Academic Positions

  • Present 2014

    Tenure-Track Assistant Professor

    Florida International University, School of Computing and Information Sciences

Education & Training

  • Ph.D. 2014

    Ph.D. in Electrical and Computer Engineering

    Cornell University, Ithaca, NY.

  • B.E.2009

    B.E. in Information Engineering

    Zhejiang University, Hangzhou, China

News

Honors, Awards and Grants

  • August 2016
    2016 Best REU and RET Poster Awards
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    Dr. Fang's mentee, Paul Naghshineh from George Washington University, won the Best REU Poster Award at the annual REU Symposium at FIU. Christian McDonald with Edda I. Rivera, teachers from Miami Jackson Senior High School and John A. Ferguson Senior High School, under Dr. Fang's mentorship, won the Best RET Poster Award. Congratulations to Paul, Christian and Edda!
  • May 2016
    2016 Ralph E. Powe Junior Faculty Enhancement Award
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    Dr. Fang has been selected to receive the 2016 Ralph E. Powe Junior Faculty Enhancement Award from Oak Ridge Associated Universities (two nominations from each institute, 35 awardees out of 132 applicants).
  • April 2016
    NSF CRII Award on "Characterizing, Modeling and Evaluating Brain Dynamics"
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    Brain dynamics, which reflects the healthy or pathological states of the brain with quantifiable, reproducible, and indicative dynamics values, remains the least understood and studied area of brain science despite its intrinsic and critical importance to the brain. Unlike other brain information such as the structural and sequential dimensions that have all been extensively studied with models and methods successfully developed, the 5th dimension, dynamics, has only very recently started receiving systematic analysis from the research community. The state-of-the-art models suffer from several fundamental limitations that have critically inhibited the accuracy and reliability of the dynamic parameters’ computation. First, dynamic parameters are derived from each voxel of the brain spatially independently, and thus miss the fundamental spatial information since the brain is ?connected?. Second, current models rely solely on single-patient data to estimate the dynamic parameters without exploiting the big medical data consisting of billions of patients with similar diseases.

    This project aims to develop a framework for data-driven brain dynamics characterization, modeling and evaluation that includes the new concept of a 5th dimension – brain dynamics – to complement the structural 4-D brain for a complete picture. The project studies how dynamic computing of the brain as a distinct problem from the image reconstruction and de-noising of convention models, and analyzes the impact of different models for the dynamics analysis. A data-driven, scalable framework will be developed to depict the functionality and dynamics of the brain. This framework enables full utilization of 4-D brain spatio-temporal data and big medical data, resulting in accurate estimations of the dynamics of the brain that are not reflected in the voxel-independent models and the single patient models. The model and framework will be evaluated on both simulated and real dual-dose computed tomography perfusion image data and then compared with the state-of-the-art methods for brain dynamics computation by leveraging collaborations with Florida International University Herbert Wertheim College of Medicine, NewYork-Presbyterian Hospital / Weill Cornell Medical College (WCMC) and Northwell School of Medicine at Hofstra University. The proposed research will significantly advance the state-of-the-art in quantifying and analyzing brain structure and dynamics, and the interplay between the two for brain disease diagnosis, including both the acute and chronic diseases. This unified approach brings together fields of Computer Science, Bioengineering, Cognitive Neuroscience and Neuroradiology to create a framework for precisely measuring and analyzing the 5th dimension – brain dynamics – integrated with the 4-D brain with three dimensions from spatial data and one dimension from temporal data. Results from the project will be incorporated into graduate-level multi-disciplinary courses in machine learning, computational neuroscience and medical image analysis. This project will open up several new research directions in the domain of brain analysis, and will educate and nurture young researchers, advance the involvement of underrepresented minorities in computer science research, and equip them with new insights, models and tools for developing future research in brain dynamics in a minority serving university.

  • April 2016
    Robin Sidhu Memorial Young Scientist Award
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    Dr. Fang was awarded the Robin Sidhu Memorial Young Scientist Award at the Annual Congress of Society of Brain Mapping and Theurapeutics. News reported on PR Newswire, Yahoo,FIU NEWS
  • March 2015
    National Science Foundation CISE CAREER Workshop Travel Award
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    Dr. Fang received a Travel Award to attend CISE NSF CAREER Proposal Writing Workshop in Arlington, VA.
  • 2014
    Hsien Wu and Daisy Yen Wu Memorial Award
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    in recognition of the excellent progress in the academic program and high potential for a successful academic career (5 awardees out of all graduate students at Cornell University)
  • 2014
    Student Travel Award at MICCAI
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    Student Travel Award at the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2014.
  • 2012
    IBM Cornell ECE Women’s Conference Travel Grant
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    IBM Cornell ECE Women’s Conference Travel Grant to attend the 15th International Conference on Medical Image Computing and Computer Assisted Intervention, 2012.
  • 2010
    Best Paper Award at IEEE ICIP
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    Best Paper Award at the 17th International Conference on Image Processing, 2010. (Top 1 out of 1190 accepted papers, first author publication)
  • 2009-2010
    Irwin and Joan Jacobs Fellowship
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    Cornell University, awarded to students who exemplify strength and potential in academics, service, and leadership, 2009-2010
  • 2010
    Best PhD Poster Award
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    Cornell Engineering Research Conference, 2010

Acknowledgements

  • Our work is supported by:

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Laboratory Members

Haodi Jiang

M.S. Alumni @ FIU 2014/10-2015/06

Now pursuing PhD at New Jersey Institute of Technology (NJIT).

Co-author papers: “Tissue-Specific Sparse Deconvolution for Brain CT Perfusion”.

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Sherman Ng

M.Eng. @ Cornell, B.S. @ Berkeley. 2010/09-2011/05 Alumni

Thesis: Interactive interface for kinship verification on family photo albums. Now work as software engineer at Exelis.

Co-author papers: “Intelligent Family Relationship Classification System for Personal Photo Album Organization“. ECCV Demo 2010, CVPR Demo 2011.

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Peng Liu

PhD @ FIU 2016/05 - Now

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Maryamossadat Aghili

PhD @ FIU 2016/05 - Now

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Nasim Sabetpour

PhD @ FIU 2016/08 - Now

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Yao Xiao

M.S. @ FIU 2015/12 - Now

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Jingan Qu

M.S. @ FIU 2015/12 - Now

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Micheal Adeyosoye

M.S. @ FIU 2016/08 - Now

Bridge to Doctorate Fellowship

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YuanYuan Zhu

Visiting Undergraduate Student From Zhejiang University 2016/08-2016/10

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Xing Pang

Visiting Master Alumni Student from Nanjing University of Science & Technology. 2015/09-2016/07 Publications: SPIE 2016, OSA 2016, WH 2015.

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Daniel Parra

M.S. Alumni @ FIU 2015/01 - 2016/07

Edda Rivera

RET Member 2016 Alumni

Best NSF RET Poster Award @ FIU SCIS RET Presentation 2016

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Christian McDonald

RET Member 2016 Alumni

Best NSF RET Poster Award @ FIU SCIS RET Presentation 2016

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Paul Naghshineh

REU Student 2016 Alumni

Best NSF REU Poster Award @ FIU SCIS REU Presentation

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Great lab Personel!

The fiber of this lab’s existence rests on both undergraduate and graduate students. We are always interested in having intelligent, motivated, and responsible students join the lab.

Research Projects

  • image

    TENDER: TEnsor Non-local Deconvolution Enabled Radiation Reduction in CT Perfusion

    Stroke is the leading cause of long-term disability and the second leading cause of mortality in the world, and exerts an enormous burden on the public health. CT remains one of the most widely used imaging modality for stroke diagnosis. However when coupled with CT perfusion, the excessive radiation exposure in repetitive imaging to assess treatment response and prognosis has raised significant public concerns regarding its potential hazards to both short- and longterm health outcomes. Tensor total variation has been proposed to reduce the necessary radiation dose in CT perfusion without comprising the image quality by fusing the information of the local anatomical structure with the temporal blood flow model. However the local search in the framework fails to leverage the non-local information in the spatio-temporal data. In this paper, we propose TENDER, an efficient framework of non-local tensor deconvolution to maintain the accuracy of the hemodynamic parameters and the diagnostic reliability in low radiation dose CT perfusion. The tensor total variation is extended using non-local spatio-temporal cubics for regularization to integrate contextual and non-local information. We also propose an efficient framework consisting of fast nearest neighbor search, accelerated optimization and parallel computing to improve the efficiency and scalability of the non-local spatio-temporal algorithm. Evaluations on clinical data of subjects with cerebrovascular disease and normal subjects demonstrate the advantage of non-local tensor deconvolution for reducing radiation dose in CT perfusion.

  • image

    Computational Health Informatics in the Big Data Age: A Survey

    The explosive growth and widespread accessibility of digital health data have led to a surge of research activity in the healthcare and data sciences fields. The conventional approaches for health data management have achieved limited success as they are incapable of handling the huge amount of complex data with high volume, high velocity, and high variety. This article presents a comprehensive overview of the existing challenges, techniques, and future directions for computational health informatics in the big data age, with a structured analysis of the historical and state-of-the-art methods. We have summarized the challenges into four Vs (i.e., volume, velocity, variety, and veracity) and proposed a systematic data-processing pipeline for generic big data in health informatics, covering data capturing, storing, sharing, analyzing, searching, and decision support. Specifically, numerous techniques and algorithms in machine learning are categorized and compared. On the basis of this material, we identify and discuss the essential prospects lying ahead for computational health informatics in this big data age.

  • image

    Near-Infrared Optical Imaging and Wound Segmentation in Lower Extremity Ulcers

    Near-Infrared (NIR) optical imaging can reveal tissue oxygenation of the wound, complementing the visual inspection of the surface granulation. Herein, graph cuts algorithm is applied to segment NIR images of the wound from its peripheries.

  • image

    Risk Factor Analysis: BRFSS

    Members: Yao Xiao, Daniel Parra

    The Behavioral Risk Factor Surveillance System (BRFSS) is the nation's premier system of health-related telephone surveys that collect state data about U.S. residents regarding their health-related risk behaviors, chronic health conditions, and use of preventive services. Established in 1984 with 15 states, BRFSS now collects data in all 50 states as well as the District of Columbia and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world.

  • image

    Big Neuron Analytics

    Members: Peng Liu, Jingan Qu, Maryam Aghili

    We are going to analysize different neuron from many species by data mining and deep learning algorithm seperately.

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34. Hemodynamic Imaging of Lower Extremity Ulcers.

Rebecca Kwasinski, Cristianne Fernandez, Kevin Leiva, Edwin Robledo, Yuanyuan Zhu, Penelope Kallis, Francesco-Perez Clavijo, Ruogu Fang, Robert Kirsner, Anuradha Godavarty.
Conference Innovations in Wound Healing, Dec. 2016.

Abstract

Clinicians employ visual inspection of the wound and reduction in its size over time to monitor its healing process. Although these are standard clinical assessments, there is a need to develop a physiological approach that can map sub-surface tissue oxygenation at and around the wound region. Recently, a non-contact, portable, hand-held near infrared optical scanner (NIROS) has been developed to functionally image wound sites and differentiate healing from non-healing in lower extremity ulcers. Past studies using NIROS focused on differentiating healing from non-healing wounds based on NIR optical contrast between the wound and healthy surrounding tissue. However, these studies did not showcase the physiological changes in tissue oxygenation. Herein, NIROS has been modified to perform multi wavelength imaging in order to obtain the oxy and deoxy- hemoglobin maps of the wound and its surroundings. Clinical studies are currently performed at two clinical sites in Miami on lower extremity ulcers (2, diabetic foot ulcers (DFUs) and 4 venous leg ulcers (VLUs to date). Preliminary results have shown changes in oxy- and deoxy-hemoglobin maps of the wound and background across weeks of the treatment process. Image segmentation studies quantified regions of varied tissue oxygenation around and beneath the wound to potentially determine sub-surface healing regions. Ongoing efforts involve systematic 8-week imaging studies to obtain physiological indicators of healing from hemodynamic studies of DFUs and VLUs.

33. Indexing and Mining Large-Scale Neuron Databases using Maximum Inner Product Search.

Zhongyu Li, Ruogu Fang, Fumin Shen, Amin Katouzian, Shaoting Zhang.
Journal Pattern Recognition, In Press.

Abstract

Morphological retrieval is an effective approach to explore large-scale neuronal databases, as the morphology is correlated with neuronal types, regions, functions, etc. In this paper, we focus on the neuron identification and analysis via morphological retrieval. In our proposed framework, multiple features are extracted to represent 3D neuron data. Because each feature reflects different levels of similarity between neurons, we group features into different hierarchies to compute the similarity matrix. Then, compact binary codes are generated from hierarchical features for efficient similarity search. Since neuronal cells usually have tree-topology structure, it is hard to distinguish different types of neurons simply via traditional binary coding or hashing methods based on Euclidean distance metric and/or linear hyperplanes. Therefore, we employ an asymmetric binary coding strategy based on the maximum inner product search (MIPS), which not only makes it easier to learn the binary coding functions, but also preserves the non-linear characteristics of the neuron morphological data. We evaluate the proposed method on more than 17,000 neurons, by validating the retrieved neurons with associated cell types and brain regions. Experimental results show the superiority of our approach in neuron morphological retrieval compared with other state-of-the-art methods. Moreover, we demonstrate its potential use cases in the identification and analysis of neuron characteristics from large neuron databases.

32. CT perfusion image super-resolution using a deep convolutional network.

Paul Naghshineh, Peng Liu, Ruogu Fang∗ (* corresponding authors)
ConferenceBMES, Biomedical Engineering Society Annual Meeting, October 5-8, 2016 in Minneapolis, Minnesota. Accepted.

31. Physiological Assessment of Wound Healing using a Near-Infrared Optical Scanner.

Anuradha Godavarty, Rebecca Kwasinki, Cristianne Fernandez, Yuanyuan Zhu, Edwin Robledo, F. Perez-Clavijo, Ruogu Fang
ConferenceBMES, Biomedical Engineering Society Annual Meeting, October 5-8, 2016 in Minneapolis, Minnesota. Accepted.

30. Abdominal Adipose Tissues Extraction Using Multi-Scale Deep Neural Network.

Fei Jiang, Huating Li, Xuhong Hou, Bin Sheng∗, Ruimin Shen, Xiao-Yang Liu, Weiping Jia, Ping Li, Ruogu Fang∗ (* corresponding authors)
Journal NeuroComputing, In Press.

Abstract

Segmentation of abdominal adipose tissues (AAT) into subcutaneous adipose tissues (SAT) and visceral adipose tissues (VAT) is of crucial interest for managing the obesity. Previous methods with raw or hand-crafted features rarely work well on large-scale subject cohorts, because of the inhomogeneous image intensities, artifacts and the diverse distributions of VAT. In this paper, we propose a novel two-stage coarse-to-fine algorithm for AAT seg- mentation. In the first stage, we formulate the AAT segmentation task as a pixel-wise classification problem. First, three types of features, intensity, spatial and contextual fea- tures, are adopted. Second, a new type of deep neural network, named multi-scale deep neural network (MSDNN), is provided to extract high-level features. In the second stage, to improve the segmentation accuracy, we refine coarse segmentation results by determining the internal boundary of SAT based on coarse segmentation results and continuous of SAT internal boundary. Finally, we demonstrate the efficacy of our algorithm for both 2D and 3D cases on a wide population range. Compared with other algorithms, our method is not only more suitable for large-scale dataset, but also achieves better segmentation results. Fur- thermore, our system takes about 2 seconds to segment an abdominal image, which implies potential clinical applications.

29. TENDER: TEnsor Non-local Deconvolution Enabled Radiation Reduction in CT Perfusion.

Ruogu Fang*, Ajay Gupta, Junzhou Huang, Pina Sanelli.
Journal NeuroComputing, In Press.

Abstract

Stroke is the leading cause of long-term disability and the second leading cause of mortality in the world, and exerts an enormous burden on the public health. CT remains one of the most widely used imaging modality for stroke diagnosis. However when coupled with CT perfusion, the excessive radiation exposure in repetitive imaging to assess treatment response and prognosis has raised significant public concerns regarding its potential hazards to both short- and longterm health outcomes. Tensor total variation has been proposed to reduce the necessary radiation dose in CT perfusion without comprising the image quality by fusing the information of the local anatomical structure with the temporal blood flow model. However the local search in the framework fails to leverage the non-local information in the spatio-temporal data. In this paper, we propose TENDER, an efficient framework of non-local tensor deconvolution to maintain the accuracy of the hemodynamic parameters and the diagnostic reliability in low radiation dose CT perfusion. The tensor total variation is extended using non-local spatio-temporal cubics for regularization to integrate contextual and non-local information. We also propose an efficient framework consisting of fast nearest neighbor search, accelerated optimization and parallel computing to improve the efficiency and scalability of the non-local spatio-temporal algorithm. Evaluations on clinical data of subjects with cerebrovascular disease and normal subjects demonstrate the advantage of non-local tensor deconvolution for reducing radiation dose in CT perfusion.

28. Computational Health Informatics in the Big Data Age: A Survey

Ruogu Fang*, Samira Pouyanfar*, Yimin Yang, Shu-Ching Chen, S. S. Iyengar (* indicates equal contributions)
Journal Journal CSUR, ACM Computing Survey, Volume 49 Issue 1, Article No. 12, June 2016

Abstract

The explosive growth and widespread accessibility of digital health data have led to a surge of research activity in the healthcare and data sciences fields. The conventional approaches for health data management have achieved limited success as they are incapable of handling the huge amount of complex data with high volume, high velocity, and high variety. This article presents a comprehensive overview of the existing challenges, techniques, and future directions for computational health informatics in the big data age, with a structured analysis of the historical and state-of-the-art methods. We have summarized the challenges into four Vs (i.e., volume, velocity, variety, and veracity) and proposed a systematic data-processing pipeline for generic big data in health informatics, covering data capturing, storing, sharing, analyzing, searching, and decision support. Specifically, numerous techniques and algorithms in machine learning are categorized and compared. On the basis of this material, we identify and discuss the essential prospects lying ahead for computational health informatics in this big data age.

27. Maximum Inner Product Search for Morphological Retrieval of Large-Scale Neuron Data

Zhongyu Li, Fumin Shen, Ruogu Fang, Sailesh Conjeti, Amin Katouzian, Shaoting Zhang.
ConferenceISBI, IEEE International Symposium on Biomedical Imaging, Prague, Czech Republic, April, 2016

Abstract

Morphological retrieval is an effective approach to explore neurons' databases, as the morphology is correlated with neuronal types, regions, functions, etc. In this paper, we focus on the neuron identification and analysis via morphological retrieval. In our proposed framework, both global and local features are extracted to represent 3D neuron data. Then, compacted binary codes are generated from original features for efficient similarity search. As neuron cells usually have tree-topology structure, it is hard to distinguish different types of neuron simply via traditional binary coding or hashing methods based on Euclidean distance metric and/or linear hyperplanes. Thus, we propose a novel binary coding method based on the maximum inner product search (MIPS), which is not only more easier to learn the binary coding function, but also preserves the non-linear characteristics of neuron morphology data. We evaluate the proposed method on more than 17,000 neurons, by validating the retrieved neurons with associated cell types and brain regions. Experimental results show the superiority of our approach in neuron morphological retrieval compared with other state-of-the-art methods. Moreover, we demonstrate its potential use case in the identification and analysis of neuron characteristics.

26. Direct Estimation of Permeability Maps for Low-Dose CT Perfusion

Ruogu Fang, Ajay Gupta, Pina C. Sanelli
Conference ISBI, IEEE International Symposium on Biomedical Imaging, Prague, Czech Republic, April, 2016

Abstract

With the goal of achieving low radiation exposure from medical imaging, computed tomography perfusion (CTP) introduces challenging problems for both image reconstruction and perfusion parameter estimation in the qualitative and quantitative analyses. Conventional approaches address the reconstruction and the estimation processes separately. Since the hemodynamic parameter maps have much lower dimensionality than the original sinogram data, estimating hemodynamic parameters directly from sinogram will further reduce radiation exposure and save computational resources to reconstruct the intermediate time-series images. In this work, we propose the first direct estimation framework for CTP that integrates the time-series image reconstruction, contrast conversion, hematocrit correction and hemodynamic parameter estimation in one optimization function, which is solved using an efficient algorithm. Evaluations on the digital brain perfusion phantom and a clinical acute stroke subject demonstrate that the proposed direct estimation framework boosts the estimation accuracy remarkably in CTP scanning with lower radiation exposure.

25. Automatic Segmentation of Lower Extremity Ulcers in Near-Infrared Optical Imaging

Ruogu Fang, Xing Pang, Arash Dadkhah, Jiali Lei, Elizabeth Solis, Suset Rodriguesz, Francisco Perez-Clavijo, Stephen Wigley, Charles Buscemi, Anuradha Godvarty.
Conference ISBI, IEEE International Symposium on Biomedical Imaging, Prague, Czech Republic, April, 2016

24. Near-Infrared Optical Imaging and Wound Segmentation in Lower Extremity Ulcers

Xing Pang, Arash Dadkhah, Jaili Lei, Elizabeth Solis, Suset Rodriguez, Francisco Perez-Clavijo, Stephen Wigley, Ruogu Fang, Anuradha Godvarty.
ConferenceOSA, Optical Society of America Annual Meeting, 2016.

Abstract

Near-Infrared (NIR) optical imaging can reveal tissue oxygenation of the wound, complementing the visual inspection of the surface granulation. Herein, graph cuts algorithm is applied to segment NIR images of the wound from its peripheries.

23. Wound Size Measurement of Lower Extremity Ulcers Using Segmentation Algorithms

Arash Dadkhah, Xing Pang, Elizabeth Solis, Ruogu Fang, Anuradha Godvarty.
Conference SPIE Proceedings in Photonics West, San Francisco, February 2016.

Abstract

Lower extremity ulcers are one of the most common complications that not only affect many people around the world but also have huge impact on economy since a large amount of resources are spent for treatment and prevention of the diseases. Clinical studies have shown that reduction in the wound size of 40% within 4 weeks is an acceptable progress in the healing process. Quantification of the wound size plays a crucial role in assessing the extent of healing and determining the treatment process. To date, wound healing is visually inspected and the wound size is measured from surface images. The extent of wound healing internally may vary from the surface. A near-infrared (NIR) optical imaging approach has been developed for non-contact imaging of wounds internally and differentiating healing from non-healing wounds. Herein, quantitative wound size measurements from NIR and white light images are estimated using a graph cuts and region growing image segmentation algorithms. The extent of the wound healing from NIR imaging of lower extremity ulcers in diabetic subjects are quantified and compared across NIR and white light images. NIR imaging and wound size measurements can play a significant role in potentially predicting the extent of internal healing, thus allowing better treatment plans when implemented for periodic imaging in future.

22. Wound Segmentation in Near-Infrared Optical Imaging

Ruogu Fang, Xing Pang, Arash Dadkhah, Jiali Lei, Elizabeth SOlis, Suset ROdriguez, Francisco Perez-Calvijo, Stephen Wigley, Charles Buscemi, Anuradha Godvarty.
Conference Innovation in Wound Healing, Hawks Cay, FL. Dec. 2015.

21. Robust Low-dose CT Perfusion Deconvolution via Tensor Total-Variation Regularization

Ruogu Fang, Shaoting Zhang, Tsuhan Chen, Pina C. Sanelli.
JournalTMI, IEEE Transaction on Medical Imaging, vol.34, no.7, pp.1533-1548, July 2015.

Abstract

Acute brain diseases such as acute strokes and transit ischemic attacks are the leading causes of mortality and morbidity worldwide, responsible for 9% of total death every year. ‘Time is brain’ is a widely accepted concept in acute cerebrovascular disease treatment. Efficient and accurate computational framework for hemodynamic parameters estimation can save critical time for thrombolytic therapy. Meanwhile the high level of accumulated radiation dosage due to continuous image acquisition in CT perfusion (CTP) raised concerns on patient safety and public health. However, low-radiation leads to increased noise and artifacts which require more sophisticated and time-consuming algorithms for robust estimation. In this paper, we focus on developing a robust and efficient framework to accurately estimate the perfusion parameters at low radiation dosage. Specifically, we present a tensor total-variation (TTV) technique which fuses the spatial correlation of the vascular structure and the temporal continuation of the blood signal flow. An efficient algorithm is proposed to find the solution with fast convergence and reduced computational complexity. Extensive evaluations are carried out in terms of sensitivity to noise levels, estimation accuracy, contrast preservation, and performed on digital perfusion phantom estimation, as well as in-vivo clinical subjects. Our framework reduces the necessary radiation dose to only 8% of the original level and outperforms the state-of-art algorithms with peak signal-to-noise ratio improved by 32%. It reduces the oscillation in the residue functions, corrects overestimation of cerebral blood flow (CBF) and under-estimation of mean transit time (MTT), and maintains the distinction between the deficit and normal regions.

20. Guest Editorial: Sparsity Techniques in Medical Imaging.

Ruogu Fang, Tsuhan Chen, Dimitris Metaxas, Pina Sanelli, Shaoting Zhang.
JournalCMIG, Elsevier Journal of Computerized Medical Imaging and Graphics, vol. 46, no. 1, 2015.

Abstract

With the advent of the age for big data and complex structure, sparsity has been an important modeling tool in compressed sensing, machine learning, image processing, neuroscience and statistics. In the medical imaging field, sparsity methods have been successfully used in image reconstruction, image enhancement, image segmentation, anomaly detection, disease classification, and image database retrieval. Developing more powerful sparsity models for a large range of medical imaging and medical image analysis problems as well as efficient optimization and learning algorithm will keep being a main research topic in this field. The goal of this special issue is to publish original and high quality papers on innovation research and development in medical imaging and medical image analysis using sparsity techniques. This special issue will help advance the scientific research within the field of sparsity methods for medical imaging.

19. Tissue-Specific Sparse Deconvolution for Brain CT Perfusion.

Ruogu Fang,Haodi Jiang, Junzhou Huang.
Journal CMIG, Elsevier Journal of Computerized Medical Imaging and Graphics, vol.46, no.1, pp. 64-72, 2015.

Abstract

Enhancing perfusion maps in low-dose computed tomography perfusion (CTP) for cerebrovascular disease diagnosis is a challenging task, especially for lowcontrast tissue categories where infarct core and ischemic penumbra usually occur. Sparse perfusion deconvolution has been recently proposed to effectively improve the image quality and diagnostic accuracy of low-dose perfusion CT by extracting the complementary information from the high-dose perfusion maps to restore the low-dose using a joint spatio-temporal model. However the lowcontrast tissue classes where infarct core and ischemic penumbra are likely to occur in cerebral perfusion CT tend to be over-smoothed, leading to loss of essential biomarkers. In this paper, we propose a tissue-specific sparse deconvolution approach to preserve the subtle perfusion information in the low-contrast tissue classes. We first build tissue-specific dictionaries from segmentations of high-dose perfusion maps using online dictionary learning, and then perform deconvolution-based hemodynamic parameters estimation for block-wise tissue segments on the low-dose CTP data. Extensive validation on clinical datasets of patients with cerebrovascular disease demonstrates the superior performance of our proposed method compared to state-of-art, and potentially improve diagnostic accuracy by increasing the differentiation between normal and ischemic tissues in the brain

18. Fast Preconditioning for Accelerated Multi-Contrast MRI Reconstruction

Ruoyu Li, Yeqing Li, Ruogu Fang,Shaoting Zhang, Hao Pan, Junzhou Huang
Conference MICCAI'15, In Proc. of the 18th Annual International Conference on Medical Image Computing and Computer Assisted Intervention, Munich, Germany, October 2015.

17. Efficient 4D Non-Local Tensor Total-Variation for Low-Dose CT Perfusion Deconvolution

Ruogu Fang, Ming Ni, Junzhou Huang, Qianmu Li, Tao Li
JournalMedical Computer Vision: Algorithms for Big Data Chapter, no.16, Lecture Notes in Computer Science, 2015.

Abstract

Tensor total variation deconvolution has been recently proposed as a robust framework to accurately estimate the hemodynamic parameters in low-dose CT perfusion by fusing the local anatomical structure correlation and temporal blood flow continuation. However the locality property in the current framework constrains the search for anatomical structure similarities to the local neighborhood, missing the global and long-range correlations in the whole anatomical structure. This limitation has led to noticeable absence or artifact of delicate structures, including the critical indicators for the clinical diagnosis of cerebrovascular diseases. In this paper, we propose an extension of the TTV framework by introducing 4D non-local tensor total variation into the deconvolution to bridge the gap between non-adjacent regions of the same tissue classes. The non-local regularization using tensor total variation term is imposed on the spatio-temporal flow-scaled residue functions. An efficient algorithm and implementation of the non-local tensor total variation (NL-TTV) reduces the time complexity with fast similarity computation, accelerated optimization and parallel operations. Extensive evaluations on the clinical data with cerebrovascular diseases and normal subjects demonstrate the importance of non-local linkage and long-range connections for low-dose CT perfusion deconvolution.

16. Robust Low-Dose CT Perfusion Deconvolution via Non-Local Tensor Total Variation

Ruogu Fang, Ming Ni, Junzhou Huang, Qianmu Li, Tao Li
Conference BMES, Biomedical Engineering Society Annual Meeting, Tampa, FL, October 2015.

Introduction

Stroke and cerebrovascular diseases are the leading cause of serious, long-term disability in the United States. Computed tomography perfusion (CTP) is one of the most widely accepted imaging modality for stroke care. However, the high radiation exposure of CTP has lead to increased cancer risk. Tensor total variation (TTV)[1] has been proposed to stabilize the quantification of perfusion parameters by integrating the anatomical structure correlation. Yet the locality limitation of the neighborhood region has led to noticeable absence or inflation of the delicate structures which are critical indicators for the clinical diagnosis. In this work, we propose a non-local tensor total variation (NL-TTV) deconvolution method to by incorporating the long-range dependency and the global connections in the spatio-temporal domain

15. A Spatio-Temporal Low-rank Total Variation Approach for Denoising Arterial Spin Labeling MRI Data

Ruogu Fang,Junzhou Huang, Wen-Ming Luh.
Conference ISBI’15, The IEEE International Symposium on Biomedical Imaging (ISBI), NYC, USA, April, 2015.

Abstract

Arterial spin labeling MRI (ASL-MRI) can provide quantitative signals correlated to the cerebral blood flow and neural activity. However, the low signal-to-noise ratio in ASL requires repeated acquisitions to improve the signal reliability, leading to prolonged scanning time. At fewer repetitions, noise and corruptions arise due to motion and physiological artifacts, introducing errors into the cerebral blood flow estimation. We propose to recover the ASL-MRI data from the noisy and corrupted observations at shorter scanning time with a spatio-temporal low-rank total variation method. The low-rank approximation uses the similarity of the repetitive scans, and the total variation regularization considers the local spatial consistency. We compare with the state-of-art robust M-estimator for ASL cerebral blood flow map estimation. Validation on simulated and real data demonstrate the robustness of the proposed method at fewer scanning repetitions and with random corruption

14. Leveraging Coupled Multi-Index for Scalable Retrieval of Mammographic Masses.

Menglin Jiang, Shaoting Zhang, Ruogu Fang, Dimitris Metaxas.
Conference ISBI’15, The IEEE International Symposium on Biomedical Imaging (ISBI), NYC, USA, April, 2015. Oral presentation 130 / 714 = 18%

13. 4-D Spatio-Temporal MR Perfusion Deconvolution via Tensor Total Variation.

Ruogu Fang
Conference ISMRM’15, International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, Toronto, Canada, May-June 2015. Oral presentation

Introduction

4-D dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) is a well-established perfusion technique for non-invasive characterization of tissue dynamics, with promising applications in assessing a wide range of diseases, as well as monitoring response of therapeutic interventions). DSC-MRI provides critical real-time information by tracking the first-pass of an injected contrast-agent (e.g. gadolinium) with T2*-weighted MRI. The spatio-temproal data, consisting of contrast concentration signals for each voxel of a volume, are deconvolved from the arterial input function (AIF) and then post-processed to generate perfusion parameter maps, typically including the cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT) and time to peak (TTP). The most popular deconvolution method is truncated singular value decomposition (TSVD)1,2 and its variants3 , which fail to exploit the spatio-temporal nature of the 4D data with both the anatomical structure and the temporal continuation. This work adapts and demonstrates the feasibility of a 4-D tensor total variation (TTV) deconvolution approach, which has been proposed for CT perfusion4 , to brain MR perfusion, with evaluation on synthetic data and clinical DSC-MRI data for glioblastomas, the most common type of brain cancer. The method is guaranteed to convergence to global optimal because of the convex cost function and presents a more elegant framework of total variation for the deconvolution, compared to recent efforts5,6 which either do not have a global optimal solution for the non-convex case or need to handcraft spatial and temporal regularization terms.

12. Improving Low-Dose Blood-Brain Barrier Permeability Quantification Using Sparse High-Dose Induced Prior for Patlak Model.

Ruogu Fang, Kolbeinn Karlsson, Tsuhan Chen, Pina C. Sanelli.
JournalMedIA’14, Medical Image Analysis, Volume 18, Issue 6, Pages 866-880, 2014.

Abstract

Blood-brain barrier permeability (BBBP) measurements extracted from the perfusion computed tomography (PCT) using the Patlak model can be a valuable indicator to predict hemorrhagic transformation in patients with acute stroke. Unfortunately, the standard Patlak model based PCT requires excessive radiation exposure, which raised attention on radiation safety. Minimizing radiation dose is of high value in clinical practice but can degrade the image quality due to the introduced severe noise. The purpose of this work is to construct high quality BBBP maps from low-dose PCT data by using the brain structural similarity between different individuals and the relations between the high- and low-dose maps. The proposed sparse high-dose induced (shd-Patlak) model performs by building a high-dose induced prior for the Patlak model with a set of location adaptive dictionaries, followed by an optimized estimation of BBBP map with the prior regularized Patlak model. Evaluation with the simulated low-dose clinical brain PCT datasets clearly demonstrate that the shd-Patlak model can achieve more significant gains than the standard Patlak model with improved visual quality, higher fidelity to the gold standard and more accurate details for clinical analysis.

11. Tensor Total-Variation Regularized Deconvolution for Efficient Low-Dose CT Perfusion.

Ruogu Fang,Pina Sanelli, Shaoting Zhang, Tsuhan Chen.
ConferenceMICCAI’14, The 17th Annual International Conference on Medical Image Computing and Computer Assisted Intervention, Boston, USA, September, 2014.

Abstract

Acute brain diseases such as acute stroke and transit ischemic attacks are the leading causes of mortality and morbidity worldwide, responsible for 9% of total death every year. ‘Time is brain’ is a widely accepted concept in acute cerebrovascular disease treatment. Efficient and accurate computational framework for hemodynamic parameters estimation can save critical time for thrombolytic therapy. Meanwhile the high level of accumulated radiation dosage due to continuous image acquisition in CT perfusion (CTP) raised concerns on patient safety and public health. However, low-radiation will lead to increased noise and artifacts which require more sophisticated and time-consuming algorithms for robust estimation. We propose a novel efficient framework using tensor totalvariation (TTV) regularization to achieve both high efficiency and accuracy in deconvolution for low-dose CTP. The method reduces the necessary radiation dose to only 8% of the original level and outperforms the state-of-art algorithms with estimation error reduced by 40%. It also corrects over-estimation of cerebral blood flow (CBF) and under-estimation of mean transit time (MTT), at both normal and reduced sampling rate. An efficient

10. Anisotropic Tensor Total Variation Regularization For Low Dose Low CT Perfusion Deconvolution.

Ruogu Fang, Tsuhan Chen, Pina C. Sanelli.
ConferenceMICCAI’14, The 17th Annual International Conference on Medical Image Computing and Computer Assisted Intervention, Workshop on Sparsity Techniques in Medical Imaging, Boston, USA, September, 2014.

Abstract

Tensor total variation (TTV) regularized deconvolution has been proposed for robust low radiation dose CT perfusion. In this paper, we extended TTV algorithm with anisotropic regularization weighting for the temporal and spatial dimension. We evaluated TTV algorithm on synthetic dataset for bolus delay, uniform region variability and contrast preservation, and on clinical dataset for reduced sampling rate with visual and quantitative comparison. The extensive experiments demonstrated promising results of TTV compared to baseline and state-of-art algorithms in low-dose and low sampling rate CTP deconvolution with insensitivity to bolus delay. This work further demonstrates the effectiveness and potential of TTV algorithm’s clinical usage for cerebrovascular diseases with significantly reduced radiation exposure and improved patient safety.

9. Towards Robust Deconvolution of Low-Dose Perfusion CT: Sparse Perfusion Deconvolution Using Online Dictionary Learning.

Ruogu Fang, Tsuhan Chen, Pina C. Sanelli.
JournalMedIA’13, Medical Image Analysis, Volume 17, Issue 4, Pages 417-428, 2013. (5 Year Impact Factor: 4.512)

Abstract

Computed tomography perfusion (CTP) is an important functional imaging modality in the evaluation of cerebrovascular diseases, particularly in acute stroke and vasospasm. However, the post-processed parametric maps of blood flow tend to be noisy, especially in low-dose CTP, due to the noisy contrast enhancement profile and the oscillatory nature of the results generated by the current computational methods. In this paper, we propose a robust sparse perfusion deconvolution method (SPD) to estimate cerebral blood flow in CTP performed at low radiation dose. We first build a dictionary from high-dose perfusion maps using online dictionary learning and then perform deconvolution-based hemodynamic parameters estimation on the low-dose CTP data. Our method is validated on clinical data of patients with normal and pathological CBF maps. The results show that we achieve superior performance than existing methods, and potentially improve the differentiation between normal and ischemic tissue in the brain.

8. Tissue-Specific Sparse Deconvolution for Low-Dose CT Perfusion.

Ruogu Fang, Tsuhan Chen, Pina C. Sanelli.
ConferenceMICCAI’13, The 16th Annual International Conference on Medical Image Computing and Computer Assisted Intervention, Japn, September, 2013.

Abstract

Sparse perfusion deconvolution has been recently proposed to effectively improve the image quality and diagnostic accuracy of low-dose perfusion CT by extracting the complementary information from the high-dose perfusion maps to restore the low-dose using a joint spatio-temporal model. However the low-contrast tissue classes where infarct core and ischemic penumbra usually occur in cerebral perfusion CT tend to be over-smoothed, leading to loss of essential biomarkers. In this paper, we extend this line of work by introducing tissuespecific sparse deconvolution to preserve the subtle perfusion information in the low-contrast tissue classes by learning tissue-specific dictionaries for each tissue class, and restore the low-dose perfusion maps by joining the tissue segments reconstructed from the corresponding dictionaries. Extensive validation on clinical datasets of patients with cerebrovascular disease demonstrates the superior performance of our proposed method with the advantage of better differentiation between abnormal and normal tissue in these patients.

7. Sparsity-Based Deconvolution of Low-Dose Perfusion CT Using Learned Dictionaries.

Ruogu Fang, Tsuhan Chen, Pina C. Sanelli.
ConferenceMICCAI’12, The 15th Annual International Conference on Medical Image Computing and Computer Assisted Intervention, Nice, France, October, 2012. Lecture Notes in Computer Science Volume 7510, 2012, pp 272-280.

6. Sparsity-Based Deconvolution Of Low-Dose Brain Perfusion CT In Subarachnoid Hemorrhage Patients.

Ruogu Fang, Tsuhan Chen, Pina C. Sanelli.
ConferenceISBI’12, The 9th International Symposium on Biomedical Imaging, pp. 872-875, Barcelona, Spain, May 2012. Oral presentation.

Abstract

Functional imaging serves as an important supplement to anatomical imaging modalities such as MR and CT in modern health care. In perfusion CT (CTP), hemodynamic parameters are derived from the tracking of the first-pass of the contrast bolus entering a tissue region of interest. In practice, however, the post-processed parametric maps tend to be noisy, especially in low-dose CTP, in part due to the noisy contrast enhancement profile and oscillatory nature of results generated by current computational methods. In this paper, we propose a sparsity-based perfusion parameter deconvolution approach that consists of a non-linear processing based on sparsity prior in terms of residue function dictionaries. Our simulated results from numericaldata and experiments in aneurysmal subarachnoid hemorrhage patients with clinical vasospasm show that the algorithm improves the quality and reduces the noise of the perfusion parametric maps in low-dose CTP, compared to state-of-the-art methods

5. Radiation dose reduction in computed tomography perfusion using spatial-temporal Bayesian methods.

Ruogu Fang, Ashish Raj, Tsuhan Chen, Pina C. Sanelli
ConferenceSPIE’12, In Proceedings of SPIE Medical Imaging, Volume 8313, Paper #831345, April, 2012.

Abstract

In current computed tomography (CT) examinations, the associated X-ray radiation dose is of significant concern to patients and operators, especially CT perfusion (CTP) imaging that has higher radiation dose due to its cine scanning technique. A simple and cost-effective means to perform the examinations is to lower the milliampere-seconds (mAs) parameter as low as reasonably achievable in data acquisition. However, lowering the mAs parameter will unavoidably increase data noise and degrade CT perfusion maps greatly if no adequate noise control is applied during image reconstruction. To capture the essential dynamics of CT perfusion, a simple spatial-temporal Bayesian method that uses a piecewise parametric model of the residual function is used, and then the model parameters are estimated from a Bayesian formulation of prior smoothness constraints on perfusion parameters. From the fitted residual function, reliable CTP parameter maps are obtained from low dose CT data. The merit of this scheme exists in the combination of analytical piecewise residual function with Bayesian framework using a simpler prior spatial constrain for CT perfusion application. On a dataset of 22 patients, this dynamic spatial-temporal Bayesian model yielded an increase in signal-tonoise-ratio (SNR) of 78% and a decrease in mean-square-error (MSE) of 40% at low dose radiation of 43mA.

4. Segmentation of Liver Tumor Using Efficient Global Optimal Tree Metrics Graph Cuts.

Ruogu Fang, Ramin Zabih, Ashish Raj, Tsuhan Chen.
ConferenceMICCAI’11, Abdominal Imaging, International Conference on Medical Image Computing and Computer Assisted Intervention, pp. 51-59, September, 2011.

Abstract

We propose a novel approach that applies global optimal tree-metrics graph cuts algorithm on multi-phase contrast enhanced contrast enhanced MRI for liver tumor segmentation. To address the difficulties caused by low contrasted boundaries and high variability in liver tumor segmentation, we first extract a set of features in multi-phase contrast enhanced MRI data and use color-space mapping to reveal spatial-temporal information invisible in MRI intensity images. Then we apply efficient tree-metrics graph cut algorithm on multi-phase contrast enhanced MRI data to obtain global optimal labeling in an unsupervised framework. Finally we use tree-pruning method to reduce the number of available labels for liver tumor segmentation. Experiments on realworld clinical data show encouraging results. This approach can be applied to various medical imaging modalities and organs.

3. Tree-Metrics Graph Cuts For Brain MRI Segmentation With Tree Cutting.

Ruogu Fang, Joyce Yu-hsin Chen, Ramin Zabih, Tsuhan Chen.
ConferenceWNYIPW’10, IEEE Western New York Image Processing Workshop, pp. 10-13, Rochesester, NY. USA. November, 2010. oral presentation

Abstract

We tackle the problem of brain MRI image segmentation using the tree-metric graph cuts (TM) algorithm, a novel image segmentation algorithm, and introduce a “tree-cutting” method to interpret the labeling returned by the TM algorithm as tissue classification for the input brain MRI image. The approach has three steps: 1) pre-processing, which generates a tree of labels as input to the TM algorithm; 2) a sweep of the TM algorithm, which returns a globally optimal labeling with respect to the tree of labels; 3) post-processing, which involves running the “tree-cutting” method to generate a mapping from labels to tissue classes (GM, WM, CSF), producing a meaningful brain MRI segmentation. The TM algorithm produces a globally optimal labeling on tree metrics in one sweep, unlike conventional methods such as EMS and EM-style geo-cuts, which iterate the expectation maximization algorithm to find hidden patterns and produce only locally optimal labelings. When used with the “tree-cutting” method, the TM algorithm produces brain MRI segmentations that are as good as the Unified Segmentation algorithm used by SPM8, using a much weaker prior. Comparison with the current approaches shows that our method is faster and that our overall segmentation accuracy is better.

2. Kinship Classification by Modeling Facial Feature Heredity.

Ruogu Fang, Joyce Yu-hsin Chen, Ramin Zabih, Tsuhan Chen.
ConferenceWNYIPW’10, IEEE Western New York Image Processing Workshop, pp. 10-13, Rochesester, NY. USA. November, 2010. oral presentation

Abstract

We tackle the problem of brain MRI image segmentation using the tree-metric graph cuts (TM) algorithm, a novel image segmentation algorithm, and introduce a “tree-cutting” method to interpret the labeling returned by the TM algorithm as tissue classification for the input brain MRI image. The approach has three steps: 1) pre-processing, which generates a tree of labels as input to the TM algorithm; 2) a sweep of the TM algorithm, which returns a globally optimal labeling with respect to the tree of labels; 3) post-processing, which involves running the “tree-cutting” method to generate a mapping from labels to tissue classes (GM, WM, CSF), producing a meaningful brain MRI segmentation. The TM algorithm produces a globally optimal labeling on tree metrics in one sweep, unlike conventional methods such as EMS and EM-style geo-cuts, which iterate the expectation maximization algorithm to find hidden patterns and produce only locally optimal labelings. When used with the “tree-cutting” method, the TM algorithm produces brain MRI segmentations that are as good as the Unified Segmentation algorithm used by SPM8, using a much weaker prior. Comparison with the current approaches shows that our method is faster and that our overall segmentation accuracy is better.

1. Towards Computational Models of Kinship Verification.

Ruogu Fang, Kevin D. Tang, Noah Snavely, Tsuhan Chen.
ConferenceICIP’10, The 17th IEEE International Conference on Image Processing , Hong Kong, October, 2010. Oral presentation ICIP 2010 Best Paper Award

Abstract

We tackle the challenge of kinship verification using novel feature extraction and selection methods, automatically classifying pairs of face images as “related” or “unrelated” (in terms of kinship). First, we conducted a controlled online search to collect frontal face images of 150 pairs of public figures and celebrities, along with images of their parents or children. Next, we propose and evaluate a set of low-level image features that for use in this classification problem. After selecting the most discriminative inherited facial features, we demonstrate a classification accuracy of 70.67% on a test set of image pairs using K-Nearest-Neighbors. Finally, we present an evaluation of human performance on this problem.

Towards Robust Deconvolution in Medical Imaging: Informatics, Diagnosis and Treatment.

Ruogu Fang
PhD ThesisSchool of Electrical and Computer Engineering, Cornell University, August 2014.

Introduction

Deconvolution in medical imaging is an inverse process to estimate the hemodynamic parameters, such as the blood flow, blood volume and mean transit time, from the measurements in the medical images, as illustrated in Figure 1.1. A traditional machinery employed for estimating these hemodynamic parameters is to compute the impulse response function of each voxel independently using matrix factorization or curve fitting. For instance, singular value decomposition (SVD) is widely used in current commercial softwares to inverse the convolution process.

System and Method For Interactive Segmentation On Mobile Devices in a Cloud Computing Environment

Ruogu Fang,Leo Grady, Gianluca Paladini.
PatentsSiemens Corporation. Patent No: US20130272587 A1, WO2012027259 A2, WO2012027259 A3, approved on 4/19/2012.

Abstract

A mobile device (160) for medical image analysis is disclosed. The mobile device (160) includes a display (162), a communication module (218), a memory (204) configured to store processor-executable instructions (224) and a processor (202) in communication with the display (162), the communication module (218) and the memory (204). The processor (202) being configured to execute the processor-executable instructions (224) to implement a compression routine to generate a compressed representation of a medical image stored in the memory (204), transmit the compressed representation to a remote device (110) via the communication module (218), receive segmented results from the remote device (110), wherein the segmented results are derived from a reconstruction of the compressed representation generated at the remote device (110), and present, via the display (162), a segmented medical image based on the received segmented results.

Currrent Teaching

  • 2017 Spring

    CAP 5771 Principles of Data Mining

    >Data Mining is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. It has gradually matured as a discipline merging ideas from statistics, machine learning, database and etc. This is an introductory graduate course for master and PhD computer science students on the topic of Data Mining. Topics include data mining applications, knowing your data, data exploration, and various data mining techniques (such as classification, association, clustering, anomaly detection) and advanced topics in data mining.

  • 2017 Spring

    CAP 4770 Introduction to Data Mining

    Data Mining is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. It has gradually matured as a discipline merging ideas from statistics, machine learning, database and etc. This is an introductory course for junior/senior computer science undergraduate students on the topic of Data Mining. Topics include data mining applications, data preparation, data reduction and various data mining techniques (such as association, clustering, classification, anomaly detection).

Teaching History

  • 2016 Fall

    CAP 4770 Introduction to Data Mining

    Data Mining is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. It has gradually matured as a discipline merging ideas from statistics, machine learning, database and etc. This is an introductory course for junior/senior computer science undergraduate students on the topic of Data Mining. Topics include data mining applications, data preparation, data reduction and various data mining techniques (such as association, clustering, classification, anomaly detection).

  • 2016 Spring

    CAP 5610 Machine Learning

    Machine learning is concerned with the question of how to make computers learn from experience. The ability to learn is not only central to most aspects of intelligent behavior, but machine learning techniques have become key components of many software systems. For examples, machine learning techniques are used to create spam filters, to analyze customer purchase data, to understand natural language, or to detect fraudulent credit card transactions.

  • 2015 Fall

    CAP 4770 Introduction to Data Mining

    Data Mining is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. It has gradually matured as a discipline merging ideas from statistics, machine learning, database and etc. This is an introductory course for junior/senior computer science undergraduate students on the topic of Data Mining. Topics include data mining applications, data preparation, data reduction and various data mining techniques (such as association, clustering, classification, anomaly detection).

  • 2015 Spring

    CAP 5610 Machine Learning

    Machine learning is concerned with the question of how to make computers learn from experience. The ability to learn is not only central to most aspects of intelligent behavior, but machine learning techniques have become key components of many software systems. For examples, machine learning techniques are used to create spam filters, to analyze customer purchase data, to understand natural language, or to detect fraudulent credit card transactions.

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Florida International University
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