Phylogenetic “family trees” are a primary tool used for studying evolution such as the spread of antibiotic resistant bacteria or transmission of COVID-19. As we collect more and more sequencing data we need better algorithms to infer and compare phylogenetic trees. The successful applicant will design and implement new algorithms and data structures based on graph theory or statistical models for complex problems and big data challenges. The ideal candidate has a strong interest in algorithm design and analysis, graph theory, or bioinformatics.

Accepting: PhD students

Express your interest in working with Dr. Chris Whidden.

1. Algorithms and data structures, especially fast and space-efficient algorithms and data structures, including succinct data structures, string algorithms and text indexing, I/O-efficient algorithms, implicit data structures, and adaptive algorithms.
2. Computational Geometry, especially efficient algorithms and data structures for computational geometry.
   

Accepting: PhD and MCS students

Express your interest in working with Dr. Meng He.

I am looking for a person with an aptitude for independent creative work, good team working skills, desire to learn and interact multidisciplinary, and excellent English language skills. 

The project combines HCI, Ubicomp, AI, and Health related research areas. The student will develop skills as user centered design, design and conduct user studies, UX/UI, design interactive systems, iterative prototyping, Ubicomp, and in the field evaluations.

The candidate should have programming skills and the confidence to interact with study participants.

Accepting: PhD students

Express your interest in working with Dr. Robert Beiko.

I am seeking dedicated graduate students passionate about applying deep learning to bioacoustics, animal vocalizations, and the broader spectrum of farm technology. Our research focus is four-fold:

1. Gaining a profound understanding of the intricacies of animal sounds amid farm noises, Adapting advanced AI models to process and interpret these vocalizations, considering the variability across and within species
2. Addressing challenges in real-time data acquisition, emphasizing reliability, scalability, and ethical considerations
3. Integrating these bioacoustic insights with physiological and behavioral metrics to holistically gauge animal welfare. 
4. Our vision is to pioneer innovations that bridge technology and livestock care, driving both knowledge and tangible farm improvements.

Accepting: PhD and MCS students

Express your interest in working with Dr. Suresh Neethirajan.

Multimodal Data Integration in Livestock Farming through Artificial Intelligence

Contemporary animal farming encompasses a myriad of facets, from nutrition, welfare, and resilience to behavior and affective states. While most current farming tools prioritize a singular modality, they often overlook the comprehensive physiological and clinical context of the animal. This singular focus limits their overall effectiveness. By harnessing the power of AI to integrate various data modalities, there's potential to significantly enhance both the accuracy and robustness of diagnostic tools, as well as predictive welfare models. Such AI models can identify novel patterns both within and across these modalities, thereby refining the design of decision support systems and intervention measures. The valuable insights drawn from these models can stimulate further research, leading to the identification of innovative welfare indicators, standards, benchmarks, and protocols, thus highlighting the interconnectivity of multimodal data in livestock farming.

Accepting: PhD and MCS students

Express your interest in working with Dr. Suresh Neethirajan.

My recent research had focused on applied Natural Language Processing (NLP) methods for authorship attribution, financial report analysis, financial forecasting, and application of deep learning methods in NLP. The second theme was centred around applied machine learning and AI for behavioural analysis, in particular analysis of play styles and responsible gaming with electronic gaming machines. 

I am looking for candidates in the general area of:

1. Natural Language Processing (NLP): applied NLP in the financial domain, authorship attribution, text visualization, question answering, and creation of virtual digital assistant; and
2. Use of AI and ML in Behavioural Analytics, with focus on analysis of playstyles of players on Electronic Gaming Machines (EGMs).

Accepting: PhD and MCS students

Express your interest in working with Dr. Vlado Keselj.

Deep learning solutions to visual reinforcement learning tasks tend to rely on convolutional neural networks. Such a starting point represents a high computational cost necessitating GPU support. Conversely, Tangled Programmed Graphs are able achieve a lot while only indexing a fraction of the visual state space. We are interested in developing sampling biases that accelerate the convergence of Tangled Program Graphs. Previous research experience deploying Tangled Program Graphs is necessary for this project.

Accepting: PhD students

Express your interest in working with Dr. Malcolm Heywood.

Tracking the location and presence of fish and marine mammals is necessary for both scientific study and conservation. The objective of this research is training integrated ML models with images, acoustics and text to answer questions about the diversity and abundance of life below water in monitored regions at scale. The successful applicant will apply contrastive learning, generative models, or other methods of learning with few or no labels. Goals include predicting climate change impacts on commercial fishes and marine mammals, regulatory monitoring of tidal energy, Carbon Dioxide Removal (CDR), and other ocean infrastructure, and modeling climate change mitigation efforts such as reducing fishing or introducing species to new areas.

Accepting: PhD students

Express your interest in working with Dr. Christopher Whidden.

I am recruiting ambitious, experienced graduate students to work on deep learning in speech and natural language processing, brain data, machine learning in healthcare, and especially any intersection of these. One program of research has four components: 1) fundamental improvements and deeper understanding of state-of-the-art, transformer-based large language models, 2) modifying these models to process medical and healthcare texts for real-world tasks, 3) solving MLOps challenges in deployment, including scalability, generalizability, transparency, robustness, safety, and mitigation against bias, and 4) clinical implementation issues, including bioethics and usability. Our goals are to make major advancement in knowledge and in real-world impact.

Accepting: PhD and MCS students

Express your interest in working with Dr. Frank Rudzicz.

AI models, specifically deep learning models, have achieved state-of-the-art performance across a multitude of domains including computer vision and natural language processing (NLP). However, despite the benefits of deep learning models, their opaqueness is a major cause of concern. These models work as a black box and it can be impossible to understand how a model solves a task. This project touches various aspects of interpreting and explaining deep learning models, and involves applications enabled by interpretation such as domain adaptation, debiasing and style generation.

I am looking for students with a strong background in machine learning and deep learning, and exceptional problem solving and programming skills. Past experience with research and development projects involving deep learning, NLP or vision is a plus. 

Accepting: PhD and MCS students

Express your interest in working with Dr. Hassan Sajjad.

My research program focuses on AI for healthcare and digital health. My data-driven AI research involving developing innovative machine learning frameworks for predictive modeling for survival analysis, therapy outcome, disease trend analysis; patient phenotyping; disease risk stratification;  public health and optimization of health systems. My knowledge-driven AI activities focus on health knowledge management to computerize clinical practice guidelines, clinical workflows, behaviour change theories in terms of point-of-care, evidence-informed clinical decision support systems. 

Accepting: Phd students

Express your interest in working with Dr. Syed Abidi.

Deep language models and conversational AI have revolutionized natural language processing by providing semantic vector representations of text (embeddings) and by offering a question-answer style of interaction with text data. My research focuses on designing text mining and NLP pipelines for specific document engineering tasks that take advantage of these new techniques, including the challenges of designing appropriate free-text questions (prompts) to the conversational AI models for a given task and of converting the model’s free-text answer into machine-readable information. Particular text mining domains of interest include administrative text, clinical notes, systematic reviews of the research literature, historical text mining, high-recall information retrieval, and interactive clustering and topic modelling. 

I seek highly motivated students with strong programming skills and a solid mathematical background. A publication record in any area of machine learning, natural language processing and text mining is desirable. 

Accepting: Phd students

Express your interest in working with Dr. Evangelos Milios.

My research focuses on understanding the human brain with a particular interest in the cerebellum. What is the interaction between the cerebellum and the neocortex, and what are the consequences of its damage? I seek to apply artificial intelligence algorithms to improve our analytic and diagnostic tools for clinical assessment, primarily for magnetic resonance imaging but also for other techniques and behavioural scales.

We have a variety of projects in my lab. In all cases, each project will have a computer science component and another component of clinical or behavioural neuroscience.

Accepting: Phd students

Express your interest in working with Dr. Carlos Hernandez.

The reward signal, which defines the goal of a Reinforcement Learning (RL) agent, is a critical part of any RL problem. For many real-world RL problems, however, the rewards are often quite sparse, most of the time only indicating whether the task is completed partially or fully. Such sparse rewards provide the agent with only rare signs of progress, making the learning of the task slow and difficult. In some scenarios, the rewards might be noisy, change over time, or even be completely unavailable for extended periods of time. Some real-world RL problems also involve goals that are difficult to translate precisely into a numerical reward signal. Learning with such poor reward signals poses serious challenges to current RL methods. I am looking for students who would be interested in working with me to develop methods that would enable RL agents to learn effectively in environments with poor reward signals and thereby facilitate the successful application of RL to a wider range of real-world tasks. 

If you are interested in working with me, please refer to the 'Prospective Students' page on my website for more information and instructions on how to apply.

Accepting: Phd and MCS students

Express your interest in working with Dr. Janarthanan Rajendran.

In this project, we explore how spatial analysis techniques traditionally used in architecture and urban planning can support authoring, implementing, and evaluating building-scale immersive augmented reality experiences.   

The project involves iterative participatory design with content creators, software toolkit development in Unity 3D, spatial analysis using tools like QGIS, DepthMapX, R, and/or Matlab, designing and running "in-the-wild" field studies, and evaluating results using qualitative and quantitative approaches. In this project you will work in an interdisciplinary team that includes researchers in architecture, urban anthropology, and media studies.

Accepting: PhD and MCS students

Express your interest in working with Dr. Derek Reilly.

Human-computer interaction (HCI) and Ubiquitous computing (Ubicomp) fields have been interested in building and developing interactive technologies for years that support family and child health and wellness. To assist families as their babies’ transition into toddlers, several forms of technology have been created and designed for specific uses (such as feeding, diapering, and exercising) related to monitoring children's health. Industry and researchers have worked to use technology to inform parents about age-appropriate developmental milestones to monitor children's health and to minimize the negative impacts of developmental impairments. 

Despite all these improvements and applications available, there are still challenges when there are multiple caregivers, false data from the perspective of caregivers, multicultural communities, and in managing real-world data. Thus, there is a need to investigate culturally relevant health informatics and opportunities to investigate how Ubicomp or collaborative tools can be designed to address such challenges

The candidate should have programming skills and the confidence to interact with study participants.

Accepting: PhD and MCS students

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

SmartFarm is a state-of-the-art dashboard designed for farmers to gain real-time insights into animal (Dairy cows, pigs, chickens, sheep, horses) participation, health metrics, and interaction quality. By continuously monitoring vital stats and behavioral patterns, it offers proactive alerts to ensure optimal animal welfare. Its customizable interface, combined with mobile integration, ensures accessibility and ease of use for farmers on-the-go. With SmartFarm, farmers are equipped to make data-driven decisions, fostering a stronger bond with their animals and boosting overall farm productivity.

Accepting: PhD and MCS students

Express your interest in working with Dr. Suresh Neethirajan.

Farmer-Animal Team Challenges: Dive into interactive sessions where both farmer and animal (Dairy cows, pigs, chickens, sheep, horses) work in tandem. Imagine a touch-sensitive panel game where animals initiate pattern sequences that farmers must mirror, fostering synchronized thinking and collaboration between species.

Accepting: PhD and MCS students

Express your interest in working with Dr. Suresh Neethirajan.

Stimulate the cognitive faculties of farm animals with engaging puzzles and challenges. From puzzle feeders that reward problem-solving skills to touch panels that release treats upon correct sequences, these games are designed to enrich mental well-being, keeping animals alert and content.

Accepting: PhD and MCS students

Express your interest in working with Dr. Suresh Neethirajan.

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.

VR and AR are becoming affordable and accessible for everyday use. One area that has become prevalent is using VR/AR for creative tasks, such as sketching in 3D. This new research project aims to create new adaptive and intelligent user interfaces for 3D design by identifying the actions and elements humans use when thinking spatially. The ideal candidate has a strong interest in VR and AR, HCI and user interface design.

Accepting: PhD and MCS students

Express your interest in working with Dr. Mayra Barrera Machuca.

It is common for training systems, simulators, and digital twins to use VR. However, VR has hardware and software limitations, potentially resulting in mis-learning. This new research project aims to understand the cognitive mechanisms involved in skill transfer from VR to real life, identify the dynamics of mis-learning within VR, and develop new strategies for mitigating their adverse effects. The ideal candidate is strongly interested in using VR for training and HCI. 

Accepting: PhD and MCS students

Express your interest in working with Dr. Mayra Barrera Machuca.

Mixed Reality Games 4 Change project using games as a tool to teach skills, increase empathy, and reduce stigma for the benefit of the players outside the game. Games are designed to challenge social norms in an interdisciplinary feminist approach to Equity, Diversity, Inclusion, Accessibility, Decolonization, and Justice. Previous experience developing in MR, VR, and games are an asset. Fabrication skills (3D printing, Arduino, Raspberry Pi, etc.) are an asset. Must like video games, research, and social good. 

Accepting: PhD students

Express your interest in working with Dr. Rina Wehbe.

In collaboration with industry partners, Solucore Atlantic Inc. we look to create safer, smarter elevators. To apply for this project, you must have had previous experience with 3D printing, fabrication, maker technology (e.g., Raspberry Pi, Arduino).

Accepting: PhD and MCS students

Express your interest in working with Dr. Rina Wehbe.

Source code analysis, code quality issue identification, and refactoring have been explored extensively in the last two decades. Despite the progress, the existing methods and tools lack efficiency, rigor, extensible support for issue identification, and comprehensive support for potential refactorings. We aim to explore the next generation methods and tools by combining traditional approaches with machine learning-based approaches to improve the state-of-the-art in code analysis and refactoring.

Accepting: PhD students

Express your interest in working with Dr. Tushar Sharma.

The role of software development in sustainability is vastly understudied. Software profoundly affects all three pillars of sustainability: Environmental, Social and Economic. Inversely, the three sustainability pillars apply to every software project. The successful applicants will not only investigate the relationship between software engineering and sustainability but also develop and empirically evaluate tools or practices for improving software project sustainability.

Accepting: PhD and MCS students

Express your interest in working with Dr. Paul Ralph.

Most software companies are either considering a hybrid workforce strategy (employees work partly remotely, partly on-site) or have already adopted one. Refusing to accommodate remote work is crushing retention across the tech sector – the so-called “Great Resignation.” Indeed, hybrid work has many advantages for companies (e.g. lower overhead costs), employees (e.g. improved flexibility for parents and other caregivers) and society (e.g. improving workplace accessibility for people with disabilities). However, remote work undermines teams’ resilience, cohesion, and performance, and causes online-fatigue, poorly regulated workdays, loneliness, and coordination problems. The successful applicants will investigate how organizations can embrace a remote or hybrid workforce while overcoming challenges surrounding team cohesion, resilience, performance, and retention.

Accepting: PhD students

Express your interest in working with Dr. Paul Ralph.

Peer review—the foundation of science—is ineffective, unreliable, prejudiced, and opaque. It can only be fixed by transitioning to more structured review processes in which reviewers evaluate papers against specific acceptance criteria tailored to a paper’s individual research methodology (e.g. case study, controlled experiment). The successful applicants will create and evaluate tools to facilitate more structured review. The ideal candidate has good knowledge of web programming (e.g. HTML, CSS, Javascript) and an interest in research methods.

Accepting: PhD and MCS students

Express your interest in working with Dr. Paul Ralph.

Software engineering professionals and researchers need better measures of code quality. Professionals need better indicators of the effects of their changes on overall system quality. Researchers need to measure code quality as a dependent variable in their studies to assess the effectiveness of their interventions. Current research on code smells is easy for professionals to use but lacks scientific rigour. Code smells are merely patterns that someone thinks may cause problems eventually. Demonstrating that code smells actually cause code quality problems presupposes that we can accurately measure code quality. However, lack of good code quality measures is why we have code smells in the first place. Successful applicants for this project will join a comprehensive effort to improve code quality measurement.

Accepting: MCS students

Express your interest in working with Dr. Paul Ralph.

The proliferation of the Internet of Things (IoT) has led to an increasing demand for wireless access solutions capable of connecting low-power devices over large areas with low data rates. My research addresses this demand by focusing on enabling massive wireless connectivity for IoT. This involves strategies such as augmenting capacity through advanced wireless access techniques, enhancing reliability in challenging radio conditions, and exploring the possibilities of integrating satellite connectivity to extend the reach of IoT connectivity. I am seeking highly self-motivated students to join my team, especially those interested in exploring real-world platforms.

Accepting: PhD and MCS students

Express your interest in working with Dr. Samer Lahoud.

In the evolution of cellular networks beyond 5G, openness and intelligence will play crucial roles in radio access networks (RAN). My research is devoted to tackling the complex challenge of controlling radio resources to optimize performance and ensure quality of service within the framework of open RANs. In these networks, different components of the RAN can be software-defined and programmed through standard open interfaces. I am actively seeking highly self-motivated students to join my team, especially those with an interest in exploring real-world platforms.

Accepting: PhD and MCS students

Express your interest in working with Dr. Samer Lahoud.

In this research project, we are going to work on monitoring and analysis of adversity and changes in the communication networks and services using machine learning and artificial intelligence approaches. I'm looking for interested students who are capable of independent as well as team based research on both wired and wireless networks, including the internet of things and vehicular networks.

Accepting: PhD and MCS students

Express your interest in working with Dr. Nur Zincir-Heywood.

The primary objective of this project is to investigate vulnerabilities, security threats and intrusions on Healthcare IoT systems, and design intrusion detection and prevention mechanisms to mitigate cyber-attacks on such systems. The project will explore an integration of machine learning approaches with biometric parameters to ensure confidentiality, integrity and authentication of healthcare data, and experimentally validate these mechanisms on a Healthcare IoT test bench.

Accepting: PhD and MCS students

Express your interest in working with Dr. Srini Sampalli.

The objective of this project is to investigate, design and implement mechanisms for the detection, prediction, and prevention of cyber-attacks such as Distributed Denial of Service (DDoS) on Supervisory Control and Data Acquisition (SCADA) systems, which form the core of critical infrastructure such as power grids, water supply control, and nuclear systems. These strategies will be developed using machine learning classifiers with extension to deep learning techniques. The project also aims to explore the integration of distributed systems, fuzzy logic and machine learning approaches to predict and detect Distributed Denial of Service (DDoS) attacks with higher detection accuracy and faster detection time.

Accepting: PhD and MCS students

Express your interest in working with Dr. Srini Sampalli.

The project entails the design of secure and reliable assistive wireless technologies for healthcare. Past work in this area by my students has led to the innovation of touchscreen-based Braille keyboards for the visually impaired, applications using wireless technologies in the areas of medication error detection and prevention, and a tool for monitoring of pregnant women for signs of premature labour. The work by my Masters student Steve Dafer's work has resulted in an IoT-based tool called EMPWRD (Enhanced Modular Platform for People with Rigid Disabilities) that uses artificial intelligence and natural language processing techniques integrated with IoT. This tool enables paralyzed patients to control devices and even make live phone calls. I am looking for students to extend work in this area.

Accepting: PhD and MCS students

Express your interest in working with Dr. Srini Sampalli.

I currently have a couple of open positions for highly self-motivated MCS, PhD students interested in wireless networking and artificial intelligence (AI). My current research project focuses on supporting real-time applications in high-confidence Internet of Vehicles (IoV). Traditional analysis tools/algorithms are unable to cope with the full complexity of IoV or adequately predict system behavior due to great challenges that arise from the high mobility, dynamic changing environment and intrinsic heterogeneity of such systems. Therefore, the goal of my research project is to reap the benefits of AI to address aforementioned challenges in IoV. 

Accepting: PhD and MCS students

Express your interest in working with Dr. Yujie Tang.

Large Language Models, such as ChatGPT, Google Bard, Copilot, and other artificial intelligence (AI) systems, possess the capability to generate code based on natural language descriptions. Previous research on AI code generators has primarily focused on assessing their usability for novice programmers and highlighting their advantages for curriculum development in educational settings. Although these studies have yielded valuable insights, there remains considerable gaps in learning sciences research with respect to the potential benefits for novice programmers who face challenges and the inherent limitations with relying extensively on code generated by these tools. Based on information processing theories related to skill acquisition and program comprehension in introductory programming, this research aims to gain insights into learning, transfer, and performance of students using AI code generators. 

Accepting: PhD students

Express your interest in working with Dr. Eric Poitras.