Dr. Snehanshu Saha

Professor

I work in the intersection of Machine learning, Predictive Analytics and Computational Modeling and apply new insights and theory in Astronomy, Scientometrics, Finance and Healthcare. I have initiated a new focus group on Astroinformatics, supported by IEEE Computer Society Bangalore Section (Please visit our research group online at http://astrirg.org/). Classical problems in astronomy now involve the accumulation of large volumes of complex data with different formats and characteristic and cannot now be addressed using classical techniques. As a result, machine learning algorithms and data analytic techniques have exploded in importance, often without a mature understanding of the pitfalls in such studies. I lead the effort along with a handful of researchers in India and abroad. We plan to capture the baseline, set the tempo for future research in India and abroad and prepared a scholastic primer that would serve as a standard document for future research.

Computer Science

Undergraduate


Awards / Achievement
  • 17 Vice Chair-Development, International AstroStatistics Association, USA.
  • Featured Research in Astronomy and Machine learning • , recently covered in a brief news article appearing in one of the issues of Science Letter, Page # 1320. NewsRx, http://tinyurl.com/tjbaagy
  • IEEE Senior Member, Less than 7% of total members are senior members, India.
  • Outstanding reviewer, CRC Press, 600 USD Award, UK.
  • Outstanding reviewer, CRC Press, 600 USD Award, UK.
  • Best research paper award, SIoT.
  • The Catalan Teaching Tool- A learning Process Design for Supplemental Instruction and Learning Process Facilitation, published in IETE Journal of Education Vol 55 No 2, Jul-Dec 2014 issue, has been selected by the IETE Jury for the coveted IETE Journal Award for the Best Paper on topic of General Interest, Kolkata
  • Full fellowship-Graduate studies, Clemson University, Clemson, USA
  • Full fellowship-Graduate studies, Clemson University, Clemson, USA


Additional Information
  • Career Highlights: Senior Member-IEEE, Senior Member-ACM, Governing Council-International Astrostatistics Association Researchgate Score: 18.56, Citations: 215 Chair-Elect, IEEE Computer Society Bangalore Chapter Cutting-Edge Research: http://astrirg.org, sahascibase.org Developed new metrics for habitability in Exoplanets Publicly accessible technical contributions/code-bases/repositories: https://github.com/AstrIRG/psopy; A SciPy compatible super fast Python implementation for Particle Swarm Optimization, AJ Theo, S.Saha and S.Basak AstroInformatics CodeBases: https://github.com/orgs/AstrIRG/dashboard Open E-Book: Handbook of Machine Learning in Astronomy: A Workman's Manual, Free Copy hosted at http://astrirg.org/projects.html SciBase repository: http://sahascibase.org/datacenter Open Source Visualization app: http://sahascibase.org/vizkit https://github.com/orgs/NeuralFuzzy/dashboard Infant Scale - A Low-Birth-Weight Prediction App; Play store Link: https://play.google.com/store/apps/details?id=io.cordova.InfantScale1
  • Professor, 2011-Present 2017 Machine Learning and Computing, PES university, Bangalore, India.
  • Topics on Pure Mathematics to Undegraduate Students with Major in Mathematics and Computing, UT ElPaso, EL Paso, USA. FUNDING AGENCY UT El Paso
  • Topics on Applied Mathematics to Undegraduate Students with Major in Mathematics and Computing, UT ElPaso, EL Paso, USA. FUNDING AGENCY UT El Paso
  • Topics on Applied/Computational Mathematics to Undegraduate Students with Major in Mathematics and Computing, UT Arlington, Arlington, USA. FUNDING AGENCY UT Arlington
  • Topics on Applied Mathematics to Undegraduate Students with Major in Mathematics and Computing, Clemson university, Clemson, USA. FUNDING AGENCY Clemson University
  • I work in the intersection of Machine learning, Predictive Analytics and Computational Modeling and apply new insights and theory in Astronomy, Scientometrics, Finance and Healthcare. I have initiated a new focus group on Astroinformatics, supported by IEEE Computer Society Bangalore Section (Please visit our research group online at http://astrirg.org/). Classical problems in astronomy now involve the accumulation of large volumes of complex data with different formats and characteristic and cannot now be addressed using classical techniques. As a result, machine learning algorithms and data analytic techniques have exploded in importance, often without a mature understanding of the pitfalls in such studies. I lead the effort along with a handful of researchers in India and abroad. We plan to capture the baseline, set the tempo for future research in India and abroad and prepared a scholastic primer that would serve as a standard document for future research.
  • I work in the intersection of Machine learning, Predictive Analytics and Computational Modeling and apply new insights and theory in Astronomy, Scientometrics, Finance and Healthcare. I have initiated a new focus group on Astroinformatics, supported by IEEE Computer Society Bangalore Section (Please visit our research group online at http://astrirg.org/). Classical problems in astronomy now involve the accumulation of large volumes of complex data with different formats and characteristic and cannot now be addressed using classical techniques. As a result, machine learning algorithms and data analytic techniques have exploded in importance, often without a mature understanding of the pitfalls in such studies. I lead the effort along with a handful of researchers in India and abroad. We plan to capture the baseline, set the tempo for future research in India and abroad and prepared a scholastic primer that would serve as a standard document for future research.
  • develop a set of fundamentally correct thumb rules and experiments, backed by solid mathematical theory and render the marriage of astronomy and Machine Learning stability and far reaching impact. In particular, I have done this in the context of specific science problems of interest to the community: the classification of exoplanets, classification of nova, separation of stars, galaxies and quasars in the survey catalogs, and the classification of multi-wavelength sources. My work on developing new metrics on signs of life outside the solar system has been recognized in peer reviewed international forums.
Expertise / list of subjects handled
  • Professor, 2011-Present 2017 Machine Learning and Computing, PES university, Bangalore, India.
  • Topics on Pure Mathematics to Undegraduate Students with Major in Mathematics and Computing, UT ElPaso, EL Paso, USA. FUNDING AGENCY UT El Paso
  • Topics on Applied Mathematics to Undegraduate Students with Major in Mathematics and Computing, UT ElPaso, EL Paso, USA. FUNDING AGENCY UT El Paso
  • Topics on Applied/Computational Mathematics to Undegraduate Students with Major in Mathematics and Computing, UT Arlington, Arlington, USA. FUNDING AGENCY UT Arlington
  • Topics on Applied Mathematics to Undegraduate Students with Major in Mathematics and Computing, Clemson university, Clemson, USA. FUNDING AGENCY Clemson University
Research Interest
  • Astroinformatics Scientometric Modeling Machine Learning Big Data
  • Astroinformatics Scientometric Modeling Machine Learning Big Data
  • FinTech and Machine Learning

Books
  • Ordinary Differential Equations: A Structured Approach, 192 pages, Publisher: Cognella (June 24, 2011), ISBN-10: 160927704X
  • Handbook of Machine Learning in Astronomy: A Workman’s Manual, Free Copy hosted at http://astrirg.org/projects.html
  • Rahul Aedula, Yashasvi Madhukumar, Snehanshu Saha, Archana Mathur, Kakoli Bora and Surbhi Agrawal, L1 Norm SVD based Ranking Scheme: A Novel Method in Big Data Mining, AISC, Springer, 2018
  • Mohammed Viquar, Suryoday Basak, Ariruna Dasgupta, Surbhi Agrawal and Snehanshu Saha, Machine Learning in Astronomy: A Case Study in Quasar-Star Classification, Advances in Intelligent Systems and Computing, Springer, 2018
  • Anirvan Maiti, Hema Veeradhi, Snehanshu Saha, Thought Co-Relation: A Quantitative Approach to Classify EEG Data for Predictive Analysis, Progress in Advanced Computing and Intelligent Engineering, Springer, pp.127-136, January 2018.
  • Anirvan Maiti, Hema Veeradhi, Snehanshu Saha, Thought Co-Relation: A Quantitative Approach to Classify EEG Data for Predictive Analysis, Progress in Advanced Computing and Intelligent Engineering, Springer, pp.127-136, January 2018.
  • Ginde,G., Aedula,R., S.Saha., Mathur.A., S.Roy Dey, G.S.Sampatrao, B.S.DayaSagar, Big Data acquisition, preparation and analysis using Apache Software Foundation Projects, Big Data Analytics Tools, Technology for Effective Planning, Taylor and Francis, 2017
  • Kusuma.M and Saha.S, Machine Learning Methods as a test bed for EEG Analysis in BCI Paradigms, Handbook of Research on Applied Cybernetics and Systems Science (IGI Global), 2017
  • Sobin.C.C, Raychaudhury.V and Saha.S, A Survey of Parallel Community Detection Algorithms, Handbook of Research on Applied Cybernetics and Systems Science (IGI Global), 2017
  • Evidence of chaos in EEG signals: an application to BCI ; Kusuma, M.,Saha, S., K.Srikantamurthy, Advances in Chaos Theory and Intelligent Control, Studies in Fuzziness and Soft Computing, Vol 337, 2016, Springer-Verlag, Germany

Conferences
  • Manikandan R, Krishna Madgula and Snehanshu Saha, Cybersecurity Text Analysis using Convolutional Neural Network and Conditional Random Fields, NAACL-Human Language Technologies, 2018 (accepted)
  • Anisha R.Y., S. Roy. Dey., Saha. S., Early Prediction of LBW Cases via Minimum Error Rate classifier: A Statistical Machine Learning Approach, IEEE Smartcomp, Hong Kong, 2017
  • Snehanshu Saha, Abu Kurian, Harsha Aladi and Aparna Basu, Predictive Analytics for Safer Smart Cities, IEEE SmartTechCon, August 2017
  • Gambhire.S.S, Dey Roy.S, Sai.P, Goswami.B and Saha.S, A Study of Revenue Cost Dynamics in Large Data Centers: A Factorial Design Approach, ACM-ICC, 2017, Churchill College, ISBN: 978-1-4503-4774-7; Cambridge, UK.
  • Agarwal, B., Ravikumar.A., Saha, S, Big Data Veracity using Crowdsourcing Techniques and Bayesian MAP and Multinomial Predictors, ICMLA 2017, URL:http://ieeexplore.ieee.org/document/7838288/.
  • Agarwal, B., Ravikumar.A., Saha, S., A Novel Approach to Big Data Veracity using Crowdsourcing Techniques and Bayesian Predictors, ACM Compute 2016, Proceedings of the 9th Annual ACM India Conference, 153-160, doi>10.1145/2998476.2998498
  • Sobin C C, Raychoudhury.V and Saha, S., An Energy-efficient and Buffer-aware Routing Protocol for Opportunistic Smart Traffic Management, ; ICDCN’17

Journals
  • Snehanshu Saha & Jyotirmoy Sarkar, Internet Data Centers; The SAGE Encyclopedia of the Internet, 478-483, DOI: http://dx.doi.org/10.4135/9781473960367.n141, Ed. by Barney Warf
  • Sobin C.C, Vaskar Raychoudhury, Snehanshu Saha; Addressing Space-constraint driven Selfishness in Smart Opportunistic Environment, International Journal of Communication Systems (Wiley), 2018 (accepted)
  • Poulami Sarkar, Snehanshu Saha, Archana Mathur, Suryoday Basak, Model Visualization in understanding rapid growth of a journal in an emerging area, J. Scientometric Research (Wolter-Kluwer), 2018 (accepted)
  • Sandra Anil, Abu Kurien, Sudeepa Roy Dey, Snehanshu Saha, G.S. Sampatrao, GENEALOGY TREE: UNDERSTANDING ACADEMIC LINEAGE OF AUTHORS VIA ALGORITHMIC AND VISUAL ANALYSIS, J. Scientometric Research (Wolter-Kluwer), 2018 (accepted)
  • Snehanshu Saha, Suryoday Basak, Margarita Safonova, Kakoli Bora, Surbhi Agrawal, Poulami Sarkar and Jayant Murthy: Theoretical Validation of Potential Habitability via Analytical and Boosted Tree Methods: An Optimistic Study on Recently Discovered Exoplanets, Astronomy & Computing (Elsevier-I.F: 2.3), Vol. 23, pp 141-150, 2018, https://doi.org/10.1016/j.ascom.2018.03.003
  • Margarita Safonova, Snehanshu Saha, Jayant Murthy, Madhu Kashyap, C. Sivaram, Suryoday Basak, Surbhi Agrawal, Kakoli Bora, Pros and Cons of Classification of Exoplanets: in Search for the Right Habitability Metric, Astrobiology Newsletter, Vol 11, 2018.
  • Bidisha Goswami, Saibal Kar, Jyotirmoy Sarkar and Snehanshu Saha, Revenue Forecasting in Technological Services: Evidence from Large Data Centers, Economic Modelling, Elsevier (accepted).
  • Fidele Hategekimana, Snehanshu Saha, Anita Chaturvedi, DYNAMICS OF AMOEBIASIS TRANSMISSION: STABILITY AND SENSITIVITY ANALYSIS, Mathematics, MDPI, Vol 5
  • Kusuma,M.., Saha, S., Srikantamurthy.K., A Communication Paradigm Using Subvocalized Speech: Translating Brain Signals into Speech, Augmented Human Research, Springer , Vol 1, 1, 1-14, 2016
  • Jangid, N., Saha, S., Mathur, A., Narasimhamurthy, A., DSRS: Estimation and Forecasting of Journal Influence in the Science and Technology Domain via a Lightweight Quantitative Approach, Collnet J.Scientometrics and Information Management, Vol 10, #1, 41-70, 2016 (Taylor and Francis), DOI: 10.1080/09737766.2016.1177939
  • Hebbar, S., Saha, S., Mathur, A., Discrete path selection and entropy based sensor node failure detection in Wireless Sensor Networks, Cybernetics and Information Technologies (Bulgarian Academy of Sciences), Vol 16, 3, 137-153
  • Sarasvathi.V, Saha, S., N.Ch.S.N. Iyengar and Koti, M.;Coefficient of Restitution based Cross Layer Interference Aware Routing Protocol in Wireless Mesh Networks, IJCNIS; Vol 7 #3, 177-186, 2016
  • Kusuma.M, Saha,S., K.Srikantamurthy, G.M.Lingaraju, Distinct adoption of k-nearest neighbour and support vector machine in classifying EEG signals of mental tasks; Int. J. Intelligent Engineering Informatics, Vol. 3, No. 3, 313-329,2015(InderScience)
  • Sarasvathi V, N.Ch.S.N.Iyengar, Saha, S., QoS Guaranteed Intelligent Routing using Hybrid PSO-GA in Wireless Mesh Networks, CYBERNETICS AND INFORMATION TECHNOLOGIES, Volume 15, No 1, 69-83, 2015 (Bulgarian Academy of Sciences)
  • Suryoday Basak, Snehanshu Saha, Archana Mathur, Kakoli Bora, Simran Makhija, Margarita Safonova, Surbhi Agrawal; CESSA Meets Machine Learning: From Earth Similarity to Habitability Classification of Exoplanets, Astronomy and Computing (Elsevier–I.F: 3.1), 30, November 2019
  • Ankush Mishra, S. Saha, Simran Makhija, Sumana Sinha, Vaskar Raychoudhury and Sobin CC; Empirical study of dynamics of amoebiasis transmission in mobile ad-hoc networks (MANETs), International Journal of Communication Systems; (Wiley–I.F: 1.9), 32 (14), November 2019; DOI: 10.1002/dac.4186
  • Simran Makhija, S.Saha, Suryoday Basak, Mousumi Das; Separating Stars from Quasars: Machine Learning Investigation Using Photometric Data, Astronomy and Computing (Elsevier–I.F: 3.1), 29, September 2019, https://doi.org/10.1016/j.ascom.2019.100313
  • Archana Mathur, S.Saha,, Saibal Kar, Gouri Ginde & Ankit Sinha, SES-RREF: The Machine Learning Approach to Credible Metrics of Scholastic Evidence via Recursive Referencing; J.Scientometric Research (Wolter-Kluwer), Special Issue on Machine Learning, September 2019 (accepted)
  • Archana Mathur, S. Saha, Poulami Sarkar and Saibal Kar, Time Reversed Delay Differential Equation Based Modeling Of Journal Influence In An Emerging Area, Integrated Design and Process Science (IoS Press-I.F: 1.0) (in press).
  • Bidisha Goswami; Jyotirmoy Sarkar, Snehanshu Saha; Saibal Kar; Poulami Sarkar, ALVEC: Autoscaling by Lotka Volterra Elastic Cloud: A QoS aware Non Linear Dynamical Allocation Model, Simulation Modeling Practice and Theory (Elsevier–I.F: 2.8), 93 (3), 262-292, May 2019
  • Suryoday Basak, Saibal Kar, Snehanshu Saha, Luckyson Khaidem & Sudeepa Roy Dey, From Forecasting to Classification: Predicting the Direction of Stock Market Price Using Tree-Based Classifiers, North American Journal of Economics and Finance, Elsevier (I.F: 1.8); Volume 47, January 2019, 552-567

Others
  • Saha, Agrawal,et al., ASTROMLSKIT: A New Statistical Machine Learning Toolkit: A Platform for Data Analytics in Astronomy, 2015, http://arxiv.org/abs/1504.07865 Basak, Saha et al., Star Galaxy Separation using Adaboost and Asymmetric Adaboost, October 2016, DOI: 10.13140/RG.2.2.20538.59842, 10/2016
  • Saha, Agrawal,et al., ASTROMLSKIT: A New Statistical Machine Learning Toolkit: A Platform for Data Analytics in Astronomy, 2015, http://arxiv.org/abs/1504.07865 Basak, Saha et al., Star Galaxy Separation using Adaboost and Asymmetric Adaboost, October 2016, DOI: 10.13140/RG.2.2.20538.59842, 10/2016