sicheng tian | Natural Language Processing Award | Best Researcher Award

Dr. sicheng tian | Natural Language Processing Award | Best Researcher Award

Student, Harbin engineering university, China

👨‍💻 Dr. Sicheng Tian is a fourth-year Ph.D. candidate at the College of Computer Science and Technology, Harbin Engineering University, China. His academic journey has been marked by excellence, progressing seamlessly from bachelor’s to master’s to doctoral studies at the same institution. Specializing in natural language processing (NLP), Dr. Tian has made notable contributions to reverse dictionary tasks, publishing two JCR Q1 papers and actively driving advancements in this niche area. He is a member of the prestigious China Computer Federation (CCF), reflecting his commitment to the computer science community.

Publication Profile

Scopus

Education

🎓 Dr. Sicheng Tian has pursued his entire academic career at Harbin Engineering University, excelling through bachelor’s, master’s, and Ph.D. programs. He is currently in his fourth year as a doctoral candidate, focusing on innovative approaches to reverse dictionary tasks in NLP.

Experience

💼 Dr. Tian has a strong background in research, contributing to multiple national-level projects, including those funded by the National Natural Science Foundation of China. His expertise extends to the development of cutting-edge models and datasets, driving advancements in natural language processing.

Research Interests

🔍 Dr. Tian’s primary research interests lie in reverse dictionary tasks within the field of natural language processing. He is particularly focused on developing models using methods such as multitask learning and multimodal information fusion, aiming to enhance computational understanding and performance.

Awards

🏆 Dr. Tian has achieved recognition for his research, including the successful publication of two high-impact JCR Q1 papers. His contributions to NLP and participation in national projects highlight his significant achievements in the field.

Publications

A prompt construction method for the reverse dictionary task of large-scale language models.” Engineering Applications of Artificial Intelligence 133 (2024): 108596. Cited by articles.

RDMTL: Reverse dictionary model based on multitask learning.” Knowledge-Based Systems 296 (2024): 111869. Cited by articles.

RDMIF: Reverse Dictionary Model Based on Multi-modal Information Fusion.” Neurocomputing (2024, In Press).

 

Fida Ullah | Natural language Processing | Data Science Contribution Award

Mr.Fida Ullah | Natural language Processing | Data Science Contribution Award

PhD Student, Institute of politechnical National, Mexico

🎓 Fida Ullah is a dedicated PhD student in Computer Science at Instituto Politécnico Nacional, Mexico, specializing in Named Entity Recognition (NER) and machine learning, with a deep passion for advancing Natural Language Processing (NLP) for low-resource languages. His expertise spans deep learning and transformer models, and he is skilled in applying these techniques to various text analysis challenges. Fida has published extensively in reputable journals and actively engages in the latest NLP developments, making him a promising researcher in this field.

Publication Profile

Google Scholar

Education

📘 PhD in Computer Science – Instituto Politécnico Nacional, Mexico (2022-Present), Thesis: Urdu Named Entity Recognition with Deep Learning
Advisor: Dr. Alexander Gelbukh. M.Sc. in Computer Science – Beijing University of Chemical Technology, China (2018-2021)

Experience

💻 Fida has hands-on experience with Python and essential machine learning libraries like TensorFlow, PyTorch, and Keras. He has worked extensively with deep learning frameworks, focusing on Named Entity Recognition, sentiment analysis, and hate speech detection in low-resource languages. His work has been showcased at international conferences, and he has collaborated with global researchers on NLP projects.

Research Interests

🔍 Fida’s research interests are centered around Natural Language Processing and Named Entity Recognition for low-resource languages, utilizing deep learning, transformer models, and data augmentation techniques. He is also intrigued by advancing explainable machine learning applications for smart city innovations.

Awards and Achievements

🏆 Awards include the CONACYT Scholarship (Mexico) and the Chinese Government Scholarship for his academic excellence and contributions to NLP research.

Publications

Ullah, Fida, Ihsan Ullah, and Olga Kolesnikova. “Urdu Named Entity Recognition with Attention Bi-LSTM-CRF Model.” Mexican International Conference on Artificial Intelligence (2022). Springer Nature Switzerland.

Fida Ullah, Alexander Gelbukh, MT Zamir, EM Felipe Revoron, and Grigori Sidorov. “Enhancement of Named Entity Recognition in Low-Resource Languages with Data Augmentation and BERT Models: A Case Study on Urdu.” Computers, MDPI (2023). https://doi.org/10.3390/computers13100258.

Muhammad Arif, MS Tash, Ainaz Jamshidi, Fida Ullah, et al. “Analyzing Hope Speech from Psycholinguistic and Emotional Perspectives.” Scientific Reports (2024). https://doi.org/10.1038/s41598-024-74630-y.

Fida Ullah, M.Ahmed, MT. Zamir, et al. “Optimal Scheduling for the Performance Optimization of SpMV Computation using Machine Learning Techniques.” IEEE Xplore (2024). https://doi.org/10.1109/ICICT62343.2024.00022.

Alberto Martínez Castro, Jesús, et al. “Suppressor of Cytokine Signaling Members in Lung Adenocarcinoma: Unveiling Expression Patterns, Posttranslational Modifications, and Clinical Significance.” Journal of Population Therapeutics and Clinical Pharmacology 30, no. 18 (2023): 2077-2091.

 

Md. Sajeebul Islam Sk. | Natural Language Processing | Excellence in Research

Mr. Md. Sajeebul Islam Sk. | Natural Language Processing | Excellence in Research

Research Assistant, BRAC University, Bangladesh

Md. Sajeebul Islam Sk. is a passionate researcher in machine intelligence, focusing on creating computational models that enhance our understanding of text, audio, image, and video data. With a solid foundation in Mathematics and core Machine Learning principles, Sajeebul has centered his work on solving intricate challenges in text and audio comprehension. Currently, as a Research Assistant at BRAC University, he continues to refine his skills in Natural Language Processing (NLP) and Computer Vision, embodying a commitment to applying mathematical insights to practical machine learning applications. 🌐📊

Publication Profile

ORCID

Education

Sajeebul holds a Master’s in Computer Science & Engineering from BRAC University, completed in 2023 with a CGPA of 3.83/4.00. His academic journey began with a Bachelor of Science in Mathematics from Khulna University, where he achieved a CGPA of 3.05/4.00. 📚🎓

Experience

Sajeebul is currently serving as a Research Assistant at the Department of Computer Science & Engineering, BRAC University, where he has been since January 2024. In this role, he focuses on advancing research in NLP and Computer Vision, leveraging his expertise in deep learning and machine learning to explore innovative solutions. 🖥️🔬

Research Focus

His research interests lie in the realms of Natural Language Processing, Machine and Deep Learning, and Computer Vision, with a particular emphasis on wavelet analysis techniques. Through his work, Sajeebul aims to expand the frontiers of computational understanding in human-computer interaction. 💡🔍

Awards and Honours

Sajeebul’s academic journey is distinguished by several scholarships and recognitions. He was awarded the BRAC University Thesis Scholarship in May 2023 and multiple Academic Merit Scholarships in 2023 and 2022. In his early years, he achieved the 1st merit position in the Biology Olympiad in Khulna and received government scholarships for academic excellence. 🏆🎖️

Publications (Top Notes)

Unveiling personality traits through Bangla speech using Morlet wavelet transformation and BiG, published in Natural Language Processing Journal, 2024, DOI link, cited by several recent studies on wavelet transformation in NLP applications.

Bangla Speech Personality Traits Data, dataset available on Mendeley Data, 2024, Dataset link, widely accessed by NLP researchers