Friday Lunch Keynote
The Guardrails of Progress: Aligning Intelligent Systems with Community, Health, and Human Values
Dusan Ramljak, Assistant Professor of Software Engineering, Penn State Great Valley
Keynote Summary
Artificial intelligence (AI) is rapidly evolving from a decision-support technology into a collaborative partner capable of recommending actions, coordinating workflows, and influencing outcomes across industries.
As organizations increasingly adopt agentic AI systems, the challenge is no longer simply building smarter algorithms but ensuring that intelligent systems operate within guardrails that preserve trust, accountability,
and human well-being. In this keynote, attendees will see how data driven decision making tools can be repurposed to build trust in AI systems from the outset, while preserving human authority over decisions.
Central to the discussion is the concept that trust must be treated as a design requirement rather than an afterthought. Examples from Penn State Great Valley faculty and industry collaborative interdisciplinary projects
illustrate how AI can support partnership development, strategic planning, talent matching, project coordination, organizational learning, address opioid-related social and economic harms, help with cybersecurity readiness,
medicinal chemistry, and improving athletic performance. In this keynote, it will be demonstrated how organizations can harness the benefits of agentic AI without surrendering responsibility.
Ultimately, The Guardrails of Progress keynote attendees will be offered a vision for an AI-enabled future where innovation and responsibility advance together. That vision includes the integration of human judgment,
community priorities, health considerations, and trust-centered governance into the design of intelligent systems that is the practical mechanisms for creating AI that amplifies human potential while preserving the
values that define and strengthen our society.
Dusan's Bio
Dusan Ramljak, Assistant Professor of Software Engineering, joined Penn State Great Valley in 2021. His research, teaching, and mentoring activities are deeply rooted in advanced computing, data science, machine learning,
digital twins, and responsible Artificial Intelligence (AI). These areas define his academic identity and position him and his students well to work with multiple industry partners and collaborators across the Pennsylvania
State University, the United States and the world.
His research portfolio includes more than fifty publications across data-driven decision making, combinatorial optimization, predictive modeling, blockchain systems, learning analytics, cybersecurity, and health and
environmental applications. These works share a common foundation: leveraging intelligent algorithms to improve real-time decision-making in complex environments. Recent projects funded under the inaugural Penn State’s
Presidential Public Impact Research Award (PPIRA) employed machine learning, AI, and cross-disciplinary data to address opioid-related social and economic harms and cybersecurity readiness. The methodological frameworks
developed, particularly those related to continuous monitoring, adaptive risk modeling, sensor-driven analytics, and optimization under uncertainty, translate naturally to the newest project of building trust in agentic AI frameworks.
In addition to his scholarly contributions, he leads multiple student research teams each year, including approximately a dozen part-time research support students and dozens of volunteers from Information Science,
Data Analytics, AI, and Software Engineering programs. This research support prepares students to work effectively with complex datasets, develop interpretable models, and communicate insights aligned with scientific
and community-based applications. Students consistently demonstrate strong proficiency, creativity, and professionalism, qualities that support his vision of aligning intelligent systems with community, health, and
human values. He has been actively supporting student initiatives and initiatives to bring science closer to a wider audience.