01
Animation
- After Effects
- C4D
Microsoft
- Filming
Project overview
Objective
I had the opportunity to work with Microsoft on a project aimed at educating healthcare professionals about the revolutionary potential of AI-driven cancer research, specifically in collaboration with UHN. The goal was to communicate complex scientific concepts, such as precision medicine and data analysis across multiple cancer treatment platforms, in an accessible and engaging way.
- Client
- Microsoft
- Industry
- Healthcare
- Year
- 2020
- Piece
- (05/10)
The transformation
Challenge
Explain how three AI platforms (PharmacoDB, SYNERGx, and CReSCENT) advance precision cancer medicine to a healthcare-professional audience, from abstract data concepts with no natural visual.
Approach
I paired directed on-location interviews with custom 3D and 2D animation in Cinema 4D and After Effects, turning cancer cells and each platform into simplified but scientifically accurate models, accessible without dumbing it down.
Outcome
Delivered a broadcast-quality explainer (original animation plus interviews and B-roll) that gave Microsoft and UHN a clear way to communicate AI-driven cancer research to clinicians.
Process& tools.
02
Video Editing
- FCPX
- Davinci Colour correction
03
Filming
- Ursa Pro
- Canon C200
- Canon r5
- Movi
04
Timelapse
- Canon 5D
In the work.
Screens and stills from the project.
On location.
Shots from the room where it happened.
The solution.
Solution
With a focus on clarity and engagement, I developed a series of 3D and 2D animations in Cinema 4D and After Effects to demystify the roles of PharmacoDB, SYNERGx, and CReSCENT within AI-powered cancer treatment. Drawing on research and scientific visual references, I created simplified models of cancer cells and other cellular structures to make the material both visually approachable and scientifically accurate.
Beyond animation, I was responsible for setting up interviews, including lighting, camera positioning, and lavalier microphone placement, ensuring high production quality. I also captured b-roll and time-lapse footage to enhance storytelling, and handled the film’s editing and color correction to maintain a polished and cohesive visual style.
By blending technical expertise with storytelling, my contributions supported Microsoft’s mission to educate healthcare professionals and highlight AI’s transformative potential in cancer research.
Inmotion.
Read transcript
Platforms such as Crescent, PharmacoDB, Synergix, and cBioPortal are using Azure to solve a problem that couldn't be solved before. How do we define a treatment that can target all the cancer cells in a tumor? Recent advances in single cell sequencing allow scientists to simultaneously analyze tens of thousands of tumor cells. Crescent uses this huge volume of genomic data to identify cancer cell populations with incredible fidelity. Through machine learning, these cancer cells can be compared with cancer cells from all other cancer patients to understand and predict the effect of treatment. This is significant because each tumor, really each patient, adds to our understanding of cancer and how it responds to treatment over time. PharmacoDB is a deep learning platform built on Azure that develops novel predictors of gene drug response. This system brings cancer drug research closer to clinicians than ever before. Clinicians can now mine this massive body of research to explore every gene, every drug, and every cancer type. Synergix is the machine learning platform that brings it all together. It takes the patient cancer cell information from Crescent and the gene drug matching results from PharmacoDB and runs an AI based simulation on each cell in a patient's tumor. It looks for the most effective drug combinations among hundreds of thousands of drugs, meaning that now we can target all the cells in a tumor on multiple fronts simultaneously.
Read transcript
Cancer reoccurrence is mainly due to the inability to treat cancer effectively. At Microsoft, we're trying to change that. We're working with the University Health Network and the Princess Margaret Research Center to bring precision medicine to life. Healthcare researchers now have access to Microsoft's Azure Cloud and advanced artificial intelligence tools, accelerating research projects in ways never before possible. At UHN, scientists have developed powerful machine learning platforms and algorithms to drive cancer research and treatment. Platforms such as Crescent, PharmacoDB, Synergix, and cBioPortal are using Azure to solve a problem that couldn't be solved before. How do we define a treatment that can target all the cancer cells in a tumor? Recent advances in single cell sequencing allow scientists to simultaneously analyze tens of thousands of tumor cells. Crescent uses this huge volume of genomic data to identify cancer cell populations with incredible fidelity. Through machine learning, these cancer cells can be compared with cancer cells from all other cancer patients to understand and predict the effect of treatment. This is significant because each tumor, really each patient, adds to our understanding of cancer and how it responds to treatment over time. PharmacoDB is a deep learning platform built on Azure that develops novel predictors of gene drug response. This system brings cancer drug research closer to clinicians than ever before. Clinicians can now mine this massive body of research to explore every gene, every drug, and every cancer type. Synergix is the machine learning platform that brings it all together. It takes the patient cancer cell information from Crescent and the gene drug matching results from PharmacoDB and runs an AI based simulation on each cell in a patient's tumor. It looks for the most effective drug combinations, meaning that now we can target all the cells in a tumor on multiple fronts simultaneously. These findings are then validated in the lab, and the results are then sent to cBioPortal, the leading oncology platform in Canada. This is the platform that actually enables oncologists to interact with the findings that the researchers who work in the labs are uploading into that portal. For the first time ever, they're going to be able to log into a portal and actually see what specific drug types are going to work for the patient that is sitting right in front of them. With these platforms now on Azure, institutions across Canada can share their own data, creating a network that enables high resolution genomic analysis for every Canadian. This is one of the most pivotal moments in the history of health care. Not only do we have access to an enormous amount of data, we can actually use artificial intelligence to process that data and bring precision medicine to life.