SYED NAVAZ A S | Computer Science | Research Excellence Award

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Dr. SYED NAVAZ A S | Computer Science | Research Excellence Award

Shine & Inspire Academy | India

Dr. A. S. Syed Navaz is an accomplished academician, researcher, and educational leader with over 14 years of teaching experience at both undergraduate and postgraduate levels in the field of Computer Science and Applications. He holds a Ph.D. in Computer Science from Prist University, Thanjavur, where his doctoral research focused on Layer-Based and Flow-Based Channel Assignment in Tree-Structured Wireless Sensor Networks for Fast Data Collection, reflecting his strong expertise in networking and data communication systems. Beyond academia, Dr. Syed Navaz plays prominent leadership roles as Publisher and Chief Editor of the International Organization of Innovative Research & Publishers (IOIRP) and as Managing Director of Shine & Inspire Academy, where he supports research, Ph.D. guidance, publications, patents, entrepreneurship training, and motivational and soft-skill development. He has also successfully mobilized government funding through DST–NSTEDB for multiple Entrepreneurship Awareness Camps, demonstrating his commitment to innovation and societal development. With multidisciplinary expertise spanning education, research, entrepreneurship, blockchain consulting, and life advisory services, Dr. A. S. Syed Navaz continues to make impactful contributions to academic excellence, research advancement, and human capacity building.

 

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Featured Publications

Entropy Based Anomaly Detection System to Prevent DDoS Attacks in Cloud
– International Journal of Computer Applications, 2013
Data Visualization: Enhancing Big Data More Adaptable and Valuable
– International Journal of Applied Engineering Research, 2016
Face Recognition Using Principal Component Analysis and Neural Networks
– International Journal of Computer Networking, Wireless and Mobile Computing, 2013
Human Resource Management System
– IOSR Journal of Computer Engineering, 2013
Flow Based Layer Selection Algorithm for Data Collection in Tree Structure Wireless Sensor Networks
– International Journal of Applied Engineering Research, 2016

Fengrui Hao | Computer Science | Best Researcher Award

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Dr. Fengrui Hao | Computer Science | Best Researcher Award

Jinan University, China

Dr. Fengrui Hao is an emerging researcher in the field of computer science, currently pursuing his Ph.D. in Cyber Security at the School of Information Science and Technology, Jinan University, Guangzhou, China. He holds a B.S. degree in Information Management and Information Systems and an M.S. degree in Computer Technology from Guilin University of Electronic Technology, which laid the foundation for his deep engagement with advanced computing and security research. His primary focus lies in adversarial machine learning and trustworthy artificial intelligence, where he has made significant contributions to strengthening AI systems against vulnerabilities and ensuring fairness, transparency, and robustness in their applications. With more than ten publications in prestigious journals and conferences such as IEEE Transactions on Dependable and Secure Computing (TDSC), IEEE Transactions on Industrial Informatics (TII), and IEEE Transactions on Knowledge and Data Engineering (TKDE), Dr. Hao has established himself as a promising scholar. His research contributions include the development of novel attack and defense mechanisms, fairness-aware algorithms, and privacy-preserving techniques for graph data publishing, all of which are shaping the foundation of secure and ethical AI. His work has been recognized through two completed or ongoing research projects, one consultancy project, and an impressive record of sixteen patents under process. With a citation index of twenty, his influence in the field continues to expand as he pursues innovative research directions. Dr. Hao’s dedication to advancing adversarial learning and trustworthy AI reflects his vision of creating resilient, secure, and human-centered AI technologies for the future.

Profiles: Scopus | Orcid

Featured Publications

  • FBA: Fairness Backdoor Attack on Graph Neural Networks – IEEE Transactions on Dependable and Secure Computing, 2025, DOI: 10.1109/TDSC.2025.3563668

  • dK-DGDP: A Differential Privacy Approach on Directed Social Network Graphs – Computers & Security, 2025, DOI: 10.1016/j.cose.2025.104647

  • GCPA: GAN-Based Collusive Poisoning Attack in Federated Recommender Systems – IEEE Transactions on Knowledge and Data Engineering, 2025, DOI: 10.1109/TKDE.2025.3579807

  • CBAs: Character-level Backdoor Attacks against Chinese Pre-trained Language Models – ACM Transactions on Privacy and Security, 2024, DOI: 10.1145/3678007

  • Collusive Model Poisoning Attack in Decentralized Federated Learning – IEEE Transactions on Industrial Informatics, 2024, DOI: 10.1109/TII.2023.3342901

 

Vincenzo Arceri | Computer Science | Best Researcher Award

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Dr. Vincenzo Arceri | Computer Science | Best Researcher Award

Dr. Vincenzo Arceri | Computer Science | University of Parma | Italy

Dr. Vincenzo Arceri is an accomplished computer scientist and Assistant Professor at the University of Parma, Italy. His expertise lies in abstract interpretation, static program analysis, blockchain security, and formal methods for ensuring software reliability. With a strong academic background and multiple research fellowships, he has established himself as a leading voice in advancing secure, dependable software systems. Dr. Arceri has contributed significantly to the development of static analysis tools, including LiSA, a generic library for static analysis, and EVMLiSA, a specialized analyzer for Ethereum smart contracts. His research extends into improving the quality and security of large language model–generated code, showcasing his commitment to addressing emerging challenges in artificial intelligence and blockchain domains. Recognized internationally through invitations to prestigious Dagstuhl Seminars, awards, and collaborations, Dr. Arceri combines research excellence with impactful teaching, mentoring students in programming and fostering the next generation of computer scientists.

Author Profiles

Orcid | Google Scholar

Education

Dr. Vincenzo Arceri pursued his academic journey at the University of Verona, Italy, where he obtained a Bachelor’s Degree in Computer Science in 2014 with a thesis on semantic analysis techniques for JavaScript. He continued his studies with a Master’s Degree in Computer Science, graduating cum laude in 2016, with a thesis focusing on static type analysis for PHP. Building upon his foundation, he earned his Ph.D. in Computer Science in 2020, presenting a dissertation titled “Taming Strings in Dynamic Languages – An Abstract Interpretation-based Static Analysis Approach.” His doctoral work, supervised by Prof. Isabella Mastroeni, was critically acclaimed by international reviewers such as Prof. Sergio Maffeis and Prof. Xavier Rival. Through this academic pathway, Dr. Arceri specialized in the rigorous application of abstract interpretation to real-world programming challenges, setting the stage for his future contributions to static analysis, software verification, and blockchain-related applications.

Experience

Dr. Vincenzo Arceri began his research career as a Postdoctoral Researcher at Ca’ Foscari University of Venice (2019–2021), where he worked on IoT applications in smart cities and the development of static analysis tools for Go, particularly in the context of blockchain smart contracts. His research there focused on formal verification and the precision–efficiency trade-offs in string analysis. In September 2021, he joined the University of Parma as an Assistant Professor, where he currently teaches Fundamentals of Programming to undergraduate students while continuing his research in advanced program analysis. His contributions include designing LiSA, a generic static analysis framework, and EVMLiSA, a static analyzer for Ethereum Virtual Machine bytecode. He has also explored static analysis for unsafe Rust programs and LLM-generated code. Dr. Arceri’s professional trajectory reflects a balance of teaching, applied research, and international collaboration with academic and industry partners.

Awards and Honors

Dr. Vincenzo Arceri’s research excellence has been recognized through prestigious awards and honors. In 2019, he received the Best Paper Award at VALID 2019 for his contribution to the operational semantics of Solidity, highlighting his innovative work in blockchain verification. His international reputation was further affirmed with scholarships such as the Marktoberdorf Summer School in 2018, which focused on engineering secure and dependable software systems. In 2023, he was awarded INdAM GNCS funding to support his participation in international conferences, workshops, and seminars. Furthermore, Dr. Arceri has been invited to the distinguished Dagstuhl Seminars in 2023 and 2025, gatherings known for shaping the future of computer science research. These invitations underscore his standing as an expert in abstract interpretation and static analysis. Collectively, these accolades reflect his academic rigor, groundbreaking contributions, and the international recognition he has garnered for advancing software reliability and security.

Research Focus

Dr. Vincenzo Arceri’s research centers on the application of abstract interpretation to improve the security, reliability, and correctness of software systems. He has dedicated his career to advancing static program analysis for a wide range of programming paradigms, from dynamic languages such as JavaScript and PHP to domain-specific blockchain applications. His work also addresses the challenges of analyzing unsafe Rust code and verifying smart contracts in Go and Ethereum. Notably, he has developed LiSA, a multilanguage static analysis framework, and EVMLiSA, a static analyzer tailored to EVM bytecode, demonstrating his ability to merge theoretical rigor with practical implementations. His recent projects explore the safety of LLM-generated code, aiming to ensure that AI-driven programming integrates robust security principles. By balancing precision and performance in static analysis, Dr. Arceri’s work provides a critical foundation for future-proof software engineering, cross-blockchain applications, and secure AI-integrated development practices.

Publications

  • Static analysis for dummies: experiencing LiSA.

  • Analyzing Dynamic Code: A Sound Abstract Interpreter for Evil Eval.

  • LiSA: a generic framework for multilanguage static analysis.

  • Static Program Analysis for String Manipulation Languages.

  • Static analysis for ECMAScript string manipulation programs.

  • Ensuring determinism in blockchain software with GoLiSA: an industrial experience report.

  • Information flow analysis for detecting non-determinism in blockchain.

  • Twinning automata and regular expressions for string static analysis.

  • Abstract domains for type juggling.

  • Relational string abstract domains.

Conclusion

Dr. Vincenzo Arceri exemplifies the qualities of a modern computer scientist—innovative, collaborative, and deeply committed to advancing the reliability of digital systems. His work bridges theory and practice, from foundational contributions in abstract interpretation to impactful tools for blockchain verification and AI-generated code analysis. With a growing body of influential publications, awards, and teaching contributions, he stands as a leading researcher shaping the future of secure and dependable software systems.

 

Ashok Ghimire | Computer Science | Research Excellence Award

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Mr. Ashok Ghimire | Computer Science | Research Excellence Award

Mr. Ashok Ghimire | Computer Science | Westcliff University | United States

Ashok Ghimire is a dynamic researcher and professional specializing in artificial intelligence, data analytics, and financial technology. Born in Nepal and currently based in Anaheim, California, he has demonstrated a strong commitment to advancing computer science applications in banking, finance, and business intelligence. With over a decade of academic and professional experience, he has transitioned from banking operations and financial management in Nepal to advanced research in AI-driven data analytics in the United States. His work emphasizes leveraging machine learning, quantum computing, and big data to address critical challenges such as fraud detection, financial inclusion, and risk management. Ashok has earned recognition for his scholarly contributions, securing multiple scholarships and publishing extensively in reputed journals. In addition to his academic excellence, he contributes to the research community as a peer reviewer and conference evaluator. His forward-looking vision integrates technology and finance to create sustainable and secure digital ecosystems.

Author Profiles

Orcid | Google Scholar

Education 

Ashok Ghimire has pursued a strong academic journey combining business, finance, and technology. He completed his high school and higher secondary education in Nepal with distinction before receiving an ICCR-funded scholarship to study for a Bachelor of Business Administration in Finance at MITSOM, Pune, India, where he graduated with distinction. Continuing his academic path, he earned an MBA in Finance and Marketing at Surya World, India, achieving high distinction and receiving support through Surya Pharmaceuticals’ CSR scholarship. Ashok’s academic excellence has been recognized with multiple scholarships at each stage of his education. Building on this foundation, he is currently pursuing a Doctor of Business Administration (DBA) in Business Intelligence and Data Analytics at Westcliff University, Irvine, California. His doctoral studies reflect his growing specialization in artificial intelligence, machine learning, and big data analytics, preparing him to make innovative contributions to the U.S. financial technology and banking sectors.

Experience 

Ashok Ghimire has built a diverse professional background, beginning his career as a Management Trainee at CG Electronics, Nepal, where he gained experience in sales enhancement, product imports, and brand management. He later advanced to Assistant Manager and then Deputy Manager at Nepal Bank Limited, one of the country’s leading financial institutions. During his tenure, he managed credit assessments, financial risk evaluations, loan negotiations, and approvals of major projects, including hydroelectric power initiatives. His responsibilities also extended to customer relations, compliance, staff supervision, and branch operations. Ashok’s professional expertise lies in analyzing business proposals, conducting industry research, and making strategic financial recommendations. Since moving to the U.S., he has focused on academic research and peer reviewing for international journals and conferences, strengthening his global engagement in computer science, AI, and data analytics. His career path reflects a seamless integration of financial management and emerging digital technologies.

Awards and Honors 

Ashok Ghimire’s academic and professional journey is distinguished by numerous awards and recognitions. He received scholarships at every stage of his higher education, beginning with Einstein Academy in Nepal, where he was awarded for his higher secondary studies. He later received the prestigious Indian Council for Cultural Relations (ICCR) scholarship for his undergraduate degree in Finance at MITSOM, Pune, India. His postgraduate studies were supported by a CSR scholarship from Surya Pharmaceuticals, enabling him to complete his MBA in Finance and Marketing with distinction. At Westcliff University, he has been recognized on the Dean’s List with Distinction and awarded the Founder’s Scholarship for Business during his doctoral studies. Beyond academia, Ashok has secured a UK Design Patent for an AI-based Facial Recognition Device (2025) and has established himself as an active peer reviewer for leading international journals and conferences, highlighting his global recognition as a scholar.

Research Focus 

Ashok Ghimire’s research focus lies at the intersection of computer science, artificial intelligence, and financial technology. His work explores how AI, machine learning, and data analytics can transform the U.S. financial sector, particularly in enhancing fraud detection, anti-money laundering (AML) compliance, financial crime prevention, and risk management. He has published extensively on AI-driven predictive modeling, big data in banking, quantum computing applications in fraud detection, and the role of sentiment analysis in cryptocurrency markets. His research also extends to healthcare, education, and agricultural industries, where AI-driven solutions are shaping innovation and efficiency. Through his doctoral studies, he aims to strengthen the adoption of business intelligence and decision-support systems in organizations, especially in resource-constrained environments. His future vision is to integrate computer science with financial strategies to support economic inclusion, transparency, and security, ultimately contributing to both national priorities and global digital transformation.

Publications 

  • Exploring Benefits, Overcoming Challenges, and Shaping Future Trends of AI in Agriculture.

  • Behavioral Intention to Adopt Artificial Intelligence in Educational Institutions.

  • Exploring the Latest Trends in AI Technologies: Current State and Impacts.

  • Applying TAM in IT Systems to Evaluate Decision Support Adoption.

  • Advances in Smart Health Care: Paradigms, Challenges, Case Studies.

  • Predictive Models Performance in Financial Services for At-Risk Customers.

  • Harnessing Big Data with AI-Driven BI Systems for Real-Time Fraud Detection.

  • AI-Powered Anomaly Detection for AML Compliance in US Banking.

  • Quantum Computing in US Banking: Future of Fraud Prevention.

  • Multi-Factor Forex Hedging Models with Reinforcement Learning.

  • Organizational Factors Influencing Predictive Analytics Adoption for FX Exposure.

  • Role of AI-Based Sentiment Detection in Forecasting Cryptocurrency Market.

  • Leveraging AI for Trade-Based Money Laundering Detection.

  • AI-Driven Drug Repurposing for Oncology Treatments.

  • Meta-Synthesis of Barriers to Decision Tree Analytics in Payment Fraud.

  • Enhancing Real-Time Fraud Detection Using RNNs.

  • Sociotechnical Framework for Business Intelligence Adoption in SMEs.

  • Data Analytics in Judicial Decision-Making.

Conclusion

Ashok Ghimire represents a new generation of scholars who bridge finance, technology, and computer science to drive innovation in real-world contexts. His academic journey, professional expertise, and extensive publications reflect a strong foundation in both theory and practice. Through his pioneering research on artificial intelligence, data analytics, and financial risk management, he is actively shaping the future of digital finance and business intelligence. His achievements, including international scholarships, patents, and peer-review contributions, highlight his credibility and leadership in the field. Positioned at the intersection of academia and industry, Ashok continues to contribute toward building smarter, safer, and more inclusive financial systems. His forward-looking vision makes him a strong candidate for recognition in the domain of Computer Science, particularly where technology and finance converge to address global challenges.

Alimul Rajee | Computer Science | Young Scientist Award

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Mr. Alimul Rajee | Computer Science | Young Scientist Award

Mr. Alimul Rajee, Dept. of ICT, Comilla University, Kotbari, Bangladesh

Alimul Rajee is a Lecturer at the Department of Information and Communication Technology, Comilla University. His academic journey includes a stellar performance with a CGPA of 3.69 in his M.Sc. in Information Technology from Jahangirnagar University. Rajee’s research interests span Machine Learning, Data Science, Artificial Intelligence, Cyber Security, and Robotics, with a focus on real-world applications such as traffic accident data analysis and smart waste management. He has contributed significantly to several research projects, and his work has been published in prestigious journals, such as Knowledge-Based Systems and Heliyon. In addition to his research, Rajee is an active educator, mentoring students and supervising projects in areas like IoT and deep learning. His dedication extends beyond the classroom to extracurricular activities, where he has received multiple awards and recognitions, including an international award for his project at Fujitsu Research Institute in Tokyo.

Professional Profile

Scopus

Orcid

Google Scholar

Suitability Summary of Young Scientist Awards

Alimul Rajee stands out as an excellent candidate for the Research for Young Scientist Award due to his impressive academic achievements, significant research contributions, and commitment to advancing in the fields of Machine Learning, Data Science, Artificial Intelligence, Cyber Security, and IoT. He has a strong educational background, earning his M.Sc. and B.Sc. with high CGPA rankings from Jahangirnagar University, which reflects his deep knowledge and dedication to his field.

Rajee’s research work is highly commendable, with several publications in reputable, Scopus-indexed journals such as Knowledge-Based Systems and Heliyon, where he has contributed to the development of novel algorithms and methodologies, especially in big data analysis, sentiment analysis, and AI-based applications. His ongoing and completed research projects, including a hybrid smart waste management system and aspect-based sentiment analysis for Bengali text, further showcase his innovative thinking and practical application of emerging technologies to address real-world problems. Additionally, his leadership in supervising over 40 academic projects and his participation in global training programs, like those held at the Fujitsu Research Institute in Japan, illustrate his proactive approach to both learning and teaching.

🎓  Education

Alimul Rajee completed his M.Sc. in Information Technology from Jahangirnagar University, securing a CGPA of 3.69 out of 4, ranking 6th in his batch. Before this, he earned his B.Sc. (Hons.) in the same field, also from Jahangirnagar University, with a CGPA of 3.71, again securing the 6th position. Rajee’s academic excellence dates back to his secondary education, where he achieved the highest CGPA of 5.00 in both his HSC and SSC exams from Govt. Ananadamohan College and Islamnagar Sailampur High School. His continuous pursuit of academic excellence earned him merit-based scholarships throughout his education. His academic prowess has laid a strong foundation for his research and professional career, as he continues to excel in his field with a focus on cutting-edge technologies such as AI and IoT.

💼 Professional Experience

Alimul Rajee’s professional career began as a Junior Data Scientist at Oculin Tech BD Ltd., where he worked from March 2020 to May 2021. He then served as a Senior Officer (ICT) at Sonali Bank PLC for a brief period before becoming a Lecturer at Comilla University in November 2021, where he currently teaches. Rajee’s teaching journey includes roles at Bangladesh University of Business and Technology (BUBT) and Jahangirnagar University (IIT-JU), where he was a Teacher Assistant. His extensive experience also includes supervising over 40 academic projects focused on machine learning, deep learning, and IoT. As an educator, he fosters a positive learning environment, guiding students through complex technical concepts while contributing to the development of innovative research and real-world applications.

🏅  Awards and Recognition

Alimul Rajee’s achievements have been recognized at both national and international levels. He has received several awards, including the UGC Research Grant from Comilla University for consecutive fiscal years, which is a testament to his research capabilities. Rajee’s work has been recognized by prestigious institutions such as Fujitsu Research Institute (FRI) in Tokyo, where his final project won 1st prize. He has also been a reviewer for the International Conference on Embracing Industry 4.0 for Sustainable Business Growth. His consistent academic and research excellence has earned him regular merit-based scholarships and fellowships, such as the National Science & Technology Fellowship from the ICT Division of Bangladesh.

🌍 Research Skills On Computer Science

Alimul Rajee specializes in the application of cutting-edge technologies such as Machine Learning, Artificial Intelligence, Cyber Security, and IoT. His research includes a diverse range of topics like traffic accident data analysis, sentiment analysis of Bengali text, and smart waste management. Rajee has honed his expertise in Data Science and deep learning methods, contributing to several high-impact publications in renowned journals such as Knowledge-Based Systems and Heliyon. His current research projects include Aspect-Category-Opinion-Sentiment Quad Extraction for Bengali Text and a Hybrid Smart Waste Management Technique using Deep Learning and IoT. Rajee’s proficiency in data analysis, algorithm design, and system integration showcases his strong research skills and his commitment to advancing technology for societal benefit.

📖 Publication Top Notes

  • “Aspect-based sentiment analysis for Bengali text using bidirectional encoder representations from transformers (BERT)”
    • Authors: MM Samia, A Rajee, MR Hasan, MO Faruq, PC Paul
    • Citation: International Journal of Advanced Computer Science and Applications, 13(12)
    • Year: 2022
  • “Detecting the provenance of price hike in agri-food supply chain using private Ethereum blockchain network”
    • Authors: MH Sayma, MR Hasan, M Khatun, A Rajee, A Begum
    • Citation: Heliyon, 10(11)
    • Year: 2024
  • “Analyzing depression on social media utilizing machine learning and deep learning methods”
    • Authors: PC Paul, MT Ahmed, MR Hasan, A Rajee, K Sultana
    • Citation: Indian Journal of Computer Science and Engineering, 14(5), 740-746
    • Year: 2023
  • “WFFS—An ensemble feature selection algorithm for heterogeneous traffic accident data analysis”
    • Authors: A Rajee, MS Satu, MZ Abedin, KMA Ali, S Aloteibi, MA Moni
    • Citation: Knowledge-Based Systems, 113089
    • Year: 2025

Iustina Ivanova | Computer Science | Best Researcher Award

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Mrs. Iustina Ivanova | Computer Science | Best Researcher Award

👤 Mrs. Iustina Ivanova, FBK, Italy

Iustina Ivanova is an accomplished researcher in the field of Artificial Intelligence (AI) with a focus on computer vision and machine learning applications in real-world scenarios. She holds a Master’s degree in Artificial Intelligence from the University of Southampton, where she earned distinction for her research on neural networks for object detection. Currently, Iustina is engaged in AI research in smart agriculture at the Fondazione Bruno Kessler in Italy. Over the years, she has contributed to a variety of high-impact projects, including developing a recommender system for outdoor sport climbers and researching sensors for sports activity analysis. Her work has earned her several well-regarded publications and recognition in the AI and computer vision communities.

Professional Profile

Scopus

Orcid

🌟 Summary of Suitability for the Research for Best Researcher Award

Iustina Ivanova demonstrates exceptional qualifications for the “Research for Best Researcher Award.” Her academic background, professional experience, and research contributions highlight her significant impact on the fields of artificial intelligence (AI), machine learning, and computer vision. Her academic journey is distinguished by a Master’s degree in Artificial Intelligence with distinction from the University of Southampton and ongoing research pursuits during her Ph.D. studies. While her Ph.D. remains incomplete, the work she has undertaken—such as her contributions to recommender systems and computer vision—showcases her ability to address complex, real-world problems.

Professionally, Iustina’s research experience is diverse and impactful. At the Fondazione Bruno Kessler, she has been actively involved in applying AI to smart agriculture, addressing sustainability and innovation in the domain. Her previous roles, including as a Computer Vision Data Scientist and Data Science Moderator, further demonstrate her ability to bridge academia and industry.

🎓 Education

Iustina Ivanova has an impressive academic background in computer science and AI. She completed her Master of Science in Artificial Intelligence with distinction at the University of Southampton, UK, in 2018. Before that, she earned a Specialist degree in Software Engineering from Bauman Moscow State Technical University, Russia, in 2013. In 2019, she pursued a PhD in Computer Science at the Free University of Bolzano, Italy, although she later decided to focus more on practical AI applications. Her academic journey includes notable achievements such as developing research in neural networks for object detection, which has been the cornerstone of her professional career in AI.

💼  Professional Experience 

Iustina Ivanova has a diverse and robust professional background in AI and computer vision. She currently works as a researcher at the Fondazione Bruno Kessler, Italy, specializing in the use of AI for smart agriculture. Prior to this, Iustina served as a Data Science Moderator at Netology, Russia, where she designed and delivered online courses in statistics and mathematics for data science students. She also worked as a Computer Vision Data Scientist at OCRV, Russia, where she helped develop a video-based tracking system for railway workers, focusing on object detection and worker time measurement. Iustina’s role as a teacher of informatics and mathematics at Repetitor.ru involved preparing high school students for their final exams, ensuring that many students successfully entered top universities. Throughout her career, she has collaborated on numerous innovative projects in AI, particularly in outdoor sports and smart agriculture.

🏅Awards and Recognition 

Iustina Ivanova’s dedication and excellence in the field of AI have been recognized through multiple prestigious awards and accolades. Notably, she won several editions of the NOI Hackathon, including the SFSCON Edition (2021, 2022, 2024) and the Open Data Hub Edition (2022), showcasing her ability to create cutting-edge solutions in AI and data science. Her contributions to research and development in AI for sports activity analysis and computer vision have been published in highly regarded journals and conferences, such as the ACM Conference on Recommender Systems and IEEE Conferences. Iustina has also received recognition for her teaching contributions, inspiring future generations of data scientists. Her projects, especially those related to sports climbers’ recommender systems and sensor data analysis, have received wide acclaim for their innovation and real-world impact.

🌍 Research Skills On Computer Science

Iustina Ivanova’s research expertise spans artificial intelligence, machine learning, computer vision, and recommender systems. She is particularly skilled in using AI techniques to solve complex problems in real-world applications. Her work with neural networks for object detection and sensor data analysis has led to significant advancements in both sports and smart agriculture sectors. Iustina is proficient in Python, OpenCV, machine learning frameworks like PyTorch and TensorFlow, and data analysis tools such as Jupyter Notebook and Git. She is well-versed in the development of recommender systems and has implemented innovative solutions for outdoor sports, including climbing crag recommendations. Her interdisciplinary approach combines knowledge from software engineering, data science, and AI to design systems that enhance user experience and improve decision-making. Iustina is committed to the continual development of her skills, evident in her participation in advanced data science and deep learning courses, as well as her extensive practical work in AI.

📖 Publication Top Notes

  • Climbing crags repetitive choices and recommendations
    • Author: Ivanova, I.
    • Citation: Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023
    • Year: 2023
    • Pages: 1158–1164
  • How can we model climbers’ future visits from their past records?
    • Authors: Ivanova, I., Wald, M.
    • Citation: UMAP 2023 – Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization
    • Year: 2023
    • Pages: 60–65
  • Introducing Context in Climbing Crags Recommender System in Arco, Italy
    • Authors: Ivanova, I.A., Wald, M.
    • Citation: International Conference on Intelligent User Interfaces, Proceedings IUI
    • Year: 2023
    • Pages: 12–15
  • Climbing crags recommender system in Arco, Italy: a comparative study
    • Authors: Ivanova, I., Wald, M.
    • Citation: Frontiers in Big Data
    • Year: 2023
    • Volume: 6, Article: 1214029
  • Map and Content-Based Climbing Recommender System
    • Authors: Ivanova, I.A., Buriro, A., Ricci, F.
    • Citation: UMAP2022 – Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization
    • Year: 2022
    • Pages: 41–45
  • Climbing Route Difficulty Grade Prediction and Explanation
    • Authors: Andric, M., Ivanova, I., Ricci, F.
    • Citation: ACM International Conference Proceeding Series
    • Year: 2021
    • Pages: 285–292
  • Climber behavior modeling and recommendation
    • Author: Ivanova, I.
    • Citation: UMAP 2021 – Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization
    • Year: 2021
    • Pages: 298–303
  • Knowledge-based recommendations for climbers
    • Authors: Ivanova, I., Andrić, M., Ricci, F.
    • Citation: CEUR Workshop Proceedings
    • Year: 2021
    • Volume: 2960
  • Climbing activity recognition and measurement with sensor data analysis
    • Authors: Ivanova, I., Andric, M., Janes, A., Ricci, F., Zini, F.
    • Citation: ICMI 2020 Companion – Companion Publication of the 2020 International Conference on Multimodal Interaction
    • Year: 2020
    • Pages: 245–249
  • Video and Sensor-Based Rope Pulling Detection in Sport Climbing
    • Authors: Ivanova, I., Andrić, M., Moaveninejad, S., Janes, A., Ricci, F.
    • Citation: MMSports 2020 – Proceedings of the 3rd International Workshop on Multimedia Content Analysis in Sports
    • Year: 2020
    • Pages: 53–60