About me

Naman is a Senior Product Manager in the data science team at Engine, where he own building advanced pricing strategies. He drives the development of pricing models and machine learning solutions that optimize decision-making frameworks and deliver business value.

Before joining Engine, Naman was a Product Manager in the Applied Science team at FLYR Inc., where he led a team of data scientists to develop machine learning models that enhanced airline revenue management systems. He spearheaded efforts to implement statistical models leveraging contextual information to optimize pricing strategies for airlines.

Naman's research expertise lies in dynamic pricing with contextual information, where he developed innovative algorithms and computational techniques to analyze revenue management data. His work has been published in renowned journals, including the Journal for Applied Analytics by the Institute for Operations Research and the Management Sciences (INFORMS) and the Airline Group of the International Federation of Operational Research Societies (AGIFORS).

His experience extends to roles such as Machine Learning Engineer, Research Infrastructure Manager, and Software Engineer at Deepair, where he built tools and frameworks utilized by global airlines and academic institutions like the University of Illinois at Urbana-Champaign, Imperial College London, and the University of Notre Dame. Notably, he was part of the team that secured New Distribution Capability (NDC) Level 4 certification from the International Air Transport Association (IATA).

Naman holds a Master of Science in Industrial Engineering from the University of Illinois at Urbana-Champaign. He is a recipient of the Japan Student Services Organization (JASSO) scholarship for the Innovation Program at the University of Tokyo, Japan, and the Computational Biology Scholarship for Associate Researchers at Ritsumeikan University, Japan. He earned a Bachelor of Technology in Chemical Engineering and a Minor in Entrepreneurship, both with academic excellence awards, from the Indian Institute of Technology, Hyderabad.

Work Experience

  • Engine San Francisco, CA
    Senior Product Manager [Oct 2024 - Current]
  • FLYR Inc. San Francisco, CA
    Product Manager [Aug 2022 - Sept 2024]
  • Deepair Solutions London, UK
    Lead Data Scientist [May 2018 - July 2022]

Education

  • University of Illinois Urbana-Champaign, IL
    Master of Science in Advanced Analytics.
    Thesis: Dynamic Pricing for Airline Ancillaries with Customer Context.
  • Indian Institute of Technology Hyderabad, India
    Bachelor of Technology in Chemical Engineering with entrepreneurship minor.

Publications

  1. P Yang, A Kolbeinsson, N Shukla.
    Deep contrastive anomaly detection for airline ancillaries prediction.
    Twenty-first IEEE International Conference on Machine Learning and Applications. 2022.
    PDF
  2. N Shukla, K Yellepeddi.
    Negotiating Networks in Oligopoly Markets for Price-Sensitive Products.
    Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS) WS. 2021.
    arXiv
  3. A Garg, N Shukla, L Marla, S Somanchi.
    Distribution Shifts in Airline Customer Behavior during COVID-19.
    Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS) WS. 2021.
    arXiv
  4. A Kolbeinsson, N Shukla, A Gupta, L Marla, K Yellepeddi.
    Galactic Air Improves Airline Ancillary Revenues with Dynamic Personalized Pricing.
    Special Issue of INFORMS Journal on Applied Analytics. 2021.
    PDF
  5. A Gupta, L Marla, R Sun, N Shukla, A Kolbeinsson.
    PenDer: Incorporating Shape Constraints via Penalized Derivatives.
    Proc. 35th AAAI Conference on Artificial Intelligence. 2021.
    PDF
  6. N Shukla, A Kolbeinsson, L Marla, K Yellepeddi.
    From Average Customer to Individual Traveler: A Field Experiment in Airline Ancillary Pricing.
    Institute for Operations Research and the Management Sciences. 2020.
    PDF
  7. N Shukla.
    Dynamic Pricing for Airline Ancillaries.
    Masters Thesis, University of Illinois at Urbana-Champaign (UIUC). 2019.
    PDF
  8. N Shukla, A Kolbeinsson, K Otwell, L Marla, K Yellepeddi.
    Dynamic Pricing for Airline Ancillaries with Customer Context.
    Proc. 25th ACM SIGKDD Intl. Conf. on Knowledge discovery and data mining (KDD). 2019.
    PDF arXiv Video
  9. A Gupta, N Shukla, L Marla, A Kolbeinsson, K Yellepeddi.
    How to Incorporate Monotonicity in Deep NetworksWhile Preserving Flexibility?
    Thirty-third Conference on Neural Information Processing Systems (NeurIPS) WS. 2019.
    Workshop arXiv Poster
  10. N Shukla, A Kolbeinsson, L Marla, K Yellepeddi.
    Adaptive Model Selection Framework: An Application to Airline Pricing
    Thirty-sixth International Conference on Machine Learning (ICML) WS. 2019.
    WorkshoparXiv Poster
  11. A Kolbeinsson, N Shukla, A Gupta, L Marla.
    Leveraging Time Dependency in Graphs.
    Thirty-third Conference on Neural Information Processing Systems (NeurIPS) WS. 2019.
    Workshop PDF

Talks

  1. A Gupta, N Shukla.
    Generating optimal bid prices via reinforcement learning with batch and shape constraints
    Airline Group of the International Federation of Operational Research Society (AGIFORS). 2024.
    Blog Slides
  2. A Garg, N Shukla.
    Mid-term decision making in airline cargo using machine learning
    Airline Group of the International Federation of Operational Research Society (AGIFORS). 2024.
    Slides
  3. A Garg, N Shukla, L Marla.
    Dealing with distribution shifts in customer choice due to COVID - 19
    Airline Group of the International Federation of Operational Research Society (AGIFORS). 2021.
    Video Slides
  4. B Kolbeinsson, N Shukla, A Kolbeinsson.
    FLAI: Reinforcement Learning Virtual Platform for Travel
    Airline Group of the International Federation of Operational Research Society (AGIFORS). 2021.
    Video Slides
  5. N Shukla, L Marla, K Yellepeddi.
    Deep Learning Algorithms for Dynamic Pricing of Airline Ancillaries with Customer Context
    Airline Group of the International Federation of Operational Research Society (AGIFORS). 2019.
    Web Slides

Softwares

  1. Deep pricing© for exchange rates: Dynamic pricing solution for pricing exchange rates for reward points.
  2. Deep pricing© for ancillaries: Dynamic pricing solution for pricing ancillaries in airline industry
  3. Fluent: Deepair's internal framework for orchestrating life cycle of pricing agents. Currently powering all pricing agents deployed by Deepair Solutions.
  4. Flai: A toolkit for developing and comparing reinforcement learning algorithms. Created with Imperial College London and University of Illinois at Urbana-Champaign.
    Github Website

Academic Projects

  1. Double Deep Q-Learning (Double DQN)
    Flappy Bird (Android game) hack using deep reinforcement learning with double Q-learning
    University of Illinois at Urbana-Champaign (UIUC). Fall 2018.
    BlogPDFRepositoryText
  2. Cycle Consistent Generative Adversarial Network (Cycle-GAN)
    A deep neural network based on cycle consistent image to image translation with generative adversarial networks
    University of Illinois at Urbana-Champaign (UIUC). Spring 2018.
    Blog PDF Repository Text
  3. Wasserstein Deep Convolutional Generative Adversarial Network (DC-GAN)
    Deep convolutional neural network that genrates pokemons using wasserstein generative adversarial networks
    University of Illinois at Urbana-Champaign (UIUC). Spring 2018.
    TensorflowPytorch Text
  4. Handwritten Digit Recognition by Kernel PCA (Kernal PCA)
    Kernal based principal component analysis for feature extraction on hand written images provided by USPS
    University of Illinois at Urbana-Champaign (UIUC). Fall 2017.
    PDF Repository Text Slides Demo

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