Wooi-Chen Khoo | Statistical Models and Inference | Best Researcher Award

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Dr. Wooi-Chen Khoo | Statistical Models and Inference | Best Researcher Award

UCSI University | Malaysia

Dr. Wooi-Chen Khoo is a distinguished academician and researcher specializing in actuarial science, business analytics, and applied statistics. With expertise in statistical modeling, time series analysis, survival models, and data analytics, she has built an impressive portfolio of teaching, research, and academic leadership. She has taught across leading institutions such as UCSI University, Sunway University, and the University of Malaya, delivering both undergraduate and postgraduate programs aligned with IFoA and SoA standards. Her research integrates applied probability, statistical inference, and multidisciplinary applications, resulting in impactful publications in high-quality journals. Dr. Wooi-Chen Khoo has successfully supervised PhD and master’s candidates, guiding them toward scholarly and industry-relevant contributions. Beyond academia, she engages with professional bodies such as the Institute and Faculty of Actuaries (IFoA), the Society of Actuaries (SoA), and the Department of Statistics Malaysia (DOSM). Her outreach includes keynote lectures, panel discussions, and workshops for industries, emphasizing data-driven decision-making.

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ORCID 

Googlescholar

Education

Dr. Wooi-Chen Khoo’s academic foundation reflects her strong commitment to mathematics and statistics. She began her journey with a Bachelor’s degree in Mathematical Science at Universiti Sains Malaysia, followed by a Master’s degree in Mathematical Science from the same institution, where she deepened her expertise in applied statistics. She later pursued her doctoral studies at Universiti Malaya, where she completed a PhD in Applied Probability and Statistics. During this period, she refined her focus on statistical modeling of time series and mixture distributions, which became central themes in her scholarly work. Her academic training has equipped her with both theoretical depth and applied skills, enabling her to bridge the gap between pure mathematics and real-world statistical challenges. Complementing her formal education, Dr. Khoo also holds professional qualifications such as HRDF Trainer certification, Google Analytics accreditation, and affiliation with the Society of Actuaries, reflecting her interdisciplinary competence and professional credibility

Experience

Dr. Wooi-Chen Khoo has built a diverse teaching and administrative career across leading Malaysian universities. At UCSI University, she currently serves as Head of the Institute of Actuarial Science and Data Analytics, where she leads programme accreditation, curriculum development, and IFoA alignment. She previously headed the Department of Applied Statistics at Sunway University, driving new programme initiatives and accreditation processes. Earlier, she contributed as a lecturer at Universiti Malaya while pursuing her doctoral studies, strengthening her teaching of probability theory and statistical methods. Her teaching portfolio spans IFoA modules CS1, CS2, and CM1, postgraduate research methodology, and empirical modeling. She has supervised numerous final-year, Master’s, and PhD students, many of whom progressed into impactful research careers. Beyond teaching, Dr. Khoo has held roles as exam coordinator, journal reviewer, invited speaker, and panelist, highlighting her academic leadership. Her consultancy and industry outreach underscore her commitment to data-driven problem solving.

Awards and Honors

Dr. Wooi-Chen Khoo has received recognition for her academic excellence and applied research contributions. She was awarded First Place in an article writing competition organized by the Department of Statistics Malaysia (DOSM) for her work on resilience and economic growth in an ageing society, aligning statistical insights with sustainable development goals. Her research excellence was also acknowledged with a Best Paper Award at the IMT-GT International Conference on Mathematics, Statistics, and Their Applications, recognizing her innovative statistical modeling of bus travel time using the Burr distribution. Additionally, she achieved Second Runner-Up for Best Technical Paper at the Malaysian Road Conference & Exhibition and the International Road Federation Asia-Pacific Regional Congress, showcasing the practical impact of her work in transportation systems. These awards reflect her ability to integrate rigorous statistical methodologies with applied contexts, contributing not only to academia but also to national policy and industry practices.

Research Focus

Dr. Wooi-Chen Khoo’s research lies at the intersection of applied probability, statistics, actuarial science, and data analytics. Her work emphasizes statistical modeling, including mixture autoregressive models, time series of counts, and Burr distributions, with applications spanning infectious disease forecasting, unemployment analysis, insurance pricing, and urban transportation. She has published extensively in reputable international journals, demonstrating both theoretical innovation and practical relevance. In transportation studies, her research has advanced understanding of bus travel time variability and reliability, while her contributions to epidemiological modeling offer insights into disease spread and preventive strategies. She has also explored statistical frameworks for socio-economic issues, such as unemployment and insurance schemes. Her current projects expand into reinforcement learning applications and AI-driven modeling, reflecting her adaptability to emerging methodologies. By supervising postgraduate candidates and collaborating with institutions like DOSM, IFoA, and SoA, Dr. Khoo continues to bridge research, policy, and professional practice

 

Publications

 

Title: Modeling time series of counts with a new class of INAR (1) model
Year: 2017
Citation count: 36

Title: Short-term impact analysis of fuel price policy change on travel demand in Malaysian cities
Year: 2012
Citation count: 28

Title: Quantifying bus travel time variability and identifying spatial and temporal factors using Burr distribution model
Year: 2022
Citation count: 19

Title: Coherent forecasting for a mixed integer-valued time series model
Year: 2022
Citation count: 7

Title: On the prediction of intermediate-to-long term bus section travel time with the Burr mixture autoregressive model
Year: 2024
Citation count: 6

Conclusion

Dr. Wooi-Chen Khoo is a passionate educator, accomplished researcher, and strategic academic leader whose contributions in applied probability, actuarial science, and data analytics have advanced knowledge, supported industry practices, and shaped future generations of statisticians.

Chaima AOUICHE | Mathematics and Bioinformatics | Outstanding Scientist Award

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Dr. Chaima AOUICHE | Mathematics and Bioinformatics | Outstanding Scientist Award

Dr. Chaima Aouiche is a dedicated academic and researcher in computer science with expertise in artificial intelligence, machine learning, cybersecurity, and bioinformatics. Born on October 24, 1990, in Tebessa, Algeria, she began her academic journey at Larbi Tebessi University and pursued her Ph.D. at Northwestern Polytechnical University (NPU), China. With international exposure, Dr. Aouiche has authored impactful publications on cancer gene prediction, data integration, and AI-based energy systems. She has collaborated across disciplines and countries, contributing to international conferences and peer-reviewed journals. Currently serving as a university teacher in Algeria, she is also a multilingual educator with teaching experience in China and Algeria. Dr. Aouiche combines technical knowledge with strong interpersonal skills and a passion for teaching, traveling, and community service, making her a well-rounded and globally competent scholar committed to innovation and education.

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🎓 Education

Dr. Chaima Aouiche holds a strong academic foundation in computer science. She earned her Bachelor’s degree (2008–2011) and Master’s degree (2011–2013) in Computer Science from Larbi Tebessi University, Algeria, where she was recognized with the “Outstanding Student Award” in 2013. She expanded her horizons by studying the Chinese language for a year (2013–2014) at Northwestern Polytechnical University (NPU) in Xi’an, China. She then pursued a Ph.D. in Computer Science and Technology at NPU (2014–2021), focusing on stage-specific gene prediction, big data integration, and artificial intelligence. Throughout her academic journey, she acquired various global certifications, including Artificial Intelligence Foundations, Advanced Machine Learning, and Deep Learning, further enriching her qualifications. With multilingual skills in Arabic, French, English, and Chinese, she integrates global perspectives into her research and teaching. Her academic path reflects both depth and international breadth.

🧪 Experience

Dr. Chaima Aouiche has a diverse background in academia, industry, and cross-cultural teaching. She began her professional career in project management at MPE-MPI Investments, Tebessa (2011–2013), where she gained hands-on technical and administrative skills. In 2017, she taught English and Arabic in Xi’an, China, enhancing her intercultural communication and educational outreach. Currently, she works as a university teacher in Algeria, engaging in teaching, research supervision, and publication. Her training includes courses in project management, AI, and big data, complemented by technical expertise in programming (Python, Java, R), MATLAB, web technologies, and networking. Her ability to communicate in four languages (Arabic, French, English, Chinese) and her volunteering and mentoring activities reflect her commitment to holistic professional development. Dr. Aouiche’s career is defined by interdisciplinary collaboration, international exposure, and a passion for applied technological solutions, making her an asset in both academia and industry.

🏅 Awards and Honors

Dr. Aouiche’s academic and professional excellence has been recognized through multiple awards and certificates. She was awarded the Outstanding Student Award by Larbi Tebessi University in 2013. Her further accolades include numerous international certifications, such as the HSK 4 Chinese Proficiency Certificate, Artificial Intelligence and Big Data Training (Xi’an Jiaotong University), AI Foundations Masterclass (2023), and Advanced Machine Learning and Deep Learning Certificates (2024). She has also been recognized for her participation in global academic initiatives, such as the International Winter Camp (2017) and the Silk Road Engineering Science Program (2020). In addition to formal honors, her significant co-authorship on high-impact publications in BMC Bioinformatics, Frontiers in Genetics, and IEEE conferences speaks to her professional standing. These accolades collectively highlight her dedication to academic distinction, global engagement, and technological innovation.

🔬 Research Focus

Dr. Aouiche’s research intersects bioinformatics, artificial intelligence, machine learning, and cybersecurity. Her work has emphasized integrating multiple datasets to predict stage-specific cancer-related genes, mapping copy number variations, and modeling aberrant genomic events. She co-authored key studies published in BMC Bioinformatics, Frontiers in Genetics, and Quantitative Biology, which propose dynamic gene modules and data-driven cancer diagnostics. Recent work explores ensemble learning and AI approaches to detect cyberattacks using integrated datasets, showing a pivot toward cybersecurity and smart systems. Additionally, her research extends into renewable energy, specifically applying AI models to optimize photovoltaic systems and MPPT (Maximum Power Point Tracking) control. Her interdisciplinary approach bridges computational biology and engineering, reflecting her adaptability and innovative vision. Dr. Aouiche is particularly interested in applied AI that addresses real-world challenges in medicine, energy, and security, with a growing focus on industry 4.0 applications.

✅ Conclusion

Dr. Chaima Aouiche is an innovative computer scientist and academic whose international education, multidisciplinary research in AI and bioinformatics, commitment to teaching, and dynamic professional experiences make her a valuable contributor to global science and technology.

Publications

Shui Yu | Reliability analysis and design optimization | Best Researcher Award

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Dr. Shui Yu | Reliability analysis and design optimization | Best Researcher Award

Yu Shui is an Associate Researcher at the University of Electronic Science and Technology of China, with a Ph.D. in Engineering and extensive academic and research experience in reliability analysis, robust design, and AI-driven robotics. He has previously held postdoctoral and lecturer roles at UESTC and Southwest Jiaotong University, respectively. His research spans intelligent systems, robust optimization, and reliability engineering, with publications in top-tier journals like Reliability Engineering & System Safety. His academic path reflects a strong commitment to developing advanced models and frameworks for time-variant reliability design and intelligent algorithms. He is an active researcher contributing to the frontiers of artificial intelligence in engineering systems.

Profile

Education 🎓

Yu Shui completed both his Bachelor’s (2009.09–2013.06) and Ph.D. (2013.09–2019.06) degrees at the University of Electronic Science and Technology of China (UESTC), majoring in engineering fields related to system reliability and optimization. His academic training provided a rigorous foundation in theoretical modeling, numerical simulations, and intelligent systems. During his doctoral studies, he focused on reliability design and probabilistic modeling under uncertainty, incorporating machine learning techniques into engineering optimization. He worked under distinguished mentors, gaining expertise in both the practical and theoretical aspects of engineering reliability. His Ph.D. research laid the groundwork for innovative solutions to complex, real-world reliability issues using AI methods.

Experience 👨‍🏫

Yu Shui started his academic career with a postdoctoral position (2019.07–2021.07) at UESTC, focusing on intelligent algorithms in reliability systems. From 2021.07 to 2024.03, he worked as a Lecturer at Southwest Jiaotong University, where he led courses and supervised research in design optimization and AI applications. In March 2024, he returned to UESTC as an Associate Researcher, contributing to high-impact projects in robotics and reliability engineering. Throughout his career, he has collaborated on interdisciplinary projects involving surrogate modeling, dynamic pruning methods, and AI-driven design optimization, earning recognition for both teaching and research contributions.

Research Interests 🔬

Yu Shui’s research centers on reliability analysis, robust design, intelligent robotics, and artificial intelligence. He develops optimization frameworks and surrogate models to improve the performance and resilience of complex engineering systems. His work incorporates Bayesian regression, dynamic pruning, and demand-objective frameworks for time-variant reliability-based design. His interdisciplinary focus bridges engineering with machine learning, pushing the boundaries of how intelligent systems can manage uncertainty in design and operations. He is particularly interested in integrating AI techniques into robust mechanical systems to enhance reliability in real-world applications.

Publications

  • Empirical Examination of the Interactions Between Healthcare Professionals and Patients Within Hospital Environments—A Pilot Study

    Hygiene
    2025-05-08 | Journal article
    DOI: 10.3390/hygiene5020020
    CONTRIBUTORS: Dimitris Charalambos Karaferis; Dimitris A. Niakas

  • Digitalization and Artificial Intelligence as Motivators for Healthcare Professionals

    Japan Journal of Research
    2025-01-01 | Journal article
    DOI: 10.33425/2690-8077.1170
    CONTRIBUTORS: Karaferis Dimitris; Balaska Dimitra; Pollalis Yanni

  • Workplace Violence in Healthcare: Effects and Preventive Measures and Strategies

    SunText Review of Case Reports & Images
    2024 | Journal article
    DOI: 10.51737/2766-4589.2024.141
    Part ofISSN: 2766-4589
    CONTRIBUTORS: Karaferis D; Balaska D

  • Enhancement of Patient Engagement and Healthcare Delivery Through the Utilization of Artificial Intelligence (AI) Technologies

    Austin Journal of Clinical Medicine
    2024-11-15 | Journal article
    DOI: 10.26420/austinjclinmed.2024.1053
    Part of ISSN: 2381-9146
    CONTRIBUTORS: Department of Economic Science, University of Piraeus, Piraeus, Greece; Dimitris Karaferis; Dimitra Balaska; Department of Economic Science, University of Piraeus, Piraeus, Greece; Yannis Pollalis; Department of Economic Science, University of Piraeus, Piraeus, Greece

Meryem Yankol-Schalck | Insurance and Machine Learning | Best Researcher Award

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Assist. Prof. Dr. Meryem Yankol-Schalck | Insurance and Machine Learning | Best Researcher Award

 

Profile

Education

She holds a Ph.D. in Econometrics and Machine Learning from the University of Orleans (2018–2022), where she investigated new machine learning approaches for financial fraud detection and survival analysis in the insurance industry under the supervision of S. Tokpavi. In addition, she earned a Data Science Certificate (Executive) from the Institute of Risk Management (IRM) in 2016–2017. Her academic background also includes a Master’s degree in Mathematical Engineering (Applied Statistics) from Paris-Sud University (2004–2007) and a Master’s degree in Mathematics from the University of Marmara in Istanbul (1995–1999). Since September 2022, she has been an Assistant Professor of Data Science at IPAG Business School in Nice and Paris. With extensive experience in the insurance sector, she integrates her professional insights into the classroom, emphasizing practical AI applications. Her curriculum reflects the latest trends in data science, fostering a dynamic learning environment tailored to students’ needs. She adapts resources and pedagogical methods to specific course objectives, utilizing tools such as Tableau for data visualization and exploring real-world business applications, including Netflix, Uber, ChatGPT, Gemini, and facial recognition technologies.

 

Work experience

She has held various academic and professional roles, combining her expertise in data science, machine learning, and business analytics. From September 2022 to January 2023, she was an adjunct faculty member at the International University of Monaco, where she taught Mathematics for Business. Prior to that, from September 2021 to August 2022, she served as an adjunct faculty member at IPAG Business School (Nice), teaching courses such as “Data Analysis for Business Management” (BBA3), “Data Processing” (MSc, e-learning), “Digital and Sales” (GEP 5th year), and “Introduction to Statistics” (BBA1). Between September 2020 and October 2021, she was an adjunct faculty member at EMLV (Paris), where she taught “Quantitative Data Analytics – SPSS” (GEP 4th year, hybrid learning) and supervised master’s theses for GEP 5th-year students.

In addition to her academic roles, she has extensive experience in the consulting and insurance sectors. From March to November 2020, she worked as a Senior Consultant at Fraeris (Paris), supporting clients in project development and providing technical solutions. She collaborated with the “Caisse de Prévoyance Sociale” (CPS) of French Polynesia, modeling healthcare expenditures using machine learning techniques. She developed predictive models to analyze healthcare costs from both the insured’s and CPS’s perspectives, offering actionable insights and data-driven forecasts to aid long-term financial planning. Prior to that, in 2019–2020, she was a Senior Manager in Pricing & Data P&C at Addactis (Paris), where she supported clients in project development, innovation, and strategic planning. As an expert referent for ADDACTIS® Pricing software, she worked on database processing for BNP Paribas Cardif, facilitating APLe software operations for quarterly account closings.

Memberships and Projects:

• Membership of the American Risk and Insurance Association (ARIA)
• Membership of the academic association AFSE.
• Member of the RED Flag Project of the University of Orléans in cooperation with CRJPothier.
• Participation at 3 Erasmus+ Projects: Artificial Intelligence to support Education (EducAItion).
• Virtual Incubator Tailored to All Entrepreneurs (VITAE).
• Artificial Intelligence in high Education (PRAIME),

Research topics:

Studies focus on the application of data science techniques to business issues, particularly in the insurance
sector, and on climate change. Another topic of study is the relationship between AI and education.

Publication

  • Yankol-Schalck, M. (2023). Auto Insurance Fraud Detection: Leveraging Cost Sensitive and Insensitive
    Algorithms for comprehensive Analysis, Insurance: Mathematics and Economics.(
    (https://www.sciencedirect.com/science/article/abs/pii/S0167668725000216)
    Banulescu‐Radu, D., & Yankol‐Schalck, M. (2024). Practical guideline to efficiently detect insurance fraud
    in the era of machine learning: A household insurance case. Journal of Risk and Insurance, 91(4), 867-
    913.
    Yankol-Schalck, M. (2022). A Fraud Score for the Automobile Insurance Using Machine Learning and
    Cross-Data set Analysis, Research in International Business and Finance, Volume 63, 101769.
    Schalck, C., Yankol-Schalck, M. (2021). Failure Prediction for SME in France: New evidence from
    machine learning techniques, Applied Economics, 53(51), 5948-5963.
    On- going research:
    Yankol-Schalck (2025). Auto Insurance Fraud Detection: Machine Learning and Deep Learning
    Applications, submitted in Journal of Risk and Insurance.
    Schalck, C., Yankol-Schalck, M. (2024). Churn prediction in the French insurance sector using Grabit
    model, revision in Journal of Forecasting.
    Schalck, C., Seungho, L., Yankol-Schalck, M. (2024). Characteristics of firms and climate risk
    management: a machine learning approach. Work in progress for The Journal of Financial Economics.
    Yankol-Schalck M.and Chabert Delio C., (2024). The application of machine learning to analyse changes in
    consumer behaviour in a major crisis. Work in progress.
    Yankol-Schalck M. and Nasseri A. (2024).An investigation into the integration of artificial intelligence in
    education: Implications for teaching and learning methods. Work in progress.

Nuo Yu | Radiomics | Best Researcher Award

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Ms. Nuo Yu | Radiomics | Best Researcher Award

Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College ,China

Nuo Yu is a Ph.D. candidate at the Cancer Institute and Hospital of the Chinese Academy of Medical Sciences, specializing in radiation oncology with a focus on esophageal squamous cell carcinoma (ESCC). His research primarily explores innovative chemoradiotherapy regimens to improve treatment outcomes for patients with locally advanced ESCC.

Yu has contributed to several peer-reviewed publications in SCI-indexed journals. Notably, he co-authored a study titled “Conversion Chemoradiotherapy Combined with Nab-Paclitaxel Plus Cisplatin in Patients with Locally Advanced Borderline-Resectable or Unresectable Esophageal Squamous Cell Carcinoma: A Phase I/II Prospective Cohort Study,” published in Strahlentherapie und Onkologie in August 2024. This research evaluated the efficacy and safety of a novel chemoradiotherapy regimen, demonstrating promising results in locoregional control and overall survival rates.

In March 2023, Yu co-authored another significant study, “Efficacy and Safety of Concurrent Chemoradiotherapy Combined with Nimotuzumab in Elderly Patients with Esophageal Squamous Cell Carcinoma: A Prospective Real-world Pragmatic Study,” published in Current Cancer Drug Targets. This research focused on treatment strategies for elderly patients with ESCC, highlighting the potential benefits of combining chemoradiotherapy with nimotuzumab.

Yu’s work has been recognized at international conferences, including presentations at the American Society for Radiation Oncology (ASTRO), the Federation of Asian Organizations for Radiation Oncology (FARO), and the Korean Society for Radiation Oncology (KOSRO). These engagements underscore his active participation in the global radiation oncology community and his commitment to advancing cancer treatment research.

While still in the early stages of his career, Yu’s focused research on ESCC and his contributions to the field of radiation oncology position him as a promising candidate for the Best Researcher Award. Continued efforts to expand his research scope, increase publication impact, and assume leadership roles in larger-scale studies will further strengthen his candidacy.

Profile

Scientific Publications