The problem of efficiently storing and retrieving information is an essential topic in computer science. During the past decades, many techniques have been developed to index data so that useful information can be retrieved almost instantaneously by performing queries for keywords or phrases. In recent years, as the size of the data has grown rapidly, many techniques that were useful for small, older systems have become infeasible for large, modern applications, because they occupy too much space to fit into faster levels of memory hierarchy. Most of this space is not raw data, but structural information added to improve search efficiency. Succinct data structures were proposed to address this problem, so that the information in large systems can be retrieved quickly, but the space requirement is little more than that of the raw data. To provide solutions to modern systems that process large data sets such as web search engines, geographic information systems, social network platforms and bioinformatics applications, this project will extend the research on succinct data structures and start new research directions on the design of efficient data structures in general.

Accepting: PhD and MCS students

Express your interest in working with Dr. Meng He.

This research project investigates the convergence of artificial intelligence, intellectual property law, and ethical considerations in modern animal agriculture. By developing advanced AI models to monitor and interpret livestock behavior and health, the project aims to enhance animal welfare and promote sustainable farming practices. It explores the legal implications surrounding data ownership and intellectual property rights of AI algorithms used in animal care. Ethical issues such as privacy, consent, and the impact of technology on farmers and animals are central to the study. The goal is to create a framework that balances technological innovation with legal and ethical responsibilities, advancing both animal welfare and the agricultural industry.

Accepting: PhD and MCS students

Express your interest in working with Dr. Suresh Neethirajan.

This research project aims to integrate the traditional knowledge and practices of the Mi'kmaq and other First Nations communities within Nova Scotia with advanced artificial intelligence technologies to improve animal welfare. By actively learning from these Indigenous communities, the project seeks to understand their deep-rooted insights into animal behavior, ethical treatment, and sustainable farming practices. These invaluable perspectives will guide the design and implementation of AI technologies—such as ML models for monitoring livestock health and well-being—to create solutions that are both culturally respectful and technologically innovative. The goal is to harmonize ancestral wisdom with modern digital tools, enhancing animal welfare while fostering sustainable agriculture. Central to the study are ethical considerations, community engagement, and the co-creation of knowledge, bridging traditional practices with cutting-edge technology for the betterment of animals and farming communities alike.

Accepting: PhD and MCS students

Express your interest in working with Dr. Suresh Neethirajan.

This research project focuses on leveraging artificial intelligence and big data to transform dairy and poultry farming practices with the goal of reducing greenhouse gas (GHG) emissions and achieving net-zero targets. By integrating AI-driven analytics, machine learning models, and precision agriculture technologies, the project aims to optimize feed efficiency, improve animal health monitoring, and enhance waste management systems. The study will analyze large datasets collected from digital livestock farming operations to identify patterns and develop predictive models that support sustainable decision-making. The ultimate goal is to create innovative, climate-smart farming methods that not only enhance productivity and animal welfare but also significantly cut down GHG emissions.

Accepting: PhD and MCS students

Express your interest in working with Dr. Suresh Neethirajan.

Understand Intelligence in Humans and Machines

The Computation and Cognition lab studies intelligence by treating both humans and AI systems as subjects of scientific investigation. Our research combines cognitive science, artificial intelligence, computational modeling, and behavioral experimentation to (1) study how people learn, reason, make decisions, and interact with AI systems and (2) build AI systems that are more capable, reliable, and aligned with human decision-making. Current projects span human–AI decision-making, AI evaluation, computational cognitive modeling, program induction, probabilistic inference, machine learning, and experimental studies of human behavior. Students may work on developing computational models, designing behavioral experiments, analyzing large datasets, or building AI systems inspired by human cognition.

Ideal applicants are curious, mathematically inclined, and excited by interdisciplinary research at the intersection of AI, cognitive science, psychology, and computer science. Students from backgrounds in cognitive science, neuroscience, mathematics, statistics, physics, computer science and engineering are encouraged to apply.

Accepting: PhD students

Express your interest in working with Dr. Marta Kryven. 

Autonomous robots increase efficiency by supporting human workers in environments such as hospitals, office spaces, industrial facilities, or personal homes. Furthermore, drones, autonomous ground vehicles and vessels provide flexible tools for outdoor information gathering environmental sciences and precision agriculture. However, end-users are typically not computer science experts. Thus, we require robotic systems that learn the preferences of end-users and adapt their behaviour to user preferences by balancing different tasks and objectives.

I am looking for motivated students to join my new robotics lab to conduct fundamental research in cognitive robotics in one of the following two topic areas:

i) Human Multi-Robot Interaction, Learning from human feedback, Preference Learning for Multi-Robot Systems.

ii) Multi-Objective Optimization and Planning, Robust Control, Planning under Uncertainty.

Accepting: PhD and MCS students

Express your interest in working with Dr. Nils Wilde.

I am recruiting students for a research project focused on developing robust predictive models that infer ecological compatibility between geographically distant regions by aligning their climate profiles. The project leverages machine learning techniques (e.g., link prediction, probabilistic forecasting, and reinforcement learning) to identify likely invasion pathways for non-native species transported via global shipping routes.

The framework is being designed to operate over high-dimensional, multi-source data streams, integrating climatological variables (e.g., temperature, precipitation, seasonality), historical species distributions, and, in future iterations, land-use, and biodiversity metrics. A key challenge lies in ensuring the generalizability of these models across scales and regions while maintaining strict control over the internal representations used for analogy detection and spatial transferability.

Accepting: PhD and MCS students

Express your interest in working with Dr. Gabriel Spadon.

I am recruiting students for a research project focused on adaptive learning frameworks that integrate spatial priors and acoustic modeling for dynamic maritime environments. The project investigates the use of synthetically generated acoustic data (derived from geographic, bathymetric, ecological, and vessel traffic features) to support classification, localization, and anomaly detection under restricted data regimes.

The research explores techniques in physics-informed generative modeling, domain adaptation, and constrained optimization to enable model deployment in unseen or data-sparse regions (also spatially apart). Emphasis is placed on achieving reliable regional generalization and supporting real-time inference pipelines.

Accepting: MCS and PhD students

Express your interest in working with Dr. Gabriel Spadon.

I am recruiting students for a research project focused on enhancing short- and long-term mobility forecasting by integrating neural network architectures with contextual information and principles derived from Newtonian physics. The objective is to develop physics-informed/inspired models that explicitly incorporate dynamic forces (e.g., drag, thrust, and buoyancy) into the trajectory prediction process, addressing limitations in conventional approaches that rely solely on geometric and coordinate-based inputs. By incorporating environmental variables, including ocean currents, wind fields, and atmospheric conditions, into the learning process, this project aims to generate highly realistic and temporally consistent trajectory forecasts.

A central component involves modeling the interactions between multiple independently moving entities (e.g., vessels) in unstructured and expansive open-water regions. This includes capturing emergent group dynamics, non-linear dependencies, and the impact of external physical drivers on mobility behaviors.

Accepting: PhD students

Express your interest in working with Dr. Gabriel Spadon.

Assessment reports such as the UN World Ocean Assessment rest on tens of thousands of scientific works. How that literature is structured, where the research was actually carried out, and who holds expertise on a given topic or region are all hard to see from the reports themselves.

This project develops interactive visualizations of scientific literature along two dimensions. The first is bibliometric, building citation, bibliographic coupling and co-citation networks over the works an assessment report cites and their surrounding neighbourhood, then clustering them into research areas. The second is geographic, extracting research sites from the text of articles using named entity recognition and gazetteer geocoding and linking those sites to the papers, authors and institutions associated with them.

Accepting: Masters students

Express your interest in working with Dr. Stephen Brooks

• Passive sensing and ubicomp for tracking and screening cognitive developmental milestones. We will explore the use and fabrication of ubicomp for passive sensing in the home setting.
• Enhancing reading and writing skills of children at school. We will explore how to improve literacy skills of children considering cultural aspects.
• Ubicomp supporting adults with autism manage anxiety and stress in real-life situations.

Accepting: PhD students

Express your interest in working with Dr. Lizbeth Olivia Escobedo Bravo.

Autonomous robots increase efficiency by supporting human workers in environments such as hospitals, office spaces, industrial facilities, or personal homes. Furthermore, drones, autonomous ground vehicles and vessels provide flexible tools for outdoor information gathering environmental sciences and precision agriculture. However, end-users are typically not computer science experts. Thus, we require robotic systems that learn the preferences of end-users and adapt their behaviour to user preferences by balancing different tasks and objectives.

I am looking for motivated students to join my new robotics lab to conduct fundamental research in cognitive robotics in one of the following two topic areas:

i) Human Multi-Robot Interaction, Learning from human feedback, Preference Learning for Multi-Robot Systems.

ii) Multi-Objective Optimization and Planning, Robust Control, Planning under Uncertainty.

Accepting: PhD and MCS students

Express your interest in working with Dr. Nils Wilde.

This research project aims to enhance the welfare of dairy cattle by developing innovative digital interfaces that facilitate better human-computer-animal interactions. By integrating artificial intelligence technologies such as machine learning, facial recognition, and natural language processing, the project seeks to interpret cows' behaviors and vocalizations to understand their health and emotional states more accurately. It involves creating user-friendly platforms for farmers to monitor real-time data on livestock well-being, enabling timely interventions and improving overall farm productivity. Ethical considerations, including data privacy and the impact of technology on both animals and farmers, are central to the study. The ultimate goal is to promote sustainable and ethical farming practices by bridging the communication gap between humans and dairy animals through advanced technological solutions.

Accepting: PhD and MCS students

Express your interest in working with Dr. Suresh Neethirajan.

Persuasive systems are interactive systems (such as mobile, web, virtual reality, and augmented reality apps and games) designed to promote desirable behaviours or discourage risky behaviours without coercion or deception. My research intersects Human-Computer Interaction, Artificial Intelligence (AI), and Persuasive Computing to design, develop, and evaluate next-generation persuasive systems that are AI-powered, highly personalized, user-adaptive, and more effective at motivating and promoting desirable behaviours in individuals and groups across diverse health domains including disease prevention and management, physical activity, and mental health.

I am looking for candidates with strong background in software development and AI techniques including machine learning, deep learning, and natural language processing. Past experience with research and development projects involving building and deploying AI models, along with developing interactive systems that integrate these models, is a plus.

Accepting: PhD and MCS students

Express your interest in working with Dr. Oladapo Oyebode.

My research focuses on the intersection of graphics, AI and interaction and, importantly, includes the concurrent goal of developing new and accessible graphics methods. These initiatives feed directly into my long-term research trajectory which aims to infuse novel graphics methods with perceptual and aesthetic elements. Ongoing topics of interest include high dynamic range graphics, navigation through virtual worlds and the structured presentation of 3D models.

Accepting: PhD students

Express your interest in working with Dr. Stephen Brooks.

Socially Sustainable Software Development means creating and maintaining software systems that promote social equity, well-being, and inclusivity by ensuring fair access to resources and opportunities, supporting diverse user needs, and fostering community engagement and participation. The successful applicant will develop and empirically evaluate tools or practices for improving software sustainability. The ideal candidate has professional experience in software development and a keen interest in ethics.

Accepting: PhD and MCS students

Express your interest in working with Dr. Paul Ralph.

Evidence standards are models of a scientific community’s expectations for research: how research should be conducted: what should be reported in scientific articles; how much and what kind of evidence is needed to justify claims about the world. The successful applicants will join the ACM SIGSOFT Evidence Standards project, improve existing evidence standards, and use them to develop tools to support research, scientific writing, and peer review. The ideal candidate has good knowledge of web programming (e.g. HTML, CSS, Javascript) and a keen interest in science and research.

Accepting: PhD and MCS students

Express your interest in working with Dr. Paul Ralph.