Amirmohammad Kazemeini

Amirmohammad Kazemeini

Associate Director, Principal Scientific R&D AI Products Lead

Sanofi

Biography

I am an applied AI and product leader focused on moving AI from research and prototypes into real enterprise systems. I currently serve as Associate Director, R&D AI Product Development at Sanofi, where I lead AI product work in scientific R&D — spanning agentic systems, LLM-based generative AI, and predictive modeling — connecting technical architecture, scientific constraints, stakeholder adoption, and deployment realities.

Previously, I led applied AI programs at the Vector Institute, partnering with enterprises and startups across the Canadian AI ecosystem on agentic AI, retrieval-augmented generation (RAG), and LLM fine-tuning and alignment. My research spans NLP, computational psychology, and large language models, with 500+ citations, an h-index of 8, and peer-review service for leading AI journals. I hold an M.Sc. in Computer Science (AI) from Western University.

Interests

  • Generative AI & Large Language Models
  • Agentic AI & Multi-Agent Systems
  • Retrieval-Augmented Generation (RAG)
  • AI for Science & Healthcare
  • Applied AI Product & Adoption

Education

  • NEXT AI Program, 2025

    NEXT Canada

  • MSc in Computer Science (Artificial Intelligence), 2021-2022

    Western University

  • NLP Research Fellow (Mitacs Globalink), 2022

    Nanyang Technological University

  • BSc in Computer Engineering, 2015-2020

    Iran University of Science and Technology

Experience

 
 
 
 
 

Associate Director, R&D AI Product Development

Sanofi

Aug 2025 – Present Toronto, Canada
  • Lead AI product work in scientific R&D, coordinating Sanofi Digital, AI development, clinical scientists, and external partners.
  • Technical lead for Talk2QSP, a multi-agent system for QSP model analysis, with a manuscript under review at ICLR GenBio 2026.
  • Established and lead a 3-year Harvard–Sanofi partnership on LLM-based GenAI and predictive models for R&D.
  • Serve as Technical Advisor within Digital R&D, assessing AI vendors, SaaS, and services for adoption decisions.
 
 
 
 
 

Applied AI Projects Lead

Vector Institute

Nov 2022 – Aug 2025 Toronto, Canada
  • Led applied AI programs with enterprise and startup partners across the Canadian AI ecosystem (TD, RBC, TELUS, Air Canada, Roche, Thomson Reuters, and more).
  • Led Vector’s first Agentic AI project across 10 enterprise partners — retrieval, RAG evaluation, ReAct-RAG, and observability — advising 150+ participants.
  • Led Vector’s largest startup AI program with 15+ startups in partnership with Google Cloud.
  • Led Vector’s highest-rated LLM fine-tuning and alignment project, training 69 specialists across 12 organizations (9.05/10 satisfaction).
 
 
 
 
 

NLP Research Fellow

Nanyang Technological University & CAMH–KCNI

Sep 2021 – Nov 2022 Singapore / Toronto
  • Mitacs Globalink Research Award at NTU’s Computational Intelligence Lab, investigating LLMs and Theory of Mind with Prof. Erik Cambria and Stanford’s Prof. Michal Kosinski.
  • Built PsyLap at CAMH–KCNI, an NLP pipeline for mental-health text delivering sentiment analysis, topic modeling, and statistical insight.

Recent Publications

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Talk2QSP: Deriving Executable Scenarios from Unstructured Literature via Human-in-the-Loop Agents

An agentic, human-in-the-loop framework that turns literature-derived scenarios into simulation-ready QSP interventions through entity definition, scenario extraction, and scenario mapping.

A Silent Voice: A Bilingual Longitudinal Analysis of Mahsa Amini's Movement Tweets

A bilingual longitudinal NLP analysis of social-media discourse around the Mahsa Amini movement, presented at IEEE ICDM 2025.

A Comprehensive Review of Recommender Systems: Transitioning from Theory to Practice

A comprehensive 2017–2024 survey of recommender systems, bridging traditional techniques and modern deep learning, graph-based, reinforcement learning, and LLM-based approaches across e-commerce, healthcare, and finance.

TbExplain: A Text-Based Explanation Method for Scene Classification Models with the Statistical Prediction Correction

A text-based explanation framework for scene classification that combines XAI techniques, object detection, and a statistical prediction-correction method.

Psychological Understanding of Textual journals using Natural Language Processing approaches

Recent advancements in artificial intelligence models that accept textual inputs are becoming more and more accurate. However, because of the differences between the nature of the artificial intelligence models and human functioning, understanding the AI outputs are becoming harder for humans. In this project, the aim is to utilize top AI models in the field of natural language processing to provide meaningful insight from psychological real-world documents that contain complex structures. The project involves two main chapters each including a different dataset. The first chapter is related to binary classification on a personality detection dataset, while the second one is about sentiment analysis and Topic Modeling of sleep-related reports.
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Psychological Understanding of Textual journals using Natural Language Processing approaches
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Recent Posts

If I was a machine, part 1: The future may not be as beautiful as we think.

Hello, this is the first part of this serie. In this story, I try to question some of the dark sides of AI, which I think are forgotten or neglected.
Sep 27, 2020 8 min read
If I was a machine, part 1: The future may not be as beautiful as we think.

IELTS Reading Band 9 Tips — An End to End Self-Study Guide

Introduction IELTS, which stands for International English Language Testing System, is a standard international English test that assesses English skills in four sections reading, writing, listening, and speaking. Most international institutes and universities put an IELTS minimum requirement for their international applicants, so they become sure that no communication issues happen for non-English speakers after the recruitment process.
Sep 12, 2020 8 min read
IELTS Reading Band 9 Tips — An End to End Self-Study Guide