CSI Faculty


Core Faculty

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Winship Distinguished Research Associate Professor​, Department of Computer Science
Research Areas: ​Information retrieval, web search, text and data mining, medical informatics, user behavior modeling.
Eugene's research spans the areas of information retrieval, natural language processing, data mining, and human computer interaction. Eugene is a Sloan Research Fellow, a past member of the DARPA Computer Science Study Group, recipient of best paper awards from SIGMOD, SIGIR, and WSDM conferences. and of the 2013 Karen Spark Jones award from the British Computer Society.

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Associate Professor, Department of Computer Science
Director of Graduate Studies in Computer Science and Informatics
Research Areas: Operating and Distributed Systems, High-Performance computing, Systems Sofware, Resilience/Fault-tolerance, HPC Tools
Extremely large (aka high-performance) computing systems have become critical instruments in theorlds grandest scientific and engineering challenges. Designed, built and managed by computer scientists and engineers, these systems' principal users are experts from other domains. Dorian studies the theory and practice of system software that makes large, complex computer systems accessible to computer systems non=experts - in particular the performance, scalability and reliability issues that abound in large scale computer environments.

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Imon Banerjee, Ph.D.
Assistant Professor​, Department of Biomedical Informatics​
Research Areas: Deep learning, natural language processing, high dimensional data analysis, longitudinal prediction modeling

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Associate Professor, Department of Computer Science​
Associate Director of Graduate Studies​ for MS
Research Areas: High Speed Networks, Multicast Communication, Mobile Networks, Queueing Theory, Performance Evaluation, Replica Control Methods.

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Assistant Professor, Department of Computer Science
Research Areas: Natural language processing, machine learning, text mining, medical informatics.
Dr. Choi has been active in the field of Natural Language Processing (NLP). He has presented many state-of-the-art NLP models that automatically derive various linguistic patterns from unstructured text. These models are publicly available through the cloud-based NLP platform called ELIT that Dr. Choi has created to promote academic and industrial research. He has also introduced novel machine comprehension models to identify personal entities and infer explicit and implicit contexts in multiparty dialogue, which can be used to build question answering systems on daily conversion. For medical informatics, Dr. Choi has developed innovative models to classify severity levels on radiology reports using deep neural networks and detect early stages of Alzheimer’s disease using meta-semantic analysis, which show similar accuracy as human experts in those domains.

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Associate Professor and Chair, Department of Biomedical Informatics
Associate Professor, Department of Biomedical Engineering @ Georgia Institute of Technology
Adjunct Faculty, Morehouse School of Medicine
Distinguished Guest Professor, Tsinghua University, Beijing, China
Deputy Editor, Physiological Measurement, Institute of Physics and Engineering
Research Areas: Data fusion, machine learning, mHealth, neural networks, resource-constrained scalable healthcare, signal processing, streaming data analytics, voting algorithms.
Signal processing, machine learning and physiological modeling to reduce costs, increase accuracy, and improve access in healthcare using high frequency multivariate data streams. Theoretical developments focus on building confidence intervals and trust metrics for fusing predictive algorithms and scaling analysis of medical data beyond conventional clinical capacity. Application areas include critical care, sleep & circadian rhythms, perinatal monitoring, and resource-constrained mHealth in the US & LMICs.

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Senior Lecturer, Department of Computer Science
Director of Undergraduate Studies (DUS)
Research Areas: Technology Enhanced Learning, Computer Science Education, Educational Technology, Intelligent Tutoring Systems, Educational Data Mining, Educational Assessment, Natural Language Processing.
I research and develop innovative technology based on Artificial Intelligence, Data Mining, and Natural Language Processing to support teaching and improve learning.

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Associate Professor, Department of Computer Science
Research Areas: Theory of computation: approximate subgraph optimization problems (such as metric traveling salesman), graph decomposition algorithms (spanners and separators), metric approximation, bioinformatic algorithms.

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Assistant Professor, Department of Computer Science
Research Areas: Data mining, machine learning, healthcare informatics, dimensionality reduction, interpretable models, electronic health records, computational phenotyping, tensor factorization.
My research focuses on the development of novel data mining and machine learning algorithms for healthcare applications. In particular, I am interested in building interpretable models using dimensionality reduction and modern time series analysis.

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Research Areas: Biomedical signal processing, machine learning, mobile health.
Dr. Li's research interests include multidimensional biomedical signal processing, advanced patient monitoring, artifact and noise analysis, machine learning, and large physiological database analysis.

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Associate Professor, Department of Computer Science
Research Areas: Applied artificial intelligence, language tools, data linking and integration.
My recent focus has been on applying artificial intelligence and language processing techniques to develop interactive tools for researchers in the health sciences. I am also interested in tools to assist in the composition process. ​

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Babak Mahmoudi, Ph.D.
Research Areas: Artificial intelligence, deep learning, reinforcement learning, optimization, neuromorphic computing, computational and systems neurosceince, neural interface systems, biomarker discovery
Dr. Mahmoudi's research is at the interface of machine learning, artificial intelligence and neuroscience my research is to better understand the information processing in the brain and restore normal function after injures or neuropsychiatric diseases by developing intelligent neural interface systems that continuously sense the dynamics of brain states and learn to optimally modulate those states to achieve a desired therapeutic or behavioral outcome.

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Associate Professor, Department of Computer Science
Director Of Undergraduate Studies (DUS)
Research Areas: Operating Systems, Networks, Open Systems.
My research focuses on tools to enhance visibility, debugging and performance in system programs and operating systems.

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Samuel Candler Dobbs Professor of Mathematics
Chair​, Department of Mathematics​
Research Areas: Numerical linear algebra, scientific computation, numerical solutions to discrete ill-posed problems, image processing.
Dr. Nagy's research expertise is in scientific computation, numerical linear algebra, and ill-posed inverse problems. He has done a substantial amount of work on the development of algorithms and software for image processing, including reconstruction, deblurring, and enhancement. His research has been funded by grants from the National Science Foundation (NSF), Air Force Office of Scientific Research (AFOSR) and the National Institutes of Health (NIH).

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Research Areas: Bioinformatics, Statistical Modeling, Epigenetics, Genomics, machine learning, sequence analysis
I have extensive experience in statistical modeling and computing with applications to statistical genetics and genomics. My recent research is focused on developing Bayesian model-based methods to analyze data generated from applications of next-generation sequencing technologies such as ChIP-seq, RNA-seq, Hi-C, WGBS, resequencing, and on developing software so that the methods can be easily adopted by the research community.. I am also actively collaborating with biomedical scientists and clinicians on projects that utilize next-generation sequencing technologies to better understand genomics and epigenomics.

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Assistant Professor, Department of Mathematics​
Research Areas: Numerical optimization, image registration, inverse problems, image reconstruction, numerical linear algebra, parallel computing.
My general field of interest is computational methods for inverse problems arising in medical and geophysical imaging. My research is interdisciplinary in nature and covers a variety of topics ranging from mathematical theory via design of numerical algorithms and efficient computational methods towards solving problems arising in real-world applications.

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Abeed Sarker, Ph.D.
Assistant Professor​, Department of Biomedical Informatics​
Research Areas: Natural Language Processing, Applied Machine Learning, Social Media Mining, Text Mining, Public Health Informatics, Medical Informatics

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Assistant Professor​, Department of Biomedical Informatics​
Research Areas: Imaging informatics, biomedical informatics, Distributed Systems, Containers, Radiogenomics, Data Fusion, Information Visualization, HPC.
Our lab focuses on developing novel systems that are used to manage, explore, integrate and process large (>1TB) biomedical, clinical, and imaging datasets, particularly those related to cancer. This is a highly collaborative effort and involves researchers from multiple disciplines and institutions. In the coming years, the research will make extensive use of Big Data systems such as Spark and Drill; multi-modal fusion of data; and novel systems to visualize and explore such diverse datasets.
Lab/Group Website:Sharma Lab​

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Associate Professor​, Department of Epidemiology
Assistant Professor​, Department of Biomedical Informatics​
Research Areas: Genomic Epidemiology of Cardiovascular Disease and Hypertension; Modeling of Complex Diseases: Machine learning and data mining, System and network analysis of complex disease phenotypes.
Dr. Sun's research focuses on the personalized and preventive health measures of chronic diseases across ethnic groups, to better understand the disease etiology and to improve the predictive modeling of the development, treatment and prevention of diseases. His research interests include novel study designs and applications using high-dimensional multi-omic analysis and phenomics approach.

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Samuel Candler Dobbs Professor of Computer Science
Chair​, Department of Computer Science
Director, Computational and Life Sciences Strategic Initiative
Research Areas: Distributed systems, high-performance computing, collaborative computing, data analytics.
Vaidy's research interests are in high performance and cloud computing, collaborative frameworks, and data science, with a focus on privacy and security. He is the principal architect of several software systems for metacomputing and collaboration, and his work is supported by grants from the National Science Foundation and the Air Force Office of Scientific Research.

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Research Areas: Numerical Analysis, Partial Differential Equations, Finite Elements, Computational Fluid Dynamics, Blood Flow Problems, Computational Electrocardiology.
Mathematical and numerical modeling of problems of real interest, with particular emphasis on cardiovascular diseases. Computational mechanics on real problems demands the most advanced numerical methods and parallel architectures. We work on image processing applied to cardiovascular sciences to perform massive simulations of patient-specific geometries for a fast and effective computation of quantities of medical interest and for the creation of decision-support tools.

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Assistant Professor, Department of Computer Science
Research Areas: Distributed systems, security, data replication, cloud computing, caching, epidemiology.
Ymir's research focuses on creating, improving and understanding the large-scale systems that organize and process information and help us communicate. He is particularly interested in socially motivated real-world problems that embody deep trade-offs within distributed data replication -- caching, live streaming and multicast -- and in security.
Ymir co-founded Syndis in 2013, a company that simulates sophisticated cyberattacks against large companies. His TEDx talk on "Why I teach people how to hack"has received over 1,000,000 views on YouTube. Ymir's software has been used in cloud products at IBM, databases at Yahoo! and at other major companies. He holds four patents.

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Research Areas: Disease Surveillance, Public Health Preparedness and Response, Safe Water and Sanitation, Statistical Modeling, Spatial Analysis/GIS
Professor Waller's research involves the development and application of statistical methods for spatially referenced data including applications in environmental justice, neurology, epidemiology, disease surveillance, conservation biology, and disease ecology. He has published in a variety of biostatistical, statistical, environmental health, and ecology journals and is co-author with Carol Gotway of the text Applied Spatial Statistics for Public Health Data (2004, Wiley).

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Assistant Professor, Department of Computer Science​
Research Areas: Storage systems, systems optimization, data mining, archival storage, reliability, computational neuroscience.
Whereas computer scientists have defined how to arrange storage to meet specific metrics such as fault tolerance and access speed, in neuroscience the metrics are observable but the system unknown. I am working to model information in the brain as a storage problem to better learn how we collect and interpret signals from our world, working towards a robust fault tolerance model for the brain.

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Hao Wu, Ph.D.
Research Areas: Biostatistics, Bioinformatics
My research has been mainly focused on bioinformatics and computational biology. I'm particularly interested in developing statistical methods and computational tools for interpreting large scale genomic data from high-throughput technologies such as microarrays and second generation sequencing. I am also interested in general machine learning, pattern recognition and large scale data mining methods with applications to biological and medical data.
Lab/Group Website: Hao Wu Lab​

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Research Areas: Data privacy and security, spatiotemporal data management, biomedical informatics.
The overarching objective of my research is to enable secure and privacy-preserving data sharing for social good. We develop models and tools that address both fundamental and applied questions at the interface of data privacy and security, data management, and health informatics.
Lab/Group Website:AIMS Lab page

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Tianwei Yu, Ph.D.
My research is focused on bioinformatics.
Groups: Metabolomics, Pharmacogenomics, Systems Biology

Affiliated Faculty

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Research Areas: Machine learning, image analysis, computer vision, convolutional networks, genomics, bioinformatics, high performance computing, human computer interaction.
The Cancer Data Science lab explores how learning algorithms can be used to improve basic cancer research and clinical care. We develop machine learning and image analysis methods to analyze and integrate imaging, genomic and clinical data with the aims of improving the precision of medical interventions and gaining insights into disease mechanisms.
Lab/Group Website: Cooper Lab

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Research Areas: Whole-Slide Microscopy Image Analysis, Biomedical Image Analysis, Computer-Aided Diagnosis, Machine Learning and Pattern Recognition, Objection Representation, Oncology Translational Research, Bioimage Informaitcs, Integration of Imaging Data and Genomics.
Dr. Kong's research interests include biomedical image analysis, computer-aid diagnosis, machine learning, 2D/3D whole-slide microscopy image processing, computer vision, bioimaging informatics, and signal processing for large-scale biomedical translational research. He has established multiple Computer-aided Diagnosis systems and quantitative data integration methods for different cancer diseases.
Groups: Biomedical Informatics​, Machine Learning

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Director of Predictive Health Analytics, University of California San Diego​​
Assistant Professor, Department of Biomedical Informatics​​
Research Areas: Machine learning, deep neural networks, reinforcement learning, time series analysis, optimization, physiological control systems, computational neuroscience, deep brain stimulation.
My research brings together concepts and tools across signal processing, information theory, control theory, optimization, and machine learning (ML) to design physiologically-inspired models and predictive analytic algorithms. A major focus of my ongoing research is in the intensive care unit (ICU) where my team is developing advanced ML algorithms capable of summarizing large volumes of continuously measured patient data, with the goal of prediction of life threatening clinical events and risk assessment.