Dr. Han Du is an Associate Professor in the Department of Psychology at the University of California, Los Angeles (UCLA). He holds a PhD from the University of Notre Dame and leads the Du Research Lab. His methodological expertise includes Bayesian statistics, longitudinal data analysis, structural equation modeling, meta-analysis techniques, and machine learning applications in psychological research. Dr. Du's substantive research applies quantitative methods to developmental, clinical, cognitive, educational, and health psychology. His recent publications focus on transgender adolescent stress assessment, LGBTQ+ mental health in military contexts, social network interventions for HIV prevention, and minority stress theory applications. He teaches advanced statistical methods and supervises graduate students in quantitative psychology.
Jakoah Brgoch is an Assistant Professor in the Department of Chemistry at the University of Houston. His research focuses on leveraging machine learning to design inorganic compounds for applications in LED-based lighting and superhard materials. Key areas include phosphor development, sparse data handling, and predicting material formation. He leads the Brgoch Group, which emphasizes interdisciplinary approaches combining computational modeling and experimental synthesis. Research interests span luminescent materials, crystal chemistry, and defect engineering, with a particular emphasis on optimizing phosphors for solid-state lighting and high-performance materials under extreme conditions. His work bridges data science and traditional materials discovery to accelerate innovation in optoelectronics and mechanical materials. Recent publications highlight advancements in cyan-emitting nitridation processes, machine learning-guided phosphor discovery, and understanding oxidation resistance in silicides. His team has developed novel phosphors like Na2CaZr2Ge3O12:Cr³⁺ for NIR bioimaging and explored luminescent properties of Sr-based solid solutions. Active in translational research, Dr. Brgoch collaborates on applications like smartphone-readable diagnostic platforms using nanophosphors and point-of-care testing. His lab emphasizes open science practices and has pioneered methods like Single-crystal automated refinement (SCAR) for structural determination.
David Blaauw is the Kensall D. Wise Collegiate Professor of Electrical Engineering and Computer Science (EECS) at the University of Michigan. His research focuses on ultra-low-power analog/mixed-signal circuits, mm-scale sensors, neural networks, and biomedical applications. He leads the Blaauw Lab, which has pioneered innovations like the Michigan Micro Mote (M^3) and neural recording probes. His work emphasizes real-world deployability, with applications in environmental monitoring (e.g., monarch butterflies), medical devices, and robotics. Education: B.S. in Physics and Computer Science, Duke University (1986) Ph.D. in Computer Science, University of Illinois Urbana-Champaign (1991) Research Interests: Blaauw’s lab explores ultra-low-power computing, mm-scale systems, RF communication, in-memory computing, and genomics acceleration. Key projects include: Millimeter-scale computers (e.g., 0.04mm³ temperature sensors) Wireless neural interfaces for brain-machine communication Energy-efficient accelerators for edge AI and genomics Micro-robotics with sensing/actuation/computation Awards: IEEE Fellow 2016 SIA-SRC Faculty Award Motorola Innovation Award Best Paper Awards at ISSCC, ISCA, and RFIC Advising & Impact: Over 600 publications, 65 patents, and 4 startup companies spun from his lab. Current research includes genome sequencing accelerators (GenAx) and neural recording dust for brain mapping. He directs the Michigan Integrated Circuits Lab and chairs major conferences like ISSCC and DAC. Labs/Teams: Blaauw Lab (University of Michigan) Michigan Integrated Circuits Lab (MICAL)
Hasan Davulcu is a Professor in the School of Computing and Augmented Intelligence at Arizona State University (ASU). He holds a B.S. in Mathematics from Middle East Technical University (Turkey) and M.S./Ph.D. in Computer Science from Stony Brook University (NY). His research focuses on sociocultural modeling, AI, machine learning, and behavioral analytics for fraud detection. He leads the CIPS-AI Lab, developing data mining tools for semantic information extraction from social media and web data. Affiliations: Senior Global Futures Scientist (Global Futures Scientists and Scholars Program), Co-founder & CIO of ARTIS MAGI (AI-driven behavioral analysis startup). Education: Ph.D. Computer Science (Stony Brook, 2002), M.S. Computer Science (Stony Brook, 1995), B.S. Mathematics (METU, 1993). Research interests include: Sociocultural modeling and persuasive AI. Web/social media mining, information extraction, and database systems. Behavioral analytics for fraud detection and countering extremist influence. Key achievements: 2011 HSCB Focus Exceptional Scientific Achievement Award for work on sociocultural modeling in the DOD Minerva project. Principal Investigator on NSF and DoD grants, including behavioral analytics for financial fraud and social influence analysis of extremist groups. Grants & Projects: NSF PFI:BIC Grant (2014-2019): Behavioral analytics for fraud detection via visual analytics infrastructure. DoD Minerva (2015-2019): Measuring social influence of extremist groups. ONR Projects (2018-2021): Modeling polarization, adversarial framing, and disinformation tracking. Labs/Teams: Cognitive Information Processing Systems (CIPS-AI) Lab, which pioneers data mining techniques for unstructured social media data and semantic representation systems.
Dr. Jennifer Koch is an Associate Professor at the Laboratory of Geo-information Science and Remote Sensing, part of Wageningen University & Research. Previously, she served as an Associate Professor and Associate Research Director at the University of Oklahoma's Data Institute for Societal Challenges. Her research integrates data-driven methods like simulation modeling to address socio-economic and climate change challenges, focusing on sustainable urbanization and environmental management. Education: She holds a Diplom (Univ.) in Geoecology from the University of Bayreuth and a Dr.-Ing. in Electrical Engineering/Computer Science from the University of Kassel. She teaches courses on geo-information management and data analytics. Research emphasizes multi-scale modeling, stakeholder engagement, and participatory approaches to socio-ecological systems. Recent work explores urban growth in Africa, methane emission monitoring, and renewable energy siting. Articles highlight interdisciplinary methods in GIS, climate policy, and community geography. Professional service includes roles with iEMSs, IALE, and the AAG. No ancillary activities reported. Her work bridges technical geospatial tools with societal challenges, emphasizing practical policy applications.
Dr. Patrick Bianchi serves as a Researcher at the Swiss Seismological Service (SED) within ETH Zurich, Switzerland, where he conducts fundamental investigations into earthquake processes and rock failure mechanisms. His work integrates laboratory experimentation with numerical modeling to advance seismic hazard assessment methodologies. His research spans seismology, rock mechanics, and experimental geophysics with emphasis on fault mechanics and earthquake physics. Dr. Bianchi employs distributed fiber-optic strain sensing, acoustic emission monitoring, and triaxial testing to study strain localization, precursory signals, and the transition from aseismic to seismic deformation in crystalline and siliclastic rocks. His experimental approaches bridge laboratory observations with natural fault behavior, focusing on how surface roughness, wear processes, and fluid pressure influence fault stability and rupture nucleation. Analysis of his 15 most recent publications (2024-2025) reveals consistent investigation of strain heterogeneities, preslip phenomena, and energy dissipation during earthquake preparation phases. Key methodological trends include scaling deep learning applications from labquakes to megathrusts, comparative laboratory-numerical modeling of pre-failure processes, and environmental loading effects on brittle failure thresholds. His work demonstrates particular expertise in distributed strain sensing techniques applied to fault zone deformation. As an integral member of the Swiss Seismological Service, Dr. Bianchi contributes to Switzerland's national seismic monitoring network and fundamental research on earthquake physics. The SED operates as ETH Zurich's center for seismic hazard analysis, maintaining real-time earthquake detection systems while conducting experimental and theoretical research to improve understanding of seismic sources and ground motion prediction.
Victoria J. Orphan is the James Irvine Professor of Environmental Science and Geobiology at Caltech, where she directs the Center for Environmental Microbial Interactions. Her research investigates microbial processes in anaerobic ecosystems including deep-sea methane seeps, hydrothermal vents, and coastal sediments using interdisciplinary approaches combining molecular biology, stable isotope techniques, and geochemistry. Her laboratory focuses on: Microbial partnerships in methane cycling Ecophysiology of uncultured archaea and bacteria Viral ecology in marine environments Biogeochemical impacts of microbial communities Blue carbon sequestration in seagrass ecosystems Professor Orphan has developed innovative methods including BONCAT-FISH and nanoSIMS for studying microbial activity in environmental samples. Her research group maintains active field programs in Monterey Canyon and hydrothermal vent systems, and develops high-pressure incubation systems for studying deep-sea microbes. She teaches courses on microbial ecology and evolution, and mentors graduate students through the Geobiology and Environmental Science programs.
May Yuan is the Ashbel Smith Professor of Geospatial Information Sciences at the University of Texas at Dallas (UT-Dallas), affiliated with the School of Economic, Political and Policy Sciences. She directs the Geospatial Analytics and Innovative Applications (GAIA) Lab. Her research focuses on space-time representation, GIS analytics, and environmental/social problem-solving (e.g., disaster risk, pollution, crime mapping). She holds a Ph.D. in Geography from SUNY Buffalo (1994) and B.S. from National Taiwan University (1987). Previously, she was Brandt Professor and Director of the Center for Spatial Analysis at the University of Oklahoma (1994–2014). Education: Ph.D. in Geography, State University of New York at Buffalo, 1994 M.A. in Geography, State University of New York at Buffalo, 1992 B.S. in Geography, National Taiwan University, 1987 Research Interests: Her work integrates space-time GIS databases with cognitive science, environmental modeling, and social dynamics. Key areas include: - Spatiotemporal query and analytics for geographic processes - GIS-based disaster risk assessment (wildfires, tornadoes) - Urban air quality modeling - Neurogeography and Alzheimer’s disease prediction using environmental complexity metrics - Deep mapping and spatial narratives. Grants & Partnerships: Supported by NSF, NASA, DoD, DHS, NOAA, EPA, and state agencies. Her GAIA Lab explores 'place' concepts in space-time analytics. Awards & Roles: Fellow, AAAS and AAG Editor-in-Chief, International Journal of Geographical Information Science (2017–present) Former President, Cartography and Geographic Information Society (2020–2021) and UCGIS (2011–2012) Member, NOAA Environmental Information Services Working Group (2016–2022) Labs/Teams: Leads the GAIA Lab, collaborating on geospatial AI, environmental health, and urban analytics.
Özer Özkahraman is a postdoctoral researcher at the Division of Robotics, Perception and Learning (RPL) at KTH Royal Institute of Technology. He works under Ivan Stenius and John Folkesson, focusing on underwater mission planning, simulation, and integration of autonomous systems. His email is ozero@kth.se . He completed his PhD at KTH under Petter Ögren, researching large-scale multi-agent coverage planning for autonomous underwater vehicles (AUVs). Current projects include the SMaRCSim multi-domain simulation platform and development of underwater vehicles like LoLo, SAM, and Evolo. Research interests span autonomous underwater systems, multi-agent coordination, control systems, and simulation infrastructure. He emphasizes modular, accessible frameworks for vehicle testing and real-world deployment. His work bridges theoretical methods (e.g., control barrier functions) with practical applications in marine robotics. Publications focus on AUV navigation, environmental sensing, and adaptive control. Projects like Real2Sim aim to align simulation with real-world vehicle dynamics using motion capture data. He collaborates internationally on topics like data-driven damage detection and model compression for resource-constrained robots. No academic awards are explicitly mentioned. He actively seeks collaborators for projects in sonar simulation, flow field modeling, and cyber-physical system integration.
Richard Jardine is a Professor of Geomechanics in the Department of Civil and Environmental Engineering at Imperial College London's Faculty of Engineering. He also serves as a College Proconsul and co-chairs the College Artworks Group. His work spans advanced geotechnical research, offshore renewable energy foundations, and international collaboration. Research Interests: His expertise includes soil properties, advanced laboratory and field measurement techniques, soil characterization, offshore geotechnics, foundation analysis, slope stability, driven pile behavior, soft ground engineering, full-scale monitoring, geotechnical instrumentation, and cold region geotechnics. His research is central to climate change adaptation and renewable power systems. Publication Trends: Recent articles focus on the mechanical behavior of chalk, sand, and glacial tills under monotonic and cyclic loading, particularly in offshore contexts. Emphasis is placed on numerical modeling (FE, MPM), pile-soil interaction, aging effects, and design method validation for offshore wind foundations. Fellow of the Royal Society (2024) Fellow of the Royal Academy of Engineering (2002) RAEng Medal (1997) British Geotechnical Association Medal (1990, 2015, 2021, 2023) Canadian Geotechnical Society Quigley Award (2019) ISSMGE McClelland Honour Lecture (2023) BGA Rankine Lecture (2016) Advising and Grants: Richard has led major international Joint Industry Projects including ALPACA, PISA, PAGE, and Unified Pile Design Method JIP, involving partners such as Orsted, NGI, Fugro, Oxford, and Zhejiang University. He has advised UK government, contractors, and energy firms on offshore projects in the Baltic Sea, Taiwan Strait, and North Sea. He holds a Royal Society Newton Advanced Fellowship with ZJU. Labs and Teams: He leads research within Imperial’s Geotechnics group and the Imperial Centre for Geohazards. His team conducts advanced laboratory and field testing, collaborates with Deltares (Netherlands), and maintains strong ties with Zhejiang University, where he is a Visiting Professor and Distinguished International Scholar.
Rayadurgam Srikant is the Fredric G. and Elizabeth H. Nearing Endowed Professor of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign, affiliated with the Coordinated Science Lab. He co-directs the C3.ai Digital Transformation Institute, focusing on AI-driven solutions for global challenges. His research spans machine learning, communication networks, stochastic systems, and game theory. Srikant has authored influential textbooks including Communication Networks: An Optimization, Control and Stochastic Networks Perspective . He holds IEEE Fellow status and has received prestigious awards like the ACM SIGMETRICS Achievement Award (2021) and IEEE Koji Kobayashi Award (2019). Over 20 of his advisees hold faculty positions globally. Education: PhD (1991), MS (1988) in Electrical Engineering from UIUC; B.Tech (1985) from IIT Madras. He has taught advanced courses on optimization, stochastic systems, and game theory. His work bridges theory and practice, with contributions to congestion control, cloud computing, and reinforcement learning. Current projects include AI applications for pandemic response and digital transformation initiatives. Research highlights include foundational work on Lyapunov drift methods for network stability and distributed algorithms. He serves as Area Editor for Mathematics of Operations Research and has led editorial roles for IEEE/ACM Transactions on Networking. His lab collaborates with industry leaders like Microsoft and C3.ai, leveraging supercomputing resources for societal impact.
Svetlana Lazebnik is a Full Professor and Willett Faculty Scholar in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), part of the Grainger College of Engineering. She holds a Ph.D. from UIUC (2006) and previously served as an Assistant Professor at the University of North Carolina at Chapel Hill (2007–2011). Her research focuses on computer vision, including generative models for virtual try-on, image stylization, scene understanding, and joint modeling of images and language. She has advised numerous Ph.D. students and postdocs, many of whom now hold prominent academic and industry roles. Education: Ph.D. in Computer Science, UIUC (2006); supervised by Jean Ponce. Research Interests: Her work spans generative adversarial networks (GANs), diffusion models, virtual try-on systems (e.g., Dressing-in-Order, Street Try-On), exemplar-based stylization, and large-scale photo analysis. She has pioneered spatial pyramid matching and contributed to binary code learning for image retrieval. Key Awards: NSF CAREER Award (2008), Microsoft Research Faculty Fellow (2009), Sloan Research Fellow (2013), IEEE Fellow (2021), and the Longuet-Higgins Prize (2016) for her CVPR 2006 paper. Teaching: Recent courses include CS 444 (Deep Learning for Computer Vision), CS 543 (Computer Vision), and a Ph.D. Job Search Seminar. She has also taught at UNC Chapel Hill. Grants & Funding: Supported by NSF, Amazon, AWS, Microsoft, Sloan Foundation, Google, ARO, and Adobe. Notable grants include CCF 2348624 and IIS 1718221. Labs/Groups: Leader in the Illinois CS Vision Group, contributing to collaborative projects on embodied AI, multi-agent systems, and visual-semantic reasoning.
Natacha Crooks Dr. Natacha Crooks is an Assistant Professor in the Department of Electrical Engineering and Computer Science (EECS) at UC Berkeley. Her research focuses on distributed systems, databases, and security, with a particular emphasis on consistency models, BFT protocols, and cloud computing. She is a founding member of the SkyLab and a core contributor to the Data Systems and Foundations Group at Berkeley. She holds a Ph.D. in Distributed Systems from the University of Texas at Austin (2019) and a BA in Computer Science and Law from the University of Cambridge (2012). Affiliations & Roles: Assistant Professor, UC Berkeley EECS Visiting Researcher at Azure Research, Security & Privacy Founding Member of SkyLab Member of Berkeley Center for Decentralized Intelligence Former Scientific Advisor at Improbable and Astronomer Research Interests: Her work bridges distributed systems and database research, addressing challenges in transactional consistency, fault-tolerant protocols, and cloud resource optimization. Key themes include: Designing scalable BFT consensus algorithms Secure multi-cloud storage solutions Optimizing distributed transaction processing Privacy-preserving distributed systems Awards & Recognition: Sloan Research Fellow (2025) NSF CAREER Award ACM SIGOPS Dennis M. Ritchie Doctoral Dissertation Award (2020) IEEE CS TCDE Early Career Award (2024) Teaching: Fall 2025: CS 294-282 (Research Culture and Community Norms), Wheeler 130. Industrial Collaboration: Prior roles include visiting researcher at Cornell University, Microsoft Research (DMX group), and Imperial College London. Industrial partnerships include work with Materialize, Astronomer, and Improbable.
Sewon Min is an Assistant Professor at UC Berkeley's Electrical Engineering and Computer Sciences (EECS) department and a research scientist at the Allen Institute for AI. Her research focuses on Natural Language Processing (NLP) and Machine Learning, particularly Large Language Models (LLMs), emphasizing data-centric approaches and ethical AI practices. She holds a Ph.D. from the University of Washington (2024) and a B.S. from Seoul National University (2018). Her work includes advancements in retrieval-based models, mixture-of-experts architectures, and data privacy in LLMs. Notable projects include FlexOlmo (flexible data use in LLMs) and OLMoE (open mixture-of-experts models). She has been recognized with the ACM Doctoral Dissertation Award Honorable Mention (2025) and WAGS/ProQuest Innovation in Technology Award. Recent articles highlight her contributions to reasoning models, data tracing (OLMoTrace), and scalable retrieval systems (MassiveDS). She leads the Berkeley NLP Group and collaborates with BAIR, exploring topics like model transparency and ethical data usage.
Dr. Changyou Chen is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo, State University of New York. His research focuses on Multi-Modal Learning Foundation Models Deep Generative Models Large-scale Bayesian Sampling with applications in document understanding, music-AI integration, and molecular representation learning. Research Trends revealed through his recent publications include Optimizing Multimodal Large Language Models Developing Novel Retrieval-Augmented Generation Frameworks Creating Benchmark Datasets for Visual Text Understanding Advancing Diffusion Models with Domain-Specific Constraints across domains from music sheets to biomedical documents. Scientific Contributions : UB Young Investigator Award (2020) Architect of LoCAL Framework for Long Document Understanding Co-developer of MusiXQA Benchmark Pioneering Work in Probability Contrastive Learning Academic Leadership includes mentoring 10+ graduate students and serving as Area Chair for major AI conferences (ICML, NeurIPS, AAAI, IJCAI). His Labs develop scalable solutions for multimodal reasoning, with recent work demonstrating practical GPU memory optimization through LoRA adapter sharing.
