CIS Transdisciplinary Award Program
The Center for Industry Studies received a large number of highly competitive applications for the 2026-2027 CIS Transdisciplinary Award Program that included PIs from twenty-four departments/disciplines from across the university. Thank you to all the research teams that submitted an application.
We are excited to announce the 2026-2027 recipients of the CIS Transdisciplinary Award Program. This funding opportunity is intended to support innovative research efforts that bridge disciplines and encourage cross-departmental collaboration.
By facilitating partnerships between faculty members from diverse academic backgrounds, this initiative seeks to generate new insights and solutions that have a tangible impact on industry practices, business strategies, technological advancements, and policy frameworks. It is expected that funded projects will serve as a catalyst for securing highly competitive external research grants and funding opportunities.
The following four proposals were awarded $25,000 for one-year of funding.
Bond-Order Descriptors for Interfacial Stability in Multiphase Alloys
Principal Investigators: John A. Keith (Dept. of Chemical & Petroleum Engineering, Swanson School of Engineering) and Jill E. Millstone (Dept. of Chemistry, Dietrich School of Arts & Sciences)
Abstract: The interfaces between phases in a multiphase alloy govern whether the material survives service. Their interfacial energies control phase stability and resistance to coarsening, and they set the microstructures that determine mechanical and thermal performance during use [1, 2]. However, predicting these energetics today requires large-scale quantum-mechanical (QM) calculations for every candidate phase pair, and the local atomic environments that emerge, especially in nanoparticles and nanocrystalline materials, defy the structural intuition that normally guides materials design.
This large and sometimes undefined design space creates a screening bottleneck: at present there is no quick way to ask which local environments will form and resist interfacial degradation before committing to an expensive computation or synthesis. This proposal tests whether one human-accessible quantity, the bond order across a phase boundary, can serve as that screening descriptor.
Noninvasive Ultrasound Control of Lysosomal Function: A Transdisciplinary Engineering–Biology Approach to Rare Disease and Brain Aging
Principal Investigators: Kirill Kiselyov (Biological Sciences, Dietrich School of Arts & Sciences), Nikhil Bajaj (Mechanical Engineering & Materials Science, Swanson School of Engineering), and Zhimin Huang (Neurological Surgery, School of Medicine)
Abstract: Cells depend on lysosomes to break down and recycle unneeded material, and when this clearance fails, waste accumulates and cells lose function. The same failure underlies both lysosomal storage diseases (LSDs), a group of rare inherited disorders that cause progressive brain injury in children, and the more gradual decline of the aging brain. This project develops noninvasive methods to restore lysosomal function using focused ultrasound, and tests them first in Mucolipidosis type IV, an LSD caused by the loss of a single lysosomal ion channel.
Two strategies are pursued with the same ultrasound hardware. The first uses low-intensity focused ultrasound to supply the cellular signal that is missing when the channel is lost. The second delivers a corrective gene whose expression is switched on only where and when ultrasound is applied, allowing controlled rather than constant expression. The work combines ultrasound engineering (Bajaj, Mechanical Engineering and Materials Science), lysosomal biology (Kiselyov, Biological Sciences), and gene delivery (Huang, Neurological Surgery).
Beyond preliminary data and a shared biomarker of treatment response, the project defines the acoustic and control-software specifications for a steerable, potentially wearable ultrasound system, establishing a pathway toward a device to be developed with an industrial partner.
Hardware-in-the-Loop: Coupling of an Event-Driven Vision Sensor with a Biologically Grounded Probabilistic Synaptic Plasticity Engine
Principal Investigators: Rajkumar Kubendran (Department of Electrical and Computer Engineering, Swanson School of Engineering) and Weifeng Xu (Department of Neuroscience)
Abstract: Modern neuromorphic vision sensors mimic the retina to emit streams of asynchronous spikes, yet the learning algorithms applied to their output remain abstract, weight-based neural networks that do not reflect how biological synapses actually change. In parallel, the biologically faithful model of synaptic plasticity has been validated only against laboratory electrophysiology, never against the noisy, event-driven data produced by real hardware. This project bridges this gap by coupling a retinomorphic vision sensor (an energy-efficient “silicon retina” developed in the Kubendran Lab) with the Silent-Synapse-Based Plasticity (SSBP) engine developed in the Xu Lab. Network learning in the SSBP model arises from the discrete, probabilistic unsilencing of silent synaptic transmission sites triggered by spiking events from the pre- and post-synaptic neurons. We will transform live sensor output into spike-train inputs, drive the SSBP engine with real visual data, and test whether a biologically grounded plasticity rule can self-organize orientation selectivity directly from a hardware retina. The work unites computational neuroscience and neuromorphic circuit engineering to lay the foundation for ultra-low-power, onsensor learning systems and to position the team for competitive federal funding.
TEAM 3D: Tracking Exposures through Additive Manufacturing of Passive Samplers for Occupational and Community Health
Principal Investigators: Carla Ng (Department of Civil & Environmental Engineering, Swanson School of Engineering) and Maureen Lichtveld (Dean, School of Public Health)
Abstract: People who live and work in industrial regions are exposed to complex chemical mixtures, yet most monitoring devices and analytical methods are built around single compounds and scale poorly to the diversity of hazards present, particularly where toxic metals and hazardous organic chemicals co-occur. This gap is especially relevant in southwestern Pennsylvania, where legacy heavy industry, an active petrochemical corridor, expanding unconventional natural gas development, and a rapid buildout of gas-powered data centers now overlap within airsheds and watersheds. Characterizing these mixtures currently depends on intensive, costly spectrometric methods, creating a persistent trade-off between chemical breadth and real-world feasibility.
TEAM 3D unites engineers and public-health scientists across the Swanson School of Engineering and the School of Public Health to address that trade-off through a one-year transdisciplinary pilot. We will (1) define and prioritize the mixtures posing the greatest health risk across two contrasting source classes (petrochemical and natural gas development / gas-powered data-center buildout); (2) design, fabricate, and test 3D-printed, multi-sorbent passive samplers that capture organic and inorganic mixtures in a single device, exploiting additive manufacturing of both polymeric and inorganic materials; and (3) develop a proof-of-concept machine-learning analysis that identifies low-cost, easily monitorable “tracer” compounds as surrogates for source-specific signature mixtures. Together, these deliverables — sampler prototypes, regionally relevant mixture data, and a tracer-identification model — establish a scalable, affordable alternative to full mixture analysis and positions the team with critical preliminary data to pursue competitive external funding (NIEHS, NSF, and SBIR/STTR) to protect worker and community health.
Congratulations to this year’s award recipients!
We look forward to announcing the RFP for the CIS Transdisciplinary Award Program next summer during our 2027-2028 funding cycle!