Highlight how digital twins and hybrid ML models (e.g., Bayesian, predictive) enable virtual experimentation and proactive troubleshooting, reducing scale-up failures and supporting more reliable process performance at commercial scale.
Shruti Vij
Associate Director, Data Analytics & Modeling
Takeda
Uncover how quantum technologies could reshape clinical trial design and optimization, from accelerating molecule-to-protocol timelines to improving patient stratification and adaptive trial modelling.
Michael Dandrea
Principal Data Scientist
Genentech
Zoran Krunic
Principal Product Manager
Amgen
Since joining Amgen R&D in 2018, Zoran Krunic has been at the forefront of applying Machine Learning to enhance patient outcomes and streamline clinical trial enrollment processes, utilizing comprehensive Electronic Health Records and clinical datasets. His pioneering work in the Quantum Machine Learning space, in collaboration with IBM's Quantum team, has been instrumental in integrating machine learning with quantum computing through IBM’s Qiskit platform.
Prior to his tenure at Amgen, Zoran developed Machine Learning algorithms at Optum to predict hardware and software failures within complex enterprise architectures. He has a strong background in data engineering and systems development, having contributed significantly to large-scale projects at renowned organizations such as Capital Group and ARCO Petroleum.
In his current full and part-time endeavors, Zoran is leading the efforts in embracing generative AI technologies, with a particular focus on OpenAI's GPT and Anthropic's Claude-2 models. His work is focused on prompt engineering and its application to code generation, advanced document analysis, and process management, with a commitment to ethical AI practices and data privacy.
A recognized voice in quantum computing circles, Zoran is a regular presenter at industry conferences and has served on numerous panels discussing the integration of quantum computing and generative AI within the Health Sciences sector.
With a Master of Science in Electrical Engineering & Computer Science, Zoran continues to explore and contribute to the evolving relationship between quantum computing and artificial intelligence, fostering groundbreaking advancements in healthcare technology.
Bolsters innovation agility by embedding ML Ops practices, aligning data science and IT workflows to ensure reliable, scalable AI deployments and a culture of continuous improvement.
Discover how ML and active learning techniques are revolutionizing the search for promising drug candidates in vast chemical libraries, accelerating hit identification.
Learn how AI models navigate ultra-large chemical spaces, prioritize bioactive compounds, and streamline the discovery of potential hits for further development.
Lingling Shen
Associate Director, Discovery Sciences
Novartis
Justin Scheer
Vice President, In Silico Discovery
Johnson & Johnson Innovative Medicine
Explore how AI accelerates antibody discovery by enabling de novo design, epitope prediction, and in silico affinity maturation for highly specific, developable therapeutics.
Learn how deep learning and structure-based models optimize antibody stability, immunogenicity and target binding to advance precision biologics.
Adam Root
Vice President & Head, Protein Sciences
Generate Biomedicines
Claudette Fuller
Vice President, Non Clinical Safety & Toxicology
Genmab
Explore how ML-enabled real-time control systems and continuous process verification improve yield predictability, reduce rework, and enable faster release - offering a direct line of sight to cost savings and product quality gains.
Strategic insights from complex, high-dimensional healthcare data, fostering integrated analytics and strengthening the organization’s competitive edge in precision medicine.
Showcasing generative models that craft hyper‑personalized outreach messages and informed consent materials, driving up engagement rates and shaving weeks off recruitment timelines.
Discover how ML‑driven forecasts for recruitment rates and optimized site selection translate into faster first‑patient‑in and lower screen‑fail/dropout rates, saving you both time and budget.
Claire Zhao
Associate Director, AI/ML & Quantitative & Digital Sciences
PFIZER
Learn how AI models enhance physics-based simulations to predict molecular interactions and optimize drug design.
Discover the synergy between machine learning and classical methods to accelerate screening and improve the accuracy of drug discovery.
Sreyoshi Sur
Former Scientist, Molecular Engineering & Modeling
Moderna