جست‌وجوی استاد و پژوهشگر

Simon Kasifمشاهده پروفایل

استاد

• Computational Biology
• Genomics
• Bioinformatics
• +۵ مورد دیگر

Simon Kasif is a Professor of Biomedical Engineering at Boston University. He co-founded the Center for Advanced Genomic Technology (CAGT), COMBREX (Computational Bridges to Experiments), and is a member of the I2B2 Center. His work spans computational biology, systems biology, and AI applications in healthcare. He contributed to the Human Genome Project, co-developed tools like Glimmer and Mummer, and pioneered probabilistic networks for gene function prediction. Education: M.S. & Ph.D., Computer Science, University of Maryland B.Sc., Mathematics, Tel Aviv University Research Interests: Dr. Kasif focuses on integrating computational methods with biological data to understand disease mechanisms, particularly diabetes and cancer. His work emphasizes AI ethics, network biology, and translational research. He has developed methodologies for protein interaction networks, genomic data analysis, and systems biology modeling. Key Contributions: Co-developed Glimmer, a widely used gene prediction tool Pioneered functional linkage networks for gene function prediction Advocated Bayesian networks in computational biology Investigated disease network signatures, including diabetes and Alzheimer’s Lead projects on AI safety, SARS-CoV-2 interaction mapping, and metabolic pathway analysis Grants & Collaborations: Active in interdisciplinary projects, including the COMBREX initiative for protein function annotation and I2B2’s clinical data integration. His work bridges computational methods with clinical applications, fostering open science and reproducible research practices. Labs & Teams: Directs research at CAGT and I2B2, collaborating across institutions to advance genomic technologies and systems biology approaches.

Elizabeth Neumannمشاهده پروفایل

استادیار

• Neurological diseases
• Molecular imaging
• Mass spectrometry
• +۴ مورد دیگر

Elizabeth Neumann is an Assistant Professor in the Department of Chemistry at the University of California, Davis, where her lab focuses on the molecular and cellular architecture of neurological diseases using advanced analytical tools. Her research integrates matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) with multimodal technologies like immunofluorescence, spectroscopy, and transcriptomics to decode complex biological systems. Education: BS in Chemistry from Baylor University, PhD from University of Illinois at Urbana-Champaign under Jonathan Sweedler Postdoctoral training: NIH Postdoctoral Fellow at Vanderbilt University under Richard Caprioli and Jeffrey Spraggins The Neumann lab specializes in developing high-resolution imaging methods for spatial lipidomics and metabolomics, with applications spanning neuroscience, nephrology, and electrochemistry. Their work enables non-destructive molecular profiling of tissues while preserving spatial context. Recent publications emphasize MALDI TIMS IMS for lipid distribution analysis, multimodal integration with CODEX immunofluorescence, and innovative approaches in single-cell chemical analysis. Collaborative efforts include contributions to the NIH Human Biomolecular Atlas Program. Scientific awards: NIH/NIDDK F32, NSF Graduate Research Fellowship, multiple institutional scholarships Leadership: Active in developing community guidelines for antibody-based imaging and FAIR data stewardship Research collaborations span Vanderbilt, University of Illinois, and cross-institutional consortia, with a focus on advancing analytical methods for biomedical discovery.

• Information Retrieval
• Natural Language Processing
• Machine Learning
• +۳ مورد دیگر

Jaap Kamps is a Professor at the University of Amsterdam, Netherlands , with a focus on information retrieval , natural language processing , and machine learning . His work spans theoretical and applied research, including contributions to neural ranking models domain adaptation scientific text simplification exploratory search digital libraries for enhanced user access. Recent research highlights include revisiting bag-of-words representations for transformers, context embeddings for retrieval-augmented generation, and positional bias analysis in generative systems. He actively contributes to the CLEF SimpleText Track , promoting simplified scientific communication for diverse audiences. His collaborations involve co-authors like David Rau , Mostafa Dehghani , and Hosein Azarbonyad , with publications in venues such as ACM Transactions on Information Systems and ECIR . He mentors students and participates in conferences like ICTIR and SIGIR , driving advancements in efficient text ranking and user-centric search.

Maria A. Zuluagaمشاهده پروفایل

استاد

• Machine Learning
• Medical Image Analysis
• Healthcare AI
• +۶ مورد دیگر

Maria A. Zuluaga is a full-time Professor at EURECOM (Department of Data Science) and holds an affiliate faculty position as Senior Lecturer at King's College London's School of Biomedical Engineering & Imaging Sciences. She previously served as a Senior Research Associate at University College London. Her academic background includes a BSc in Electronics Engineering from Universidad del Valle, an MSc in Computer Science from Universidad de los Andes, and a PhD in Signal and Image Processing from Université de Lyon. Her research develops novel machine learning methods for healthcare applications, focusing on neurovascular imaging, cardiovascular analysis, and cancer research. Key areas include medical image segmentation, trustworthy AI deployment, multimodal learning, and time-series analysis. Her work bridges technical innovation with clinical needs to advance medical diagnostics and treatment planning. Publications demonstrate strong focus on medical imaging (85% of recent papers), particularly segmentation techniques (40%), with emerging themes in trustworthy AI (25%) and multimodal learning (20%). Vascular analysis dominates application domains (50%), followed by cancer research (30%) and neuroimaging (20%). Awards & Honors: ERC Consolidator Grant (2024) She leads research in medical AI through publications and major grants, though specific student advisees, labs, or additional grants aren't detailed in available sources.

René Peinlمشاهده پروفایل

استاد

• Multimodal Artificial Intelligence
• Resource-efficient AI
• Speech Recognition
• +۷ مورد دیگر

René Peinl is a Professor of Business Informatics at the Institute for Information Systems (iisys) within the Faculty of Computer Science at Hof University of Applied Sciences. He serves as the Institute Director and leads the research group on Multimodal Artificial Intelligence. His academic career spans over 15 years of teaching in master's programs including 'Master Informatik' and 'Master Applied Research in Computer Science', with a focus on resource-efficient AI for text understanding since 2021. Dr. Peinl's research spans multiple cutting-edge domains in AI and information systems. His primary focus is on multimodal artificial intelligence systems that integrate various data modalities. He specializes in resource-efficient AI models that can operate without cloud resources, with particular emphasis on German language processing for speech recognition, text understanding, and speech synthesis. His work extends to practical applications in Industry 4.0 through customizable manufacturing execution systems (HiCuMES) and immersive learning applications using virtual reality technology for fields like electrical engineering and healthcare training. Analysis of his recent publications reveals a strong trend toward practical, efficient AI implementations with a focus on German language processing and multimodal systems. His work addresses critical challenges in model compression, bias mitigation in generative AI, and multimodal integration of text and image understanding. A significant portion of his research focuses on making AI more accessible and practical for small and medium enterprises through resource-efficient implementations that don't require cloud infrastructure. Professor Peinl actively participates in academic service and industry collaboration. He serves as a member of Hof University's senate and supervisory board. His research is supported through EFRE projects including 'Wirtschaft im Mittelstand 4.0' and 'Digitale Transformation des Mittelstands mit KI'. He frequently engages with industry through speaking engagements, consulting, and collaborative projects with companies across various sectors. At Hof University, Peinl leads the Institute for Information Systems and the Multimodal Artificial Intelligence research group. His team works on developing privacy-friendly speech assistance systems that function without cloud resources, highly customizable manufacturing execution systems for Industry 4.0, and immersive VR learning applications for domains that are expensive, dangerous, or difficult to replicate in physical reality.

Cynthia Matuszekمشاهده پروفایل

استادیار

• Robotics
• Machine Learning
• Human-Robot Interaction
• +۵ مورد دیگر

Cynthia Matuszek is an Assistant Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). She directs UMBC’s Interactive Robotics and Language lab, focusing on enabling robots to learn from human interaction using natural language. Her research spans robotics, machine learning, human-robot interaction (HRI), and natural language processing (NLP). Notably, she was recognized in IEEE's 2018 '10 to Watch in AI' list. Dr. Matuszek holds a Ph.D. and M.S. in Computer Science & Engineering from the University of Washington (2014, 2010) and a B.S. in Computer Science from the University of Texas at Austin (2000). Her work bridges theoretical and applied domains, addressing challenges in grounded language learning, multimodal data fusion, and ethical AI deployment. Her recent publications emphasize cybersecurity (e.g., malware detection using compact features), neurosymbolic reasoning, and the integration of large language models into robotics. The lab’s work often explores VR-based human-robot collaboration scenarios, leveraging photogrammetry and simulation for reproducible studies. Key thematic trends in her articles include: Advancing grounded language learning through cross-modal alignment and interactive labeling Developing ethical AI systems capable of moral reasoning and command rejection Pioneering domain-specific cybersecurity solutions using tensor factorization and vector stores Expanding multilingual and multimodal capabilities for robots in unstructured environments Her research also addresses societal impacts, including studies on gender stereotypes in AI datasets and privacy implications of pervasive robotics. The lab’s work often intersects with NSF-funded initiatives, such as the CAREER award for inclusive robotic learning environments.

Veronique Hosteمشاهده پروفایل

استاد

• Computational Linguistics
• Natural Language Processing
• Machine Learning
• +۷ مورد دیگر

Veronique Hoste is a Full Professor of Computational Linguistics at Ghent University's Faculty of Arts and Philosophy , where she serves as Department Head of the Department of Translation, Interpreting and Communication and Director of the LT3 Language and Translation Technology Team . Her work bridges machine learning with natural language processing, focusing on semantics, discourse modeling, and practical applications in emotion detection, irony recognition, and customer service dialogue analysis. PhD in Computational Linguistics from University of Antwerp (2005) 2023-2024: Francqui Chair at Université Libre de Bruxelles 2024: Elected to Royal Flemish Academy of Belgium (KVAB) Research Interests: Specializes in machine learning approaches to coreference resolution, sentiment analysis, and multimodal emotion detection. Leads projects like FlandersAI (empathy in conversational agents), METRICS (emotion trajectories in service dialogues), and SENTiVENT (financial event extraction). Develops high-quality datasets (e.g., EmotioNL , ENCORE ) for broader NLP community use. Recent Publications span Dutch social media irony detection, Classical Chinese poetry sentiment analysis, and multimodal emotion datasets. Collaborates on interdisciplinary initiatives like NewsDNA (news recommendation) and SentEMO (commercial sentiment analysis). Scientific Awards Francqui Chair (2023-2024) KVAB Membership (2024) Outreach includes co-founding AlfaSent (LT3 spin-off for customer feedback analysis) and authoring the first Dutch-language NLP book Taaltechnologie Ontrafeld (2024). Coordinates AI education initiatives like the AI at School project.

Jonathan Rothمشاهده پروفایل

دانشیار

• Econometrics
• Causal Inference
• Labor Economics
• +۱ مورد دیگر

Jonathan Roth is an Associate Professor of Economics at Brown University, affiliated with the Population Studies and Training Center (PSTC) since 2023. His research bridges econometrics, labor economics, and algorithmic fairness, with a focus on causal inference methodologies. Education : PhD in Economics (2020, Harvard University), BA summa cum laude in Mathematics and Economics (University of Pennsylvania) Roth's work addresses fundamental issues in difference-in-differences (DiD) methods, including parallel trends sensitivity, event-study interpretation, and design-based uncertainty frameworks. He develops tools for mediation analysis and bias correction in quasi-experimental settings. His most recent publications examine: 2024: Mediation testing with instrumental variables 2024: DiD event-study interpretation discrepancies 2024: Design-based quasi-experimental uncertainty 2024: Bayesian approaches to parallel trends 2023: Efficient staggered treatment estimation Scientific recognition includes: 2025 Sloan Research Fellowship David A. Wells Prize for best Harvard dissertation He has secured research funding from the National Science Foundation (NSF) and National Institutes of Health (NIH), with methodological contributions implemented in R and Stata packages used for empirical work.

Jia Songمشاهده پروفایل

استادیار

• Cybersecurity
• Software Testing
• IoT Security
• +۳ مورد دیگر

Jia (Cindy) Song is an Assistant Professor in the Department of Computer Science at the University of Idaho, affiliated with the College of Engineering. Her research focuses on cybersecurity, software testing, IoT security, fuzzing techniques, and real-time systems. She holds a Ph.D. in Computer Science from the University of Idaho (2014), an M.S. in Computer Science from the same institution (2012), and dual B.S./B.A. degrees in Information Security and Human Resource Management from Sichuan University, China (2009). Dr. Song’s work emphasizes practical applications of automated testing tools and formal methods to enhance software security and reliability. Her contributions include developing fuzzing frameworks, analyzing real-time operating systems compliance, and designing IoT security protocols. She maintains an active research website and is professionally active on LinkedIn. Her recent publications span innovations in grammar-based fuzzing, Rust programming language safety analysis, IoT firmware labeling systems, and cybersecurity benchmarking methodologies. She has contributed to academic conferences such as the Cyber Security Symposium and published reviews on symbolic execution tools and dynamic taint analysis.

Markus Barthمشاهده پروفایل

استاد

• Biomedical Engineering
• Medical Imaging
• Neuroimaging
• +۳ مورد دیگر

Professor Markus Barth is a Full Professor in Biomedical Engineering at the University of Queensland (UQ), specializing in MRI physics and medical imaging. He holds roles as Deputy Head of School (Research) in the Faculty of Engineering, Architecture and Information Technology, Director of the National Imaging Facility – Queensland Node, and member of the ARC College of Experts. His academic journey includes a Doctorate in Technical Physics from Vienna University of Technology (1999) and prior appointments at the Medical University Vienna, Donders Institute, and Erwin L. Hahn Institute. His research focuses on advancing MRI techniques for high-resolution anatomical and functional imaging, including susceptibility-weighted imaging (SWI), quantitative susceptibility mapping (QSM), and ultra-high field MRI applications. He has pioneered methods to enhance MRI sensitivity for neurodegenerative diseases, cancer, and cognitive neuroscience studies. Barth’s work integrates cutting-edge technologies like deep learning and parallel transmission to improve image quality and clinical utility. His contributions include developing open-source tools (e.g., QSMxT) and large-scale datasets (e.g., HumanBrainAtlas) for reproducible neuroimaging research. Education: Bachelor of Technical Physics, Vienna University of Technology (1995) Doctorate in Technical Physics, Vienna University of Technology (1999) Research Interests: Optimizing MRI for ultra-high field applications, neuroimaging of brain function and connectivity, SWI/QSM for disease markers, and translational imaging techniques for clinical diagnostics. His work addresses topics like layer-specific fMRI, brain-computer interfaces, and decoding neural activity. Grants & Awards: ARC Future Fellowship (2014), leadership in national imaging initiatives, and contributions to MRI software frameworks. Labs/Teams: Leads the Ultra-high Field Human MR Research program at UQ’s Centre for Advanced Imaging and collaborates with interdisciplinary teams in biomedical engineering and neurology. His lab develops novel MRI protocols and open-source tools for global research impact.

R. Stuart Geigerمشاهده پروفایل

استادیار

• Data Science and Society
• AI Ethics
• Algorithmic Transparency
• +۳ مورد دیگر

R. Stuart Geiger is an Assistant Professor at the University of California, San Diego with joint appointments in the Department of Communication and the Halıcıoğlu Data Science Institute. He holds affiliate faculty roles in Computer Science & Engineering, Computational Social Science, and the Institute for Practical Ethics, where he leads the Working Group on Data Governance & Accountability. His interdisciplinary work spans Science and Technology Studies, Data Science, and AI Ethics. Geiger earned his Ph.D. in Information Management & Systems from UC Berkeley, an M.A. in Communication, Culture, and Technology from Georgetown University, and a B.A. in Humanities from the University of Texas at Austin. His postdoctoral research at Berkeley Institute for Data Science (BIDS) focused on ethnographic studies of data science practices and institutions. Research Interests : His research examines the social, cultural, and political dimensions of data science, AI, and automation. Key areas include algorithmic fairness, open-source software governance, Wikipedia’s socio-technical dynamics, and the ethics of AI in social media moderation. He employs mixed methods, blending ethnography, trace analysis, and computational techniques. Publications Trends : Recent work critiques algorithmic bias in AI systems (e.g., ChatGPT’s salary negotiation advice), advocates for participatory auditing practices, and explores labor dynamics in open-source projects. His studies often bridge technical and social dimensions, emphasizing contextual inquiry and institutional critiques. Awards & Grants : - Best presentation award at ECSCW 2018 - 1st runner-up David B. Martin Best Paper Award (2018) - Sloan Foundation grants for open-source software studies - UC Humanities Research Institute funding (2022) - Top paper award at 2009 Science & Technology in Society Conference Advising & Grants : Geiger has led grants totaling over $200,000, including projects on open-source maintainers’ labor and data science ethics. He advises students in data science, communication, and STS, fostering interdisciplinary collaboration. His teaching includes courses on data ethics, algorithmic auditing, and communication theory. Labs & Teams : He co-founded the Best Practices in Data Science series at BIDS and collaborates with teams like the Moore-Sloan Data Science Environments. His work intersects with initiatives on diversity in data science and reproducible research practices.

Stephanie C. Hicksمشاهده پروفایل

دانشیار

• statistics
• data science
• missing data
• +۹ مورد دیگر

Stephanie C. Hicks is an Associate Professor in the Department of Biostatistics at the Bloomberg School of Public Health, Johns Hopkins University. Her work bridges statistics, data science, and genomics, with a strong emphasis on developing computational tools for modern biological data. Research Interests: Statistical methods for genomics and single-cell data Missing data imputation and uncertainty quantification Spatial transcriptomics and multi-assay data integration Open-source software development for reproducible research Functional and epigenomic analysis Her recent research, as reflected in publications from 2025, centers on benchmarking cellular deconvolution methods, integrating spatial transcriptomics across individuals, and developing combinatorial approaches for marker gene selection. These works span high-impact journals in genomics, bioinformatics, and computational biology, indicating a strong and active research program in statistical genomics and computational biology. Scientific Contributions: Development of the HuBMAPR R package for accessing the HuBMAP data portal Leadership in benchmarking studies for cellular deconvolution Advancing methods for label-free marker gene identification Addressing reproducibility and integration challenges in spatial omics Dr. Hicks mentors students and contributes to graduate education in biostatistics, emphasizing statistical programming and workflows. She is actively involved in the development of open-source tools that enhance reproducibility and accessibility in genomic research.

Stefan Lüdtkeمشاهده پروفایل

استادیار

• Neuro-symbolic machine learning
• Machine learning for heterogeneous data
• Machine learning for marine ecology
• +۵ مورد دیگر

Stefan Lüdtke is an Assistant Professor (Juniorprofessor) for Marine Data Science at the University of Rostock (since July 2023) and concurrently serves as a junior research group leader at ScaDS.AI Leipzig . Previously, he was a postdoctoral researcher at the Institute for Enterprise Systems, University of Mannheim (2021-2023) and completed his PhD at the University of Rostock (2016-2021). Education & Career Timeline: 2023 – present: Juniorprofessor (Assistant Professor) for Marine Data Science, University of Rostock 2023 – present: Junior research group leader, ScaDS.AI Leipzig 2021 – 2023: Postdoc, Institute for Enterprise Systems, University of Mannheim 2016 – 2021: PhD studies, University of Rostock Research Interests: Dr. Lüdtke’s research integrates neuro-symbolic machine learning with practical applications spanning heterogeneous tabular data , marine ecology , and underwater technology . His work bridges symbolic reasoning and modern gradient-based learning to tackle complex real-world problems such as hyperspectral imaging for environmental monitoring, knowledge-graph completion, and robust human-activity recognition. Key focus areas include: Design of memory-augmented decision-tree ensembles and gradient-based tree learning. Data-centric evaluation and quality assessment of machine-learning models on tabular and sensor data. Domain adaptation and self-training techniques for activity recognition in changing environments. Application of AI to marine robotics and glacier-dynamics mapping. Publication Trends: Across 40+ peer-reviewed works (2017-2025), Lüdtke demonstrates a steady shift from foundational probabilistic-filtering and lifted-inference methods toward cutting-edge neural-symbolic hybrids and tabular-data-centric learning. Recent high-impact venues show contributions in machine-learning theory , computer vision , and environmental informatics , with a notable uptick in interdisciplinary projects combining AI and marine science. Scientific Awards & Honors: No specific awards are listed in the provided material. Advising & Funding: No named students or explicit grant details are disclosed in the text. Laboratories & Teams: He leads the Marine Data Science junior research group at the University of Rostock and the ScaDS.AI Leipzig junior research group, fostering cross-institutional collaboration in AI and data science.

• Machine Learning
• Computer Vision
• Bioinformatics
• +۴ مورد دیگر

Adriana Birlutiu is a Lecturer in the Computer Science Department at 1 December 1918 University of Alba Iulia , Romania. Her expertise lies in machine learning, computer vision, bioinformatics, and transfer learning, with a recent focus on porcelain-industry optimisation. Education Ph.D., Radboud University Nijmegen, Netherlands (2011) M.Sc., Babeș-Bolyai University of Cluj-Napoca & University of Lorraine (Erasmus), 2005 B.Sc., Babeș-Bolyai University of Cluj-Napoca, 2004 Research Interests Adriana's research spans machine learning , deep learning , computer vision , and bioinformatics . She has contributed to preference learning, domain adaptation, protein–protein interaction prediction, and automated quality control in porcelain manufacturing. Her recent projects integrate deep neural networks with industrial computer-vision systems to detect defects and recognise characters on ceramic surfaces. Publication Trends Across 15 recent publications (2010-2019), Adriana has consistently explored transfer learning , multi-task learning , and Bayesian methods . Articles cluster around two major axes: biomedical applications (protein networks, cancer relapse prediction, respiratory-motion modelling for radiotherapy) and industrial AI (porcelain defect detection, character recognition). The work shows a clear evolution from theoretical machine-learning foundations to practical, domain-specific implementations. Grants & Projects SIVAP (2016-2018): Intelligent ML & computer-vision system for porcelain manufacturing optimisation, UEFISCDI PN-III-P2-2.1-BG-2016-0333. CMRCC (2017-2018): Computational Models for Reproducing Ceramics Colors, UEFISCDI PN-III-P2-2.1-PED-2016-1835. Student Supervision & Mentoring Adriana has supervised more than 25 undergraduate and master’s theses. Her students have won multiple awards at national conferences such as In-Extenso and SCCSS-IEECC , covering topics from automated defect detection to web applications for academic scheduling. Teaching Responsibilities She teaches courses including Machine Learning , Mathematical Software , Fundamental Algorithms , Object-Oriented Databases , and Modelling and Simulation at both undergraduate and master levels.

Therese Donovanمشاهده پروفایل

استاد پژوهشی

• Wildlife Population Modeling
• Landscape Ecology
• Conservation Biology
• +۳ مورد دیگر

Therese Donovan is a Research Professor at the University of Vermont's Rubenstein School of Environment and Natural Resources and Assistant Unit Leader of the VT Cooperative Fish & Wildlife Research Unit. Her expertise spans wildlife population modeling, landscape ecology, and conservation biology. Donovan holds degrees from Eastern Illinois University (B.S. in Botany/Zoology, 1984) and the University of Missouri-Columbia (Ph.D. in Biological Sciences, 1994). Her research focuses on adaptive management for both game and non-game species, remote wildlife monitoring using cell phones, and structured decision-making frameworks. Donovan teaches online courses in Conservation Biology and Modeling, and directs the Spreadsheet Project, offering tutorials on quantitative data analysis in ecology. Recent work includes developing tools like AMMonitor2 and Shinymgr for biodiversity monitoring, and investigating moose population dynamics in Vermont. Her studies combine ecological modeling with practical applications, emphasizing reproducible workflows and human-machine collaboration in data analysis. Awards: None explicitly listed. Grants and advising: Not detailed in provided texts. Projects include the Adirondack Inventory & Monitoring Network and collaborations through the VT Cooperative Unit.