Jason Ritt is an Associate Professor of Brain Science (Research) and Scientific Director of Quantitative Neuroscience at the Robert J. and Nancy D. Carney Institute for Brain Science, Brown University. He holds affiliations with the Data Science Institute and collaborates across disciplines on quantitative research methods. Education : B.S., M.A., and Ph.D. in Neuroscience from Boston University (1997–2003). Research : Focuses on neural processing during active sensing and neuroengineering for neurostimulation. Combines electrophysiology, optogenetics, and theoretical approaches in rodent models. Develops closed-loop systems for studying sensory neural prosthetics and brain-machine interfaces. Key areas include synaptic diversity, neurocontrol algorithms, and sensory restoration. Teaching : Instructs NEUR 2100 NeuroPracticum, integrating hands-on neuroscience research training.
Massimo Mischi is a Full Professor at the Faculty of Electrical Engineering of the Eindhoven University of Technology (TU/e) and chairs the Signal Processing Systems (SPS) Division , the largest division at TU/e with over 250 researchers. He founded the Biomedical Diagnostics (BM/d) Lab in 2012, which now includes 180 researchers and clinical/industrial advisors, focusing on biomedical signal processing for diagnostics and monitoring.
Rex Ying is an Assistant Professor in the Department of Computer Science at Yale University's School of Engineering & Applied Science. He leads research in graph neural networks, geometric representation learning, and explainable AI, with applications spanning physical simulations, biology, knowledge graphs, and recommender systems. His lab actively recruits PhD students interested in geometric deep learning, graph neural networks, and trustworthy AI. Dr. Ying received his PhD in Computer Science from Stanford University under Jure Leskovec, with a thesis titled "Towards Expressive and Scalable Deep Representation Learning for Graphs." Prior to that, he graduated from Duke University in 2016 with highest distinction, majoring in Computer Science and Mathematics. His research focuses on three interconnected areas: advancing graph neural network architectures for improved expressiveness, scalability, and interpretability; innovating in geometric representation learning for data with diverse characteristics; and developing real-world applications across scientific domains. He has pioneered influential algorithms including GraphSAGE, PinSAGE, and GNNExplainer, and developed the first billion-scale graph embedding services at Pinterest as well as graph-based anomaly detection algorithms at Amazon. His recent publication trends show a strong focus on hyperbolic geometry for foundation models, non-Euclidean representation learning, and multimodal applications in computational biology. The research demonstrates increasing integration of geometric deep learning with large language models and foundation model architectures. KDD 2022 Dissertation Award 2019 Baidu Scholarship in Artificial Intelligence Dr. Ying actively serves the research community as a committee member for major conferences including AAAI, ICML, NeurIPS, ICLR, KDD, and WebConf for over seven years, and as area chair for LoG 2022. He co-leads the open-source PyTorch Geometric project and has organized numerous workshops on graph learning. His industry collaborations include Pinterest, Amazon, Facebook AI Research, DeepMind, Siemens, SLAC National Accelerator Laboratory, and Saudi Aramco. He teaches "Deep Learning for Graph-Structured Data" at Yale and mentors students in developing cutting-edge graph learning algorithms. His research lab collaborates with both academic institutions and industry partners to advance the state-of-the-art in graph representation learning, with particular emphasis on geometric deep learning and its applications to scientific discovery and real-world systems.
Sebastian Schemm is a Heisenberg Fellow at the Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge, a position regarded as equivalent to a non-permanent Associate Professor. He leads research within the Atmosphere-Ocean Dynamics group and previously held an ERC Starting Grant-funded Assistant Professorship (without tenure track) at ETH Zurich. Education and Career Path PhD (2013) and MSc (2010), ETH Zurich, Switzerland Postdoctoral researcher, University of Bergen, Norway (2014–2017) Postdoctoral researcher, Laboratoire de Météorologie Dynamique, ENS Paris (2017–2018) Assistant Professor (ERC Starting Grant), ETH Zurich (2020–2024) Heisenberg Fellow, DAMTP, University of Cambridge (2025–present) Research Focus Schemm’s work centres on atmospheric and climate dynamics, spanning turbulence to planetary scales. Core themes include the physics of extratropical cyclone life cycles, jet-stream and storm-track dynamics, Rossby waves and teleconnection patterns, high-resolution atmospheric modelling, and the integration of machine-learning techniques for parameter estimation, data assimilation, and kilometre-scale global simulations. He also contributes to large-scale initiatives such as ECMWF’s WeatherGenerator. Scientific Awards and Editorial Service DFG Heisenberg Fellowship (2025) ERC Starting Grant (2020–2024) European Meteorological Society Young Researcher Medal (2019) Co-Editor, Weather and Climate Dynamics (EGU) Co-Editor, Quarterly Journal of the Royal Meteorological Society PhD Supervision & Funding He currently supervises PhD students at both Cambridge and ETH Zurich, with funding streams including the Cambridge CREATES Doctoral Training Partnership and Swiss/EU grants. Ongoing students explore reinforcement-learning parameterisations, jet-stream–storm-track relationships, mid-latitude eddy energetics, machine-learning ensemble forecasting, and Bayesian parameter estimation in LES. Active Projects EU Horizon project WeatherGenerator (led by ECMWF) PASC HiRAD-Gen : High-Resolution Atmospheric Downscaling Using Generative Models
Ryuko Kubota is a Professor in the Department of Language and Literacy Education at the University of British Columbia (UBC), Faculty of Education, since 2009. Her research spans critical applied linguistics , race and language education , and decolonial pedagogies . She has contributed extensively to understanding language ideologies , multicultural education , and critical literacies . Education: BA in English Linguistics, Rikkyo University (1980) MAT in TESOL, School for International Training (1987) PhD in Education, OISE/University of Toronto (1992) Her research interests focus on the intersection of race, language policies, and critical pedagogies. She examines neoliberal paradoxes in language learning, translingual practices , and antiracist engagement in global contexts. Her work addresses systemic inequities in English language education and immersion programs . Recent publications highlight critical approaches to World Englishes , AI-assisted writing , and raciolinguistic hegemony . These studies span interdisciplinary sub-fields including critical race theory , decolonial education , and language teacher identity . Scientific awards include the DSSA Award 2020 . She supervises graduate students like Charu Gupta (PhD) and collaborates with institutions such as the Language Sciences Institute at UBC. Her teaching includes courses on critical multicultural education and language politics .
Jeremy Dahl is a Professor of Radiology (Pediatric Radiology) at Stanford University School of Medicine. He directs the Ultrasound Imaging & Instrumentation Lab and serves as Director of Research Academic Affairs in the Department of Radiology since 2020. He holds multiple affiliations across Stanford including Bio-X, the Cardiovascular Institute, Wu Tsai Human Performance Alliance, Maternal & Child Health Research Institute, Stanford Cancer Institute, and Wu Tsai Neurosciences Institute. Dr. Dahl received his B.S. in Electrical Engineering from the University of Cincinnati (1999) and Ph.D. in Biomedical Engineering from Duke University (2004). His research focuses on developing ultrasonic beamforming and image reconstruction methods for diagnostic imaging applications, particularly techniques that generate high-quality images in difficult-to-image patients. His laboratory specializes in B-mode and Doppler imaging techniques that utilize additional information from ultrasonic wavefields to improve image quality and develop real-time imaging systems for clinical applications including cardiac, liver, and fetal imaging. Dr. Dahl's research has led to significant advancements in ultrasound molecular imaging platforms, sound speed estimation, aberration correction, and reverberation noise suppression. His work often bridges engineering innovation with clinical applications for cancer detection and other diseases. His recent publications demonstrate strong focus on machine learning applications in ultrasound, distributed aberration correction, and molecular imaging techniques. Fellow, American Institute of Ultrasound in Medicine (2021) Senior Member, Institute of Electrical and Electronics Engineers (2020) Distinguished Investigator Award, The Academy for Radiology & Biomedical Imaging Research (2018) Outstanding Paper Award, IEEE Ultrasonics, Ferroelectrics, and Frequency Control Society (2011) Dr. Dahl serves in editorial roles for major journals including IEEE Transactions on Medical Imaging (2017-2024) and IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (2013-Present). His laboratory has successfully translated numerous innovations into clinical applications, with multiple patents including recent developments in pulsed focused ultrasound therapy and speed of sound quantification.
Shiyu Chang is an Associate Professor in the Department of Computer Science at the University of California, Santa Barbara , focusing on machine learning with applications in natural language processing and computer vision . He previously worked as a research scientist at the MIT-IBM Watson AI Lab alongside Prof. Regina Barzilay and Prof. Tommi Jaakkola, and earned both his B.S. and Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign , advised by Prof. Thomas S. Huang. Education : PhD, University of Illinois at Urbana-Champaign BS, University of Illinois at Urbana-Champaign His research centers on enhancing AI systems through human-AI interaction , aiming to improve interpretability , transferability , and adversarial robustness in LLMs. Recent work includes LLM watermarking defense , uncertainty decomposition , and self-denoised smoothing for model robustness. His publications span premier venues like ICML , NeurIPS , CVPR , and ACL , with recurring themes in diffusion models , LLM optimization , and ethical AI (e.g., hallucination detection, unlearning frameworks). He actively mentors students, several of whom are marked as advisees (☆) in his publications.
Yang Weng is an Associate Professor at the School of Electrical, Computer and Energy Engineering, Arizona State University. He leads the U.S.-Israel International Consortium on Energy Cyber Initiative on Cybersecurity R&D and directs a research lab focused on smart grid resilience and machine learning applications. Previously, he was a TomKat Postdoctoral Scholar at Stanford University. Education: Ph.D. in Electrical and Computer Engineering, Carnegie Mellon University M.S. in Machine Learning, Carnegie Mellon University Research: His interdisciplinary work bridges power systems, machine learning, and cybersecurity, emphasizing renewable integration, grid optimization, and cyber-physical resilience. Key themes include physics-informed AI, adversarial robustness in energy infrastructure, and real-time control algorithms for dynamic grids. Publications: Recent articles (2024–2025) demonstrate strong trends in AI-driven grid security, adaptive control under uncertainty, and climate-impact modeling. Dominant domains include neural network applications for stability guarantees, cyber-attack mitigation, and data-efficient renewable integration. Awards: NSF CAREER Award (2021), Amazon Research Award (2023) Best Paper Awards at IEEE SmartGridComm (2012, 2013), PES GM (2014), PMAPS (2016) IEEE Senior Member, Sun Award (ASU), Centennial Award (ASU) Grants & Leadership: Secured DOE, NSF, and AFOSR funding for projects on AI-enhanced grid resilience. Advises PhD/postdoc candidates and chairs the U.S.-Israel Energy Center consortium. Organized international workshops (e.g., ICRDE 2023) and validated research via hardware-in-the-loop experiments. Lab & Team: Directs a research group developing deployable ML solutions for utilities (e.g., OPAL-RT collaborations). Focus areas: cybersecurity toolchains, reinforcement learning for grid control, and anomaly detection architectures.
Song Mei is an Assistant Professor in the Department of Statistics and Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. She received her Ph.D. from Stanford University in 2020 under Andrea Montanari and maintains active research collaborations with institutions including Amazon (as a 2023 Amazon Research Award recipient) and OpenAI (where she is currently on leave). Her research spans the intersection of statistics, machine learning, information theory, and computer science, with particular emphasis on foundational theories for modern AI systems. Key interests include language models, diffusion models, quantum algorithms, and high-dimensional statistics, often leveraging insights from statistical physics literature. Analysis of her recent publications reveals a strong focus on theoretical underpinnings of generative AI, with significant contributions to understanding contrastive pre-training (CLIP), attention mechanisms in LLMs, and mathematical foundations of diffusion models. Her work demonstrates consistent interdisciplinary connections between statistical theory and practical AI development. Sloan Research Fellowship (2025) Noether Early Career Scholar Award (2025) Google Research Scholar Award (2024) Amazon Research Award (2024) She actively advises graduate students through MA programs and has secured significant research grants including Amazon Research Awards. Her work with AGI Labs at Amazon demonstrates applied impact of theoretical research. Current projects focus on mechanistic interpretability of large language models and mathematical frameworks for generative AI. Professor Mei leads research on the statistical principles behind frontier AI models, with particular focus on developing rigorous theoretical frameworks for understanding emergent behaviors in large-scale systems.
Alfred O. Hero, III is the John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science and the R. Jamison and Betty Williams Professor of Engineering at the University of Michigan, Ann Arbor. He is currently on leave from the University of Michigan as a Program Director in the CISE Directorate at the National Science Foundation. His primary appointment is in the Department of Electrical Engineering and Computer Science (EECS), with secondary appointments in the Department of Biomedical Engineering and the Department of Statistics. He is also affiliated with the UM Center for Computational Medicine and Bioinformatics (CCMB), the UM Graduate Program in Applied and Interdisciplinary Mathematics (AIM), the UM Applied Physics Program, and the Michigan Institute for Data Science (MIDAS), which he co-founded from 2015-2018. Hero's research focuses on data science, developing theory and algorithms for multimodality data collection, fusion, analysis and visualization that use statistical machine learning and distributed optimization. His work has applications in wearable technologies for personalized health and predictive medicine, spatio-temporal networks in biology, climate, and social discourse, anomaly detection, and data analysis for international security. His recent research interests include high dimensional spatio-temporal data analysis, multimodal data integration, statistical signal processing, and machine learning, with particular emphasis on predictive mathematical models for biological and physical sciences, social networks, network security and forensics, and personalized health and disease. His recent publications demonstrate a strong focus on high-dimensional statistical methods, contrastive learning, neural network optimization, change detection in temporal graphs, and applications in microbiome analysis and epidemic modeling. The research spans theoretical foundations in information theory and statistical learning while addressing practical applications across multiple domains. Scientific Awards: IEEE Signal Processing Society Best Paper Award (1998) Best Original Paper Award from Journal of Flow Cytometry (2008) Best Magazine Paper Award from IEEE Signal Processing Society (2010) SPIE Best Student Paper Award (2011) IEEE ICASSP Best Student Paper Award (2011) IEEE Signal Processing Society Technical Achievement Award (2014) IEEE Signal Processing Society Society Award (2015) IEEE Fourier Award (2020) University of Michigan Distinguished Faculty Achievement Award (2011) Hero has advised over 60 PhD students and 30 postdocs in areas including modeling, computation, and inference for large scale time varying data in the biosciences. He has received significant research funding from the Department of Energy, Army Research Office, Air Force Office of Scientific Research, and National Science Foundation. He has held leadership positions including President of the IEEE Signal Processing Society (2006-2007), Director of Division IX (Signals and Applications) on the IEEE Board of Directors (2009-2011), and Chair of the Committee on Applied and Theoretical Statistics of the US National Academies (2018-2020).
Kevin C. Zhou is an Assistant Professor in the Department of Biomedical Engineering at the University of Michigan. His research focuses on developing high-performance computational optical imaging systems with unprecedented spatiotemporal throughput, integrating advanced optical instrumentation with machine learning-driven algorithms to analyze big data in biology and medicine. His lab specializes in creating imaging systems capable of capturing high-resolution, high-speed, and high-dimensional datasets. Dr. Zhou holds a Ph.D. in Biomedical Engineering from Duke University (NSF GRFP Fellow) and a B.S. in Biomedical Engineering from Yale University (Barry Goldwater Scholar). Prior to joining U-M, he was a Schmidt Science Fellow and postdoctoral researcher at UC Berkeley. Key research areas include: High-throughput microscopy (gigapixel-scale systems) 3D tomographic imaging Light field and Fourier-based imaging modalities Machine learning for image reconstruction and analysis Biomedical applications in cellular/molecular imaging His recent work has advanced technologies like multi-camera array microscopes (MCAM/MCAS) and Fourier light field mesoscopes, achieving video-rate 3D imaging of freely moving organisms. These innovations enable applications in digital cytopathology, behavioral tracking, and high-content biological studies. Notable awards include the NSF Graduate Research Fellowship and Barry Goldwater Scholarship. His research has been featured in top journals and conferences with a focus on advancing optical imaging hardware and computational pipelines.
Benyuan Liu is a Professor at the Miner School of Computer and Information Sciences within the Kennedy College of Sciences at the University of Massachusetts Lowell . He serves as Director and Graduate Coordinator for Ph.D. programs, with expertise in Data and Computer Communication Networks, Mobile and Wireless Networks, and Internet Technologies & Applications. Education: B.S., University of Science and Technology of China M.S., Yale University Ph.D., University of Massachusetts Amherst His research focuses on Artificial Intelligence in Medical Imaging , Deep Learning for Endoscopy , and Edge Computing Systems . Recent work includes automated lesion detection, 3D reconstruction from sensor data, and predictive models for financial and reproductive health domains. The 15 most recent publications highlight applications of deep learning in medical diagnostics (thyroid nodules, gastric lesions, dental caries), computer vision (attention mechanisms, transformers), and financial technology (market psychology analysis). Technical themes include mmwave radar processing, diffusion models for synthetic data, and multi-scale feature extraction. Benyuan Liu leads the Computer Networking Lab and CHORDS initiative at UMass Center for Digital Health. His work bridges network optimization with healthcare AI , emphasizing real-time systems and portable diagnostics.
Michael U. Gutmann is a Senior Lecturer in Machine Learning at the School of Informatics, University of Edinburgh, and a member of the Institute for Adaptive and Neural Computation. His research lies at the intersection of machine learning, statistics, and scientific applications, with a focus on developing inference methods for complex and implicit models. Education: PhD in Computational Neuroscience, University of Tokyo MSc in Engineering and Applied Mathematics, Swiss Federal Institute of Technology (ETH) Zurich MSc, Ecole Centrale Paris His primary research interests include Bayesian inference, likelihood-free inference, optimal experimental design, unsupervised learning, and applications in computational biology and neuroscience. He is best known for introducing Noise-Contrastive Estimation (NCE), a foundational technique for training unnormalized statistical models. His recent work spans variational inference, density ratio estimation, flow models for missing data, and AI-driven experimental design in behavioral and biological sciences. His publications, including in NeurIPS , ICML , JMLR , and eLife , demonstrate a strong emphasis on methodological innovation for scientific discovery. He has contributed to open-source tools such as ELFI (Engine for Likelihood-Free Inference) and developed practical implementations of robust inference algorithms. Scientific Awards: No specific awards listed in the provided texts. Michael Gutmann actively supervises students and collaborates with leading researchers in machine learning and computational biology. He has secured research funding from EPSRC and BBSRC for projects in generative modeling and infectious disease epidemiology. He teaches advanced courses such as Probabilistic Modelling and Reasoning and Data Mining, reflecting his deep engagement with both theoretical and applied aspects of machine learning. Labs and Research Groups: Institute for Adaptive and Neural Computation (ANC), University of Edinburgh Former affiliations with Department of Mathematics and Statistics and Department of Computer Science at the University of Helsinki and Aalto University
Joemon M Jose is Professor of Information Retrieval at the University of Glasgow's School of Computing Science. His research develops adaptive information retrieval systems, multimodal interaction techniques, and machine learning approaches for recommender systems and social media analysis. Current work explores reinforcement learning frameworks combined with large language models for recommendation, neural approaches to multimodal representation learning, and affective computing for engagement prediction. Recent innovations include LLM-driven policy optimization and transformer-based sequential recommendations. Contributions span personalized search, diversity-aware retrieval, and evaluation methodologies. Collaborative projects investigate cross-modal alignment, temporal query modeling, and fairness in information access systems.
Georgia Zellou is an Associate Professor in the Department of Linguistics at the University of California, Davis, where she co-directs the Phonetics Lab and conducts award-winning research at the intersection of phonetics, speech perception, and human-AI interaction. Her work investigates how phonetic detail is cognitively represented through variations in speech production, with significant contributions to understanding speech alignment with voice assistants, face-masked speech intelligibility, and cross-linguistic perception of synthetic voices. Her academic credentials include a Ph.D. in Linguistics from the University of Colorado at Boulder (2012), an M.A. in Linguistics from Stony Brook University (2007), and a B.A. in Linguistics & Anthropology from the University of Florida (2005, Cum Laude, Phi Beta Kappa). Ph.D., Linguistics, University of Colorado at Boulder (2012) M.A., Linguistics, Stony Brook University (2007) B.A., Linguistics & Anthropology, University of Florida (2005) Professor Zellou's research program centers on laboratory phonology approaches to real-world communication challenges, examining how acoustic-phonetic details influence speech perception across contexts. Her studies span speech alignment with voice-AI systems (e.g., Amazon Alexa), sociophonetic variation in bilingual speech, and the cognitive mechanisms underlying perceptual compensation for coarticulation. She employs experimental methods including eye-tracking, acoustic analysis, and perceptual testing to uncover how phonetic variation functions pragmatically in human communication and human-machine interaction. Analysis of her 15 most recent publications (2023-2025) reveals three dominant research trajectories: (1) human-AI voice interaction dynamics, including prosodic alignment and social evaluation of TTS voices; (2) intelligibility optimization in challenging contexts (face masks, clear speech for diverse listeners); and (3) cross-linguistic phonetic variation in vowelless words and consonant clusters. These works consistently bridge theoretical phonology with applied speech technology, demonstrating how fine-grained phonetic detail influences communication effectiveness in both human-human and human-machine contexts. Her scientific recognition includes: Fulbright Scholar (2022) for research in France Chancellor’s Award for Excellence in Undergraduate Mentoring (2019) Fellow of the Linguistic Society of America (2020) Amazon Faculty Research Award (2019) for Alexa-related speech studies Dean’s Fellow designation at UC Davis (2020-2023) Professor Zellou maintains an active mentoring practice recognized with the Chancellor’s Award, supervising undergraduate researchers in the Phonetics Lab while teaching core linguistics courses from introductory to advanced graduate levels. Her research program is supported by competitive grants including NSF funding, Amazon Research Awards, and UC Davis internal grants (Hellman Foundation, ISS Junior Faculty Grant), reflecting the translational value of her work for speech technology development. She has co-directed major initiatives including the 2019 LSA Linguistic Institute. The Phonetics Lab she co-leads serves as a hub for experimental phonetics research, focusing on speech production-perception relationships through projects investigating vocal accommodation to voice assistants, nasal coarticulation dynamics, and cross-linguistic prosody. Current collaborations with industry partners aim to implement human speech adaptation principles into voice assistant design to enhance naturalness and engagement.
