March 2025 Events
Join us for March Data talks at Temple University! In partnership with TUAmStat and Temple's Data Science Club. TUAmStat: The purpose of this organization shall be to foster in the broadest manner, statistical science, and its applications, and to promote unity among all groups at Temple University having an interest in statistical science or data analytics. As an official chapter of the American Statistical Association, this organization shall aim to expose students to both opportunities and experiences within professional fields relating to the application of both statistical science and data analytics. Website: https://www.templeamstat.com LinkedIn: @TU American Statistical Association Instagram: @tuamstat
Temple Data Science Community (TDSC) is a student organization in the Computer & Information Sciences department that focuses on interdisciplinary data science and its applications. Through workshops, guest speakers, networking events, and social events, we create a community to bring together all students interested in data science, regardless of their major. Instagram is @tu_dsc
Event Schedule:
Doors opens at 6:00 pm ET
6:00 - 6:45: Event start and networking
6:45 - 7:00 DataPhilly kicks off event
7:00 - 7:35: Marc Leprince, Senior Risk Consultant & Solutions Architect, https://bambou.xyz: "Running Credit Risk Models at Scale - Components, Methodology & Governance of Generating Regulatory Reports"
7:35 -7:45: Break
7: 45 - 8:20: Brian M. Green, MS | Chief AI Ethics Officer & Strategist, Health-Vision.AI, Website, MediumBlog: "Terminator vs. Tricorder: Autonomous or Assisted AI Futures and the Need for AI Governance."
After 8:20: Networking time
Speakers
Marc Leprince, Senior Risk Consultant & Solutions Architect, https://bambou.xyz: "Running Credit Risk Models at Scale - Components, Methodology & Governance of Generating Regulatory Reports"
Summary: This presentation will cover and review the context of credit risk models, (what they are and why they are run) and also how they are built and the quirks of them that make them unique from other kinds of models. Then I will cover how these aspects translate into a model execution environment that has layers of access controls and governance to ensure data accuracy and integrity throughout the process of generating regulatory numbers for a bank's financial statements. I will draw parallels to how it takes and expands upon the stages of a modeling project or modeling course - and how in this application there are significant deviations from what might be seen in a classroom setting.
Speaker Bio: Marc Leprince graduated from Villanova in 2015 with a BA in MIS. Starting as a technology consultant at SAS in Raleigh, he grew familiar with configuring software solutions for Model Risk and Credit Risk business requirements. This later led to becoming an implementation architect in charge of installation, configuration and design of software installations, upgrades and integrations. Transitioning to pre-sales, he took on the role of a solutions architect for financial services companies. In the last 4 years, he has returned to consulting as a lead consultant and architect helping large banks and insurance companies implement, optimize, and streamline regulatory reporting software, processes, data pipelines. These help teams effectively use software recouping their IT investments and gain business agility and speed.
Brian M. Green, MS | Chief AI Ethics Officer & Strategist, Health-Vision.AI, Website, MediumBlog: "Terminator vs. Tricorder: Autonomous or Assisted AI Futures and the Need for AI Governance."
Summary: Society stands at a crossroads between two futures: one where autonomous systems operate independently, risking unintended harm (Terminator), and another where AI serves as a collaborative tool to augment human decision-making (Tricorder). This presentation explores these divergent paths' ethical, technical, and societal implications. It underscores the urgent need for robust AI governance and data governance to steer innovation toward equitable and accountable outcomes. This discussion contrasts the risks of unchecked autonomy (e.g., biased algorithms, opaque decision-making) with the promise of human-centered AI collaboration (e.g., contextual and layered explainability). It examines how governance frameworks like the EU AI Act, NIST AI Risk Framework, and sector-specific regulations can mitigate harm while fostering trust in AI systems. The presentation also addresses critical implementation challenges, including regulatory fragmentation, model evaluation challenges, algorithmic transparency gaps, and the tension between innovation speed and ethical safeguards. By advocating for hybrid governance models that blend proactive risk assessments, dynamic consent mechanisms, and cross-sector collaboration, the talk offers actionable strategies to align AI development with societal values. Ultimately, this session argues that the choice between Terminator and Tricorder futures hinges not on technological capability but on AI governance. Policymakers, technologists, and civil society must collaborate to ensure that AI remains a force that empowers human agency, equity, and safety.
Speaker Bio: Brian M. Green is the founding Chief AI Ethics Officer and strategist at Health-Vision.AI, LLC. With a strong foundation in data analytics, digital innovation, and AI strategy, Brian consults with startups and other businesses to align AI use cases and integrations with business goals. He specializes in helping organizations navigate the complexities of responsible AI adoption and ensures strategies are impactful and sustainable.