Akhil Gupta

[Dec'21] Left Walmart Global Tech, and started as a Data Scientist @ Shelf Engine, Seattle, WA.

[Sep'21] MS thesis available on IDEALS!

[Jul'21] Starting as Senior Data Scientist @ Walmart Global Tech, Sunnyvale, CA.

[May'21] Graduated with MS in Industrial Engineering (Advanced Analytics concentration) from UIUC.

[Apr'21] Thesis deposited with Graduate College.

[Dec'20] Our work "PenDer: Incorporating Shape Constraints via Penalized Derivatives" accepted to the main track at AAAI'21. First first-author full paper! :)

[Nov'20] Paper accepted to the Challenges of Real World Reinforcement Learning Workshop @ NeurIPS'20.

[Jun'20] Spending Summers @ Deepair.

[Dec'19] Paper on Guaranteeing Monotonicity available on arXiv!

[Oct'19] Paper accepted to the Machine Learning with Guarantees Workshop @ NeurIPS'19, Vancouver, BC, Canada.

[Oct'19] Paper accepted to the Graph Representation Learning Workshop @ NeurIPS'19, Vancouver, BC, Canada.

[Aug'19] Started Grad Life @ UIUC.

[Jun'19] Started Summer Internship @ Deepair.

[May'19] Left ZS after 2 years as a Data Science Associate.

Arinbjörn Kolbeinsson, Naman Shukla, Akhil Gupta, Lavanya Marla, Kartik Yellepeddi (2021). Galactic Air Improves Ancillary Revenues with Dynamic Personalized Pricing, published in INFORMS Journal on Applied Analytics. [SSRN Copy] [Informs PubsOnline]

Akhil Gupta, Lavanya Marla, Ruoyu Sun, Naman Shukla, Arinbjörn Kolbeinsson (2021). PenDer: Incorporating Shape Constraints via Penalized Derivatives, published in Proceedings of the AAAI Conference on Artificial Intelligence, 35 (13), 11536-11544. [Paper]

Benedikt Kolbeinsson, Arinbjörn Kolbeinsson, Naman Shukla, Akhil Gupta, Lavanya Marla, Kartik Yellepeddi (2020). The Challenges of Reinforcement Learning for Airline Seat Pricing, accepted to NeurIPS 2020 Workshop on Challenges of Real-World RL.

Akhil Gupta, Naman Shukla, Lavanya Marla, Arinbjörn Kolbeinsson (2019). How to Incorporate Monotonicity in Deep Networks While Preserving Flexibility?, accepted to NeurIPS 2019 Workshop on Machine Learning with Guarantees, Vancouver, BC, Canada. [arXiv]

Arinbjörn Kolbeinsson, Naman Shukla, Akhil Gupta, Lavanya Marla (2019). Leveraging Time Dependency in Graphs, accepted to NeurIPS 2019 Workshop on Graph Representation Learning, Vancouver, BC, Canada. [Paper]

Akhil Gupta (2017). Time Series Modeling for Dream Team in Fantasy Premier League, presented at International Conference on Sports Engineering (ICSE), Jaipur, India. [arXiv]

Akhil Gupta, Durga Toshniwal, Sumit Singh, Shreyas Verma (2017). KATSCAN: K-Means Associated Terrorism Clustering Avoiding Noise, presented at Data Science Congress (DSC), Mumbai, India.

Sumit Singh, Durga Toshniwal, Akhil Gupta, Shreyas Verma (2016). GenIt-e-MoTo: Generation of intelligence to envisage modus operandi of terror outfits, published in Proceedings of 15th IEEE/ACIS International Conference on Computer and Information Science, Okayama, Japan. [IEEE Xplore]