Ankur Bist
Editor At Large at Springer Nature
Aalborg, North Denmark Region, Canada
Summary
👤
Senior🎓
Top SchoolAnkur Bist is a Senior AI Scientist and consultant with a decade of experience bridging academic research and applied ML across higher education and industry. He holds a PhD in Computer Science and has contributed to notable open-source projects such as SpeechBrain (implementing LEAF audio frontends) and MLX examples for ResNet and keyword-spotting transformers, reflecting hands-on expertise in speech and audio ML. As an editor, founding editor at Springer Nature and Editor At Large at SciBase, he shapes research dissemination while mentoring startups and students through multiple advisory and mentorship roles. His background spans malware analysis, research trends, and practical model engineering, and he combines deep technical rigor with a knack for turning research insights into usable tools and educational resources.
10 years of coding experience
9 years of employment as a software developer
Master's Degree, Computer Science, Master's Degree, Computer Science at College Of Technology, G.B.P.U.A&T, PantnagarDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Sri Venkateswara UniversityBachelor of Technology - BTech, Computer Science, Bachelor of Technology - BTech, Computer Science at Uttar Pradesh Technical University
kumauni
Stackoverflow
21reputation
23kreached
1answer
0questions
Github Skills (17)
transformers10
pytorch10
python10
machine-learning10
audio-processing10
mask-rcnn10
deep-learning10
faster-rcnn10
speech-recognition10
computer-vision9
speech-to-text8
deeplearning-ai8
audio7
autocomplete6
intellisense6
Programming languages (5)
Github contributions (5)
ml-explore/mlx-examples
Dec 2023 - Dec 2023
Examples in the MLX framework
Role in this project:
ML Engineer
ML EngineerContributions:11 reviews, 3 PRs, 15 comments in 8 days
Contributions summary:Ankur implemented a ResNet model for CIFAR-10 using the MLX framework and a Keyword Spotting Transformer (KWT) model for speech command recognition. They added training and testing functionalities, including data loading and optimization. Furthermore, the user expanded the example's performance by incorporating throughput metrics.
mlx
speechbrain/speechbrain
Apr 2022 - Jun 2022
A PyTorch-based Speech Toolkit
Role in this project:ML Engineer
ML EngineerContributions:6 reviews, 12 commits, 19 comments in 2 months
Contributions summary:Ankur primarily contributed to the implementation and refinement of the LEAF audio frontend within the speechbrain framework. Their work involved adding the GaborConv1d and Leaf modules, which are core components of the LEAF architecture. Furthermore, the user updated the code to address pre-commit formatting issues.
voice-recognitionasrspeech-recognitionspeech-separationspeaker-verification
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.