Nirupam Roy is an Assistant Professor at the Department of Computer Science, University of Maryland, College Park, and Director of the iCoSMoS research lab. His work bridges wireless networking, mobile computing, and acoustic sensing with applications in IoT, localization, healthcare, security, and wearables. Research Focus: Wireless Networking & Mobile Sensing Awards: Best paper award, MobiSys 2022 Best demo award, MobiSys 2021 CSL Ph.D. Thesis Award, UIUC 2019 Students: Nakul Garg, Yang Bai, Irtaza Shahid, Harshvardhan Takawale, Aritrik Ghosh, Ayushi Mishra, Sumbul Zehra, Justin Goodman Grants: NSF CAREER award (2023), Meta Research Award (2023)
Huamin Qu is a Chair Professor in the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST). He serves as the Founding Dean of the Academy of Interdisciplinary Studies (AIS), Founding Head of the Division of Emerging Interdisciplinary Areas (EMIA), and was the Founding Acting Head of Computational Media and Arts (CMA) at HKUST(GZ). Qu directs the VisLab and coordinates the Human-Computer Interaction (HCI) group. He obtained his BS in Mathematics from Xi'an Jiaotong University and MS/PhD in Computer Science from Stony Brook University. Qu's research integrates Data Visualization , Human-Computer Interaction , and Human-Centered AI , with applications in urban informatics, social networks, and explainable AI. His work focuses on developing interactive systems for big data analytics, visual storytelling, and AI-driven decision support. Research extends to multimodal communication, fintech, and augmented reality applications. His publications emphasize visual analytics for complex datasets (mobility, social media, financial), interaction techniques for immersive environments, and AI-enhanced visualization tools. Recent works explore explainable AI interfaces and large-scale data communication frameworks. IEEE Visualization Academy (2020) IEEE VGTC Technical Achievement Award AI 2000 Most Influential Scholar (2019, 2023, 2024) 21 Best Paper/Honorable Mention awards IBM Faculty Award (2009) APICTA Merit Award (2015) Yelp Dataset Grand Prize (2018) Qu has advised 48 PhD graduates (21 now faculty at institutions like UC Davis, University of Minnesota, Texas A&M) and 30 MPhil students. He secured major grants including RGC theme-based projects (digital citizenship, air pollution), UGC AoE (slope safety), and China's 973 Program. As VisLab director, he leads 20+ researchers in visualization/HCI projects adopted by Microsoft, IBM, Huawei, and Tencent.
Professor Gabriel Brostow is a faculty member in the Department of Computer Science at University College London (UCL), where he leads research in Computer Vision and Human-Computer Interaction. He also serves as Chief Research Scientist and Senior Director of the R&D Team at Niantic, the company behind Pokémon GO. His work bridges academic research and industry applications, focusing on developing AI systems that enhance human capabilities through what he terms 'Human in the Loop AI'—now commonly referred to as Human-Centered AI. Brostow completed his BS in Electrical Engineering at UT Austin, followed by a PhD with Irfan Essa at Georgia Tech. He then pursued postdoctoral research with Roberto Cipolla's Computer Vision & Robotics Group at Cambridge University as a Marshall Sherfield Fellow, and with Marc Pollefeys in ETH Zurich's CVG Group. His research explores how AI, particularly Computer Vision, can serve as 'super-tools' for professionals across various domains including filmmaking, architecture, robotics, and scientific research. Specific interests include assistive technology for everyday life, authoring systems that maximize user effort, 3D reconstruction, depth estimation, and vision-language models. His work often involves creating systems that are validated through real-world human interaction to ensure practical utility. Analysis of his recent publications reveals a strong focus on practical applications of Computer Vision that directly interact with humans. His research spans 3D scene understanding, depth estimation, sketch-based interfaces, and multimodal AI systems. There's a clear emphasis on creating benchmarks and tools that facilitate human-AI collaboration, with applications in assistive technology, urban planning, filmmaking, and biodiversity monitoring. His work frequently appears at top conferences including CVPR, NeurIPS, ECCV, and CHI. Marshall Sherfield Fellowship Brostow actively mentors PhD students, with current advisees including Ross Murphy, Skanda Koppula, Gizem Unlu, Omiros Pantazis, and Jamie Watson. His alumni include numerous PhD graduates and MSc students who have gone on to successful careers in academia and industry. He emphasizes selecting students based on passion and potential rather than just academic credentials, valuing traits like helpfulness, drive, and hunger to learn. His research is supported through collaborations with major institutions and companies including DeepMind, MIT, and the University of Edinburgh. He leads a research group at UCL that collaborates closely with Niantic's R&D team, creating a unique bridge between academic research and industry application. His team's work frequently involves developing novel Computer Vision techniques that are validated through real-world human interaction, ensuring practical utility alongside technical innovation. The group explores blue-sky research problems with applications ranging from assistive technology to professional tools for filmmakers, architects, and scientists studying diverse environments.
Richard E. Turner is a Professor of Machine Learning at the University of Cambridge's Department of Engineering and Research Lead for AI for Weather Prediction at the Alan Turing Institute. He serves as Cambridge Lead for the EPSRC Probabilistic AI Hub and previously held roles including Visiting Researcher at Microsoft Research, Co-Director of the AI4ER CDT, and Course Director for the Machine Learning and Machine Intelligence MPhil program. Current research focuses on probabilistic machine learning fundamentals, environmental prediction (weather/climate), and spatio-temporal modeling combining deep learning with Bayesian methods Supervised 26 PhD students (13 graduated) and 7 research assistants/associates Secured over £30M in research funding from EPSRC, Microsoft, Toyota, Google, DeepMind, Amazon, and Improbable Featured in BBC Radio 5 Live's The Naked Scientist, BBC World Service's Click, and Wired Magazine His recent publications demonstrate expertise in diffusion models for PDE simulations, Gaussian Processes for environmental applications, and Bayesian methods for spatio-temporal forecasting. Key trends include climate modeling using ML, neural PDE solvers, and scalable probabilistic inference. Awards : Cambridge Students' Union Teaching Award for Lecturing; supervised Qualcomm Innovation Fellowship winner Collaborations : Microsoft Research (AI4Science), Alan Turing Institute, EPSRC Probabilistic AI Hub Turner leads the Turner Group within Cambridge's Machine Learning Group, focusing on uncertainty-aware ML for scientific applications. Current research assistants work on topics like meta-learning, Bayesian inference, and climate science applications.
Amir Ali Ahmadi is a Professor at Princeton University's Department of Operations Research and Financial Engineering (ORFE), with affiliations across multiple disciplines including PACM, Computer Science, Mechanical & Aerospace Engineering, Electrical Engineering, and the Center for Statistics and Machine Learning. He serves as Director of Princeton's Optimization and Quantitative Decision Science Certificate Program and has taken temporary roles at Citadel GQS (2021-2022) and Google Brain (2020-2021). His research bridges optimization theory , dynamical systems , and control theory , focusing on scalable algorithms for complex problems in robotics, autonomous systems, and machine learning. He has pioneered DSOS/SDSOS relaxations as alternatives to traditional sum-of-squares methods, enabling faster solutions through linear/second-order cone programming. Recent publications explore: Higher-order Newton methods for socially responsible investment Data-efficient learning of dynamical systems Computational complexity of local minima Robust-to-dynamics optimization frameworks Award highlights include: 2024 Egon Balas Prize in Optimization 2024 Princeton Engineering Council Teaching Award 2023 Distinguished Teaching Award (Princeton SEAS) 2019 NSF CAREER Award 2017 DARPA Young Faculty Award 2017 Sloan Fellowship in Computer Science He advises prominent researchers like Georgina Hall (Tucker Prize finalist) and Bachir El Khadir (Goldstine Fellow), and leads the Princeton Optimization Seminar and MURI project on Control-Oriented Learning on the Fly.
Mohamed Noureldin is an Assistant Professor at the Department of Civil Engineering, Aalto University , Finland, with prior academic roles at Sungkyunkwan University, South Korea (2015–2022). His expertise lies in integrating Artificial Intelligence (AI) with Structural Health Monitoring (SHM) , Structural Digital Twin , Predictive Maintenance , and Seismic Retrofitting . Research Focus : AI-powered sustainable structural design, smart retrofitting, predictive maintenance, structural material innovation, and next-generation performance-based seismic/wind design. Industrial Experience : 20+ years in offshore/onshore structural engineering (Hyundai Heavy Industries, Samsung Engineering, Arab-Swiss Engineering Company, Zuhair Fayez Partnership). Teaching : Courses in structural analysis, seismic design, dynamics, and reinforced concrete at Aalto and Sungkyunkwan Universities. Laboratory : Leads the Structural Design AI Lab (SDAI), focusing on AI-driven resilient infrastructure. Contact : mohamed.noureldin@aalto.fi , +358504544861. His publications explore cutting-edge applications of AI, ML, and DL in seismic retrofitting, structural durability, soil stabilization, and hybrid damping systems. Collaborative work emphasizes life-cycle cost assessment and augmented reality for predictive maintenance.
Kevin M. Lynch is a Professor of Mechanical Engineering at Northwestern University's McCormick School of Engineering, where he also serves as Director of the Center for Robotics and Biosystems. His research spans multiple domains of robotics, with particular expertise in dynamics, motion planning, and feedback control of mechanical systems. Dr. Lynch received his Ph.D. in Robotics from Carnegie Mellon University in 1996, with a thesis on "Nonprehensile Robotic Manipulation: Controllability and Planning" under advisor Prof. Matthew T. Mason. He earned his B.S.E. with honors in Electrical Engineering from Princeton University in 1989. His research interests focus on robotics, particularly dynamics, motion planning, and feedback control of mechanical systems. He investigates mechanics, planning, and control of robotic manipulation (juggling, throwing, pushing, rolling, vibration, etc.) and locomotion. His work also explores self-organizing systems, particularly decentralized control of mobile sensor networks and swarm robotics, underactuated dynamic systems, and physical human-robot interaction with industrial applications. Recent research has expanded into bio-inspired active electrosense, underwater robotics, control and optimization for robot swarms, swarm shape control, and functional electric stimulation. Dr. Lynch's recent publications demonstrate a strong trend toward rehabilitation robotics and human-robot interaction, particularly in lower-limb exoskeletons for gait training and rehabilitation. His work bridges fundamental robotics research with practical applications in healthcare, showing increasing integration of swarm robotics principles with human-centered design. The publications also reveal continued strong contributions to fundamental robotics theory, particularly in swarm formation control and manipulation dynamics. George Saridis Leadership Award in Robotics and Automation (2022) Harashima Award for Innovative Technologies (2017) IEEE Fellow (2010) Charles Deering McCormick Professor of Teaching Excellence (2007-10) Society of Automotive Engineers Ralph R. Teetor Educational Award (2007) Early Career Award in Robotics and Automation (2001) McCormick School of Engineering and Applied Science Teacher Of The Year Award (1998-1999) NSF Career Award (1998) As Director of the Center for Robotics and Biosystems, Dr. Lynch oversees significant research grants and initiatives in robotics. He has made substantial contributions to robotics education through his "Modern Robotics" book and associated Coursera specialization, which has reached thousands of students worldwide. His professional service includes serving as Editor-in-Chief of IEEE Transactions on Robotics, where he oversaw a 90% increase in submissions during his tenure. Dr. Lynch leads research groups focusing on swarm robotics and rehabilitation robotics, with particular emphasis on the Center for Robotics and Biosystems at Northwestern University. His teams integrate expertise from mechanical engineering, electrical engineering, computer science, and rehabilitation medicine to develop innovative robotic systems for both industrial applications and healthcare solutions.
Claus Lamm is a Full Professor of Biological Psychology at the University of Vienna , where he leads the Social, Cognitive and Affective Neuroscience Unit (SCAN-Unit) . He serves as Vice Dean for Research and Advancement of Early Career Researchers at the Faculty of Psychology and holds affiliations with the Vienna Cognitive Science Hub , Environment & Climate Change Hub , and Austrian Academy of Sciences . His academic career spans international collaborations and formative research experience abroad. Scientific Focus: Lamm investigates the neural underpinnings of empathy and prosocial behavior , employing multi-modal approaches combining neuroimaging, psychopharmacology, and psychoneuroendocrinology . His work extends to comparative studies with ravens and dogs, and explores environmental social neuroscience through climate change decision-making research. Recent publications show trends in cross-cultural psychology , machine learning applications , and neurobiological pathways related to social behavior. Awards & Grants: Recipient of the APS Mentor Award for his support of early career researchers. Funded by European Research Council , Austrian Science Fund , Vienna Science and Technology Fund , and intramural grants exceeding €10 million. Key projects include "Unravelling the opioid system in empathy" and "Comparative dog-human fMRI" . Media Engagement: A prominent public science communicator, Lamm has appeared in Nature , Science Magazine , and Austrian media outlets like Ö1 Mittagsjournal and ORF2 , discussing topics from pandemic psychology to social media effects . He maintains active outreach through Science TV and educational programs .
Lorena Sosa is an Associate Professor at the Faculty of Law, Economics and Governance, Utrecht University, specializing in International and European Law. She is affiliated with the Netherlands Institute of Human Rights (SIM) and the research program Family & Law (UCERF). Her roles include membership of the management board of the Center for Global Challenges, Director of Education at UGlobe since 2022, and a community-engaged learning ambassador for her faculty since 2023. Education: PhD (cum laude) in Law from Tilburg University (2015), LL.M. in International Public Law (cum laude, National University of Córdoba), and a law degree from the same institution. Prior to Utrecht, she held positions at the University of Buenos Aires, University of Turku, and others. Research focuses on intersectional discrimination, gender-based violence, and human rights law. Key areas include the Istanbul Convention, feminist legal frameworks, and trans rights in criminal procedures. She employs socio-legal methods and collaborates with international organizations like the EU, Council of Europe, and UN. Publications span topics like femicide, domestic violence, and transgender experiences. Recent works include contributions to the Routledge International Handbook on Femicide and Feminicide (2023) and Preventing and Combating Violence Against Women (2023). Awards include the Marie Skłodowska-Curie Fellowship (2018), VENI Grant (2020), and the Max van der Stoel Award (2015) for her PhD on intersectionality in violence against women frameworks. Teaching includes courses on human rights law at Utrecht’s Legal Research Master, PIL Master, and University College Utrecht. She advises on projects related to victim rights, gender diversity, and empirical legal studies.
Ranjan Singh is a Professor at the Division of Physics, Nanyang Technological University (NTU) Singapore, specializing in terahertz photonics and metamaterials. He holds an elected fellowship from OPTICA (OSA) for pioneering work in ultrafast terahertz photonics, active metamaterials, and sensors. His research focuses on hybrid THz-electronic-photonic technologies for 6G communications, topological photonics, spintronics, quantum materials, and high-Tc superconductors. Education: B.Eng. in Telecommunications (Bangalore University, 2001); M.Tech in Photonics (Cochin University, 2004); Ph.D. in Photonics (Oklahoma State University, 2009). Postdoctoral research at Los Alamos National Laboratory (2009–2013). Research emphasizes on-chip THz topological photonics for next-gen communication systems, with notable achievements including a $7M grant for TERACOMM (on-chip THz topological photonics). His work integrates AI-driven beamforming, reconfigurable metasurfaces, and phase-change materials for adaptive THz systems. Key awards include the 2020 Web of Science 'Top 1% Highly Cited Researcher' distinction. His lab, TeraX Labs (founded 2013), develops cutting-edge technologies like THz brain-computer interfaces, quantum emitters, and spintronic sensors. Over $12M in competitive grants has fueled innovations in THz integrated circuits, tunable optical coatings, and ultra-sensitive biosensors. Advancing 6G/XG wireless, Singh's team designs topological beamformers, intelligent reflecting surfaces (IRS), and terahertz metamaterials for multi-link systems. His work bridges theoretical physics and applied engineering, with a focus on energy-efficient, reconfigurable photonic systems.
Professor Omar Matar is a Professor of Fluid Mechanics and RAEng/PETRONAS Research Chair in Multiphase Fluid Dynamics at the Department of Chemical Engineering, Imperial College London. He leads the Matar Fluids Group, focusing on interfacial fluid mechanics, multiphase flows, computational fluid dynamics (CFD), and applications in energy, manufacturing, and nanotechnology. His roles include Head of Department of Chemical Engineering, Director of the PETRONAS Centre for Engineering of Multiphase Systems (PETCEMS), and Editor-in-Chief of the Journal of Engineering Mathematics. Education: PhD in Chemical Engineering, Princeton University (1993) MEng Chemical Engineering, Imperial College London (1989) Research Interests: Interfacial fluid mechanics, multiphase flows, CFD, and machine learning 2D materials exfoliation and scale-up, immersive technologies (AR/VR) Applications in energy systems, nanotechnology, and personalized education Awards: Fellow of the Royal Academy of Engineering (2020) Recipient of the Imperial College President’s Medal (2020) EPSRC Programme Grant Principal Investigator (MEMPHIS, PREMIERE) Grants & Projects: MEMPHIS: £5M EPSRC-funded Programme Grant (2012–2017) PREMIERE: EPSRC Programme Grant (2019–present) PETCEMS: PETRONAS-funded Centre for Multiphase Systems Engineering Labs & Collaborations: Leads the Matar Fluids Group, collaborating with institutions like UCL, University of Edinburgh, and industry partners such as BP and First Light Fusion. Active in developing high-performance CFD codes (e.g., BLUE) and machine learning-driven models for multiphase systems.
Zakia Hammal is an Assistant Research Professor with dual appointments at Carnegie Mellon University, holding positions in the Robotics Institute within the School of Computer Science and the Department of Biomedical Engineering in the College of Engineering. Her work bridges computer science, machine learning, artificial intelligence, and social/behavioral psychology to advance computational models for human behavior analysis. Dr. Hammal's educational background includes a PhD in Computer Science, a Master of Artificial Intelligence and Algorithmic with specialization in Image Processing, and an Engineer's degree in Computer Science with specialization in Computer Systems. Her academic journey has positioned her at the intersection of technical expertise and healthcare applications. Her research focuses on multimodal human behavior modeling in social interaction, with particular emphasis on health informatics and affective computing (Emotion AI). Dr. Hammal's work has pioneered computational models for multimodal assessment of psychiatric disorders, including depression severity evaluation, automatic pain intensity measurement, assessment of expressiveness in children with facial abnormalities, analysis of non-verbal communication in mother-infant interaction, and identification of behavioral markers in autism spectrum disorder. Her approach integrates computer vision, machine learning, and behavioral psychology to create systems that can objectively measure human behaviors that are often subjective in clinical settings. Analysis of her recent publications reveals a consistent trajectory toward more sophisticated multimodal approaches to healthcare challenges, particularly in pain assessment and mental health diagnostics. Her work increasingly emphasizes interpretable AI models that can translate complex behavioral patterns into clinically meaningful insights, with growing attention to applications for vulnerable populations including infants, elderly patients, and those with craniofacial abnormalities or autism spectrum disorder. Women in AI Awards North America 2023 – AI Researcher of the Year Award Outstanding Reviewer Award at FG 2015 Best Paper award at ACII 2015 Outstanding Paper award at ICMI 2012 Dr. Hammal has secured significant research funding, primarily from the U.S. National Institutes of Health, including an R01 grant for developing a Multimodal Behavioral AI platform for pain assessment and management, and additional grants for automatic pain assessment in older adults with dementia. Her leadership extends to mentoring through her involvement in organizing workshops and conferences that train the next generation of researchers in affective computing and health informatics. As an active leader in her field, Dr. Hammal serves as ACM ICMI Steering Board Committee Member, Associate Editor for IEEE Transactions on Affective Computing and IEEE Transactions on Multimedia, and has organized numerous influential workshops including the International Workshop on Automated Assessment of Pain and Face and Gesture Analysis for Health Informatics. She is set to serve as Program Chair for FG 2025, ACII 2025, and ICMI 2026, demonstrating her growing influence in shaping the future direction of research in multimodal interaction and affective computing.
Elizabeth A. Simpson is an Associate Professor in the Department of Psychology at the University of Miami's College of Arts and Sciences. She serves as Associate Director of the Child Division and directs the Social Cognition Laboratory, focusing on infant social and cognitive development through interdisciplinary methodologies. Education: Ph.D. in Psychology (unspecified institution) Key Research Areas: Developmental Psychology, Autism, Social Cognition, Primate Studies, Visual Attention, Infant Behavior Her research investigates individual differences in infant visual attention, social motivation, and physiological markers like salivary oxytocin. Recent studies demonstrate: Stability of attentional patterns from newborn to 14 months Sex differences in early face detection Links between oxytocin levels and social affect in infants Neurophysiological markers in newborns later diagnosed with ASD Scientific awards include the NSF CAREER grant for neonatal imitation research. She contributes to open science initiatives through the ManyBabies consortium and develops remote eye-tracking methods for broader accessibility. Current lab work explores: Parent-infant affective interactions during still-face episodes Predictive power of early attentional biases Development of pathogen avoidance behaviors Cross-species comparisons of social cognition
Arno De Caigny is an Associate Professor at IÉSEG School of Management in France, specializing in Marketing Analytics. He holds a Ph.D. in Sales and Marketing from the University of Lille and Masters in Economics/Mathematics and Finance from Ghent University. His professional experience includes work as a Business Analyst at Deloitte. His primary research interests include customer churn prediction, AI applications in marketing, explainable AI for business, and life event-based marketing. He develops advanced machine learning models for customer behavior prediction and retention strategies. De Caigny's recent publications demonstrate strong focus on developing interpretable machine learning models for business applications, particularly in customer churn prediction and financial decision support. His work increasingly incorporates deep learning and natural language processing techniques.
Vassilis Zikas is an Associate Professor at the School of Cybersecurity and Privacy and holds a courtesy appointment in the School of Computer Science at Georgia Tech. He previously held positions at Purdue University, the University of Edinburgh, and Rensselaer Polytechnic Institute (RPI). His research focuses on cryptography, blockchain technologies, cryptocurrencies, game theory, and distributed computing. He leads the Blockchain Lab at Georgia Tech and serves as Chief Scientist at Sunday Group, where he contributes to the Mobby blockchain architecture. His work is supported by the NSF, DoD, Swiss NSF, and industry partners like Sunday Group and Algorand Foundation. He has organized major conferences including PKC 2020 and has served on program committees for EUROCRYPT, CRYPTO, and TCC. He advises PhD students in cryptography and related fields and has mentored postdocs and master’s students. Zikas’ publications span topics like secure multi-party computation (MPC), blockchain security, and cryptographic protocol design. His recent work includes advancements in privacy-preserving protocols, adaptive security frameworks, and composability in blockchain systems. Notable awards include the 2023 Leadership Award from Purdue College of Science and an AnlytiXIN Fellowship. He actively contributes to both theoretical and applied aspects of cybersecurity and privacy through academic and industrial collaborations.
