🤖🧬 Where Technology Meets Science

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Appsilon

15 episodes

4 months ago

Where Science Meets Technology by Appsilon is a series of interviews and discussions focused on how technology can accelerate data science processes, particularly within life sciences and biopharmaceutical companies.

Each episode will explore various aspects of innovation and its potential to address challenges where traditional methods may fall short. We will feature internal experts from our delivery teams, along with clients and partners as guest speakers, providing valuable insights into the intersection of science and technology.

The podcast is brought to you by Appsilon.

Appsilon empowers Fortune 500 companies to leverage open-source technology for faster, data-driven decision-making in regulated environments

All content for 🤖🧬 Where Technology Meets Science is the property of Appsilon and is served directly from their servers with no modification, redirects, or rehosting. The podcast is not affiliated with or endorsed by Podjoint in any way.

Episodes (15/15)

🤖🧬 Where Technology Meets Science

#010 Centralized Monitoring: Risk-based Approach to Clinical Trial Processes

#datascience #centralizedmonitoring #clinicaltrials #technologyinpharma #pharmatransformation

In this episode, Jennifer Krohn, Associate Director of Risk-Based Quality Management at Gilead Sciences, shares how centralized monitoring is transforming clinical trial oversight, improving data quality, participant safety, and trial efficiency. From statistical tools and open-source innovation to CRA training and AI advancements, Jenn shares what it takes to detect critical data signals earlier, ensure trial integrity, and foster cross-functional collaboration in pharma through the open-source community.

Materials shared in the episode:
PHUSE RBQM Working Group RBQM Education Project
PHUSE CM White Papers:

Jennifer Krohn
https://www.linkedin.com/in/jenn-krohn/
Nat Chrzanowskahttps://www.linkedin.com/in/nat-chrzanowska/
__________________________________________
More about Appsilon:
► https://www.appsilon.com/
Appsilon empowers pharmaceutical and life sciences companies to leverage open-source technology for faster, data-driven decision-making in regulated environments.

Schedule a free consultation with our expert ► https://www.appsilon.com/contact-us
_________________________________________
For more insights about how technology helps scientists push the boundaries of data analysis and reporting check out our blog:
► http://appsilon.com/blog
LinkedIn: https://www.linkedin.com/company/appsilon/

4 months ago

41 minutes

🤖🧬 Where Technology Meets Science

#009 GenAI in Clinical Reporting: Beyond the Buzz, Real Use Cases, and Future Directions

#datascience #genAI #clinicaltrials #clinicalreporting #AIinpharma #GenerativeAI #Roche #AIautomation #pharmaceuticalindustry #AIchallenges #AIbot #chatbots #copilot #codingassistant

In this episode, we speak with Vincent Shen, Senior Principal Data Scientist at Roche, about the actual impact of generative AI in clinical data analysis. Vincent shares insights from his work implementing AI-powered tools, such as knowledge chatbots and coding assistants, that support clinical reporting at Roche. We discuss the evolving role of AI, the challenges of integrating it into clinical trial workflows, and the importance of rethinking the processes to ensure effective AI use.In this episode:

AI will augment rather than replace data scientists in clinical trial workflows.
Roche has successfully deployed a GenAI-powered knowledge bot, saving an estimated 25 hours per 100 questions.
Coding assistants help data science teams accelerate time-to-insight.
Providing detailed specifications is essential for AI to deliver accurate and meaningful results.
Training AI models requires extra effort to ensure they correctly interpret context and domain-specific requirements.
The next big step is to rethink how processes are designed, not just automate existing ones.

Vincent Shen
https://www.linkedin.com/in/vincent-shen-vs/
Nat Chrzanowska
https://www.linkedin.com/in/nat-chrzanowska/
__________________________________________
More about Appsilon:
► https://www.appsilon.com/

Appsilon empowers pharmaceutical and life sciences companies to leverage open-source technology for faster, data-driven decision-making in regulated environments.
Schedule a free consultation with our expert ► https://www.appsilon.com/contact-us

We design scalable and user-friendly Shiny dashboards to help you make data-driven decisions.
► https://www.appsilon.com/services/data-dashboards
We design and implement Statistical Computing Environment for R and Python for efficient data analysis. ► https://www.appsilon.com/services/sce
We design, implement, and optimise data analysis environments so you can focus focus on insights and innovation. ► https://www.appsilon.com/services/platform

Where Technology Meets Science podcast is available on all podcasting platforms:
► Spotify: https://hubs.li/Q037KddD0
► Apple Podcasts: https://hubs.li/Q037KlLl0
► YouTube: Show more...

5 months ago

35 minutes

🤖🧬 Where Technology Meets Science

#4 Pharma Brief: Generative AI Advances at FDA, Pharma’s Autonomous Agents, and Open-Source Tools Spotlight
#datascience #dataanalysis #technology #pharmabrief #clinicaltrials #technologyinpharma #pharma #pharmanews

Pharma Brief’s fourth edition spotlights generative AI’s rapid expansion across pharma and regulatory landscapes. This issue covers the FDA’s planned rollout of generative AI tools across all centers, including the launch of Elsa to streamline reviews and inspections. We explore Anthropic’s Model Context Protocol as a new standard for context-aware AI, plus pharma’s growing use of autonomous agents in clinical operations and medical writing, with insights from BCG’s latest report.Benchling’s integration of Claude into biotech R&D workflows highlights significant time savings and smarter data management. Catch a recap of the AWS Life Sciences Symposium keynote and FDA’s public call for comments on the CDISC Dataset-JSON standard, which could reshape study data exchange.Open-source tools like {mergen}, {kuzco}, and {cheetahR} are featured to empower your R workflows with AI, computer vision, and blazing-fast data rendering.

You can follow Pharma Brief on LinkedIn: https://www.linkedin.com/newsletters/pharma-brief-7300489155535380480/
Now also available in audio on your favorite podcast platforms.

Links from the episode:
FDA’s Generative AI Rollout: https://www.fda.gov/news-events/press-announcements/fda-announces-completion-first-ai-assisted-scientific-review-pilot-and-aggressive-agency-wide-aiBCG
Report on AI Agents in Pharma: https://www.linkedin.com/posts/bcg-platinion_ai-agents-and-the-model-context-protocol-activity-7330864988070309891-zhCZ
Benchling + Claude Integration: https://www.anthropic.com/customers/benchling
AWS Life Sciences Symposium 2025 & Keynote: https://aws.amazon.com/blogs/industries/7th-annual-aws-life-sciences-symposium/
https://www.youtube.com/watch?v=3hohXZF_3ys
FDA Request for Comments on CDISC Dataset-JSON: https://www.federalregister.gov/documents/2025/04/09/2025-06051/electronic-study-data-submission-data-standards-clinical-data-interchange-standards-consortium

Open Source Highlights:
{muttest} https://github.com/jakubsob/muttest
{mergen}   https://bioinformatics.mdc-berlin.de/mergen/
gptstudio & gpttools https://github.com/MichelNivard/gptstudio
{kuzco}  https://github.com/frankiethull/kuzco
{cheetahR} Show more...
5 months ago
6 minutes

🤖🧬 Where Technology Meets Science

#008 Machine Learning Modeling in Neuroscience Clinical Trials Design
#datascience #dataanalysis #technology #machinelearning #clinicaltrials #placebo

In this episode, Jing Dai, Director of Biostatistics at Jazz Pharmaceuticals, shares insights from the PHUSE US Connect conference and her work on applying machine learning to neuroscience clinical trials. She discusses challenges like high placebo response and attrition, the value of interdisciplinary collaboration, and how AI/ML can shape trial design, improve regulatory readiness, and move the field toward more objective, data-driven outcomes.
In this episode, you will learn:
How machine learning can help address high placebo response and attrition in neuroscience clinical trials.
Why traditional statistical models struggle with high-dimensional clinical data.
Key regulatory frameworks (GxP, GMLP) for ensuring AI/ML models meet compliance standards in drug development.
Practical tips for fostering interdisciplinary collaboration between biostatisticians, clinicians, and data scientists.
Nat Chrzanowska https://www.linkedin.com/in/nat-chrzanowska/
Jing Dai https://www.linkedin.com/in/jingdai1009/
__________________________________________

More about Appsilon: ► https://www.appsilon.com/

Appsilon empowers pharmaceutical and life sciences companies to leverage open-source technology for faster, data-driven decision-making in regulated environments.

Schedule a free consultation with our expert

► https://www.appsilon.com/contact-us
__________________________________________

For more insights about how technology helps scientists push the boundaries of data analysis and reporting check out our blog: ► http://appsilon.com/blog

LinkedIn: https://www.linkedin.com/company/appsilon/
Show more...
5 months ago
34 minutes

🤖🧬 Where Technology Meets Science

#007 How Open Source and Community Efforts Drive R-Based FDA Submissions
#datascience #dataanalysis #technology #datascience #opensource #pharmaverse #pharma #dataanalysis #clinicaltrials

In this episode, Ben Straub, Principal Programmer at GSK, explores the shift from proprietary software to open source tools in the pharmaceutical industry. He shares insights into regulatory challenges, the rise of R, and the impact of {admiral} and other open-source packages on clinical data analysis and submissions. From pilot programs to enterprise-wide adoption, learn why collaboration is key to reducing risk and shaping the future of regulatory workflows.

In this episode, you will learn:
Lessons learned from GSK’s open source adoption journey,
How collaboration via pharmaverse, PHUSE, and R Consortium accelerates regulatory submissions
How {admiral} package for ADaM datasets was one of the triggers for widespread open source adoption in pharma
Why R is a dominant language in pharma
What are the next milestones to modernize outdated regulatory standards
Webinar about GSK's R Journey: From Pilot Projects to Enterprise Adoption: https://www.youtube.com/watch?v=xDrt6txplek
Admiral package: https://pharmaverse.github.io/admiral/ __________________________________________
More about Appsilon:
► https://www.appsilon.com/
Appsilon empowers pharmaceutical and life sciences companies to leverage open-source technology for faster, data-driven decision-making in regulated environments.
Schedule a free consultation with our expert
► https://www.appsilon.com/contact-us
__________________________________________
For more insights about how technology helps scientists push the boundaries of data analysis and reporting check out our blog:
► http://appsilon.com/blog
LinkedIn: https://www.linkedin.com/company/appsilon/
Show more...
6 months ago
35 minutes

🤖🧬 Where Technology Meets Science

#3 Pharma Brief: FDA's Animal Testing Phase-Out, AI in Clinical Development, and Open-Source News
#datascience #dataanalysis #technology #pharmabrief #clinicaltrials #technologyinpharma #pharma #pharmanews

Pharma Brief is back with its third edition, packed with essential industry insights and the latest developments in pharma and biotech. This issue covers the FDA’s plan to phase out animal testing for monoclonal antibodies in favor of AI models and NAMs, insights from Stanford’s AI Index 2025 and McKinsey’s analysis of AI in clinical development, plus new tools like CDISC Dataset Generator, scMultiSim, open-source releases from Novo Nordisk and Genentech and more!

You can follow Pharma Brief on LinkedIn: https://www.linkedin.com/newsletters/pharma-brief-7300489155535380480/
And now it’s available in audio on all your favorite podcasting platforms.

Links from the episode:
FDA’s New Plan for Phasing Out Animal Testing: https://www.fda.gov/news-events/press-announcements/fda-announces-plan-phase-out-animal-testing-requirement-monoclonal-antibodies-and-other-drugs
Stanford’s AI Index Report 2025: https://hai-production.s3.amazonaws.com/files/hai_ai_index_report_2025.pdf
Summary & takeaways: https://hai.stanford.edu/ai-index/2025-ai-index-report
McKinsey & Company’s article on clinical development with AI & ML: https://www.mckinsey.com/industries/life-sciences/our-insights/unlocking-peak-operational-performance-in-clinical-development-with-artificial-intelligence
CDISC Dataset Generator for Synthetic Data: https://cdiscdataset.com/
scMultiSim for Omics Data Simulation: https://www.nature.com/articles/s41592-025-02651-0
Revolutionising Participant Safety Monitoring with Advanced Solutions (Risk Based Quality Management PHUSE Working Group): https://phuse.s3.eu-central-1.amazonaws.com/Archive/2025/Webinar/Worldwide/Virtual/REC_CF12.mp4 Slides: https://phuse.s3.eu-central-1.amazonaws.com/Advance/Community+Forums+/Revolutionizing+Participant+Safety+Monitoring+with+Advanced+Solutions.pdf
Clinical Data Analysis: Open Source in Pharma (Free eBook): https://hubs.li/Q03kf2Pk0
FDA Pilots Session (ShinyGatherings): https://youtu.be/zZMGFq57wnE
Open-Source packages:
Novo Nordisk’s {connector} Package: https://novonordisk-opensource.github.io/connector/
Genentech’s BRAID Foundation Models: Show more...
6 months ago
6 minutes

🤖🧬 Where Technology Meets Science

#006 Inside Novo Nordisk’s Path to Open Source for the Pharma and Beyond
Ari Siggaard Knoph from Novo Nordisk shares how the company transitioned from SAS to R for FDA submissions. From early Shiny apps to full-scale open-source workflows, learn how this shift accelerated innovation, attracted top talent, and redefined clinical programming.

In this episode, you will learn:
How Novo Nordisk transitioned from legacy systems to R for regulatory submissions
How Shiny applications became the catalyst for internal buy-in
How open-source adoption has broadened the company’s talent pool and improved productivity
Future directions in pharma tech
__________________________________________
More about Appsilon:
► https://www.appsilon.com/
Appsilon empowers pharmaceutical and life sciences companies to leverage open-source technology for faster, data-driven decision-making in regulated environments.
Schedule a free consultation with our expert
► https://www.appsilon.com/contact-us
__________________________________________
or more insights about how technology helps scientists push the boundaries of data analysis and reporting check out our blog:
► http://appsilon.com/blog
LinkedIn: https://www.linkedin.com/company/appsilon/
Show more...
6 months ago
32 minutes

🤖🧬 Where Technology Meets Science

#005 Shiny’s Evolution: From Prototyping Tool to Critical Technology in Pharma
#datascience #dataanalysis #technology #clinicaltrials #rshiny #shinyforpython #pharmatech

Shiny paved the way for R users to create interactive, production-ready applications without switching stacks. In this episode, Eric Nantz reflects on Shiny’s origins, its "lazy by design" reactivity model, and how the ecosystem matured. We dive into how Shiny for Python expands this power to new audiences, and how Shiny is becoming key to modern clinical trial workflows. Eric shares real-world examples, user reactions, and the future of interactive data science.

In this episode, you will learn:
How Shiny became a game changer for people working with R
How the framework evolved from a simple prototyping tool to a critical asset in life sciences
How open source is surpassing proprietary software
The future of Shiny in drug development
Links for the episode:
R-Podcast Episode 18 Interview with Joe Cheng https://r-podcast.org/018-interviews-with-the-rstudio-team/
Joe Cheng - The Past and Future of Shiny (rstudio::conf 2022 keynote) https://www.youtube.com/watch?v=HpqLXB_TnpI
echarts4r - Interactive visualizations for R via Apache ECharts https://echarts4r.john-coene.com
reactable -Interactive data tables https://glin.github.io/reactable/
htmlwidgets Gallery https://gallery.htmlwidgets.org/
renv - Project environments for R https://rstudio.github.io/renv/articles/renv.html
rix - Reproducible data science environments for R with Nix https://docs.ropensci.org/rix/
R Weekly https://rweekly.org
R Weekly Highlights podcast https://serve.podhome.fm/r-weekly-highlights
Shiny Developer Series https://shinydevseries.com
__________________________________________
More about Appsilon: ► https://www.appsilon.com/
Appsilon empowers pharmaceutical and life sciences companies to leverage open-source technology for faster, data-driven decision-making in regulated environments.
Schedule a free consultation with our expert
► https://www.appsilon.com/contact-us
_________________________________________
For more insights about how technology helps scientists push the boundaries of data analysis and reporting check out our blog:► http://appsilon.com/blog
LinkedIn:Show more...
7 months ago
35 minutes

🤖🧬 Where Technology Meets Science

#2 Pharma Brief: AI’s Impact on Clinical Trials, Must-See Open Source Tools, and Upcoming Pharma Events
Pharma Brief is back with the latest edition, packed with valuable insights and upcoming events you don’t want to miss. This month, we’re exploring AI’s impact on clinical development (trials could be 30% faster!), along with innovative tools that streamline processes. We’ve also highlighted open-source packages to optimize your workflows.

You can follow Pharma Brief on LinkedIn: https://www.linkedin.com/newsletters/pharma-brief-7300489155535380480/ And now it’s available in audio on all your favorite podcasting platforms.

Links from the episode:
NVIDIA’s “State of AI in Healthcare and Life Sciences” Report: https://blogs.nvidia.com/blog/ai-healthcare-life-sciences-survey-2025/
McKinsey & Company’s “Faster, Smarter Trials: Modernizing Biopharma’s R&D IT Applications”: https://www.mckinsey.com/industries/life-sciences/our-insights/faster-smarter-trials-modernizing-biopharmas-r-and-d-it-applications
NovoScribe by Novo Nordisk: https://www.mongodb.com/solutions/customer-case-studies/novo-nordisk
'Not a cost-cutting exercise': Novo Nordisk Chief Scientific Officer Marcus Schindler explains rationale behind pharma's R&D shake-up: https://www.fiercebiotech.com/biotech/not-cost-cutting-exercise-novo-nordisk-reshapes-rd-setup
Novartis joins the Pharmaverse Council: https://pharmaverse.github.io/blog/posts/2025-03-14_welcoming__.../welcoming__novartis_to_the__pharmaverse__council!.html
Johnson & Johnson's Open Source Journey with R in Clinical Trials | Webinar | Posit https://www.youtube.com/live/_bqeYh2kNgY
Why Clinical Trials Fail | Where Technology Meets Science Podcast | Appsilon https://youtu.be/UXfUUA1vo4I
Open-Source packages:
mall: https://mlverse.github.io/mall/querychat: https://github.com/posit-dev/querychat
multideploy: https://r-pkg.thecoatlessprofessor.com/multideploy/chatlas: https://posit.co/blog/announcing-chatlas/
nipals: https://github.com/johnsonandjohnson/open_nipals

Events:
Real World Data Spring Event 2025 | 9–10 April 2025 | Virtual
ShinyConf 2025: Life Sciences/Pharma track | 9-11 April, 2025 | Virtual
PHUSE Risk Based Quality Management Working Group Webinar “Revolutionising Participant Safety Monitoring with Advanced Solutions” | 23 April, 2025 | Virtual
Shiny Gathering: FDA Pilots: Key Insights, Lessons...
Show more...
7 months ago
5 minutes

🤖🧬 Where Technology Meets Science

#004 Technology Transformation in Clinical Trial Analysis
#datascience #dataanalysis #technology #clinicaltrials #opensource #rshiny #techtransformation

The opinions shared here do not represent the official position of Roche or Novartis.

We explore why clinical trials lag behind in innovation—even as the research side of drug development makes massive leaps. We’ll uncover why attracting top talent is critical to driving the industry forward, particularly in adopting open-source technologies. Dive into tools like R, test automation frameworks, and data abstractions, and learn how they’re reshaping tech transformation in clinical trials. Joining us are Novartis experts Orla Doyle and James Black to unpack the challenges and opportunities ahead.

In this episode, you will learn:
Why R and open-source tools are dethroning outdated proprietary systems
How modern tools attract new talent and retain seasoned experts
The persistent inefficiency puzzle in clinical data analysis
Decentralized trials: balancing accessibility with data integrity
Building regulatory trust in open-source and agile workflows
AI’s future in trials, decentralized models, and global standards

Orla Doyle
► https://www.linkedin.com/in/orla-doyle-53a5019a/
James Black
► https://www.linkedin.com/in/epijim/
Nat Chrzanowska
https://www.linkedin.com/in/nat-chrzanowska/
__________________________________________
More about Appsilon:
► https://www.appsilon.com/
Appsilon empowers pharmaceutical and life sciences companies to leverage open-source technology for faster, data-driven decision-making in regulated environments. Schedule a free consultation with our expert
► https://www.appsilon.com/contact-us
__________________________________________
For more insights about how technology helps scientists push the boundaries of data analysis and reporting check out our blog:
► http://appsilon.com/blog
LinkedIn: https://www.linkedin.com/company/appsilon/
Show more...
7 months ago
40 minutes

🤖🧬 Where Technology Meets Science

#1 Pharma Brief: J&J Moves to Open Source, AI-powered Data Extraction, and Top Clinical Trials for 2025
Pharma Brief delivers your monthly pulse on pharma’s evolving landscape. Stay ahead with curated insights on FDA/EMA updates, AI-driven trends, and must-attend global events. We unpack breakthroughs—from novel drug approvals to open-source tools like R/Shiny—and arm you with actionable resources to in tech transformations in clinical trials. Whether it’s GxP compliance strategies or clinico-genomic advancements, we’re here to sharpen your expertise.
You can follow it on LinkedIn: https://www.linkedin.com/newsletters/pharma-brief-7300489155535380480/ and now it’s available in the audio on all your favorite podcasting platforms.
Links from the episode:
The FDA draft guidance on AI: https://www.fda.gov/news-events/press-announcements/fda-proposes-framework-advance-credibility-ai-models-used-drug-and-biological-product-submissions
AWS re:Invent Talk about Natera’s AI-Powered Data Extraction: https://www.youtube.com/watch?v=csbfwFbg8zE
2024 CDER Novel Drug Approvals: https://www.fda.gov/drugs/novel-drug-approvals-fda/novel-drug-approvals-2024
360i Kick off CDISC recording: https://www.cdisc.org/events/webinar/360i-program-kickoff-enabling-standards-driven-automation-study-design-through
The 360i Program is a transformative initiative designed to automate the entire clinical research data lifecycle. You can watch the recording here and the slide deck here.
Top clinical trials for 2025: https://www.nature.com/articles/s41591-024-03383-y/tables/1
A new study on personalized cancer treatment: https://www.nature.com/articles/s41467-024-55251-5
Generative Artificial Intelligence (AI) on AWS: https://aws.amazon.com/developer/generative-ai/?ref=dc&id=b2
You can now learn and build AI applications with Amazon Bedrock, SageMaker, and more.
Johnson & Johnson's Open Source Journey with R in Clinical Trials”: https://www.youtube.com/watch?v=IloY1jiDD_Y
About Pharmaverse: https://pharmaverse.org/
Overview of CDISC standards and their role in supporting clinical research: https://www.cdisc.org/standards
Recordings from the recent GxP Validation Summit: Show more...
7 months ago
5 minutes

🤖🧬 Where Technology Meets Science

#003 The Future of GxP Compliance in Pharma: R-based Submissions
#datascience #dataanalysis #GxP #PharmaTech #SoftwareValidation #FDARegulations #ClinicalTrials #LifeSciences #R #PharmaInnovation #TechInPharma
GxP compliance is shaping the future of pharmaceutical software engineering, yet many companies still struggle with implementation. In this episode, Appsilon’s co-founder, Paweł Przytuła, unpacks GxP best practices, software validation, and how R-based submissions are gaining traction with regulatory bodies like the FDA. He also shares industry insights from Roche, Novo Nordisk, and GSK, explores emerging trends like AI in validation, and discusses how pharma companies can leverage open-source tools while ensuring compliance. Don’t miss this deep dive into the intersection of software and life sciences!
In this episode, you will learn:
What is GxP in software engineering and why it’s critical for regulatory approval in life sciences
What GxP compliance means for pharma software
How leading pharma companies approach R-based submissions
What are the risks of ignoring GxP in software development
How AI, statistical computing environments (SCEs), and open-source innovations are transforming software validation in pharma
How to build a strong GxP-ready data science team

Guest: Paweł Przytuła https://www.linkedin.com/in/pawelprzytula
Your host: Nat Chrzanowska https://www.linkedin.com/in/nat-chrzanowska/
__________________________________________
Mentioned resources:
Paper on R-based Submissions to the FDA: https://pharmasug.org/proceedings/2024/SS/PharmaSUG-2024-SS-344.pdf
Presentation on Novo Nordisk's Journey towards open source: https://phuse.s3.eu-central-1.amazonaws.com/Archive/2023/Connect/EU/Birmingham/PRE_TT12.pdf
GSK's R Journey to Open Source: https://www.youtube.com/watch?v=xDrt6txplek&ab_channel=PositPBC
Roche's End-to-End R Journey to Submission: https://www.youtube.com/watch?v=BlJNILSoZlM&ab_channel=PositPBC
__________________________________________
More about Appsilon:
► https://www.appsilon.com/
Appsilon empowers pharmaceutical and life sciences companies to leverage open-source technology for faster, data-driven decision-making in regulated environments. Schedule a free consultation with our expert
► https://www.appsilon.com/contact-us
__________________________________________
For more insights about how technology helps scientists push the boundaries of data analysis and reporting check out our blog:
► http://appsilon.com/blog
LinkedIn: https://www.linkedin.com/company/appsilon/
Show more...
8 months ago
34 minutes

🤖🧬 Where Technology Meets Science

#002 How NASA Chooses the Right Talent for Space Exploration
In this episode of Technology Meets Science, we welcome David Meza, Head of Analytics at NASA’s Human Capital Office. David shares how data science and AI are transforming talent management at NASA, ensuring the right individuals are matched with critical missions. From knowledge graphs to generative AI, he explains how NASA is leveraging technology to optimize workforce planning, upskilling, and recruitment. Tune in for insights on AI-driven talent analytics, the challenges of data accessibility, and what it takes to land a career at NASA.

In this episode, you will learn:
How NASA uses data science, analytics, knowledge graphs, and AI to align employees' skills and competencies with space mission roles
How NASA tackles cleaning and structuring workforce data
How AI models help to assess employee skills, identify gaps, and recommend training programs to upskill the workforce.
How open-source tech stack, including Python, R, SQL, Databricks, and knowledge graphs enable smarter decision-making
What it takes to work at NASA, from passion-driven careers to the importance of continuous learning
__________________________________________

More about Appsilon:
► https://www.appsilon.com/
Appsilon empowers pharmaceutical and life sciences companies to leverage open-source technology for faster, data-driven decision-making in regulated environments.
Schedule a free consultation with our expert
► https://www.appsilon.com/contact-us

__________________________________________

For more insights about how technology helps scientists push the boundaries of data analysis and reporting check out our blog:
► http://appsilon.com/blog
LinkedIn: https://www.linkedin.com/company/appsilon/
Show more...
8 months ago
27 minutes

🤖🧬 Where Technology Meets Science

#001 Why Clinical Trials Fail: Lessons from Two Decades in Pharma
Together with Eric Genevois-Marlin, former Head of Data Sciences in R&D and Biostatistics at Sanofi, we uncover the reasons behind clinical trial failures. Drawing from his 20+ years of experience leading science and technology teams in bringing new drugs to market, we'll examine real-world examples and extract lessons from both successes and failures.In this episode, you will learn:
What the risks are in clinical trials
The main causes of clinical trial failures
Examples of success stories
Lessons learned from clinical trials that go wrong
How to minimize the risks of failure
__________________________________________
More about Appsilon:
► https://www.appsilon.com/

Appsilon empowers pharmaceutical and life sciences companies to leverage open-source technology for faster, data-driven decision-making in regulated environments.

Schedule a free consultation with our expert
► https://www.appsilon.com/contact-us
__________________________________________
For more insights about how technology helps scientists push the boundaries of data analysis and reporting check out our blog:
► http://appsilon.com/blog
LinkedIn: https://www.linkedin.com/company/appsilon/
Show more...
8 months ago
33 minutes

🤖🧬 Where Technology Meets Science

#000 Where Technology Meets Science by Appsilon Trailer
"Where Technology Meets Science" is a podcast series diving into the intersection of technology and life sciences. We bring together industry experts, innovators, and thought leaders to explore how advanced technologies are reshaping data science processes in biotechnology, biopharma, environmental sciences, genetics, and similar fields. Together with experts from companies like Roche, Novartis, NASA, and J&J, we uncover solutions that address complex challenges, accelerate research, and push beyond traditional scientific methods, offering listeners insights into the evolving landscape of scientific discovery.
Show more...
9 months ago
1 minute