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Pratinav Seth

Lead Research Scientist @ Lexsi Labs | Prev @ Mila, Bosch Research | AAAI UC-23 Scholar | Manipal |

Kolkata

5+ years of work experience

Lexsi Labs
Apr 20265 mosPresent

Leading & Working on Research around Alignment, Safety, Interpretability, Agents, Applied Research OSS Library.
Lexsi Labs
Jul 20241 yr 9 mosMar 2026

India

Research Scientist / Senior Research Scientist India Research Lab - Alignment & Safety, Interpretability driven Model Optimization & Safety, Post Training LLM, Applied Research. Post Hoc Interpretability & Tabular Foundation Models .
Mila - Quebec Artificial Intelligence Institute
Jan 20246 mosJun 2024

Montreal, Quebec, Canada

Worked on a project (as a part of my Thesis) focused on computer vision and deep learning for geospatial applications targeting climate change, specifically in detecting abandoned oil and gas wells. Engaged with domain experts from McGill University to create a new geospatial dataset and benchmark deep learning models. PI & Mentor: Dr. David Rolnick Publication : Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery Accepted at ICML 2025 and TCCAI Workshop, ICLR 2025 .
Bosch Research
Jun 20235 mosOct 2023

Bengaluru, Karnataka, India

Worked on a project focused on vision-based generative AI for autonomous driving, utilizing Latent Diffusion Models to generate additional data for difficult or misclassified samples. This approach aimed to improve the fine-tuning and optimization of the downstream task network. Mentor: Kaustav Mullick (CR/RTC-IN)
Indian Institute of Technology, Kharagpur
May 20221 yr 8 mosDec 2023

Remote

Worked extensively with chest radiographs, leveraging deep learning techniques to integrate domain knowledge using Graph Neural Networks (GNNs) into convolutional neural networks (CNNs) for hierarchical disease identification. Focused on enhancing model explainability. Mentor: Rakshith Satish PI: Dr. Debdoot Sheet
Mars Rover Manipal
Jun 20212 yrs 7 mosDec 2023

Manipal

Progressed from a Student Trainee to a Senior Researcher, leading a student led independent team in the AI Research Wing. Contributed to projects in machine learning, deep learning, computer vision, and natural language processing, focusing on image-to-image translation, AI4SG, medical imagery, multi-modal AI, robust neural networks, and sentiment analysis. This work led to multiple publications at various A*/A conferences workshops. Also mentored over 10 undergraduates, with many collaborations resulting in publications in with some being the mentees' first publications. Publications: 1. Lapgsr: Laplacian reconstructive network for guided thermal super-resolution. (Journal Paper under Review) 2. Lamar: Laplacian pyramid for multimodal adaptive super resolution. (Student Abstract and Poster, AAAI 2024) 3. Corefusion: Contrastive regularized fusion for guided thermal super-resolution. (CVPR Workshop 2023) 4. Refuseg: Regularized multi-modal fusion for precise brain tumour segmentation. (BrainLes Workshop, MICCAI 2023) 5. Uatta-ens: Uncertainty aware test time augmented ensemble for pirc diabetic retinopathy detection. (Medical Imagery Meets NeurIPS Workshop, NeurIPS 2022) 6. Evaluating predictive uncertainty and robustness to distributional shift using real world data. (Bayesian Deep Learning Workshop, NeurIPS 2021) 7. Agrillm: Harnessing transformers for farmer queries. (NLP for Positive Impact Workshop, EMNLP 2024) 8. Performance evaluation of deep segmentation models on landsat-8 imagery. (Tackling Climate Change with Machine Learning Workshop, NeurIPS 2022) 9. SSS at SemEval-2023 task 10: Explainable detection of online sexism using majority voted fine-tuned transformers. (SemEval Workshop Shared Task, ACL 2023) 10. Analyzing effects of fake training data on the performance of deep learning systems. Pre-Print.

Pratinav’s AI Stack

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Education

Manipal institute of technology

B.Tech, Data Science

Manipal institute of technology

Bharatiya Vidya Bhavan's

CBSE, High School/Secondary Diplomas and Certificates

Bharatiya Vidya Bhavan's

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Headline

Lead Research Scientist @ Lexsi Labs | Prev @ Mila, Bosch Research | AAAI UC-23 Scholar | Manipal |

About

I was an 2023 AAAI Undergraduate Consortium Scholar and 2026 Mentor. I have reviewed and presented research at A*/A conferences and workshops at conferences such as NeurIPS, ICML, ICLR, CVPR, WACV, AAAI, ACL, NAACL, EMNLP, MICCAI & MIDL . For a more detailed overview of my professional journey, projects, and contributions, feel free to take a look at my Resume.

Location

Kolkata

AI stack

claude code

codex

cursor

hugging face

opencode

huggingface

Work experience

Lead Research Scientist at Lexsi Labs, Apr 2026 - Present · 5 mos, Leading & Working on Research around Alignment, Safety, Interpretability, Agents, Applied Research OSS Library.

Research Scientist at Lexsi Labs, Jul 2024 - Mar 2026 · 1 yr 9 mos, India, Research Scientist / Senior Research Scientist India Research Lab - Alignment & Safety, Interpretability driven Model Optimization & Safety, Post Training LLM, Applied Research. Post Hoc Interpretability & Tabular Foundation Models .

Researcher at Mila - Quebec Artificial Intelligence Institute, Jan 2024 - Jun 2024 · 6 mos, Montreal, Quebec, Canada, Worked on a project (as a part of my Thesis) focused on computer vision and deep learning for geospatial applications targeting climate change, specifically in detecting abandoned oil and gas wells. Engaged with domain experts from McGill University to create a new geospatial dataset and benchmark deep learning models. PI & Mentor: Dr. David Rolnick Publication : Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery Accepted at ICML 2025 and TCCAI Workshop, ICLR 2025 .

Computer Vision Research Engineer Intern at Bosch Research, Jun 2023 - Oct 2023 · 5 mos, Bengaluru, Karnataka, India, Worked on a project focused on vision-based generative AI for autonomous driving, utilizing Latent Diffusion Models to generate additional data for difficult or misclassified samples. This approach aimed to improve the fine-tuning and optimization of the downstream task network. Mentor: Kaustav Mullick (CR/RTC-IN)

Research Intern at Indian Institute of Technology, Kharagpur, May 2022 - Dec 2023 · 1 yr 8 mos, Remote, Worked extensively with chest radiographs, leveraging deep learning techniques to integrate domain knowledge using Graph Neural Networks (GNNs) into convolutional neural networks (CNNs) for hierarchical disease identification. Focused on enhancing model explainability. Mentor: Rakshith Satish PI: Dr. Debdoot Sheet

Researcher at Mars Rover Manipal, Jun 2021 - Dec 2023 · 2 yrs 7 mos, Manipal, Progressed from a Student Trainee to a Senior Researcher, leading a student led independent team in the AI Research Wing. Contributed to projects in machine learning, deep learning, computer vision, and natural language processing, focusing on image-to-image translation, AI4SG, medical imagery, multi-modal AI, robust neural networks, and sentiment analysis. This work led to multiple publications at various A*/A conferences workshops. Also mentored over 10 undergraduates, with many collaborations resulting in publications in with some being the mentees' first publications. Publications: 1. Lapgsr: Laplacian reconstructive network for guided thermal super-resolution. (Journal Paper under Review) 2. Lamar: Laplacian pyramid for multimodal adaptive super resolution. (Student Abstract and Poster, AAAI 2024) 3. Corefusion: Contrastive regularized fusion for guided thermal super-resolution. (CVPR Workshop 2023) 4. Refuseg: Regularized multi-modal fusion for precise brain tumour segmentation. (BrainLes Workshop, MICCAI 2023) 5. Uatta-ens: Uncertainty aware test time augmented ensemble for pirc diabetic retinopathy detection. (Medical Imagery Meets NeurIPS Workshop, NeurIPS 2022) 6. Evaluating predictive uncertainty and robustness to distributional shift using real world data. (Bayesian Deep Learning Workshop, NeurIPS 2021) 7. Agrillm: Harnessing transformers for farmer queries. (NLP for Positive Impact Workshop, EMNLP 2024) 8. Performance evaluation of deep segmentation models on landsat-8 imagery. (Tackling Climate Change with Machine Learning Workshop, NeurIPS 2022) 9. SSS at SemEval-2023 task 10: Explainable detection of online sexism using majority voted fine-tuned transformers. (SemEval Workshop Shared Task, ACL 2023) 10. Analyzing effects of fake training data on the performance of deep learning systems. Pre-Print.

Education

B.Tech, Data Science, Manipal institute of technology

CBSE, High School/Secondary Diplomas and Certificates, Bharatiya Vidya Bhavan's