Faculty

Institution

Unit

Venue

Sanguthevar Rajasekaran

Board of Trustees Distinguished Professor Pratt & Whitney Chair Professor of CSE

University of Connecticut

Affiliations

College of Engineering

School of Computing

Sanguthevar Rajasekaran is currently serving as a College Of Engineering Board of Trustees Distinguished Professor Pratt & Whitney Chair Professor of CSE at University of Connecticut.

Honors

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Experiences

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Education

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Sanguthevar Rajasekaran

University of Connecticut

Board of Trustees Distinguished Professor Pratt & Whitney Chair Professor of CSE

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computer science machine learning data science bioinformatics parallel computing systems engineering applied mathematics data mining scientific computing pattern recognition informatics sorting algorithms distributed computing randomized algorithms computational biology efficient algorithms parallel processing mathematical optimization network routing theoretical computer science distributed systems sequence analysis data compression combinatorial optimization optimization problems distributed processing scheduling problems scheduling (computing) pattern discovery record linkage mesh optimization image compression motif search biomedical informatics computational optimization knowledge discovery information technology feature selection engineering optimization data privacy network routing algorithm routing protocols health monitoring underwater technologies nanobiotechnology sequence motif mesh networks massively-parallel computing parallel disk systems protein science vehicular technology discrete algorithms data security computational genomics pattern matching array processing structure discovery algorithm design efficient algorithm parallel computation frequent itemsets fast algorithm reliability engineering respiratory medicine array computing lossless compression wireless routing practical algorithms exact algorithms reconfigurable optical buses web system fastq files selection algorithms time efficient algorithms correlated component failures membership inference attack data-intensive computing computational science big data science computational intelligence memetic computing artificial intelligence high-performance data analytics multidisciplinary ai intelligent computing big data

Notable Scholarly Works

Notable Federal Grants

Collaborators

Randomized Feature Selection Based Semi-Supervised Latent Dirichlet Allocation for Microbiome Analysis. Scientific Reports (2024)

Namitha PaisNalini RavishankerSanguthevar RajasekaranGeorge WeinstockDong-Binh Tran

scientific computingsemi-supervised learningdata sciencemicrobiome analysisscientometricsscientific databioinformaticsfeature selectionhealth sciencesmachine learning

Supervised Dynamic Correlated Topic Model for Classifying Categorical Time Series. Algorithms (2024)

Namitha PaisNalini RavishankerSanguthevar Rajasekaran

computer sciencedata scienceclassification methodclusteringtopic modelsobject categorizationapplied mathematicscategorical time seriestime seriesautomatic classificationmachine learning

A Fast Privacy-Preserving Patient Record Linkage of Time Series Data. Scientific Reports (2023)

Ahmed SolimanSanguthevar RajasekaranPatrick TomanNalini Ravishanker

data preservationdata sciencedata privacytime series datatime serieshealth sciencesmachine learning

Automatic Subnetwork Search Through Dynamic Differentiable Neuron Pruning. (2023)

Zigeng WangBingbing LiXia XiaoTianyun ZhangMikhail A. BraginBing YanCaiwen DingSanguthevar Rajasekaran

computational neurosciencecyberneticsautomatic subnetwork searchneuroscienceneural computation

Identifying Suitable Attributes for Record Linkage Using Association Analysis. 2021 IEEE International Conference on Big Data (Big Data) (2023)

Nachiket DeoSanguthevar RajasekaranRany Kamel

record linkagedata sciencesuitable attributesmachine learning

KE: A Knowledge Enhancing Framework for Machine Learning Models. The Journal of Physical Chemistry A (2023)

Yijue WangNidhibahen ShahAhmed SolimanDan GuoSanguthevar Rajasekaran

computer sciencephysical chemistrymaterials sciencephysicsmachine learning

Latent Autoregressive Student-T Prior Process Models to Assess Impact of Interventions in Time Series. Future Internet (2023)

Patrick TomanNalini RavishankerNathan LallySanguthevar Rajasekaran

statisticslatent modelingfuture internetimpact assessmenttime series

Novel Blocking Techniques and Distance Metrics for Record Linkage. Lecture notes in computer science (2023)

Nachiket DeoJoyanta BasakAhmed SolimanDaniel WeinbergRebecca SteortsSanguthevar Rajasekaran

computer scienceinformation retrievaldata sciencerecord linkagecomputer engineeringdistance metricsmachine learning

On Computing the Jaro Similarity Between Two Strings. Lecture notes in computer science (2023)

Joyanta BasakAhmed SolimanNachiket DeoKenneth HaaseAnup MathurKrista ParkRebecca SteortsDaniel WeinbergSartaj SahniSanguthevar Rajasekaran

computer sciencejaro similaritystring processingstring theorypattern recognitiondata miningstring-searching algorithmtheory of computationmachine learning

Online Evidential Nearest Neighbour Classification for Internet of Things Time Series. International Statistical Review (2023)

Patrick TomanNalini RavishankerSanguthevar RajasekaranNathan Lally

data sciencetime seriesstatisticsinternet of thingsmachine learning

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Sanguthevar Rajasekaran

Board of Trustees Distinguished Professor Pratt & Whitney Chair Professor of CSE

University of Connecticut

Affiliations

College of Engineering

School of Computing

Sanguthevar Rajasekaran is currently serving as a College Of Engineering Board of Trustees Distinguished Professor Pratt & Whitney Chair Professor of CSE at University of Connecticut.

Honors

Create free account to unlock more features

Experiences

Create free account to unlock more features

Education

Create free account to unlock more features

Sanguthevar Rajasekaran

University of Connecticut

Board of Trustees Distinguished Professor Pratt & Whitney Chair Professor of CSE

You can click a keyword to filter the displayed items

Notable Scholarly Works

Notable Federal Grants

Collaborators

Randomized Feature Selection Based Semi-Supervised Latent Dirichlet Allocation for Microbiome Analysis. Scientific Reports (2024)

Namitha PaisNalini RavishankerSanguthevar RajasekaranGeorge WeinstockDong-Binh Tran

scientific computingsemi-supervised learningdata sciencemicrobiome analysisscientometricsscientific databioinformaticsfeature selectionhealth sciencesmachine learning

Supervised Dynamic Correlated Topic Model for Classifying Categorical Time Series. Algorithms (2024)

Namitha PaisNalini RavishankerSanguthevar Rajasekaran

computer sciencedata scienceclassification methodclusteringtopic modelsobject categorizationapplied mathematicscategorical time seriestime seriesautomatic classificationmachine learning

A Fast Privacy-Preserving Patient Record Linkage of Time Series Data. Scientific Reports (2023)

Ahmed SolimanSanguthevar RajasekaranPatrick TomanNalini Ravishanker

data preservationdata sciencedata privacytime series datatime serieshealth sciencesmachine learning

Automatic Subnetwork Search Through Dynamic Differentiable Neuron Pruning. (2023)

Zigeng WangBingbing LiXia XiaoTianyun ZhangMikhail A. BraginBing YanCaiwen DingSanguthevar Rajasekaran

computational neurosciencecyberneticsautomatic subnetwork searchneuroscienceneural computation

Identifying Suitable Attributes for Record Linkage Using Association Analysis. 2021 IEEE International Conference on Big Data (Big Data) (2023)

Nachiket DeoSanguthevar RajasekaranRany Kamel

record linkagedata sciencesuitable attributesmachine learning

KE: A Knowledge Enhancing Framework for Machine Learning Models. The Journal of Physical Chemistry A (2023)

Yijue WangNidhibahen ShahAhmed SolimanDan GuoSanguthevar Rajasekaran

computer sciencephysical chemistrymaterials sciencephysicsmachine learning

Latent Autoregressive Student-T Prior Process Models to Assess Impact of Interventions in Time Series. Future Internet (2023)

Patrick TomanNalini RavishankerNathan LallySanguthevar Rajasekaran

statisticslatent modelingfuture internetimpact assessmenttime series

Novel Blocking Techniques and Distance Metrics for Record Linkage. Lecture notes in computer science (2023)

Nachiket DeoJoyanta BasakAhmed SolimanDaniel WeinbergRebecca SteortsSanguthevar Rajasekaran

computer scienceinformation retrievaldata sciencerecord linkagecomputer engineeringdistance metricsmachine learning

On Computing the Jaro Similarity Between Two Strings. Lecture notes in computer science (2023)

Joyanta BasakAhmed SolimanNachiket DeoKenneth HaaseAnup MathurKrista ParkRebecca SteortsDaniel WeinbergSartaj SahniSanguthevar Rajasekaran

computer sciencejaro similaritystring processingstring theorypattern recognitiondata miningstring-searching algorithmtheory of computationmachine learning

Online Evidential Nearest Neighbour Classification for Internet of Things Time Series. International Statistical Review (2023)

Patrick TomanNalini RavishankerSanguthevar RajasekaranNathan Lally

data sciencetime seriesstatisticsinternet of thingsmachine learning

1
2
3
4
5
36
10 / page

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