Di Mao | Data Analysis | Best Researcher Award

Mr. Di Mao | Data Analysis | Best Researcher Award

Mr. Di Mao at Peking University Third Clinical School of Medicine, China

👨‍🎓 Profile

👩‍⚕️ Summary

Di Mao is a dedicated medical student at Peking University Health Science Center, specializing in clinical research. With a focus on reproductive health, she investigates the implications of various health conditions on fertility and pregnancy outcomes.

🎓 Education

Currently enrolled in the Eight-Year Undergraduate Program in Clinical Medicine at Peking University Health Science Center since 2021.

💼 Professional Experience

As a co-author of several research publications in high-impact journals, Di has contributed to studies examining the effects of artificial sweeteners, mental health disorders, and reproductive health in women.

🔍 Research Interests

Di’s research interests encompass reproductive health, infertility, and pregnancy outcomes, as well as the impact of mental health on fertility and thyroid function in assisted reproductive technologies. She also explores trends in postoperative complications and delirium.

📖  Top Noted Publications

Treatment outcomes of infertile women with endometrial hyperplasia undergoing their first IVF/ICSI cycle: A matched-pair study

  • Authors: Jing Yang, Mingmei Lin, Di Mao, Hongying Shan, Rong Li
    Journal: European Journal of Obstetrics & Gynecology and Reproductive Biology
    Year: 2024

Artificial Sweetener and the Risk of Adverse Pregnancy Outcomes: A Mendelian Randomization Study

  • Authors: Di Mao, Mingmei Lin, Zhonghong Zeng, Dan Mo, Kai-Lun Hu, Rong Li
    Journal: Nutrients
    Year: 2024

 Common mental disorders and risk of female infertility: a two-sample Mendelian randomization study

  • Authors: Di Mao, Mingmei Lin, Rong Li
    Journal: Frontiers in Endocrinology
    Year: 2024

 Impact of mildly evaluated thyroid-stimulating hormone levels on in vitro fertilization or intracytoplasmic sperm injection outcomes in women with the first fresh embryo transfer: a large study from China

  • Authors: Mingmei Lin, Di Mao, Kai-Lun Hu, Ping Zhou, Fen-Ting Liu, Jingwen Yin, Hua Zhang, Rong Li
    Journal: Journal of Assisted Reproduction and Genetics
    Year: 2024

International Data Mastery Achievement Award

International Data Mastery Achievement Award

Introduction: Welcome to the pinnacle of data excellence - the 'International Data Mastery Achievement Award.' This prestigious accolade recognizes trailblazers in the field of data science, honoring those whose groundbreaking work reshapes our understanding and utilization of data.

Eligibility: Open to data enthusiasts worldwide, this award has no age limits. Candidates must possess a proven track record of remarkable achievement in the realm of data science.

Qualification: Candidates should demonstrate exceptional proficiency in data analysis, algorithm development, and innovative data-driven solutions. A minimum of 5 years of professional experience in the field is required.

Publications and Requirements: Applicants should have a history of impactful publications showcasing their contributions to the data science community. Original research papers, articles, or case studies are highly encouraged.

Evaluation Criteria: Judged on innovation, impact, and contribution to the field, candidates will be assessed by a panel of distinguished experts in data science.

Submission Guidelines: Submissions must include a comprehensive biography, an abstract of the candidate's work, and supporting files highlighting the significance of their contributions. Ensure all documents are submitted in a standard format.

Recognition: Winners will receive global recognition for their outstanding achievements, with a featured spotlight in prominent industry publications and events.

Community Impact: Candidates should demonstrate a positive influence on the data science community through mentorship, knowledge-sharing, or community-driven initiatives.

Biography: Provide a detailed biography outlining your journey, achievements, and impact on the data science landscape.

Abstract and Supporting Files: Include a compelling abstract summarizing your key contributions and attach supporting files that showcase the depth and impact of your work.