Innovative Projects Realized

Raymond James Ltd. – Tax Services Process Innovation Project

Raymond James Ltd. is a leading financial services firm dedicated to helping clients achieve their financial goals through tailored wealth management, investment, and tax advisory solutions. The organization operates in a fast-paced, compliance-driven environment where accuracy, timeliness, and client trust are paramount. As tax regulations evolve and digital transformation continues to reshape the financial landscape, Raymond James faces the innovation challenge of optimizing and modernizing its tax reporting and preparation processes to ensure efficiency, accuracy, and scalability during peak tax seasons. The primary problem to be solved lies in enhancing the firm’s ability to leverage data-driven tools, automation, and digital workflows to streamline complex tax documentation, reduce manual errors, and improve client service turnaround times.

Estimation non paramétrique dans le cadre du q-calcul en élaborant un estimateurs des fonctions q-densités des q-distributions avec application aux données du vivant

Le calcul quantique, ou q-calcul, constitue une approche moderne de l’analyse mathématique dans laquelle les opérateurs différentiels et intégrals classiques sont généralisés par des opérateurs dépendant d’un paramètre q. Ce cadre établit un lien fécond entre les mathématiques et la physique
(Yamano, 2002) et possède de nombreuses applications dans les probabilités, la théorie des nombres, les fonctions hypergéométriques de base et les polynômes orthogonaux (Bangerezako & Hounkonnou, 2003 ; Tsallis, 1998).
Dans le domaine de la statistique non paramétrique, cette idée est particulièrement prometteuse : la généralisation des estimateurs de densité au cadre du q-calcul offre de nouveaux outils pour traiter des données présentant des distributions non classiques (asymétriques, à queues épaisses ou fortement
dépendantes). Ces propriétés sont notamment rencontrées dans le domaine médical, où les distributions issues d’observations biologiques ou cliniques s’écartent souvent des hypothèses gaussiennes.
Le présent projet s’inscrit donc dans une démarche théorique, algorithmique et appliquée, visant à construire et étudier des estimateurs de q-densités, à en analyser les propriétés asymptotiques, et à les implémenter dans un environnement open-source (R), avec des applications à des données médicales
réelles.

Integrating Model-Based Systems Engineering and System Architecture Optimization

Concordia University’s Aircraft Systems Lab and the Institute of System Architectures in Aeronautics at the German Aerospace Center (DLR) will collaborate to develop innovative digital methodologies for aircraft system design. The project bridges two important aspects of engineering product development: model-based systems engineering (MBSE), which unifies complex system representations within a single computational platform, and system architecture optimization (SAO), which generates and evaluates thousands of design possibilities. While MBSE excels at describing and representing high-level characteristics, it lacks quantitative evaluation capabilities. Conversely, SAO efficiently compares many designs, but sometimes lacks the rigor and traceability of MBSE. This project will connect widely used and industry-proven MBSE platforms with state-of-the-art SAO tools from the DLR, enabling engineers to explore design alternatives within a proven framework using powerful optimization tools. A Concordia PhD student will spend three months in Hamburg collaborating with the DLR’s architecture experts, strengthening institutional relationships and enabling joint publications, academic exchanges, and tool development. Concordia will gain access to advanced optimization methodologies and international experience, while the DLR will benefit from developments in collaborative contexts and the Aircraft Systems Lab’s safety-centered architecture evaluation expertise. Both institutions will advance digital engineering practices for developing safer and more environmentally sustainable aircraft.

Influence of habitat complexity on optimal movement choices in wild emperor tamarins (Saguinus imperator) at the Los Amigos Biological Station, Peru

Movements—such as locomotion, gestures, and group coordination—are influenced by individual, social, and environmental factors. Gaining a better understanding of how habitat complexity—defined as the abiotic and biotic components that shape habitat structure—influences movement has the potential to generate new knowledge on an understudied species, the emperor tamarin (Saguinus imperator), and, in the context of increasing habitat fragmentation and degradation, to provide new guidelines for tamarin conservation. This project aims to examine how habitat complexity may influence movement choices in a wild population of emperor tamarins at the Los Amigos Biological Station in Peru, using the framework of optimal movement theory (OMT). The project lies at the intersection of cognitive ecology, behavioural ecology, and geography. It also stands to benefit both institutions: it will establish a new collaboration with Field Projects International (FPI) and reinforce Concordia University’s contribution to research and conservation of non-human primates. Furthermore, as FPI is an NGO dedicated to animal monitoring and conservation, with an active program in movement ecology, this project will contribute to their research, support scientific publications, advance knowledge, and potentially increase their visibility in Canada.

Enhancing Multi-Agent Coordination for Real-Time Detection of Obfuscated Shilling Attacks

This research project investigates how agentic AI—autonomous AI agents capable of perceiving, reasoning, coordinating, and acting in dynamic environments—can be advanced to protect recommendation systems against increasingly obfuscated shilling attacks. Shilling attacks occur when malicious actors inject fake profiles, ratings, or reviews to manipulate recommendation outcomes for financial, competitive, or political advantage. As attackers adopt more subtle, disguised, and time-distributed behaviors, traditional detection mechanisms become less effective. Building on the foundational work of Project IT47858, the extension focuses on developing a more robust and collaborative multi-agent defense architecture that not only identifies suspicious user behavior patterns but also infers hidden or obfuscated attack signals through enhanced inter-agent communication and reasoning. The goal is to create an adaptive, real-time agentic AI framework capable of detecting attacks that are intentionally designed to mimic legitimate user activity. Through improved coordination strategies, real-time monitoring pipelines, and extended adversarial simulation environments, the project will enable agents to share insights, cross-validate observations, and dynamically adjust defense decisions to preserve system integrity and user trust. The outcome will be a more resilient, scalable, and intelligent defense layer capable of protecting modern recommendation systems against the next generation of stealthy manipulation threats.

Alignment and characterization of multimode-fibre compatible superconducting nano-wire single-photon detector

This project aims to couple a multimode optical fiber(MMF) to a superconducting nanowire single-photon detector(SNSPD) and characterize it. SNSPDs are currently the most sensitive single-photon detectors available. But most of the current SNSPDs are coupled using single-mode fibers(SMF), which collect photons in only one spatial mode. This is inefficient when collecting photons from biological samples, as they are highly scattered and have multiple spatial modes.

MMFs on the other side allow the collection from different spatial modes, making them the perfect choice for applications in biomedical imaging and quantum biophotonics. But this creates an additional challenge as MMFs have a larger core area, and increases the background counts. Developing large area SNSPDs is difficult to fabricate and presents various challenges when it is coupled to MMF. This project aims to tackle these challenges and align and characterize the detector. This includes systematically measuring the detector efficiency, background counts, time jitter, and spatial resolution, and then optimizing these parameters. Once the detector performance has been properly characterized and optimized, it will be used to study ultra-weak biophoton emission from different biological samples. These photons originate from different biological processes and provide a new way of studying and understanding biological mechanisms.

Here, There, and In Between: Remembering Markets in Manila and Montréal

This project examines how the disappearance, relocation, and redevelopment of neighborhood markets in Manila reshape cultural memory, daily routines, and community belonging. Markets such as palengke, sari-sari stores, and small commercial corridors have long served as social infrastructures where familiarity, recognition, and shared practices form through everyday interaction. As many of these spaces are demolished or replaced by malls and large redevelopment projects, the environments that once sustained collective life are altered or erased. Through fieldwork in Manila and comparative research in Montreal, this project will document market sites, gather oral histories, and analyze how communities remember and adapt to these spatial changes. The research will strengthen collaboration between Canadian and Philippine institutions and contribute new insight into how urban redevelopment affects cultural memory, community networks, and everyday forms of place-based belonging.

MITACS Globalink Research Internship – Investigation of Cerebral Cortex Development Under the Influence of Pharmacological Treatments

This project explores how the anti-epileptic drug valproic acid (VPA) may affect early brain development during pregnancy. While VPA is known to increase the risk of neurodevelopmental conditions such as autism and learning difficulties in children, the exact effects on the developing human brain are not well understood. To study this, the project uses a new laboratory human brain model called “cerebroids”. These models closely mimic how the brain normally develops, allowing researchers to study VPA’s effects in more detail and under realistic conditions. The project will look at how VPA influences brain cell growth, movement, and gene activity. The findings will help improve understanding of how certain medications may impact the brain during pregnancy and could guide safer treatment strategies. This research strengthens collaboration across institutions by combining advanced tissue models, imaging, and genetic analysis, and supports the broader goal of improving maternal and child health through evidence-based science

Enhanced Optical Rulers from Nonlinear SNAP Resonators

This project brings together leading Canadian and European researchers to explore a new type of tiny optical device called a SNAP resonator, which can guide and control light with extreme precision. These devices may play an important role in future technologies such as environmental sensing, low-energy optical computing, and advanced communication systems. During a 12-week visit to Concordia University, the researcher will work closely with experts to develop new theoretical models that explain how light behaves inside these resonators, helping to interpret ongoing experiments and guide future ones. The collaboration will strengthen both institutions by combining Concordia’s world-class experimental facilities with the visitor’s specialised expertise in light-based physics, opening new research directions and supporting long-term international partnerships.

Misalignment Detector — Mining and Modelling Misaligned Code Change Descriptions Using Large Language Models

Code review plays a key role in software development by helping teams understand why a change was made and how it fits into the broader project. A major part of this understanding comes from the commit message, which is supposed to accurately describe the intent and scope of the corresponding code changes. However, in many real-world projects, commit messages are incomplete, outdated, or simply do not match what the code actually does. This misalignment creates confusion for reviewers and increases the time needed to understand and validate a change.

To address this challenge, many open-source communities discuss and correct misleading commit messages during code review. These discussions provide valuable examples of how commit messages should be improved and why certain messages fail to reflect the code.

This project builds on that real-world practice by creating a dataset of aligned and misaligned commit messages drawn from open-source code review discussions. We then train language models to automatically detect when a commit message does not accurately reflect the underlying changes and to suggest improvements.

The goal is to support developers and reviewers by making commit messages clearer, reducing review time, and improving overall code quality.

Smart mannequin platform for compression testing

This project is a collaborative research partnership designed to support Tression, a Canadian health-tech apparel company, in enhancing its product development. The primary challenge in the therapeutic apparel industry is the difficulty of accurately and efficiently testing the performance of medical-grade compression garments. The current process can be slow and subjective, making it difficult to innovate rapidly. To solve this challenge, this project will develop, build, and validate an advanced R&D testing device: a “smart mannequin leg.” This innovative prototype will feature a mechatronic system to adjust its physical dimensions, allowing it to simulate a wide variety of body types. It will also be integrated with a sophisticated sensor array to capture real-time, objective data on the exact pressure a garment applies. This new technology will provide Tression with a rapid, reliable, and data-driven process for product validation. The expected outcomes include a significant improvement in R&D productivity, a reduction in material waste from physical prototyping, and a stronger capacity for innovation.

Pure Environmental BSI Internship

This project will help Pure Environmental improve how it collects and maps important environmental and regulatory data. Pure has already built an initial version of a mapping tool in ArcGIS, and the intern will help make this system more reliable, easier to use, and able to pull in new information automatically. By strengthening this platform, Pure will be able to plan waste and water management more efficiently and identify solutions that reduce long-distance trucking and related emissions. The project benefits Pure by creating a smoother, faster digital process for updating key datasets, while supporting the company’s broader goals of environmental sustainability and operational improvement.