Ying Zhang | Decision Sciences | Best Researcher Award

Published on 06/06/202506/06/2025 by TOT Awards

Prof. Ying Zhang | Decision Sciences
| Best Researcher Award

Researcher at Yunnan University of Finance and Economics, China.

Professor Ying Zhang, born in 1980 (Bai nationality, Australian PR), is a tenured professor at Yunnan University of Finance & Economics since 2019. Her expertise spans International Human Resource Management (IHRM), Organizational Behavior (OB), cultural intelligence, expatriation, and management science. With over 30 peer-reviewed papers—14 in JCR Q1 and 5 in Q2 journals—and seven authored books, her cumulative impact factor exceeds 280. She has led multiple national and international research projects and received 12 prestigious awards. Renowned for mentoring postgraduate students from minority backgrounds in mountainous regions, she guided SSCI-published research and earned China’s First Prize for National Master Thesis Instruction, highlighting her impact in both research excellence and inclusive education.

professional profile

Scopus

Education

Professor Ying Zhang earned her Ph.D. in Management from Charles Sturt University (CSU), Australia, in 2014, with a focus on international management and human resource development. She holds a Master of Education from the University of Melbourne (2004), where she specialized in cross-cultural and curriculum studies. Her undergraduate studies were completed at Yunnan University (1998–2002), majoring in Foreign Languages, which laid the foundation for her bilingual teaching and international orientation. Her academic trajectory reflects a strong blend of management, education, and cultural fluency—qualities that fuel her interdisciplinary research and global collaborations. Her diverse academic background empowers her to bridge Western and Eastern educational philosophies and manage research projects with international relevance and impact.

Work Experience

Since 2015, Professor Ying Zhang has served as a professor at the International Business School, Yunnan University of Finance & Economics, a CSU partner institution. She teaches across undergraduate to doctoral levels, delivering courses in English and bilingual formats, such as HRM, Strategic Marketing, Global Business, and Cross-cultural Management. She supervises master’s and Ph.D. students in management science, engineering, and international business. Her practical experience includes leading international collaborations, including three Sino-Australian projects. She has also been actively involved in academic research, project management, and community outreach. Her dual role as a scholar and mentor demonstrates a well-rounded professional commitment to teaching, research, and inclusive academic development in diverse regions.

Research Focus

Professor Ying Zhang’s research concentrates on International Human Resource Management (IHRM), Organizational Behavior (OB), cultural intelligence, and expatriation management. Her work critically examines how culture influences management practices, employee behavior, and global business operations. She explores the dynamics of expatriate adaptation, leadership development in multicultural contexts, and organizational strategies for global talent retention. With a focus on evidence-based analysis and cross-cultural applicability, she integrates qualitative and quantitative methodologies to drive innovation in management science. Her projects often intersect with real-world policy, addressing economic development in remote and minority communities. Her research not only advances theoretical frameworks but also provides practical models for effective intercultural engagement in today’s globalized business environment.

Conclusion

Ying Zhang is highly suitable for the Research for Best Researcher Award. Her academic profile, research leadership, international collaborations, and exceptional teaching contributions collectively make her a top-tier candidate. Minor advancements in global visibility and applied impact would further elevate her candidacy, but she already demonstrates an outstanding record worthy of high recognition.

Publications to Noted

📄 An investigation of the impact of intellectual capital on entrepreneurial performance
Year: 2024 | 🔍 Journal of International Management (JCR Q1, IF=6.200, ABDC-A) | 📚

📄 Knowledge management of MNCs in the post-COVID Era: The role of cultural intelligence and knowledge-oriented leadership
Year: 2024 | 🔍 Journal of Knowledge Management (JCR Q1, IF=9.961, ABDC-A) | 📚

📄 Team Psychological Resource as A Critical Enabler for Innovation and Knowledge Creation
Year: 2023 | 🔍 Journal of Industrial Integration and Management (ESCI Q1, IF=9.000) | 📚

📄 Modelling the Influence of Individual Differences on Knowledge Hiding
Year: 2022 | 🔍 Journal of Knowledge Management (JCR Q1, IF=9.961, ABDC-A) | 📚

📄 Exploring the longitudinal effects of emotional and cultural intelligence on knowledge management processes
Year: 2022 | 🔍 Asia Pacific Journal of Management (JCR Q2, IF=6.721, ABS***, ABDC-A) | 📚

Guanbo Wang | Biostatistics | Best Researcher Award

Published on 02/06/202502/06/2025 by TOT Awards

Assist. Prof. Dr. Guanbo Wang | Biostatistics | Best Researcher Award

Assistant professor at The Dartmouth Institute, United States

Dr. Guanbo Wang is an Assistant Professor at The Dartmouth Institute for Health Policy and Clinical Practice and the Department of Biomedical Data Science, Dartmouth College. He specializes in biostatistics, causal inference, and machine learning, with a commitment to advancing statistical methodologies for improving public health. His work focuses on data integration, trial design, and personalized medicine using multi-source data such as randomized clinical trials and electronic health records. Dr. Wang holds a Ph.D. and M.Sc. in Biostatistics from McGill University, with research stints at Harvard University. He has collaborated with leading institutions like Harvard T.H. Chan School of Public Health, Biogen, and Roche, contributing to diverse therapeutic areas including cancer, cardiovascular diseases, and pediatric health. His academic contributions are evidenced by his prolific publication record and roles in high-impact research projects. Dr. Wang is also an active mentor, reviewer, and member of professional societies in statistics and epidemiology. 📊

professional profile

Google Scholar

Scopus

ORCID

Education

Dr. Guanbo Wang earned his Ph.D. in Biostatistics (2017–2022) and M.Sc. in Biostatistics (2015–2017) from McGill University, under esteemed mentors such as Robert Platt, Mireille Schnitzer, and Andrea Benedetti. His doctoral and master’s work emphasized causal inference, high-dimensional data, and clinical applications. He also completed a visiting scholar stint at Harvard University’s Department of Population Medicine in 2019, guided by Rui Wang. Currently, he is completing a Postdoctoral Fellowship (2022–2025) in Epidemiology at CAUSALab, Harvard T.H. Chan School of Public Health, mentored by Issa Dahabreh. His academic training integrates theoretical statistics with real-world medical challenges, contributing to a solid foundation in methodological development and applied health sciences. With additional statistical training from China, Dr. Wang’s global academic background uniquely equips him to tackle multifaceted health data challenges. 📘📈

Work Experience

Dr. Wang’s professional journey spans academia, industry, and collaborative health research. Since 2023, he has served as a research collaborator at Harvard’s CBAR on pediatric HIV/AIDS studies. Previously, he interned at Genentech/Roche, where he augmented early-phase oncology trials using external data. He consulted for Biogen on machine learning in neurology and supported various clinical research projects at McGill-affiliated hospitals. Earlier roles include internships with firms such as KPMG, AXA Insurance, and the National Bureau of Statistics of China, where he developed analytics and business strategies. Dr. Wang has also taught graduate courses and mentored students at McGill and Harvard. These experiences reflect his ability to bridge complex statistical theory with practical solutions in both research and corporate settings. 🏥📊

Awards and Honors

Dr. Wang has earned widespread recognition through prestigious awards and fellowships reflecting his scholarly excellence. He is a co-investigator on the $31M PCORI-funded RAPTOR-CIED project, supporting cardiac device care optimization. He was awarded the FRQS Doctorate Fellowship (2019–2022), valued at CAD 70,000, for his work on machine learning in anticoagulant effectiveness. Additional honors include the Research Institute of MUHC Fellowship (CAD 9,125), Harvard Pilgrim Fellowship (USD 4,000), and the CRM StatLab Award. Dr. Wang has also received the McGill Graduate Excellence Award (CAD 40,227), the McGill GREAT Award, and travel grants from the Statistical Society of Canada. His competitive success in acquiring NIH and CIHR funding as a trainee and collaborator further reflects his leadership in health data science. These distinctions underscore his impact on high-stakes research, statistical innovation, and mentorship within interdisciplinary academic and clinical communities. 🏆📑

Research Focus

Dr. Wang’s research lies at the intersection of causal inference, machine learning, and health policy. He focuses on developing robust statistical methods for data integration—particularly combining randomized controlled trials with real-world data sources to generate generalizable and actionable evidence. In personalized medicine, his work aims to evaluate treatment heterogeneity by leveraging multi-source longitudinal datasets and time-to-event outcomes. He also develops advanced methods for complex clinical trial designs, including adaptive, early-phase, and cluster-randomized trials. Another research stream explores how to incorporate prior clinical knowledge into high-dimensional prediction models to improve interpretability and clinical relevance. Methodologically, Dr. Wang draws on non/semi-parametric statistics, optimization, survival analysis, and computational methods. His work addresses diseases across pediatrics, oncology, cardiology, infectious diseases, and mental health. With a mission to inform precision health decisions, Dr. Wang’s research supports a deeper understanding of treatment effects across diverse populations and care settings. 🧬🧠📉

Conclusion

Dr. Guanbo Wang is an exceptional candidate for the Research for Best Researcher Award. His profile demonstrates a rare blend of theoretical innovation, real-world applicability, and academic leadership. His interdisciplinary and collaborative approach, especially in causal inference for healthcare, aligns perfectly with the award’s objectives to recognize researchers who push the boundaries of impactful, rigorous science.

Publications to Noted

Title: Causal inference with multiple concurrent medications: A comparison of methods and an application in multidrug-resistant tuberculosis
Authors: AA Siddique, ME Schnitzer, A Bahamyirou, G Wang, TH Holtz, GB Migliori, …
Citations: 31
Year: 2019

Title: Estimating treatment importance in multidrug-resistant tuberculosis using Targeted Learning: An observational individual patient data network meta-analysis
Authors: G Wang, ME Schnitzer, D Menzies, P Viiklepp, TH Holtz, A Benedetti
Citations: 18
Year: 2020

Title: Using Effect Scores to Characterize Heterogeneity of Treatment Effects
Authors: G Wang, PJ Heagerty, IJ Dahabreh
Citations: 17
Year: 2024

Title: Modeling treatment effect modification in multidrug-resistant tuberculosis in an individual patient data meta-analysis
Authors: Y Liu, ME Schnitzer, G Wang, E Kennedy, P Viiklepp, MH Vargas, …
Citations: 11
Year: 2022

Title: Evaluating hybrid controls methodology in early-phase oncology trials: a simulation study based on the MORPHEUS-UC trial
Authors: G Wang, MP Costello, H Pang, J Zhu, HJ Helms, I Reyes-Rivera, RW Platt, …
Citations: 9
Year: 2023

Title: Predictive factors of detectable viral load in HIV-infected patients
Authors: A Bouchard, F Bourdeau, J Roger, VT Taillefer, NL Sheehan, M Schnitzer, G Wang, …
Citations: 9
Year: 2022

Title: Robust integration of external control data in randomized trials
Authors: G Wang, R Karlsson, JH Krijthe, IJ Dahabreh
Citations: 6
Year: 2024

Title: Penalized G-estimation for effect modifier selection in the structural nested mean models for repeated outcomes
Authors: A Jaman, G Wang, A Ertefaie, M Bally, R Lévesque, R Platt, M Schnitzer
Citations: 6
Year: 2024

Title: Combining an experimental study with external data: study designs and identification strategies
Authors: L Ung, G Wang, S Haneuse, M Hernán, I Dahabreh
Citations: 4
Year: 2024

Sana Mujeeb | Predictive Analysis | Best Researcher Award

Published on 28/10/202429/10/2024 by TOT Awards

Dr. Sana Mujeeb | Predictive Analysis | Best Researcher Award

Assistant Professor at Iqra University Islamabad Campus, Pakistan

Dr. Sana Mujeeb is a highly qualified Assistant Professor in the Department of Computing and Technology at Iqra University, Islamabad. She holds a Ph.D. in Computer Science with a specialization in Data Science, focusing on data-driven energy optimization. With over 570 citations, an H-index of 10, and several publications in high-impact journals, her research interests include data-driven electricity theft detection, renewable energy optimization, and driver drowsiness detection. Dr. Mujeeb’s professional experience spans over seven years as a research associate and more than four years in teaching, demonstrating a solid foundation in both academic and practical applications of her expertise.

professional profile

Google Scholar

ORCID

Education

Dr. Sana Mujeeb pursued her education with a focus on Computer Science and Data Science, starting with an MCS from the National University of Modern Languages (NUML), Islamabad, where she completed her thesis on gender classification. She continued her studies at COMSATS University Islamabad, earning an MS in Computer Science with a specialization in Artificial Intelligence and Natural Language Processing. Her thesis work on a diagnostic chatbot underscored her early interests in practical, technology-driven solutions for real-world problems. She later obtained her Ph.D. in Computer Science from COMSATS, with a research emphasis on energy optimization in smart grids, achieving a CGPA of 3.75. This program refined her expertise in big data analytics, machine learning, and data science, which now inform her impactful research in electricity theft detection and sustainable energy, positioning her as a leading researcher in data-driven approaches to modern energy challenges.

work Experience

Dr. Mujeeb has over 11 years of professional experience, encompassing both teaching and research. She currently serves as an Assistant Professor at Iqra University, Islamabad, where she teaches advanced courses in data science and artificial intelligence. Previously, she was an Assistant Professor at Hamdard University, where she honed her teaching and curriculum development skills. Her research experience includes seven and a half years as a Research Associate at COMSATS University’s Communication over Sensors (ComSens) Lab, where she contributed to cutting-edge projects in data-driven energy optimization and artificial intelligence applications. Dr. Mujeeb has also served as a Visiting Lecturer at Punjab University Jhelum Campus and as a lecturer at various institutions, including the National University of Modern Languages and Grafton College. Her extensive background in both academia and research has given her a robust foundation for tackling contemporary technological challenges.

Research Focus

Dr. Sana Mujeeb’s research focuses on data-driven applications in energy optimization, predictive analytics, and sustainable technology. Her work addresses critical societal issues such as electricity theft detection, where she leverages machine learning and big data to develop accurate predictive models. She is also deeply involved in green energy initiatives, exploring renewable energy’s role in reducing carbon emissions and advancing a sustainable energy mix in Pakistan. Her interest in artificial intelligence extends to driver safety, where she researches image-based drowsiness detection systems to enhance road safety. Her interdisciplinary expertise includes machine learning, deep learning, data visualization, and exploratory data analysis using Python and MATLAB. Dr. Mujeeb’s research embodies a blend of theoretical rigor and practical application, with an emphasis on creating scalable, data-driven solutions that contribute to a sustainable and technologically advanced future.

Awards and honors

Dr. Sana Mujeeb has received several accolades for her academic and research achievements. She was awarded a Certificate of Appreciation for her outstanding Ph.D. coursework at COMSATS University Islamabad, reflecting her dedication to her studies. Additionally, she earned certificates of excellence for her MCS final-year project titled “Gender Classification Tool” at the prestigious Visio Spark 2010 event held at COMSATS Wah and the ACM Softcom 2010 at Ghulam Ishaq Khan Institute (GIKI). Her active participation in the Data Science Hands-on Workshop and the Data Science & Machine Learning Course further highlights her commitment to continuous learning and professional development. Furthermore, her presentations at the Frontier of Information Technology Conference in 2019 showcase her engagement with the broader academic community, enhancing her reputation as a researcher dedicated to advancing the field of data science and artificial intelligence.

Conclusion

Dr. Sana Mujeeb’s research contributions, high citation metrics, and focus on practical, socially relevant applications make her a strong candidate for the Best Researcher Award. She has demonstrated excellence in academic research, teaching, and practical application, especially in energy and data analytics. By expanding her recognition through additional awards, fostering industry partnerships, and engaging further with the international community, she could strengthen her candidacy even further.

Publications to Noted

“Deep Long Short-Term Memory: A New Price and Load Forecasting Scheme for Big Data in Smart Cities”

Authors: S Mujeeb, N Javaid, M Ilahi, Z Wadud, F Ishmanov, MK Afzal

Journal: Sustainability 11 (4), 987

Citations: 149

Year: 2019

“Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics”

Authors: S Mujeeb, TA Alghamdi, S Ullah, A Fatima, N Javaid, T Saba

Journal: Applied Sciences 9 (20), 4417

Citations: 110

Year: 2019

“ESAENARX and DE-RELM: Novel Schemes for Big Data Predictive Analytics of Electricity Load and Price”

Authors: S Mujeeb, N Javaid

Journal: Sustainable Cities and Society 51, 101642

Citations: 73

Year: 2019

“Aquabot: A Diagnostic Chatbot for Achluophobia and Autism”

Authors: S Mujeeb, MH Javed, T Arshad

Journal: International Journal of Advanced Computer Science and Applications 8 (9)

Citations: 59

Year: 2017

“Big Data Analytics for Price and Load Forecasting in Smart Grids”

Authors: S Mujeeb, N Javaid, M Akbar, R Khalid, O Nazeer, M Khan

Conference: Advances on Broadband and Wireless Computing, Communication and Applications

Citations: 46

Year: 2019

“Electricity Load Forecasting in Smart Grids Using Support Vector Machine”

Authors: N Ayub, N Javaid, S Mujeeb, M Zahid, WZ Khan, MU Khattak

Conference: Advanced Information Networking and Applications

Citations: 45

Year: 2020

“Short-Term Electricity Price and Load Forecasting Using Enhanced Support Vector Machine and K-Nearest Neighbor”

Authors: M Ali, ZA Khan, S Mujeeb, S Abbas, N Javaid

Conference: HCT Information Technology Trends (ITT)

Citations: 18

Year: 2019

“Electricity Theft Detection with Automatic Labeling and Enhanced RUSBoost Classification Using Differential Evolution and Jaya Algorithm”

Authors: S Mujeeb, N Javaid, A Ahmed, SM Gulfam, U Qasim, M Shafiq, JG Choi

Journal: IEEE Access 9, 128521-128539

Citations: 17

Year: 2021

“Wind Power Forecasting Based on Efficient Deep Convolution Neural Networks”

Authors: S Mujeeb, N Javaid, H Gul, N Daood, S Shabbir, A Arif

Conference: P2P, Parallel, Grid, Cloud and Internet Computing

Citations: 17

Year: 2020

“DE-RUSBoost: An Efficient Electricity Theft Detection Scheme with Additive Communication Layer”

Authors: S Mujeeb, N Javaid, R Khalid, M Imran, N Naseer

Conference: IEEE International Conference on Communications (ICC)

Citations: 14

Year: 2020

Ying Zhang | Decision Sciences | Best Researcher Award

Prof. Ying Zhang | Decision Sciences
| Best Researcher Award

professional profile

Education

Work Experience

Research Focus

Conclusion

Publications to Noted

Guanbo Wang | Biostatistics | Best Researcher Award

Assist. Prof. Dr. Guanbo Wang | Biostatistics | Best Researcher Award

professional profile

Education

Work Experience

Awards and Honors

Research Focus

Conclusion

Publications to Noted

Sana Mujeeb | Predictive Analysis | Best Researcher Award

Dr. Sana Mujeeb | Predictive Analysis | Best Researcher Award

professional profile

Education

work Experience

Research Focus

Awards and honors

Conclusion

Publications to Noted

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Prof. Ying Zhang | Decision Sciences | Best Researcher Award

professional profile

Education

Work Experience

Research Focus

Conclusion

Publications to Noted

Assist. Prof. Dr. Guanbo Wang | Biostatistics | Best Researcher Award

professional profile

Education

Work Experience

Awards and Honors

Research Focus

Conclusion

Publications to Noted

Dr. Sana Mujeeb | Predictive Analysis | Best Researcher Award

professional profile

Education

work Experience

Research Focus

Awards and honors

Conclusion

Publications to Noted

Prof. Ying Zhang | Decision Sciences
| Best Researcher Award