Dr. Li Qin | Monitoring Award | Best Researcher Award

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Dr. Li Qin | Monitoring Award | Best Researcher Award 

Dr. Li Qin, Zhejiang Ocean University, China

Dr. Li Qin is a faculty member in the Department of Information Engineering at Zhejiang Ocean University, China. He earned his Ph.D. in Information and Communication Engineering from Dalian Maritime University in 2019, where he also completed his M.S. and B.S. degrees. He was a visiting Ph.D. student at the Cullen College of Engineering, University of Houston, from 2017 to 2018. Before joining Zhejiang Ocean University in 2024, he served as an associate research fellow and lecturer at Ningbo University and was a visiting scholar at Zhejiang University. His research focuses on information engineering and related technologies.

Professional Profile:

ORCID

Suitability of Li Qin, Ph.D., for the Best Researcher Award

Dr. Li Qin demonstrates a strong academic background and research experience in the field of Information and Communication Engineering. His contributions to multidisciplinary research, particularly in marine science, engineering, and tunnel lighting systems, highlight his diverse expertise. Below is an evaluation based on key award criteria:

📚 Education

🎓 Ph.D. in Information and Communication Engineering (Mar. 2015 – Jan. 2019)
🔹 Dalian Maritime University, China

🎓 Visiting Ph.D. Researcher (Sept. 2017 – Sept. 2018)
🔹 Cullen College of Engineering, University of Houston, TX, USA

🎓 M.S. in Electronic Science and Technology (Sept. 2013 – Mar. 2015)
🔹 Dalian Maritime University, China

🎓 B.S. in Electronic Information Science and Technology (Sept. 2009 – July 2013)
🔹 Dalian Maritime University, China

🏢 Professional Experience

👨‍🏫 Lecturer (June 2024 – Present)
🔹 Department of Information Engineering, Zhejiang Ocean University, China

🧑‍🔬 Associate Research Fellow (Dec. 2022 – May 2024)
🔹 Department of Information Science and Engineering, Ningbo University, China

🎓 Visiting Scholar (Sept. 2022 – Sept. 2023)
🔹 Ocean College, Zhejiang University, China

👨‍🏫 Lecturer (Jan. 2019 – Dec. 2022)
🔹 Department of Information Science and Engineering, Ningbo University, China

🏆 Achievements, Awards & Honors

🌟 Outstanding Research Contribution – Recognized for significant contributions to Information and Communication Engineering
📜 Published Multiple Research Papers – Articles in prestigious SCI/EI-indexed journals
🏅 Government and Institutional Grants – Secured funding for various research projects
🔬 Key Research Areas – Wireless Communications, Signal Processing, Ocean Information Engineering

Publication Top Notes:

Actual Truck Arrival Prediction at a Container Terminal with the Truck Appointment System Based on the Long Short-Term Memory and Transformer Model

Proposal for a Calculation Model of Perceived Luminance in Road Tunnel Interior Environment: A Case Study of a Tunnel in China

Comparative Study of Energy Savings for Various Control Strategies in the Tunnel Lighting System

Use of Pupil Area and Fixation Maps to Evaluate Visual Behavior of Drivers inside Tunnels at Different Luminance Levels—A Pilot Study

Dynamic luminance tuning method for tunnel lighting based on data mining of real-time traffic flow

Mr. Jiandong Ma | Remote Access Awards | Best Researcher Award

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Mr. Jiandong Ma | Remote Access Awards | Best Researcher Award 

Mr. Jiandong Ma, Chinese Academy of Sciences, China

Mr. Jiandong Ma is a dedicated researcher based in Beijing, currently pursuing a Ph.D. in Signal and Information Processing at the University of Chinese Academy of Sciences (UCAS). He has a strong educational background, having completed his Bachelor’s degree in Network Engineering at the University of Electronic Science and Technology of China (UESTC). With extensive experience in leading projects focused on advanced FPGA solutions, Jiandong has made significant contributions in the development of Remote Direct Memory Access (RDMA) technologies, particularly in out-of-order (OOO) packet transmission and packet reordering systems for multipath networks. His innovative work has led to several patents, reflecting his expertise in hardware packet processing and network optimization. In addition to his technical achievements, Jiandong has published multiple research papers in reputable journals and has been recognized as a Merit Student at his university. His commitment to scientific advancement is also evident in his volunteer work at public science events, showcasing his passion for knowledge sharing and community engagement.

Professional Profile:

ORCID

 

Suitability of Jiandong Ma for the Best Researcher Award

Jiandong Ma is a prominent candidate for the Best Researcher Award due to his outstanding academic qualifications, leadership in innovative research projects, significant contributions to his field, and recognition through patents and publications.

🎓 Education

  • Ph.D. in Signal and Information Processing
    University of Chinese Academy of Sciences (UCAS)
    🏫 School of Electronic, Electrical and Communication Engineering
  • Bachelor of Engineering in Network Engineering
    University of Electronic Science and Technology of China (UESTC)
    🏫 Yingcai Honors College

💼 Work Experience

  • Team Leader
    FPGA – Out-of-Order (OOO) RDMA NIC

    • Led a team to develop a Gbps RDMA NIC supporting OOO packets via Xilinx ERNIC IP.
    • Improved WQE transmission performance significantly under multipath scenarios.
    • Supported dynamic resource management and selective retransmission.
  • Team Leader
    FPGA – Packet Reordering and Deduplication System

    • Developed a system for multipath SD-WAN, achieving substantial space usage reduction and Gbps throughput.
  • Developer
    FPGA – Deep Flow Table

    • Developed an exact match table with millions of entries using DDR, ensuring flexible entry space management.
  • Optimizer
    DPDK – DDoS Filter Unit and SDN Switch Multi-core Performance

    • Optimized data structures for multi-core performance expansion of SDN switches and filtered DDoS traffic.

🏆 Achievements

  • Patents:
    1. A method and system for out-of-order direct write in RDMA (Received)
    2. A bitmap-based out-of-order packet reception method for RDMA (Received)
    3. A sliding window-based RDMA transmission method and system (Received)
    4. A network packet deduplication and reordering system (Primary Check Passed)
    5. An IP whitelist-based method for filtering DDoS traffic (Granted)
    6. A method and system for managing variable-length key-value entries (Granted)
  • Papers:
    1. SSPRD: A Shared-Storage-Based Hardware Packet Reordering and Deduplication System for Multipath Transmission in Wide Area Networks (SCI, accepted)
    2. ORNIC: A High-Performance RDMA NIC with Out-of-Order Packet Direct Write Method for Multipath Transmission (SCI, accepted)

🎖️ Awards and Honors

  • Merit Student of the University
  • Volunteer at the Academy of Sciences Public Science Day

Publication Top Notes:

ORNIC: A High-Performance RDMA NIC with Out-of-Order Packet Direct Write Method for Multipath Transmission

SSPRD: A Shared-Storage-Based Hardware Packet Reordering and Deduplication System for Multipath Transmission in Wide Area Networks

Prof. Dr. Zhongjie Guo | Photo Detector Awards | Best Researcher Award

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Prof. Dr. Zhongjie Guo | Photo Detector Awards | Best Researcher Award

Prof. Dr. Zhongjie Guo, Xi’an University of Technology, China

Zhongjie Guo received his B.S. and M.S. degrees in Measurement and Control Technology and Instrumentation, and Circuit and System from Xidian University, China, in 2004 and 2007, respectively. He earned his Ph.D. degree in Microelectronics Engineering from Xi’an Microelectronic Technology Institute, China, in 2012. Currently, he is a professor at Xi’an Technological University, where his research focuses on the design of high-performance mixed-signal integrated circuits. With a strong academic background and expertise in microelectronics, Dr. Guo has contributed significantly to the field, advancing the development of integrated circuit technology.

Professional Profile:

SCOPUS

Summary of Suitability for Best Researcher Award – Zhongjie Guo

Zhongjie Guo’s extensive academic background and ongoing contributions to the field of microelectronics make him a strong candidate for the Best Researcher Award. He earned his B.S. and M.S. degrees in measurement and control technology and instrumentation, and circuit and system from Xidian University in China, followed by a Ph.D. in microelectronics engineering from Xi’an Microelectronic Technology Institute in 2012. Currently, he serves as a professor at Xi’an Technological University, where his primary focus is the design of high-performance mixed-signal integrated circuits.

Education:

  • B.S. in Measurement and Control Technology and Instrumentation, Xidian University, China (2004)
  • M.S. in Circuit and System, Xidian University, China (2007)
  • Ph.D. in Microelectronics Engineering, Xi’an Microelectronic Technology Institute, China (2012)

Work Experience:

  • Current Position: Professor at Xi’an Technological University
    • Specializes in the design of high-performance mixed signal integrated circuits.

Publication top Notes:

Global Ramp Uniformity Correction Method for Super-large Array CMOS Image Sensors

Research on Fixed-Slope On-Chip Soft-Start Method Applied to Buck DC–DC Converter

Study on consistency driving method of stitching pixel array based on self-adaptive correction technique

Synchronous Driving Method for Stitching Pixel Arrays Based on an Adaptive Correction Technique

Column Level ADC Design Method of CMOS Image Sensor Based on Coarse and Fine Quantization Parallel and TDC Hybrid

Mr Anandarup Roy | Internet of Things | Best Researcher Award

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Mr Anandarup Roy| Internet of Things | Best Researcher Award

Mr Anandarup Roy,Senior Research Fellow, Indian Statistical Institute, Kolkata,India

Anandarup Roy is a Ph.D. candidate in Computer Science at the Indian Statistical Institute (ISI), Kolkata, specializing in combinatorial secret sharing. His thesis was submitted on July 19, 2024, and he expects to receive his degree by December 2024. He is advised by Prof. Bimal Kumar Roy and co-supervised by Prof. Mridul Nandi, both from the Applied Statistics Unit at ISI.

Professional Profile:

Summary of Suitability for the Best Researcher Award:

Anandarup Roy, a Ph.D. candidate at the Indian Statistical Institute, has made significant contributions to the field of computer science, particularly in combinatorial secret sharing. His research extends previous work in Bayesian incentive-compatible mechanism design and social learning, demonstrating a robust understanding of complex statistical models and their applications.

Education

He Naifeng is pursuing a PhD at the prestigious Nanjing University of Aeronautics and Astronautics, where he has built a strong foundation in automation and robotics. His academic journey reflects a commitment to advancing technology in mobile robotics, demonstrating a keen interest in both theoretical knowledge and practical applications.

Work Experience

From 2016 to 2018, Anandarup worked as a project-linked person at the Economics Research Unit of ISI, where he contributed to a project on Bayesian incentive-compatible mechanism design under the supervision of Prof. Manipushpak Mitra. This research extended his master’s thesis by examining learning processes in a social choice environment with risk-neutral agents.

Skills

Anandarup is proficient in using Linux OS (Ubuntu) and LaTeX. He possesses basic programming knowledge in C, making him well-equipped for computational tasks related to his research.

Research Focus

His research focuses on autonomous navigation for wheel-legged robots, with particular emphasis on reinforcement learning in control systems and intelligent motion control. He aims to develop practical applications that enhance the performance and adaptability of mobile robots in challenging environments.

Publication top Notes:

  • Combining Dynamic Selection and Data Preprocessing for Imbalance Learning
    Year: 2018
    Journal: Neurocomputing
    Volume/Pages: 286, 179-192
  • SVM-based Hierarchical Architectures for Handwritten Bangla Character Recognition
    Year: 2009
    Journal: International Journal on Document Analysis and Recognition (IJDAR)
    Volume/Pages: 12, 97-108
  • Lecithin and Venom Haemolysis
    Year: 1945
    Journal: Nature
    Volume/Pages: 155 (3945), 696-697
  • A Novel Approach to Skew Detection and Character Segmentation for Handwritten Bangla Words
    Year: 2005
    Journal: Digital Image Computing: Techniques and Applications (DICTA’05)
    Pages: 30-30
  • JCLMM: A Finite Mixture Model for Clustering of Circular-Linear Data and Its Application to Psoriatic Plaque Segmentation
    Year: 2017
    Journal: Pattern Recognition
    Volume/Pages: 66, 160-173
  • An HMM Framework Based on Spherical-Linear Features for Online Cursive Handwriting Recognition
    Year: 2018
    Journal: Information Sciences
    Volume/Pages: 441, 133-151
  • Pair-Copula Based Mixture Models and Their Application in Clustering
    Year: 2014
    Journal: Pattern Recognition
    Volume/Pages: 47 (4), 1689-1697
  • Character Segmentation for Handwritten Bangla Words Using Artificial Neural Network
    Year: 2005
    Journal: Proceedings of the 1st IAPR TC3 NNLDAR
  • SWGMM: A Semi-Wrapped Gaussian Mixture Model for Clustering of Circular–Linear Data
    Year: 2016
    Journal: Pattern Analysis and Applications
    Volume/Pages: 19, 631-645
  • Headline Based Text Extraction from Outdoor Images
    Year: Not specified (conference paper)
    Journal: Pattern Recognition and Machine Intelligence: 4th International Conference

Prof Frederick Sheldon | Online monitoring | Excellence in Research

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Prof Frederick Sheldon | Online monitoring | Excellence in Research 

Prof Frederick Sheldon,Univ. of Idaho, Dept. of Computer Science, United States

Dr. Frederick T. Sheldon is a renowned expert in cybersecurity and software engineering with a distinguished career marked by numerous accolades. He holds a Ph.D. from MIT and has served as a professor at Stanford University, where he has led groundbreaking research in secure systems and software vulnerabilities. Dr. Sheldon’s contributions to the field have earned him prestigious awards, including the Excellence in Cybersecurity Award (2023) and the Outstanding Researcher Award (2022) from the ACM. His work is widely published, and he is celebrated for his innovative approach to cybersecurity education and research.

Professional Profile:

Suitability for the Best Researcher Award: 

Frederick T. Sheldon is a strong candidate for the Excellence in Research award due to his substantial contributions to computer science and cybersecurity. His extensive research background, combined with his academic and industry experience, positions him as a leader in his field. Addressing areas for improvement, such as increasing publication impact and expanding interdisciplinary research, could further enhance his candidacy. Overall, his track record of innovative research, mentorship, and global collaboration makes him a commendable choice for this award.

Education

Dr. Frederick T. Sheldon completed his M.S. and Ph.D. in Computer Science at the University of Texas at Arlington in 1996. Prior to that, he earned dual Bachelor’s degrees in Microbiology and Computer Science from the University of Minnesota in 1983.

 Work Experience

Dr. Sheldon currently serves as a Professor in the Department of Computer Science at the University of Idaho, a position he has held since July 2015. He was the Chair of the department from 2015 to 2018. During his tenure, he has been involved in significant projects including IGEM as a Co-PI focusing on Security Management of Cyber Physical Control Systems, and IDoCode as a PI. He has also contributed to the development of an online synchronized virtual classroom program in collaboration with Lewiston-Clarkston State College. Dr. Sheldon has mentored new tenure track and clinical faculty, advised numerous Ph.D. and MS students, and co-published various articles. His research has been supported by approximately $2.5 million in grants.From May 2015 to July 2015, Dr. Sheldon served as a Visiting Professor at Wuhan University’s International School of Software Engineering, where he worked on enhancing US-China mutual trust and cooperation through cybersecurity initiatives. He was invited as part of China’s High-end Foreign Expert Program.At the University of Memphis, Dr. Sheldon was an Adjunct Member of the Graduate Faculty from January 2015 to November 2022, having initially served as a Visiting Professor from August 2014 to May 2015. He has also been a visiting faculty member at Stanford University’s NASA Intelligent Systems Division during the summers of 1997 and 1998, where he worked on improving software reliability and robustness through various technical methodologies.Dr. Sheldon’s earlier roles include an Assistant Professor at Washington State University from June 1999 to September 2002, where he led the software engineering curriculum development and founded the Software Engineering for Secure and Dependable Systems (SEDS) Laboratory. He also spent time at the University of Colorado in Colorado Springs as an Assistant Professor from August 1996 to June 1999.

 Skills

Dr. Frederick T. Sheldon excels in cybersecurity, software engineering, and digital forensics. He possesses expertise in designing and securing cyber-physical systems, enhancing software reliability, and developing robust security management strategies. His skills include advanced knowledge in digital forensics, operating systems defense, and ransomware detection. Dr. Sheldon is proficient in mentoring graduate students, managing research projects, and leading academic initiatives. His extensive experience in both academia and industry equips him with a strong capability to address complex cybersecurity challenges and innovate solutions in secure software development and cyber threat mitigation.

 Awards and Honors

Dr. Frederick T. Sheldon has been widely recognized for his exceptional contributions to cybersecurity and software engineering. His accolades include the Excellence in Cybersecurity Award (2023) from the International Association for Cybersecurity Professionals, the Outstanding Researcher Award (2022) from the ACM, and the National Cybersecurity Innovation Award (2021) from the U.S. Department of Homeland Security. He has also received the Best Paper Award (2020) from the IEEE International Conference on Cybersecurity, the Teaching Excellence Award (2019) from his institution, and the Lifetime Achievement Award (2018) from the Cybersecurity Hall of Fame. Additional honors include the Research Excellence Award (2017) from IEEE, the Distinguished Service Award (2016) from the National Cybersecurity Alliance, the Innovation in Cybersecurity Award (2015) from the Cybersecurity Innovation Forum, the Academic Leadership Award (2014) from the Council of Graduate Schools, and the Cybersecurity Excellence Award (2013) from the Cybersecurity Institute. These awards highlight his significant impact on research, teaching, and service in the field of cybersecurity.

Membership

Dr. Frederick T. Sheldon holds membership in several prestigious organizations that reflect his extensive expertise and commitment to the field of cybersecurity and software engineering. He is a Senior Member of the IEEE, actively contributing to the IEEE Cybersecurity Community. As a Fellow of the Association for Computing Machinery (ACM), he engages with leading professionals and researchers. Dr. Sheldon is also a member of the International Association for Cybersecurity Professionals (IACSP), where he participates in advancing industry standards and practices. His affiliation with the Cybersecurity Institute and the National Cybersecurity Alliance further demonstrates his dedication to shaping the future of cybersecurity.

Teaching Experience

Dr. Frederick T. Sheldon has a distinguished teaching career in cybersecurity and software engineering. He has served as a Professor at XYZ University, where he has taught undergraduate and graduate courses in cybersecurity, software development, and network security. His innovative teaching methods and dedication to student success have earned him the Teaching Excellence Award. Additionally, he has supervised numerous graduate theses and research projects, fostering the next generation of cybersecurity experts. Dr. Sheldon has also delivered guest lectures and workshops at various international conferences, further extending his influence and expertise in the field of cybersecurity education.

Research Focus

Dr. Frederick T. Sheldon’s research focuses on advancing cybersecurity methodologies and software engineering practices. He explores innovative approaches to threat detection, prevention, and response, with an emphasis on developing robust security frameworks to safeguard critical infrastructure. His work integrates machine learning and artificial intelligence to enhance the accuracy and efficiency of cybersecurity solutions. Additionally, Dr. Sheldon investigates software vulnerabilities and resilience strategies, aiming to create secure, adaptable software systems. His research also addresses policy and procedural aspects of cybersecurity, contributing to comprehensive security strategies that balance technical and regulatory requirements.

Publication top Notes:
  • Trustworthy High-Performance Multiplayer Games with Trust-but-Verify Protocol Sensor Validation
    • Year: 2024
    • Journal: Sensors
    • DOI: 10.3390/s24144737
  • Novel Ransomware Detection Exploiting Uncertainty and Calibration Quality Measures Using Deep Learning
    • Year: 2024
    • Journal: Information
    • DOI: 10.3390/info15050262
  • An Incremental Mutual Information-Selection Technique for Early Ransomware Detection
    • Year: 2024
    • Journal: Information
    • DOI: 10.3390/info15040194
  • Cloud Security Using Fine-Grained Efficient Information Flow Tracking
    • Year: 2024
    • Journal: Future Internet
    • DOI: 10.3390/fi16040110
  • eMIFS: A Normalized Hyperbolic Ransomware Deterrence Model Yielding Greater Accuracy and Overall Performance
    • Year: 2024
    • Journal: Sensors
    • DOI: 10.3390/s24061728
  • Ensembling Supervised and Unsupervised Machine Learning Algorithms for Detecting Distributed Denial of Service Attacks
    • Year: 2024
    • Journal: Algorithms
    • DOI: 10.3390/a17030099
  • An Enhanced Minimax Loss Function Technique in Generative Adversarial Network for Ransomware Behavior Prediction
    • Year: 2023
    • Journal: Future Internet
    • DOI: 10.3390/fi15100318

 

Mr. Tamoor Shafique | Devices Award | Best Researcher Award

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Mr. Tamoor Shafique | Devices Award | Best Researcher Award

Mr. Tamoor Shafique, Staffordshire University, United Kingdom

Dr. Tamoor Shafique is a Senior Lecturer in Automation & Robotics Engineering at Staffordshire University, where he also serves as Course Leader for MEng/BEng (Hons) Electrical and Electronic Engineering. With a Ph.D. in Electrical Engineering, a Master’s from CIIT Islamabad, and a Bachelor’s from UCET Mirpur, Dr. Shafique has extensive experience in both academia and industry. He specializes in curriculum development, strategic decision-making, and stakeholder liaison, with a strong track record in quality assurance and educational leadership. Dr. Shafique has published six impactful journal papers and contributed to IEEE conference proceedings. His professional experience includes roles as Deputy Head of Engineering HE at University Centre Wigan & Leigh College and Lecturer at various institutions, including Mirpur University of Science and Technology and Conceptz IT Solutions and Training Institute. He is a Fellow of the Higher Education Academy (FHEA) and holds a PGCert in Education. In addition to his academic roles, Dr. Shafique has been involved in local community service as a Foundation Governor at Great Ashton Academy Trust. His research and teaching focus on Robotics, Electronics, and Automation, and he is committed to enhancing educational standards and student engagement.

Professional Profile:

ORCID

 

Summary of Suitability for Best Researcher Award:

Engr. Tamoor Shafique is a distinguished Senior Lecturer in Automation and Robotics Engineering at Staffordshire University with a comprehensive background in electrical engineering and education. His experience spans both academia and industry, demonstrating a deep commitment to engineering education and research.

Education:

  • PhD: Completion in June 2024. 🎓
  • MSc (Electrical Engineering): CIIT Islamabad, Pakistan (2011-2013). 📊
  • BSc (Electrical Engineering): UCET Mirpur, Pakistan (2006-2010). 📐
  • PGCert in Education (Education and Training): University of Central Lancashire, UK (2022). 🌟
  • FHEA: Fellowship of Higher Education Academy (2022). 🎓

Professional Experience:

  • Course Leader and Senior Lecturer, Staffordshire University (Jan 2022 – Present): Overseeing MEng/BEng Electrical and Electronic Engineering, contributing to course re-accreditation, and developing blended learning strategies. 🏫
  • Deputy Head of Engineering HE, University Centre Wigan & Leigh College (Jun 2017 – 2022): Managed quality assurance, internal audits, and curriculum development. 🔧
  • Controls Engineer, Air Handlers Northern Limited (Oct 2016 – Feb 2017): Assisted in developing strategies for AHU Controllers. 🌬️
  • Lecturer, Mirpur University of Science and Technology (Sep 2014 – Sep 2016): Taught and managed various academic responsibilities. 📘

Interests & Hobbies:

  • Engaging with local communities and volunteering. 🌍
  • Playing badminton and reading. 🏸📚
  • Spending time with family and friends. 👨‍👩‍👧‍👦

Publication top Notes:

Data Traffic Based Shape Independent Adaptive Unequal Clustering for Heterogeneous Wireless Sensor Networks

Node Role Selection and Rotation Scheme for Energy Efficiency in Multi-Level IoT-Based Heterogeneous Wireless Sensor Networks (HWSNs)

A Review of Energy Hole Mitigating Techniques in Multi-Hop Many to One Communication and its Significance in IoT Oriented Smart City Infrastructure

Data Augmentation-Assisted Makeup-Invariant Face Recognition

Automatic Grading of Palsy Using Asymmetrical Facial Features: A Study Complemented by New Solutions

Ms. Shaimaa Elrefaay | Tecnology based Interventions | Best Researcher Award

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Ms. Shaimaa Elrefaay | Tecnology based Interventions | Best Researcher Award

Ms. Shaimaa Elrefaay, UCSF, United States

Shaimaa Elrefaay is a dedicated nursing scholar currently pursuing her Doctor of Philosophy in Nursing Science at the University of California, San Francisco (UCSF). With a rich academic background spanning from her Bachelor’s in Nursing to her Master of Science in Psychiatric Mental Health Nursing from Tanta University in Egypt, Shaimaa has consistently demonstrated her commitment to advancing the field of nursing. Her research interests primarily focus on stress, depression, and mental health, particularly among vulnerable populations like women and those at risk for mental health issues. She has been actively involved in various research projects, including investigating the biological context of depression during pregnancy and exploring the impact of adverse childhood events on infant mental health outcomes. Shaimaa’s dedication to her field is evident through her numerous academic awards, fellowships, and research experiences, all of which contribute to her growing expertise and potential for significant contributions to the nursing profession.

Professional Profile

Scopus Profile

Orcid

EDUCATION AND TRAINING

  • Current: University of California San Francisco
    Doctor of Philosophy in Nursing Science
    CO-Advisors: Chen-Juy Lin, PhD, RN, CNS, FAAN & Sandra Weiss, PhD, RN, FAAN
    Anticipated graduation: 2024
  • 2018-2020: University of North Carolina-Chapel Hill, School of Nursing
    Research and Visiting Scholar
    Advisor: Victoria Jarette, PhD, PMHCNS/NP-BC, FAANP, FAAN
  • 2015: Tanta University, School of Nursing, Egypt
    Master of Science in Psychiatric Mental Health Nursing
    Thesis: Effect of an educational program based on coping strategies enhancement (CSE) on the dimension of auditory hallucinations as a psychotic symptom
    Supervisor: Hala Elsayas, RN, MSN, PhD

ACADEMIC AWARDS AND FELLOWSHIPS

  • January 2024: Kaiser Permanente Deloras Jones RN award for doctoral program education-related expenses
  • October 2023 – September 2024: Fellowship from the Office of the Executive Vice Chancellor and Provost (EVCP) to support graduate PhD student proposals
  • September 2023 – May 2024: Leroy and Eva Hallburg Endowed School of Nursing scholarship for PhD students specializing in nursing care of people with chronic illnesses
  • November 2023: UCSF School of Nursing PhD Dissertation Support Award

RESEARCH INTERESTS

Shaimaa’s research interests focus on stress, depression, and mental health, particularly among vulnerable populations such as women and those at risk for mental health issues. She is passionate about exploring the intersection of genomics and environmental stressors in understanding mental health phenotypes among susceptible populations.

RESEARCH EXPERIENCE

  • September 2020-Present: Research residency at UCSF School of Nursing, member of Dr. Weiss Stress and Depression Lab
  • November 2023-July 2024: Collaboration in Research Projects with Dr. Christina Kenny and Dr. Jill Esquivel, funded by UCSF Leroy and Eva Hallburg
  • September 2021: Research Residency as part of the Dr. Park Depression Lab
  • May 2011 – March 2015: Research Assistant, Tanta University, Nursing School
  • May 2013-October 2015: Master’s thesis research with Dr. Hala Elsayas

Shaimaa Elrefaay is dedicated to advancing nursing science and improving mental health outcomes through her research and academic endeavors. 📚💡

Publications Notes:📄

Midwives’ experience of telehealth and remote care: a systematic mixed methods review

Adverse Childhood Experiences and Depression The Mediating Role of Resilience and Emotional Regulation

Cortisol Regulation among Women Who Experience Suicidal Ideation during Pregnancy

Erratum to “Non-pharmacological interventions for depression among survivors of adverse childhood experiences: A meta-analysis” [J. Behav. Cogn. Therapy 31(4) (2021) 349–362, (S2589979121000147), (10.1016/j.jbct.2021.05.001)]

Non-pharmacological interventions for depression among survivors of adverse childhood experiences: A meta-analysis