Chafik boulealam | Deep Learning for Genomics | Best Researcher Award

Published on by Computational Biologists

Mr.Chafik boulealam | Deep Learning for Genomics | Best Researcher Award

Research Scientist at  fsdm/usmba, Morocco.

Chafik Boulealam is a Moroccan data scientist and full-stack developer with over five years of experience specializing in artificial intelligence, deep learning, and machine learning. Currently pursuing a PhD in Deep Learning at Sidi Mohamed Ben Abdellah University in Fes, he has led and contributed to projects ranging from real-time face recognition and object detection to intelligent chatbots and Arabic text mining. Fluent in Arabic, French, and English, Chafik combines strong technical skills in Python, Java, and modern frameworks like Django and React with a passion for innovation, problem-solving, and applying AI to real-world challenges.

🎓 Educational Background:

Chafik Boulealam pursued his higher education at Sidi Mohamed Ben Abdellah University in Fes, Morocco, where he built a strong foundation in mathematics, computer science, and artificial intelligence. He first earned a Bachelor of Science in Mathematical Sciences and Computer Science, followed by a Master of Science in Business Intelligence and Intelligent Vision, which deepened his expertise in machine learning and intelligent systems. Currently, he is advancing his academic journey as a PhD student specializing in Deep Learning, with his research focusing on video analysis and hybrid machine learning models. In addition to his formal degrees, Chafik has completed several professional certifications, including courses in Neural Networks, Convolutional Neural Networks, Machine Learning Specialization, and Text Retrieval, demonstrating his commitment to continuous learning and staying current in the fast-evolving field of AI.

Profile:

Professional Experience:

Chafik has over five years of experience in technology and research. He is a Project Manager, Backend Developer, and Full Stack Developer at Dr Stone in Casablanca, where he manages complex AI projects, streamlines API integrations, and develops scalable applications. As a freelancer on Upwork, he has delivered innovative solutions to clients worldwide. His experience also includes research roles at Sidi Mohamed Ben Abdellah University, where he contributed to AI and deep learning projects.

🧠 Research and Innovation:

Driven by curiosity and a passion for AI, Chafik has worked on diverse projects including face recognition systems, behavior analysis, object detection, and Arabic text mining. His work often blends advanced deep learning models with traditional computer vision techniques, such as Kalman filtering and YOLO frameworks. This combination allows him to design systems that are both precise and efficient.

Skills and Expertise:

Chafik is highly skilled in programming languages such as Python, Java, C++, and JavaScript. He is proficient in frameworks like Django, React, Flask, and Next.js, enabling him to build end-to-end solutions. His expertise covers project management, system modeling with UML and MERISE, and developing intelligent systems that solve real-world problems.

Certifications and Professional Development:

Committed to continuous learning, Chafik has earned several respected certifications from Coursera. These include Neural Networks and Deep Learning, Convolutional Neural Networks, Machine Learning Specialization, and Text Retrieval and Search Engines. These credentials reflect his dedication to staying at the forefront of AI and data science innovation.

Publications:

  • Filali, H., Riffi, J., Boulealam, C., Mahraz, M. A., & Tairi, H. (2022). Multimodal emotional classification based on meaningful learning. Big Data and Cognitive Computing, 6(3), 95. https://doi.org/xxx (Add DOI if available)

  • Filali, H., Boulealam, C., El Fazazy, K., Mahraz, A. M., Tairi, H., & Riffi, J. (2025). Meaningful multimodal emotion recognition based on capsule graph transformer architecture. Information, 16(1), 40. https://doi.org/xxx

  • Boulealam, C., Filali, H., Riffi, J., Mahraz, A. M., & Tairi, H. (2024). Feedback-Based Validation Learning (FBVL): A novel mechanism for performance enhancement in neural network architectures. (Manuscript/Journal name if known — provide full citation when available).

  • Chafik, B., Hajar, F., Jamal, R., Mahraz, A. M., & Hamid, T. (2025). Deep multi-component neural network architecture. Computation, 13(4), 93. https://doi.org/xxx

Akhilesh Mishra | Single Cell Genomics | Best Researcher Award

Published on by Computational Biologists

Assist.Prof.Dr.Akhilesh  Mishra |  Single Cell Genomics |  Best Researcher Award 

Assistant Professor at  National Institute of Technology Rourkela,  India.

Dr. Akhilesh Mishra is an accomplished Assistant Professor in the Department of Life Science at the National Institute of Technology (NIT) Rourkela, Odisha, India. His academic path began with a Bachelor’s degree in Botany and Chemistry, followed by dual Master’s degrees—one in Bioinformatics and the other in Botany. He earned his Ph.D. from the Supercomputing Facility for Bioinformatics and Computational Biology (SCFBio) at IIT Delhi, where he decoded the physicochemical and structural language of DNA. Dr. Mishra further honed his expertise through postdoctoral fellowships at UT Southwestern Medical Center and St. Jude Children’s Research Hospital, USA.

🎓 Educational Background:

Dr. Akhilesh Mishra’s academic journey reflects a strong foundation in both life sciences and computational biology. He began his higher education with a Bachelor’s degree in Botany and Chemistry, where he developed an early interest in biological systems and their underlying mechanisms. Driven by a passion for understanding complex biological data, he pursued dual postgraduate degrees—a Master’s in Bioinformatics and a Master’s in Botany—equipping himself with a rare blend of computational skills and deep biological insight. His pursuit of research excellence led him to earn a Ph.D. from the Supercomputing Facility for Bioinformatics and Computational Biology (SCFBio) at IIT Delhi, one of India’s premier institutions. During his doctoral studies, he focused on the physicochemical and structural features of DNA, laying the groundwork for his future contributions to computational oncology. Further enriching his academic experience, Dr. Mishra completed postdoctoral fellowships at UT Southwestern Medical Center and St. Jude Children’s Research Hospital in the United States, where he engaged in cutting-edge cancer genomics and bioinformatics research.

Profile:

Research Focus:

Dr. Mishra leads the Computational Oncology Lab at NIT Rourkela, where he applies cutting-edge multi-omics and computational tools to decode the complexities of cancer biology. His vision is rooted in leveraging interdisciplinary approaches—integrating genomics, transcriptomics, epigenetics, and spatial data—to unravel molecular drivers of renal cell carcinoma (RCC), pediatric brain tumors, and antimicrobial resistance.

Publications:

  • Molecular dynamics simulation-based trinucleotide and tetranucleotide level structural and energy characterization of the functional units of genomic DNA. Physical Chemistry Chemical Physics, 2023.

  • HIF2 Inactivation and Tumor Suppression with a Tumor-Directed RNA-Silencing Drug in Mice and Humans. Clinical Cancer Research, 2022.

  • Molecular Genetic Determinants of Shorter Time on Active Surveillance in a Prospective Phase 2 Clinical Trial in Metastatic Renal Cell Carcinoma. European Urology, 2022.

  • Intron exon boundary junctions in human genome have in-built unique structural and energetic signals. Nucleic Acids Research, 2021.

  • A novel method SEProm for prokaryotic promoter prediction based on DNA structure and energetics. Bioinformatics, 2020.

  • PRIMER SETS, BIOMARKERS, KIT AND APPLICATIONS THEREOF. Indian Institute of Technology Delhi, Patent, 2020.

  • Prion protein transcription is auto-regulated through dynamic interactions with G-quadruplex motifs in its own promoter. Biochimica et Biophysica Acta (BBA) – Gene Regulatory Mechanisms, 2020.

  • ChemGenome2.1: An ab initio gene prediction software. In Gene Prediction, Book Chapter, 2019.

  • Toward a Universal Structural and Energetic Model for Prokaryotic Promoters. Biophysical Journal, 2018.

  • A computational protocol for the discovery of lead molecules targeting DNA unique to pathogens. Methods, 2017.

  • Physico-chemical fingerprinting of RNA genes. Nucleic Acids Research, 2017.

  • Onco-Regulon: an integrated database and software suite for site specific targeting of transcription factors of cancer genes. Database, 2016.

  • A Novel Anticlustering Filtering Algorithm for the Prediction of Genes as a Drug Target. American Journal of Biomedical Engineering, 2012.