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Anivilla Shashank

Assistant Professor (OC), School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore

Qualification: B. Tech., M.Tech.
a_shashank@cb.amrita.edu
Orcid Profile
Research Interest: Geo-AI & Remote Sensing Analytics, Hydrological Modelling & Disaster Risk Assessment, Generative Deep Learning & Ensemble Architectures, Applied AI (Cybersecurity, Biomedical & Ecological Mapping),Advanced Image Interpretation & Time-Series Analysis.

Bio Publications Qualification Experience Teaching Workshops
Bio

Shashank Anivilla is an Assistant Professor (On Contract) and Ph.D. Research Scholar at Amrita Vishwa Vidyapeetham, Coimbatore, specializing in Artificial Intelligence and Hydrological Modelling in Amrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham. His doctoral research focuses on developing advanced AI-driven hydrological forecasting systems, integrating remote sensing indicators and flood-vulnerability modelling. His thesis is titled “Hydrological and Data-Driven Approaches for Flood Vulnerability and Risk Assessment.” He has designed high-performance streamflow prediction frameworks utilizing Transformer, BiGRU, and ensemble architectures, and contributed to the development of MTV19ANet, a multi-tier deep learning model for flood early-warning systems. His work validates these architectures by integrating geospatial predictors and climate forcing variables from multi-continental CAMELS basins.

Beyond hydrological forecasting, his interdisciplinary expertise spans applied AI for cybersecurity and ecological monitoring. He has contributed to the development of deep convolutional neural network-based intrusion detection systems (DCNN-IDS) and explored frameworks for handling imbalanced data in network security environments. Additionally, his research in advanced image analysis includes developing semantic segmentation workflows using Conditional Generative Adversarial Networks (C-GAN) to detect epiphytes in forest

He has published in Water Resources Management, ISPRS Archives, and several Springer and IEEE conference proceedings.

Publications

Conference Paper

Year : 2025

Deep Learning-Driven MRI Stratification for Intracranial Tumor Subtype Resolution

Cite this Research Publication : K. B. Sivachandra, P. Geetha, Kritesh Kumar Gupta, A. Shashank, Deep Learning-Driven MRI Stratification for Intracranial Tumor Subtype Resolution, Lecture Notes in Networks and Systems, Springer Nature Singapore, 2025, https://doi.org/10.1007/978-981-95-0681-1_12

Publisher : Springer Nature Singapore

Year : 2025

Deciphering the Intricacies of Pancreatic Malignancy Through Advanced Deep Learning Methodologies

Cite this Research Publication : C. M. Rohith, P. Geetha, Kritesh Kumar Gupta, A. Shashank, Deciphering the Intricacies of Pancreatic Malignancy Through Advanced Deep Learning Methodologies, IFIP Advances in Information and Communication Technology, Springer Nature Switzerland, 2025, https://doi.org/10.1007/978-3-031-98356-6_15

Publisher : Springer Nature Switzerland

Year : 2025

Optimizing Terrain Classification with Multi-spectral Imaging Using Machine Learning Models

Cite this Research Publication : Parthvi Manoj, Munnangi Pranish Kumar, K. Satya Narayana Reddy, Geetha Parameswaran, Shashank Anivilla, Optimizing Terrain Classification with Multi-spectral Imaging Using Machine Learning Models, IFIP Advances in Information and Communication Technology, Springer Nature Switzerland, 2025, https://doi.org/10.1007/978-3-031-98356-6_18

Publisher : Springer Nature Switzerland

Year : 2020

DCNN-IDS : Deep Convolutional Neural Network based Intrusion Detection System

Cite this Research Publication : Sriram Srinivasan, Shashank A, vinayakumar R, Soman KP, DCNN-IDS : Deep Convolutional Neural Network based Intrusion Detection System, null, Institute of Electrical and Electronics Engineers (IEEE), 2020, https://doi.org/10.36227/techrxiv.12151734.v1

Publisher : Institute of Electrical and Electronics Engineers (IEEE)

Year : 2020

Identifying epiphytes in drones photos with a conditional generative adversarial network (C-GAN)

Cite this Research Publication : Shashank Anivilla, Sajith Variyar V. V., Sowmya V., Dr. Soman K. P., Ramesh Sivanpillai, and Greg Brown, “Identifying epiphytes in drones photos with a conditional generative adversarial network (C-GAN)”, in ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020, pp. 99-104.

Publisher : ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Journal Article

Year : 2025

MTV19ANet: A Multi-tier Visual Geometry Group 19 with Attention Network-Based Streamflow Prediction System

Cite this Research Publication : Shashank A, Geetha P, Jyothish Lal G, Sankaran Rajendran, MTV19ANet: A Multi-tier Visual Geometry Group 19 with Attention Network-Based Streamflow Prediction System, Water Resources Management, Springer Science and Business Media LLC, 2025, https://doi.org/10.1007/s11269-025-04113-w

Publisher : Springer Science and Business Media LLC

Qualification
  • PhD (Year): 2020 – Present
    Specialization: Artificial Intelligence and Hydrological Modelling
    Thesis title: Hydrological and Data-Driven Approaches for Flood Vulnerability and Risk Assessment
  • M.Tech (Year): Jan 2020, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Ettimadai, Coimbatore, Tamil Nadu.
    Specialization: Remote Sensing and Wireless Sensor Networks
    Thesis title: Identifying Epiphytes in Drone Imagery using Conditional GAN (C-GAN)
  • B. Tech (Year): July 2017, Lendi Institute of Engineering and Technology (Affiliated with Jawaharlal Nehru Technological University-Gurajada Vizianagaram, AP).
    Specialization: Computer Science and Engineering.
    Thesis Title: Customer & Inventory Analysis System Using Statistical Modelling.
Experience
  • Assistant Professor (On Contract) – Feb 2026 -Present
  • Ph.D. Research Scholar (2020 – Present)
  • Teaching and Research Assistant, Amrita Vishwa Vidyapeetham, Coimbatore (Apr 2022 – Feb 2026)
Teaching

Courses Taught

  • 23AID112 -Data Structures and Algorithms – Even Semester 2025-2026 (B. Tech Artificial Intelligence and Data Science)
  • 25AID202 -Introduction to Computer Networks – Odd Semester 2026-2027 (B. Tech Artificial Intelligence and Data Science (with Specialization in Quantum Technology, Cyber-Physical Systems , Autonomous Agents and Robotic Systems)

Doctoral advisor

  • Thesis Title: Hydrological and Data-Driven Approaches for Flood Vulnerability and Risk Assessment
    Year :2020

Doctoral Committee Members

Thesis Advisor Dr. Geetha P(CEN)
Co-Advisor Dr. JYOTHISH LAL G(CEN)
DC Member Dr. Gopalakrishnan E.A(CEN)
DC Member Dr. NEETHU MOHAN(AI)
Workshops

GIAN/Workshops/Short-term training programs attended:

    1. Two-Day Workshop on SAR Data Analysis (IIRS/ISRO), Dec 2022.
    2. Summer Workshop on Remote Sensing Algorithms (RARSA 2022), NITK Surathkal, July 2022.
    3. National Workshop on Geospatial Technology for Coastal Disaster Studies (GEO-CODISM), Annamalai University, Nov 2022.
    4. DST-NGP Summer School in Geospatial Technologies, TNAU, Coimbatore, June 2024.
    5. IIT Palakkad Workshop on AI in Time Series, Jan 2024.
    6. IIT-T–NIF Workshop on Hyperspectral Image Analysis, Jan 2024.
    7. CIS Summer School: AI for Disaster Resilience, NIT Calicut , Dec 2025.
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