Find Academic Supervisors

• Natural Language Processing
• Machine Translation
• Statistical Machine Translation
• +4 more

Cristina España i Bonet is a Professor in the Department of Computer Science at the Polytechnic University of Catalonia (UPC), working within the Natural Language Processing group (GPLN) of the Center for Technologies and Applications of Language and Speech (TALP). She holds a Physics degree and a PhD in Cosmology from the University of Barcelona, later transitioning to Natural Language Processing and Machine Translation. Her academic journey includes teaching at both the Faculty of Physics of the University of Barcelona and currently at the Barcelona School of Informatics of UPC. Her research spans multiple areas of computational linguistics with a strong focus on Machine Translation. She has extensive experience in statistical and hybrid translation systems, document-level translation, multilingual systems, and machine learning applications in NLP. Her work often addresses real-world challenges with diverse text genres including news, patents, Wikipedia articles, and social media content. She has made significant contributions to developing translation systems that leverage context beyond the sentence level to improve coherence and quality. Cristina has been actively involved in numerous European and national research projects including OPENMT, OPENMT2, MOLTO, and TACARDI, where she has contributed both research and project coordination. Her recent work shows a growing interest in sign language translation, low-resource language processing, and the intersection of large language models with traditional machine translation paradigms. She has supervised multiple PhD and Master's students in topics related to machine translation and multilingual systems. Member of the Natural Language Processing group (GPLN) at TALP Research Center Supervisor of doctoral and master's theses in NLP and Machine Translation Lead researcher in multiple EU-funded projects on multilingual translation Developer of resources and tools for Wikipedia-based multilingual corpora Her research has evolved from statistical machine translation to incorporate neural approaches while maintaining focus on document-level context and multilingual applications. She has made significant contributions to understanding translation artifacts, developing methods for low-resource language translation, and creating resources for sign language processing. Her work bridges theoretical advances with practical applications across diverse language pairs and domains.

• surface physics
• thin film physics and technology
• physics of atomic hydrogen interactions
• +3 more

Professor Volodymyr Mykhailovych Matyushin is a distinguished faculty member at Zaporizhzhia Polytechnic National University, serving in the Department of Physical Materials Science within the Engineering-Physics Faculty. With over four decades of academic experience since 1981, he has established himself as a leading expert in surface physics and thin film technology, particularly in atomic hydrogen interactions with solid materials. Education: Zaporizhzhia Machine-Building Institute named after V.Ya. Chubar (1978) - Design and production of radio equipment PhD in Physics (1982, Donetsk State University) - "Low-temperature chemostimulated heterodiffusion in germanium" Doctor of Physics and Mathematics (2005, Kharkiv Institute of Physics and Technology) - "The influence of atomic hydrogen on mass transfer processes from thin metal films in systems with limited solubility" Professor Matyushin's research focuses on the intricate interactions between atomic hydrogen and solid surfaces, particularly examining how these interactions affect thin film properties and semiconductor behavior. His work bridges fundamental physics with practical applications in materials science, with special emphasis on low-temperature diffusion processes, surface modification techniques, and the development of advanced measurement methodologies. His expertise has contributed significantly to understanding how atomic-scale phenomena translate to macroscopic material properties, with applications in semiconductor manufacturing and electronic device development. His publication record demonstrates a consistent trajectory from fundamental research on hydrogen-semiconductor interactions to applied work in metrology and educational materials. The most recent publications show an expansion into curriculum development and practical training materials, reflecting his commitment to education alongside research. His scholarly output spans multiple disciplines including physics, materials science, and engineering education. Professor Matyushin teaches courses including Technological Fundamentals of Electronics, Thin Film Technology, Production Technology of IC/GIS/VET, Functional Electronics, and Physics of Hydrogen Technologies, shaping the next generation of engineers and scientists through both classroom instruction and laboratory work.

Antonio Carlos CostaView profile

Researcher

• Biophysics
• Animal Behavior
• Dynamical Systems
• +4 more

Antonio Carlos Costa is a postdoctoral researcher at École Normale Supérieure – PSL University , affiliated with the Laboratoire de physique de l’ENS (LPENS) and the Institut de biologie de l’ENS (IBENS) . His work bridges theoretical physics and biology to study animal behavior dynamics. Research Focus : Physics of animal behavior, neuromuscular control, dynamical systems, and information theory Current Affiliation : Institut du Cerveau (ICM Paris) as a postdoctoral researcher Key Research Areas Costa investigates how animals translate neural activity into adaptive behavior across scales, from muscle mechanics to environmental navigation. His methodologies combine: High-resolution tracking data analysis Markovian and stochastic modeling Statistical mechanics applications Open-source tool development (e.g., WormPose) Scientific Contributions His publications focus on C. elegans locomotion, predictive modeling of behavior, and fluctuating landscapes in biological systems, reflecting interdisciplinary approaches to understanding complexity.

Kathleen DESVEAUDView profile

Professor

• Artificial Intelligence
• Data Marketing
• Natural Language Processing
• +5 more

Kathleen DESVEAUD serves as a permanent Professor and Researcher specializing in Data Marketing & Artificial Intelligence at Kedge Business School in Marseille. She directs the Digital & Data Marketing Research Lab and maintains active academic engagement through publications in top-tier journals including Journal of Business Research and Journal of Consumer Marketing. Her educational background includes: Preparatory class (ECS) PGE Master's degree from Toulouse Business School PhD in Management Sciences from Toulouse Capitole University Visiting Researcher position at National University of Singapore (Department of Quantitative Psychology) DESVEAUD's research focuses on the intersection of artificial intelligence and marketing, with particular emphasis on brand perceptions, natural language processing applications, and ethical implications of AI adoption. Her work combines quantitative methodologies with practical marketing applications, examining how consumers interact with AI-driven technologies across various contexts including autonomous vehicles and chatbot interfaces. Recent publications demonstrate growing interest in gender dynamics within AI systems and the psychological mechanisms underlying consumer trust in automated technologies. Her publication record shows a clear trajectory from foundational work in brand loyalty and customer database management toward cutting-edge research on AI ethics and implementation. The 2024 book 'Artificial Intelligence Decrypted' represents a significant contribution to understanding ethical risks in AI systems, while her 2025 article on gendered AI in autonomous vehicles indicates ongoing exploration of social dimensions in emerging technologies. Teaching responsibilities include Big Data, Artificial Intelligence, and Machine Learning courses within the PGE program, reflecting her commitment to translating research insights into practical business education. As director of the Digital & Data Marketing Research Lab, DESVEAUD leads initiatives that bridge academic research with industry applications, particularly in the areas of social media analytics, consumer data management, and AI implementation strategies for marketing organizations.

Raul AranovichView profile

Associate Professor

• Theoretical Linguistics
• Syntax
• Morphology
• +8 more

Raul Aranovich is an Associate Professor in the Department of Linguistics at the University of California, Davis, where he has been a faculty member since 2001. Prior to his appointment at UC Davis, he held faculty positions at the Ohio State University and the University of Texas in San Antonio. He received his Ph.D. in Linguistics from UC San Diego in 1996 under the direction of Professors S.-Y. Kuroda and John Moore. Professor Aranovich is a theoretical linguist whose research focuses on the interfaces between syntax, morphology, and semantics, with particular attention to grammatical mismatches across these linguistic levels. His work spans both theoretical and empirical approaches, utilizing natural language processing and corpus linguistics tools. His primary language specializations include Spanish and other Romance languages, as well as Fijian and Shona, representing significant cross-linguistic diversity in his research portfolio. His recent publications demonstrate an expanding interdisciplinary focus, bridging traditional linguistic theory with computational applications, including work in cybersecurity and neural machine translation. This reflects his evolving research trajectory from purely theoretical syntax and morphology toward computational and applied linguistics. His work on computer-mediated communication represents a contemporary extension of his longstanding interest in language structure and variation. Among his notable recognitions is being named a Fellow of the Linguistic Society of America, highlighting his contributions to the field. Fellow, Linguistic Society of America Professor Aranovich's research demonstrates remarkable breadth across linguistic subfields and languages, connecting historical linguistic theory with contemporary computational approaches. His work on Romance languages maintains a strong theoretical foundation while his more recent cybersecurity and machine translation publications show successful adaptation to emerging interdisciplinary opportunities. His continued publication record through 2024 indicates active ongoing research contributions across multiple linguistic domains.

Susan Galloway HilsenbeckView profile

Professor

• Biostatistics
• Cancer Research
• Clinical Trials
• +4 more

Susan Galloway Hilsenbeck, Ph.D., is a Professor at Baylor College of Medicine and Director of the Quantitative Sciences Shared Resource at the Dan L Duncan Comprehensive Cancer Center. She specializes in biostatistics and informatics collaboration in cancer research, with a focus on clinical trials and biomarker analysis. Education: Ph.D. in Applied Biostatistics, University of Miami (1990) Her research interests include the design and statistical analysis of translational experiments, clinical trials, and prognostic/predictive biomarker studies. She plays a critical role in multi-center clinical trials and leads biostatistics and informatics initiatives at Baylor. Scientific Awards: Fellow, American Statistical Association Certified Professional Statistician (PStat), American Statistical Association Fellow, American Association for Cancer Research Hilsenbeck actively mentors students, teaches biostatistics courses for biomedical graduate students, and contributes to national and international workshops. She collaborates with teams such as the Advanced Technology Cores at BCM and the Smith Breast Center.

Evgueni SmirnovView profile

Assistant Professor

• Machine Learning
• Conformal Prediction
• Time Series Forecasting
• +3 more

Evgueni Smirnov is an Assistant Professor in the Artificial Intelligence Department of Advanced Computing Sciences within the Faculty of Science and Engineering . His research focuses on Machine Learning , Conformal Prediction , Time Series Forecasting , and Biomedical Data Analysis . Key research areas include data prediction , classification , instance transfer , and conformal prediction with applications in both computer science and biomedical domains. His work spans predictive modeling , gene expression analysis , and inflammatory response mechanisms in cardiovascular diseases. He has contributed to editorial activities, including co-editing books through ML Research Press , and organized academic events like the Symposium on Conformal and Probabilistic Prediction with Applications . His publications demonstrate expertise in transfer learning , multivariate time-series forecasting , and cross-species biomedical modeling .

Sean O'DonoghueView profile

Professor

• Bioinformatics
• Structural Biology
• Systems Biology
• +3 more

Sean O'Donoghue is a Conjoint Professor at the School of Biotechnology and Biomolecular Science, University of New South Wales (UNSW). He concurrently serves as a Laboratory Head and Senior Faculty Member at the Garvan Institute of Medical Research and a Visiting Scientist at CSIRO Data61. He holds a B.Sc. (Hons) and Ph.D. in Biophysics from the University of Sydney. His research integrates bioinformatics, structural biology, and data visualization to decode complex biological systems. Key interests include: Development of computational tools for protein structure/function analysis Visual analytics for genomics and multiomics data Mechanisms of viral protein assembly (e.g., SARS-CoV-2) Epigenetic dynamics and cancer transcriptomics Recent publications (2018–2022) demonstrate a strong focus on: Protein annotation frameworks and dark proteome characterization SARS-CoV-2 structural mechanisms Single-cell transcriptomics in breast cancer Innovations in biological data visualization tools He leads the VIZBI initiative (advancing bioinformatics visualization) and VizbiPlus (public science outreach). No awards or student advisories are detailed in the source material.

Jeff JonesView profile

Researcher

• Proteomics
• Mass Spectrometry
• Bioinformatics
• +4 more

Jeff Jones is a Senior Proteomics Bioinformatician at the Roukes Group within the Division of Physics, Mathematics and Astronomy at California Institute of Technology (Pasadena, CA). He holds a Ph.D. in Analytical Chemistry (2005, University of Arkansas) and dual BS degrees in Biochemistry and Microbiology (2001, Cal Poly). Expert in proteomics, mass spectrometry, and data science Developed algorithms for single-cell proteomics and spatial proteomics Created open-source tools: tidyproteomics , msfastar Recipient of patents for tandem identification engines and sample workflow systems Research interests span ultra-sensitive mass spectrometry , machine learning , and infectious disease proteomics . Current projects include NEMS for drug-target interactions , spatial proteomics of tuberculosis , and single-cell proteome analysis . His scientific contributions include: Over 20 years of experience in proteomics and bioinformatics Commercial development of colorectal cancer biomarker tests Collaborations with Prof. Steyn (AHRI)

• Reproductive Biology
• Placental Pathology
• Maternal-Fetal Medicine
• +6 more

Virginia D. Winn, MD, PhD is a Professor of Obstetrics and Gynecology at Stanford University School of Medicine, where she directs the Reproductive, Stem Cell and Perinatal Biology Division and co-leads the Dunlevie Maternal-Fetal Medicine Center . Her research focuses on human placental biology , particularly the molecular mechanisms underlying preeclampsia and maternal-fetal immune tolerance. Education: PhD in Biochemistry, University of Rochester School of Medicine (1994) MD, University of Rochester (1996) Research: Investigates placental chimerism and vascular remodeling Develops multi-omics predictive models for preeclampsia Explores sex differences in placental gene expression Her lab’s recent studies (2022) highlight Siglec-6 signaling in preeclampsia pathogenesis, ancestral HMOX1 polymorphisms in hypertensive disorders, and exosome-mediated communication between trophoblasts and maternal systems. She has received the MCHRI Harman Faculty Scholar award and leads the NIH K12 Women’s Reproductive Health Research Program . Dr. Winn’s clinical expertise includes Maternal-Fetal Medicine , with a focus on early placental dysfunction. Key Collaborations: Stanford Cardiovascular Institute Bio-X interdisciplinary research hub Maternal and Child Health Research Institute (MCHRI)

• Psychiatry
• Respiratory Medicine and Allergy
• Pediatric Epidemiology
• +4 more

Anna Hedman is a Senior Research Specialist and Lecturer at Karolinska Institutet's Department of Medical Epidemiology and Biostatistics (MEB), where she leads the Pediatric and respiratory epidemiology research group under Catarina Almqvist Malmros. Her primary affiliations include examiner and coordinator roles for medical degree projects at KI's medical program (semester 8), and she serves as Course Director for scientific writing and methodology courses. Education: Post-doc in Epidemiology, MEB, Karolinska Institutet (2015-2019) PhD in Neuroscience, Rudolf Magnus Brain Center, Utrecht University (2013) Her research integrates pediatric epidemiology with advanced methodologies including twin modeling, longitudinal data analysis, and biomarker studies (IgE, FeNO, cytokines, metabolic markers) to investigate atopic diseases and non-communicable conditions in children. Current work focuses on asthma endotypes, maternal-child health interactions, and genetic/environmental determinants of respiratory and psychiatric disorders through large-scale cohorts like STOPPA and Born into Life. Analysis of her 15 most recent publications reveals dominant themes in pediatric respiratory epidemiology (60%), psychiatric comorbidity research (25%), and methodological innovation in multi-omics integration (15%). Key trends include leveraging twin registries for causal inference, developing non-invasive biomarkers for asthma control, and exploring bidirectional relationships between mental health and immune disorders. Teaching & Mentorship: Examiner/coordinator for medical degree projects (2017–present) Lecturer in scientific writing (Basvetenskap 1, 2022–2023) Group leader for Medical Scientific Methodology courses (2017–2021) Supervisor for master's theses in medicine Her work operates within Karolinska Institutet's Pediatric and respiratory epidemiology research group, utilizing national registries, clinical cohorts, and biobanks including the Swedish Twin Registry. Current projects emphasize translational applications of inflammatory proteomics and epigenetic clocks for personalized pediatric care.

Vlad Stefan BARBUView profile

Associate Professor

• semi-Markov and Hidden semi-Markov processes
• Markov and Hidden Markov processes
• statistical inference for stochastic processes
• +11 more

Vlad Stefan BARBU is an Associate Professor of Mathematics (Statistics) at the Laboratory of Mathematics Raphaël Salem UMR 6085, University of Rouen - Normandy (URN) - CNRS, France. He serves as Director of the Research Federation Normandy-Mathematics, Scientific Secretary of the Romanian Society of Probability and Statistics, and Vice-president of the Romanian Society of Applied and Industrial Mathematics. His research focuses on stochastic processes, particularly semi-Markov models and their applications in reliability, survival analysis, and biostatistics. Education: HDR (Habilitation to Conduct Research) in Statistics (2017) PhD in Statistics (2005) Master in Applied Statistics and Optimization (1997-1998) BA in Mathematics (Bac + 5) (1992-1997) Barbu's research interests center on semi-Markov and Hidden semi-Markov processes, Markov models, statistical inference for stochastic processes, and nonparametric estimation. His work extends to reliability and survival analysis, biostatistics with applications in DNA modeling, entropy and divergence measures, and model selection. He has developed several R packages for semi-Markov modeling, demonstrating his commitment to translating theoretical advances into practical tools for researchers and practitioners. Analysis of his recent publications reveals a consistent focus on advancing semi-Markov theory with applications across diverse domains. His work spans theoretical developments in estimation methods, hypothesis testing, and reliability analysis, while maintaining strong connections to practical applications in reliability engineering, biostatistics, and risk modeling. The interdisciplinary nature of his research is evident in publications spanning statistics journals, mathematics journals, and applied fields. Research Grants: Coordinator of project 'Reliability and Survival Analysis of Multi-State Random Systems' (2024-2025) Team leader for LMRS in ANR project 'Hidden Semi Markov Models: INference, Control and Applications' (2022-2025) Participant in ANR project 'Swimming and Para-swimming: All United for our Champions' (2020-2024) Participant in multiple regional and international research projects Barbu has supervised numerous PhD students and served on doctoral committees in France and abroad. His research leadership extends to coordinating significant research projects and organizing international conferences. He maintains active research collaborations across Europe, with frequent visits to institutions in Greece, Romania, and other countries. His work bridges theoretical statistics with practical applications, particularly in reliability engineering and biostatistics. As Director of the Research Federation Normandy-Mathematics, Barbu leads a substantial mathematical research network. His international engagement is further evidenced by his leadership roles in Romanian statistical societies and his participation in European research networks and projects.

• Atherosclerosis
• Biomechanics
• Cardiovascular system
• +5 more

Diego Gallo is an Associate Professor in Industrial Bioengineering at the Department of Mechanical and Aerospace Engineering (DIMEAS), Politecnico di Torino, and a member of the PolitoBIOMed Lab - Biomedical Engineering Lab. With a PhD in Biomedical Engineering from Politecnico di Torino and a Marie Sklodowska-Curie Global Fellowship at the University of Toronto's Biomedical Simulation Laboratory, Gallo focuses on the interplay between blood fluid mechanics, vascular morphometry, and cardiovascular disease development/diagnosis. He has authored 120+ publications and holds 4 patents in cardiovascular device optimization. Editorial Roles: Associate Editor for Frontiers in Pediatrics and Frontiers in Cardiovascular Medicine , Associate Editor for Cardiovascular Engineering and Technology , Topic Editor for Fluids (MDPI). Research Themes: Computational hemodynamics, digital twins, vascular biomechanics, helical flow analysis, and clinical translation of cardiovascular devices/surgical techniques. Scientific Awards: Jack Perkins Prize (2018) Elsevier Highly Cited Research (2016) ISMRM Awards (2014) TERMIS Travel Award (2012) Premio Enzo Belardinelli (2012) Teaching Activities: Courses in fluid biomechanics, bioimaging processing, and cardiovascular surgical strategies for Bioengineering programs. Supervises PhD students in cardiovascular device design, hemodynamic modeling, and computational medical technology.

Brooke N. WolfordView profile

Research Fellow

• Statistical Genetics
• Precision Public Health
• Cardiovascular Disease Epidemiology
• +5 more

Brooke N. Wolford is a Marie Skłodowska-Curie Postdoctoral Fellow at the Norwegian University of Science and Technology (NTNU), affiliated with the Department of Public Health and Nursing within the Faculty of Medicine and Health Sciences. She leads the ProtectHearts and HUNT AI for Heart Health projects at the HUNT Center for Molecular and Clinical Epidemiology, while also contributing to the EU-funded INTERVENE project through the Finnish Institute for Molecular Medicine. Her work bridges statistical genetics, computational biology, and clinical applications to advance precision public health initiatives. Dr. Wolford earned her PhD in Bioinformatics and Master's in Statistics from the University of Michigan, where she completed her dissertation on 'Genetic Discovery and Precision Medicine in Cardiovascular Diseases Using Electronic Health Record-linked Biobanks' under Dr. Cristen Willer and Dr. Michael Boehnke. Her undergraduate training includes a Bachelor of Science in Quantitative Biology with highest honors from the University of North Carolina at Chapel Hill, where she was a Phi Beta Kappa honors graduate. Her research program focuses on artificial intelligence-driven precision public health, particularly in cardiovascular disease prediction and prevention. By integrating genomics, proteomics, and clinical data from large biobanks like HUNT, UK Biobank, and FinnGen, she develops novel risk prediction models that address critical gaps in women's heart health and young adult disease prevention. Her work on polygenic risk scores and proteomic biomarkers aims to transform population-scale screening programs through explainable AI methodologies. Dr. Wolford's recent publications demonstrate significant contributions to genetic epidemiology, with high-impact work in Nature Genetics, Nature Communications, and Circulation: Genomic and Precision Medicine. Her research outputs reveal consistent themes in cross-population polygenic score development, sex-specific disease mechanisms, and innovative biobank data integration approaches that enhance cardiovascular risk prediction across diverse populations. 2022/2023 Best Dissemination Award (K.G. Jebsen Centers) 2022 ASHG Trainee Research Excellence Award Finalist 2021 ASHG Trainee Research Excellence Award Semi-Finalist National Science Foundation Graduate Research Fellowship Genome Sciences Predoctoral Traineeship University of Michigan Program in Biomedical Sciences 20th Anniversary Award As a dedicated mentor, Dr. Wolford supervises multiple graduate students across Master's and PhD programs at NTNU, focusing on proteomic risk prediction for diabetes, cardiovascular-Alzheimer's disease connections, and polygenic scoring for women's heart health. She actively promotes open science through R-ladies Trondheim leadership and develops educational resources like the Health AI in R workshop. Her ongoing projects with INTERVENE and ProtectHearts secure substantial European research funding for translational genomics initiatives. Dr. Wolford co-leads the HUNT AI for Heart Health initiative and ProtectHearts project within the HUNT Center for Molecular and Clinical Epidemiology. These teams integrate computational biologists, clinicians, and epidemiologists to develop explainable AI models that translate genomic discoveries into clinical prevention strategies, with particular emphasis on addressing health disparities in cardiovascular outcomes for women and younger populations.

Lloyd M. SmithView profile

Professor

• Mass Spectrometry
• Proteomics
• Bioinformatics
• +3 more

Lloyd M. Smith is the W. L. Hubbell Professor and Hall-Fischer Professor of Chemistry at the University of Wisconsin–Madison. His research focuses on developing cutting-edge proteomics technologies that bridge biology, chemistry, and computational analysis. Key research areas include comprehensive proteoform identification Development of tools like ProteaseGuru, MetaMorpheus, and FlashLFQ Applications in neurodegenerative diseases (Alzheimer's), viral biology (HIV), and diabetes Research trends show strong emphasis on: Mass spectrometry innovations RNA-protein interaction networks Proteoform family concept development Machine learning integration in proteomics