Chris Harrison is an Associate Professor at Carnegie Mellon University's School of Computer Science, directing the Future Interfaces Group . His research focuses on novel human-computer interaction technologies, including haptics, AR/VR/XR, and ubiquitous computing. Research Interests: Ubiquitous Computing, Human-Centered AI, Physical Interfaces (Sensing, Haptics, Fabrication), AR/VR/XR, Social Computing Advisees: Daehwa Kim, Nathan DeVrio, Vimal Mollyn, Vivian Shen Scientific Recognition: Forbes 30 Under 30 (Science), MIT Technology Review 35 Innovators Under 35, Smithsonian Innovator (2013), Google/Microsoft/Qualcomm Fellows Contact: chris.harrison@cs.cmu.edu His recent publications explore advanced haptic systems (Reel Feel, Fluid Reality), body-centric sensing (SkinTrack), and environment-embedded interfaces (Wall++). Current work integrates UWB/IMU fusion for pose estimation and synthetic jet haptics.
Taina Bucher is a Professor at the Department of Media and Communication, University of Oslo, specializing in algorithmic studies, digital infrastructure, and human-machine communication. She leads the Screen Cultures research group and serves as Principal Investigator for HumAIn , a five-year humanities hub reimagining AI (2024-2029). Research Focus: Her work examines Algorithmic power and politics Cultural imaginaries of digital technology Temporalities of algorithmic media Feminist epistemologies in digital contexts Monitorial citizenship in platform societies Publications: Bucher has authored two monographs: Facebook (Polity Press, 2021) and IF…THEN: Algorithmic Power and Politics (Oxford University Press, 2018). Her recent articles explore facial recognition systems, algorithmic temporality, and digital divides. Academic Background: PhD in Media Studies (University of Oslo, 2012), MSc in Culture & Society (London School of Economics, 2007), with visiting scholar appointments at The New School, Ryerson University, and New York University. Teaching: She supervises and teaches topics including Digital infrastructure Algorithm studies Screen cultures Media theory Critical data studies Digitalization of public sectors Editorial Roles: Active on editorial boards for Journal of Communication , New Media & Society , and Information, Communication & Society .
Joydeep Biswas is an Associate Professor in the Computer Science Department at the University of Texas at Austin, where he serves as the Director of the Autonomous Mobile Robotics Laboratory (AMRL). He is also affiliated with Texas Robotics, the UT Machine Learning Laboratory, and UT Good Systems. Previously, he was an Assistant Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst. Dr. Biswas earned his PhD in Robotics from Carnegie Mellon University in 2014 and his B.Tech in Engineering Physics from the Indian Institute of Technology Bombay in 2008. His educational background has provided him with a strong foundation in both theoretical and applied aspects of robotics and artificial intelligence. Dr. Biswas's research focuses on enabling long-term autonomy for mobile robots operating in human environments. His work spans robot perception, motion planning, control systems, and AI, with the ultimate goal of creating self-sufficient autonomous mobile robots that can perform tasks accurately and robustly in real-world settings. He is particularly interested in perception, planning, and failure recovery for autonomous mobile robots, which supports his vision of having autonomous service mobile robots deployed at campus-to-city scale, both indoors and outdoors, performing assistive tasks over deployments spanning years. His IJCAI 2019 Early Career Spotlight talk summarizes much of his research to date and ongoing interests. His recent research has shown a strong trend toward social navigation, human-robot interaction, and the application of machine learning techniques to robotics problems. There's a clear progression from fundamental robotics research toward more complex, real-world applications that require robots to understand and navigate human social spaces effectively. His work increasingly integrates large language models and other advanced AI techniques with traditional robotics approaches, as evidenced by his recent publications on topics like preference-conditioned navigation, social navigation benchmarks, and instruction-following navigation systems. Dr. Biswas has received numerous prestigious awards including the NSF CAREER Award (2021), J.P. Morgan Faculty Research Award (2019), Amazon Research Award (2019), and a grant from Northrop Grumman Mission Systems (2018). These awards recognize his innovative contributions to the field of robotics and autonomous systems. As a dedicated educator and mentor, Dr. Biswas actively supervises PhD and master's students, with his PhD student Sadegh Rabiee winning the student poster award at the Northrop Grumman University Symposium 2019. He has secured significant grant funding from the National Science Foundation for projects including 'Introspective Perception and Planning for Long-Term Autonomy' and 'Interactive Synthesis and Repair For Robot Programs,' demonstrating his ability to secure competitive research funding and his commitment to advancing the field. Dr. Biswas leads the Autonomous Mobile Robotics Laboratory (AMRL), which serves as a hub for interdisciplinary research in mobile robotics. The lab has developed notable resources such as the UT Campus Object Dataset (CODA) for 3D perception research and SOCIALGYM, a framework for benchmarking social robot navigation. His team regularly deploys robots on the UT Austin campus and in urban environments to test and refine their approaches in realistic settings, bridging the gap between simulation and real-world application.
Zhiyong Huang is an Associate Professor at the National University of Singapore (NUS) School of Computing. He holds multiple leadership roles, including Deputy Director of the NUS Business Analytics Centre, Director of the Computing Translational Research & Development (C-TReND) Centre, and Assistant Dean (Industry Relations). He is also a Senior Principal Investigator at the NUS Chongqing Research Institute. Education: PhD in Computer Science from École Polytechnique Fédérale de Lausanne (EPFL), MEng and BEng in Computer Engineering from Tsinghua University. Leadership: Senior Member of ACM and IEEE, Pioneer Member of ACM SIGGRAPH, and Chair of the Singapore ACM SIGGRAPH Chapter. Research Interests: His work spans Data Analytics, Machine Learning, Computer Vision, Human-Robot Interaction, and Computer Graphics. Key projects include the NUS Digital Twin initiative and secure data analytics pipelines. Article Trends: Recent publications focus on time series generation, cryptocurrency benchmarks, medical image registration, and phishing detection. These works integrate machine learning, computer vision, and multimodal systems. Scientific Awards: Finalist, World Technology Summit & Awards (Entertainment, 2010) Bronze, National Science and Technology Progress Award (1992) Tsinghua 12.9 Distinguished Young Teacher Award (1989) Grants & Service: Extensive involvement in Singapore's IT Standards Committee, review panels for EDB SIIRD projects, and editorial roles. He has served as PC co-chair, local chair, and reviewer for numerous conferences and journals.
Giulio Jacucci is a Professor in the Department of Computer Science at the University of Helsinki, affiliated with the Helsinki Information Technology Research Institute. He serves as a Supervisor in the Doctoral Programme in Computer Science, mentoring PhD candidates in human-computer interaction and related fields. His academic career spans over two decades with continuous research productivity from 2006 through 2025. Professor Jacucci's research interests center on Human-Computer Interaction , particularly focusing on Virtual Reality , Brain-Computer Interfaces , and Neuroadaptive Systems . His work explores how technology can adapt to users' cognitive and physiological states, with significant contributions to social virtual reality environments, embodied agents, and information retrieval systems. He investigates how virtual representations affect social behavior, time perception, and communication in digital spaces. His publication record shows a clear evolution from foundational work in information retrieval toward increasingly sophisticated neuroadaptive systems. Recent publications (2024-2025) demonstrate his leadership in examining social dynamics within virtual reality platforms, including votekicking mechanisms, mirror watching behaviors, and communication tools like mutes. His work bridges technical innovation with deep understanding of human social behavior in digital environments. Professor Jacucci currently leads multiple research projects funded by the Academy of Finland, including DataLit: Datalukutaito ja vastuullinen päätöksenteko (2023-2026) and MyModel: Selitettävyys suositusten hallinnassa (2023-2027). These projects focus on data literacy, responsible decision-making, and explainability in recommendation systems, reflecting his commitment to ethical and user-centered technology development. His research has received media attention, particularly around brain-computer interfaces and virtual reality applications, with coverage in outlets discussing how deep learning transforms mobile applications and sensor landscapes. He has presented his work at major conferences including CHI 2017 and continues to be an active contributor to the international research community.
Dhruv Jain is an Assistant Professor in the Computer Science and Engineering Department at the University of Michigan , with affiliations in the School of Information and Michigan Medicine . He leads the Soundability Lab , focusing on transforming hearing into a programmable interface through human-centered AI systems. PhD from University of Washington MS from MIT Media Lab Formerly worked at Microsoft Research, Google, and Apple Research Interests intersect Human-Computer Interaction (HCI) , Audio AI , Accessibility , and Hearing Health . His work develops systems like SoundWatch (smartwatch-based sound awareness) and HomeSound (IoT sound visualization) to expand auditory access for DHH individuals and other domains. Recent Publications (2023–2025) span top venues like CHI , ASSETS , and ICMI , with trends including generative AI for real-time captioning , adaptive soundscapes , and clinical communication tools . SIGCHI Outstanding Dissertation Award (2023) William Chan Memorial Dissertation Award (2023) Best Poster Award at ASSETS 2024 Google Academic Research Award (2024) NIH Grant ($450k, 2023) William Demant Foundation Grant ($740k, 2025) Teaching includes Accessible Computing (undergraduate) and Advanced Accessibility (graduate), alongside global DIY workshops in five countries. The Soundability Lab collaborates with neuroscientists, clinicians, and Deaf scholars to create deployable systems like clinical communication tools now at Michigan Medicine and features integrated into Apple devices.
Dr. Muhammad Abdul-Mageed is an Associate Professor in the School of Information at The University of British Columbia, with joint appointments in Linguistics and an associate membership in Computer Science. He holds the Canada Research Chair in Natural Language Processing and Machine Learning. His research focuses on deep learning, socio-pragmatics, and speech/language technologies, particularly for Arabic and African languages. He leads the UBC Deep Learning & NLP Group and co-directs SSHRC-funded grants like I Trust AI and Ensuring Full Literacy. He is a founding member of the Center for Artificial Intelligence Decision making and Action and a member of the Institute for Computing, Information, and Cognitive Systems. His work spans automatic speech recognition, machine translation, computational socio-pragmatics, and low-resource language technologies. Notable projects include developing Arabic speech recognition systems, multidialectal Arabic benchmarks, and tools for African language processing. He has authored over 100 peer-reviewed papers and leads initiatives like the NADI Arabic Dialect Identification shared task and the NileChat project for culturally-aware LLMs. His research aims to create equitable, socially-aware AI systems for health, social media, and information management.
Dr. Zhenman Fang is an Associate Professor at the School of Engineering Science , Simon Fraser University (SFU) , where he founded and directs the HiAccel Lab . He also holds an associate membership in the School of Computing Science at SFU. His research focuses on customizable computing with software-defined hardware acceleration , addressing performance, energy-efficiency, and reliability in post-Moore’s law computing across domains like machine learning , big data analytics , quantum chemistry , and precision medicine . Education: Ph.D. in Computer Science from Fudan University (2014), with a visit to University of Minnesota during his studies. Postdoctoral Work: University of California, Los Angeles (UCLA) (2014-2017). Industry Experience: Staff Software Engineer at Xilinx (2017-2019). Dr. Fang’s research spans the entire computing stack , including application characterization , accelerator-rich architecture design , and programming/tool support . He has developed frameworks like HiSpMV , SyncNN , and SQL2FPGA , emphasizing FPGA acceleration for vision transformers , quantum chemistry , and spiking neural networks . His work has been recognized with 3 best paper awards (FPL 2024, TCAD 2019, MEMSYS 2017) and 3 best paper nominees (FCCM 2025, HPCA 2017, ISPASS 2018). Recent publications highlight trends in low-precision machine learning ( ShiftQuant , ESRU ), quantum chemistry acceleration ( SERI ), and vision transformer optimization ( Quasar-ViT ). His HiAccel Lab actively mentors PhD and MASc students , with notable graduates like Alec Lu (PhD 2024, now at Meta) and Philip Stachura (MASc, now with BC Graduate Scholarship). Scientific Awards: Inaugural SFU Research Excellence Award - Horizon Award (2025) FPL 2024 Stamatis Vassiliadis Best Paper NSERC Alliance Award (2020) CFI JELF Award (2019) Xilinx University Program Award (2019) IEEE Senior Member (2023) Grants: NSERC Discovery Grant (2019) CFI JELF Funding (2019) Huawei and Xilinx sponsorships Dr. Fang leads open-source initiatives like SyncNN , PASTA , and SQL2FPGA , and serves as General Chair for ASAP 2025 and Program Co-Chair for RAW 2025 . His lab collaborates globally with institutions such as UCLA , Northeastern University , and Xidian University .
Janne Lindqvist is an Associate Professor at the Department of Computer Science, Aalto University. His research bridges security engineering, human-computer interaction (HCI), and privacy, with a focus on making security systems usable and user-centric. University: Aalto University Department: Department of Computer Science Rank: Associate Professor Email: janne.lindqvist@aalto.fi Lindqvist’s work spans security engineering , privacy systems , and user research , emphasizing practical authentication methods, password management, and human behavior in security contexts. He explores how users interact with systems like TPM APIs, gesture passwords, and mobile authentication mechanisms. Recent publications highlight trends in authentication systems , ubiquitous computing security , and mobile user behavior . Key themes include biometric authentication, gesture-based security, and balancing usability with cryptographic robustness. CHI'25 Honorable Mention Award CHI'24 Best Paper Award His research integrates empirical studies with technical implementations, such as analyzing password forgetting patterns and developing acoustic sensing for vehicle detection (e.g., Auto++, BO-Ear). Collaborative efforts span machine learning, psychology, and embedded systems.
Dr. Joey Paquet is a Tenured Associate Professor and Department Chair in the Department of Computer Science and Software Engineering at Concordia University in Montreal, Canada. He holds a PhD and specializes in research areas including Design and Implementation of Programming Languages, Context-Driven Computing, and Demand-Driven Computing. His work focuses on advancing programming paradigms and their applications in cybersecurity, distributed systems, and autonomic computing. Dr. Paquet is actively involved in thesis supervision across Computer Science and Software Engineering programs at both master's and doctoral levels. Research interests are centered around programming language design, particularly in demand-driven and context-aware systems. His contributions include frameworks like GIPSY and OpenISS, enabling scalable data processing and forensic computing. Recent work explores autonomic intent-driven networking, real-time gesture recognition, and IoT forensics. These efforts highlight his expertise in both theoretical constructs and practical implementations. His advising role supports students in MCompSc, MASc, and PhD programs. While specific grants are not detailed, his projects often involve interdisciplinary collaboration. Dr. Paquet is affiliated with platforms like LinkedIn and ResearchGate, reflecting an active academic presence. Key technical contributions include pioneering work on forensic computing backends, intent expression languages, and multimodal interaction systems. His research addresses challenges in cybersecurity, distributed systems, and service composition, with a focus on resilience and scalability.
Frank L. Hammond III serves as Assistant Professor at Georgia Tech's Woodruff School of Mechanical Engineering since April 2015, directing the Adaptation Robotic Manipulation (ARM) Laboratory. A Carnegie Mellon PhD graduate, he previously held postdoctoral positions at MIT and Harvard as a Ford Fellow. His interdisciplinary work bridges mechanical engineering, biomedical applications, and computational design. Education Ph.D. in Mechanical Engineering, Carnegie Mellon University M.S. in Mechanical Engineering, University of Pennsylvania M.S. in Electrical Engineering, University of Pennsylvania B.S. in Electrical Engineering & Biomedical Engineering, Drexel University Hammond's research pioneers adaptive robotic manipulation (ARM) systems that operate in unstructured human environments through bioinspired computational design. His lab develops xenomorphic (non-biomorphic) robots using soft pneumatic actuation, flexible electronics, and machine learning to achieve biological-level versatility. Key application domains include wearable human augmentation devices , haptic-enabled surgical teleoperation , and autonomous soft platforms for medical and industrial use. The ARM methodology integrates empirical biomechanics characterization with simulation-driven optimization and rapid prototyping. Analysis of his 15 most recent publications (2023-2025) reveals three dominant trends: (1) Medical rehabilitation breakthroughs through intention-driven exoskeletons with soft bioelectronics, (2) Novel locomotion strategies for soft robots in complex environments (sand, water, cluttered spaces), and (3) Advanced haptic feedback systems leveraging multimodal sensory substitution for proprioceptive restoration. These works consistently bridge biomechanics, control theory, and human factors. Awards Ford Postdoctoral Research Fellowship at Harvard School of Engineering Hammond actively mentors graduate researchers including PhD candidates Lucas Tiziani (soft actuators) and Bangyuan Liu (earthworm robotics), and Master's student Alex Hart (pediatric haptics). His lab secures research funding for projects like tunable mechanical interfaces for neuropathy treatment and cognition-focused wearable devices, with strong industry and clinical partnerships evident in co-authored medical device publications. The ARM Lab maintains robust collaborations across Georgia Tech's robotics, neuroscience, and biomedical engineering communities. The Adaptation Robotic Manipulation Laboratory operates from Whitaker Building Room 4102, housing specialized facilities for soft robot fabrication (3D printing, shape deposition manufacturing) and biomechanics testing. Current projects include pediatric haptic feedback displays, biomimetic swimming robots, and kirigami-skinned earthworm robots for subsurface locomotion. The lab emphasizes translational research with multiple pending medical device patents and active participation in K-12 STEM outreach programs.
Prof. Justus Thies is Full Professor for 3D Graphics & Vision at the Technical University of Darmstadt and leads the Neural Capture & Synthesis research group at the Max Planck Institute for Intelligent Systems. His research develops AI methods to capture and synthesize the real world using commodity hardware, focusing on markerless motion capture of faces and bodies, and photorealistic neural rendering. His work has been recognized with the German Pattern Recognition Award, Eurographics Young Researcher Award, and an ERC Starting Grant (all 2024). Recent publications focus on Gaussian-based avatars, neural human reconstruction, and diffusion models for scene synthesis.
Zhang Daqing is a Professor at Peking University (Boya Chair Professor since 2014) and Full Professor at Telecom SudParis, France (2007–present). Previously, he held roles such as Department Head at the Institute for Infocomm Research (2004–2007) and Research Scientist positions in Singapore and Italy. He earned his Ph.D. in Physics from the University of Rome 'La Sapienza' in 1996. His research focuses on urban computing, context-aware systems, wireless sensing, and big data analytics. His work has been cited over 14,400 times with an H-index of 59. Key awards include the IEEE Fellow (2019) and multiple best paper awards at conferences like ACM UbiComp and IEEE UIC. Zhang has served as General Chair for 17 international conferences and is an associate editor for journals such as IEEE Pervasive Computing and ACM Transactions on Intelligent Systems. His contributions to context modeling have significantly impacted pervasive computing and service computing communities.
Raquel Fernández is Full Professor of Computational Linguistics and Dialogue Systems at the University of Amsterdam, where she leads the Dialogue Modelling Group at the Institute for Logic, Language & Computation (ILLC). As Vice-Director for Research at ILLC and a Fellow of the ELLIS Society, she bridges computational linguistics, cognitive science, and artificial intelligence through her research on language use in multimodal and conversational contexts. PhD in Computational Linguistics from King's College London Prior research positions at University of Potsdam and Stanford University's CSLI Her work explores how cognitive constraints, social interaction, and perception shape language use, with a focus on: Visually-grounded language processing Multimodal dialogue modeling Model uncertainty and calibration Language grounding in multimodal data Language learning and semantic change Dialogue reference resolution Recent publications analyze multimodal reasoning limitations, cross-lingual knowledge consistency, and uncertainty modeling in dialogue systems. She has received multiple accolades including an ERC Consolidator Grant , NWO VENI/VIDI/Aspasia fellowships , and EMNLP/GenBench awards . Outstanding Paper Award (EMNLP 2023) Best Data Award (GenBench Workshop 2023) ELLIS Society Fellow ERC Consolidator Grant #819455 recipient NWO VENI/VIDI/Aspasia awardee As a leader in academic service, she serves on the SIGDAT Executive Committee and chairs multiple conference committees. Her lab develops models for multimodal dialogue, visual storytelling, and grounded language understanding.
Dr. Aditya Joshi is a Senior Lecturer in the School of Computer Science & Engineering at the University of New South Wales (UNSW). He specializes in Natural Language Processing (NLP), with a focus on sarcasm detection, dialectal NLP, and ethical AI applications in public health and cybersecurity. He joined UNSW in 2023 following industry roles at SEEK, Notiv, and Fractal Analytics, where he developed NLP systems for recommendation engines and meeting analytics. His research has garnered over 3,000 citations (h-index 26) and secured $3.1M in grants, including Defence Trailblazer and Google exploreCSR awards. Education: Joint PhD (2018) from IIT Bombay (India) and Monash University (Australia); MTech in CSE (2011) from IIT Bombay. Research Interests: Making NLP models robust for non-native English speakers and the LGBTI+ community, algorithmic enhancements to transformers, and applications in public health, cybersecurity, and societal issues. His work spans epidemic intelligence (collaborations with EPIWATCH and IFCYBER), cybersecurity tools like AuditNet, and inclusive AI initiatives such as queer-inclusive workshops funded by Google. He designed UNSW's new NLP course (COMP6713) and co-authored a Wiley textbook on NLP. Notable grants include the A$1.4M 'Comprehensive Defence Data Platform' (Lead CI) and A$92K Google exploreCSR grant for benchmarking dialectal sentiment. His awards include the Best PhD Thesis from IITB-Monash and Best Paper accolades at FAccT 2023 and MoMM 2020. He supervises projects on kernel-based attention reformulation, prompt-based sarcasm detection, and multilingual small-scale LLMs. His service roles include Executive Committee Member at ALTA and arXiv moderator for computational linguistics.
