collectively aim to advance the design of intelligent, explainable, and sustainable machine learning systems for real-world, high-impact applications

Featured Publications

Tummalapalli, V. (2025). Understanding distance metrics in KNN imputation: Theoretical insights and applications. Journal of Mathematical & Computer Applications, 4(4), 1–4. https://doi.org/10.47363/JMCA

Tummalapalli, V. (2025). Machine learning pipeline for automotive propensity models. International Journal of Core Engineering & Management, 8(3), [Issue-03].

Tummalapalli, V. (2025). Outlier detection & treatment for machine learning models. International Journal of Innovative Research and Creative Technology, 11(3).

Tummalapalli, V. (2025). Stratified sampling in cohort-based data for machine learning model development. International Scientific Journal of Engineering and Management, 4.

Sharmila More | Machine Learning | Women Researcher Award

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Dr. Sharmila More | Machine Learning | Women Researcher Award

MIT Arts, Commerce & Science College| India

Dr. Sharmila More is an accomplished academician and researcher, currently serving as Assistant Professor in the Department of Science and Computer Science at MIT ACSC, Alandi (D), Pune. With over nine years of teaching and administrative experience, along with eight years of dedicated research expertise, she has significantly contributed to the fields of computer science, data science, cyber security, and artificial intelligence. She holds a Ph.D. in Computer Science from MATS University, Raipur, an MCA from Pune University, and a Postgraduate Diploma in Core Competency from Shivaji University. Dr. More has published 18 research papers in reputed journals, presented 20 papers at national and international conferences, and authored a book titled Solving Security Issues in Personal Identification using Fuzzy Approach and Multimodal Images. She has also guided several students in research and academic projects. Her innovations are reflected in multiple patents, including an Indian patent at the FER stage, one design patent, and granted patents in Australia, Germany, and the UK. She is actively engaged in academic committees, curriculum development under the NEP framework, and serves as an editorial board member for journals. Her professional memberships include the Computer Society of India, Indian Science Congress Association, and Soft Computing Research Society. Recognized for her excellence, she has received prestigious honors such as the International Research Excellence Awards for Outstanding Researcher and Distinguished Researcher, Best Teacher Award, and multiple prizes at research competitions. A sought-after resource person and reviewer, she has delivered expert lectures and contributed as co-supervisor and examiner for Ph.D. programs. Her citation index stands at 67, reflecting her impactful scholarship. Dr. More continues to advance interdisciplinary research in computer science, focusing on biometric systems, machine learning, cryptography, and emerging technologies, while inspiring future scholars through teaching, mentorship, and innovation.

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Featured Publications

More, S. S. (2025). Revolutionizing military operations: The role of deep learning with YOLO v7 in the evolution of drones. Advancements in Intelligent Systems, 33–43. Computing & Intelligent Systems.

More, S. S. (2024). Pythonic learning: Advancements and innovations in machine intelligence. International Journal of Advanced Research in Science, Communication and Technology.

More, S. S. (2023). Statistical and fuzzy inference system analysis of multimodal. Solovyov Studies ISPU.

More, S. S. (2023). Statistical and fuzzy inference system analysis of multimodal images using NMRSA. Solovyov Studies ISPU, 71(11), 122–127.

More, S., Narain, B., & Jadhav, B. T. (2023). Privacy conserving using fuzzy approach and blowfish algorithm for malicious personal identification. In Institute of Engineering (pp. xx–xx). Springer.

 

Xinxin Luo | Computer Science| Best Researcher Award

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Dr. Xinxin Luo | Computer Science | Best Researcher Award

Southeastern University, China

Xinxin Luo is a dedicated Ph.D. candidate in Cyberspace Security at Southeast University, China, with research interests in trustworthy AI, causal inference, and spatio-temporal graph neural networks. She holds a Master’s in Intelligent Science and Technology from NUPT and a Bachelor’s in Electronic Information Engineering from Hefei Normal University. Xinxin has authored several high-impact papers and presented at international conferences, contributing to robust AI systems by integrating causal structures into machine learning. Her work blends deep theoretical knowledge with practical implementations in AI-driven forecasting and decision support.

Profile

🎓 Education

Xinxin Luo is pursuing her Ph.D. (2021–2025) at the School of Cyber Science and Engineering, Southeast University, focusing on trustworthy AI. She earned her Master’s degree in Intelligent Science and Technology (2018–2021) from the College of Automation & AI at NUPT, and her Bachelor’s degree in Electronic Information Engineering (2013–2017) from Hefei Normal University. Her educational path reflects a deep foundation in intelligent systems, machine learning, and electronic engineering, forming the basis for her cutting-edge work in AI and causal inference.

💼 Experience

Xinxin has actively contributed to AI R&D through national key projects, where she developed anomaly detection algorithms and integrated ML with big data for real-time monitoring. She interned at Zhejiang Zijiang Laboratory, applying deep learning for human motion recognition. Xinxin also led a provincial project creating an interpretable SME rating system, showcasing her ability to translate theoretical insights into practical applications. Her hands-on experience spans spatio-temporal modeling, causality analysis, and real-world AI implementations in both academic and industrial environments.

🏅 Awards & Honors

Xinxin Luo has received recognition through multiple high-impact publications, including journals like Engineering Applications of AI and Journal of Artificial Intelligence Research. Her accepted conference presentations (e.g., ICCBR2024, PRICAI2024) and under-review articles in top-tier journals reflect academic excellence. She holds a patent in zero-shot learning and a software copyright for AI-based chart recognition. Her contributions to national and provincial research projects and successful leadership in AI system design highlight her innovation and technical prowess.

🔬 Research Focus

Xinxin focuses on trustworthy AI through the lens of causal inference and spatio-temporal graph neural networks. Her research explores dynamic causal structure learning for time series forecasting, addresses confounding bias, and incorporates counterfactual reasoning into predictive models. She investigates causal mechanisms behind data to enhance fairness, interpretability, and robustness in AI systems. Additionally, Xinxin integrates situational awareness with causal levels (observation, intervention, counterfactual) to improve intelligent decision-making. Her vision aims at building secure, human-centered, and future-aware AI.

 Conclusion

Xinxin Luo exemplifies the qualities of an outstanding researcher: deep technical expertise, a strong publication record, and a proven ability to translate theory into practice. By augmenting her grant-acquisition skills and widening her collaborative networks, she can elevate her already remarkable contributions to even greater international prominence. Her dedication to human-centered, trust-grounded AI renders her a highly deserving candidate for the Best Researcher Award.

Publication

  1. A Novel Approach of Causality Matrix Embedded into the Graph Neural Network for Forecasting the Price of Bitcoin

    • Authors: Xinxin Luo, Wei Yin, Xiao Bo

    • Journal: Engineering Applications of Artificial Intelligence

  2. Dynamic Causal Structure Learning for Spatio-Temporal Graph Forecasting

    • Authors: Xinxin Luo, Wei Yin, Zhuang Li

    • Journal: IEEE Transactions on Reliability

  3. Causal Spatio-Temporal Graph Forecasting Against Confounding Bias

    • Authors: Xinxin Luo, Wei Yin, Xiao Bo, Fan Wu

    • Journal: Journal of Artificial Intelligence Research

  4. Deconfounded Spatio-temporal Prediction with Causal-based Graph Neural Networks

    • Authors: Xinxin Luo, Wei Yin, Zhuang Li

    • Journal: Complex & Intelligent Systems

    • Ranking: JCR Q1

  5. Multi-view Deep Generative Dual Fusion Network for Zero-shot Learning

    • Authors: Xinxin Luo, Wei Yin, Xiao Bo

    • Journal: Multimedia Tools and Applications

    • Ranking: JCR Q2

  6. Forecasting Cryptocurrencies’ Price with the Financial Stress Index: A Graph Neural Network Prediction Strategy

    • Authors: Wei Yin, Ziling Chen, Xinxin Luo, Berna Kirkulak-Uludag

    • Journal: Applied Economics Letters

    • Ranking: JCR Q3