Education:
Dr. Rouhollah Ahmadian is currently pursuing a Ph.D. in Computer Science at Amirkabir University of Technology in Tehran, Iran, where he has achieved an impressive overall GPA of 4.0/4.0 (18.49/20). His academic journey began with a Bachelor of Science in Computer Science from the University of Tabriz, graduating in July 2015 with a GPA of 3.3/4.0 (16.77/20), where he ranked in the top 1% of his cohort. He then continued his studies at Amirkabir University, obtaining a Master of Science in Computer Science in October 2020, with a GPA of 3.61/4.0 (17.48/20) and ranking in the top 1% of his master’s program. His selected coursework throughout his education has included advanced topics such as Data Analytics, Data Mining, Machine Learning, Deep Learning, Computational Data Mining, and Advanced Nonlinear Optimization, highlighting his strong foundation in computer science and artificial intelligence.
Professional Experience:
Dr. Rouhollah Ahmadian has gained substantial professional experience in the field of computer science, particularly in data science and software development. Currently, he serves as a Data Scientist at the NORC Amirkabir University of Technology, where he has been involved in innovative projects such as License Plate Recognition, focusing on the application of advanced algorithms and data analytics. In addition to his academic role, Dr. Ahmadian has worked extensively as a freelance Android Developer since 2013, creating a diverse array of applications that include municipal automation tools, real estate management platforms, and messaging applications. His tenure at Noor Islamic Sciences Research Center and Al-Zahra Society further honed his skills in mobile app development, where he contributed to projects aimed at managing educational and religious resources. Additionally, he co-founded Mojafzar Company, serving as both a shareholder and developer, which underscores his entrepreneurial spirit and ability to lead technical initiatives. This blend of academic and practical experience equips Dr. Ahmadian with a comprehensive skill set that he applies to his research and development efforts.
Research Interests:
Dr. Rouhollah Ahmadian’s research interests lie at the intersection of artificial intelligence, data science, and machine learning. He is particularly focused on the development of advanced algorithms for data analytics, which includes exploring techniques such as deep learning and neural networks to solve complex problems in various domains. His work has prominently featured driver identification systems using sensor data, highlighting his commitment to leveraging technology for practical applications. Additionally, Dr. Ahmadian is interested in the integration of data mining techniques and computational models to enhance data processing and interpretation, especially in spatiotemporal data analysis. He is also passionate about investigating innovative approaches to anomaly detection and object recognition, utilizing frameworks like TensorFlow and PyTorch to develop robust and scalable solutions. Through his research, Dr. Ahmadian aims to contribute to the advancement of smart technologies that can improve decision-making processes and enhance user experiences across multiple industries.
Skills:
Dr. Rouhollah Ahmadian possesses a diverse and robust skill set that spans multiple domains within computer science and software development. He is proficient in several programming languages, including Python, C, Java, and Kotlin, which enables him to tackle various software engineering challenges effectively. His expertise extends to artificial intelligence and machine learning, where he has hands-on experience with frameworks such as TensorFlow, PyTorch, and Scikit-learn, particularly in areas like deep learning, neural networks, and data mining. Dr. Ahmadian is well-versed in modern software development methodologies, including Agile and Scrum, which he applies to enhance project management and collaboration. He is adept in database management, utilizing systems like MySQL and Cassandra to design and implement efficient data storage solutions. Additionally, Dr. Ahmadian has developed skills in version control with Git and GitHub, ensuring seamless code collaboration and tracking. His capabilities also include advanced data processing techniques, such as anomaly detection and natural language processing (NLP), making him a versatile asset in any technology-driven environment.
Conclusion:
Dr. Rouhollah Ahmadian exemplifies the qualities of a top researcher through his outstanding academic record, diverse professional experience, and impactful research contributions. His commitment to advancing the field of computer science, particularly in artificial intelligence and data analytics, makes him a deserving candidate for the Best Researcher Award.
Publication Top Noted:
Discrete wavelet transform for generative adversarial network to identify drivers using gyroscope and accelerometer sensors
- Authors: R. Ahmadian, M. Ghatee, J. Wahlström
- Journal: IEEE Sensors Journal
- Year: 2022
- Cited by: 12
- Volume: 22, Issue 7, Pages 6879-6886
- DOI: 10.1109/JSEN.2022.3156679
Driver Identification by an Ensemble of CNNs Obtained from Majority-Voting Model Selection
- Authors: R. Ahmadian, M. Ghatee, J. Wahlström
- Conference: International Conference on Artificial Intelligence and Smart Vehicles
- Year: 2023
- Cited by: 2
- Pages: 120-136
- DOI: N/A (check conference proceedings)
Superior scoring rules for probabilistic evaluation of single-label multi-class classification tasks
- Authors: R. Ahmadian, M. Ghatee, J. Wahlström
- Platform: arXiv preprint
- Year: 2024
- Cited by: 1
- DOI: arXiv:2407.17697
Training of Neural Networks to Classify Spatiotemporal Data by Probabilistic Fusion on Hopping Windows: Theory and Experiments
- Authors: R. Ahmadian, M. Ghatee, J. Wahlström
- Platform: SSRN
- Year: N/A
- Cited by: 1
- DOI: SSRN:4616995
Uncertainty Quantification to Enhance Probabilistic Fusion Based User Identification Using Smartphones
- Authors: R. Ahmadian, M. Ghatee, J. Wahlström, H. Zare
- Journal: IEEE Internet of Things Journal
- Year: 2024
- Cited by: N/A
- DOI: N/A (article in press)
Calibrated SVM for Probabilistic Classification of In-Vehicle Voices into Vehicle Commands via Voice-to-Text LLM Transformation
- Authors: M. Moeini, R. Ahmadian, M. Ghatee
- Conference: 2024 8th International Conference on Smart Cities, Internet of Things and Applications
- Year: 2024
- Cited by: N/A
- DOI: N/A (check conference proceedings)
Driver Identification by Neural Network on Extracted Statistical Features from Smartphone Data
- Authors: R. Ahmadian, M. Ghatee
- Platform: arXiv preprint
- Year: 2020
- Cited by: N/A
- DOI: arXiv:2002.00764