Innovative Projects Realized

Model Application for Estimating Total Suspended Solids in Eastern Amazonian Waters

Suspended sediments influence water quality, ecosystem functioning, and navigation in rivers and coastal environments. In the Eastern Amazon, estuarine systems such as the Guamá River and Guajará Bay experience intense variability in suspended sediment concentrations due to the combined effects of river discharge and tidal dynamics. These processes produce changes in water turbidity and optical properties, making continuous monitoring challenging using field measurements alone.
This project aims to improve the estimation of Total Suspended Solids (TSS) in Amazonian coastal waters by integrating satellite remote sensing, in situ observations, and machine learning techniques. Multispectral satellite imagery will be combined with field-based TSS measurements to develop models capable of capturing the optical and hydrodynamic complexity of tide-dominated environments.
By explicitly accounting for tidal influence and optical variability, the project addresses key limitations of existing TSS monitoring approaches, which are often developed for river-dominated systems.
The results will include multi-temporal maps of suspended sediment distribution, providing valuable information for environmental monitoring, water resource management, and navigation planning. The project also strengthens international collaboration and develops transferable methods applicable to other dynamic coastal and estuarine systems.

Multi-Objective Optimization for path planning: Comparing MOAstar to MOACO, older adults case study

As populations age, many older adults face increasing difficulty moving safely and comfortably through their cities due to challenges such as limited walking endurance, steep slopes, poor lighting, and the need for regular rest. This project aims to improve pedestrian route planning by developing and comparing methods that generate routes adapted to older adults’ needs, considering factors such as distance, slope difficulty, and access to rest amenities. By testing these approaches on real urban environments in different countries, the project will contribute to safer, more comfortable, and more inclusive mobility for older adults. At the same time, it will strengthen the participating institutions’ expertise in intelligent transportation systems and data-driven urban mobility, support the training of highly qualified researchers, and advance research on inclusive, age-friendly mobility solutions.

Developing a User-Friendly Digital Platform for Coastal and Conservation Guardian Volunteers

This project will be conducted in collaboration with Island Nature Trust (INT) to develop a user-friendly digital platform for volunteers in their Coastal Guardian and Conservation Guardian Programs. Currently, the organization faces an innovation challenge in coordinating and supporting a geographically dispersed network of volunteers, many of whom have limited technical experience. Also, existing processes for scheduling, training, data collection and communication rely on multiple platforms and manual updates. The new platform will centralize these functions into a single, accessible hub where volunteers can access training materials, submit surveys, share photos, coordinate schedules, and view interactive maps highlighting priority sites. By streamlining volunteer engagement and program coordination, this project strengthens community participation and improves conservation outcomes.

Lowering Costs in Biotherapeutics: Raman Spectroscopy for Real-Time Antifoam Detection in Perfusion Bioreactors

This project aims to improve the production of monoclonal antibody (mAb) based medicines by developing a novel method for measuring antifoaming agents within perfusion bioreactors used to produce mAbs. Antifoams are necessary to keep bioreactors running smoothly, but they are also widely believed to contribute to reactor membrane fouling, which increases costs and severely limits how long these manufacturing systems can operate. During the placement, the intern will work with the host institution to develop and validate a Raman spectroscopy–based method to measure antifoam concentrations in real time, then bring this method back to the home institution for use in ongoing perfusion bioreactor research. The project benefits the host institution by applying its advanced analytical expertise to a new and practical manufacturing challenge, while the home institution gains new analytical tools, trained personnel, and transferable methods that strengthen its continuous biomanufacturing research capacity. Together, this collaboration supports more efficient bioprocess development and contributes to long-term improvements in biologics manufacturing research.

ML-optimization of tensor contraction operations (TCOs) in the PyBEST software package

The main goal of this project is to integrate advanced machine learning techniques into the open-source PyBEST quantum chemistry software package to substantially accelerate quantum chemical calculations through the automated selection of optimal computational strategies. The project specifically targets the AI-driven optimization of tensor contraction operations (TCOs), which constitute the primary computational bottleneck in many quantum chemistry methods. By enabling data-driven prediction of the most efficient contraction schemes and execution pathways across different problem sizes and hardware configurations, the proposed approach will significantly reduce time-to-solution, enhance scalability on modern GPU architectures, and improve computational resource efficiency, while preserving numerical accuracy and scientific reliability.

NouLife: Synthesis of Linked Conjugates

In 2023, Innovotech acquired a 60% interest in NouLife Sciences Inc., a company that developed the intellectual property for the linking of two antioxidant molecules commonly used in skin care products – alpha lipoic acid and acetyl-L-carnitine – into one molecule. The linking of the two molecules has been indicated by NouLife to increase the beneficial properties of the separate molecules by improving their penetration through the skin, thus promoting antioxidant activity and improving overall skin health as a result. Innovotech used the intellectual property to create three different conjugated molecules, and the next step is to confirm that the linking of the molecules provides more effective antioxidant activity in skincare applications, focusing initially on permeability, solubility, and antioxidant properties. This testing of the linked molecules will seek to confirm that the linkage itself significantly improves the function of the molecules with respect to skin treatment so as to encourage their use in nutraceutical and cosmetic applications. The proposed project is to work with collaborators at the University of Alberta to synthesize the additional quantities of the conjugated molecules needed for the next phase of testing.

L’origine leibnizienne du principe de la moindre action

Ce projet vise à rechercher dans la dynamique de Leibniz une nouvelle compréhension de la notion d’action de Maupertuis qui pourrait, croit-on, résister aux critiques des commentateurs qui ont suggéré que la quantité d’action est définie par Maupertuis de manière ad hoc et réhabiliter la preuve métaphysique de Maupertuis. J’avance l’hypothèse selon laquelle l’action de Maupertuis est une notion métaphysique qu’il est possible de préciser mathématiquement par l’usage de principes métaphysiques adéquats, de la même manière que Leibniz le fait dans Dynamica de potentia.

Scheduled Tandem Parking to Relieve Parking Demand at Commuter Organizations

This project develops and validates a tandem parking simulation and operating framework for large commuter organizations such as hospitals, universities, and municipal complexes facing persistent parking shortages. Conducted in partnership with pointA, a non-profit supporting sustainable commuter programs, the study explores how assigning two vehicles to a shared stall can safely and reliably increase effective capacity without new construction. Using shift-based data, stochastic modeling, and discrete-event simulation, the PhD intern will test pairing rules, buffer times, and participation rates to quantify utilization gains and blocking risks. The outputs—a simulation model, operational handbook, and planning guidelines—will provide evidence-based insights for future pilot implementation, helping organizations expand parking efficiency, reduce congestion, and improve access within existing footprints.

How Generative AI Changes Problem Solving in Software Development

Use of generative artificial intelligence (GenAI) has skyrocketed in software development. Although gains in workers’ productivity have received significant recent attention, little is known regarding the deeper long-term effects of these tools. Researchers from the University of Victoria, Canada and the University of Zurich, Switzerland are conducting an experimental study to evaluate the potential risks the technology poses to the critical thinking skills of software developers. The results of their research will help ensure GenAI is responsibly integrated into software engineering education curriculum at the participating institutions, and beyond benefiting mentors in the field, inform individual software developers.

Development and Characterization of Zr-Doped Multifunctional High-Entropy Alloy Coatings

This project aims to develop multifunctional high-entropy alloy (HEA) coatings that protect industrial components from multiple types of damage at the same time, including erosion, corrosion, and hydrogen attack. Using laser-directed energy deposition (L-DED), the research will study how adding zirconium to AlCoCr2FeMo0.5Ni –Zrx changes the coating’s microstructure and improves its performance for real industrial applications, such as aerospace, energy, marine, and hydrogen transport systems. The goal is to create long-lasting, reliable coatings that enhance the durability of critical components in harsh environments. Both participating institutions will benefit from shared expertise, access to advanced manufacturing and microscopy facilities, and opportunities for student training and collaborative research, strengthening their capacity to design and evaluate next-generation multifunctional protective coatings.

Studying collaborative interactions with physical props in extended reality

People in VR can see each other and see the same virtual objects, but the feeling of touch is usually weak or missing. It is not realistic to give everyone a full set of physical replicas for every virtual item they use. Instead, this project will explore how one or two simple physical objects can be reused in clever ways so they feel like many different virtual tools or parts. We will create a shared virtual space where two people, each in their own room with a VR headset, collaborate on tasks such as moving, aligning, or inspecting virtual objects. Both will hold only a small number of physical objects, but in VR, they will experience them as several different items that can be passed or shared. We will test various techniques to make this illusion feel natural and convincing, and then conduct a study to assess how well people can cooperate and when the illusion fails. The final goal is to offer clear, practical recommendations to help future VR tools make remote collaboration feel more physical, shared, and affordable.

Automated Structural Analysis and Change Detection for No-Code Automation Workflows

This project focuses on developing and evaluating automated methods for analyzing complex no-code automation workflows used in enterprise environments. Platforms such as Workfront Fusion and Make.com allow organizations to build powerful automations quickly, but as these workflows grow in size and importance they often become difficult to understand, maintain, and safely modify. Changes are frequently made without clear visibility into downstream impacts, which increases operational risk and makes long-term governance challenging. The project will investigate techniques for parsing automation configurations, identifying structural relationships within workflows, and detecting meaningful differences between versions over time. In addition, the project will explore ways to generate clear, human-readable summaries and visual explanations that help technical and non-technical stakeholders understand how automations function.

The expected benefit to the partner organization is improved clarity and control over their automation systems. By making the structure and behavior of complex workflows easier to understand, the organization can reduce the risk of errors when changes are introduced, support more informed technical decision-making, and improve collaboration between developers, administrators, and business stakeholders. These capabilities will also help the organization scale its use of automation more responsibly by supporting better documentation, auditing, and long-term maintainability. Overall, the project aims to transform opaque automation configurations into understandable, manageable systems that can evolve safely as organizational needs change.