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  1. Home
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  4. Vijayalakshmi Saravanan
Vijayalakshmi Saravanan
Faculty
Assistant Professor Computer Science
Bio Image for Faculty Member Vijayalakshmi Saravanan

Contact Us

211 UAS -
(605) 677-5388
Vijayalakshmi.Sarava@usd.edu

Dr. Vijayalakshmi Saravanan is currently working as Assistant Professor, Computer Science, at the University of South Dakota. Prior to this, she was a Visiting Assistant Professor, Computer Science, Vassar College, NY and an Adjunct Faculty at Rochester Institute of Technology, USA. Earlier she was a Postdoctoral Associate at University at Buffalo, SUNY, USA and University of Waterloo, Canada under the prestigious "Schlumberger Faculty for the Future" Fellowship award (2015-2017). She completed her Ph.D. under the prestigious Erasmus Mundus EU-Govt Fellowship award at Malardalen University, Sweden as a research exchange student. Prior to this, she was Assistant Professor of Practice at UTSA, USA. She is serving as a program committee member for reputed conferences & journals such as GHC, SIGCSE and Springer. Her research interests include Power-Aware Processor Design, Big Data and SW/HW Co-Design of MultiCore Architecture. She is also a lead editor for Pattern Recognition Letters (2020), Special Issue of Multimedia Tools and Applications (2020) and CRC Press Taylor & Francis, USA. She is a Senior Member of IEEE, ACM, ACM Distinguished speaker, CSI and Chair of IEEE WIE Affinity Group at VIT University during 2009-2015, Chair of NPA (National Postdoctoral Association) Annual Meetings and a Board Member of N2WOMEN. She is a recipient of SRP-HPC ECP Fellow.

Data Science, Big Data, Machine learning, Data Mining, Data Visualization, HPC, AI and Computer Architecture

Big Data, HPC, AI and Computer Architecture

  • PostDoc, Computer Science and Engineering, University of Waterloo, Canada, 2017
  • PostDoc, Computer Science and Engineering, University at Buffalo, USA, 2016
  • Ph D, Computer Science and Engineering, VIT, 2014
  • MS, Computer Science, M.S University, 2002
  • BS, Electrical and Electronics Engineering, Bharathiar University, 1999
  • AI for Everyone deeplearning.ai, deeplearning.ai
  • Postdoctoral Scholars Teaching Training for Academic Careers Certificate Program, University at Buffalo, USA
  • EMC certified "Data Scientist" professional; EMC verified certificate Data Science Associate (EMCDSA), EMC
  • EMC Data Lakes for Big Data , EMC

Citations listed below are presented in a standardized, modified format for display purposes only. They do not necessarily reflect the preferred style and conventions of the faculty member or discipline.

  • Saravanan, Vijayalakshmi, and Singh, Akansha. Machine Learning & Internet of Things (IoT) for Urban Intelligence. In Machine Learning & Internet of Things (IoT) for Urban Intelligence.
  • Saravanan, Vijayalakshmi, and Alagan, Anpalagan. Securing IoT and Big Data: Towards Next Generation Intelligence. In Securing IoT and Big Data: Towards Next Generation Intelligence.
  • US DoE (Department of Energy) SRP-HPC ECP Fellow, DoE, 2022
  • ACM Distinguished Speaker Program (DSP), ACM, 2020
  • Schlumberger Foundation "Faculty for the Future" PostDoc Award, Schlumberger, 2015
  • Senior Member, IEEE, IEEE, 2014
  • Erasmus-Mundus EU-Government Doctoral Fellowship, Erasmus-Mundus, 2009
  • IEEE-WIE(Women in Engineering), Chair, Affinity Groups, IEEE, 2009
  • Saravanan, Vijayalakshmi, An Exploration of an Enhanced Scheduling Algorithm Approach with Feasibility Analysis on a Single CPU System. Presented at the 15th IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC 2022), (January 2022)
  • Saravanan, Vijayalakshmi, ADCCF: Adaptive deep concatenation coder framework for visual question answering. Presented at the Elsevier, Pattern Recognition Letters (January 2022)
  • Saravanan, Vijayalakshmi, . Employing BERT-DCNN with sentic knowledge base for social media sentiment analysis. Presented at the J Ambient Intell Human Comput (January 2022)
  • Saravanan, Vijayalakshmi, Deep learning assisted convolutional auto-encoders framework for glaucoma detection and anterior visual pathway recognition from retinal fundus . Presented at the J Ambient Intell Human Computing (January 2022)
  • Saravanan, Vijayalakshmi, Predictive Analytics of Drug Discovery and Early Detection of Infections Using Deep Learning and AI. Presented at the SRP-HPC (January 2022)
  • Saravanan, Vijayalakshmi, Applying Artificial Intelligence to fundus photography for early detection of diabetic retinopathy . Presented at the USD IdeaFest 2022 (January 2022)
  • Saravanan, Vijayalakshmi, .A Hybrid CNN-LSTM: A Deep Learning Approach for Consumer Sentiment Analysis Using Qualitative User-Generated Contents. . Presented at the ACM Trans. Asian Low-Resour. Lang. Inf. Process (January 2021)
  • Saravanan, Vijayalakshmi, SACNN: self-attentive convolutional neural network model for natural language inference. . Presented at the ACM Transactions on Asian and Low-Resource Language Information Processing, 20(3), 1-16. (January 2021)
  • Saravanan, Vijayalakshmi, Mathematical Modelling of an Application Specific Processor Architecture with Power Optimization. Presented at the IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE (January 2021)
  • Saravanan, Vijayalakshmi, Sparse self-attentive network-based aspect-aware model for sentiment analysis.. Presented at the J Ambient Intell Human Computing (January 2021)
  • Saravanan, Vijayalakshmi, Creating Collision-Free Communication in IoT with 6G Using Multiple Machine Access Learning Collision Avoidance Protocol. Presented at the Mobile Netw Appl (January 2021)
  • Saravanan, Vijayalakshmi, An Efficient Optimizing Hybrid Deep Learning Model for Big Data in Healthcare and Urban Intelligence. Presented at the SRP-HPC (January 2021)
  • Saravanan, Vijayalakshmi, The optimized energy-efficient sensible edge processing model for the internet of vehicles in smart cities. Presented at the Sustainable Energy Technologies and Assessments (January 2021)
  • Saravanan, Vijayalakshmi, Machine learning for mobile network payment security evaluation system. Presented at the Transactions on Emerging Telecommunications Technologies (January 2021)
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