Faculty Profiles

Scouten, A., Gong, H., Tesic, J., & Wang, F. (n.d.). Deep Learning Pipeline for Modeling Pavement Cracks with an Imbalanced Dataset (MoPaC).

Elizondo, M., Yu, J., Payan, D., Feng, L., & Tesic, J. (2025). Novel Considerations in the ML/AI Modeling of Large-Scale Learning Loss. IEEE Access, 13, 7780–7792. https://doi.org/10.1109/access.2025.3526412

Zeraatkar, E., Farough, S. A., & Tesic, J. (2025). ViSIR: Vision Transformer Single Image Reconstruction Method for Earth System Models. IEEE Pulse, 16(1), 23–25. https://doi.org/10.1109/mpuls.2025.3526506

Akter Tani, T., Scouten, A., Ortiz, E. G., McLean, R. J. C., & Tesic, J. (2025). Automated Corrosion Identification in Metal Imagery: Traditional vs. Deep Learning. In Lecture Notes in Computer Science (Vol. 15047). Springer. https://doi.org/10.1007/978-3-031-77389-1_21

Biswas, D., & Tesic, J. (2024). Domain Adaptation With Contrastive Learning for Object Detection in Satellite Imagery. IEEE Transactions on Geoscience and Remote Sensing, 62, 1–15. https://doi.org/10.1109/tgrs.2024.3391621

Rahman, M. M. M., & Tesic, J. (2024). Stratified Graph Indexing for Efficient Search in Deep Descriptor Databases. International Journal of Multimedia Information Retrieval, 13(3). https://doi.org/10.1007/s13735-024-00342-8

Shebaro, M., Nogueira, L., & Tesic, J. (2024). Improving Association Discovery through Multiview Analysis of Social Networks. Social Network Analysis and Mining. https://doi.org/https://doi.org/10.1007/s13278-023-01197-3

Biswas, D., & Tesic, J. (2024). Unsupervised Domain Adaptation with Debiased Contrastive Learning and Support-Set Guided Pseudo Labeling for Remote Sensing Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https://doi.org/10.1109/JSTARS.2024.3349541

Elizondo, M., Gobert, D. V. N., & Tesic, J. (2024). AI/ML Pipeline for Predicting Diabetic Readmissions from CMS OASIS dataset. IEEE.

Akter Tani, T., & Tesic, J. (2024). Advancing Retinal Vessel Segmentation with Diversified Deep Convolutional Neural Networks. IEEE Access, 1–1. https://doi.org/10.1109/access.2024.3467117

Rahman, M. M. M., Biswas, D., Chen, X., & Tesic, J. (2024). Image Deduplication Using Efficient Visual Indexing and Retrieval: Optimizing Storage, Time and Energy for Deep Neural Network Training. Signal, Image, and Video Processing. https://doi.org/10.21203/rs.3.rs-4682458/v1

Biswas, D., & Tesic, J. (2024). Binarydnet53: a lightweight binarized CNN for monkeypox virus image classification. Signal, Image and Video Processing, 18(10), 7107–7118. https://doi.org/10.1007/s11760-024-03379-8

Tesic, J., & Shebaro, M. (2023). On Discovering Consensus States in Large Signed Graphs. Retrieved from https://www.insna.org/events/sunbelt-2023

Elizondo, M., Musal, R. M., & Tesic, J. (2023). Clinical Tabular Data in the Wild: A Data Science Perspective. Retrieved from https://ieeeichi.github.io/ICHI2023/

Gong, H., Tesic, J., Tao, J., Luo, X., & Wang, F. (2023). Automated Pavement Crack Detection with Deep Learning Methods: What Are Main Factors and How to Improve the Performance? Transportation Research Record. https://doi.org/https://doi.org/10.1177/036119812311613

Shebaro, M., & Tesic, J. (2023). Identifying Stable States of Large Signed Graphs. ACM. Retrieved from https://dl.acm.org/doi/10.1145/3543873.3587544

Shebaro, M., & Tesic, J. (2023). Identifying Stable States of Large Signed Graphs. In Companion Proceedings of the ACM Web Conference 2023 (Vol. 22, pp. 594–597). ACM. https://doi.org/10.1145/3543873.3587544

Rahman, M. M. M., & Tesic, J. (2023). Hybrid Approximate Nearest Neighbor Indexing and Search (HANNIS) for Large Descriptor Databases. IEEE. Retrieved from https://ieeexplore.ieee.org/document/10020464

Rahman, M. M. M., & Tesic, J. (2023). Poster: Evaluating Hybrid Approximate Nearest Neighbor Indexing and Search (HANNIS) for High-dimensional Image Feature Search. Retrieved from https://ieeexplore.ieee.org/document/10021048

Tomasso, M. E., Rusnak, L. J., & Tesic, J. (2022). Advances in Scaling Community Discovery Methods for Large Signed Graph Networks. Network Science, 10(3). https://doi.org/10.1093/comnet/cnac013

Strauch, G., Lin, J., & Tesic, J. (2022). Overhead Projection Approach For Multi-Camera Vessel Activity Recognition. In Proceedings of IEEE International Conference on Big Data (pp. 5626–5632). IEEE. https://doi.org/10.1109/BigData52589.2021.9671274

Biswas, D., Rahman, M. M. M., Zong, Z., & Tesic, J. (2022). Improving the Energy Efficiency of Real-time DNN Object Detection via Compression, Transfer Learning, and Scale Prediction. IEEE.

Biswas, D., & Tesic, J. (2022). Small Object Difficulty (SOD) Modeling for Objects Detection in Satellite Images. IEEE. Retrieved from https://ieeexplore.ieee.org/abstract/document/10008383

Biswas, D., Rahman, M. M., Zong, Z., & Tesic, J. (2022). Improving the Energy Efficiency of Real-time DNN Object Detection via Compression, Transfer Learning, and Scale Prediction. In 2022 IEEE International Conference on Networking, Architecture and Storage (NAS). IEEE. https://doi.org/10.1109/nas55553.2022.9925528

Lommatzsch, A., Kille, B., Ozgobek, O., Zhou, Y., Tesic, J., Bartolomeu, C., … Larson, M. (2022). NewsImages: Addressing the Depiction Gap with an Online News Dataset for Text-Image Rematching. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3524273.3532891

Lommatzsch, A., Kille, B., Özgöbek, Ö., Zhou, Y., Tesic, J., Bartolomeu, C., … Larson, M. (2022). NewsImages. In Proceedings of the 13th ACM Multimedia Systems Conference. ACM. https://doi.org/10.1145/3524273.3532891

Tomasso, M. E., Rusnak, L. J., & Tesic, J. (2022). Cluster boosting and data discovery in social networks. In Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing (pp. 1801–1803). New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3477314.3507243

Nogueira, L., & Tesic, J. (2022). pytwanalysis: Twitter Data Management And Analysis at Scale. In 2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS) (Vol. 3, pp. 1–8). IEEE. https://doi.org/10.1109/SNAMS53716.2021.9732079

Rusnak, L. J., & Tesic, J. (2021). Characterizing Attitudinal Network Graphs through Frustration Cloud. Data Mining and Knowledge Discovery, 35(6), 2498–2539. https://doi.org/10.1007/s10618-021-00795-z

Shebaro, M., Oliver, J., Olarewaju, T., & Tesic, J. (2021). Enhancing Tweet Content Classification with Adapted Language Models. Retrieved from https://ceur-ws.org/Vol-3181/paper62.pdf

Alabandi, G. A. H., Tesic, J., Rusnak, L. J., & Burtscher, M. (2021). Discovering and balancing fundamental cycles in large signed graphs. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (pp. 1–17). ACM. https://doi.org/10.1145/3458817.3476153

Ford, B., Qasem, A., Tesic, J., & Zong, Z. (2021). Migrating Software from x86 to ARM Architecture: An Instruction Prediction Approach. Retrieved from 601 University Dr

Ford, B. W., Qasem, A. M., Tesic, J., & Zong, Z. (2021). Migrating Software from x86 to ARM Architecture: An Instruction Prediction Approach. In 2021 IEEE International Conference on Networking, Architecture and Storage (NAS) (pp. 1–6). IEEE. https://doi.org/10.1109/nas51552.2021.9605443

Magill, A., Nogueira de Moura, L., Tomasso, M. E., Elizondo, M., & Tesic, J. (2021). Enriching Content Analysis of Tweets Using Community Discovery Graph Analysis (Vol. 2882). Retrieved from http://ceur-ws.org/Vol-2882/paper66.pdf

Tesic, J., & Tamir, D. (2020). Computing with Words in Maritime Piracy and Attack Detection Systems. In Lecture Notes in Computer Science (Vol. 12197, pp. 434–444). Cham, U.S.: Springer International Publishing. https://doi.org/https://doi.org/10.1007/978-3-030-50439-7_30

Tesic, J., Warren, N., & Heyse, D. B. (2019). Identifying maritime vessels at multiple levels of descriptions using deep features. In Artificial Intelligence and Machine Learning for Multi-Domain Operations (Vol. 11006). United States. https://doi.org/https://doi.org/10.1117/12.2519248

Samimi, H., Tesic, J., & Ngu, H. H. (2019). Patient Centric Data Integration for Improved Diagnosis and Risk Prediction. In Lecture Notes in Computer Science (pp. 185–195). Springer International Publishing. https://doi.org/10.1007/978-3-030-33752-0_13

Samimi, H., Tesic, J., & Ngu, H. H. (2019). Patient Centric Data Integration for Improved Diagnosis and Risk Prediction. In Heterogeneous Data Management, Polystores, and Analytics for Healthcare (pp. 185–195). Springer, Cham: Springer International Publishing. https://doi.org/https://link.springer.com/chapter/10.1007/978-3-030-33752-0_13

Dunstatter, N., Tahsini, A., Guirguis, M. S., & Tešić, J. (2019). Solving Cyber Alert Allocation Markov Games With Deep Reinforcement Learning. In 10th Conference on Decision and Game Theory for Security (GameSec).

Mauldin, T., Ngu, A., Metsis, V., Canby, M., & Tesic, J. (2019). Experimentation and Analysis of Ensemble Deep Learning in IoT Applications. Open Journal of Internet Of Things (OJIOT), 5(1), 133–149. Retrieved from http://nbn-resolving.de/urn:nbn:de:101:1-2018080519304951282148

Heyse, D., Warren, N., & Tesic, J. (2019). Identifying maritime vessels at multiple levels of descriptions using deep features. In T. Pham (Ed.), Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications. SPIE. https://doi.org/10.1117/12.2519248

Nogueira De Moura, L., & Tesic, J. (2019). Spread of English Neologisms through Brazilian Portuguese Online Chatter, A Data Science Perspective. Retrieved from https://www.journals.elsevier.com/lingua

Tesic, J., Warren, N., Garrard, B., & Staudt, E. (2018). Transfer Learning of Deep Neural Networks for Visual Collaborative Maritime Asset Identification. IEEE. https://doi.org/10.1109/CIC.2018.00041

Tesic, J., Sullivan, K., Manjunath, B. S., & Chandrasekaran, S. (2015). Scalable Video Indexing, Search, and Retrieval. NAVAIR Journal.

Tesic, J. (2015). An end-to-end system for content-based video retrieval using behavior, actions, and appearance with interactive query refinement.

Xie, L., Yan, R., Tesic, J., Natsev, A., & Smith, J. R. (2010). Probabilistic visual concept trees.

Dantone, M., Sullivan, K., & Tesic, J. (2010). Multimedia Event Detection (MED) Evaluation Task.

Xie, L., Yan, R., Tesic, J., Natsev, A., & Smith, J. R. (2010). Probabilistic visual concept trees. In Proceedings of the 18th ACM international conference on Multimedia (Vol. 33, pp. 867–870). ACM. https://doi.org/10.1145/1873951.1874099

Natsev, A., Jiang, W., Merler, M., Smith, J. R., Tesic, J., Xie, L., & Yan, R. (2008). IBM Research trecvid-2008 video retrieval system.

Golder, S., & Tešić, J. (2008). Collaborative Tagging of Multimedia. IEEE Multimedia, 15(3), 12–13. https://doi.org/10.1109/mmul.2008.58

Natsev, A., Smith, J. R., Tesic, J., Xie, L., & Yan, R. (2008). IBM Multimedia Analysis and Retrieval System - Video Olympics People’s choice award.

Campbell, M., Haubold, A., Natsev, A., Smith, J. R., Tesic, J., Xie, L., … Yang, J. (2007). IBM research trecvid-2007 video retrieval system.

Natsev, A., Haubold, A., Tesic, J., Xie, L., & Yan, R. (2007). Semantic concept-based query expansion and re-ranking for multimedia retrieval.

Natsev, A. (Paul), Haubold, A., Tesic, J., Xie, L., & Yan, R. (2007). Semantic concept-based query expansion and re-ranking for multimedia retrieval. In Proceedings of the 15th ACM international conference on Multimedia. ACM. https://doi.org/10.1145/1291233.1291448

Yan, R., Tesic, J., & Smith, J. R. (2007). Model-shared subspace boosting for multi-label classification.

Yan, R., Tesic, J., & Smith, J. R. (2007). Model-shared subspace boosting for multi-label classification. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM. https://doi.org/10.1145/1281192.1281281

Tesic, J., Natsev, A., & Smith, J. R. (2007). Cluster-based Data Modeling for Semantic Video Search.

Tesic, J., Natsev, A., Seidl, J., & Smith, J. R. (2007). IBM marvel interactive video threading.

Natsev, A., Smith, J. R., Tesic, J., Xie, L., & Yan, R. (2007). IBM Multimedia Search and Retrieval System.

Tesic, J., Natsev, A., Xie, L., & Smith, J. R. (2007). Data modeling strategies for imbalanced learning in visual search.

Xie, L., Tesic, J., & Natsev, A. (2007). Dynamic multimodal fusion in video search.

Tesic, J., Natsev, A., Xie, L., & Smith, J. R. (2007). Data Modeling Strategies for Imbalanced Learning in Visual Search. In Multimedia and Expo, 2007 IEEE International Conference on (pp. 1990–1993). IEEE. https://doi.org/10.1109/icme.2007.4285069

Xie, L., Natsev, A., & Tesic, J. (2007). Dynamic Multimodal Fusion in Video Search. In Multimedia and Expo, 2007 IEEE International Conference on (pp. 1499–1502). IEEE. https://doi.org/10.1109/icme.2007.4284946

Natsev, A., Tesic, J., Xie, L., Yan, R., & Smith, J. R. (2007). IBM multimedia search and retrieval system. In Proceedings of the 6th ACM international conference on Image and video retrieval (pp. 645–645). ACM. https://doi.org/10.1145/1282280.1282373

Tesic, J., Natsev, A., Seidl, J., & Smith, J. R. (2007). IBM multimodal interactive video threading. In Proceedings of the 6th ACM international conference on Image and video retrieval (pp. 124–126). ACM. https://doi.org/10.1145/1282280.1282302

Tesic, J., Natsev, A. (Paul), & Smith, J. R. (2007). Cluster-based data modeling for semantic video search. In Proceedings of the 6th ACM international conference on Image and video retrieval (pp. 595–602). ACM. https://doi.org/10.1145/1282280.1282365

Tesic, J., & Smith, J. R. (2006). Efficient Summarizing of Multimedia Archives Using Cluster Labeling. In Lecture Notes in Computer Science (pp. 518–520). Springer Berlin Heidelberg. https://doi.org/10.1007/11788034_58

Campbell, M., Haubold, A., Ebadollahi, S., Naphade, M. R., Natsev, A., Seidl, J., … Xie, L. (2006). IBM Research trecvid-2006 video retrieval system.

Wang, J. Z., Boujemaa, N., Del Bimbo, A., Geman, D., Hauptmann, A. G., & Tesic, J. (2006). Diversity in multimedia information retrieval research. In Proceedings of the 8th ACM international workshop on Multimedia information retrieval (pp. 5–12). ACM. https://doi.org/10.1145/1178677.1178681

Naphade, M., Smith, J. R., Tesic, J., Chang, S. F., Hsu, W., Kennedy, L., … Curtis, J. (2006). Large-Scale Concept Ontology for Multimedia. IEEE Multimedia Magazine, 13.

Tesic, J., & Smith, J. R. (2006). Efficient summarizing of multimedia archives using cluster labeling.

Tesic, J., & Smith, J. R. (2006). Semantic labeling of multimedia content clusters.

Tesic, J., & Smith, J. (2006). Semantic Labeling of Multimedia Content Clusters. In 2006 IEEE International Conference on Multimedia and Expo (pp. 1493–1496). IEEE. https://doi.org/10.1109/icme.2006.262825

Christel, M., Naphade, M., Natsev, A., & Tesic, J. (2006). Assessing the filtering and browsing utility of automatic semantic concepts for multimedia retrieval.

Tesic, J. (2005). Metadata Practices for Consumer Photos. IEEE Multimedia Magazine, 12.

Smith, J. R., Naphade, M., Natsev, A. (Paul), & Tesic, J. (2005). Multimedia Research Challenges for Industry. In Lecture Notes in Computer Science (pp. 28–37). Springer Berlin Heidelberg. https://doi.org/10.1007/11526346_4

Natsev, A., Naphade, M. R., & Tesic, J. (2005). Learning the Semantics of Multimedia Queries and Concepts from a Small Number of Examples.

Smith, J. R., Naphade, M. R., Natsev, A., & Tesic, J. (2005). Multimedia research challenges for industry.

Amir, A., Argillander, J., Campbell, M., Haubold, A., Ebadollahi, S., Kang, F., … Volkmer, T. (2005). IBM Research trecvid-2005 video retrieval system.

Natsev, A. (Paul), Naphade, M. R., & Tesic, J. (2005). Learning the semantics of multimedia queries and concepts from a small number of examples. In Proceedings of the 13th annual ACM international conference on Multimedia (Vol. 4210, pp. 598–607). ACM. https://doi.org/10.1145/1101149.1101288

Smith, J. R., Campbell, M. S., Naphade, M. R., Natsev, A., & Tesic, J. (2005). Learning of semantic concepts in broadcast video.

Tesic, J. (2004). Managing Large-scale Multimedia Repositories. Ph.D. Thesis, UC Santa Barbara.

Newsam, S., Tesic, J., Wang, L., & Manjunath, B. S. (2004). Issues in Managing Video Datasets.

Amir, A., Chang, S. F., Franz, M., Iyengar, G., Kender, J., Lin, C. Y., … Tesic, J. (2004). IBM research TRECVID-2004 video retrieval system.

Newsam, S. D., Tesic, J., Wang, L., & Manjunath, B. S. (2003). <title>Issues in managing image and video data</title> In M. M. Yeung, R. W. Lienhart, & C.-S. Li (Eds.), SPIE Proceedings (Vol. 5307, pp. 280–291). SPIE. https://doi.org/10.1117/12.538096

Tesic, J., Bhagavathy, S., & Manjunath, B. S. (2003). Issues Concerning Dimensionality and Similarity Search.

Bhagavathy, S., Tesic, J., & Manjunath, B. S. (2003). On the Rayleigh nature of Gabor filter outputs.

Tesic, J., & Manjunath, B. S. (2003). Issues concerning multimedia mining, methods for mining video data.

Tesic, J., & Manjunath, B. S. (2003). Nearest Neighbor Search for Relevance Feedback.

Tesic, J., Newsam, S., & Manjunath, B. S. (2003). Mining Image Datasets using Perceptual Association Rules.

Tesic, J., Newsam, S., & Manjunath, B. S. (2002). Scalable Spatial Event Representation.

Tesic, J., Newsam, S., & Manjunath, B. S. (2002). Challenges in Mining Large Image Datasets. IPAM Short Program on Mathematical Challenges in Scientific Data Mining. Los Angeles, CA.

Tesic, J., & Manjunath, B. S. (2000). Mining image datasets. Workshop on Mining Scientific Datasets, Minneapolis, MN.

Orton, G. S., Fisher, B. M., Baines, K. H., Stewart, S. T., Friedson, A. J., Ortiz, J. L., … Parija, K. C. (1998). Characteristics of the Galileo probe entry site from Earth-based remote sensing observations. Journal of Geophysical Research.

Orton, G. S., Fisher, B. M., Baines, K. H., Stewart, S. T., Friedson, A. J., Ortiz, J. L., … Parija, K. C. (1998). Characteristics of the Galileo probe entry site from Earth‐based remote sensing observations. Journal of Geophysical Research: Planets, 103(E10), 22791–22814. https://doi.org/10.1029/98je02380

Dujković, D., Tesic, J., Mashanovich, M., Rakoćevic, I., Milosavljevi´c, I., Reljin, B., & Kostić, P. (1998). Automated segmentation and pathogen cell detection in tissues (in Serbian).

Tesic, J., & Arantes, D. (1998, February). Imaging of electrocardiogram (ECG) signals. Computer Science Dept, University of Campinas, Brazil.