Robert West is an Associate Professor at EPFL (École polytechnique fédérale de Lausanne) in the School of Computer and Communication Sciences , leading the Data Science Lab (dlab) . His research focuses on Natural Language Processing , Machine Learning , and Computational Social Science , analyzing human-generated data from the web, social media, and online platforms. Education : PhD in Computer Science (2016) - Stanford University MSc in Computer Science (2010) - McGill University BSc in Computer Science (2007) - Technische Universität München Research Interests : West develops algorithms for analyzing large-scale web data, with emphasis on multilingual NLP , social network analysis , and AI ethics . His work bridges machine learning with social science to understand digital human behavior. Scientific Awards : ICWSM’22 Adamic–Glance Distinguished Young Researcher Award Google Faculty Research Award Facebook Research Award Multiple Outstanding Paper Awards at ICWSM and WWW Advising & Grants : He advises 12 PhD students and has secured funding from the Swiss National Science Foundation , Swiss Data Science Center , and industry partners. His lab maintains collaborations with Microsoft Research and CROSS . Labs & Collaborations : West leads the Data Science Lab at EPFL, which focuses on web-scale data analysis , privacy-preserving machine learning , and AI for social good . The lab develops tools like Wikispeedia and Quotebank for public data exploration.
Erik B. Sudderth is a Professor of Computer Science and Statistics and Chancellor's Fellow at the University of California, Irvine (UCI). He leads the Learning, Inference, & Vision Group and directs multiple research centers, including the UCI Center for Machine Learning and Intelligent Systems and the HPI Research Center in Machine Learning and Data Science. He previously served as an Associate Professor at Brown University. Education: B.S. (summa cum laude) in Electrical Engineering from UC San Diego (1999), M.S. and Ph.D. in EECS from MIT (2002, 2006). His research focuses on statistical methods for scalable machine learning, Bayesian nonparametrics, probabilistic graphical models, and applications in computer vision, AI, and environmental science. Key areas include nonparametric clustering, deep generative models, and particle-based inference algorithms. Research interests span diverse topics: advancing Bayesian nonparametric models for medical time series, scalable variational inference, and AI ethics. Notable contributions include the NET-VISA seismic monitoring system (ISBA Mitchell Prize, 2014), the BNPy toolbox (NSF CAREER Award), and work on diverse particle max-product algorithms for continuous inference. Scientific awards include the NSF CAREER Award, ISBA Mitchell Prize, and recognition as one of "AI's 10 to Watch" (IEEE). He has served as editor for top journals (JMLR, IEEE PAMI) and conference chairs (NeurIPS, CVPR). His work bridges theory and practice, with applications in robotics, climate science, and healthcare. Labs/Teams: UCI Learning, Inference, & Vision Group; UCI Center for Machine Learning; CREATE Technology Center. Grants include NSF funding for visually impaired collaboration tools and soil biogeochemical modeling.
Michael McAlpine is a Professor in the Mechanical Engineering department at the University of Minnesota . He also holds affiliations with the Biomedical Engineering and Electrical and Computer Engineering departments. His research focuses on 3D printing functional materials & devices , Nanoscale inks , Biomedical devices , Bioelectronics , and Flexible Microsystems . Research Interests : 3D Printing, Biomedical Engineering, Nanotechnology, Flexible Electronics, Microfluidics Labs : ME 361/363 Contact : mcalpine@umn.edu , (612) 626-3303, ME 117 Recent Research Trends include 3D Printed Biomedical Devices , Flexible Electronics , and Bioprinting Applications . His work spans from Spinal Organoid Formation to Programmable Drug Release Capsules . Scientific Award : Circulation Research 2020 Best Manuscript Award
Olindo Isabella serves as a Full Professor within the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology. She heads the Photovoltaic Materials and Devices (PVMD) research group, driving innovation in solar energy conversion technologies. Her academic role encompasses teaching, research leadership, and extensive collaboration with industry and international institutions to advance photovoltaic science and engineering. Professor Isabella's research spans multiple domains of photovoltaics, including silicon and perovskite solar cells, thin-film technologies, offshore floating systems, and agrivoltaics. She investigates material properties, device physics, and system performance to enhance efficiency, reliability, and environmental sustainability of solar energy solutions. Her work integrates experimental and computational approaches for comprehensive analysis. Analysis of her recent publications indicates a strategic focus on machine learning for PV-climate classification, impedance spectroscopy of silicon solar cells, offshore floating platform engineering, and perovskite crystallization processes. These studies collectively address key barriers to large-scale solar deployment, such as performance prediction, structural integrity in marine environments, and novel material synthesis. Scientific Awards: The available information does not mention any specific awards or honors for Professor Isabella. She has guided the research of 20 students and secured competitive funding for impactful projects. Currently, she leads SYMBIOSYST (2023-2026), which explores symbiotic relationships between solar PV and agriculture, and recently completed TRUST-PV (2020-2024), aimed at improving PV plant integration across market segments through machine learning and monitoring technologies. The PVMD group under her direction operates state-of-the-art laboratories for solar cell fabrication and characterization. The team collaborates with global partners on field trials, data analysis, and technology development, contributing to both fundamental knowledge and practical applications in renewable energy.
Xiaoxiang Zhu is a Professor for Data Science in Earth Observation at the Technical University of Munich (TUM) and serves as the Director of the International AI Future Lab - AI4EO. She is also on the Board of Directors of the Munich Data Science Institute (MDSI) and has held various leadership positions in research institutions including as Spokesperson for Helmholtz AI Research Field "Aeronautics, Space and Transport" (MASTr). Her educational background includes a doctorate (Dr.-Ing.) and habilitation from TUM. She has held positions as Private Dozent at TUM (2013-2015), TUM Junior Fellow (2013-2015), and Research Group Leader for "SparsEO" at Munich Aerospace (2011-2016). Professor Zhu's research focuses on the intersection of remote sensing, artificial intelligence, and data science. Her work primarily addresses global urban mapping, sustainable development goals, and climate change monitoring through Earth observation technologies. She develops advanced signal processing techniques and machine learning algorithms specifically tailored for satellite imagery and geospatial data analysis. Her research has significant applications in urban planning, environmental monitoring, and disaster management. Her team has pioneered approaches that combine synthetic aperture radar (SAR) with deep learning for improved Earth observation capabilities. Professor Zhu has received numerous prestigious awards including being named an IEEE Fellow (2021), receiving the Geodesy Award of the Nico Rüpke Foundation (2020), and being awarded an ERC Proof of Concept Grant (2020, 2022). She is also a Fellow of the Academia Europaea (2024) and AAIA Fellow (2024). Her publication record includes highly cited works such as "Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources" (2017). She leads a substantial research team comprising numerous PhD students and postdoctoral researchers working on various projects including Horizon Europe - ThinkingEarth, EarthCare, and AI4TWINNING. Her research group, the Chair of Data Science in Earth Observation, is actively involved in multiple large-scale European and German research initiatives focused on Earth observation and AI applications.
Andreas Rietbrock is Professor and Director of the Geophysical Institute (GPI) at the Karlsruhe Institute of Technology (KIT) , Germany, where he also serves as Dean of Studies for Geophysics . He is a leading expert in earthquake seismology, seismic tomography, and subduction zone dynamics, with a strong focus on integrating advanced observational techniques and computational methods. Education: While specific degrees are not listed in the provided text, his extensive publication record and leadership roles indicate advanced academic training in geophysics and seismology. Research Interests: His work spans a wide range of topics including: Seismic imaging of subduction zones (e.g., Nazca, Lesser Antilles) Earthquake rupture dynamics and fault mechanics Volcanic seismology and magma transport Full waveform inversion and AI-enhanced seismic analysis Distributed Acoustic Sensing (DAS) applications Induced seismicity and reservoir monitoring Research Trends: His recent publications (2022–2025) emphasize the use of dense seismic arrays, AI-based data processing, and multi-method tomography to study complex tectonic environments. Key themes include high-resolution imaging of slab structures, fluid migration in subduction zones, and the integration of DAS and machine learning for seismic monitoring. Scientific Contributions: Andreas has led major international projects such as the ANTICS Large-N deployment in Albania and the VoiLA project in the Lesser Antilles. He has published extensively in top-tier journals like Nature , Geophysical Research Letters , and Journal of Geophysical Research , with over 200 peer-reviewed articles. Teaching and Supervision: He teaches courses such as "Introduction to Geophysics II", "Seismology", and "Current Topics in Seismology and Risk". While specific student names are not listed, his role as Dean and principal investigator on numerous projects indicates active supervision of graduate students and postdocs. Labs and Teams: He leads the seismology group at GPI, coordinating large-scale deployments of seismic instruments, including ocean-bottom seismometers and fiber-optic DAS systems. His team collaborates globally with institutions in Europe, South America, and Asia.
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.
Paolo Gardoni is the Alfredo H. Ang Family Professor and an Excellence Faculty Scholar in the Department of Civil and Environmental Engineering at the University of Illinois Urbana-Champaign, with additional professorial appointments in Industrial & Enterprise Systems Engineering and Biomedical & Translational Sciences. He also serves as Director of the MAE Center and Editor-in-Chief of Reliability Engineering & System Safety. Education Ph.D. in Civil Engineering, University of California, Berkeley (2002) M.A. in Statistics, University of California, Berkeley (2001) M.Eng. in Structural Engineering, University of Tokyo (1997) Laurea (BS+MS equivalent) in Structural Engineering, Politecnico di Milano (1997) Research Interests Gardoni’s scholarship integrates probabilistic methods with large-scale infrastructure systems to advance reliability, risk, and life-cycle analysis. His work quantifies the performance of deteriorating systems under natural and anthropogenic hazards, models societal impacts of disasters, and develops decision frameworks for sustainable and resilient infrastructure. He also examines ethical, social, and legal dimensions of risk, and investigates optimal strategies for hazard mitigation, disaster recovery, and climate adaptation. Across more than 250 refereed journal papers, he has advanced sub-fields ranging from probabilistic mechanics and earthquake engineering to catastrophe bond pricing and engineering ethics, leveraging tools such as stochastic differential equations, Bayesian networks, and physics-informed machine learning. Awards & Honors Alfredo Ang Award on Risk Analysis and Management of Civil Infrastructure (ASCE, 2021) Best Paper Awards in ASCE Journal of Sustainable Water in the Built Environment (2019) and Geotechnical Research (2018 Telford Premium Prize) Fellowships and named professorships: Alfredo H. Ang Family Professor, Excellence Faculty Scholar, and courtesy or honorary professorships at Tsinghua, IIT Guwahati, Tongji, Jianghan, and Loughborough universities. Research Leadership & Funding Gardoni has secured over $58 million in research funding from NSF, DHS, NIST, USAID, Qatar National Research Fund, and other agencies. He directs the MAE Center—formerly an NSF Engineering Research Center—focused on multi-hazard engineering approaches, and is Editor-in-Chief of Reliability Engineering & System Safety (Elsevier, IF 9.4). He founded and formerly led the journal Sustainable and Resilient Infrastructure (Taylor & Francis) and serves on editorial boards of nine additional journals. Advising & Mentorship He has graduated 27 PhD and 35 Master’s students, many of whom now hold faculty positions worldwide. His group maintains an active pipeline of doctoral and post-doctoral researchers working on resilience analytics, infrastructure monitoring, and risk-informed decision-making. Laboratories & Collaborations He leads the MAE Center and is affiliated with the Critical Infrastructure Resilience Institute (CIRI) and the Biomedical and Translational Sciences group. International collaborations span the UK (Loughborough), India (IIT Guwahati), and China (Tsinghua, Tongji, Jianghan), fostering cross-disciplinary research in reliability and resilience engineering.
Yehuda Ben-Zion is a Professor of Earth Sciences at the University of Southern California (USC), affiliated with the Dornsife College of Letters, Arts and Sciences. He serves as Director of the Statewide California Earthquake Center (SCEC). His expertise lies in geophysics and seismology, with a focus on earthquake mechanics, fault dynamics, and seismic hazard assessment. He holds a Ph.D. in Geophysics and Seismology from USC (1990) and a B.S. in Geology and Physics from The Hebrew University of Jerusalem (1982). Research interests include physics of earthquakes and faults, high-resolution fault zone imaging, earthquake source properties, and dynamic rupture processes. Recent work emphasizes multi-scale modeling of rupture zones, seismic velocity monitoring using anthropogenic signals (e.g., train tremors), and probabilistic seismic hazard analysis frameworks like CyberShake. He leads projects such as Quakeworx, an open-source earthquake simulation platform, and investigates fault zone architecture in regions like the San Andreas, San Jacinto, and Marmara faults. His studies address critical questions about large earthquake mechanisms, ground motion prediction, and the interplay between tectonic stress and seismicity patterns. He has pioneered the use of dense seismic arrays and machine learning to analyze seismic data, advancing understanding of fault zone processes and their implications for hazard mitigation.
Dr. Binbin Xie is an Assistant Professor in the Department of Computer Science and Engineering at The University of Texas at Arlington (UTA). She holds a Ph.D. from the Manning College of Information and Computer Sciences at the University of Massachusetts Amherst, where she was a recipient of the prestigious Google PhD Fellowship in Mobile Computing. Her research focuses on wireless sensing, mobile health, cyber-physical systems, smart IoT, and wireless networking. Dr. Xie’s academic journey includes a tenure-track position at UTA since September 2024. She has received notable awards such as the Emerging Rockstar (IEEE Pervasive Computing, 2025), Rising STARs Award (UT System, 2024), and multiple scholarships including the Krithi Ramamritham Computer Science Scholarship (2023). Her work spans innovative applications in wireless sensing, leveraging LoRa, mmWave radar, and RFID technologies for real-world challenges like in-vehicle sensing and indoor localization. Her recent publications highlight advancements in LoRa sensing coexistence with communication, mmWave radar human sensing using secondary reflections, and combating interference in multi-target localization. She actively contributes to the academic community through service roles, including Technical Program Committee memberships for MobiSys 2025 and EWSN 2025, and as a reviewer for top-tier conferences like IMWUT/UbiComp and journals such as ACM Transactions on Sensor Networks. Dr. Xie’s teaching includes courses like CSE 4321 (Software Testing & Maintenance). Her research and educational efforts aim to bridge theoretical innovations with practical implementations in wireless and IoT systems.
Dr. Yunjie Yang is an Associate Professor at the University of Edinburgh's School of Engineering, with affiliations at the Edinburgh Futures Institute (EFI), the Edinburgh Generative AI Laboratory (GAIL), and the Edinburgh Centre for Robotics. He previously held the Chancellor's Fellow in Data Driven Innovation (2018-2023) and Bayes Innovation Fellow (2023-2024) positions. His research focuses on AI-powered sensing and imaging, machine learning, and soft sensors & electronics for robotics. Yang received his PhD in Engineering Electronics from the University of Edinburgh, MSc in Control Science & Engineering from Tsinghua University, and BEng in Measurement & Control Engineering from Anhui University. After his PhD, he worked as a Postdoctoral Research Associate in Chemical Species Tomography before securing his lectureship. His research interests center on developing intelligent sensing systems that replicate human perception capabilities for robotics and intelligent systems. He pioneers flexible sensing and imaging technologies across various scales through innovative multi-modal sensors, soft electronics, and their modeling using machine learning approaches. His work aims to enable autonomous physical artificial intelligence by bridging the gap between robotic systems and human-like perception. Analysis of his recent publications reveals a strong focus on soft robotics perception, particularly through electrical impedance tomography (EIT) and transformer-based architectures. His research spans medical imaging applications, digital twin modeling for industrial processes, and machine learning approaches for sensor data interpretation. The trend shows increasing integration of physics-informed deep learning with traditional tomographic techniques to achieve higher accuracy and efficiency. European Research Council (ERC) Starting Grant (2024) IEEE J. Barry Oakes Advancement Award (2024) IEEE I&M Society Graduate Fellowship Award (2015) Multiple Best Paper Awards Senior Member of IEEE Fellow of the International Society for Industrial Process Tomography Fellow of the Higher Education Academy ESI highly cited papers Dr. Yang serves as Associate Editor for IEEE Transactions on Instrumentation and Measurement and holds editorial positions with Scientific Reports and IEEE Sensors Journal. His research has been licensed to overseas research institutes and industry partners and received wide media coverage including BBC, EFE, USA Today, and STV. He has secured significant grant funding including the prestigious ERC Starting Grant. He leads the Edinburgh SMART Lab (Sensing/imaging + Machine Learning + Robotics), which aims to replicate human perception capabilities for robotics and advance flexible sensing technologies through innovative multi-modal sensors and machine learning approaches. The lab focuses on enabling autonomous physical artificial intelligence with applications spanning medical diagnostics, industrial monitoring, and advanced robotics systems.
Dr. Jiaqi Gong serves as Associate Professor in Computer Science and Adjunct Associate Professor in Mechanical Engineering at The University of Alabama's College of Engineering, while directing the Alabama Center for the Advancement of Artificial Intelligence. His academic foundation includes: B.S. in Engineering, China University of Geoscience (2004) Ph.D. in Engineering, Huazhong University of Science and Technology (2010) Dr. Gong's research pioneers human-AI convergence through cyber-physical systems and smart health technologies, developing mobile/wearable platforms to enhance human perceptual, cognitive, and physical capabilities. His work spans artificial intelligence, machine learning, computer vision, and IoT with applications in healthcare, environmental monitoring, and education. The Sensor-Accelerated Intelligent Learning (SAIL) laboratory he founded drives innovation in behavior change interventions, human movement modeling, and educational data mining. Recent publications reveal strong interdisciplinary trends: healthcare AI dominates with medication adherence prediction and surgical classification systems, while environmental applications feature flood-risk communication and drought analysis. His work increasingly integrates generative AI and LLMs across domains, demonstrating methodological innovation in federated learning, knowledge graphs, and explainable storytelling frameworks. Notable recognitions include: Best Student Paper Award, IEEE/ACM Connected Health Conference (2022) Best Student Paper Award, Body Sensor Networks Conference (2019) Data Challenge Win, IEEE Biomedical Health Informatics (2018) Best Paper Award, Body Area Networks Conference (2014) Best Demonstration Award, IEEE Wireless Health Conference (2014) Dr. Gong leads significant funded projects including a $2M CDC/NIOSH grant for first responder safety and $3M NSF funding for hydrologic research. As SAIL laboratory director, he mentors students in developing clinically deployed technologies for multiple sclerosis, dementia, and mental health. Future work focuses on scaling AI applications in chronic disease management and climate resilience through the Alabama AI Center. The SAIL laboratory (founded 2017) operates as a multidisciplinary hub developing wearable/mobile systems for health applications, with active collaborations across medical clinics and engineering departments for real-world deployment of behavior change interventions and movement analysis tools.
Clark Olson is a Professor in the Division of Computing & Software Systems at the University of Washington Bothell, part of the School of Science, Technology, Engineering & Mathematics. He earned his Ph.D. in Computer Science from UC Berkeley (1994), M.S. in Electrical Engineering (1990), and B.S. in Computer Engineering (1989) from the University of Washington, Seattle. Education: Ph.D. in Computer Science (2017) from University of California, Berkeley M.S. in Electrical Engineering (1990) from University of Washington, Seattle B.S. in Computer Engineering (1989) from University of Washington, Seattle His research focuses on computer vision, robot navigation, and clustering algorithms. He has developed techniques for Mars rover terrain mapping, subspace clustering, and geometric feature matching. His work bridges theory and application in autonomous systems and image analysis. Analysis of his publications reveals expertise in computer vision (8 papers), clustering algorithms (4 papers), and robotics (5 papers). Key subtopics include Mars exploration (3 papers), Hough transforms (3 papers), and probabilistic methods (3 papers). Professor Olson teaches courses ranging from introductory programming (CSS 161-162) to advanced topics in computer vision (CSS 487-587) and algorithm design (CSS 549). He also advises on the CSSE Capstone (CSS 497) projects requiring rigorous prerequisites and structured evaluation criteria.
Prof. Baker Mohammad serves as Professor and Director of the System on Chip Lab in the Department of Computer and Information Engineering at Khalifa University. With over 15 years of industrial experience at Intel and Qualcomm designing microprocessors and DSP chips, he bridges academic research with real-world engineering challenges in high-performance computing and low-power systems. His educational background includes: Ph.D. in Electrical and Computer Engineering, University of Texas at Austin (2008) M.S. in Electrical and Computer Engineering, Arizona State University B.S. in Electrical Engineering, University of New Mexico Dr. Mohammad's research spans cutting-edge domains where VLSI design converges with AI acceleration and emerging memory technologies . His work pioneers Memristor applications in environmental sensing (radiation, vacuum, glucose) and neuromorphic computing, while advancing energy harvesting systems for wearable electronics. The integration of in-memory computing with security primitives represents a paradigm shift in hardware design, moving beyond traditional CMOS limitations. His publication trajectory reveals accelerating focus on self-powered neuromorphic systems and RRAM-based architectures, with recent work (2021-2023) emphasizing hardware-software co-design for edge AI. Over 75% of his recent publications involve cross-disciplinary collaborations spanning materials science, chemistry, and biomedical engineering. Notable scientific recognition includes: IEEE TVLSI Best Paper Award 2016 IEEE MWSCAS Myrill B. Reed Best Paper Award Qualcomm Qstar Award for Performance Leadership KUSTAR IP Excellence Award Multiple SRC Techon Best Session Papers As a dedicated mentor, he has supervised over 15 graduate students while securing competitive funding from Khalifa University, ADEK, Qualcomm, Tii, and UAE space agencies. His grant portfolio demonstrates exceptional translational impact, converting fundamental research in memristive devices into drone flight computers and medical sensors. Current projects integrate academic rigor with industrial deployment timelines. The System on Chip Lab operates as a multidisciplinary hub where semiconductor physicists collaborate with AI researchers to develop RISC-V-based secure processors and piezoelectric nanogenerator systems. Recent expansions include partnerships with Tii for aerospace applications and medical device startups for glucose monitoring technology.
Assoc Prof Ng Teng Yong is an Associate Professor at the School of Mechanical & Aerospace Engineering (NTU), specializing in numerical modeling and simulation. With a background as Research Manager at A*STAR Institute of High Performance Computing, his work spans materials science, nanotechnology, and aerospace engineering. Current focus on graphene-based desalination membranes Expertise in molecular dynamics simulations Investigates nanoscale fluid mechanics and structural dynamics Recent publications highlight advancements in energy-efficient electrodialysis, smart robotics, and nonlinear vibration analysis. His interdisciplinary approach integrates computational methods with experimental validation in additive manufacturing and soft material mechanics.
