Head of Machine Learning services leveraging extensive industry experience in executing simulation model projects, creating trustworthy machine learning solutions and developing efficient methods and tools, with a passion for data quality, integration of large language models and ontologies to propel progress and foster sustainability.
About Dr Suyuthi
Abdillah is Head of Machine Learning services, supporting clients across various industries to develop and operate trustworthy and reliable machine learning solutions across multiple industry domains including maritime, oil & gas, energy, and railways, handling diverse data types including environmental, industrial, and business process data.
His niche is acquiring and developing smarter and more efficient methods and tools, which has led him to participate in various assessments, verifications, and development projects related to simulation models and software, as well as authoring several industry guidelines. In addition, he is running assurance projects for data-driven and machine learning solutions. He is also venturing to combine the best of two worlds: large language models and ontologies. His work has a significant impact on customers’ ability to leveragecutting-edge simulation models, robust data-driven solutions, and innovative machine learning applications, ultimately enhancing their operational efficiency and decision-making capabilities.
His expertise includes data science, developing data pipelines, ensuring data quality, and creating machine learning solutions for predictive maintenance, anomaly detection, and image recognition.
I am fueled by an unwavering commitment to continuous learning, and my passion lies in propelling progress, fostering sustainability, and illuminating a brighter future.
Dr Abdillah Suyuthi
Assurance of B2B/B2C API for micro-offsetting carbon trading transactions.
Assurance of machine learning models for predicting gas emissions in maritime applications.
Development of an AI solution that detects anomalies in the sound sensor readings from incoming trains at railroad crossings; and development of a set of machine learning models to consistently predict the arrival of incoming trains based on sound sensor readings, thereby replacing the deterministic algorithms previously used for this task.
Development of an AI solution to predict the remaining useful life of a fleet of gas turbines in a number of compressor stations as part of a national gas distribution network.
EU Horizon 2020 AI-Mind “artificial intelligence for dementia prevention” : development of data governance/management related documents and Python implementations of data quality control.
Haraldsen, I.H., Hatlestad-Hall, C., Marra, C., Renvall, H., Maestú, F., Acosta-Hernández, J., Alfonsin, S., Andersson, V., Anand, A., Ayllón, V., Babic, A., Belhadi, A., Birck, C., Bruña, R., Caraglia, N., Carrarini, C., Christensen, E., Cicchetti, A., Daugbjerg, S., Di Bidino, R., Diaz-Ponce, A., Drews, A., Giuffrè, G.M., Georges, J., Gil-Gregorio, P., Gove, D., Govers, T.M., Hallock, H., Hietanen, M., Holmen, L., Hotta, J., Kaski, S., Khadka, R., Kinnunen, A.S., Koivisto, A.M., Kulashekhar, S., Larsen, D., Liljeström, M., Lind, P.G., Marcos Dolado, A., Marshall, S., Merz, S., Miraglia, F., Montonen, J., Mäntynen, V., Øksengård, A.R., Olazarán, J., Paajanen, T., Peña, J.M., Peña, L., lrabien Peniche, D., Perez, A.S., Radwan, M., Ramírez-Toraño, F., Rodríguez-Pedrero, A., Saarinen, T., Salas-Carrillo, M., Salmelin, R., Sousa, S., Suyuthi, A., Toft, M., Toharia, P., Tveitstøl, T., Tveter, M., Upreti, R., Vermeulen, R.J., Vecchio, F., Yazidi, A. and Rossini, P.M., 2024. Intelligent digital tools for screening of brain connectivity and dementia risk estimation in people affected by mild cognitive impairment: the AI-Mind clinical study protocol.Frontiers in Neurorobotics, 17, p.1289406.
