Background and activities
Ramampiaro is an Associate Professor at the Department of Computer Science, NTNU Trondheim. He is Head of the Data and Artificial Intelligence (DART) group, and serves as Deputy Head (Vice Chair) of the department. 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.
I am teaching/lecturing in the following courses:
- TDT4117 Information Retrieval
- IT8802 Advanced Information Retrieval (Doctoral course)
- TDT4305 Big Data Architecture
- IT3902 - Informatics Postgraduate Thesis: Data and Information Management
I have been involved in a number of projects during recent years, including:
- MUSED - Strategic Research Project (2016-2019)
Role: Principal Investigator
Funding 1 PhD and 1 Postdoc fellowship (approx. 1M USD)
- Big Data - Faculty Strategic Researh Area (2013-present)
Role: Project Coordinator/Leader.
Funding 2 PhD and 1 Postdoc fellowships (approx. 1M USD).
- "CAIM - Context-Aware Image Management" (2009-2013)
Role: Co-Principal Investigator.
Funded by the research council of Norway (the VERDIKT programe) - Approx. 1.5M USD
In addition I am participating/PMC in the following project:
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
- (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 (S2).
- (2019) Locality-adapted kernel densities of term co-occurrences for location prediction of tweets. Information Processing & Management. vol. 56 (4).
- (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.
- (2018) Spatial Statistics of Term Co-occurrences for Location Prediction of Tweets. Lecture Notes in Computer Science. vol. 10772.
- (2018) Effective hate-speech detection in Twitter data using recurrent neural networks. Applied intelligence (Boston). vol. 48 (12).
- (2018) Extracting news events from microblogs. Journal of Statistics & Management Systems. vol. 21 (4).
- (2017) Content-Based Social Recommendation with Poisson Matrix Factorization. Lecture Notes in Computer Science. vol. 10534 LNAI.
- (2017) Efficient High Utility Itemset Mining using Buffered Utility-Lists. Applied intelligence (Boston). vol. 48 (7).
- (2015) Geo-Temporal Distribution of Tag Terms for Event-Related Image Retrieval. Information Processing & Management. vol. 51 (1).
- (2014) Learning to Rank for Personalized Fashion Recommender Systems via Implicit Feedback. Lecture Notes in Computer Science. vol. 8891.
- (2014) A scalable algorithm for extraction and clustering of event-related pictures. Multimedia tools and applications. vol. 70 (1).
- (2013) Event-Related Image Retrieval: Exploring Geographical and Temporal Distribution of User Tags. International Journal of Multimedia Information Retrieval.
- (2013) Exploring Temporal Proximity and Spatial Distribution of Terms in Web-based Search of Event-Related Images. ACM Hypertext Proceedings.
- (2011) DNA Sequence Search Using Content-Based Image Search Approach. Advances in Intelligent and Soft Computing. vol. 93.
- (2011) Supporting BioMedical Information Retrieval: The BioTracer Approach. Lecture Notes in Computer Science. vol. 6990.
- (2011) NTNU@MediaEval 2011 Social Event Detection Task (SED). CEUR Workshop Proceedings. vol. 807.
- (2010) BioMedical Information Retrieval: The BioTracer Approach. Lecture Notes in Computer Science. vol. 6266.