Bei Guan | Computer Science | Best Researcher Award

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Assoc. Prof. Dr. Bei Guan | Computer Science | Best Researcher Award

Assoc. Prof. Dr. Bei Guan, Institute of Software, Chinese Academy of Sciences, China

Dr. Bei Guan is a distinguished Senior Engineer (Associate Professor) at the Institute of Software, Chinese Academy of Sciences. With deep-rooted expertise in Big Data Analytics, Cyber Security, and Knowledge Graph-based systems, he has established himself as a key contributor to intelligent system development. Dr. Guan earned prominence through innovative work in operating system virtualization, malicious domain detection, and traditional Chinese medicine analytics. His postdoctoral research at QCRI, Qatar, led to the breakthrough “Guilt by Association” framework for cyber threat detection. Beyond academia, he has led impactful national and industrial projects ranging from AI in civil aviation to smart manufacturing platforms. Passionate about applying data science to real-world problems, Dr. Guan consistently pushes the frontier of technological application in intelligent diagnostics and threat intelligence systems. His career exemplifies a balance of theoretical rigor and practical innovation in computer science.

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Suitability Summary for Research for Best Researcher Award: Bei Guan

Bei Guan demonstrates strong qualifications that align well with the prestigious Research for Best Researcher Award. As a Senior Engineer (Associate Professor) at the Institute of Software, Chinese Academy of Sciences, his academic and professional journey shows a deep and sustained commitment to cutting-edge research in multiple high-impact areas such as Big Data Analytics, Cyber Security Analytics, Threat Intelligence, Virtualization, and Knowledge Graphs.

His research portfolio reflects significant contributions, particularly in developing novel algorithms and systems for detecting malicious cyber activities—work that has practical applications in national and global cybersecurity. The “Guilt by Association” graph inference technique he helped develop has been recognized as a major achievement, evidencing not only innovation but also real-world impact. Moreover, his leadership roles in major funded projects (with grants up to 1.5 million RMB) on intelligent diagnosis in Traditional Chinese Medicine and big data applications in industrial manufacturing highlight his capability to manage complex, interdisciplinary research programs successfully.

🎓 Education 

Dr. Bei Guan holds a Ph.D. in Computer Science, during which he cultivated his proficiency in virtualization, cloud computing, and security systems. His academic training emphasized system-level design and optimization, culminating in research focused on enhancing VM communication efficiency and integrity measurement in virtual environments. Notably, he contributed to Google Summer of Code (GSoC) projects from 2011 to 2013, where he optimized I/O performance in Xen environments and advanced support for OVMF virtual firmware. These global collaborations provided hands-on experience with open-source communities and cutting-edge system architecture. Additionally, he participated in the prestigious Chinese Academy of Sciences (CAS) Main Direction Program for Cloud OS development, solidifying his role in trusted computing. This rigorous academic foundation, enriched by diverse international projects, laid the groundwork for Dr. Guan’s pioneering efforts in secure computing and big data analysis, which now anchor his research and leadership roles at the Chinese Academy of Sciences.

💼 Professional Experience 

Dr. Bei Guan currently serves as a Senior Engineer (Associate Professor) at the Institute of Software, Chinese Academy of Sciences, where he has led national and industry-backed projects since 2018. Before that, from 2014 to 2018, he was a postdoctoral researcher at Qatar Computing Research Institute (QCRI), contributing to the renowned MADA project on malicious activity intelligence. His key roles involved developing graph-based inference systems to detect stealthy domains and contributing to one of QCRI’s major milestones, the “Guilt by Association” algorithm. At ISCAS, he spearheaded intelligent diagnostic systems using Traditional Chinese Medicine (TCM) data and big data analytics. He also managed AI-centric platforms in industrial manufacturing and civil aviation, employing microservices and neural networks for predictive analytics. Earlier in his career, he made significant contributions to virtualization and system security under GSoC and CAS initiatives. His work bridges academic excellence with practical, scalable system deployments.

🏅 Awards and Recognition 

Dr. Bei Guan has earned notable recognition for his impactful research in cybersecurity and big data systems. His co-authored paper, “A Domain is only as Good as its Buddies,” presented at CODASPY 2018, received the Best Paper Award, affirming the importance of his graph-based malicious domain inference technique. His breakthrough work under the “Guilt by Association” framework was also prominently highlighted on the official website of QCRI as one of their leading achievements. In addition, Dr. Guan was a three-time recipient of Google’s highly selective Summer of Code (GSoC) grant, which underscored his technical innovation and collaboration with the open-source community. His continued success in securing significant national funding, including 1.5 million RMB from China’s Ministry of Science and Technology for TCM diagnostics, showcases the trust placed in his leadership. These honors reflect Dr. Guan’s ability to merge academic rigor with real-world impact in computer science.

🌍 Research Skills On Computer Science

Dr. Bei Guan demonstrates a multidisciplinary research portfolio combining system security, data analytics, knowledge representation, and AI. He is proficient in developing inference algorithms, designing knowledge graphs, and building data pipelines in complex domains like Traditional Chinese Medicine, civil aviation, and manufacturing. His core technical skills include graph-based anomaly detection, neural networks, virtualization technologies (Xen, OVMF), and microservice architecture. Dr. Guan effectively utilizes big data frameworks such as Hadoop and applies machine learning to detect malicious activity in DNS logs, IP clusters, and online behavior. His “Guilt by Association” model represents a milestone in cybersecurity analytics. Equally adept at theoretical modeling and system deployment, he integrates entity extraction, deep learning, and natural language processing in domain-specific knowledge bases. As a project manager and team leader, he brings strategic vision and execution capability to research translation. His dynamic skills enable him to contribute effectively across academic and industrial research collaborations.

📖 Publication Top Notes

  • Large language models meet nl2code: A survey
    Authors: D. Zan, B. Chen, F. Zhang, D. Lu, B. Wu, B. Guan, Y. Wang, J.G. Lou
    Citation: 202
    Year: 2022

  • CERT: Continual pre-training on sketches for library-oriented code generation
    Authors: D. Zan, B. Chen, D. Yang, Z. Lin, M. Kim, B. Guan, Y. Wang, W. Chen, J.G. Lou
    Citation: 140
    Year: 2022

  • Discovering malicious domains through passive DNS data graph analysis
    Authors: I. Khalil, T. Yu, B. Guan
    Citation: 135
    Year: 2016

  • When language model meets private library
    Authors: D. Zan, B. Chen, Z. Lin, B. Guan, Y. Wang, J.G. Lou
    Citation: 79
    Year: 2022

  • CIVSched: A Communication-aware Inter-VM Scheduling Technique for Decreased Network Latency between Co-located VMs
    Authors: B. Guan, J. Wu, Y. Wang, S.U. Khan
    Citation: 48
    Year: 2014

  • Private-library-oriented code generation with large language models
    Authors: D. Zan, B. Chen, Y. Gong, J. Cao, F. Zhang, B. Wu, B. Guan, Y. Yin, Y. Wang
    Citation: 32
    Year: 2023

  • Predictive value of serum thyroglobulin for structural recurrence following lobectomy for papillary thyroid carcinoma
    Authors: S. Xu, H. Huang, X. Zhang, Y. Huang, B. Guan, J. Qian, X. Wang, S. Liu, Z. Xu, …
    Citation: 31
    Year: 2021

  • A domain is only as good as its buddies: Detecting stealthy malicious domains via graph inference
    Authors: I.M. Khalil, B. Guan, M. Nabeel, T. Yu
    Citation: 30
    Year: 2018

  • Following passive DNS traces to detect stealthy malicious domains via graph inference
    Authors: M. Nabeel, I.M. Khalil, B. Guan, T. Yu
    Citation: 28
    Year: 2020

  • Return-Oriented Programming Attack on the Xen Hypervisor
    Authors: B. Ding, Y. Wu, Y. He, S. Tian, B. Guan, G. Wu
    Citation: 27
    Year: 2012

 

Md. Nahid Hasan | Computer Science | Best Researcher Awards

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Mr. Md. Nahid Hasan | Computer Science | Best Researcher Awards

Mr. Md. Nahid Hasan, Dhaka International University, Bangladesh

Md. Nahid Hasan is a dedicated academic and researcher in Computer Science and Engineering, currently serving as a Lecturer at Dhaka International University. With a strong foundation in software development, machine learning, and data science, he has published several peer-reviewed articles in reputed journals and international conferences. He is known for blending advanced AI techniques with real-world challenges, particularly in health analytics, text classification, biosensors, and cybersecurity. Md. Hasan is pursuing his M.Sc. Engineering in CSE from BUET with a CGPA of 3.75 and previously graduated with distinction from Khulna University. His diverse research has garnered international attention, reflecting his deep curiosity, discipline, and passion for innovation. A former winner of the IEEE YESIST12 Innovation Challenge, he continues to contribute to both academia and industry with impactful research and teaching. Md. Hasan envisions a future driven by ethical AI and smart technologies that elevate human potential.

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Suitability Assessment for Research for Best Researcher Award: Md. Nahid Hasan

Md. Nahid Hasan demonstrates a strong profile for the Research for Best Researcher Award based on his academic background, research contributions, and professional engagement in the field of Computer Science & Engineering. Currently pursuing an M.Sc. in Computer Science & Engineering at Bangladesh University of Engineering and Technology (BUET), he has already established a solid foundation with a B.Sc. degree where he graduated with a commendable GPA of 3.87 and secured the 2nd position in his class.

His employment history highlights consistent academic involvement as a lecturer at reputed universities, including Dhaka International University and Daffodil International University, showcasing his dedication to both teaching and research simultaneously. This professional experience provides him with a practical platform to influence and contribute to academic development.

🎓 Education

Md. Nahid Hasan’s educational journey exemplifies academic excellence and dedication. He is currently pursuing his M.Sc. Engineering in Computer Science and Engineering from the prestigious Bangladesh University of Engineering and Technology (BUET), holding a CGPA of 3.75 with thesis remaining. His undergraduate studies were completed at Khulna University, where he graduated with a CGPA of 3.87 and secured the second position in his class. His strong foundation was built at Dinajpur Govt. College and Dinajpur Zilla School, where he achieved perfect GPAs of 5.00 in both HSC and SSC. Throughout his academic life, he has demonstrated exceptional analytical skills, logical reasoning, and innovative thinking. His curriculum has been enriched with practical programming, AI applications, and research projects, which paved the way for his contributions in machine learning, cybersecurity, and biosensor modeling. This educational background not only underpins his current research but also fuels his ambitions for advancing intelligent technologies.

💼 Professional Experience

Md. Nahid Hasan has steadily progressed through various academic roles, currently holding a Lecturer position in the Department of Computer Science and Engineering at Dhaka International University since January 2024. Prior to this, he served as a Lecturer at Daffodil International University (Jan 2023 – Jan 2024) and previously at Dhaka International University (Feb 2022 – Dec 2022). In these roles, he has taught core CSE subjects, mentored undergraduate research, and contributed to academic course development. His teaching philosophy centers around interactive learning, analytical thinking, and real-world application of computing principles. Outside the classroom, he is actively involved in research collaborations, interdisciplinary projects, and conference presentations. His industry-relevant insight and academic rigor allow him to bridge the gap between theoretical knowledge and emerging technologies. Through his academic appointments, Md. Hasan continues to inspire students, encourage innovation, and strengthen institutional research output in Bangladesh’s higher education landscape.

🏅 Awards and Recognition 

Md. Nahid Hasan’s academic journey is adorned with several accolades that reflect his brilliance and commitment. Notably, he was the Winner of the IEEE YESIST12 Innovation Challenge Track 2021, an internationally recognized competition that celebrates innovative technological solutions. He has also been a recipient of multiple merit-based scholarships throughout his undergraduate studies at Khulna University, a testament to his consistent academic performance and leadership potential. His research works have been accepted and presented at esteemed IEEE international conferences across Europe and Asia. With journal articles published in reputed outlets like Array and EAI Endorsed Transactions on IoT, he is quickly gaining recognition in global research circles. Md. Hasan’s contributions span across machine learning, bioinformatics, and cybersecurity—areas critical to the digital transformation of society. His awards not only highlight his technical abilities but also his potential to drive meaningful change through data-driven innovation.

🌍 Research Skills On Computer Science

Md. Nahid Hasan possesses a rich blend of research skills at the intersection of artificial intelligence, machine learning, and computational modeling. His expertise includes advanced statistical analysis, neural networks (ANN, LSTM, Bi-LSTM), and ensemble learning models, often applied in areas such as mental health prediction, biosensor simulation, natural language processing, and cybersecurity. He is proficient in PyTorch, Python, SQL, and C++, and utilizes LaTeX for scholarly writing. His research often involves building predictive models, performing comparative classifier analyses, and optimizing AI pipelines for complex data systems. He is also skilled in academic publishing, technical documentation, and collaborative research design. With hands-on experience in multiple IEEE conferences, Md. Hasan continues to refine his methodologies through peer feedback, interdisciplinary collaboration, and continual learning. His ability to translate real-world problems into algorithmic solutions exemplifies a future-ready research mindset grounded in ethical and impactful innovation.

📖  Publication Top Notes

  • Title: Computing Confinement Loss of Open-Channels Based PCF-SPR Sensor with ANN Approach
    Authors: N. Islam, M.S.I. Khan, M.N. Hasan, M.A. Yousuf
    Citation: 4
    Year: 2023

  • Title: Computing Optical Properties of Open–Channels Based Plasmonic Biosensor Employing Plasmonic Materials with ML Approach
    Authors: N. Islam, I.H. Shibly, M.M.S. Hasan, M.N. Hasan, M.A. Yousuf
    Citation: 4
    Year: 2023

  • Title: A Comparative Study on Machine Learning Classifiers for Cervical Cancer Prediction: A Predictive Analytic Approach
    Authors: K.M.M. Uddin, I.A. Sikder, M.N. Hasan
    Citation: 1
    Year: 2024

  • Title: An Ensemble Machine Learning-Based Approach for Detecting Malicious Websites Using URL Features
    Authors: K.M.M. Uddin, M.A. Islam, M.N. Hasan, K. Ahmad, M.A. Haque
    Citation: 1
    Year: 2023

  • Title: Stacked Ensemble Method: An Advanced Machine Learning Approach for Anomaly-based Intrusion Detection System
    Authors: A. Rahman, M.S.I. Khan, M.D.Z.A. Eidmum, P. Shaha, B. Muiz, N. Hasan, …
    Citation: — (citation not provided)
    Year: 2025

  • Title: Language Prediction of Twitch Streamers using Graph Convolutional Network
    Authors: M.N. Hasan, N. Saha, M.A. Rahman
    Citation: — (citation not provided)
    Year: 2025

  • Title: Artificial Neural Network-Assisted Confinement Loss Prediction of D-Shaped PCF-SPR Biosensor
    Authors: N. Islam, M.M.S. Hasan, M.N. Hasan, I.H. Shibly, M.A. Yousuf, M.Z. Uddin
    Citation: — (citation not provided)
    Year: 2024

  • Title: Credibility Analysis of Robot Speech Based on Bangla Language Dialect
    Authors: M.N. Hasan, R. Azim, S. Sharmin
    Citation: — (citation not provided)
    Year: 2024

  • Title: A Comparative Study on Machine Learning Classifiers for Early Diagnosis of Cervical Cancer
    Authors: I.A. Sikder, M.N. Hasan, R. Jahan, A. Mohamed, Y. Dirie
    Citation: — (citation not provided)
    Year: 2024

  • Title: Machine Learning Classification Approach for Refractive Index Prediction of D-Shape Plasmonic Biosensor
    Authors: N. Islam, M.N. Hasan, M.M.S. Hasan, I.H. Shibly, M.A. Yousuf, M.Z. Uddin
    Citation: — (citation not provided)
    Year: 2024

Vijay Srinivas Tida | Computer Science | Excellence in Research

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Dr. Vijay Srinivas Tida | Computer Science | Excellence in Research

Dr. Vijay Srinivas Tida, College of St Benedict and St John’s university, United States

Dr. Vijay Srinivas Tida is a dedicated researcher and academician currently serving as a Tenure-track Assistant Professor at the College of St. Benedict and St. John’s University, Minnesota. With a strong foundation in Electronics, Computer Engineering, and Deep Learning, he has developed a notable reputation in the fields of differential privacy, federated learning, and FPGA hardware acceleration. His Ph.D. dissertation at the University of Louisiana at Lafayette explored optimizing transpose convolution operations—a critical component in CNNs. Dr. Tida’s academic journey has taken him through top institutions including Illinois Institute of Technology and Koneru Lakshmaiah University, consistently achieving high academic honors. He has actively contributed to privacy-preserving machine learning for healthcare and has authored several journal articles and conference papers. Passionate about teaching, he also mentors students in deep learning and hardware systems, making him a valuable contributor to modern computer science education.

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Suitability for Research for Excellence in Research Award: Vijay Srinivas Tida

Vijay Srinivas Tida stands out as a highly deserving candidate for the Research for Excellence in Research Award due to his exceptional contributions in the fields of deep learning optimization, differential privacy, federated learning, and hardware accelerator design. His academic journey reflects consistent excellence, culminating in a Ph.D. in Computer Engineering with a remarkable GPA of 3.9/4.0 from the University of Louisiana at Lafayette. Complemented by a strong foundation in Electrical and Computer Engineering from Illinois Institute of Technology and Electronics and Communication Engineering from Koneru Lakshmaiah University, his educational background is solid and highly relevant.

Throughout his academic and professional career, Vijay has demonstrated a commitment to pioneering research, particularly focusing on the optimization of deep convolutional neural networks, privacy-preserving machine learning models, and hardware security. His doctoral dissertation on optimizing transpose convolution operations and his multiple research projects emphasize innovative approaches that enhance the efficiency and security of AI models, which are critical in today’s technology-driven healthcare and security domains.

🎓 Education

Dr. Vijay Srinivas Tida earned his Ph.D. in Computer Engineering from the University of Louisiana at Lafayette (2018–2023), under the mentorship of Dr. Sonya Hsu and Dr. Xiali Hei, graduating with an impressive GPA of 3.9/4.0. His dissertation focused on optimizing transpose convolution operations for efficient deep learning computation. Prior to this, he completed his Master’s degree in Electrical and Computer Engineering from Illinois Institute of Technology (2016–2018), working with Dr. Erdal Oruklu and maintaining a GPA of 3.8/4.0. He began his academic journey with a Bachelor of Science in Electronics and Communication Engineering from Koneru Lakshmaiah University (2011–2015), guided by Dr. Nalluri Siddaiah, achieving a perfect GPA of 4.0/4.0. His academic background reflects a blend of theoretical knowledge and practical experience in machine learning, hardware design, and optimization algorithms, which forms the core of his current research and teaching interests.

💼 Professional Experience

Dr. Tida’s professional trajectory spans across academic teaching and innovative research. He currently holds the position of Assistant Professor at the College of St. Benedict and St. John’s University, where he teaches and mentors students in computer science. Previously, he served as a Postdoctoral Research Assistant at the University of Louisiana at Lafayette (May–Aug 2023), contributing to projects in privacy-preserving AI and FPGA-based accelerators. From 2018 to 2022, he was a Graduate Teaching Assistant and Lab Instructor, where he taught courses including Computer Architecture and Computer Engineering Labs. He also held Research Assistant roles across institutions like Illinois Institute of Technology and Koneru Lakshmaiah University, engaging in high-impact projects on energy harvesting, sensor security, and neural networks. Dr. Tida’s teaching is complemented by his commitment to community outreach, where he has conducted programming workshops for high school students and offered deep learning sessions to Ph.D. candidates.

🏅 Awards and Recognition

Dr. Tida has been the recipient of numerous honors recognizing both his academic excellence and research contributions. Notably, in 2024, he received $1,750 to attend the prestigious SIGCSE Technical Symposium on Computer Science Education. He was awarded a $6,500 Summer Collaborative Research Grant and $1,000 by the Faculty Development Research Committee for conference travel. In 2023, the College of St. Benedict and St. John’s University provided him with high-performance computing resources worth $16,000. During his doctoral studies, he earned a Dissertation Completion Fellowship and secured consistent Graduate Teaching and Research Assistantships from 2018 to 2022. These accolades reflect his capabilities in leading cutting-edge projects and fostering academic excellence. His continued association with academic conferences such as HICSS and ACM further underscores his recognition within the computing research community.

🌍 Research Skill On Computer Science

Dr. Tida’s research skills encompass a dynamic combination of deep learning, optimization, hardware acceleration, and data privacy. His expertise lies in the development and optimization of Convolutional Neural Networks (CNNs), especially with transpose convolution operations—a subject central to his doctoral work. His focus on Differential Privacy and Federated Learning reflects his commitment to secure and ethical AI, particularly for healthcare data applications. He is adept at hardware-level design using Field Programmable Gate Arrays (FPGAs), enabling real-time and efficient AI computations. With a solid command over Natural Language Processing, he has also published in areas like fake news detection and spam classification using models such as BERT. Dr. Tida’s proficiency spans Python, Arduino C, and hardware descriptive languages, supported by his consistent role in mentoring and peer reviewing. His integration of theoretical algorithms with practical systems development defines his impactful presence in modern computational research.

📖 Publication Top Notes

  • Universal Spam Detection using Transfer Learning of BERT Model
    Author(s): VSTDS Hsu
    Citation: 89
    Year: 2022

  • A reliable diabetic retinopathy grading via transfer learning and ensemble learning with quadratic weighted kappa metric
    Author(s): SV Chilukoti, L Shan, VS Tida, AS Maida, X Hei
    Citation: 45
    Year: 2024

  • Transduction shield: A low-complexity method to detect and correct the effects of EMI injection attacks on sensors
    Author(s): Y Tu, VS Tida, Z Pan, X Hei
    Citation: 38
    Year: 2021

  • Design and Analysis of High Efficient UART on Spartran-6 and Virtex-7 Devices
    Author(s): KH Kishore, CA Kumar, TV Srinivas, GV Govardhan, CNP Kumar, …
    Citation: 20
    Year: Not specified (likely between 2015–2018 based on journal timeline)

  • A unified training process for fake news detection based on fine-tuned BERT model
    Author(s): VS Tida, S Hsu, X Hei
    Citation: 10
    Year: 2022

  • Privacy-Preserving Deep Learning Model for Covid-19 Disease Detection
    Author(s): Vijay Srinivas Tida, Sai Venkatesh Chilukoti, Sonya H. Y. Hsu, Xiali Hei
    Citation: 8
    Year: 2023

  • Kernel-Segregated Transpose Convolution Operation
    Author(s): Vijay Srinivas Tida, Sai Venkatesh Chilukoti, Sonya H. Y. Hsu, Xiali Hei
    Citation: 5
    Year: 2023

  • Deep Learning Approach for Protecting Voice-Controllable Devices From Laser Attacks
    Author(s): VS Tida, R Shah, X Hei
    Citation: 2
    Year: 2022

  • Unified Kernel-Segregated Transpose Convolution Operation
    Author(s): VS Tida, MI Hossen, L Shan, SV Chilukoti, S Hsu, X Hei
    Citation: Not listed
    Year: 2025

  • Differentially private fine-tuned NF-Net to predict GI cancer type
    Author(s): SV Chilukoti, IH Md, L Shan, VS Tida, X Hei
    Citation: Not listed
    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.

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🌟 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