Fatma Sule Bilgic- Graph abstract – Graph Analytics Research Excellence Award

Posted on by ResearchW

Dr.  Fatma Sule Bilgic  distinguished academic and researcher in the field Graph Abstract. She has taken on various administrative roles, serving as the Quality and Accreditation Department Commission Representative and as a member of the Zero Waste Commission. Proficient in English with good reading comprehension, moderate speaking, and writing skills, she scored 60 in the KPDS/ÜDS exam. In terms of her academic achievements, she holds a Master’s thesis on the examination of the effect of the mother’s nutritional status on the content of breast milk’s macronutrients, supervised by Assoc. Prof. Dr. Gülçin Bozkurt and Prof. Dr. Asuman Çoban. Her doctoral thesis, supervised by Prof. Dr. Gülçin Bozkurt, focuses on the impact of web-based infant care education based on Meleis’ Transition Theory on maternal self-confidence and newborn health. With a Google Scholar citation count of 63 and an h-index of 3, and a Web of Science citation count of 25 and an h-index of 2, her research has made a notable impact. She has also contributed to national journals with publications such as an article evaluating the emotional and mental impact of midwifery students’ experiences in the delivery room, and another discussing the dimension of male contraception in reproductive health.

 

🌐 Professional Profiles

Educations📚📚📚

The individual completed their education journey, culminating in a Doctorate from Istanbul University-Cerrahpaşa Graduate School of Education, Department of Midwifery, in 2023, following a Master’s degree in Midwifery from the same institution in 2020, and a Bachelor’s degree in Midwifery from Haliç University in 2014. Prior to their academic pursuits, they attended Bakırköy 70th Year Health Vocational High School for their secondary education, graduating in 2006. Their professional experience includes roles as a Lecturer at Haliç University Faculty of Health Sciences, Department of Midwifery, starting in 2020, and as a Research Assistant at Haliç University School of Health Sciences, Department of Midwifery, from 2014 to 2020. Before their academic career, they worked as a Nurse-Midwife at Haseki Training and Research Hospital Neonatal Intensive Care Unit from 2011 to 2014, and as a Nurse at Dağkapı Children’s Diseases Hospital Neonatal Intensive Care Unit from 2010 to 2011.

 

Research 

She, along with her colleagues, has been actively involved in conducting various research studies on topics related to reproductive health and sexuality. Some of her recent publications include studies on the impact of COVID-19 on family planning and sexuality, attitudes and beliefs regarding sexuality during pregnancy, breastfeeding success after cesarean delivery, the effect of yoga on sexual function and body image in pregnant women, and nonpharmacological interventions for infantile colic. Additionally, she has contributed to understanding gender perception and dating violence attitudes among women students of health sciences. Her research also extends to investigating the severity and impact of symptoms in women with urinary incontinence. Moreover, she has co-authored a systematic review and meta-analysis on the effect of telehealth on incontinence severity in women. Furthermore, she has contributed a chapter on postpartum contraception in the international book “Postpartum Period,” edited by Yılmaz T. She continues to make significant contributions to the field of reproductive health through her research endeavors.

 Publication📚✨📚✨

 

 

Rong Yin – Graph Neural network – Best Researcher Award

Posted on by ResearchW

Rong Yin – Graph Neural network

Dr. Rong yin  distinguished academic and researcher in the field Graph Neural Network. She is a highly accomplished researcher with a focus on cutting-edge areas in artificial intelligence and machine learning. Over the past five years, she has made significant contributions, publishing over ten top conference and journal papers in esteemed venues such as NeurIPS, ICML, AAAI, IJCAI, IEEE TKDE, IEEE TNNL, PR, and IEEE TC. Recognized for her expertise, she has been invited to serve as a program committee member and reviewer for prestigious conferences and journals, including ICML, NeurIPS, ICLR, AAAI, and JMLR. One of her notable contributions includes proposing an efficient unsupervised learning algorithm based on the unified randomized sketches framework, paving the way for the application of machine learning in international important fields with massive data scenarios.

Additionally, she has designed a series of approximation algorithms for large-scale tasks such as regression, classification, ranking, and distributed learning. Her theoretical analyses have yielded optimal convergence rates for large-scale unsupervised learning, regression, and distributed learning, making significant strides in machine learning theory. As the principal or core backbone of more than 20 national or provincial key projects, including the Youth/General Fund of the National Natural Science Foundation of China and the National Key R&D Program, she has demonstrated leadership in advancing research in the field.

 

Professional Profiles:

Education

She earned her Ph.D. in Computer Science from the Institute of Information Engineering at the Chinese Academy of Sciences in July 2020, under the guidance of Prof. Dan Meng. Her research during this period has significantly contributed to the field, as evidenced by her subsequent achievements. Prior to her doctoral studies, she completed her M.S. in Computer Science at Harbin Institute of Technology in China in July 2016, with Prof. Xiaohong Su as her advisor. Her academic journey commenced with a B.S. in Thermal Energy and Power Engineering from Shenyang Aerospace University, China, in July 2014, under the mentorship of Prof. Rangshu Xu. Throughout her educational trajectory, she has demonstrated a commitment to academic excellence and has seamlessly transitioned from her undergraduate studies to achieving a Ph.D. in Computer Science, showcasing a continuous pursuit of knowledge and expertise in her chosen field.

Experience

She has been an integral part of the Institute of Information Engineering at the Chinese Academy of Sciences in China, serving as an Associate Researcher since December 2023. Her journey with the institute commenced in August 2020 when she was designated as an Associate Researcher to be Appointed, a position she transitioned into officially in December 2023 and continues to hold to the present. In her capacity as an Associate Researcher, she plays a crucial role in the ongoing research activities of the institute, contributing her expertise and insights to further the objectives of the organization. Her commitment to the field and the institute is evident in her sustained role, where she actively contributes to the research endeavors of the Institute of Information Engineering.

Academic achievements

  • Rong Yin, Yong Liu, Weiping Wang, Dan Meng. Scalable Kernel k-Means with Randomized Sketching: From Theory to Algorithm. In IEEE Transactions on Knowledge and Data Engineering, 2023, 35(9): 9210 – 9224. (TKDE 2023) (CCF-A, SCI-1, IF: 9.235). [PDF]
  • Rong Yin, Yong Liu, Weiping Wang, Dan Meng. Randomized Sketches for Clustering: Fast and Optimal Kernel k-Means. In Proceedings of Advances in Neural Information Processing Systems, 2022, 35: 6424-6436. (NeurIPS 2022) (CCF-A). [PDF]
  • Rong Yin, Yong Liu, Weiping Wang, Dan Meng. Distributed Nystrom Kernel Learning with Communications. In Proceedings of the 28th International Conference on Machine Learning, PMLR, 2021: 12019-12028. (ICML 2021) (CCF-A). [PDF]
  • Rong Yin, Yong Liu, Dan Meng. Distributed Randomized Sketching Kernel Learning. In Proceedings of the 36th AAAI Conference on Artificial Intelligence, 2022, 36(8): 8883-8891. (AAAI 2022) (CCF-A). [PDF]
  • Ruyue Liu, Rong Yin*, Yong Liu, Weiping Wang. ASWT-SGNN: Adaptive Spectral Wavelet Transform-based Self-Supervised Graph Neural Network. In Proceedings of the 38th AAAI Conference on Artificial Intelligence, 2024. (AAAI 2024) (CCF-A, Corresponding author).
  • Rong Yin, Yong Liu, Lijing Lu, Weiping Wang, Dan Meng. Divide-and-Conquer Learning with Nyström: Optimal Rate and Algorithm. In Proceedings of the 34th AAAI Conference on Artificial Intelligence, 2020, 34(04): 6696-6703. (AAAI 2020) (CCF-A). [PDF]
  • Xueyan Wang, Jianlei Yang, Yinglin Zhao, Xiaotao Jia, Rong Yin, Xuhang Chen, Gang Qu, Weisheng Zhao. Triangle counting accelerations: From algorithm to in-memory computing architecture. IEEE Transactions on Computers, 2021, 71(10): 2462-2472. (TC 2021) (CCF-A, SCI-1, IF: 3.7). [PDF]
  • Rong Yin, Yong Liu, Weiping Wang, Dan Meng. Sketch Kernel Ridge Regression using Circulant Matrix: Algorithm and Theory. In IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(9): 3512-3524. (TNNLS 2020) (SCI-1, IF: 11.683). [PDF]
  • Ruyue Liu, Rong Yin*, Yong Liu, Weiping Wang. Unbiased and Augmentation-Free Self-Supervised Graph Representation Learning. In Pattern Recognition, 2024. (PR 2024) (SCI-1, IF: 8, Corresponding author). [PDF]
  • Rong Yin, Yong Liu, Weiping Wang, Dan Meng. Extremely sparse Johnson-Lindenstrauss transform: From theory to algorithm. In Proceedings of IEEE International Conference on Data Mining, 2020: 1376-1381. (ICDM 2020) (CCF-B). [PDF]
  • Lijing Lu, Rong Yin, Yong Liu, Weiping Wang. Hashing Based Prediction for Large-Scale Kernel Machine. In Proceedings of the International Conference on Computational Science, 2020: 496-509. (ICCS 2020). [PDF]
  • Jian Li, Yong Liu, Rong Yin, Weiping Wang. Multi-Class Learning using Unlabeled Samples: Theory and Algorithm. In Proceedings of the 28th International Joint Conference on Artificial Intelligence, 2019: 2880-2886. (IJCAI 2019) (CCF-A). [PDF]
  • Jian Li, Yong Liu, Rong Yin, Weiping Wang. Approximate Manifold Regularization: Scalable Algorithm and Generalization Analysis. In Proceedings of the 28th International Joint Conference on Artificial Intelligence, 2019: 2887-2893. (IJCAI 2019) (CCF-A). [PDF]

Publication

  Dr. Saeid Shabanlou | Graph Data Structures

Posted on by ResearchW

  Dr. Saeid Shabanlou – Graph Data Structures

Academician/Research Scholar

Congratulations,Dr. Saeid Shabanlou- Graph Data Structures and Algorithms , on winning the esteemed Best Researcher Award from Sfconference! Your dedication, innovative research, and scholarly contributions have truly made a significant impact in your field. Your commitment to advancing knowledge and pushing the boundaries of research is commendable. Here’s to your continued success in shaping the future of academia and making invaluable contributions to your field. Well done!

Wishing you continued success and many more milestones in your illustrious research journey. Your impact on the field is truly commendable and serves as a beacon of inspiration for aspiring researchers.

Warmest congratulations once again on this well-deserved recognition!

 

Professional Profiles:

Education:

    • Kermanshah Branch, Islamic Azad University
    • Saeid Shabanlou currently works at the Department of Water, Islamic Azad University Kermanshah Branch. Saeid does research in Water Resources, Environmental Engineering and Civil Engineering. Their current project is ‘WEAP 2015 Water Evaluation And Planning System.’

publication;

1. Simulation of groundwater level using MODFLOW, extreme learning machine and Wavelet-Extreme Learning Machine models
2. Combination of computational fluid dynamics, adaptive neuro-fuzzy inference system, and genetic algorithm for predicting discharge coefficient of rectangular side orifices.

3.A novel approach for prediction of monthly ground water level using a hybrid wavelet and non-tuned self-adaptive machine learning model

4.Free surface and velocity field in a circular channel along the side weir in supercritical flow conditions

5.A pareto design of evolutionary hybrid optimization of ANFIS model in prediction abutment scour depth

6.The flow pattern in triangular channels along the side weir for subcritical flow regime

7.A comparative study of artificial intelligence models and a statistical method for groundwater level prediction

8.A novel approach using CFD and neuro-fuzzy-firefly algorithm in predicting labyrinth weir discharge coefficient

9.Determining the scour dimensions around submerged vanes in a 180 bend with the gene expression programming technique

10.Modelling qualitative and quantitative parameters of groundwater using a new wavelet conjunction heuristic method: wavelet extreme learning machine versus wavelet neural networks