Dr Kiran Kalidindi is a full-stack data scientist.
My goal is to produce positive disruptive change in different industries through data science (6 years), engineering (4+ years) and product management. My experience in different roles and industries supports my ability to bridge these three disciplines, with particular recent emphasis on delivering solutions to production.
My technical skills include the use of Django for web applications, python, R, dash-plotly or R-Shiny for interactive visualisations. For analysis, I have experience with various machine learning and statistical methods including, gradient boosted trees, random forests, linear, logisitic and Cox regressions, time-series analysis and deep-learning (DL) with Keras. And for deployment and acceleration of analysis, I have used cloud computing resources to deploy APIs on Amazon Web Service (AWS) and DL neural networks on Google Cloud (GCP).
PUBLICATIONS
Journal articles
Andrew Howes, Geoffrey B. Duggan, Kiran Kalidindi, Yuan-Chi Tseng and Richard L. Lewis. Predicting Short-Term Remembering as Boundedly Optimal Strategy Choice. Cognitive Science, doi: 10.1111/cogs.12271, Aug 21, (2015)
Usha Hartgill, Kiran Kalidindi, Signe R Kaste and Sol Britt Molin. Screening for Chlamydia trachomatis and Mycoplasma genitalium; is first void urine or genital swab best? Sexually Transmitted Infections, doi:10.1136/sextrans-2014-051666 (2014).
Sujatha Tadiparthi, Alina Enache, Kiran Kalidindi, James O’Hara, Vinidh Paleri. Hospital stay following complex major head and neck resection: what factors play a role? Clinical Otolaryngology, 39(3): 156-63 (2014).
Kiran Kalidindi, Stephen Phelps, Howard Bowman, Andrew Howes and Richard Lewis. The non-generalizability of the softmax action-selection rule. (Working paper.)
Kiran Kalidindi and Howard Bowman. Using e-greedy reinforcement learning methods to further understand ventromedial prefrontal patients’ deficits on the Iowa Gambling Task. Neural Networks, 20(6): 676-689 (2007).
Conference papers
Kiran Kalidindi, Howard Bowman and Brad Wyble. A consideration of decision-making, motivation and emotions within Dual Process theory: supporting evidence from Somatic-Marker theory and simulations of the Iowa Gambling task. Edited by Dylan Evans and Lola Canamero. Proceedings of the Symposium on Agents that Want and Like: Motivational and Emotional Roots of Cognition and Action. AISB 2005 Convention, Published by www.aisb.org.uk p51-54. (April 2005).
Kiran Kalidindi, Howard Bowman and Brad Wyble. An investigation of the ‘myopia’ for future consequence theory of VMF patient behaviour on the Iowa Gambling task; an abstract neural network simulation. Edited by Angelo Cangelosi, Guido Bugmann, and Roman Borisyuk. Volume 1 of Progress in Neural Processing. Published by World Scientific p331-335 (2005).
Acknowledgement
Tadiparthi S, Staley H, Collis N and O’Donoghue JM. An analysis of the motivating and risk factors for conversion from implant-based to total autologous breast reconstruction. Plast Reconstr Surg. Jul;132(1):23-33 (2013).