Find Academic Supervisors

• Epidemiologic Factors
• Epidemiologic Methods
• Glioblastoma
• +7 more

Katsuhito Yasuno is a Research Scientist in the Department of Neurosurgery at Yale School of Medicine. He works within the Gunel Lab, focusing on neurogenetic research related to brain disorders, vascular conditions, and tumors. His work involves genomic analyses and molecular studies to understand the genetic basis of various neurological conditions. Education: Postdoctoral Fellow, Japan Science and Technology Corporation (2008) Postdoctoral Fellow, Japan Biological Informatics Consortium (2006) Postdoctoral Fellow, Tokai University School of Medicine (2004) PhD in Theoretical Physics, Tokyo Institute of Technology (2002) MS in Theoretical Physics, Tokyo Institute of Technology (1999) BS in Physics, Tokyo Metropolitan University (1997) Dr. Yasuno's research spans multiple areas of neurogenetics and molecular neuroscience. His primary interests include epidemiologic factors and methods related to brain tumors (particularly glioblastoma and meningioma), intracranial aneurysms, nervous system diseases and malformations, and vascular diseases. His physics background contributes to sophisticated analytical approaches in genetic research. His publication record shows consistent contributions to understanding the genetic mechanisms underlying neurological disorders, with recent work focusing on meningioma genetics, brain malformations, and vascular disorders. The research demonstrates a progression from basic genetic discovery to translational applications, with particular emphasis on molecular pathways that could serve as therapeutic targets. Dr. Yasuno collaborates extensively with leading researchers in the field, most notably with Dr. Murat Günel (28 common publications) and Dr. Kaya Bilguvar (34 common publications). His work involves both computational analysis and laboratory validation of genetic findings. As part of the Gunel Lab, Dr. Yasuno contributes to the lab's three major research interests: developmental neurogenetic disorders, neurovascular disorders, and brain tumor biology. The lab utilizes advanced genomics techniques and bioinformatic analysis for gene discovery followed by functional studies.

Hongwei WangView profile

Researcher

• Natural Language Processing
• Interconnected Systems
• Large Language Models
• +6 more

Dr. Hongwei Wang is a Senior Researcher at Tencent AI Lab Seattle , specializing in applied machine learning for Natural Language Processing and Interconnected Systems . His work bridges Knowledge Graphs , Recommender Systems , and Graph Neural Networks , with a focus on large language models and retrieval-augmented generation. Ph.D. (2018), B.E. (2014) in Computer Science from Shanghai Jiao Tong University Postdoctoral Researcher : Stanford University (2019-2021), University of Illinois Urbana-Champaign (2021-2022) Dr. Wang’s research explores integrating Knowledge Graphs with Graph Neural Networks to enhance recommendation systems, language models, and information retrieval. His work spans Retrieval-Augmented Generation , Representation Learning , and GAN-based Graph Modeling , with recent papers on State-Space Exploration for LLM agents and Semantic Watermarking . His 15 most recent publications (2022-2024) focus on Retrieval Granularity , Interactive Memory , and Agent Systems , with keywords spanning Computer Science , Machine Learning , and Knowledge Graphs . Trends highlight advancements in Token-Level Semantic Matching , Schema-Guided Event Prediction , and Multi-Document Summarization . Scientific Awards: 2020 CCF Outstanding Doctoral Dissertation Award 2018 Google Ph.D. Fellowship Dr. Wang contributes to open-source projects like DKN and RippleNet , with 11 repositories on GitHub. He actively engages in Knowledge Graph Conferences (KDD, WWW, AAAI) and studies Chinese Classical Poetry and Film Arts .

Natalia Díaz RodríguezView profile

Assistant Professor

• Artificial Intelligence
• Deep Learning
• Reinforcement Learning
• +6 more

Natalia Díaz Rodríguez is an Assistant Professor of Artificial Intelligence at ENSTA ParisTech, where she works in the Computer Science and Systems Engineering department within the Autonomous Systems and Robotics Lab (U2IS). She is also affiliated with the INRIA Flowers team, focusing on developmental robotics. Her research spans deep learning, reinforcement learning, continual learning, and symbolic AI, with applications in explainable AI, computer vision, and robotics for social good. Her academic background includes a double PhD in Artificial Intelligence from Abo Akademi University and the University of Granada, alongside MSc degrees in Soft Computing and Computer Engineering from the University of Granada. She contributes to interdisciplinary AI, particularly in robotics, ethics, and healthcare applications, and co-organizes workshops on continual learning. Double PhD in Artificial Intelligence (2015), Abo Akademi University and University of Granada Doctoral diploma on Innovation and Entrepreneurship (2017), EIT Digital MSc in Soft Computing and Intelligent Systems (2012), University of Granada MSc in Computer Engineering (2010), University of Granada Her recent publications focus on trustworthy AI, including bias identification, counterfactual explanations, and continual learning strategies, reflecting her commitment to ethical and robust AI systems. She also explores AI applications in structural engineering, climate visualization, and financial risk assessment, emphasizing practical deployment and interpretability.

Mikhail GelfandView profile

Professor

• molecular evolution
• comparative genomics
• systems biology
• +7 more

Mikhail Gelfand is a Full Professor and Director of the Center for Molecular and Cellular Biology at Skolkovo Institute of Science and Technology (Skoltech), where he also serves as Vice President for Biomedical Research. His distinguished career spans multiple prestigious institutions including Lomonosov Moscow State University and the Higher School of Economics. His educational background includes: 1985: MSc in mathematics (functional analysis) 1993: PhD in physics-mathematics (biophysics) 1998: DSc in biology (molecular biology) 2007: full professor (bioinformatics) Professor Gelfand's research focuses on molecular evolution, comparative genomics, systems biology, and metagenomics. His work examines eukaryotic processes including alternative splicing, mRNA editing, and chromatin structure, as well as bacterial genome evolution and transcription regulation. His lab combines data on three-dimensional chromatin structure, epigenetic states, and gene expression to obtain an integrated view of genome functioning across diverse organisms from humans to amoebae. One major research direction focuses on the evolution of transcript splicing and editing, while comparative analysis of bacterial genomes yields functional annotations of novel enzymes, transporters, and transcription factors. His recent publications demonstrate a strong focus on RNA editing in cephalopods, bacterial genome analysis, and computational approaches to understanding chromatin structure. The work spans molecular biology, evolutionary biology, and bioinformatics, with particular emphasis on how RNA editing contributes to adaptation and molecular evolution across metazoans. His research shows how edited adenines are more frequently substituted with guanine in evolution than their unedited counterparts, suggesting RNA editing may enhance adaptation. His notable awards include: The President of Russian Federation's Award for Young Doctors of Science (2000) The "Best Scientist of the Russian Academy of Sciences" award (2004) A. A. Baev Prize in Genomics and Genoinformatics (2007) Member of Academia Europaea (2010) As Director of the Center for Molecular and Cellular Biology, Professor Gelfand leads a research group that combines computational and experimental approaches to study genome function and evolution. His lab's work has significant implications for understanding molecular mechanisms of evolution and adaptation across diverse biological systems, from bacteria to complex eukaryotes. His research on metagenomics extends to practical applications in areas including coral disease, aphids, and oil wells.

Marat I. LatypovView profile

Assistant Professor

• Materials Science
• Computational Materials Science
• Materials Informatics
• +4 more

Marat I. Latypov serves as Assistant Professor in the Department of Materials Science and Engineering at the University of Arizona's College of Engineering. He is also a member of the Applied Mathematics Graduate Interdisciplinary Program and leads the Materials Informatics Lab. His research spans computational materials science, sustainable alloy design, and machine learning applications for materials development. Dr. Latypov holds a PhD in Materials Science and Engineering from Pohang University of Science and Technology (POSTECH, South Korea, 2014) and a Dipl.-Ing. in Engineering Physics from Ufa State Aviation Technical University (Russia, 2011). His postdoctoral training included appointments at Georgia Tech/CNRS in France and the University of California, Santa Barbara. His research focuses on materials informatics , physics-informed machine learning , and sustainable structural alloys . Key methodologies include graph neural networks for polycrystal mechanics, vision transformers for microstructure representation, and adaptive experimental design for materials optimization. Recent work emphasizes circular economy applications through construction waste recycling and copper mine tailings valorization. Analysis of his publication record reveals strong emphasis on computational microstructure-property linkages (35% of recent work), machine learning for materials design (30%), and sustainable materials processing (25%), with growing integration of large language models for materials knowledge extraction. NSF CAREER Award (2025) : For damage control in recycled aluminum alloys ISTI Distinguished Faculty Scholar (2024) : At Los Alamos National Laboratory Novelis Hackathon First Prize (2021) : Computer vision application Acta Materialia Outstanding Reviewer (2018) Young Researcher Award (2017) : NanoSPD7 Conference Dr. Latypov advises PhD students including Herbold Fellow Zhuocheng Huang and leads projects funded by NSF and the Grantham Foundation. Current initiatives include chalcopyrite leaching optimization for copper mining and graph neural network development for fatigue prediction. His Materials Informatics Lab maintains collaborations with Los Alamos National Laboratory, MIT, and industry partners including Novelis. The lab operates at the intersection of metallurgy , machine learning , and high-performance computing , with capabilities spanning deep learning, Bayesian inference, and cloud-based computational infrastructure. Recent news highlights participation in CODAS-HEP summer school and publication of vision transformer work in Acta Materialia.

Brandon SchmandtView profile

Professor

• Seismology
• Tectonics
• Volcanology
• +4 more

Brandon Schmandt is a Professor in the Department of Earth, Environmental and Planetary Sciences at Rice University, where he leads research using seismology to investigate Earth systems. His work integrates interdisciplinary approaches, data science, and numerical modeling to study tectonic processes, magmatic systems, and environmental interactions. His educational background includes a PhD in Geological Sciences from the University of Oregon (2011) and a BA in Environmental Studies from Warren Wilson College (2006). Dr. Schmandt's research focuses on seismology, tectonics, volcanology, and surface processes , with emphasis on seismic imaging of subsurface structures. His group employs innovative time-series analysis and field projects to resolve geologic history and contemporary Earth dynamics, particularly examining fault zones, magmatic reservoirs, and deep convective processes. Key methodologies include dense seismic arrays and machine learning applications. Analysis of his recent publications (2023-2025) reveals dominant trends in seismic event discrimination (earthquakes vs. explosions), magmatic system imaging (Yellowstone, Cascades), and global mantle structure studies. There is strong emphasis on induced seismicity, machine learning applications, and high-resolution imaging of Earth's discontinuities using dense arrays. His distinguished honors include: Aki Award of the AGU Seismology Section GSA Donath Medal AGU Macelwane Medal Body Dr. Schmandt directs an active research group conducting field projects across diverse settings including the Raton Basin, Yellowstone, Antarctica, and the Caribbean. While specific student advisees and grant details aren't provided in available materials, his group's work involves collaborative data collection, advanced computational modeling, and development of novel seismic analysis techniques applicable to both natural and anthropogenic seismic sources. The research program maintains focus on magmatic systems beneath volcanic regions, induced seismicity mechanisms, and global mantle structure using dense node arrays and interdisciplinary approaches to address fundamental questions in Earth dynamics.

• Machine Learning
• Educational Data Mining
• Time Series Forecasting
• +5 more

Irena Koprinska is a prominent researcher at the University of Sydney with over 150 publications from 1996 to 2025. Her work spans multiple interdisciplinary domains with significant contributions to machine learning applications in educational technology, time series forecasting, and health informatics. She maintains strong research collaborations, particularly with Kalina Yacef (38 joint publications), Mashud Rana (26 papers), and Bryn Jeffries (22 papers), indicating leadership in her research group. Her research interests focus on practical applications of machine learning across diverse domains. In educational data mining, she has pioneered methods for predicting student performance in programming courses, analyzing syntax errors, and developing automated hint generation systems. Her work in time series forecasting has made significant contributions to solar power prediction using advanced neural network architectures. Additionally, she has applied machine learning techniques to medical domains, particularly in sleep disorder detection and analysis. The analysis of her 15 most recent publications (2022-2025) reveals a continued focus on educational technology and time series analysis, with increasing attention to interpretable methods and health applications. Her work demonstrates a consistent trajectory of applying sophisticated machine learning techniques to solve real-world problems across multiple domains, with particular emphasis on creating practical tools for education and renewable energy management. Notable Research Contributions: Development of the HINTS framework for automated programming hint generation Innovative approaches to multistep-ahead time series forecasting Applications of deep learning to sleep disorder detection Methods for predicting student performance in programming education Her publication record in top venues including Machine Learning journal, AIED, EDM, and IJCNN demonstrates significant impact in both machine learning and educational technology communities. The consistent output of high-quality research over nearly three decades indicates sustained scholarly productivity and leadership in her fields of expertise.

Lyudmila MihaylovaView profile

Professor

• Signal Processing
• Bayesian Methods
• Monte Carlo Methods
• +9 more

Professor Lyudmila Mihaylova is a distinguished academic at the University of Sheffield's School of Electrical and Electronic Engineering, where she holds the position of Professor of Signal Processing and Control. She has established herself as a leading researcher in the fields of signal processing, Bayesian methods, and autonomous systems, with significant contributions to particle filtering techniques for intelligent transportation systems. Her work bridges theoretical developments with practical applications across multiple domains including transportation, healthcare, and industrial automation. Prof. Mihaylova's research interests center on nonlinear filtering, sequential Monte Carlo methods, statistical signal processing, and sensor data fusion. Her work spans both theoretical advancements and practical implementations, with particular focus on high-dimensional problems including vehicular traffic flow estimation, image processing, and localization in sensor networks. She has extensive experience with various image modalities such as optical, thermal, LIDAR, SAR, and hyperspectral imaging. Her group actively develops novel methods for autonomous intelligent systems focusing on sensing, tracking, decision making, and machine learning applications. Analysis of Prof. Mihaylova's recent publications reveals a strong trend toward uncertainty quantification in machine learning models, particularly for safety-critical applications. Her work increasingly integrates traditional signal processing techniques with modern deep learning approaches, with applications spanning sewer inspection robotics, medical diagnostics (particularly sleep apnea detection), UAV swarm tracking, industrial manufacturing, and autonomous vehicle systems. A significant portion of her recent research focuses on developing robust methods that can handle incomplete or outlier-corrupted data while providing reliable uncertainty estimates. Among her notable professional achievements: President of the International Society of Information Fusion (ISIF) Senior member of the IEEE Signal Processing Society Associate Editor for IEEE Transactions on Aerospace and Electronic Systems Associate Editor for Elsevier Signal Processing Journal Prof. Mihaylova has successfully mentored numerous PhD students and postdoctoral researchers, many of whom have gone on to prominent academic and industry positions. Her research has been supported by major funding bodies including EPSRC, EU, MOD/DSTL, and industry partners, with recent projects including 'Protecting Environments with UAV Swarms' (InnovateUK, 2022-2024), 'ShiRAS: Towards Safe and Reliable Autonomy in Sensor Driven Systems' (NSF-EPSRC, 2019-2023), and 'Confident safety integration for Cobots' (Lloyd's Register Foundation, 2019-2020). Her research group follows a collaborative approach with the philosophy 'We share knowledge, we grow.' Prof. Mihaylova maintains active research collaborations with institutions worldwide and has held previous academic positions at Lancaster University (2006-2013) and University of Bristol (2004-2006), along with research visiting positions at the University of Ghent, Katholic University of Leuven, and the Bulgarian Academy of Sciences.

Sophie CaronView profile

Associate Professor

• Neuroscience
• Neurobiology
• Sensory Systems
• +5 more

Sophie Caron is an Associate Professor in the Department of Biological Sciences at the University of Utah, where she leads a research laboratory investigating fundamental mechanisms of brain function using Drosophila melanogaster as a model system. Her work focuses on how the brain generates internal representations of the external world, stores memories, and translates these into behavior through multisensory integration. Education: B.S. from Université de Montréal Ph.D. from New York University Dr. Caron's research centers on the Drosophila mushroom body—a critical brain center for learning and memory—with emphasis on multisensory integration mechanisms. Her lab investigates how the brain combines information from multiple sensory modalities (olfaction, vision, etc.) to form unified percepts, challenging traditional views of sensory processing. Key discoveries include the demonstration that mushroom body connectivity follows near-random patterns that maximize memory capacity, and the identification of cross-modal sensory pathways beyond olfaction. Current work explores evolutionary adaptations in neural circuits across Drosophila species and developmental mechanisms establishing sensory wiring. Analysis of her 15 most recent publications (2019-2024) reveals three dominant research trajectories: (1) high-resolution mapping of Kenyon cell inputs using advanced techniques like dye electroporation, (2) computational modeling of how connectivity patterns shape sensory representation and learning, and (3) evolutionary studies of circuit architecture across Drosophila species. Her work consistently bridges experimental neuroanatomy with theoretical frameworks, emphasizing the interplay between random and structured wiring principles in neural circuit design. Scientific Awards: NSF CAREER Award (2021) for research on brain size evolution and neuronal circuit adaptation Dr. Caron mentors graduate students in the University of Utah's Molecular Biology Program and directs an active laboratory utilizing genetic, imaging, and behavioral approaches. Her research program is supported by competitive grants including the NSF CAREER award, with collaborations spanning neuroscience and evolutionary biology. Current projects investigate multisensory integration mechanisms, evolutionary drivers of neural circuit specialization, and developmental basis of sensory wiring. The Caron Lab maintains specialized facilities for Drosophila neurogenetics, advanced microscopy, and behavioral analysis. Her team collaborates with the University of Utah's Brain Institute and Center for Cell and Genome Science, contributing to interdisciplinary initiatives in neural circuit mapping and evolutionary neuroscience. Ongoing work explores how sensory representations evolve in response to ecological pressures and how circuit architecture enables flexible behavior in complex environments.

Mengsen ZhangView profile

Assistant Professor

• Complex Systems
• Neuroscience
• Computational Mathematics
• +5 more

Mengsen Zhang is an Assistant Professor at Michigan State University (MSU) in the Department of Computational Mathematics, Science and Engineering and the Neuroscience Program. She bridges complex systems science, neuroscience, computational mathematics, and topological data analysis (TDA) to study brain dynamics and coordination mechanisms across scales. Education: B.S. in Psychology and Pharmaceutical Sciences (Peking University); M.S. in Criminology (University of Pennsylvania); Ph.D. in Complex Systems and Brain Sciences (Florida Atlantic University, with Drs. Emmanuelle Tognoli and J. A. Scott Kelso). Postdoctoral Training: Stanford University (with Dr. Manish Saggar) and University of North Carolina at Chapel Hill (with Dr. Flavio Frohlich). Her research focuses on the intersection of topological data analysis and dynamical systems, particularly in understanding brain oscillations, neural networks, and social coordination dynamics. She explores how third-party interventions can stabilize or disrupt coordination in biological and social systems, using both empirical and theoretical approaches. Her recent publications (2022–2025) highlight applications of transcranial alternating current stimulation (tACS) in psychiatric disorders, metastability in brain dynamics, and novel computational methods for analyzing neural and behavioral data. These works span neuroscience, psychiatry, and computational modeling. She teaches courses such as CMSE 381: Fundamentals of Data Science Methods (MSU) and STT 381: Fundamentals of Data Science Methods, integrating computational tools into academic training.

• Algorithms
• Algorithmics
• Big Data
• +7 more

Stephen Alstrup is a Professor in the Algorithms and Complexity section at the Department of Computer Science (DIKU), University of Copenhagen, Faculty of Science. His research bridges theoretical computer science with practical applications in modern computational challenges. His primary research interests include: Algorithm design and analysis Graph algorithms and data structures Big Data processing techniques Streaming algorithms and Internet distribution Theoretical foundations with practical implementations Alstrup's work demonstrates how theoretical algorithm research can lead to real-world applications, as evidenced by his development of Octoshape technology for large-scale Internet streaming. His research spans from fundamental theoretical problems to applications in Big Data, cloud computing, and information retrieval systems. He has published extensively with 93 research outputs including journal articles, conference proceedings, and books. His recent work focuses on graph spanners, semantic hashing, recommendation systems, and universal graph structures, showing continued productivity in theoretical computer science. Alstrup actively engages with industry and media, contributing to discussions about Big Data applications, technology innovation, and how businesses can collaborate with universities to access cutting-edge knowledge and funding opportunities. His work has been featured in 10 media contributions discussing practical applications of algorithms in education, municipal IT projects, and business innovation.

• Business Process Management
• Process Mining
• Complex Event Processing
• +5 more

Prof. Dr. Matthias Weidlich is a faculty member at Humboldt University of Berlin within the Institute of Computer Science under the Faculty of Mathematics and Natural Sciences . His research focuses on Process Mining , Complex Event Processing , and Data Privacy with applications in Business Process Management and Scientific Workflows . Research Interests: Business Process Management and Process Mining Complex Event Processing and Stream Data Analysis Data Privacy and Security in Process Systems Scientific Workflow Systems and User Behavior Heterogeneous Network Embeddings Algorithm Design and Optimization Recent Publications (2023-2025) demonstrate expertise in: Efficient stream processing techniques Privacy-preserving process mining frameworks Scientific workflow analysis tools Graph neural network applications Multi-modal data integration Adaptive querying systems Contact: Office: Unter den Linden 6, 10099 Berlin Phone: 030 2093-41277 Email: matthias.weidlich@hu-berlin.de Web: hu.berlin/data

• Experimental Gravity
• Astrophysical Signals
• Nuclear Physics
• +8 more

Reed Essick is an Assistant Professor at the Canadian Institute for Theoretical Astrophysics (CITA), University of Toronto. His research focuses on experimental gravity, astrophysical signals, and nuclear physics, with particular emphasis on neutron stars, black holes, and gravitational waves. He develops advanced statistical methods like hierarchical Bayesian inference and nonparametric analysis for interpreting observational data from pulsars and gravitational wave detectors. Dr. Essick collaborates extensively with international observatories such as LIGO, Virgo, and KAGRA, contributing to cutting-edge projects like multimessenger astronomy and precision cosmology. His work bridges computational astrophysics with observational techniques, addressing fundamental questions about dense matter and strong-field gravity. Key contributions include studies on gravitational wave equation-of-state constraints, pulsar timing analysis, and the application of machine learning to detector data. His research leverages both ground-based interferometers and space-based observations to explore extreme astrophysical environments.

Klas TybrandtView profile

Professor

• Soft Electronics
• Bioelectronics
• Organic Electronics
• +3 more

Professor Klas Tybrandt leads the Soft Electronics group at Linköping University's Laboratory of Organic Electronics (LOE), focusing on stretchable materials and bioelectronics integration with the human body. He holds a Master's (2007) and PhD (2012) from LiU, followed by postdoctoral research at ETH Zurich (2013-2014). Promoted to Professor in 2024, he oversees the Wallenberg Wood Science Center (WWSC), WISE, and AFM initiatives. His work spans Stretchable batteries Neural interfaces Organic thermoelectrics and has earned awards like the ERC Consolidator Grant (2023) and Wallenberg Academy Fellow (2022). His research emphasizes sustainable materials and energy harvesting. Education: PhD in Organic Bioelectronics (LiU, 2012) ETH Zurich Postdoc (2013-2014) Research interests include soft electronics and biohybrid systems , with breakthroughs in gold nanowire electrodes and stretchable electrofluid batteries . Over 70 peer-reviewed articles and 7 patents underscore his contributions to organic electronics. Grants & Funding: ERC Consolidator Grant (€2M) Wallenberg Academy Fellowship (SEK 36M) Labs/Teams: Head of Soft Electronics group (LOE), active in WWSC and WISE consortia.

Marco SerafiniView profile

Assistant Professor

• Database Systems
• Distributed Systems
• Data Science
• +3 more

Marco Serafini is an Assistant Professor in the Department of Computer Science at the University of Massachusetts Amherst, affiliated with the College of Information and Computer Sciences (CICS). He leads the DREAM Lab (Data systems Research for Exploration, Analytics, and Modeling) and is part of the Center for Data Science. Prior to UMass, Serafini worked as a Senior Scientist at the Qatar Computing Research Institute (QCRI) and held a postdoctoral fellowship at Yahoo! Research in Barcelona. He earned his PhD in Computer Science from TU Darmstadt (Germany), where his thesis was recognized through nominations for best thesis awards across German, Swiss, and Austrian computer science societies. His research focuses on the intersection of database systems, distributed systems, and data science, emphasizing scalable architectures for big data analytics and machine learning. Key areas include computation pushdown in cloud DBMSs, graph neural network training systems, and efficient graph pattern matching. His work addresses challenges in tail latency mitigation, resource optimization, and transparent scaling of ML models. Serafini has contributed to influential systems like Arabesque (for distributed graph mining), E-Store (elastic partitioning), and Aion (event-time stream processing). He has been awarded an NSF CNS Core grant to advance scalable GNN training. His publications span top venues such as ACM SIGOPS, VLDB, and ICDE, reflecting his expertise in both theoretical foundations and practical system implementations. Professional recognition includes thesis nominations from major computer science societies and sustained contributions to open-source projects in distributed computing. Serafini advises students through the DREAM Lab, focusing on preparing the next generation of data systems researchers.