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Santosh Palaskar

Research Scholar (2020 - Present)                                                          

Industrial Engineering and Operations Research
IIT Bombay

Office: PhD lab, Third Floor, IEOR building, IIT Bombay,      
Powai, Mumbai, Maharashtra - 400076
Email: santoshpalaskar77[at]iitb[dot]ac[dot]in
            santoshpalaskar77[at]gmail[dot]com

Supervisor:  Prof. Narayan Ranagaraj, Prof. Nandyala Hemachandra

Research Interest
Time Series Forecasting, Supply Chain, Learning Theory, Deep Learning

Academic Background

BSc. Fergusson College, Pune  [2015-2018]

MSc-PhD. IEOR, IIT Bombay [2018-Present]

Academic Publications

  • Palaskar, Santosh, et al. "AutoMixer for Improved Multivariate Time-Series Forecasting on BizITOps Data."  Accepted in  Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-24). [Paper Link].

Academic Internships and Workshops

  • "Selecting a Best Cloud Provider", TCS TRDDC, Mentor: Mangesh Gharote, [2019]
  • "Hierarchical Probabilistic Forecasting Using Optimization-Based Reordering Method", IBM Research India, Mentor: Surya Shravan Kumar Sajja, [2022]
  • "AutoMixer for Improved Multivariate Time-Series Forecasting on BizITOps Data", IBM Research India, Mentor: Vijay Ekambaram, Arindam Jati, Neelamadhav Gantayat, Seema Nagar, [2023]
  • "Data Science Winter School by CMI AND IISA", [2018]
  • "Advanced Forecasting Training by CCmath", [2021]

Teaching and TAship

  • Computer Programming Lab
  • Quantitative Models for Supply Chain Management
  • Operations Analysis
  • OR Applications in Infrastructural and Service Sectors

Measure Courses 

  • Credited: IE 609 Mathematical Optimisation Techniques, IE 621 Probability and Stochastic Processes I, IE 683 Topics in Learning Algorithms, IE 718 Networks, Games and Algorithms, IE 684 IEOR Lab, IE 663 Advanced Topics in Deep Learning, IE 622 Probability and Stochastic Processes II, IE 614 Linear Systems, IE 714 Markov Decision Processes, IE 504 Service and Infrastructure Systems, IE 716 Integer Programming: Theory and Computations, CS 725 Foundations of Machine learning, IE 613 Online Learning, ME 781 Engineering Data Mining and Applications