Arijit Das

Worked as Senior Machine Learning Scientist at Zendesk.

Worked as Founder at Selfsupervised AI.

Design and Develop scalable AI operationalization pipelines optimized for scalability, reliability and cost.

I have developed and deployed 5+ models into production for large-scale Intelligent Document Processing, leveraging Deep Learning Models including BERT, Masked Auto Encoder, ViT, MosaicBERT, Llama3.1 and Mistralv0.3, automating 40% of 100 million previously manually processed documents.

I led designing and implementing a proprietary multi-node distributed training platform, enabling efficient orc

Spearheaded the development and implementation of fairness-aware algorithms, decreasing bias in automated decision-making especially using Deep Learning Models by 25%, enhancing ethical standards across 10 institutions, and improving decision accuracy by 20%
through comprehensive data analysis and cross-disciplinary collaboration.

Published a comprehensive paper in the British Actuarial Journal t

Secured a Köln Fortune Research Grant of €120,000 for developing Automated Breast Cancer Screening technology.

Supervised four master's theses and collaborated with two doctors on their PhD theses, working on advancements in Statistically Robust Machine Learning.

Developed anomaly detection methods in multi-parametric MRIs using Deep Convolutional Neural Networks with FDR control, enhancing dete

Developed a Discrete Compound Process model for single-cell modeling, incorporating a novel cost function with regularization, improving parameter estimation consistency in under-sampled regimes by 20%.

Automated breast cancer screening using multiparametric MRI and Deep Convolutional Neural Networks, enhancing early detection accuracy by 30%.

Enhanced interpretability of Deep Bayesian Convoluti

Designed and analyzed algorithms to control false discoveries, developing machine learning techniques to manage generalization errors. Achieved state-of-the-art results in Genome-Wide Association Studies (GWAS) for breast cancer, reducing false discoveries by 25%.

Developed an efficient sampling algorithm to sparsify a kernel matrix with bounded error in O (n log n) time, improving computational

Work in the field of signal processing, wireless communications and machine learning. Applied variational bayes techniques to turbo coding algorithms.

Design and Analysis of Unsupervised Learning Algorithms, prediction of time series. Worked on a project with EDF (Électricité de France) to predict on a daily/weekly basis the consumption patterns of their customers (tens of millions all over Europe). It involved working with very huge (100's of gigabytes) data sets.

Implemented EM algorithm in C to exploit multi-core architectures and provided API for parallel computing.

Doctoral research in machine learning and computational biology, focusing on controlling false discoveries and developing efficient algorithms for large-scale data analysis.