Responses from Faculty and Staff – “Enhancing Transdisciplinary Research at the University of Pittsburgh & Beyond”

Economics & Engineering Seminar (2-20-26)

On February 20, 2026, CIS co-hosted its second "Getting Out of Your Silos" panel discussion with the Department of Economics. This discussion was titled, "Enhancing Transdisciplinary Research at the University of Pittsburgh & Beyond" and took place between the Engineering and Economics faculty in the Economics Department in Posvar Hall.  The discussion was lively and wide-ranging. 

1) Daniel G. Cole (Associate Professor, Mechanical Engineering and Materials Science; Director, Cyber Energy Center, Ph.D., P.E.)

Reducing cyber risk by orders of magnitude requires a multidisciplinary approach that weaves together engineering, computer science, policy, and economic incentives rather than treating each as a separate domain.  Engineers and systems designers can embed security from the earliest stages of a product's lifecycle, eliminating attack surfaces through deliberate architectural choices rather than patching vulnerabilities after the fact.  But this approach only scales when economic signals reward prevention over remediation.  Economists and policy scholars can help realign that by modeling how liability rules, insurance markets, and regulatory standards shift who bears the cost of insecurity, that responsibility falls on the parties best positioned to act.  Computer and network scientists contribute the formal and analytical tools needed to specify system properties with precision, verify that software behaves as intended, and quantify the uncertainties that make cyber risk so difficult to reason about, including questions of provenance, dependency chains, and exploitability.  At the intersection of these fields lies a rich opportunity: building risk-quantification frameworks that translate technical assurance evidence into the language of financial exposure, enabling insurers, regulators, and executives to make better-informed investment decisions. 

Here are three examples of collaborative projects that could help turn these ideas into practice:

1. A Cyber Risk Pricing Model for Critical Infrastructure. An interdisciplinary team of economists, engineers, and computer scientists could develop an empirically grounded model that links measurable technical properties of industrial control systems to sound insurance premiums. This would give operators and insurers a shared, evidence-based language for valuing security investments and could inform regulatory minimum standards across sectors such as energy and water.

2. A Formal Methods Adoption Roadmap for Mid-Sized Organizations. While large technology firms have begun integrating formal verification into their development pipelines, smaller operators of critical systems lack the resources and expertise to follow suit. A collaborative project spanning computer science, policy, and organizational economics could design a tiered, cost-sensitive roadmap for partial certification.  This would identify which software properties are most valuable to verify first, what automation tools lower the barrier to entry, and what policy incentives, such as safe harbor provisions or procurement preferences, would accelerate adoption.

3. Frameworks for Matching Economic and Policy Goals to Impactful Technology. A persistent challenge in cybersecurity is that the technologies with the greatest potential to reduce systemic risk are not always the ones that receive investment, regulatory support, or policy attention. A collaborative project bringing together economists, policy scholars, and technologists could develop structured frameworks for mapping specific policy objectives to the technical interventions most likely to achieve them at scale. By making these connections explicit and evidence-based, such frameworks could help governments, standards bodies, and industry associations direct resources toward the highest-leverage opportunities rather than defaulting to familiar but incremental approaches.

2) Osea Giuntella (Associate Professor of Economics, Department of Economics)

Thank you again for organizing the February 20 discussion. I found it very valuable and came away thinking there is real scope for practical collaboration around shared research infrastructure at Pitt.

One concrete opportunity that may fit well with the Center’s mission would be to explore whether it could help support continued access to the NielsenIQ Consumer Panel (Homescan) data for the broader Pitt research community. The dataset has been an important resource for faculty and PhD students in Economics, and Katz has played a valuable role in making that access possible. As subscription arrangements are reconsidered (and Katz is planning to discontinue or reduce their involvement), it may be worth thinking about whether there is a broader institutional model that could preserve and potentially expand access across Pitt.

This seems especially relevant to the Center’s focus on industry studies, since the data are highly useful for research on prices, consumer demand, product differentiation, retail dynamics, competition, and firm strategy. The Consumer Panel subscription also includes several complementary survey modules at no additional cost, including surveys on health and wellness, economic expectations, financial habits, debt, tax rebates, and additional demographic characteristics. Together, these resources make Nielsen a versatile shared platform for work not only in empirical IO and industry studies, but also in health, labor, household finance, and public policy.

What also makes this especially attractive is the cost-value ratio. My understanding is that the annual institutional fee is about $7,000, while comparable access purchased on an individual basis would be substantially more expensive. Framed that way, this is a relatively modest annual cost for maintaining a research asset with broad use across faculty and graduate students. One could also imagine a cost-sharing arrangement or modest user-fee model to help recover part of the expense.

I thought this might be a useful example of the kind of cross-unit collaboration we discussed, where a shared data resource could support both faculty research and graduate training. I would be very happy to discuss this further if helpful.

And sorry I said Homescan, but the license covers retail scanner, consumer panel and related surveys (https://www.chicagobooth.edu/research/kilts/research-data/nielseniq)

3) Sungwan Hong (Assistant Professor, Department of Economics)

I’m excited about the potential for transdisciplinary collaboration at Pitt, especially around two related research areas. First, I’m interested in combining economics with engineering, policy, and data science to study how trade and industrial policy shape renewable-energy supply chains—especially solar panels, batteries, and wind turbines—and what these policies imply for costs, adoption, and emissions. This could include building shared datasets linking production networks, trade flows, and policy exposure, and pairing quantitative models with technical knowledge about technologies and constraints. Second, I’m eager to collaborate on the spatial impacts of datacenters, including how large electricity-demand shocks affect local electricity prices, grid operations, and carbon emissions. A central question is how AI-driven growth redistributes gains and costs across regions: benefits may concentrate where AI-related activity and high-value downstream services cluster, while datacenter hosting regions bear more of the electricity-system and emissions burdens. I welcome joint projects with colleagues across engineering, public policy, and environmental science to develop empirical strategies, integrate data, and translate findings into actionable guidance for regional planning and decarbonization.

4) Dr. Takashi Daniel Yoshida Kozai (Ernest E. Roth Professor, Department of Bioengineering)

Brain-computer interfaces (BCIs) are transitioning from research tools to clinical technologies, with market projections exceeding $400 billion (Morgan Stanley). Yet the economic frameworks needed to evaluate, price, and sustain these technologies barely exist. My research focuses on the biological features of implant use that degrade device performance over the lifetime of an implant. A defining feature of this problem is that BCIs involve biological and machine co-learning, where the brain continuously adapts to the device while decoding algorithms simultaneously adapt to the brain. This breaks standard models for evaluating safety, efficacy, and cost-effectiveness of medical devices, which assume a fixed intervention with predictable performance. How do you build reimbursement structures, insurance actuarial models, or warranty frameworks for a technology whose long-term outcomes are co-determined by the user's biology and adaptive AI? These questions extend into the economics of neural data, where continuous, high-dimensional information generated from inside the brain introduces unresolved problems around data security, valuation, and market incentives that parallel but exceed the complexities seen in genomic data markets. Regulatory uncertainty compounds the economic challenge. Ongoing work with the Center for Research Ethics at Pitt is examining participant-centered design and informed consent for adaptive neural prosthetic systems, questions that directly shape commercialization timelines, compliance costs, and investor confidence. Through the UP NExT Initiative (University of Pittsburgh Neural Engineering Cross-Translation), a cross-school effort spanning Engineering, Medicine, and Arts & Sciences, we are building infrastructure to connect biological and engineering science to exactly these kinds of economic and policy questions. Pitt has the disciplinary depth to lead in this space, and the CIS brainstorming initiative is a natural catalyst for making these connections concrete and fundable.

5) Dr. Xu Liang (Professor in the Department of Civil and Environmental Engineering)

Dr. Xu Liang is a Professor in the Department of Civil and Environmental Engineering at the University of Pittsburgh. Her research includes hydrological and land surface modeling, hydroinformatics using advanced statistical methods, cyber system development, and the application of sensors and wireless sensor networks in environmental systems. Her work integrates physical understanding and computational representation to enable new ways of modeling complex environmental systems. She combines process-based modeling, quantitative analysis, and multiscale observations, including field measurements, radar, and satellite data, to investigate the coupled physical, hydrological, and eco-biological processes across the soil–plant–atmosphere continuum. She is deeply engaged in interdisciplinary collaborations with atmospheric scientists, plant biologists, computer scientists, and geo-engineering colleagues. 

Currently, she leads the multi-institutional, interdisciplinary cyberinfrastructure development project – CyberWater. This NSF-funded, open-source, and community-oriented platform streamlines data discovery, integrates heterogeneous data sources, enables flexible two-way coupling of diverse models, and supports reproducible workflows through HPC and cloud resources. CyberWater (https://cyber-water.luddy.indianapolis.iu.edu/ ) significantly lowers technical barriers and enables researchers, small teams, and students to conduct complex, hypothesis-driven studies that bridge disciplines through integrated data and models. 

6)  Svitlana V Maksymenko, Ph.D. (Teaching Professor, Academic Director - Study Abroad Program in Central and Eastern Europe, Department of Economics)

Svitlana Maksymenko is a Teaching Professor at the Department of Economics and the affiliated faculty member at the Katz Business School, School for Public and International Affairs, and the University Center for International Studies (UCIS). Svitlana teaches   undergraduate and graduate courses, incl.  International Economics, Global Cybersecurity, Managerial Economics, Financial Economics, and International Economic Policy Analysis. Her research focuses on policy modeling, economic growth and reforms in emerging market economies. 

Ideas for collaboration: guest speakers in Economics of Cybersecurity course; co-teaching collaborations; economic policy analysis; AI in teaching and students' learning.

7) Dr. Paul R. Ohodnicki, Jr. (RK Mellon Faculty Fellow in Energy, Director, Center for Energy)

There is tremendous opportunity for coupling engineering and economics research and education to produce highly unique and impactful outcomes in a wide range of fields and disciplines.  In my specialization area of energy, for example, the relationship between technical engineering solutions and economic analysis could not be more important.  Economic assessments play a critical role in guiding prioritization of engineering research directions at the federal, state, and local level as well as within the private sector, and pairing together economists engineers, and scientists within the university environment allows us to be responsive to and also to provide thought leadership in areas of emerging importance such as critical materials, electric power systems, energy system interdependencies, energy and the environment, energy infrastructure, intersectionality of the emergence of AI and energy, and many others.  Through a collaboration between the Center for Energy and the Center for Industry Studies, I am looking to identify opportunities to leverage these synergies in natural and novel ways to amplify our research and educational impacts here at the University of Pittsburgh specifically within the domain of energy engineering, science and policy.

8) Margarita Igorevna Zabelina (Associate Teaching Professor, Department of Economics) 

Interdisciplinary Research on AI in University Teaching

As AI tools become increasingly prevalent in education, we all face similar challenges: How do we integrate AI while maintaining academic rigor? How do we ensure equitable access and ethical use? How do we measure whether AI actually improves learning? In my Intermediate Macroeconomics course (Spring 2026), I am examining these questions through structured AI homework assignments that address equity, ethics, and effectiveness. However, these challenges transcend any single discipline—faculty across STEM, social sciences, and humanities are grappling with how AI is reshaping our teaching and our students' learning. I propose we collaborate on research that examines AI integration across multiple courses and disciplines, using shared frameworks while respecting our different pedagogical contexts. By comparing our experiences and findings, we could develop evidence-based practices that benefit the entire university community. This is truly an interdisciplinary topic that affects all of us, and collaborative research would produce more valuable insights than any of us could achieve alone.