Dr A. I. Shihab is a Senior Lecturer at Kingston University's Faculty of Engineering, Computing and the Environment, Department of Networks and Digital Media. He teaches programming languages (C++/Java), data structures, web development, and AI/machine learning. His research focuses on affective computing and machine learning applications including: Acoustic event detection in sports environments Audio signal analysis for tennis match modeling Multi-camera visual surveillance systems Medical imaging analysis using fuzzy clustering techniques Publications demonstrate expertise in combining audio/video modalities for sports analytics (tennis rallies, court-shots) and developing Markov models for sound event sequence analysis. Contact: a.shihab@kingston.ac.uk
Kevin Crowston is a Distinguished Professor of Information Science at Syracuse University's School of Information Studies (iSchool), where he examines how information technology enables new organizational forms through empirical studies, theoretical modeling, and system design. His work focuses on coordination-intensive processes in virtual settings, with significant contributions to citizen science, data science teamwork, and journalism transformation. Education A.B. in Applied Mathematics (Computer Science), Harvard University, 1984 Ph.D. in Information Technologies, MIT Sloan School of Management, 1991 Research Focus : Crowston investigates coordination mechanisms in human-AI collaboration, particularly through projects like Gravity Spy (combining citizen scientists with machine learning for gravitational wave analysis) and journalism innovation (e.g., ReelFramer for AI-assisted news-to-video translation). His framework addresses how intelligent systems reshape work design, knowledge production, and team dynamics in scientific and media contexts. Publication Trends : Recent articles (2024-2025) reveal three dominant threads: (1) Human-AI co-creation in journalism (deskilling/upskilling dynamics, creative tool adoption), (2) Citizen science evolution with AI (co-learning systems, lexical entrainment), and (3) Socio-technical governance of intelligent machines (control-accountability alignment, project archetypes). These reflect his central inquiry into how technology reconfigures work structures. Scientific Recognition ACM Distinguished Speaker Research Leadership : Crowston currently directs two major NSF initiatives: (1) HCC grant 21-06865 on intelligent support for non-expert information navigation, and (2) FW-HTF grant 21-29047 exploring human-technology collaboration in journalism. He spearheaded a Research Coordination Network establishing socio-technical frameworks for work in the age of intelligent machines, culminating in a special issue of Information, Technology & People . Collaborative Infrastructure : He co-leads the Gravity Spy citizen science ecosystem (integrating LIGO physicists, machine learning systems, and volunteers) and serves as co-editor-in-chief of Information, Technology and People , previously editing ACM Transactions on Social Computing . His MIDST platform research advances stigmergic coordination for data science teams.
Dr. Bo Liu is an Associate Professor in the School of Computer Science at the University of Technology Sydney (UTS), where he serves as a core member and director of the AI Security and Privacy (AISP) Research Lab at the Australian Artificial Intelligence Institute (AAII). With expertise spanning cybersecurity, privacy protection, AI and machine learning, and wireless communications, Dr. Liu has established himself as a leading researcher in the field of AI security and privacy. Dr. Liu earned his PhD from the Department of Electronic Engineering at Shanghai Jiao Tong University in 2010. His academic journey at UTS has progressed from Senior Lecturer (November 2019-December 2022) to his current position as Associate Professor (January 2023-present). Dr. Liu's research focuses on the critical intersection of artificial intelligence and security, particularly addressing emerging threats in the age of advanced AI systems. His work spans multiple dimensions of security and privacy, including deepfake detection, privacy-preserving data synthesis, AI model security, and fair machine learning. He has pioneered approaches to detect AI-generated content, protect visual privacy through de-identification techniques, and address the complex relationship between algorithmic fairness and privacy preservation. His publication record demonstrates significant contributions across multiple cutting-edge research areas, with particular emphasis on detecting and mitigating threats from generative AI systems. His recent work reveals a strong focus on deepfake detection across multiple modalities (images, video, and audio), privacy-preserving techniques for sensitive data, and the security implications of emerging AI architectures like Retrieval-Augmented Generation systems. Dr. Liu has secured substantial research funding, including as Lead Chief Investigator on multiple ARC Discovery and Linkage Projects, totaling over $3.5 million AUD. His industry collaborations include partnerships with the NSW Department of Planning and the Reserve Bank of Australia, demonstrating the practical applicability of his research. As an academic leader, Dr. Liu serves as Associate Editor for IEEE Transactions on Broadcasting and actively contributes to the academic community through conference organization, peer review for top-tier venues, and assessment for ARC grant schemes. He also teaches courses including Penetration Testing, Ethical Hacking and Offensive Security, and supervises Masters and PhD students in cybersecurity and privacy research.
Professor David Taubman is a distinguished academic serving as Professor and Deputy Head of School (Research) at the School of Electrical Engineering and Telecommunications (EE&T) at the University of New South Wales (UNSW) in Sydney, Australia. He is also co-director of Kakadu Software Pty. Ltd. and its affiliates Kakadu R&D and Kakadu GPU. With a career spanning over three decades, Professor Taubman has made significant contributions to the field of image and video compression, most notably as the author of the EBCOT coding algorithm adopted in the JPEG2000 international standard. Professor Taubman earned his B.Sc. in Mathematics and Computer Science (1986) and B.E. (Medal) in Electrical Engineering (1988) from the University of Sydney, followed by an M.Sc. (1992) and Ph.D. (1994) in Electrical Engineering from the University of California at Berkeley. His professional journey includes engineering work at the Electricity Commission of N.S.W. (1988-1990), research positions at Hewlett-Packard Laboratories in Palo Alto (1994-1998), and an academic career at UNSW where he progressed from Senior Lecturer (1998-2003) to Associate Professor (2004-2009) and finally to Professor (2009-present). He has held various leadership roles including Head of the EE&T Telecommunications Research Group (2003-2014), Head of the EE&T Signal Processing Research Group (2014-present), Director of Research for the School of EE&T (2011-2016), and Deputy Head of School (Research) since 2017. Professor Taubman's research interests center on image and video compression, with particular expertise in JPEG2000 standards and implementations. His work spans signal processing, wavelet transforms, scalable video coding, motion modeling, and multimedia systems. He has pioneered numerous compression algorithms and frameworks, including the EBCOT coding algorithm that became central to the JPEG2000 standard. His recent research focuses on efficient motion modeling with cuboidal partitioning, learned lifting-based transform structures, and high-throughput implementations of JPEG2000 for video applications. His work bridges theoretical foundations with practical implementations, as evidenced by the commercially successful Kakadu Software tools that have garnered around 500 commercial licensees. Analysis of Professor Taubman's recent publications reveals a consistent focus on advancing compression technologies with particular emphasis on scalability, efficiency, and adaptability. His work spans traditional image compression (JPEG2000 extensions), video coding (cuboid-based partitioning for UHD/360-degree video), and emerging applications (nanopore sequencing data compression). A notable trend is the integration of machine learning techniques with traditional compression frameworks, as seen in his work on learned lifting-based transform structures. His research maintains strong connections to real-world applications across diverse domains including medical imaging, astronomical data processing, and genomic sequencing. IEEE Fellow Engineers Australia Fellow (by invitation) Professor Taubman has served as Associate Editor for the IEEE Transactions on Image Processing for two four-year appointments (2003-2005 and 2010-2013). He has been actively involved in numerous research grants focused on image and video compression technologies, particularly those related to the JPEG2000 standard and its extensions. His work has received significant industry support, reflected in his consultancy with various U.S., Japanese, and Australian corporations. He has also contributed to international standards development as a member of Standards Australia Technical Committee MS-065 (mirroring ISO TC42 on Digital Photography) and as a constitutional member of Standards Australia Technical Committee IT-029 (Coded Representation of Picture, Audio and Multimedia/Hypermedia Information). Professor Taubman co-directs Kakadu Software Pty. Ltd. and its research affiliates Kakadu R&D and Kakadu GPU, which have developed the commercially successful Kakadu Software tools for JPEG2000. His research group at UNSW focuses on advanced image and video compression techniques, with particular expertise in wavelet-based methods, scalable coding, and motion modeling. The group maintains strong industry connections and has contributed significantly to the development and standardization of image compression technologies worldwide.
Sadaf Salehkalaibar is an Assistant Professor in the Department of Computer Science at the University of Manitoba, Winnipeg, Canada. She holds an office in the EITC building (E2-416) and has previously held academic positions at the University of Tehran, University of Toronto as a research associate, and visiting roles at McMaster University, Telecom Paristech, and National University of Singapore. Her research focuses on explainable artificial intelligence, generative models, and information theory with an emphasis on rate-distortion-perception tradeoffs in video and image processing. Her educational background includes teaching courses such as Signals and Systems, Digital Signal Processing, and Network Security at the University of Tehran. She currently teaches COMP4190 (Artificial Intelligence) at the University of Manitoba. Research interests revolve around developing efficient algorithms for AI systems, with key contributions in learned video compression, federated learning, and privacy-preserving techniques. Notable work includes the M22 algorithm for communication-efficient federated learning and the NSERC Discovery Grant-funded project on data-driven learning efficiency. Recent publications highlight advancements in perception loss functions, Gaussian vector source analysis, and secure distributed hypothesis testing. She actively serves on editorial boards (e.g., IEEE Transactions on Communications) and conferences (ISIT, ITW). Awards include the prestigious NSERC Discovery Grant (2025). Supervision highlights 13 MSc students at the University of Tehran, focusing on topics like privacy-preserving systems and distributed learning. Labs/teams: Leads research group at University of Manitoba focusing on AI and information theory applications in multimedia systems.
Beichuan Zhang serves as Associate Department Head and Professor in the Department of Computer Science at the University of Arizona, maintaining office GS 723 with contact details 520-621-4817 and bzhang@cs.arizona.edu. His academic leadership spans network architecture research and departmental administration within the university's computing ecosystem. Zhang holds a Ph.D. from the University of California at Los Angeles (2003), establishing his foundation in advanced networking systems. His doctoral work catalyzed a career focused on internet infrastructure evolution. Research centers on computer networks with specific expertise in Internet routing architecture, protocols, topology, and multicast systems. Zhang is a principal investigator in Named Data Networking (NDN), driving innovations in stateful forwarding planes, in-network caching (e.g., Nb-cache, BLEnD), and congestion control mechanisms. His work bridges theoretical networking models with practical implementations for wireless, satellite, and live-streaming environments. Analysis of 2021-2025 publications reveals strategic expansion into Low Earth Orbit satellite networks, where Zhang pioneers NDN adaptations for handover resilience and outage detection. Concurrently, his group optimizes video streaming protocols and wireless performance through interest bundling techniques. This dual trajectory demonstrates systematic progression from terrestrial networking to space-ground integrated architectures.
Professor Adrian Hilton is a distinguished faculty member at the University of Surrey, serving as Director of the Centre for Vision, Speech and Signal Processing (CVSSP) and Director of the Surrey Institute for People-Centred AI. He is affiliated with the School of Computer Science and Electronic Engineering and leads the Visual Media Research Lab (V-Lab). His research focuses on pioneering next-generation 4D computer vision technologies that enable machines to understand and model dynamic real-world scenes. Key areas include 3D/4D shape capture, computer vision, machine learning, graphics, and animation for applications in sports analysis, film/TV production, virtual reality, and medical imaging. His work bridges the gap between real and computer-generated imagery, with notable contributions in volumetric capture, motion capture, and free-viewpoint video. Hilton's recent publications demonstrate a strong trend toward multimodal integration, particularly combining audio and visual processing for spatial audio applications, while advancing 4D reconstruction techniques for human performance capture. His work increasingly incorporates transformer architectures and neural rendering techniques for improved illumination estimation, shadow modeling, and multi-view consistency. Scientific Awards and Recognition Two EU IST Innovation Prizes Manufacturing Industry Achievement Award Royal Society Industry Fellowship (2008-2011) Royal Society Wolfson Research Merit Award in 4D Vision (2013-2018) Fellow of the Royal Academy of Engineering (FREng) Fellow of the International Association for Pattern Recognition (FIAPR) Fellow of the Institution of Engineering and Technology (FIET) Hilton actively mentors PhD and post-doctoral researchers through his leadership of CVSSP, which has a grant portfolio exceeding £31M and comprises 170 researchers. He has successfully commercialized several technologies, including systems used by the BBC for sports commentary visualization. His research collaborations span major industry partners including BBC, BT, Sony, Framestore, and The Foundry. He co-founded the G3 Games forum and the CVMP Conference on Visual Media Production, demonstrating strong engagement with the creative industries. Current research projects include the S3A Programme Grant in Future Spatial Audio and InnovateUK's ALIVE project for 360 video reconstruction.
Prof. Dr. Aljosa Smolic is a Professor and Co-Head of the Immersive Realities Research Lab at Lucerne School of Computer Science and Information Technology, Lucerne University of Applied Sciences and Arts. He joined HSLU in 2022 and became Co-Head in 2023. Previously, he served as SFI Research Professor at Trinity College Dublin (2016-2021) where he led the V-SENSE group in visual computing, combining computer vision, graphics, and media technology. His career includes positions as Senior Research Scientist at Disney Research Zurich (2009-2016) and Scientific Project Manager at Fraunhofer HHI (2001-2009). He holds a PhD from RWTH Aachen University. Research focuses on immersive technologies including AR/VR, volumetric video, light-fields, and deep learning applications in visual computing. His work has resulted in over 50 Disney R&D projects, publications, patents, and technology transfers. Publications emphasize VR evaluation, volumetric video applications, 3D reconstruction, and XR in education, frequently employing deep learning and computer vision techniques. Awards and Recognition: IEEE ICME Star Innovator Award 2020 TCD Campus Company Founders Award 2020 Multiple best paper awards Co-founded Volograms (volumetric video startup) and holds editorial roles including Associate Editor for IEEE Transactions on Image Processing.
Steven E. Clayman is a Professor in the Department of Sociology at the University of California, Los Angeles (UCLA), specializing in the organization of human interaction within social institutions through conversation analysis. His educational background includes: Ph.D. from the University of California, Santa Barbara Clayman's research centers on media-politics interactions (particularly presidential press conferences and broadcast interviews), extending to institutional settings like law, medicine, and public safety. His methodology integrates audio/video recordings of natural behavior with statistical analysis, examining how interaction shapes journalistic norms, political accountability, and sociopolitical legitimacy. Key contributions include co-authoring foundational texts The News Interview (2002) and Talk in Action (2010) with John Heritage. Publication analysis reveals an evolution from historical studies of presidential questioning (2002-2007) toward contemporary applications in legal contexts (jury selection) and emergency response systems (police dispatch), consistently employing conversation analytic techniques to dissect institutional power dynamics. Clayman has not received major scientific awards documented in the source material. He secured a National Science Foundation grant (2001-2003) for The Evolution of Questioning in Presidential News Conferences , investigating historical trends in press-presidential relations. Current collaborative projects include gender dynamics in presidential questioning (with Heritage/Hill), marriage equality discourse (with Loeb), and police radio dispatch systems (with Kevoe-Feldman), though no formal advisees are listed.
Jing Yang is a full Professor and Director of MS CS Program in the Computer Science Department at the University of North Carolina at Charlotte (UNCC). She has been actively involved in data visualization and visual analytics research since joining UNCC in 2005 after completing her PhD at Worcester Polytechnic Institute. Dr. Yang earned her Bachelor's degrees in Engineering Mechanics and Computer Science from TsingHua University in 1997 and completed her Ph.D. in Computer Science from Worcester Polytechnic Institute in May 2005 under advisors Matthew O. Ward and Elke A. Rundensteiner. Her research focuses on developing visual analytics techniques for abstract data including multidimensional data, time-oriented data, networks, hierarchies, text documents, and trajectory data. She conducts design studies for application domains such as sports, bioinformatics, finance, network security, and health, while exploring fundamental visualization topics like interactions, insight management, clustering-based approaches, and animations. Her recent work emphasizes sports analytics, urban data, and multivariate time series visual analytics. Analysis of Dr. Yang's publication record reveals a strong focus on practical applications of visualization techniques across diverse domains. Her work consistently bridges theoretical visualization frameworks with real-world data challenges, particularly in transportation (taxi trajectories), sports (tennis match analysis), financial transactions, and bioinformatics. The publications demonstrate an evolution from foundational visualization techniques to increasingly sophisticated domain-specific applications. Dr. Yang has secured substantial research funding from NSF, EPA, DHS, and industry partners including Google and Bank of America. Her grants portfolio includes significant projects like TrajAnalytics (NSF, $200,950), Visualizing Event Dynamics (NSF EAGER, $75,317), and Visualizing High Dimensional Categorical Datasets (Google Faculty Research Award, $60,000), among others totaling over $4 million in research support. As an educator, Dr. Yang has taught numerous courses including Visual Analytics, Information Visualization, and Database Design. She directs the Charlotte Visualization Center and has mentored numerous students through research projects. Her collaborative approach is evident in her extensive co-authorship network spanning multiple institutions and disciplines. Current research directions include narrative animation for streaming text visualization and advanced techniques for exploring high-dimensional categorical datasets.
Dag Johansen is a Professor in the Department of Informatics at UiT The Arctic University of Norway, Tromso campus. His work spans multiple research areas at the intersection of computer science, sports science, medicine, health technology, and nutrition science. He leads the interdisciplinary "Corpore Sano" research center and is actively involved in several research groups including the Cyber Security Group (CSG) and Crime Control and Security Law. Professor Johansen's research focuses on developing fundamental software solutions for secure and error-free data processing in heterogeneous distributed systems, ranging from lightweight "Internet of Things" devices and mobile phones to large-scale cloud solutions. His work particularly emphasizes applications in sports technology, edge computing, and compliance technology. His research interests include distributed systems, cybersecurity, sports technology, edge computing, data privacy, AI for sports analytics, multimedia forensics, and compliance technology. His recent publication trends show a strong focus on AI applications for sports video analysis, particularly in soccer and ice hockey, where his team has developed AI-based cropping systems for social media representations. He also has significant work in data privacy and GDPR compliance, especially regarding the "third country problem," as well as applications of AI in sustainable fishing practices. His 2024-2025 publications demonstrate continued work in self-healing microservices, lightweight encryption for video feeds, and virtual reality training environments. Professor Johansen is actively involved in mentoring students and research collaborators, as evidenced by his extensive publication record with numerous co-authors including doctoral students and postdoctoral researchers. His work has received funding through various research projects focused on data analytics, privacy technology, cybersecurity, and sports technology applications. He leads the interdisciplinary "Corpore Sano" center, which brings together researchers from computer science, sports science, medicine, health technology, and nutrition science. His work also involves collaboration with the "Njord" project focused on sustainable fishing through AI applications, and he's involved in developing the "Áika" distributed edge system for AI inference.
Dr. Rui Dai is an Associate Professor in the Department of Computer Science at the University of Cincinnati's College of Engineering and Applied Science. Her research focuses on wireless sensor networks, multimedia communications, and video analytics for healthcare and surveillance applications. She directs multiple NSF and NIST-funded projects on perceptual-quality-aware video systems. Research interests include quality-of-experience optimization for video analytics, compressed domain feature extraction, and edge computing frameworks for intelligent surveillance. Recent work develops deep feature compression techniques, multi-camera fall detection systems, and quality-aware video distribution strategies for 5G networks. Publications demonstrate consistent innovation in video processing for resource-constrained environments, with applications spanning healthcare monitoring, public safety networks, and embedded vision systems. Current projects investigate metaverse communication challenges for 6G networks and PHP vulnerability detection through hybrid static-fuzzing analysis.
Miroslaw Bober is Professor of Video Processing at the University of Surrey, where he joined in 2011. He leads the Visual Media Analysis team within the Centre for Vision, Speech and Signal Processing (CVSSP) in the School of Computer Science and Electronic Engineering. His extensive industry experience includes 15 years as General Manager of the Mitsubishi Electric R&D Centre Europe and Head of Research for its Visual & Sensing Division. BSc and MSc in Electrical Engineering from AGH University of Science and Technology, Krakow, Poland (1990) MSc in Machine Intelligence with distinction from Surrey University (1991) PhD in Computer Vision from Surrey University (1995) Professor Bober's research focuses on novel techniques in signal processing, computer vision and machine learning with applications in industry, healthcare, big-data and security. His expertise particularly lies in image and video analysis and retrieval, including visual search, object recognition, and analysis of motion, shape and texture. His algorithms for shape analysis, image/video fingerprinting, and visual search are considered world-leading and have been selected for ISO International standards within MPEG, with applications used by organizations like the Metropolitan Police. His recent publication trends show a strong focus on hybrid network architectures, scene graph generation, medical imaging applications, and augmented reality publishing systems. His work spans both theoretical advancements in computer vision and practical implementations addressing real-world challenges in media, healthcare, and security domains. The research demonstrates a consistent pattern of bridging academic innovation with industrial applications, particularly in visual search technology and media analysis. Presidential Award for strengthening the TV business in Japan via innovative 'Visual Navigation' content access technology (2010) Mitsubishi Best Invention Award for Image Signature Technology (2008) Professor Bober serves as Programme Director for the MSc in Multimedia Signal Processing and Communications and holds various teaching and mentoring roles. He has secured over 30 research and industrial grants totaling more than £16M, including the BRIDGET FP-7 project (5.28 M€) as coordinator and PI, and the CODAM project (£1.05 M) as PI. His work with the BBC, Huawei, and other industry partners demonstrates strong industry-academia collaboration. As chair of MPEG technical work on Compact Descriptors for Visual Search (CDVS) and Compact Descriptors for Video Analysis (CDVA), Professor Bober leads international standardization efforts. His Visual Media Analysis team develops cutting-edge visual search and media analysis algorithms with applications across broadcast, security, and healthcare domains.
Ma Xufeng is an Assistant Professor in the Animation Department at the School of Art and Media, Tongji University , Shanghai. She earned her PhD in Image Arts from Chung-Ang University, South Korea , and completed a postdoctoral fellowship at the School of Journalism and Communication, Tsinghua University . Her research bridges intelligent imaging, animation games, AIGC, new media art, and the metaverse , focusing on intelligent image production and dissemination, AI-driven art therapy, and serious games . Education: PhD in Image Arts, Chung-Ang University, South Korea (2018.09–2021.02) Master of Image Arts, Chung-Ang University, South Korea (2016.09–2018.06) Bachelor of Art Design (Animation), Zhongnan University of Economics and Law, China (2011.09–2015.07) Research Interests: Dr. Ma’s scholarship sits at the intersection of artificial intelligence, digital media, and therapeutic applications . She explores how AI-generated content (AIGC) can revolutionize animation, game design, and immersive storytelling . A second strand investigates serious games and VR systems for autism rehabilitation and emotional therapy , leveraging game psychology to craft interventions that blend aesthetics, technology, and mental health . Publications & Research Trends: Her most recent work (2021-2024) charts a trajectory from VR-based rehabilitation games to AI painting for art therapy , demonstrating a shift from interactive technology prototypes toward critical and aesthetic theories of AI art . Collectively, her publications map the evolution of intelligent media ecosystems and their societal impact. Scientific Awards & Grants: Principal Investigator, Korean BK21+ CFT Convergence Research Project – VR immersive game for autistic children (2020) Principal Investigator, Korean BK21+ CFT Convergence Research Project – Fitness-bicycle functional game (2019) Special Selection Award, Seoul International Animation & Cartoon Festival (2020) Nomination, Golden Kuker–Sofia International Animation Festival (2020) Teaching & Labs: At Tongji University, Dr. Ma contributes to undergraduate and postgraduate curricula in animation, new media art, and game design . She is affiliated with the Omnimedia Research Institute and the Media Experiment and Practice Teaching Center , where she guides students in AI-driven content creation and VR/AR prototyping .
Dr. Faiza Hirji is a Professor in the Department of Communication Studies and Media Arts at McMaster University, currently serving as President of the Canadian Communication Association (CCA) after previously holding roles as Vice President and member at large. She also contributed to the Hamilton Council of Aging Board of Directors from 2015 to 2021. Her research focuses on critical intersections of identity and media, with key interests including: Media representation of race, religion, ethnicity, and gender Media's role in identity construction Media within diasporic/transnational communities Representation of Muslim women in contemporary media Intersectional community communications as critical EDI work Hirji's scholarly output reveals sustained engagement with representation politics, particularly examining South Asian and Muslim communities in Canadian media landscapes. Her work spans Bollywood narratives, algorithmic bias in streaming platforms, Islamophobic discourse, and social media activism, consistently challenging normative frameworks through decolonial and intersectional lenses. She co-directs an SSHRC-funded project on intersectional community communications and has co-edited significant special issues: 'Bollywood, Power and Politics' (Global Media Journal – Canadian Edition, 2021) and 'racism and colonialism in Canadian communication studies' (Canadian Journal of Communication, 2022). Her book 'Dreaming in Canadian: South Asian Youth, Bollywood and Belonging' (UBC Press, 2010) established foundational insights into diasporic youth identity. Hirji collaborates with leading scholars including Sikata Banerjee, Yasmin Jiwani, and Kirsten McAllister on research initiatives addressing systemic inequities. Her teaching portfolio includes doctoral seminars in Communication Studies and undergraduate courses like 'Race, Religion and Media', reflecting her commitment to critical pedagogy through 2025.
