DEPARTMENTS
Dr. Shivanand Kumar Veesam
Dr. Shivanand Kumar Veesam

Assistant Professor

Office Address:

Department of Chemical Engineering, National Institute of Technology Calicut, Kozhikode, Kerala-673601, India

Contact no:

Home Address:

  • Ph.D., Chemical Engineering, Indian Institute of Science (2020)

  • M.E., Chemical Engineering, Indian Institute of Science (2011)

  • B.Tech., Chemical Engineering, University College of Technology, Osmania University (2009)

  • Educational Qualifications

    • Ph.D., Chemical Engineering, Indian Institute of Science (2020)

    • M.E., Chemical Engineering, Indian Institute of Science (2011)

    • B.Tech., Chemical Engineering, University College of Technology, Osmania University (2009)

    Journals

    1. Nikhil V. S. Avula, Shivanand K. Veesam, Sudarshan Behera, and Bala Subramanian, Building Robust Machine Learning Models for Small Chemical Science Data: The case of Shear Viscosity, Machine Learning: Science and Technology (2022), 3, 045032.

    2. Dinesh C, Shivanand K. Veesam, Emanuele B, Lara F, Sudeep N. P., Modeling of effective interactions between ligand coated nanoparticles through symmetry functions, J. Chem. Phys. (2021), 155, 244901.

    3. Shivanand K. Veesam and Sudeep N. P., Computation of the dissociation temperature of TBAB semi-clathrate in an aqueous solution using molecular simulations, J. Phys. Chem. B (2020), 124, 9195-9203.

    4. Ravi K R A, Shivanand K. Veesam, and Sudeep N. P., Review of the Frenkel-Ladd technique for computing free energies of crystalline solids, Molecular Simulation (2020), 47, 824-830.

    5. Shivanand K. Veesam and Sudeep N. P., vdWP-FL: An Improved Thermodynamic Theory for Gas Hydrates with Free-Energy Contributions due to Hydrate Lattice Flexibility, J. Phys. Chem. C (2019), 123, 26406-26414.

    6. Shivanand K. Veesam, Srikanth R, and Sudeep N. P., Recent advances in thermodynamics and nucleation of gas hydrates using molecular modeling, Current Opinion in Chemical Engineering (2019), 23, 14-20.

    7. Hrushikesh Pimpalgaonkar, Shivanand K. Veesam, and Sudeep N.P., Theory of gas hydrates: Effect of the Approximation of Rigid water lattice, J. Phys. Chem. B (2011), 115, 10018-10026.

    Conferences

    1. Shivanand K. Veesam and Sudeep N. P.: Improving the robustness of the van der Waals and Platteeuw theory for gas hydrates, 10th Liblice conference on statistical mechanics of liquids, SRNI, Czech Republic on June 17-22, 2018.

    2. Shivanand K. Veesam, and Sudeep N.P: Theory of Gas Hydrates: Effect of the Approximation of Rigid Water Lattice, AIChE Annual Meeting, Pittsburgh, PA, 2012.

    3. Shivanand K. Veesam and Sudeep N. P.: On the Approximation of Rigid Water Lattice in van der Waals and Platteeuw Theory, 7th International Conference on Gas Hydrates, Edinburgh, Scotland, United Kingdom, 2011.

    4. Shivanand K. Veesam and Sudeep N. P.: Phase Equilibria for Clathrate Hydrates using Monte Carlo Simulation, International symposium on Recent and Emergent Advances in Chemical Engineering (REACH), IIT Madras, Chennai, India, 2010.

    Professional Experience

    • Assistant Professor (Grade II), Dept. of Chemical Engineering, NIT Calicut, Dec 2022 to present

    • Postdoctoral Research Associate (Advisor: Prof. Balasubramanian Sundaram), CPMU, JNCASR, Bangalore, May 2021 - Dec 2022.

    • Postdoctoral Research Associate (Advisor: Prof. Sudeep N Punnathanam), Dept. of Chemical Engineering, IISc, Bangalore, Nov 2020 - May 2021

    • Project Assistant, Dept. of Chemical Engineering, IISc, Bangalore, July 2011 - June 2012.

    • Kumar – Gandhi Award, Inhouse Symposium (2011), Dept. of Chemical Engineering, IISc, Bangaore

    • Best Poster Award, Inhouse Symposium (2019), Dept. of Chemical Engineering, IISc, Bangaore

    • Best Poster Award, CTTC Conference (2022), BARC, Mumbai

    • Thermodynamic Modelling of Gas Hydrates
    • Flow Assurance
    • Transport Properties of Complex Fluids
    • Force Field Development of Metal Oxides
    • Crystal Nucleation and Polymorphism
    • Molecular Simulations (MD/MC)
    • Machine Learning