Find Academic Supervisors

• Signal Processing
• Error Correction Coding
• Information Theory
• +3 more

Professor Sarah Johnson is a distinguished academic in the School of Engineering at the University of Newcastle, specializing in Electrical and Computer Engineering. She holds a PhD in Electrical Engineering from the same institution and has been awarded prestigious fellowships including an ARC Future Fellowship. Her research applies engineering solutions to digital information processing and error correction coding, with significant applications in secure communications and biomedical technologies. Her research interests span: Signal processing for secure data transmission Error correction codes for reliable communication systems Biomedical applications of digital signal processing Quantum-enabled secure communications Internet of Things communication protocols Her publications primarily focus on information theory, wireless communication systems, and biomedical engineering. Recent work shows strong emphasis on index coding optimizations, quantum cryptography implementations, and biomedical signal processing techniques for neuroimaging applications. Significant awards include: NSW Premier's Prize for Excellence (2017) Pro Vice-Chancellor's Research Excellence Award (2007) Professor Johnson has secured multiple ARC Discovery grants and industry-sponsored projects, including collaborations with Quintessence Laboratories on quantum key distribution. She co-founded HunterWISE to promote women in STEM fields and has supervised numerous graduate students across engineering disciplines. She leads interdisciplinary collaborations with biomedical researchers on rehabilitation technologies and neuroimaging analysis, developing systems to monitor recovery processes and brain activity patterns.

• Planetary Seismology
• Geophysics
• Mars Geology
• +3 more

Nikolaj Louis Dahmen is a Lecturer at the Institute of Geophysics, ETH Zurich, within the School of Earth and Planetary Sciences. His research focuses on Martian seismology, leveraging data from NASA’s InSight mission to analyze seismic activity and crustal structure. Dahmen specializes in applying machine learning techniques for seismic denoising and event detection, significantly expanding the catalog of marsquakes and improving their analysis. He earned his Doctoral Thesis in 2024, titled 'The Seismicity of Mars Recorded by InSight,' supervised by Prof. Domenico Giardini. His work addresses challenges in low-signal-to-noise ratio environments and contributes to understanding Martian tectonic processes. Education: Doctoral Thesis (2024) at ETH Zurich Key Research Areas: Planetary seismology, machine learning applications, seismic data processing, Martian crustal structure Dahmen’s research has led to advancements in detecting and cataloging marsquakes, including the discovery of the largest recorded marsquake (S1222a). His methods enhance seismic signal analysis by mitigating environmental noise, enabling precise event localization and waveform interpretation. Collaborations include the Marsquake Service (MQS) and projects like MarsQuakeNet, which automate event detection using deep learning. His contributions extend to crustal studies through surface wave analysis and meteorite impact investigations, providing insights into Mars’ subsurface composition. Dahmen’s work bridges planetary science and computational methods, advancing our understanding of Mars’ geophysical dynamics.

Sven ScheweView profile

Professor

• Automata Theory
• Game Theory
• Formal Verification
• +12 more

Sven Schewe is a Professor in the Department of Computer Science at the University of Liverpool, affiliated with the School of Electrical Engineering, Electronics and Computer Science. He leads the AI Section and is a founding member and former leader of the Verification Group. He also has secondary affiliations with the Algorithms, Complexity Theory and Optimisation Group and the Institute for Risk and Uncertainty. Research Interests: His research centers on automata theory and game theory, particularly their applications in the verification and synthesis of reactive and safety-critical systems. He investigates infinite-duration games, automata over infinite words and trees, and develops algorithms and tools for automated verification, synthesis, and learning of optimal control strategies. His work extends to reinforcement learning with formal guarantees, cyber-physical systems, and AI safety. Recent Research Trends: His recent publications demonstrate a strong integration of formal methods with machine learning, particularly in adversarial training, neural network robustness, and model-free reinforcement learning under omega-regular objectives. He also applies formal reasoning to interdisciplinary domains such as chemical space exploration and materials science. Scientific Awards: Finalist for the ERCIM Cor Baayen Award 2010 Dr. Eduard Martin Preis 2009 GI Dissertation Award 2008 Advising and Grants: He actively supervises numerous PhD students and postdoctoral researchers. He is Principal Investigator (PI) or Co-Investigator (CI) on multiple major grants, including EPSRC Programme Grants, Royal Society Fellowships, and Horizon Europe projects. His funded research spans topics such as game theory, verification, synthesis, reinforcement learning, and risk analysis. He has hosted visiting researchers and collaborated internationally with institutions in Germany, France, India, Taiwan, and the US. Labs and Teams: He co-founded and led the Verification Group and previously led the AI Section at the University of Liverpool. These groups focus on formal methods, automata, games, and their applications in AI and safety-critical systems.

Alexander Miserlis HoyleView profile

Researcher

• Computational Social Science
• Natural Language Processing
• Machine Learning
• +6 more

Alexander Hoyle is a Researcher at the ETH Zürich AI Center, concurrently contributing to natural language processing/machine learning and social science groups. He holds a PhD in Computer Science from the University of Maryland (advised by Philip Resnik) and a Master's in Computational Statistics from University College London (advised by Sebastian Riedel and Jeff Mitchell). His research focuses on computational social science, emphasizing methods for latent construct identification (e.g., topic models, ideal point models) and evaluation frameworks grounded in validity. Key areas include bias/fairness in AI, political science applications, and mental health constructs like suicidality. He pioneered frameworks like PairScale (attitude measurement via pairwise comparisons) and TopicGPT (prompt-based topic modeling). Education: PhD in Computer Science, University of Maryland (2020-2023) MS in Computational Statistics & Machine Learning, University College London (2018) Bachelor's degree (pre-PhD details omitted) Research Interests: Combining NLP with social science needs, particularly in evaluation rigor and interpretability. Active in interdisciplinary work between NLP and computational social science (e.g., measuring attitude evolution on Reddit, improving topic model validity). Advocates for human-in-the-loop approaches to address LLM limitations in tasks like document clustering and sentiment analysis. Grants & Projects: Contributed to a landmark $2.2B DOJ settlement on NYC public housing via econometric modeling at The Brattle Group. Active in graduate labor advocacy (Maryland state legislature testimony) and mentorship (Científico Latino's mentorship program). Labs & Teams: Leads initiatives at the ETH Zürich AI Center, collaborating with groups like Microsoft Research (FATE) and AI2's AllenNLP. Involved in multi-university projects (e.g., University of Maryland's Computational Linguistics lab).

Jun ChenView profile

Professor

• Statistical Methods for Microbiome Data
• Single-Cell Sequencing Data Analysis
• High-Dimensional Data Analysis
• +3 more

Jun Chen, Ph.D., is a Professor of Biostatistics in the Department of Quantitative Health Sciences at Mayo Clinic, Rochester, Minnesota. His research focuses on developing robust statistical methods for high-dimensional omics data, particularly microbiome and single-cell sequencing data, with applications in disease subtyping, biomarker discovery, and integrative genomics. Education: Postdoctoral Fellowship in Biostatistics, Harvard School of Public Health Ph.D. in Genomics and Computational Biology (Biostatistics emphasis), University of Pennsylvania M.A. in Statistics, University of Pennsylvania M.E. in Computer Science (Pattern Recognition and Machine Intelligence), Shanghai Jiao Tong University M.D., Fudan University Dr. Chen’s work addresses challenges in microbiome data (phylogeny-constrained, zero-inflated counts) through methods for association testing, subtype discovery, and predictive modeling. He also develops techniques for single-cell sequencing data, including clustering, differential abundance analysis, and multimodal integration, while incorporating biological priors and structural constraints. His collaborative research spans diverse diseases, aiming to quantify microbiome contributions to pathogenicity and inform clinical decision-making via personalized microbiome profiling. Professional Highlights: Associate Editor, Frontiers in Genetics (2021–present) Associate Editor, Statistics in Biosciences (2021–present) Bioinformatics Section Editor, PeerJ (2019–present)

Ethan Katz-BassettView profile

Associate Professor

• Networking
• Distributed Systems
• Internet Measurement
• +7 more

Ethan Katz-Bassett is an Associate Professor in the Electrical Engineering Department at Columbia University, with an affiliation in the Computer Science Department. He is also a member of the Computer Engineering Program and the Data Science Institute's Sense, Collect and Move Data Center. Previously, he held the Andrew and Erna Viterbi Early Career Chair at the University of Southern California, where he led a networking and systems research group. His research focuses on improving the reliability and performance of Internet services through measurement-driven system design, with a focus on networks, distributed systems, and cloud infrastructure. Affiliations: Columbia University (Current), University of Southern California (Former) Key Projects: Edge Fabric (Facebook), Sibyl (Route Oracle), PoiRoot (Route Change Analysis), Odin (Microsoft CDN) His research interests include Internet measurement, routing optimization, content delivery networks (CDNs), TCP performance, and mobile web performance. He has pioneered tools like PEERING, a BGP testbed for scalable experiments, and developed systems such as LIFEGUARD for network failure detection. His work often bridges academia and industry, with collaborations at Google, Microsoft, and Facebook. Ethan has received numerous awards, including the SIGCOMM Rising Star Award (2019), IETF Applied Networking Research Prize (2019), and NSF CAREER Award (2014). His funding includes grants from Google, Facebook, Comcast, and the NSF. He holds a Ph.D. from the University of Washington, advised by Tom Anderson and Arvind Krishnamurthy. Grants and Labs: Works with the M-Lab, RIPE Atlas, and SpeedChecker platforms. Current funding includes Google Faculty Awards, an M-Lab grant, and NSF support.

Joseph GlazView profile

Professor

• Applied Probability
• Geometrical Probability
• Probability Approximations
• +7 more

Joseph Glaz is a Professor in the Department of Statistics at the University of Connecticut. His work focuses on applied probability and statistical methodology, particularly in scan statistics and related inference techniques. Contact: joseph.glaz@uconn.edu Phone: (860) 486-4193 (Storrs Campus) Research highlights: Develops scan statistics for discrete , continuous , and conditional data frameworks Specializes in change-point detection for normal data mean/variance Innovates robust methods for outlier-prone datasets Extends scan statistics to network and graph structures Contributed to foundational texts like the Handbook of Scan Statistics Publication trends include: Scan statistics for genomic data (Hi-C translocation detection) Nonparametric and Bayesian extensions of scan methods Approximations and inequalities for sequential testing Applications in quality control , medical imaging , and sensor networks

Katelyn ByrneView profile

Assistant Professor

• Pancreatic Cancer
• Immunotherapy
• Tumor Microenvironment
• +4 more

Katelyn Byrne is an Assistant Professor at Oregon Health and Science University (OHSU) in the Cell, Developmental and Cancer Biology department, affiliated with the Knight Cancer Institute and Brenden-Colson Center for Pancreatic Care. Her research focuses on the immunobiology of pancreatic cancer, particularly mechanisms bridging innate and adaptive immunity to enhance immunotherapy efficacy. Education: B.A. (2007) from Boston University, Ph.D. (2013) from Dartmouth College, and postdoctoral training as a Senior Parker Fellow. Her work leverages genetically engineered mouse models of pancreatic cancer to study tumor microenvironment dynamics , with clinical translation in immunotherapy trials. Key contributions include CD40 agonism for CD4+ T cell-mediated tumor rejection, immune checkpoint blockade synergy, and spatial transcriptomics for infiltrate heterogeneity. Recent articles (2024) highlight machine learning in immune profiling and MHC class I-deficient tumor targeting. Dr. Byrne is a recipient of the American Cancer Society Post-doctoral Fellowship and Senior Parker Fellowship . She mentors graduate students Margaret Haerr and Yi Zhang in her lab, which collaborates across OHSU’s cancer research centers. Current projects aim to develop novel immunotherapy combinations and T cell infiltration strategies for pancreatic cancer.

John Darrell Van HornView profile

Professor

• Human Neuroimaging
• Brain Health and Disease Biomarkers
• Computational Neuroscience
• +12 more

John Darrell Van Horn is a Professor at the University of Virginia, holding dual appointments in the School of Data Science and the Department of Psychology . He joined UVA in 2019 and is actively engaged in research and teaching, including mentoring capstone projects in the M.S. in Data Science program and teaching brain mapping with MRI. Education: Ph.D. in Psychology, University of London M.S. in Electrical and Computer Engineering, University of Maryland, College Park B.A. in Psychology, Eastern Washington University Dr. Van Horn's research centers on human neuroimaging and the application of data science to understand brain health and disease. His work integrates high-performance computation , multivariate statistics , network theory , and image processing to extract meaningful patterns and biomarkers from brain imaging data. He is a strong advocate for FAIR data principles and open science , promoting transparency and reproducibility in neuroscience research. His publications span top journals including Science , Nature , Nature Neuroscience , NeuroImage , and Biological Psychiatry , reflecting a sustained impact in the fields of neuroscience and data science. The body of work demonstrates a consistent focus on computational modeling of brain structure and function, with applications in psychiatry and cognitive neuroscience. Scientific Recognition: Fellow, Organization for Human Brain Mapping (OHBM) Dr. Van Horn has served as a mentor for capstone research projects in the M.S. in Data Science program. While specific grant funding is not detailed, his research has been widely disseminated and featured in both academic and popular media, indicating successful funding and recognition. His prior academic roles at UCLA, USC, Dartmouth, and NIH fellowships underscore a distinguished career trajectory. He is affiliated with the broader neuroscience and data science communities through publications, open science advocacy, and professional recognition. His lab or research group is not explicitly named, but his work clearly involves advanced computational analysis of neuroimaging datasets, likely involving collaborative teams focused on brain mapping and data sharing initiatives.

Jyoti NangaliaView profile

Professor

• Somatic mutagenesis
• Clonal evolution in blood cancers
• Genomic sequencing in clinical decision-making
• +3 more

Dr. Jyoti Nangalia is a Principal Investigator at the Wellcome-MRC Cambridge Stem Cell Institute and a Cancer Research UK Clinician Scientist at the Wellcome Sanger Institute. She holds a dual affiliation with the Department of Haematology at the University of Cambridge. Dr. Nangalia is also a Consultant Haematologist specializing in myeloproliferative neoplasms (MPN). Her research focuses on somatic mutagenesis, clonal evolution in blood cancers, and the integration of genomic data into clinical practice. Education: MBBS (Medicine) from the University of Cambridge, followed by a PhD at the Cambridge Institute of Medical Research, where she discovered CALR mutations in JAK2-unmutated MPNs. This discovery has become a standard diagnostic criterion in clinical practice. Research Interests: Her lab explores clonal trajectories in blood cancers, methylation dynamics across human lifespan, and personalized cancer prognosis modeling. Recent work includes tracing the evolutionary history of MPNs over decades, revealing disease origins decades before clinical presentation. Grants & Funding: Supported by CRUK, Rosetrees Trust, Alborada Trust, and MPN Research Foundation. Her group collaborates with clinical teams to inform early intervention strategies and refine diagnostic tools. Labs/Teams: The Nangalia Group operates at both the Stem Cell Institute and Sanger Institute, focusing on translational research in haematological malignancies. Advising: Supervises postgraduate students (e.g., Aleksandra Kamizela, Sreeya Kodavali) and mentors visiting researchers in genomic analysis and clonal evolution studies.

• Microbial Ecology
• Plant-Microbe Interactions
• Mycology
• +6 more

Dr. Gian Maria Niccolo’ Benucci is a Data Analyst and Bioinformatician at the Great Lakes Bioenergy Research Center (GLBRC) and the Department of Plant, Soil and Microbial Sciences at Michigan State University (MSU). He holds a Ph.D. in Biology and Biotechnology from the University of Perugia, Italy, with expertise in microbial ecology, plant-microbe interactions, and mycology. His research focuses on holobiont dynamics, microbial community responses to environmental changes, and microbiome manipulation to enhance host health. He has published over 55 peer-reviewed articles and collaborates with 160+ researchers globally. Dr. Benucci's work integrates genomics, bioinformatics, and machine learning to study microbiome structure and function across ecosystems. He co-leads GLBRC's bioinformatics support hours and is involved in the National Microbiome Data Collaborative. His teaching includes courses like Statistics for Biologists (STT464) and modules in Advanced Mycology . Key research areas: Mycorrhizal symbioses, truffle microbiome dynamics, and sustainable agriculture practices. His contributions span from fungal systematics (e.g., Tuber rugosum discovery) to bioenergy crop optimization.

Christopher Stuart HenshilwoodView profile

Professor

• Archaeology
• Human Behavioral Evolution
• Middle Stone Age Research
• +4 more

Christopher Stuart Henshilwood is a Professor and Director of the SFF Centre for Early Sapiens Behaviour (SapienCE) at the University of Bergen. His work focuses on early Homo sapiens behavior in Africa, particularly during the Middle Stone Age (100,000–50,000 years ago). He leads interdisciplinary research integrating archaeology, climate science, and cognitive studies to explore the origins of modern human behavior, including symbolic culture, technological innovation, and environmental adaptation. Key sites include Blombos Cave and Klipdrift Shelter in South Africa, where discoveries like ochre engravings and pressure-flaked tools have redefined understanding of human cognitive evolution. His research highlights the southern Cape as a critical region for early human innovation, with studies on ochre processing, shell beads, and engraved ostrich eggshells. Outreach includes international exhibitions (e.g., 'Origins of Early Southern Sapiens Behaviour') and educational programs, such as simulated excavations at the University Museum of Bergen. Awards include top citations in Social Sciences and recognition for transformative contributions to human origins research.

Anna FarihaView profile

Assistant Professor

• Data Management
• Human-Centered Systems
• Usability Engineering
• +4 more

Anna Fariha is an Assistant Professor in the Kahlert School of Computing at the University of Utah. She co-leads the Data Management Research Center for Human-centered, Efficient, and Scalable Systems. Her research focuses on enhancing data system usability, explainability, and trustworthiness through algorithmic innovations and practical implementations. She holds a Ph.D. from the Manning College of Information and Computer Sciences at the University of Massachusetts, Amherst, under Prof. Alexandra Meliou. Education: Ph.D. in Computer Science, University of Massachusetts Amherst (2021) Master's in Computer Science, University of Massachusetts Amherst (2020) Undergraduate work in Computer Science, unspecified institution Her research interests include data wrangling tools, constraint discovery, conversational AI for data science, and human-centered database systems. Recent work emphasizes automated data summary recommendations, constraint violation detection, and educational tools for data science programming. Grants & Awards: NSF CIRC: ENS/Grand: POWDER-ENS Award (2024) Stena Center Seed Grant for Fintech Data Analysis (2025) Microsoft Research Dissertation Grant (2020) SIGMOD 2022 Comprehensive Reproducibility Award Teaching: Recently taught Advanced Database Systems , Human-Centric Data Management , and Deep Learning courses at the University of Utah.

Mitra BaratchiView profile

Associate Professor

• Spatio-Temporal Data Analysis
• Machine Learning
• Mobility Data Modeling
• +5 more

Mitra Baratchi is an Associate Professor at the Leiden Institute of Advanced Computer Science (LIACS) , Leiden University. She leads the Spatio-temporal data Analysis and Reasoning (STAR) research group, co-leads the Automated Design of Algorithms (ADA) group, and founded the Special Interest Group on Spatio-Temporal Data Mining (SIG-SDTM) . PhD from University of Twente (Mobility Data) Master’s/Bachelor’s in Computer Engineering, Iran Research Interests focus on automated pattern extraction from spatio-temporal data across urban, environmental, and industrial domains. Key applications include: Automated Machine Learning (AutoML) for Earth Observations Time-Series Forecasting for public health (e.g., pandemic modeling) Urban Mobility Optimization with ESA, Honda, and municipalities Reliable Vehicular Communication Systems Smart Garments for Health Risk Detection Geocast Protocols for Internet-wide Communication Grant Highlights include €120K NWO-Aspasia, €2.9M Marie Skłodowska-Curie, €350K NWO-KLEIN, and €135K Center for BOLD Cities funding. She has supervised 12 PhD students and 4 current Master’s students since 2011, with notable best paper award at WWIC'16. Teaching includes Machine Learning (2020-present) and Urban Computing (2018-present) at Leiden, plus past courses in Data Visualization, Software Engineering, and Research Methods.

Julien de WitView profile

Associate Professor

• Exoplanets
• Planetary Science
• Atmospheric Characterization
• +12 more

Julien de Wit is an Associate Professor in the Department of Earth, Atmospheric and Planetary Sciences (EAPS) at the Massachusetts Institute of Technology (MIT), where he leads research in exoplanet detection and atmospheric characterization. He began his faculty appointment in July 2018 after completing his PhD and postdoctoral work at MIT under Professor Sara Seager. His work bridges data science and planetary science to uncover new worlds and understand their physical processes. His research focuses on exoplanets , particularly the development of novel data analysis techniques to map planetary atmospheres, infer mass from transmission spectra, and study planet-star interactions. He has played a pivotal role in the discovery and characterization of the TRAPPIST-1 system, one of the most promising targets in the search for life beyond Earth. His group utilizes data from space telescopes such as Hubble, Spitzer, and the James Webb Space Telescope (JWST), as well as ground-based facilities like TRAPPIST and SPECULOOS. The recent publications highlight a strong trend in atmospheric mapping , transmission spectroscopy , and planet-star interactions , with a focus on terrestrial and hot Jupiter exoplanets. His work spans observational astronomy, theoretical modeling, and data science, aiming to contextualize planetary properties within broader physical frameworks. He is actively involved in proposing and analyzing JWST observations to probe the atmospheres of temperate Earth-sized planets. Innovators Under 35 2017 Innovator of the Year, Belgium Julien de Wit has advised and collaborated with numerous researchers and students, contributing to major discoveries such as the first cloud map of an exoplanet and the detection of stellar pulsations induced by a planet. He is leading the deployment of the SPECULOOS Northern Observatory (ARTEMIS) in Tenerife and is involved in multiple NASA missions including TESS and GPX. His work is supported by institutional and mission-based grants, particularly through MIT and NASA collaborations. He leads a research group focused on exoplanet discovery and characterization , working closely with the SPECULOOS consortium and the TRAPPIST team. His lab develops advanced data analysis pipelines and theoretical models to interpret photometric and spectroscopic observations. The team is preparing for future JWST cycles and expanding ground-based survey capabilities to find the nearest potentially habitable worlds.