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  • 12:14 AM, Thursday, 26 Nov 2020

Department of Mathematics
     
Sumitra S., Ph.D.
Associate Professor
 
Office
Tel:+91-471-2568657
Fax:
Email:sumitra@iist.ac.in













Education
  • Ph.D. in Machine Learning, Department of Automatic Control & Systems Engineering, The University of Sheffield, UK.
  • M.Tech. in Computer and Information Science, Department of Computer ScienceCochin University of Science and Technology, India.
  • M.Sc. in Mathematics, Department of Mathematics, Cochin University of Science and Technology, India.

Experience
  • Researcher, Center for Environmental Implications of Nanotechnology, The University of California, Los Angeles, US.
  • Researcher, Terry Fox Laboratory, British Columbia Cancer Research Center, Vancouver, Canada.
  • Researcher, INSERM/U887, UFR STAPS, University of Burgundy, Dijon, France.

Research Work / Area

I am interested in the development of theoretical frame work for Machine Learning algorithms and its application to real world problems. 

Ph. D.
  1. Asif Salim. Graph Data (Started: August 2016)
  2. Shiju S.S. Formulation of Multiple Kernel Learning Using Composite Architectures (Completed: 2019)
M. Tech.
  1. Adarsh K. Disentanglement of Dependent Causal Factors (2019)
  2. Jitendra Kumar Kushwaha. Question Image Co-attention by Low-Rank Bilinear Model for Visual Question Answering (2019)
  3. Karthika S. Dual Attention RNN for Multitask Learning of Time Series Data (2019)
  4. Navneet Agarwal. Variational Inference based Mini-batch Learning for Graphs (2019)
  5. V S Silpa. GAN based approaches for Active Learning (2019)
  6. Bhartendu Thakur. Deep-RL Models for Autonomous Driving (2018)
  7. Nasibullah. Video to text in the Wild (2018)
  8. Rahul Vashisht. Study of Uncertainty in Machine Learning using Bayesian Neural Networks and Gaussian Processes (2018)
  9. H.  Viji. Deep Learning Based Damage Assessment for Structural Health Monitoring (2018)
  10. Niranjan GS. Topological Data Analysis (2017)
  11. Asif Salim. Graph Kernels and Multi-view Learning (2016)
  12. Akhil P. M. Deep Multi-layer Kernel Machines (2016)
  13. Aravindh A. Kernel Online Multi-task Learning ( 2015)                        
  14. Mohan Kashyap. P. Business Event Recognition from Online News Articles (Internal Supervisor) ( 2015)
  15. Manusubramanian S. Data Mining System for Post Flight Launch Vehicle Performance Analysis (2014)
  16. Jaison John K. Integrated Anomaly Detection and Diagnostics System for Satellite Launch Vehicle (2012)
  17. Narayana Rao G  S. Checkout Software for Remote Sensing Payloads (2012)
B. Tech.
  1. Pratik Wankhede. 3D Brain Tumour Segmentation: An overview and approach with deep learning (2019)
  2. Shubhankur Biswas. Text Classification using Graph based Semi Supervised Learning (2019)
  3. Konatham Samuel and Jitendra Kumar Dayma. Page Ranking and Search Implementation (2014)

 

  1. MA873 Graphical Models and Deep Learning (M.Tech., Jan - April  2020)
  2. MA624 Advanced Machine Learning (M.Tech., Jan - April  2020)
  3. MA618 Foundations of Machine Learning (Shared course) (M.Tech., Aug – Nov 2019)
  4. MA613 Data Mining (M.Tech., Aug – Nov 2019)
  5. MA624 Advanced Machine Learning (M.Tech., Jan - April  2019)
  6. MA871 Advanced Kernel Methods (M.Tech., Jan - April  2019)
  7. MA618 Foundations of Machine Learning (Shared course) (M.Tech., Aug – Nov 2018)
  8. MA613 Data Mining (M.Tech., Aug – Nov 2018)
  9. MA841 Advanced Learning Models (M.Tech., Jan - April  2018)
  10. MA622 Pattern Recognition and Machine Learning (M.Tech., Jan - April  2018)
  11. MA613 Data Mining (M.Tech., Aug – Nov 2017)
  12. MA867 Reinforcement Learning (M.Tech., Jan - April 2017)
  13. MA622 Pattern Recognition and Machine Learning (M.Tech., Jan - April  2017)
  14. MA613 Data Mining (M.Tech., Aug – Nov 2016)
  15. MA867 Reinforcement Learning (M.Tech., Jan - April 2016)
  16. MA622 Pattern Recognition and Machine Learning (M.Tech., Jan - April  2016)
  17. MA613 Data Mining (M.Tech., Aug – Nov 2015)
  18. CHM613 Mathematical Modeling and Simulation (Topic: Mathematical Modeling) (M.Tech., Aug – Nov 2015)
  19. MA867 Reinforcement Learning (M.Tech., Jan – April 2015)
  20. MA622 Pattern Recognition and Machine Learning (M.Tech., Jan – April 2015)
  21. MA613 Data Mining (M.Tech., Aug – Nov 2014)
  22. CHM613 Mathematical Modeling and Simulation (Topic: Mathematical Modeling) (M.Tech., Aug – Nov 2014)
  23. MA622 Pattern Recognition and Machine Learning (M.Tech., Jan – April 2014)
  24. MA613 Data Mining (M.Tech., Aug – Nov 2013)
  25. AV215 Computer Organization and DBMS (Topic: DBMS) (B.Tech., July – Nov2013)
  26. MA643 Data Mining (M.Tech., Jan – April 2013)
  27. MA311 Probability & Statistics (B.Tech., July – Nov 2012)
  28. MA614 Pattern Recognition and Machine Learning (M.Tech., Aug – Nov 2012)
  29. MA643 Data Mining (M.Tech., Feb – May 2012)
  30. MA614 Pattern Recognition and Machine Learning (M.Tech., Sep – Dec 2011)
  31. MA869 Intelligent Agents (M.Tech., Feb – May 2011)
Journals
  1. Salim A., Shiju S.S., Sumitra S. (2020).  Design of multi-view graph embedding using multiple kernel learning. Engineering Applications of Artificial Intelligence 90,  https://authors.elsevier.com/a/1abK43OWJ8wVf9.
  2.  Aravindh A., Shiju S.S. and  Sumitra S. (2019).  Kernel Collaborative Online Algorithms for Multi-Task Learning.  Ann Math Artif Intell  86269–286  doi:10.1007/s10472-019-09650-w.
  3. Shiju S. S and Sumitra S. (2017).  Multiple Kernel Learning using Single Stage Function Approximation for Binary Classification Problems. International Journal of Systems Science, 48(16): pp. 3569-3580, doi:10.1080/00207721.2017.1381892.
  4. Shiju S. S, Asif Salim and Sumitra S. (2017).  Multiple Kernel Learning using Composite Kernel Functions. Engineering Applications of Artificial Intelligence, 64, 391-400.
  5. Sumitra S. Nair and T. J. Todd. (2015). Supervised Pre-clustering for Sparse Regression. International Journal of Systems Science, 46(7): pp. 1161-1171.
  6. R. Liu, R. Rallo, S.George, Z. Ji, Sumitra. Nair, A. E. Nel, and Y. Cohen (2011). Classification NanoSAR Development for Cytotoxicity of Metal Oxide Nanoparticles. Small 7 (8), 1118-1126.
  7. R. Rallo, B. France, R. Liu, Sumitra. Nair, S. George, R. Damoiseaux, F. Giralt, A. E. Nel, K. A. Bradley, and Y. Cohen (2011). Self-Organizing Map Analysis of Toxicity-Related Cell Signaling Pathways for Metal and Metal Oxide Nanoparticles. Environmental Science & Technology, 45(4): 1695-1702.
  8. T. J. Dodd , Sumitra. Nair, and R. F. Harrison (2010). The Effect of the Order of Parameterisation in Gradient Learning for Kernel Methods. IET Control Theory and Applications, 4(10), 2141-2151.
  9. Sumitra. Nair, R. French, D. Laroche, and E. Thomas (2010). The Application of Machine Learning Algorithms to the Analysis of Electromyographic Patterns from Arthritic Patients IEEE Transactions on Neural Systems & Rehabilitation Engineering , Vol 18, No. 2, pp-174-184.
Conference Papers
  1. Rahul Vashisht, H.Viji, T.Sundarajan, D.Mohankumar, S.Sumitra. (2018).  Structural Health Monitoring of Cantilever Beam, A case study – using Bayesian Neural Networks and Deep Learning. 2nd International Conference on Structural Integrity (ICONS).
  2. Salim A., Shiju S.S., Sumitra S. (2017).  Effectiveness of Representation and Length Variation of Shortest Paths in Graph Classification. In: Shankar B., Ghosh K., Mandal D., Ray S., Zhang D., Pal S. (eds) Pattern Recognition and Machine Intelligence. PReMI 2017. Lecture Notes in Computer Science, vol 10597. Springer, Cham.
  3. Shiju S.S., Salim A., Sumitra S. (2017).  Formulation of Two Stage Multiple Kernel Learning Using Regression Framework. In: Shankar B., Ghosh K., Mandal D., Ray S., Zhang D., Pal S. (eds) Pattern Recognition and Machine Intelligence. PReMI 2017. Lecture Notes in Computer Science, vol 10597. Springer, Cham. [Received Best Student Presentation Award from Springer].
  4.  S. Sumitra and A. Aravindh. Kernel online multi-task learning (2016). In  Computational Intelligence, Cyber Security and Computational Models, volume 412 of Advances in Intelligent Systems and Computing, pages 55-64. Springer Singapore.
  5.  Manu Subramanian S, Jishy Samuel and Sumitra S (2014). Comparative study of similarity measures for launch vehicle telemetry data. Proc. of IEEE International Conference on Information, Communication and Embedded Systems (ICICES) , Chennai , pp 253-258.
  6.  T. J. Dodd, Sumitra. Nair, and R. F. Harrison (2005). Gradient Based Methods: Functional vs Parametric Forms.Proceedings of the 16th IFAC World Congress Prague.
Ph.D. Thesis
  • Function Estimation Using Kernel Methods for Large Data Sets (2007), Department of Automatic Control & Systems Engineering, The University of Sheffield, UK.

Google Scholar


Invited Talks
      1. Introduction to Machine Learning: Talk delivered in "National Workshop on Deep Learning” (NWDL'2020)", Organized by Department of Computer Science and Research Centre, University of Kerala, Kariavattom, Thiruvananthapuram, on 13 January 2020.
      2. Machine Learning and Neural Networks: Talk delivered in "KTU Sponsered FDP on Computer Vision and Machine Learning", Organized by Department of Computer Science and Engineering and Department of Information Technology College of Engineering, Perumon, on 24 July 2019.
      3. Machine Learning and its Applications:  Talk delivered in "AICTE-QIP Sponsered One Week Short Term Course on  Research Issues and Challenges in Data Science and Big data Analytics", Organized by Department of Information Technology, Thiagarajar College of Engineering, Madurai, on 20 March 2019.
      4. Advanced Machine Learning: Talk delivered in "AICTE Sponsered 5 Day Workshop on Artificial Intelligence and Machine Learning", Organized by Additional Skill Acquisition Programme (ASAP),  at Barton Hill College of Engineering, Trivandrum, on 25 February, 2019.
      5. Machine Learning: Talk delivered in "UGC Sponsered Refresher Course in Mathematics and Statistics", Organized by the UGC-Human Resource Development Center, Bharathiar University, Coimbatore, on 23 February  2019.
      6. Fundamental Algorithms in Machine Learning and its Research Perspectives:  Talk delivered in "Faculty Development Programme on Research Trends in Biomedical and Satellite Image Processing", Organized by Department of Computer Science & Engineering, TKM College of Engineering, Kollam, on 04 January 2019.
      7. Basic Machine Learning Algorithms:  Talk delivered in "AICTE-ISTE  Sponsered One Week Refresher Programme on Deep Learning", Organized by Department of Computer Science & Engineering, College of Engineering, Muttathara, Thiruvananthapuram, on 12 December 2018.
      8. Machine Learning Algorithms: Talk delivered in "TEQIP Two day Workshop on Recent Trends and Research Challenges in Deep Learning", Organized by Thiagarajar College of Engineering, Madurai, on 30 August 2018.
      9. Kernel  Algorithms: Talk delivered in "Training Programme on Artificial Intelligence", Organized by VSSC, Thiruvananthapuram, on 20 August 2018.
      10. Supervised and Unsupervised Learning: Talk delivered in "Training Programme on Artificial Intelligence", Organized by VSSC, Thiruvananthapuram, on 16 August 2018.
      11. Regression Algorithms: Talk delivered in "Faculty Development Program on Artificial Intelligence and Machine Learning", Organized by Electronics and Communication Department, Mohandas College of Engineering, Thiruvananthapuram, on 17 July 2018.
      12. Mathematical Foundations of Machine Learning Algorithms:  Talk delivered in "Faculty Development Program on Machine Learning", Organized by Department of Computer Science, St. Thomas College of Engineering & Technology, Kozhuvalloor - Chengannur, on 13 July 2018.
      13. Kernel Deep Learning: Talk delivered in "Short Term Course on Deep Learning and Applications", Organized by Center for Interdisciplinary Research, College of Engineering, Trivandrum, on 19 April 2018.
      14. Learning with Data: Talk delivered in "Workshop on Statistical Methods for (Astro) Physics", Organized by Pure & Applied Physics, Mahatma Gandhi University, on 16 March 2018.
      15. Kernel Methods: Talk delivered in the "National Seminar on Machine Intelligence", Organized by Department of Computer Science, University of Kerala, on 28 March 2017.
      16. Classification Algorithms:  Talk delivered in the TEQIP II Sponsored Faculty Development Programme  on  "Recent Trends in Signal Processing", Organized by Department of Electronics and Communication Engineering, College of Engineering, Cherthala, on 3 March 2017.
      17. Support Vector Machines:  Talk delivered in the TEQIP II Sponsored Research Colloquium  on  "Recent Advances in Soft Computing", Organized by Department of Computer Science & Engineering and Information Technology,  College of Engineering, Kidangoor, on 27 February 2017.
      18. Introduction to Data Mining:  Talk delivered in the TEQIP II Sponsored Faculty Development Programme  on  "Mathematics for Engineers", Organized by Department of Applied Science,  College of Engineering, Adoor, on 20 January 2017.
      19. Pattern Recognition & Machine Learning Methods for Image Processing: 
        Talk delivered in the  Faculty Training Programme on "Tools and Techniques In Image Processing", Organized by Department of Computer Engineering, College of Engineering,  Chengannur, on 18 January 2017.
      20. The Framework of Kernel Methods: Talk delivered in the National Workshop on "Machine Learning and Big Data", Organized by Department of Computer  Science,  Amrita School of Engineering, Amritapuri Campus,  on 09 June 2016.
      21. Optimization Techniques in Machine Learning: Talk delivered in the TEQIP II Sponsored Faculty Development Programme  on  "Contemporary Developments in Optimization Techniques and its Applications", Organized by Department of Computer  Application and  Department of Electrical & Electronics Engineering,  TKM College of Engineering, Kollam,  on 20 May 2016.
      22. Mathematics of Kernel Methods: Talk delivered in the TEQIP II Sponsored Short Term  Training Programme in "Mathematical Models in Data Mining", Organized by Division of Applied Sciences & Humanities, School of Engineering,  Cochin University of Science and Technology, on 04 April 2016.
      23. Linear Algebra Applications in Computer Vision
        Talk delivered in the Workshop on "Computer Vision: Techniques & Applications", Organized by Department of Computer Science & Engineering,College of Engineering, Karunagappally, on 17 March 2016.
      24. Supervised Learning Algorithms: Talk delivered in the TEQIP II Sponsored  Faculty Training Programme on "Advancements And Algorithms In Image Processing", Organized by Department of Computer Science & Engineering,College of Engineering,  Karunagappally, on 28 January 2016.
      25. Theory of Kernel Methods: Talk delivered in the  AICTE sponsered 14 Days Summer School Faculty Development Training Programme on “Soft Computing Techniques for the Engineering Research and its Applications”(SCTERA’15) , Organized by Department of Electronics and Communication Engineering, Sri Ramakrishna Engineering College, Coimbatore, 12 June 2015.
      26. Applications of Machine Learning in the field of Biomedical Engineering: Talk delivered in the   "National Seminar on Transforms and Medical Data Interpretation", Organized by Department of Biomedical Engineering, Sri Ramakrishna Engineering College, Coimbatore, 18 February, 2015.
      27. Applications of Machine Learning in the field of Image Processing: Talk delivered in the  "Short term training  programme on Digital Image Processing", Organized by Department of Computer Science and Engineering, Rajiv Gandhi Institute of Technology, Kottayam, 05 – 09 January 2015.
      28. Regression prediction model,: Talk delivered in the "Short term training  programme on Predictive Analytics", Organized by Department of Computer Science and Engineering, Rajiv Gandhi Institute of Technology, Kottayam, 25 – 27 June 2014.
      29. Methods for Knowledge Extraction: Talk delivered in the "Faculty Development Programme on Soft Computing", Organized by Department of Computer Science, Government Engineering College, Sreekrishnapuram, 09 – 11 January 2014.
      30. Algorithms for Mining the Web: Talk delivered in the "National Seminar on Computing and Communication", Organized by School of Computer Sciences, Mahatma Gandhi University, 13 December 2013.
      31. Introduction to Machine Learning Algorithms: Talk delivered in the  "Five-day Course on  Spatial Statistical Tools in Data Processing and Analysis", Organized by Systems Science & Informatics Unit, Indian Statistical Institute, Bangalore, 26 – 30 November 2012.
      32. RKHS Methods in Machine Learning: Talk delivered in the  "National Conference on Applied Linear Algebra and Transform Techniques", Organized by Department of Sciences and Humanities, Mar Baselios College of Engineering and Technology, Thiruvananthapuram, 10 – 11 July 2012.
      33. Function Approximation in RKHS: Talk delivered in the "National Conference on Mathematics of Soft Computing", Organized by Department of Mathematics, National Institute of Technology, Calicut, 5 – 7, July 2012.
      34. Introduction to Machine Learning: Talk delivered in the  "Short Term Course on Soft and Evolutionary Computing", Organized by Department of Avionics, Indian Institute of Space Science and Technology,  Thiruvananthapuram, 19 – 21, December 2011.


Session Chair

    1. Session on Computing, International Conference on Computing, Communication and Signal Processing (ICCCSP-2016),  organized by the Department of Electronics and Communication Engineering and the Department of Computer Science and Engineering of College of Engineering  Karunagappally, 8 July 2016.
    2. Women in Computing Symposium, IEEE International Conference on Recent Advances in Intelligent Computational Systems 2015, 10 December 2015.
    3. Session on Big Data, IEEE International Conference on Recent Advances in Intelligent Computational Systems 2013, 20 December 2013.

 

Interested candidates may send  the resume and research statement.