About Me
Dr. Suthaharan is a Professor of Computer Science at the University of North Carolina at Greensboro (UNCG). He joined UNCG in 2001 as an Assistant Professor, and promoted to Associate Professor in 2005, then promoted to Professor in 2014. He also served as Director of Undergraduate Studies for more than 10 years and as Interim Head in Fall 2015 at UNCG. He played a major role in leading the committee and maintaining ABET accreditation of the undergraduate program successfully. Dr. Suthaharan is also the author of the high impact and high quality textbook on the state-of-the-art topics of big data analytics and machine learning. Notably this book was reviewed by ACM Computing Reviews and received a “Reviewer Recommended” rating. He is also an inventor of the key management and encryption technology that has been patented in Australia, Japan, and Singapore. He also served as an elected Chair for the IEEE Central North Carolina section for two terms. Dr. Suthaharan’s research interests mainly fall under the state-of-the-art themes of big data and machine learning. He studies advanced mathematical, statistical, and computational techniques to formulate smart machine learning models and algorithms that can help accomplish secure big data analytics research in interdisciplinary settings.
“If you can’t explain it simply, you don’t understand it well enough.” — Albert Einstein
“Life gives to the givers and takes from the takers.” — Joe Polish
“An equation means nothing to me unless it expresses a thought of God.” — Srinivasa Ramanujan
Academic Positions
-
2014 - present Full Professor
Computer Science
UNC-Greensboro -
2005-2014 Associate Professor
Computer Science
UNC-Greensboro -
2001-2005 Assistant Professor
Computer Science
UNC-Greensboro -
1999-2001 Assistant Professor
Computer Science
Tennessee State University
Selected Honors and Awards
Dr. Suthaharan is a recipient of several awards, including the research awards from University of Pittsburgh Medical Center, Emory University, UC-Irvine, UC-Berkeley, University of Sydney-Australia, and University of Melbourne-Australia, and visited these highly prestigious universities to perform collaborative research.
Distinguished Speaker of the ACM (2018-2021)
Appointed a “Distinguished Speaker” by the Association for Computing Machinery (ACM) for 2018-2021 (https://speakers.acm.org/speakers/suthaharan_9163).
Visiting Scholar Award at UC-Berkeley (2013-2014)
Received this research award to study the perceptually inspired deep learning approach. Funded by the Department of Statistics, UC-Berkeley.
Faculty First Research Award at UNC-Greensboro (2018-2019)
Received this award to study big data characteristics of mixed fruits and vegetables for their automatic classification using machine learning approaches.
Research Assignment Award from UNCG (2019-2020)
Received this research award to study ophthalmic data science and machine learning problems at the University of Pittsburgh Medical Center.
Visiting research Professor Award from UPMC (2019-2020)
Received this research award from the UPMC Eye Center, University of Pittsburgh School of Medicine to develop smart machine learning for ophthalimic research.
Visiting Faculty Fellowship at Emory (2016-2017)
Received this research fellowship to study data privacy in fMRI data. Funded by the Institute for Quantitative Methods and Theory at Emory University.
Outstanding Faculty Mentor Award at UNCG (2018-2019)
Nominated by the department for the outstanding faculty mentor award presented by the Graduate School at UNC-Greensboro.
International Visiting Research Fellowship (2010-2011)
Received this research award to study data fusion problems in wireless sensor networks. Funded by the University of Sydney, Australia.
College Teaching Excellence Award (2018-2019)
Nominated by the department for the College Teaching Excellence Award presented by the College of Arts and Science at UNC-Greensboro.
Students Honors and Awards
Consistent success in advising students for the last 10 years!
Congratulations to Firoozeh Karimi! Our paper “An Enhanced Support Vector Machine Model for Urban Expansion Prediction” has been published in the Elsevier journal of Computers, Environment, and Urban Systems (2019).
Congratulations to Michael Ellis and Naseeb Thapaliya! Our poster for Stanford Compression Workshop 2019 titled “Illuminating privacy weaknesses in predictive models of fMRI data using compressed sensing and compressed learning” has been accepted (2019).
Congratulations to Vishali Vadakattu! Our paper “Feature Extraction using Apparent Power and Real Power for Smart Home Data Classification” has been published in the ICMLA 2018 conference proceedings (2018).
Congratulations to Shravya Yalamanchili! She received an award (course fellowship) to attend the short course on big data images processing and analysis (BigDIPA) offered by the Center for Complex Biological Systems, University of California Irvine (2017).
Congratulations to Tyler Wedell! He successfully defended his master’s thesis entitled “Feature Extraction and Feature Reduction for Spoken Letter Recognition,” directed by me. It has been published at https://libres.uncg.edu/ir/uncg/listing.aspx?styp=ti&id=19822 (2016).
Congratulations to Aswini Sen! She won an Outstanding Student Presentation Award in the graduate student category at the 12th Annual UNCG Regional Mathematics and Statistics Conference, on November 12, 2016. She presented our joint work on “Study of the Sensitivity of Supervised Classification Models Towards the Increased Complexity of Data” (2016).
Congratulations to Mokhaled Al-Hamadani! He presented our joint research work on the “Evaluation of The Performance of Deep Learning Techniques,” at the 2015 Graduate Research and Creativity Expo, on April 9, (2015).
Congratulations to Jeff Whitworth! He presented our joint work on “Security problems and challenges in a machine learning-based hybrid big data processing network systems at the ACM SIGMETRICS and it has been published in the ACM SIGMETRICS Performance Evaluation Review (2014).
Congratulations to Kiranmayi Kotipalli! She presented our joint research work on “Modeling of class imbalance using an empirical approach with spambase dataset and random forest classi fication”, now published in the proceedings of the ACM SIGITE/RIIT conference (2014).
Congratulations to Harry Rybacki! for presenting his research and senior project work entitled “An IPython Notebook Based Collaborative Platform for Ellipsoid Modeling Sensor Data,” (directed by me) at the 2014 State of North Carolina Undergraduate Research and Creativity Symposium in Raleigh (2014).
Congratulations to Laxmi Sunkara and Sweta Keshapagu! Our joint research work on “Lame Curve-based signature discovery learning technique for network traffic classi fication has been published in the proceedings of the Workshop on Signature Discovery for Intelligence and Security. IEEE International Conference on Intelligence and Security Informatics (2013).
Congratulations to Archana Polisetti! She won the Best Paper Presentation Awards in the graduate student category at the International Conference on Advances in Interdisciplinary Statistics and Combinatorics. She presented our joint research work on “Simultaneous Classification and Feature Selection for Intrusion Detection Systems” (2012).
Congratulations to Karthik Vinnakota! Our joint research work on “An approach for automatic selection of relevance features in intrusion detection systems” has been published in the proceedings of the International Conference on Security and Management (2011).
Congratulations to Mohammed Alzahrani! Our research work on “Labelled data collection for anomaly detection in wireless sensor networks has been published in the proceedings of the 6th International Conference on Intelligent Sensors, Sensor Networks and Information Processing (2010).
Congratulations to Abhinav Chawade! Our research work on “Energy ecient DNA-based scheduling scheme for wireless sensor networks has been pubslished in proceedings of the International Conference on Wireless Algorithms, Systems and Applications (Lecture Notes in Computer Science) (2009).
Congratulations to Surender Kumar! Our research work on “Measuring available bandwidth: pathChirps chirp train structure remodeled” has been published in the proceedings of the Australian Telecommunications Networks and Application Conference (2008).
Selected Publications
Dr. Suthaharan published more than 100 peer-reviewed articles in international journals and conference proceedings. He also presented his recent work on deep learning at Lawrence Berkeley National Lab, and presenting compressed machine learning work at Stanford Compression Workshop.
Research Projects
Dr. Suthaharan’s research interests fall predominantly under the state-of-the-art themes of big data analytics and machine learning. In big data analytics research, he studies various data characteristics — data heterogeneity, complexity, scalability, and unpredictability — of big data for extracting knowledge to understand the data source that produced the big data. In machine learning research, Dr. Suthaharan studies advanced mathematical, statistical, and computational techniques to formulate efficient machine learning models and algorithms that can help accomplish big data analytics research. His research includes the selection and optimization of hyperparameters of machine learning models using Bayesian analysis to make machine learning highly usable in big data analytics in interdisciplinary settings. Dr. Suthaharan is also interested in exploring software engineering models and designs to support big data analytics and machine learning research.
