Artificial Intelligence and Deep Learning Applications for Automotive Manufacturing. - André Luckow; Ken Kennedy; Marcin Ziolkowski; Emil Djerekarov; Matthew Cook 004; Edward B. Duffy; Michael Schleiss; Bennie Vorster; Edwin Weill; Ankit Kulshrestha; Melissa C. Smith (IEEE BigData, 2018)

Coping with Mistreatment in Fair Algorithms. - Ankit Kulshrestha; Ilya Safro (CoRR, 2021)

FAIRLEARN: Configurable and Interpretable Algorithmic Fairness. - Ankit Kulshrestha; Ilya Safro (CoRR, 2021)

Accelerating COVID-19 Research with Graph Mining and Transformer-Based Learning. - Ilya Tyagin; Ankit Kulshrestha; Justin Sybrandt; Krish Matta; Michael Shtutman; Ilya Safro (AAAI, 2022)

BEINIT: Avoiding Barren Plateaus in Variational Quantum Algorithms. - Ankit Kulshrestha; Ilya Safro (QCE, 2022)

Cobol2Vec: Learning Representations of Cobol code. - Ankit Kulshrestha; Vishwas Lele (CoRR, 2022)

Learning to Optimize Quantum Neural Networks Without Gradients. - Ankit Kulshrestha; Xiaoyuan Liu 001; Hayato Ushijima-Mwesigwa; Ilya Safro (QCE, 2023)

QArchSearch: A Scalable Quantum Architecture Search Package. - Ankit Kulshrestha; Ilya Safro; Yuri Alexeev (SC Workshops, 2023)

Learning To Optimize Quantum Neural Network Without Gradients. - Ankit Kulshrestha; Xiaoyuan Liu 001; Hayato Ushijima-Mwesigwa; Ilya Safro (CoRR, 2023)

QArchSearch: A Scalable Quantum Architecture Search Package. - Ankit Kulshrestha; Danylo Lykov; Ilya Safro; Yuri Alexeev (CoRR, 2023)

QAdaPrune: Adaptive Parameter Pruning For Training Variational Quantum Circuits. - Ankit Kulshrestha; Xiaoyuan Liu 001; Hayato Ushijima-Mwesigwa; Bao Bach; Ilya Safro (QCE, 2024)