Past Seminars: 2019

Abstract: TechnologyAlberta is pleased to feature - TenSpeed Technologies (10spd.com) a product-driven technology company with offices in Amsterdam and the Advanced Technology Centre (ATC) in the Edmonton Research Park. One of their solutions focuses on high-stakes examinations; with the use of artificial intelligence, deeper insights is provided into candidate intelligence while tracking down potential examination fraud. TenSpeed's vision is to provide cognitive student profiles rather than traditional A/B/C scoring structures. With a real understanding of candidate potential, students can be guided into careers where they can excel and enjoy.

Bio: Mike Priest is an entrepreneur and technologist who immigrated to Canada in 2008 from Liverpool, England. Mike founded TenSpeed in 2014 and has launched 18 software products across Europe and North America. Specializing in process and document-driven solutions, Mike's latest achievements were the acquisition of JumpSeat training software in 2017 and Trifork Learning in 2019. Mike runs the TenSpeed Team from their Edmonton offices.

Abstract: Because AI can now accept medical images directly as inputs to identify anatomy and pathology, many teams are working on AI analysis of CT, MRI and X-ray. Ultrasound images are more difficult to work with, but unlike CT/MRI, ultrasound probes are inexpensive and portable. Ultrasound could become the 21st century stethoscope, with handheld wireless probes generating images interpreted by AI. This can transform medical care. As a candidate for the CIFAR AI Chair, Dr. Jaremko will outline progress to date, his vision for the future of AI in ultrasound, and why we at UofA in Edmonton are particularly well equipped to lead in this field.

Bio: Jacob Jaremko is a Radiologist, Associate Professor and AHS Endowed Chair at UofA. He obtained an MD and a PhD in Biomedical Engineering at the University of Calgary, residency training in Diagnostic Radiology at UofA, and two clinical fellowships: Pediatric Radiology at the Royal Children's Hospital in Melbourne, Australia, and Musculoskeletal Radiology at Massachusetts General Hospital in Boston, USA. His background in deep learning began in 1999 with use of genetic algorithm-neural networks for his PhD thesis. His main research interests are in the use of artificial intelligence to automate image analysis, particularly of ultrasound, and the development and degeneration of bones and joints.

Abstract: The term XAI refers to a set of tools for explaining AI systems of any kind, beyond Machine Learning. Even though these tools aim at addressing explanation in the broader sense, they are not designed for all users, tasks, contexts and applications. This presentation will describe progress to date on XAI by reviewing its approaches, motivation, best practices, industrial applications, and limitations.

Bio: Dr. Freddy Lecue is the Chief Artificial Intelligence (AI) Scientist at CortAIx (Centre of Research & Technology in Artificial Intelligence eXpertise) at Thales in Montreal - Canada. He is also a research associate at INRIA, in WIMMICS, Sophia Antipolis - France. Before joining the new R&T lab of Thales dedicated to AI, he was AI R&D Lead at Accenture Labs in Ireland from 2016 to 2018. Prior joining Accenture he was a research scientist, lead investigator in large scale reasoning systems at IBM Research from 2011 to 2016, a research fellow at The University of Manchester from 2008 to 2011 and research engineer at Orange Labs from 2005 to 2008.

Abstract: Machine learning models, or Algorithm applications (Aapps), are designed to analyze pixels and spectral features combined with shape information to identify a particular target. The target could be a disease or fungus on a plant, the level of protein in a sample of seeds, nutrients in soil, a deformed object, cancer cells, or other things a business finds value in detecting. Stream Technologies has pioneered and specialized in developing multi-band convolutional neural nets that offer unprecedented accuracy over spatial analytics alone. Their experience in machine learning has grown into an offering (Stream.ML) that can enable the wider use of these technologies.

Bio: John Murphy is a graduate from the University of Alberta, is CEO and co-founder of Stream Technologies Inc., a startup in the photonics / analytics space. John has over 30 years of technology commercialization experience, including founding and growing multiple start-ups. He is active on several advisory boards, Chairman of nanocluster Alberta, is a local angel investor, and was on the founding board of the A100 Organization. John works from Stream Technologies' offices at the Advanced Technology Centre (ATC) in the Edmonton Research Park on the Southside of Edmonton. Through the collaboration and mentorship of John and others at ATC and the UofA, now over half of the Tech companies at the ATC are incorporating AI/ML into their solutions.

Abstract: Monte Carlo tree search (MCTS) has been extensively used for two-player games whose state-space can be regarded as an AND/OR graph. The method can flexibly incorporate heuristic knowledge. In AlphaGo and its successors AlphaGo Zero and Alpha Zero, game-specific knowledge is learned and expressed via a two-head network architecture. A policy-head output is used for move selection and a value-head provides move evaluation in MCTS.

We propose a three-head architecture that adds an action-value prediction head to the neural network. Using the game of Hex as a test domain, we empirically verify the merits of this architecture in two scenarios (1) supervised learning, and (2) AlphaZero style reinforcement learning. In (1), we find that the action-value head can achieve similar prediction accuracy as the state-value head with minimal overhead. Using the additional action-value head in MCTS leads to improved playing strength. We also discuss advantages of the new algorithm for delayed node expansion and for augmented training data using a minimax principle. In (2), using the additional action-value head for AlphaZero style training and play also results in improved accuracy of both policy and evaluation compared to the standard approach.

Bio: Chao Gao is a PhD student advised by Martin Mueller and Ryan Hayward. Since 2015, his major interest is developing algorithms for solving and playing the game of Hex, with a particular focus on search algorithms with deep neural networks. He developed MoHex-CNN which became the strongest Hex player in 2017 on 13x13 Hex, and MoHex-3HNN which won 2018 Computer Olympiad competition on both 13x13 and 11x11 Hex by defeating a strong competitor DeepEzo. Collaborated with researchers Pablo Hernandez-Leal and Bilal Kartal at Borealis AI, as an internship work, he developed a player that won 2nd place in the learning category on Pommeran, a multi-agent game competition held at NeurIPS 2018. He currently works as a research intern at Huawei Edmonton

Abstract: At present, the vast majority of building blocks, techniques, and architectures for training deep neural networks are based on real-valued computations and representations. However, representations based on complex numbers have started to receive increased attention. Despite their compelling properties complex-valued deep neural networks have been neglected due in part to the absence of the building blocks required to design and train this type of networks. The lack of such a framework represents a noticeable gap in deep learning tooling

We aim to fill this gap by providing new methods that go far beyond a simple theoretical generalization of real-valued neural networks. More specifically, we develop some key atomic components for complex-valued deep neural networks and apply them to convolutional feed-forward networks and convolutional LSTMs. Complex convolutions and new algorithms for complex batch-normalization and complex weight initialization strategies form the bedrock of the proposed framework.

We also provide an extension that builds on the results obtained in the first article to provide a novel fully complex-valued pipeline for automatic signal retrieval and signal separation in the frequency domain. We call this model a Deep Complex Separator. It is a novel masking-based method founded on a complex-valued version of Feature-wise Linear Modulation (FiLM). This new masking method is the key approach underlying our proposed speech separation architecture. We also propose a new explicitly phase-aware loss, which is amplitude and shift-invariant, taking into account the complex-valued components of the spectrogram. We compare it to several other phase-aware losses operating in both time and frequency domains.

We also examine issues related to the nature of orthogonal matrices employed to train recurrent neural networks. Unitary transition matrices and orthogonal constraints are shown to provide an appropriate solution to the vanishing and exploding gradients problem in recurrent neural networks. However, imposing hard constraints has been shown to be computationally expensive and needless for some real-world tasks. In this part, we aim at assessing the utility of different types of soft and hard orthogonality constraints by controlling the extent of the deviation of the corresponding transition weight matrix from the manifold of orthogonal transformations and measuring the generalization performance of the associated recurrent neural network.

Bio: Chiheb Trabelsi received his PhD in Computer Engineering from the Quebec Artificial Intelligence Institute (Mila) where he was supervised by Christopher Pal and defended his Thesis in July 2019. He is currently finishing a long-term internship at Element AI. During his PhD he mainly focused on learning hypercomplex-valued representations and how to stabilize the learning process for deep complex and real-valued neural networks. Before that, he received a M.Sc in Computer Science from the University of Montreal where his research was conducted on Statistical Machine Translation into morphologically rich language. Trabelsi obtained his B.Sc in Computer Science and Management from the University of Tunis where he was ranked 2nd among 257 students.

Abstract: Many economists have argued convincingly that automated systems employing present-day, narrow artificial intelligence have caused massive technological displacement, which has led to stagnant real wages, fewer middle-income jobs, and increased economic inequality in developed countries like Canada and the United States. To address this problem various authors have proposed measures to increase workers’ real incomes, including the adoption of a universal basic income, increased public investment in education, increased minimum wages, increased worker control of firms, and investment in a Green New Deal that will provide substantial employment in transitioning to green buildings, agriculture, and energy. In this presentation I argue that both left-wing and right-wing positions in political philosophy, such as Rawls’s Justice as Fairness and Nozick’s Entitlement Theory, are committed to the conclusion that we should take political action to counteract the effects of technological displacement by undertaking such measures to increase workers’ real incomes.

Bio: Howard Nye is an Associate Professor of Philosophy at the University of Alberta. He works primarily in the areas of normative ethics, practical ethics, and metaethics, and has related interests in political philosophy, the philosophy of mind, and decision theory. One line of Howard’s current research concerns the ethics of consumption and ecological preservation, focusing on the argument that individual actors and institutions should reduce their contributions to harmful practices because their contributions have small chances of making very important differences. Another of line of his research investigates what it takes for an entity to have desires and beliefs that represent the world in a sense that admits of genuine, underviative error. A third line of Howard’s research investigates challenges to the common assumption that life is less of a morally important benefit to beings who lack the intellectual abilities of typical human adults.

Abstract: The development and clinical potential of the Alberta linac-MR, the Aurora-RT TM is presented. Being the first to merge an MRI to a linac, the laboratory built a further prototype and is installing a third to be used clinically after regulatory clearance. The significantly improved soft-tissue visualization of live cine MR imaging while irradiating the tumour, even if moving due to breathing or other process, offers the potential of significantly improving the tight detection of the tumour and the precise delivery of the therapeutic dose to the tumour whole avoiding surrounding healthy tissues. The Aurora-RT features an isocentrically mounted linac that rotates with a biplanar MR system with the radiation beam's central axis parallel to the MR system's magnetic Bo field. Various AI-based tumour auto-contouring for real-time MR guided RT currently developed will be presented with respect to reconstruction speed, various data-acquisitions and performance. Also discussed is data-acquisition speed- increases achieved with compressed sensing and principal analyses. Novel direct acquisition beam’s eye view MR images during on-line delivery is also introduced and tested. Validation of Monte Carlo depth doses and ion-chamber angular dependences in parallel and perpendicular linac-to-Bo configurations are presented. Extremely accurate deterministic calculations within magnetic fields is presented for parallel and perpendicular configurations, which is verified with GEANT4, but calculated tremendously faster. The Cross Cancer Institute is preparing clinical studies to show the potential of the Aurora-RT in improving outcome for solid tumours, in general, and even, in decreasing the number of treatment sessions for some tumours.

Bio: Fallone is Professor and Director of Medical Physics, Dept of Oncology (University of Alberta) , and Director of Medical Physics (Cross Cancer Institute ). He previously held faculty positions at McGill University and at University of Texas M D. Anderson Cancer Center (Houston). Most recently, he successfully developed the first operational integrated linac-MR system (www.linac- MR.ca), the first whole-body pre-commercial linac-MR system and is presently completing the commercial/linac system. The linac-MR takes continuous MRI images to guide, in real-time, the linac radiation beam to the tumour avoiding healthy tissues. This capability would result in decreased side effects and improved clinical outcome for all cancer tumors treated by radiotherapy (RT), as well as, tumors which currently. Fallone is senior author on over 70 peer-reviewed publications of the physics and engineering on the linac-MR integration. Lifetime, Fallone has, co-authored over 325 peer-reviewed research articles, 330 published abstracts, 315 posters, 272 conference presentations, 130 externally invited conferences, and over 15 patent groups, directly supervised 80 graduate theses, and 25 medical physics residents. He has received numerous international awards, the most notably, being Knighthood to the Order of Merit, Italy for his contributions to cancer research, especially MR-guided Radiotherapy.

Abstract: Monte-Carlo Tree Search (MCTS) is a popular search technique used for games with huge search spaces, especially when lacking good heuristic evaluation functions.

Most MCTS algorithms have a node-selection mechanism (UCT) derived from the UBC1 formula, even though it is well-known that the scenario addressed by the UCB1 scheme is cumulative regret, which is actually irrelevant to game tree search.

Instead, we define batch value of perfect information (BVPI) in game trees as a generalization of value of computation as proposed by Russell and Wefald, and use it for selecting nodes to sample in MCTS. We show that computing the BVPI is NP-hard, but it can be approximated in polynomial time. In addition, we propose methods that intelligently find sets of fringe nodes with high BVPI, and quickly select nodes to sample from these sets. We apply our new BVPI methods to partial game trees, both in a stand-alone set of tests, and as a component of a full MCTS algorithm. Empirical results show that our BVPI methods outperform existing node-selection methods for MCTS in different scenarios.

(This is an expanded version of a UAI-2017 paper, joint work with Shachaf Shperberg and Ariel Felner)

Bio: Professor Solomon Eyal Shimony (BSc in EE, Technion, 1982) works in both theoretical aspects and applications of artificial intelligence. He has a Masters in computer graphics from BGU (1986), and a Ph.D. in artificial intelligence (computer science department, Brown University, 1991) with a dissertation on (probabilistic) abductive reasoning. His current research on probabilistic reasoning includes interest in decision-making under uncertainty, meta-reasoning and flexible computation. and applications thereof in robotics, manufacturing inspection, and AI in games. He has been with the computer science department at BGU since 1991, and ha been serving as an associate editor for IEEE-SMC-B (now IEEE-CYB) since 2001.

Abstract: In the medical domain the expectations to automatic AI systems are high, particularly in disciplines requiring prognostic models (oncology) and/or decision support (radiology, pathology). Due to the raising ethical, social, and legal issues governed by the European Union, the field of explainable AI is becoming extremely important. The problem of explainability is as old as AI itself, and classic rule-based approaches have been comprehensible "glass-box" approaches. Nevertheless, their weakness was in dealing with non-linearities and the intrinsic uncertainties of medical data. The progress of probabilistic machine learning, the availability of big data and computational power has made AI successful today, and in certain medical tasks deep learning even exceed human performance. However, such approaches are considered as “black box”- models, and even if we understand the underlying mathematical principles of such models, they still lack explicit declarative knowledge. Consequently, in the future we need context-adaptive procedures, i.e. systems that construct contextual explanatory models for classes of real-world phenomena. One possible step is in linking probabilistic learning methods with large knowledge representations (ontologies), thus allowing to understand how a machine decision has been reached. Our aim is to make machine decisions re-traceable, interpretable and comprehensible. The aim is to explain why a certain machine decision has been reached because the “why” is often more important than the mere classification result. The re-traceability and interpretability on demand shall foster reliability and trust ensuring that the human remains in control, so to augment human intelligence with artificial intelligence and vice versa.

Bio: Andreas Holzinger is lead of the Human-Centered AI Lab (Holzinger Group) at the Medical University Graz and since 2016 he is Visiting Professor for machine learning in health informatics at Vienna University of Technology. Andreas was Visiting Professor for Machine Learning & Knowledge Extraction in Verona; the RWTH Aachen, University College London and Middlesex University London. He serves as consultant for the Canadian, US, UK, Swiss, French, Italian and Dutch governments, for the German Excellence Initiative, and as national expert in the European Commission. He is in the advisory board of the Artificial Intelligence Strategy “AI Made in Germany 2030” of the German Federal Government and in the advisory board of the “Artificial Intelligence Mission Austria 2030”. Andreas Holzinger promotes a synergistic approach to Human-Centred Artificial Intelligence (HC-AI) and has pioneered in interactive machine learning (iML) with the human-in-the-loop. Andreas’ goal is to augment human intelligence with artificial intelligence to help to solve problems in health informatics. Andreas obtained a Ph.D. in Cognitive Science from Graz University in 1998 and his second Ph.D. in Computer Science from TU Graz in 2003. He serves as Austrian Representative for AI in IFIP TC 12, he is organizer of the IFIP Cross-Domain Conference “Machine Learning & Knowledge Extraction (CD-MAKE)” and is member of IFIP WG 12.9 Computational Intelligence, the ACM, IEEE, GI, the Austrian Computer Science and the Association for the Advancement of Artificial Intelligence (AAAI). More information: https://www.aholzinger.at

Abstract: I wish we had a clue how to best leverage AI for home builders and trades. What I do have however is the will, funding and countless opportunities to engage via an Edmonton PropTech company, Tech2.Build. Be ready, I’ll be performing the most un-inspiring example you’ve ever seen of AI (a talking bear that attempts to sell you a home) and I’ll show you where we are going next (which is prime with AI opportunity). It will be up to you to tell me where and what I should do next when it comes to AI, if anything at all ;)

Bio: Alexandre Gosselin is the Chief Technology Officer for Tech2.Build, a local an Edmonton PropTech startup pursuing next-level tech software for home builders and trades by 2021. He forged this company from the tech solutions developed within Rohit Group of Companies (a real-estate company from Edmonton) where Tech2.Build is currently commercializing the efforts. Alex believes that true innovation requires personal risk and is motived to do so in the pursuit of the sublime feeling of accomplishment in completing complex solutions. He brings 7+ years of business of startup experience,

10 years of IT leadership roles and has brought 32 software development projects to successful completion. Alex holds a Bachelors of Science with a Major in Software Engineering from the University of Victoria.

Abstract: Text matching is the problem of identifying the relationship between two text objects. Text generation is to generate a text object given an input passage or sentence. Both text matching and generation are key to many NLP tasks such as question answering (QA), query-document matching in search, machine reading comprehension (MRC), and chatbots. With the rise of deep neural networks, sequential modeling via recurrent neural networks and LSTMs has been dominant in text encoding. In this talk, I will describe our recent contributions to structural modeling of natural language sentences and documents, especially their graphical modeling, and the use of graph convolutional networks (GCNs) to embed text. By introducing proper graphical modeling for each specific task, we observe remarkable and sometimes even dramatic performance gain on a wide range of tasks, including long document relationship identification, query-document matching, sentence pair matching, question generation in machine reading comprehension, and search query generation and recommendation in search engines. Results of these studies have been widely published in ACL, KDD, WWW, and CIKM, etc., in the recent 2 years.

Bio: Dr. Di Niu is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Alberta. His research interests include NLP and text modeling, knowledge base construction, optimization methods for efficient and private distributed machine learning, statistical learning theory, and networks. He received the B.Eng. from Sun Yat-sen University, Guangzhou, China in 2005 and the MSc and PhD degrees from the University of Toronto, Toronto, Canada, in 2009 and 2013, respectively. He has coauthored and published more than 60 papers in top venues including many top conferences in AI and data mining. He was the winner of the Extraordinary Award (No. 1 out of all 18 grant holders worldwide) of the CCF-Tencent Rhino Bird Open Grant 2016 for his work on news document understanding. His innovations on deep text matching and generation, and knowledge based construction for search engines have been adopted by Tencent, one of the largest IT organizations in China and in the world.

Abstract: Pommerman is a difficult multi-agent game that was the subject of a NeurIPS-18 contest (also at NeurIPS-19). This talk begins by discussing why the game is very challenging, and the award-winning agent that Chao Gao developed during his internship at Borealis AI. We then talk about three extensions to the deep reinforcement learning agent using auxiliary tasks. In particular, we show improvements to our A3C agent by 1) leveraging a weak UCT agent to provide demonstrations, 2) predicting when an episode will end, and 3) using explicit opponent modelling.

Bio: Pablo Hernandez Leal is a researcher at Borealis AI in Edmonton in the group lead by Matt Taylor. Previously, Pablo was a postdoctoral researcher at CWI, the national research institute for Mathematics and Computer Science of the Netherlands. Pablo is interested in multiagent systems, reinforcement learning and game theory.

Abstract: In recent applications of Markov decision process (MDP), transition probabilities and rewards are often estimated from large-scale sequential data. In cases where sequences are obtained by simulating a single system, one can safely assume that all sequences follow the same model. However, in health applications, sequential data is collected from a population where each sequence corresponds to a person. Thus, there may be sub-populations that exhibit heterogeneous transition patterns. For example, in a large cohort with a certain disease, there may be patients whose disease status progresses faster than other patients. For such a group, estimating a separate transition probability matrix and applying the corresponding optimal treatment plan can improve their outcomes. In this work, I formally define the benefit of modeling heterogeneity and derive a probabilistic bound on the benefit. The theoretical bound gives us intuition that as the transition models of sub-populations become more “similar” to each other, modeling heterogeneity becomes less beneficial. I present empirical analysis illustrating the theoretical results and show that we need big enough samples to identify the benefit of modeling heterogeneity. I also suggest a method to estimate the benefit of modeling heterogeneity based on bootstrapping and empirically illustrate it.

Bio: Ilbin Lee is an assistant professor in operations management at the University of Alberta School of Business. He was a postdoctoral fellow in the School of Industrial and Systems Engineering at Georgia Institute of Technology. He obtained his PhD in Industrial and Operations Engineering at the University of Michigan in 2015. His research interests include sequential decision-making based on data and prediction, computational optimization, and wildfire and healthcare applications.

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Abstract: Autonomous systems are becoming pervasive in everyday life, and many of these systems are safety-critical and complex. To provide safety guarantees, formal verification methods such as reachability analysis are needed. However, verification is computationally intractable for complex systems. In the first part of the seminar, I present recent techniques that leverage system structure to make reachability analysis tractable. To perform complex tasks with complex systems, reinforcement learning (RL) has demonstrated great potential. However, performing RL on robotic systems is especially challenging due to the poor sample complexity and generalizability of RL. In the second part of the seminar, I discuss recent advances in effectively incorporating prior knowledge about robotic systems to greatly improve sample complexity.

Bio: Mo Chen is an Assistant Professor in the School of Computing Science at Simon Fraser University, Burnaby, BC, Canada, where he directs the Multi-Agent Robotic Systems Lab. He completed his PhD in the Electrical Engineering and Computer Sciences Department at the University of California, Berkeley with Claire Tomlin in 2017, and received his BASc in Engineering Physics from the University of British Columbia in 2011. From 2017 to 2018, Mo was a postdoctoral researcher in the Aeronautics and Astronautics Department in Stanford University with Marco Pavone. His research interests include multi-agent systems, safety-critical systems, reinforcement learning, and human-robot interactions.

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As you read this, algorithmic systems are evaluating who enters Canada, who receives disability benefits, and who is targeted for police scrutiny. These are legal decisions: they require immigration, welfare, and criminal laws to be interpreted and applied to individual cases. As such, they are ruled by “administrative law,” which specifies how officials ought to proceed as they make such decisions. Administrative law assumes that front-line officials reach outcomes independently. Today, however, such decisions are co-created by humans and a widening array of assistive technologies, from case management software to risk prediction matrices. Technology has long influenced how law functions at “street level,” especially for marginalized people. Yet, the newest tools more directly affect legal outcomes. Their objective appearance encourages front-line officials to favour them over other evidence, and officials may have few realistic opportunities to correct software-generated outcomes. In this presentation, I will share some of my research into how humans and technological systems co- produce decisions, and the new questions this arrangement raises for interdisciplinary research in law, sociology, and computer science. In doing so, I will review how developments in algorithmic decision- making systems have been critically evaluated on bias and transparency issues. I will then show how such systems also raise fundamental administrative law issues, specifically when it comes to the processes that are required for an outcome to be considered “fair” in law. This talk will close by offering preliminary reflections on how the legal model of fair processes might resemble, and how it might diverge from, notions of appropriate or fair process in computer science.

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Dr. Jennifer Raso is an Assistant Professor at the University of Alberta’s Faculty of Law investigating the relationship between discretion, algorithmic systems, and administrative law. She is particularly intrigued by how humans/non-humans collaborate and diverge as they produce institutional decisions, and the consequences of this hybrid arrangement for procedural fairness and substantive justice. This work builds on Dr. Raso’s doctoral research, which included a qualitative socio-legal study of how municipal caseworkers locate and use discretion to deliver the notoriously rule-bound Ontario Works program. An award-winning scholar, her research has been funded by the Social Sciences and Humanities Research Council (Canada) and the Endeavour Fellowships Program (Australia), and recognized by the Canadian Law and Society Association (best article prize, 2018) and the University of Cambridge (Richard Hart Prize, 2016). In 2017-18, she was a postdoctoral fellow at the Allens Hub for Technology, Law and Innovation at UNSW Law School and a visiting researcher at Yale Law School’s Information Society Project. Before pursuing graduate studies, Dr. Raso litigated social welfare, administrative, and human rights matters with the City of Toronto's Legal Services Division. Her scholarship appears in the Canadian Journal of Law & Society, the Journal of Law & Equality, and PoLAR: Political and Legal Anthropology Review.