Adrian Barbu

  Professor, Department of Statistics, Florida State University.
  Currently Serving as Graduate Student Director.
  Please note that I am not in the admissions committee, so I cannot answer any admissions related questions.
  
   Research interests:
      - Machine Learning
      - Computer Vision
      - Medical Imaging

Current Research:

1. Object Segmentation - Using PCA as a shape model for object segmentation. (link)
2. Large Scale Unsupervised Learning - Algorithms with theoretical guarantees for estimating mixtures of Gaussians in the presence of outliers.(arxiv)
3. Large Scale Supervised/Semi-Supervised Learning - Incremental methods for learning models from millions of observations with thousands of classes.(IEEE)
4. Online Learning - Updating sufficient statistics in an online fashion to be able to extract models of different complexities at any time.(arxiv)
5. Robust Learning - Learning models that know when the data is outside the training distribution. (arxiv)
6. Feature Selection with Annealing - A generic method for feature selection and model learning that outperforms boosting and methods based on sparsity inducing penalties such as L1, SCAD, and MCP. (arxiv)

Representative Publications

1. C. Long, S. Nag, A. Barbu. PCA-UNet for Object Segmentation, IEEE International Conference on Image Processing (ICIP), 2024. (link, GitHub)
2. Y. Zhou, K. Gallivan, A. Barbu. Scalable Clustering: Large Scale Unsupervised Learning of Gaussian Mixture Models with Outliers. Journal of Computational and Graphical Statistics, 1-17, 2024. (link, arxiv, GitHub)
3. L. Sun, M. Wang, A. Barbu. A Novel Framework for Online Supervised Learning with Feature Selection. Journal of Nonparametric Statistics, 2024. (link, arxiv, slides)
4. H. Pabuccu, A. Barbu. Feature Selection for Forecasting. Financial Innovation 10, No. 27, 2024. (link, arxiv)
5. K. Han, A. Barbu. Large-Scale Few-Shot Classification with Semi-supervised Hierarchical k-Probabilistic PCAs. IJCNN, 2024. (link)
6. S. Liu, A. Barbu. Unsupervised Learning of Mixture Models with a Uniform Background Component. (arxiv)
7. N. Lay, A.P. Harrison, S. Schreiber, G. Dawer, A. Barbu. Random Hinge Forest for Differentiable Learning. (arxiv)
8. B. Wang, A. Barbu. Hierarchical Classification for Large-Scale Learning. Electronics 12, No. 22, 4646, 2023. (link, GitHub)
9. S. Manna, M. Wang, A. Barbu, C. V. Ciobanu. Machine-learning of Piezoelectric Coefficients for Wurtzite Crystals. Materials and Manufacturing Processes, 38, No. 16, 2081-2092, 2023. (link)
10. Y.She, J.Shen, A. Barbu. Slow Kill for Big Data Learning. IEEE Trans. Information Theory, 69, No. 9, 5936-5955, 2023. (IEEE)
11. A. Barbu. Training a Two Layer ReLU Network Analytically. Sensors 23, No. 8, 4072, 2023 (arxiv, link, GitHub)
12. M. Wang, A. Barbu. Online Feature Screening for Data Streams with Concept Drift. IEEE Trans. on Knowledge and Data Engineering, 2023. (arxiv, IEEE, GitHub)
13. B. Wang, A. Barbu. Scalable Learning with Incremental Probabilistic PCA. IEEE International Conference on Big Data, Special Session on Machine Learning for Big Data, 2022. (pdf, GitHub)
14. O. Akal, A. Barbu. Fast 3D Liver Segmentation Using a Trained Deep Chan-Vese Model. Electronics 11 No. 20, 3323, 2022 (link, GitHub)
15. C. Long, A. Barbu. A Study of Shape Modeling Against Noise. IEEE International Conference on Image Processing (ICIP), 611-615, 2022. (pdf, slides)
16. A. Barbu, H. Mou. The Compact Support Neural Network. Sensors 21 No. 24, 8494, 2021. (arxiv,link)
17. H. Huang, A. Barbu. Predicting Lane Change Decision Making with Compact Support. IEEE Intelligent Vehicles Symposium, 2021. (pdf)
18. Y.Guo, A. Barbu. A study of local optima for learning feature interactions using neural networks. IJCNN 2021 (arxiv)
19. Y.Guo, Y. She, A. Barbu. Training Efficient Network Architecture and Weights via Direct Sparsity Control. IJCNN 2021 (arxiv)
20. B. R. Bartoldson, A. S. Morcos, A. Barbu, G. Erlebacher. The Generalization-Stability Tradeoff in Neural Network Pruning. Neural Information Processing Systems (NeurIPS), 2020,(arxiv)
21. G. Dawer, Y.Guo, A. Barbu. Generating Compact Tree Ensembles via Annealing. IJCNN 2020 (arxiv)
22. G. Dawer, Y.Guo, S. Liu, A. Barbu. Neural Rule Ensembles: Encoding Sparse Feature Interactions into Neural Networks. IJCNN 2020 (arxiv)
23. D. Li, A. Barbu. Training a Steerable CNN for Guidewire Detection. CVPR 2020, (pdf)
24. A. Barbu, S.C. Zhu. Monte Carlo Methods. Springer 2020 (Springer, Amazon)
25. H. Huang, A. Barbu. Playing Atari Ball Games with Hierarchical Reinforcement Learning. (arxiv)
26. M. Wang, A. Barbu. Are screening methods useful in feature selection? An empirical study. PLoS One 14, No. 9 (2019) (arxiv,link)
27. O. Akal, A. Barbu. Learning Chan-Vese. ICIP 2019, Taipei, Taiwan (pdf)
28. D. Li, A. Barbu. Training a CNN for Guidewire Detection. ICIP 2019, Taipei, Taiwan (pdf)
29. N. Lay, Y. Tsehay, R. Cheng, S. Gaur, A. Barbu, L. Lu, B. Turkbey, P. Choyke , P. Pinto, R. Summers. A Decomposable Model for Prostate Cancer Detection in Multi-Parametric MRI. MICCAI, 2018, Granada, Spain (pdf)
30. H. Mou, A. Barbu. Accurate Dictionary Learning with Direct Sparsity Control. ICIP 2018, Athens, Greece (pdf, GitHub)
31. J. Anaya, A. Barbu. RENOIR - A Dataset for Real Low-Light Image Noise Reduction. Journal of Visual Comm. and Image Rep. 51, No. 2, 144-154, 2018 (arxiv, link, data)
32. A. Gupta, A. Barbu. Parameterized Principal Component Analysis. Pattern Recognition 78, No. 6, 215–227, 2018 (arxiv, link)
33. A. Barbu, N. Lay, G.Gramajo. Face Detection with a 3D Model. "Academic Press Library in Signal Processing Volume 6: Image and Video Processing and Analysis and Computer Vision". pp 237-259, 2018. Editors: R. Chellappa and S. Theodoridis.(arxiv, link)
34. A. Barbu. A Directed Graph Approach to Active Contours. ICIP 2017 (pdf)
35. A. Barbu, Y. She, L. Ding, G. Gramajo. Feature Selection with Annealing for Computer Vision and Big Data Learning. IEEE Trans. PAMI, 39, No. 2, 272-286, 2017. (arxiv, link, GitHub)
36. A. Barbu, L. Lu, H. Roth, A. Seff, R.M. Summers. An Analysis of Robust Cost Functions for CNN in Computer-Aided Diagnosis. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2016. (pdf, link)
37. L. Ding, A. Barbu. Scalable Subspace Clustering with Application to Motion Segmentation. Current Trends in Bayesian Methodology with Applications, Chapman & Hall/CRC Press. Editors: Dipak K. Dey, Umesh Singh and A. Loganathan. pp 267-286, 2015 (pdf)
38. A. Barbu, T.F. Wu, Y. N. Wu. Learning Mixtures of Bernoulli Templates by Two-Round EM with Performance Guarantee. Electronic Journal of Statistics 8, No. 2, 3004-3030, 2014 (pdf, arxiv)
39. A. Barbu, M. Pavlovskaia, S.C. Zhu. Rates for Inductive Learning of Compositional Models. AAAI Workshop Replearn 2013 (pdf)
40. A. Barbu. Multi-Path Marginal Space Learning for Object Detection. Academic Press Library in Signal Processing: Volume 4: Image, Video Processing and Analysis, Hardware, Audio, Acoustic and Speech Processing. pp 271-291, 2013 (pdf)
41. A. Barbu. Hierarchical Object Parsing from Structured Noisy Point Clouds. IEEE PAMI, 35, No. 7, 1649-1659, 2013. (pdf, arxiv, slides)
42. L. Ding, A. Barbu, A. Meyer-Baese. Learning a Quality-Based Ranking for Feature Point Trajectories. ACCV 2012 (pdf)
43. L. Ding, A. Barbu, A. Meyer-Baese. Motion Segmentation by Velocity Clustering with Estimation of Subspace Dimension. ACCV Workshop DTCE 2012 (pdf)
44. A. Barbu, N. Lay. An Introduction to Artificial Prediction Markets for Classification. Journal of Machine Learning Research, 13, 2177-2204, 2012. (pdf)
45. A. Barbu, M. Suehling, X. Xu, D. Liu, S. K. Zhou, D. Comaniciu. Automatic Detection and Segmentation of Lymph Nodes from CT Data. IEEE Trans Medical Imaging, 31, No. 2, 240-250, 2012.(pdf)
46. W. Wu, T. Chen , A. Barbu, P. Wang, N. Strobel, S. Zhou, D. Comaniciu. Learning-based Hypothesis Fusion for Robust Catheter Tracking in 2D X-ray Fluoroscopy. CVPR 2011 (pdf)
47. F. Bunea and A.Barbu. Dimension reduction and variable selection in case control studies via regularized likelihood optimization. Electronic Journal of Statistics, 3, 2009. (pdf)
48. A. Barbu. Training an Active Random Field for Real-Time Image Denoising. IEEE Trans. Image Processing, 18, November 2009. (pdf, ppt)
49. S. Seifert, A. Barbu, S. Zhou, D. Liu, J. Feulner, M. Huber, M. Suehling, A. Cavallaro, D. Comaniciu. Hierarchical parsing and semantic navigation of full body CT data. SPIE Medical Imaging, 2009 (pdf)
50. Y. Zheng, A. Barbu, B. Georgescu, M. Scheuering and D. Comaniciu. Four-Chamber Heart Modeling and Automatic Segmentation for 3D Cardiac CT Volumes Using Marginal Space Learning and Steerable Features. IEEE Trans Medical Imaging, November 2008. (pdf)
51. L. Lu, A. Barbu, M. Wolf, J. Liang, M. Salganicoff, D. Comaniciu. Accurate Polyp Segmentation for 3D CT Colonography Using Multi-Staged Probabilistic Binary Learning and Compositional Model. CVPR 2008.(pdf)
52. A. Barbu, V. Athitsos, B. Georgescu, S. Boehm, P. Durlak, D. Comaniciu. Hierarchical Learning of Curves: Application to Guidewire Localization in Fluoroscopy. CVPR 2007 (pdf)
53. A. Barbu, S.C. Zhu. Generalizing Swendsen-Wang for Image Analysis. J. Comp. Graph. Stat. 16, No 4, 2007 (pdf)
54. A. Barbu, S.C. Zhu. Generalizing Swendsen-Wang to sampling arbitrary posterior probabilities, PAMI, 27, August 2005 (pdf)
55. A. Barbu, S.C. Zhu. Multigrid and Multi-level Swendsen-Wang Cuts for Hierarchic Graph Partition, CVPR 2004 (pdf)

Awards:

1. Graduate Faculty Mentor Award, Florida State University, 2023
2. 2011 Thomas A. Edison Patent Award. System and Method for Segmenting Chambers of a Heart in a Three Dimensional Image. Patent No. 7,916,919. video

Funding:

1. Fundamental Limits of Learning, DARPA, $142,000, 09/14/2016-12/31/2017. (PI on Subcontract from UCLA).
2. Learning Homogeneous Knowledge Representation from Heterogeneous Data for Quantitative and Qualitative Reasoning in Autonomy. DARPA, $42,000, 06/29/2015- 07/28/2016. (PI on Subcontract from UCLA)
3. A Novel Platform for Biological Information Integration and Knowledge Discovery, NSF, $70,000. 07/01/2014 - 06/30/2015, 2015 (Co-PI)
4. SEE on a Unified Foundation for Representation, Inference and Learning, DARPA, $257,000. (PI on subcontract from UCLA)
5. MCS: Research on Detection and Classification of 2D and 3D Shapes in Cluttered Point Clouds. NSF, $400,000. (CO-PI)
6. Statistical and Semantic Approaches for Object, Activity and Intent Recognition. ONR, $443,000 (CO-PI)
7. Landmark Detection Using Discriminative Anatomical Network And Active Random Fields. Siemens, $31,000 (PI)
8. Cooperative Systems: Task Allocation for Heterogeneous Agent Teams Via Stochastic Clustering Auctions. ARO, $16,000 (subcontract)

Invited Talks

1. Object Segmentation: A Journey from Level Sets to Shape Denoising. Keynote presentation at the International Conference in Medical Imaging and Computer-Aided Diagnosis (MICAD), 2022 (pdf)
2. Learning Nonlinear Feature Interactions in the Data Starved Regime. 2021 Florida ASA Chapter Meeting, April 2021 (pdf)
3. A Novel Framework for Online Supervised Learning with Feature Selection. 2021 JMM/AMS Meeting, January 2021 (pdf)
4. Online Learning with Model Selection. NC State Univ., February 1st, 2019 (pdf)
5. Artificial Intelligence and the Future of Humanity, CWT Solutions Group Meeting, Miami Beach, FL, February 2016. (pdf)
6. Face Detection with a 3D Model. UCLA, June 19, 2015
7. Feature Selection with Annealing for Big Data Learning. Johns Hopkins University, October 28, 2014
8. Feature Selection with Annealing for Regression and Classification. Temple University, October 15, 2014
9. The Artificial Prediction Market, ICML Workshop on Markets, Mechanisms and Multi-Agent Models, Edinburgh, July 1st, 2012 (pdf)
10. Hierarchical Object Parsing from Noisy Point Clouds, Siemens Corporate Research, August 16th, 2011
11. Artificial Prediction Markets for Classification, Regression and Density Estimation, UCLA, August 11th, 2011
12. Automatic Detection and Segmentation of Lymph Nodes. NIH, December 8th 2010
13. Supervised Aggregation of Classifiers using Artificial Prediction Markets, SRCOS 2010 (pdf)
14. Supervised Aggregation using Artificial Prediction Markets. UCLA, November 10th, 2009
15. Marginal Space Learning for Fast Object Detection in Medical Imaging. Tutorial on Discriminative Learning Methods in Medical Imaging, MICCAI 2009
16. Training an Active Random Field for Real-Time Image Denoising. Max Plank Institute, Saarbrucken, Germany, July 16th, 2008
17. The Swendsen-Wang Cuts Algorithm with Applications in Computer Vision, Georgia Tech University, June 2008
18. Active Random Fields for Real-Time Image Denoising, Siemens Corporate Research, May 2008
19. Hierarchical Image-Motion Segmentation using Swendsen-Wang Cuts, Third Cape Cod MCMC Workshop , Harvard, 2007 (ppt)
20. A General Clustering Sampling Method for Bayesian Inference, Joint Statistical Meetings, Minneapolis, August 10, 2005
21. Swendsen-Wang for Perceptual Grouping. Second Cape Cod Workshop on Monte Carlo Methods, 2004

Patents:

1. J. Anaya, A. Barbu. System and Method for Image Processing using Automatically Estimated Tuning Parameters. Patent no. 10,032,256
2. J. Anaya, A. Barbu. System and Method for Generating a Dataset for Real Noise Reduction Evaluation. Patent no. 9,591,240
3. Y. Zheng, A. Barbu, B. Georgescu, M. Lynch, M. Scheuering, D. Comaniciu. Method and system for generating a four-chamber heart model. Patent no 9,275,190
4. A. Barbu. Systems and Methods for Training an Active Random Field for Real-Time Image Denoising. Patent no 8,866,936
5. A. Barbu, W. Zhang, N. Strobel, A. Galant, U. Bill, D. Comaniciu. Method and system for catheter detection and tracking in a fluoroscopic image sequence. Patent no 8,396,533
6. A. Barbu, M. Suehling, X. Xu, D. Liu, S.K. Zhou, D. Comaniciu. Method and System for Automatic Detection and Segmentation of Axillary Lymph Nodes. Patent no 8,391,579
7. W. Zhang, Y. Zhu, A. Barbu, R. Socher, S. Boehm, P. Durlak, D. Comaniciu. Methods and Apparatus for Virtual Coronary Mapping. Patent 8,355,550
8. L. Lu, A. Barbu, M. Wolf, S. Lakare, L. Bogoni, M. Salganicoff, D. Comaniciu. Method and System for Polyp Segmentation for 3D Computed Tomography Colonography. Patent 8,184,888
9. L. Lu, A. Barbu, M. Wolf, S. Lakare, L. Bogoni, M. Salganicoff, D. Comaniciu. User Interface for Polyp Annotation, Segmentation and Measurement in 3D Computed Tomography Colonography. Patent 8,126,244
10. R. Socher, A. Barbu, B. Georgescu, W. Zhang, P. Durlak, S. Bohm, D. Comaniciu. Method and system for vessel segmentation in fluoroscopic images. Patent 8,121,367
11. Y. Zhu, W. Zhang, A. Barbu, S. Prummer, M. Ostermeier, C. Reddy, D. Comaniciu. System and Method for Coronary Digital Subtraction Angiography. Patent 8,094,903
12. W. Zhang, A. Barbu, S. Prummer, M. Ostermeier and D. Comaniciu. Method and System for Evaluating Image Segmentation Based on Visibility. Patent 8,086,006
13. A. Barbu, L. Lu, L. Bogoni, M. Salganicoff, D. Comaniciu. Method and System for Detection and Registration of 3D Objects Using Incremental Parameter Learning. Patent 8,068,654
14. A. Barbu, V. Athitsos, B. Georgescu, P. Durlak, D. Comaniciu. System and Method for Online Optimization of Guidewire Visibility in Fluoroscopic Systems. Patent 8,050,482
15. A. Barbu, Y. Zheng, Y. Zhing, B. Georgescu, D. Comaniciu. System and Method for Detecting an Object in a High Dimensional Space. Patent 8,009,900
16. B. Georgescu, P. Durlak, V. Athitsos, A. Barbu, D. Comaniciu. System and Method for Simultaneously Subsampling Fluoroscopic Images and Enhancing Guidewire Visibility. Patent 7,970,191
17. W. Zhang, A. Barbu, S. Prummer, M. Ostermeier, C. Reddy, D. Comaniciu. System and Method for Coronary Digital Subtraction Angiography. Patent 7,940,971
18. Y. Zheng, A. Barbu, B. Georgescu, M. Scheuering, D. Comaniciu. System and Method for Segmenting Chambers of a Heart in a Three Dimensional Image. Patent 7,916,919
19. S.K. Zhou, J. Shao, J. Dowdall, A. Barbu, B. Georgescu, D. Comaniciu. System and Method for Using a Similarity Function to Perform Appearance Matching in Image Pairs. Patent 7,831,074
20. A. Barbu, V. Athitsos, B. Georgescu, P. Durlak, S. Boehm, D. Comaniciu. System and Method for Detecting and Tracking a Guidewire In A Fluoroscopic Image Sequence. Patent 7,792,342
21. A. Barbu, L. Lu, M. Wolf, L. Bogoni, M. Salganicoff. Interface Utilisateur pour une Annotation, une Segmentation et une Mesure de Polype en Colonoscopie Virtuelle 3D. Europe Patent WO/2009/042034
22. A. Barbu, L. Bogoni, D. Comaniciu. System and Method For Detecting A Three Dimensional Flexible Tube In An Object. Patent 7,783,097
23. Z. Tu, A. Barbu. Probabilistic Boosting Tree Framework for Learning Discriminative Models. Patent 7,702,596
24. Z. Tu, X. Zhou, D. Comaniciu, L. Bogoni, A. Barbu. System and Method for Using Learned Discriminative Models to Segment Three dimensional Colon Image Data. Patent 7,583,831
25. Z. Tu, A. Barbu, D. Comaniciu. Method for Detecting Polyps in a Three Dimensional Image Volume. Patent 7,558,413

Teaching:

• Applied Machine Learning - STA 4634/5635 (syllabus) Spring 2008-2016, Fall 2016-present, Spring 2022-present
• Statistical Computing with Python - STA 5934-0002 (syllabus) Spring 2019-2021
• Applied Linear Regression - STA 4203/5207 (syllabus) Fall 2007-2013,2015-2016
• ANOVA and Design of Experiments - STA 4202/5206 (syllabus) Spring 2016-2017
• Introduction to Applied Statistics - STA 5126 (syllabus) Fall 2012
• Medical Image Analysis, (syllabus) Spring 2010,2012

Education: