Heri Ramampiaro
Background and activities
Ramampiaro is Head of department and Professor at the Department of Computer Science, NTNU Trondheim. He has previously been Head of the Data and Artificial Intelligence (DART) group. Ramampiaro has been central in the establishment of the Telenor–NTNU AI-Lab, an AI research center at NTNU (now Norwegian Open AI-Lab), for which he was NTNU's scientific coordinator. His current main research interests include information retrieval, information extraction, data/text mining and machine learning.
He has been leader for the Norwegian goverment's expert group on sharing of industrial data (2021-2022). More information about this work can be found here and here (in Norwegian).
For more information about Ramampiaros research activities, please refer to his personal homepage.
Scientific, academic and artistic work
A selection of recent journal publications, artistic productions, books, including book and report excerpts. See all publications in the database
Journal publications
- (2022) Evaluating Top-N Recommendations Using Ranked Error Approach: An Empirical Analysis. IEEE Access. vol. 10.
- (2022) Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk. JAMA Network Open. vol. 5 (7).
- (2022) Towards human-level performance on automatic pose estimation of infant spontaneous movements. Computerized Medical Imaging and Graphics. vol. 95.
- (2022) Detection of Potential Manipulations in Electricity Market using Machine Learning Approaches. Proceedings of the International Conference on Agents and Artificial Intelligence (ICAART). vol. 3.
- (2021) Exploring Decomposition for Solving Pattern Mining Problems. ACM Transactions on Management Information Systems (TMIS). vol. 12 (2).
- (2021) Density Guarantee on Finding Multiple Subgraphs and Subtensors. ACM Transactions on Knowledge Discovery from Data. vol. 15 (5).
- (2021) Fully automated clinical movement analysis from videos using skeleton-based deep learning. Gait & Posture. vol. 90.
- (2021) New automatic, efficient, and highly precise tracking of infant spontaneous movements. Developmental Medicine & Child Neurology. vol. 63.
- (2021) Machine Learning in Financial Market Surveillance: A Survey. IEEE Access. vol. 9.
- (2020) Space–time series clustering: Algorithms, taxonomy, and case study on urban smart cities. Engineering Applications of Artificial Intelligence. vol. 95.
- (2020) Fast and accurate group outlier detection for trajectory data. Communications in Computer and Information Science. vol. 1259.
- (2020) EfficientPose: Scalable single-person pose estimation. Applied intelligence (Boston).
- (2020) Prediction Intervals: Split Normal Mixture from Quality-Driven Deep Ensembles. Proceedings of Machine Learning Research (PMLR). vol. 124.
- (2019) Towards efficiently mining closed high utility itemsets from incremental databases. Knowledge-Based Systems. vol. 165.
- (2019) Deep Learning‐based infant motion tracking facilitating early detection of cerebral palsy. Developmental Medicine & Child Neurology. vol. 61.
- (2019) Locality-adapted kernel densities of term co-occurrences for location prediction of tweets. Information Processing & Management. vol. 56 (4).
- (2019) Forecasting Intra-Hour Imbalances in Electric Power Systems. Proceedings of the AAAI Conference on Artificial Intelligence. vol. 33 (1).
- (2018) A deep network model for paraphrase detection in short text messages. Information Processing & Management. vol. 54.
- (2018) Applying temporal dependence to detect changes in streaming data. Applied intelligence (Boston). vol. 48 (12).
- (2018) High utility drift detection in quantitative data streams. Knowledge-Based Systems. vol. 157.