Ju Sun is an Assistant Professor at the University of Minnesota, Twin Cities, in the Computer Science & Engineering department. He leads the Group of Learning, Optimization, Vision, Healthcare, and X (GLOVEX) and plays key roles in the UMN Data Science Initiative (DSI), Program for Clinical AI, and AI-CLIMATE institute. Research Focus : Theoretical foundations of machine learning, computer vision, and numerical optimization with applications in healthcare, inverse problems, and medical imaging. Grants : $4.5M+ in funding including NSF ACED Program and NIH R01 grants for constrained deep learning and imbalanced classification. Teaching & Leadership : Featured in UMN seminars and AI institutes, with affiliations across Electrical and Computer Engineering, Health Informatics, and Medical School. Recent Publications address inverse problems, federated learning, imbalanced classification, and phase retrieval using deep generative priors and diffusion models. His group website details these innovations. Scientific Awards : McKnight Land-Grant Professorship (2025–2027) 2021 AAAI New Faculty Highlights Advising : Mentored three PhD graduates now at Meta, Amazon, and UCLA. Collaborations span medicine, materials science, and biomedical engineering, integrating physics-informed constraints into AI.
Prof. Xiaojing Huang is a Professor of Information and Communications Technology at the University of Technology Sydney (UTS), serving as Head of Discipline for SEDE Communications and Electronics within the School of Electrical and Data Engineering. He leads the Mobile Sensing and Communications program at the Global Big Data Technologies Centre. With over 30 years of experience, he has authored over 300 publications and 31 patents, focusing on wireless communications, signal processing, and antenna technologies. Education: PhD (Electrical Engineering, Shanghai Jiao Tong University, 1989). Previous roles include Principal Research Scientist at CSIRO (2009-2014), Associate Professor at University of Wollongong (2004-2009), and key industry roles at Motorola and Shanghai Yang Tian Science and Technology Corporation. Research interests include full-duplex wireless systems, millimeter-wave and terahertz communications, massive antenna arrays, and mixed-signal processing platforms. His work on the CSIRO Ngara backhaul system earned multiple awards, including the 2012 CSIRO Chairman's Medal and Australian Engineering Innovation Award. Recent grants include $4.2M (AUD) for projects like 'Radio Frequency Camera for Radar Imaging' (ARC DP220101158) and 'Terabit mm-Wave Backbones for Integrated Space Networks' (ARC DP200101532). He has supervised numerous students in high-speed communication systems and full-duplex technologies. Awards include: 2013 CSIRO Leadership Achievement Award, 2012 Australian Engineering Innovation Award, and IEEE Sumner Award (nominee). Active in IEEE standards (802.11/802.15) and collaborations with institutions like Tsinghua University.
Ian Bradley is an Assistant Professor in the Department of Civil, Structural and Environmental Engineering at the University at Buffalo, State University of New York. His research focuses on creating sustainable biological processes to address needs in engineered and natural systems for water and wastewater treatment and resource recovery. Education: PhD in Environmental Engineering, University of Illinois at Urbana-Champaign (2017) MS in Environmental Engineering, University of Illinois at Urbana-Champaign (2011) MS in Civil Engineering (Structures), University of Illinois at Urbana-Champaign (2010) Research Interests: Dr. Bradley specializes in microalgal-based nutrient recovery, wastewater surveillance for public health monitoring, PFAS degradation using nanomaterials, and sustainable resource recovery systems. His work integrates biological processes with environmental engineering to optimize wastewater treatment efficiency and develop predictive models for water quality and health outcomes. Publications: His recent research includes advancements in microalgal cultivation (EcoRecover process), wastewater-based epidemiology for SARS-CoV-2 tracking, and computational enzyme design for PFAS remediation. These studies demonstrate interdisciplinary expertise spanning environmental engineering, biotechnology, and public health analytics.
Dr. Chih-Hung (James) Chen is a Professor in the Department of Electrical & Computer Engineering at McMaster University. His research focuses on noise-related issues in semiconductor devices, low-noise circuit design for medical and communication applications, and thermal noise characterization in nano-scale transistors. He holds senior member status in IEEE and is a licensed Professional Engineer in Ontario. Education: Ph.D., McMaster University, 2002 M.A.Sc., Simon Fraser University, 1997 B.Sc., National Central University, Taiwan, 1991 Research interests include biomedical technologies, microelectronics & VLSI, and digital/smart systems. He has collaborated with companies like Sony Corporation, United Microelectronics Corporation, and Focus Microwaves. His work is supported by grants from the Canada Foundation for Innovation (CFI), NSERC, and the Ontario Innovation Trust (OIT). Notable achievements include serving on the International Advisory Committee of the International Conference on Noise and Fluctuations (2015) and as an editor for the Journal of Low Power Electronics and Applications since 2022. Teaching includes courses like Analysis and Design of RF ICs for Communications and Electronic Devices and Circuits 2. His research lab focuses on advancing noise measurement techniques and designing ultra-low-power analog circuits for emerging applications.
Elisa Kreiss is an Assistant Professor in the Department of Communication at UCLA, affiliated with the College of Letters & Science. She leads the Coalas Lab (Computation and Language for Society Lab), focusing on advancing understanding of how communicative context shapes language use through natural language processing, psycholinguistics, and human-computer interaction. Her work addresses challenges in image accessibility for visually impaired users, supported by grants from Google, NSF, and Stanford initiatives. Education: Ph.D. in Linguistics, Stanford University (advised by Christopher Potts) Research Interests: Her research bridges AI ethics, image accessibility, and human-centered evaluation of machine learning systems. Key themes include: Generating context-aware image descriptions for accessibility Ethical implications of vision-language models Interpretable AI through causal reasoning Awards: Google Research Awards National Science Foundation Grant Stanford Human-Centered AI Initiative Support Stanford Community Impact Award (2022) Lab & Advocacy: Directs the Coalas Lab, emphasizing inclusive research environments. Advocates for diversity in STEM and accessible technology design.
David Whitney is a Professor of Psychology at the University of California, Berkeley , with affiliations in Cognitive Science and Neuroscience . His research focuses on visual perception, particularly how humans process information in cluttered environments through mechanisms like ensemble perception , serial dependence , and perceptual crowding . He employs techniques such as psychophysics, fMRI, and TMS to study these phenomena. Whitney's recent work examines serial dependence in schizophrenia, emotion perception in crowds , and medical image analysis for dermatology and radiology. His studies reveal how the brain uses dynamic predictive templates and motion cues to stabilize perception despite neural processing delays. Publications span Current Biology , Nature Reviews Psychology , and PLoS ONE , with a strong emphasis on interdisciplinary applications of perceptual science. Scientific contributions include foundational studies on visuomotor control , blind spot filling-in , and holistic face processing . His lab investigates perceptual stability across eye movements, spatial localization, and cross-modal interactions. Whitney has received grants such as NIH EY018216 to support his research on motion-dependent visual coding. Detailed information about his work is available on the Whitney Lab website .
Aurora Maccarone serves as an RAEng Research Fellow within the Institute of Photonics and Quantum Sciences at Heriot-Watt University's School of Engineering & Physical Sciences. Her work focuses on advanced photonics applications for challenging environments, particularly underwater and obscured conditions. Her research expertise spans: Single-photon LiDAR systems for underwater 3D imaging Photon-counting detector arrays for depth profiling Real-time reconstruction algorithms for obscurant-penetrating imaging Quantum sensing applications in marine environments Recent publications demonstrate consistent innovation in single-photon imaging techniques, with emphasis on underwater applications (2023-2024) and obscurant penetration (2022). Her work shows strong interdisciplinary connections between optical engineering, computational imaging, and environmental sensing. Key publications reveal growing impact in underwater LiDAR technology, with multiple high-citation papers on photon-efficient imaging systems. Dr. Maccarone actively supervises PhD students and has created significant research datasets. Her collaborations span international institutions, with notable contributions to sensor hardware development and computational imaging algorithms. Recent work shows increasing integration of machine learning techniques with traditional photon-counting approaches.
Dr. Kim Yong-Joe is an Associate Professor in the J. Mike Walker ’66 Department of Mechanical Engineering at Texas A&M University. He serves as the Director of the Acoustics and Signal Processing Laboratory (ASPL), founded in 2009. His research focuses on acoustics, applied signal processing, nonlinear acoustics, biomedical acoustics, noise and vibration control, and structural dynamics. He has received notable awards including the 2014 Department of Mechanical Engineering Graduate Teaching Award and the 2014 Pioneer Natural Resources Faculty Fellow II. His lab specializes in wave propagation analysis, ultrasonic structural health monitoring, and acoustophoresis in microfluidic systems. He has collaborated with sponsors like the National Science Foundation, Qatar National Research Fund, and Samsung Techwin. His research has led to advancements in noise reduction technologies, biomedical diagnostics, and nondestructive evaluation methods.
Meng Li is the Noah Harding Associate Professor of Statistics at Rice University's School of Engineering. He specializes in Bayesian analysis, machine learning, and statistical theory. His research bridges methodological development and applications in biomedical sciences, materials informatics, and neuroimaging. Li holds a Ph.D. from North Carolina State University and a B.S. from Sun Yat-sen University. He has been recognized with awards including the 2020 Rice Engineering Excellence Award and the Ralph E. Powe Junior Faculty Enhancement Award. Li's research focuses on probabilistic modeling of complex data such as images, functional data, and networks. His funded projects include AI frameworks for pancreatic cancer biomarkers and Bayesian spatiotemporal modeling of marine ecosystems. He collaborates with institutions like Houston Methodist and Baylor College of Medicine on medical applications. His teaching includes advanced courses like Bayesian Statistics and Advanced Bayesian Inference. He advises over 30 students, many of whom have pursued academic and industry roles. Li serves as an associate editor for Bayesian Analysis and the new ACM Transactions on Probabilistic Machine Learning.
Behnaam Aazhang is the J.S. Abercrombie Professor of Electrical and Computer Engineering at Rice University and Director of the Rice Neuroengineering Initiative (NEI). He holds a B.S., M.S., and Ph.D. from the University of Illinois at Urbana-Champaign. His roles include leading the multi-university Rice Neuroengineering Initiative and directing the Center for Neuroengineering. He has held an Academy of Finland Distinguished Visiting Professorship (FiDiPro) at the University of Oulu (2006-2014) and received an Honorary Doctorate from the University of Oulu in 2017. Education: Ph.D. in Electrical Engineering, University of Illinois at Urbana-Champaign (1986) M.S. in Electrical Engineering, University of Illinois at Urbana-Champaign (1983) B.S. in Electrical Engineering, University of Illinois at Urbana-Champaign (1981) Research Interests: Dr. Aazhang’s work focuses on signal/data processing, information theory, and neuroengineering applications. Key areas include: Neuronal circuit connectivity and learning impacts Real-time closed-loop neuromodulation for neurological disorders (epilepsy, Parkinson’s, depression) Patient-specific cardiac pacing systems Cybersecurity in cloud computing Awards & Honors: 2022 Rice Outstanding Doctoral Thesis Advisor Award 2019 SIGMOBILE Test of Time Award 2017 Honorary Doctorate (University of Oulu) 2013 IEEE Communication Society Advances in Communication Award AAAS and IEEE Fellowships (2012 and 1999) Grants & Advising: His research is supported by multi-university collaborations and grants. He has advised numerous graduate students in electrical engineering and neuroengineering, though specific names are not listed here. Labs & Teams: Leads the Aazhang Lab and the Rice Neuroengineering Initiative, focusing on translational technologies for neurological and cardiac disorders, including non-invasive neuromodulation and cloud security systems.
Dr James Herbert-Read is an Associate Professor and Whitten Lecturer in Marine Biology at the Department of Zoology, University of Cambridge. He serves as Deputy Head of Department (Postgraduate Education) and leads the Marine Behavioural Ecology Group. His research focuses on understanding how animals, particularly marine organisms, collect and process information from their environments to make behavioral decisions, with emphasis on social interactions, adaptation mechanisms, and ecological constraints. His group employs theoretical frameworks, controlled experiments, and quantitative field studies to investigate behavioral diversity in marine species. Key themes include collective behavior, predator-prey dynamics, camouflage strategies, and the impacts of environmental stressors on animal decision-making. Recent publications highlight work on lionfish vocalization mechanisms, cuttlefish camouflage, citizen science applications in marine research, and behavioral responses to visual and acoustic noise. Scientific awards and affiliations include: Whitten Lecturer in Marine Biology Associate Professor, University of Cambridge He has supervised research projects on topics such as: Social attraction in invasive fish species Evolution of coordinated movement Neurophysiological basis for leadership in shoals Maternal effects on offspring exploration
Yuxin Chen is a Professor at the University of Pennsylvania , holding joint appointments in the Department of Statistics and Data Science and the Department of Electrical and Systems Engineering . Prior to UPenn, he was an Assistant Professor at Princeton University (2017-2021) and a Postdoctoral Researcher at Stanford University (2015-2017). His research spans statistics, optimization, reinforcement learning theory, diffusion models, and information theory , with a focus on theoretical foundations and practical algorithms for machine learning. Education : Ph.D. in Electrical Engineering (Stanford, 2015), M.S. in Statistics (Stanford, 2013), M.S. in Electrical and Computer Engineering (UT Austin, 2010), B.E. in Electrical/Microelectronics (Tsinghua, 2008). Research Interests encompass theoretical and applied aspects of machine learning, including nonconvex optimization , sample complexity analysis , low-dimensional adaptation , and generative modeling . His work bridges mathematical rigor with real-world applications, particularly in scientific imaging and high-dimensional data analysis. Scientific Awards include the SIAM Activity Group on Imaging Science Best Paper Prize (2024) Alfred P. Sloan Fellowship (2022) NSF Career Award (2022) Google Research Scholar Award (2022) IEEE Transactions on Power Electronics Prize Paper Award (2024) Advising and Grants : He has mentored numerous students who have transitioned to academic roles at institutions like UIUC and UW-Madison. His research is supported by grants from the NSF , Amazon , and Google , with recent projects focusing on controllable diffusion models and efficient reinforcement learning algorithms .
Dr. Matloob Khushi serves as a Senior Lecturer in Computer Science at Brunel University London's College of Engineering, Design and Physical Sciences. With over 25 years of combined academic and industry experience, his work bridges theoretical AI advancements with practical applications in finance, healthcare, and public health domains. His research has established significant collaborations with international banks, healthcare institutions, and technology startups. Dr. Khushi earned his PhD in AI and Data Science from the University of Sydney, developing novel algorithms for genomic data analysis. His postdoctoral research at the Children's Medical Research Institute (2014-2017) pioneered AI-based diagnostic tools for medical condition detection. More recently, he developed bioinformatics tools for environmental assessment under a UKRI NEC grant. Research Focus FinTech Innovation : Creator of the SS Ratio (incorporating volatility and drawdown sensitivities), advanced portfolio optimization models, and synthetic data generation techniques for fraud detection and credit risk assessment Bioinformatics Leadership : Developer of AI tools for genomic analysis and early cancer detection, featured in SBS News and The Daily Telegraph Public Health NLP : Architect of systems for vaccine misinformation detection, mental health monitoring, and health surveillance on social media His publication portfolio shows consistent growth from foundational bioinformatics work to current multimodal AI applications, with increasing interdisciplinary collaboration across finance and healthcare sectors. Awards and Recognition Ranked among Stanford/Elsevier's top 2% of global AI scientists Recipient of Best Paper Awards from IEEE Transactions on Computational Social Systems and PeerJ Media recognition for cancer detection research by major news outlets Mentorship and Teaching Dr. Khushi has supervised six PhD candidates to completion and over 100 postgraduate dissertations. He teaches CS3002 Artificial Intelligence and mentors students in Final Year Projects. His supervision focuses on Deep Learning/NLP for FinTech prediction and Public Health Surveillance applications, emphasizing practical implementation of theoretical concepts.
Brett Laursen is a Professor of Psychology at Florida Atlantic University's Charles E. Schmidt College of Science, with additional Docent Professor appointments in Educational Psychology at the University of Helsinki and in Social Developmental Psychology at the University of Jyväskylä in Finland. His research focuses on developmental psychology, particularly adolescent development, peer relationships, and social dynamics. Dr. Laursen earned his Ph.D. and M.A. in Child Psychology from the Institute of Child Development at the University of Minnesota and his B.A. in Psychology from Nebraska Wesleyan University. He also holds an Honorary Ph.D. from Örebro University in Sweden. His research program examines influence within close relationships, with particular focus on peer relationships during childhood and adolescence. Current projects include longitudinal studies of elementary and middle school children in Florida, Lithuanian youth transitioning from middle to secondary school, and child characteristics affecting parent engagement in literacy activities. His work consistently explores how social dynamics shape development, with special attention to friendship formation, dissolution, and influence processes. Analysis of his recent publications reveals a strong focus on peer relationships, social status, and developmental transitions. His research employs sophisticated longitudinal and genetically informed designs to examine how social contexts shape development across childhood and adolescence, with particular emphasis on the mechanisms through which peer influence operates. Fellow, American Psychological Association (Division 7, Developmental; Division 8, Social) Fellow and Charter Member, Association for Psychological Science Fellow, International Society for the Study of Behavioural Development Distinguished Alumnus, College of Education and Human Development, University of Minnesota Florida Atlantic University Scholar of the Year (2023-24 and 2016-17) Dr. Laursen has mentored numerous doctoral and master's students who have gone on to successful careers in academia and research. His research has been supported by major funding sources including the US National Institute of Child Health and Human Development, the US National Institute of Mental Health, the US National Science Foundation, Trygfonden, the Jacobs Foundation, and the European Social Fund. As Editor-in-Chief of Merrill Palmer Quarterly and Founding Editor of Cambridge Elements in Research Methods for Developmental Science, he plays a significant role in shaping the field's scholarly discourse. The Laursen Lab operates as a collaborative research team involving current students, alumni, and international collaborators including Professors Goda Kaniušonytė and Rita Žukauskienė of Mykolas Romeris University in Lithuania. The lab regularly presents research at major conferences including the Society for Research on Child Development.
Sanne Cottaar is a researcher at the Department of Earth Sciences, University of Cambridge, specializing in seismology and deep Earth structure. Her work integrates seismic waveform analysis, mineral physics, and geodynamic modeling to investigate mantle plumes, ultra-low velocity zones (ULVZs), and core-mantle boundary dynamics. Key research areas include: Seismic imaging of deep Earth heterogeneity Core-mantle boundary and mantle transition zone structure Multidisciplinary approaches with mineral physics and geodynamics Development of seismic tools like BurnMan for thermodynamic modeling Public engagement through educational initiatives such as Deep Earth Explorers Her recent publications focus on mapping ULVZs using Sdiff and Pdiff waves, resolving mantle plume origins, and benchmarking seismic methods against geodynamic constraints. She actively supervises doctoral projects in seismology and deep Earth dynamics.
