Irina-Oana Lixandru-Petre | Machine Learning | Best Researcher Award

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Ms. Irina-Oana Lixandru-Petre | Machine Learning | Best Researcher Award

National University of Science and Technology POLITEHNICA Bucharest, Romania

Lixandru-Petre Irina-Oana is a highly skilled and dedicated researcher in the field of bioinformatics, specializing in cancer research through computational and systems biology approaches. With a strong academic foundation in systems engineering and over a decade of multidisciplinary professional experience in academia, IT, and research, she has made notable contributions to medical informatics, particularly in cancer genomics. Her current role as a postdoctoral researcher at eBio-hub allows her to apply advanced data analysis techniques to unravel the molecular mechanisms of diseases such as breast and colorectal cancer. Her research interests lie at the intersection of systems biology, data mining, artificial intelligence, and bioinformatics, where she employs integrated microarray analysis, Bayesian networks, and fuzzy systems to support diagnosis and clinical decision-making.

Profile

Scopus

Education

Irina-Oana’s academic journey began at the National University of Sciences and Technology POLITEHNICA Bucharest (UNSTPB), where she pursued a Bachelor’s Degree in Systems Engineering from 2008 to 2012. Her strong academic performance culminated in a perfect score in her final exam. She continued at the same institution for her Master’s in Intelligent Control Systems between 2012 and 2014, graduating with a GPA of 9.81 and a top dissertation grade. Her educational experience included a strong focus on control algorithms, decision techniques, and distributed processing systems. From 2014 to 2022, she pursued her PhD in Systems Engineering at UNSTPB. Her doctoral thesis, titled “Analysis of the molecular pathogenesis of breast cancer using integrated microarray analysis and gene modeling,” earned the distinction Magna Cum Laude and reflected her ability to merge computational intelligence with biological research.

Experience

Irina-Oana has held several significant roles throughout her career. Since 2023, she has worked as a postdoctoral researcher in bioinformatics at eBio-hub, focusing on high-impact research related to cancer genomics. Her responsibilities include publishing peer-reviewed articles, participating in conferences, and applying for competitive research grants at both national and international levels. Prior to this, she worked from 2013 as a computer systems programmer at GBA, where she developed expertise in PL/SQL, data analysis, and IT system monitoring. From 2012 to 2020, she served as a Laboratory Assistant at UNSTPB, teaching the course “Diagnostic and Decision Techniques,” where she employed tools like Weka, dTree, and Netica for teaching decision support systems. Her diverse experience across academia, IT, and research has made her a multidisciplinary contributor to biomedical informatics.

Research Interest

Irina-Oana’s research is centered around bioinformatics, cancer genomics, decision support systems, and data-driven medical diagnostics. She applies systems engineering techniques to analyze complex biomedical data, with a particular emphasis on breast and colorectal cancers. Her work frequently involves the integration of microarray gene expression data using advanced modeling techniques such as Bayesian networks and fuzzy logic systems. She has also explored the classification of malignant subtypes, diabetes modeling, and the use of artificial intelligence in thyroid cancer detection and prognosis. Her multidisciplinary approach bridges systems engineering with life sciences, making her research highly impactful in personalized medicine and computational biology.

Award

Irina-Oana’s commitment to scientific advancement was recognized when she was selected as the project director in the Romanian Academy of Sciences’ 2024–2025 research project competition for young researchers under the “AOSR-TEAMS-III” program. This award highlights her innovative contributions and leadership in medical bioinformatics, particularly in data-driven cancer research.

Publication

Irina-Oana has authored numerous scientific publications, of which the following seven are particularly noteworthy:

“An integrated gene expression analysis approach”, E-health and Bioengineering Conference, 2015 – Cited in WoS:000380397900095.

“Microarray Gene Expression Analysis using R”, International Conference on Advancements of Medicine and Health Care through Technology, 2016 – DOI: 10.1007/978-3-319-52875-5_74.

“A colon cancer microarray analysis technique”, E-health and Bioengineering Conference, 2017 – WOS:000445457500067.

“Modeling a Bayesian Network for a Diabetes Case Study”, E-Health and Bioengineering Conference, 2020 – WOS:000646194100054.

“An integrated breast cancer microarray analysis approach”, U.P.B. Scientific Bulletin, Series C, 2022 – WOS:000805648400007.

“Fast detection of bacterial gut pathogens on miniaturized devices: an overview”, Expert Review of Molecular Diagnostics, 2024 – DOI: 10.1080/14737159.2024.2316756.

“Machine Learning for Thyroid Cancer Detection, Presence of Metastasis, and Recurrence Predictions—A Scoping Review”, Cancers, 2025 – DOI: 10.3390/cancers17081308.

Each of these works contributes uniquely to the scientific community, particularly in the domain of bioinformatics and medical diagnostics, and several are indexed in prestigious databases such as Web of Science and IEEE Xplore.

Conclusion

Lixandru-Petre Irina-Oana stands at the forefront of bioinformatics research in Romania, combining her deep knowledge in systems engineering with a profound commitment to advancing biomedical sciences. Her work continues to explore innovative solutions in cancer diagnosis and decision-support systems, driven by a passion for translating computational methods into clinical insights. As a researcher, educator, and project leader, she exemplifies a model of interdisciplinary excellence and contributes meaningfully to the future of precision medicine.

Daojun Liang | Time Series Analysis | Best Researcher Award

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Mr. Daojun Liang | Time Series Analysis | Best Researcher Award

PhD student | Shandong University | China

Mr. Daojun Liang is a dedicated PhD student at Shandong University with a solid academic background in computer science. He earned his BS from Taishan University in 2016 and his MS from Shandong Normal University in 2019. Currently pursuing his doctoral studies, Daojun has established himself as a researcher with expertise in uncertainty quantification, time series analysis, and large language models (LLM). Recognized for his independent research skills, Daojun has published several high-level papers in prestigious journals and serves as a reviewer for reputable organizations like IEEE, ACM, Elsevier, and Springer.

Profile

Scholar

Education

Daojun Liang began his academic journey with a Bachelor’s degree in Computer Science from Taishan University in 2016. Driven by a passion for innovation, he pursued a Master’s degree in Information Science and Engineering at Shandong Normal University, which he completed in 2019. His commitment to academic excellence led him to Shandong University, where he is currently advancing his research as a PhD candidate. His educational foundation has equipped him with the skills necessary for cutting-edge research and practical problem-solving in the fields of artificial intelligence and computational sciences.

Experience

Daojun’s research and professional experience demonstrate his versatility and expertise. He has contributed to several impactful projects, such as the development of intelligent vehicle networking technologies and the creation of advanced forecasting methods for 6G communication systems. His work with data-driven analysis and artificial intelligence for industrial applications highlights his ability to address complex challenges. Additionally, his role as an SCI reviewer for leading journals and collaborations with esteemed institutions like Fortiss GmbH and Shanghai Jiao Tong University reflect his strong academic and professional network.

Research Interests

Daojun’s research interests encompass long-term time series forecasting, uncertainty quantification, and the development of probabilistic inference methods. He focuses on analyzing intrinsic patterns in data to propose efficient and lightweight solutions. His work has implications for a variety of industries, including energy, manufacturing, and telecommunications. Daojun is also exploring the intersection of deep learning, natural language processing, and computer vision, ensuring his research remains at the forefront of innovation.

Awards and Recognitions

Daojun has been nominated for the Best Researcher Award in recognition of his outstanding contributions to academia and industry. His innovative methods for time series analysis and uncertainty quantification have not only been published in high-impact journals but have also been widely adopted in industrial applications. He has been honored as a reviewer for leading journals and conferences, which underscores his influence in the research community.

Publications

Liang, D., Zhang, H., Yuan, D., Zhang, M. (2024). Progressive Supervision via Label Decomposition: A Long-Term and Large-Scale Wireless Traffic Forecasting Method. Knowledge-Based Systems, 305, p.112622. (SCI Q1, IF = 7.2). Cited by 10.

Liang, D., Zhang, H., Yuan, D., Zhang, M. (2024). Periodformer: An Efficient Long-Term Time Series Forecasting Method Based on Periodic Attention. Knowledge-Based Systems, 304, p.112556. (SCI Q1, IF = 7.2). Cited by 8.

D. Liang, H. Zhang, D. Yuan, M. Zhang. (2024). Multi-Head Encoding for Extreme Label Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence. (SCI Q1, IF = 20.8). Cited by 15.

Liang, D., Yang, F., Wang, X., et al. (2019). Multi-Sample Inference Network. IET Computer Vision, 13(6), 605-613. (SCI Q1, IF = 1.7). Cited by 12.

Liang, D., Zhang, H., Yuan, D., et al. (2025). DistPred: A Distribution-Free Probabilistic Inference Method for Regression and Forecasting. ACM SigKDD 2025. Cited by 5.

Conclusion

Daojun Liang exemplifies the qualities of a modern researcher: innovative, dedicated, and collaborative. His contributions to uncertainty quantification, time series analysis, and large language models are reshaping academic and industrial practices. With numerous publications, collaborative projects, and a commitment to advancing knowledge, Daojun stands as a promising figure in his field.