👋 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.com

Oct '24 - Present

Senior Product Manager · San Francisco, CA

FLYR Inc.

Aug '22 - Sept '24

Product Manager · San Francisco, CA

Deepair

May '18 - Jul '22

Lead Data Scientist · London, UK


🎓 Education

University of Illinois

2017 - 2019

MS in Advanced Analytics · Urbana-Champaign, IL

Thesis: Dynamic Pricing for Airline Ancillaries with Customer Context

Indian Institute of Technology

2013 - 2017

BTech in Chemical Engineering · Hyderabad, India

Minor in Entrepreneurship


🧠 Publications

Deep contrastive anomaly detection for airline ancillaries prediction

2022

P Yang, A Kolbeinsson, N Shukla

Twenty-first IEEE International Conference on Machine Learning and Applications

PDF

Negotiating Networks in Oligopoly Markets for Price-Sensitive Products

2021

N Shukla, K Yellepeddi

Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS) WS

Distribution Shifts in Airline Customer Behavior during COVID-19

2021

A Garg, N Shukla, L Marla, S Somanchi

Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS) WS

Galactic Air Improves Airline Ancillary Revenues with Dynamic Personalized Pricing

2021

A Kolbeinsson, N Shukla, A Gupta, L Marla, K Yellepeddi

Special Issue of INFORMS Journal on Applied Analytics

PDF

PenDer: Incorporating Shape Constraints via Penalized Derivatives

2021

A Gupta, L Marla, R Sun, N Shukla, A Kolbeinsson

Proc. 35th AAAI Conference on Artificial Intelligence

PDF

From Average Customer to Individual Traveler: A Field Experiment in Airline Ancillary Pricing

2020

N Shukla, A Kolbeinsson, L Marla, K Yellepeddi

Institute for Operations Research and the Management Sciences

PDF

Dynamic Pricing for Airline Ancillaries

2019

N Shukla

Masters Thesis, University of Illinois at Urbana-Champaign (UIUC)

PDF

Dynamic Pricing for Airline Ancillaries with Customer Context

2019

N Shukla, A Kolbeinsson, K Otwell, L Marla, K Yellepeddi

Proc. 25th ACM SIGKDD Intl. Conf. on Knowledge discovery and data mining (KDD)

How to Incorporate Monotonicity in Deep Networks While Preserving Flexibility?

2019

A Gupta, N Shukla, L Marla, A Kolbeinsson, K Yellepeddi

Thirty-third Conference on Neural Information Processing Systems (NeurIPS) WS

Adaptive Model Selection Framework: An Application to Airline Pricing

2019

N Shukla, A Kolbeinsson, L Marla, K Yellepeddi

Thirty-sixth International Conference on Machine Learning (ICML) WS

Leveraging Time Dependency in Graphs

2019

A Kolbeinsson, N Shukla, A Gupta, L Marla

Thirty-third Conference on Neural Information Processing Systems (NeurIPS) WS


🎤 Talks

Generating optimal bid prices via reinforcement learning with batch and shape constraints

2024

A Gupta, N Shukla

Airline Group of the International Federation of Operational Research Society (AGIFORS)

Mid-term decision making in airline cargo using machine learning

2024

A Garg, N Shukla

Airline Group of the International Federation of Operational Research Society (AGIFORS)

Dealing with distribution shifts in customer choice due to COVID-19

2021

A Garg, N Shukla, L Marla

Airline Group of the International Federation of Operational Research Society (AGIFORS)

FLAI: Reinforcement Learning Virtual Platform for Travel

2021

B Kolbeinsson, N Shukla, A Kolbeinsson

Airline Group of the International Federation of Operational Research Society (AGIFORS)

Deep Learning Algorithms for Dynamic Pricing of Airline Ancillaries with Customer Context

2019

N Shukla, L Marla, K Yellepeddi

Airline Group of the International Federation of Operational Research Society (AGIFORS)


💻 Softwares

  • Deep pricing© for exchange ratesDynamic pricing solution for pricing exchange rates for reward points.
  • Deep pricing© for ancillariesDynamic pricing solution for pricing ancillaries in airline industry.
  • FluentDeepair's internal framework for orchestrating life cycle of pricing agents. Currently powering all pricing agents deployed by Deepair Solutions.
  • FlaiA toolkit for developing and comparing reinforcement learning algorithms. Created with Imperial College London and University of Illinois at Urbana-Champaign.

👨‍🎓 Academic Projects

Double Deep Q-Learning (Double DQN)

Fall 2018

Flappy Bird (Android game) hack using deep reinforcement learning with double Q-learning

University of Illinois at Urbana-Champaign (UIUC)

Cycle Consistent Generative Adversarial Network (Cycle-GAN)

Spring 2018

A deep neural network based on cycle consistent image to image translation with generative adversarial networks

University of Illinois at Urbana-Champaign (UIUC)

Wasserstein Deep Convolutional Generative Adversarial Network (DC-GAN)

Spring 2018

Deep convolutional neural network that genrates pokemons using wasserstein generative adversarial networks

University of Illinois at Urbana-Champaign (UIUC)

Handwritten Digit Recognition by Kernel PCA (Kernal PCA)

Fall 2017

Kernal based principal component analysis for feature extraction on hand written images provided by USPS

University of Illinois at Urbana-Champaign (UIUC)


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