جست‌وجوی استاد و پژوهشگر

• Digital Agriculture
• Agricultural Informatics
• Data Science
• +۳ مورد دیگر

Ankita Raturi is an Assistant Professor in the Department of Agricultural & Biological Engineering at Purdue University, leading the Agricultural Informatics Lab. Her work focuses on human-centered design, information modeling, and software engineering to enhance resilience in food systems. She develops decision support tools for cover cropping, agronomic data services, and soil health technologies. Her research integrates digital agriculture applications across field crops, livestock, and food systems, emphasizing open-source solutions. Key projects include modular decision tools for diversified farming systems and information management frameworks for community food resilience. Publications highlight her expertise in sustainable tech adoption, precision agriculture, and ecosystem impact design. She actively bridges engineering and agriculture through interdisciplinary collaborations. Dr. Raturi's lab explores digital tools for smallholder farmers, pandemic response systems, and ecological wealth restoration through technology. She emphasizes trust-building in tech-mediated research and advocates for nonhuman-centric design in agroecosystems.

Junhong Chenمشاهده پروفایل

استاد

• Hybrid Nanomaterials
• 2D Nanomaterials
• Chemical and Biological Sensors
• +۳ مورد دیگر

Junhong Chen is the Crown Family Professor of Molecular Engineering at the University of Chicago's Pritzker School of Molecular Engineering and Lead Water Strategist at Argonne National Laboratory. His research focuses on hybrid nanomaterials, 2D materials, sensors for chemical/biological molecules, and energy devices. He has pioneered innovations in real-time water sensing and energy storage, with applications in environmental sustainability and healthcare. Chen holds a PhD from the University of Minnesota (2002) and a postdoc from Caltech (2003). He previously directed the NSF Industry-University Cooperative Research Center on Water Equipment & Policy and served as a NSF program director. Education: PhD in Mechanical Engineering (2002, University of Minnesota), Postdoc in Chemical Engineering (2002–2003, Caltech) Research Interests: Nanomaterials, Sensors, Energy Storage, Water Pollution Control Awards: Fellow of National Academy of Inventors, ASME, IAAM Medal, Wisconsin Innovation Award (2016) Chen's lab group develops nanosensors and energy devices using molecular engineering, with a focus on scalable manufacturing and AI integration. Recent work includes graphene-based sensors for real-time water monitoring and novel battery technologies. His research also addresses global challenges like PFAS contamination and sustainable manufacturing.

Rebecca Nugentمشاهده پروفایل

استاد

• Clustering Methodology
• Record Linkage
• Educational Data Mining
• +۵ مورد دیگر

Rebecca Nugent is the Stephen E. and Joyce Fienberg Professor of Statistics & Data Science and Department Head at Carnegie Mellon University. She holds a PhD in Statistics from the University of Washington (2006), an MS in Statistics from Stanford (2006), and a BA in Mathematics, Statistics, and Spanish from Rice University (2002). Her research spans clustering methodology , record linkage , educational data mining , public health , and semantic organization , with a focus on high-dimensional data and adaptive learning environments. She leads the Integrated Statistics Learning Environment (ISLE) and Corporate Capstone programs, emphasizing low-barrier data platforms for education and industry collaboration. Academic Roles : Department Head, Carnegie Mellon; Affiliated Faculty, Block Center for Technology and Society Research Grants : NSF (2017-2019), NIH (2018), Carnegie Mellon ProSEED/Simon Initiative (2020, 2018), Berkman Fund (2014) Her 15 most recent publications focus on data science pedagogy, clustering algorithms, record linkage applications in historical and medical data, educational data mining, and semantic organization studies. Awards include the ASA Waller Education Award (2015) and the William H. and Frances S. Ryan Award (2015) . She mentors a diverse group of PhD, Master's, and undergraduate students, with alumni pursuing careers in academia, industry, and sports analytics.

Giang Tranمشاهده پروفایل

دانشیار

• Sparse Modeling
• Compressed Sensing
• Image Processing
• +۲ مورد دیگر

Dr. Giang Tran is an Associate Professor in the Department of Applied Mathematics at the University of Waterloo, where she leads research in sparse modeling and computational mathematics. She holds a PhD from UCLA and previously served as a Bing Instructor at the University of Texas at Austin. Her research explores sparse optimization techniques with applications in medical imaging, dynamical systems, and data science. Recent publications focus on developing novel algorithms for sparse random feature expansions and dynamical system identification. She mentors numerous graduate and undergraduate researchers through projects on neural networks, transformers, and epidemic forecasting. Awards include the NSERC Discovery Grant and SIAM Student Paper Prize. Dr. Tran teaches advanced courses in numerical methods and functional analysis, contributing to curriculum development in computational mathematics.

• Cybersecurity
• Digital Forensics
• AI Security
• +۶ مورد دیگر

Assoc. Prof. Nhien An Le Khac is an Associate Professor at the School of Computer Science, University College Dublin. He serves as Programme Director for the MSc in Forensic Computing & Cybercrime Investigation, which has trained over 1,500 law enforcement officers globally. His research focuses on cybersecurity, digital forensics, AI security, and secure healthcare IT systems. He holds a PhD from Institut National Polytechnique de Grenoble (France) and has supervised 9 PhD students. His work includes pioneering contributions to electromagnetic side-channel analysis (EM-SCA) for IoT forensics, blockchain forensics, and AI-based fraud detection. Education: BSc/MSc: Vietnam National University, Ho Chi Minh City PhD: Institut National Polytechnique de Grenoble, France Professional Certificate in University Teaching & Learning: UCD Research Interests: Cybersecurity, Digital Forensics, AI Security, Machine Learning, Cloud Computing, Big Data Analytics, Healthcare IT Security. Recent Article Trends: Focus on EM-SCA for IoT device forensics, illicit Bitcoin transaction tracking, and cross-device ML portability. His work bridges theoretical AI advancements with practical forensic applications, emphasizing privacy preservation and explainable AI. Awards & Recognition: World’s Top 2% Scientists (2024) UCD Teaching Excellence Awards (2022, 2018) Best Paper Awards at Elsevier, AI-2022, and DFRWS conferences Grants & Advising: Principal Investigator on grants like Cloud Atlas, CERBERUS, and Urban ARK. Advised 9 PhD students who now work in academia/research globally. Active in funding initiatives like ML-Labs (SFI-funded). Labs & Teams: Leads ASEADOS Lab and maintains datasets like EM-SCA and InSDN. Collaborates globally on forensic frameworks and cybersecurity tools.

Sören Laueمشاهده پروفایل

استاد

• Machine Learning
• Optimization Algorithms
• GPU Computing
• +۳ مورد دیگر

Prof. Dr. Sören Laue is a Professor of Machine Learning at the University of Hamburg's Department of Informatics. His research focuses on optimization algorithms, machine learning frameworks, and high-performance computing. He leads the Machine Learning research group and developed the GENO optimization framework and the Matrix Calculus toolset. His work emphasizes GPU acceleration, tensor operations, and scalable solutions for classical machine learning problems. Projects: GENO solver (Python-based optimization), Matrix Calculus (derivative computation), and SQL-based tensor operations. Key Research Themes: Optimization frameworks, GPU computing, neural network scalability, and algorithm design. Selected recent publications highlight contributions to tensor calculus benchmarks, GPU-optimized machine learning pipelines, and novel optimization methods. His work bridges theoretical foundations and practical software tools for the machine learning community.

Jackie Chaمشاهده پروفایل

استادیار

• Human Factors and Ergonomics
• Healthcare Systems
• Surgical Robotics
• +۴ مورد دیگر

Dr. Jackie Cha serves as an Assistant Professor in the Department of Industrial Engineering within Clemson University's College of Engineering, Computing and Applied Sciences. Her research bridges human factors engineering with healthcare innovation, focusing on surgical robotics, physiological signal analysis, and wearable medical technologies to enhance clinical performance and safety. Her academic foundation includes advanced degrees from leading institutions: Ph.D. in Industrial Engineering from Purdue University M.S.E. in Biomedical Engineering from the University of Michigan B.S.E. in Biomedical Engineering from the University of Michigan Cha's research program centers on quantifying human performance in high-stakes medical environments through sensor-based metrics. She investigates nontechnical skills in surgical teams, mental workload during robotic procedures, and ergonomics of exoskeleton implementation in operating rooms. Her work integrates physiological signals, eye-tracking, and proximity sensors to develop objective assessment tools for surgical proficiency and team dynamics. Analysis of her 2023-2025 publications reveals consistent thematic focus on human-robot collaboration in surgery, with emerging trends in AI-driven workload detection (s-DResNet), neural correlates of surgical expertise, and environmental factors affecting robotic surgery outcomes. Key methodological approaches include scoping reviews of human-robot interaction metrics, mixed-methods evaluations of exoskeleton efficacy, and extended reality applications for nontechnical skills training. She leads the ECHO Lab (Engineering for Clinical and Human Outcomes) at Clemson, which develops translational solutions for healthcare human factors challenges. Her lab's work spans from fundamental physiological signal analysis to applied interventions in operating rooms and emergency medical settings.

Nazli Goharianمشاهده پروفایل

استاد بالینی

• Information Retrieval
• Text Mining
• Natural Language Processing
• +۱ مورد دیگر

Nazli Goharian is a Clinical Professor of Computer Science at Georgetown University and Associate Director of the Information Retrieval Lab. She holds a PhD from Florida Institute of Technology and joined Georgetown in 2010 after industry experience and previous academic positions at Illinois Institute of Technology. Education: PhD Computer Science, Florida Institute of Technology (2001) MSc Computer Science, George Mason University (1995) BSc Computer Science, Dortmund University (1992) Her research spans information retrieval, text mining, and natural language processing with applications in health/medical domains. She focuses on developing computational methods for medical search, mental health analysis from social media, clinical text summarization, and adverse drug reaction detection. Her recent publications (2020-2016) predominantly focus on neural ranking models, transformer architectures for document retrieval, and clinical NLP applications. Notable trends include work on BERT-based re-ranking, zero-shot multilingual retrieval, and ontology-aware medical summarization. Awards & Honors: EMNLP 2017 Best Long Paper Award COLING 2018 Honorable Mention & Area Chair Favorite Julia Beveridge Award for Faculty (IIT, 2009) Multiple Teaching Excellence Awards (2002-2007) Research Leadership: She has supervised 6 PhD students to completion with placements at leading institutions. Secured over $500,000 in research funding from NSF, Adobe, and international partners. Founded the Semi-Annual Graduate Research Presentation Days at Georgetown and served as Program Chair for ECIR 2024. She leads the Information Retrieval Lab which focuses on developing novel algorithms for efficient document retrieval, cross-lingual search, and specialized applications in healthcare text analysis.

Marco Valtortaمشاهده پروفایل

استاد

• Artificial Intelligence
• Bayesian Networks
• Causal Inference
• +۵ مورد دیگر

Marco Valtorta is a Professor and Graduate Director in the Department of Computer Science and Engineering at the University of South Carolina’s Molinaroli College of Engineering and Computing. He specializes in Artificial Intelligence, with a focus on normative reasoning under uncertainty, Bayesian networks, causal models, and computational complexity. His work includes developing algorithms for structure learning in graphical models, causal inference, and applications in multiagent systems. Education: Ph.D., Computer Science, Duke University (1987) M.A., Computer Science, Duke University (1984) Laurea, Electrical Engineering, Politecnico di Milano (1980) Research Interests: Dr. Valtorta’s work integrates logical and probabilistic reasoning, with contributions to causal models, chain graphs, and adversarial machine learning. His funded projects include collaborations with the Office of Naval Research (ONR), IARPA, and the U.S. Department of Agriculture (USDA). Notable collaborations include applying Bayesian networks to healthcare and developing frameworks for trustworthiness assessment in AI systems. Grants & Collaborations: Multi-institution IARPA project on Wigmorean/Bayesian networks for argumentation ONR-funded research on Markov properties of directed hypergraphs with Dr. Linyuan Lu Causal analysis for performance modeling of configurable systems His recent publications emphasize causal inference in AI, automated evaluation of text and sentiment analysis systems, and robustness of foundation models. He has pioneered algorithms for learning chain graphs and addressing adversarial attacks in probabilistic models.

• Closed-loop neurotechnology
• Neuromodulation
• Machine learning
• +۴ مورد دیگر

Dr. Eric Meyers is an Assistant Professor in the Department of Bioengineering at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. He holds a Ph.D. in Biomedical Engineering and dual Bachelor's/Master's degrees in Electrical Engineering from the same institution. His research focuses on closed-loop neurotechnology, neuromodulation, and bioelectronic medicine to enhance recovery from nervous system injuries. Key projects include developing wearable EMG sleeves for stroke rehabilitation and closed-loop neuromodulation systems to restore motor function. Education: B.S. (2012), M.S. (2018), Electrical Engineering; Ph.D. (2017), Biomedical Engineering – all from UTD His research interests span machine learning applications in neurorehabilitation, biomarker discovery for neurological conditions, and clinical translation of bioelectronic therapies. Recent work emphasizes wearable devices for real-time motor function assessment and neuromodulation-driven recovery strategies. Publications highlight advancements in EMG-based neural interfaces, closed-loop algorithms for stroke therapy, and innovative FES systems. His lab actively collaborates on projects funded by NIH and industry partnerships, with a focus on translating technologies to clinical settings.

Reza Curtmolaمشاهده پروفایل

استاد

• Cybersecurity
• Cloud Computing
• Network Security
• +۷ مورد دیگر

Reza Curtmola is a Professor in the Department of Computer Science at NJIT. His research focuses on cybersecurity, distributed systems, and network security with an emphasis on secure routing, cloud computing, and privacy-preserving technologies. He holds a Ph.D. in Computer Science from Johns Hopkins University (2007), an M.S. from the same institution (2003), and a B.S. from the Politehnica University of Bucharest (2001). Dr. Curtmola’s work addresses challenges in wireless mesh networks, vehicular communication systems, and mobile-cloud integration. His contributions include innovative solutions for secure network coding, distributed resource management (e.g., parking assignment systems), and auditable data storage mechanisms. He has developed middleware frameworks like Moitree for mobile-cloud applications and has explored defenses against side-channel attacks, cache leaks, and entropy-based network vulnerabilities. His research also extends to privacy in vehicular DSRC protocols, dynamic traffic optimization, and verifiable code review systems. He has published extensively on topics ranging from cryptographic defenses in distributed systems to practical implementations of remote data checking in untrusted clouds. Current research activities include advancing secure cloud infrastructure, improving mobile crowdsensing reliability, and mitigating threats in IoT-enabled urban environments. His work often bridges theoretical foundations with practical system implementations, emphasizing real-world applicability in smart cities and critical infrastructure systems.

Lei Liuمشاهده پروفایل

استاد

• Biostatistics
• Data Science
• Survival Analysis
• +۵ مورد دیگر

Lei Liu, PhD, is a Professor of Biostatistics, Medicine, and Statistics and Data Science at Washington University in St. Louis. He holds positions in the Roy and Diana Vagelos Division of Biology & Biomedical Sciences (DBBS), the Institute for Informatics, Data Science and Biostatistics (I2DB), and the Center for Biostatistics and Data Science (CBDS). His research focuses on biostatistical and data science methods, including survival analysis, longitudinal data modeling, and machine learning applications in healthcare. He collaborates with clinicians across disciplines like cardiology, ophthalmology, and addiction medicine. Dr. Liu’s work emphasizes high-dimensional omics data analysis, medical cost modeling, and joint multi-outcome models. He is an Associate Editor of Biometrics and a former member of the NIH Biostatistical Methods and Research Design Study Section. He mentors underrepresented minority researchers through the NHLBI PRIDE program.

• Evolutionary Biology
• Genetics
• Zoology
• +۹ مورد دیگر

Alfried Vogler is a Professor of Molecular Systematics with a joint appointment at Imperial College London's Department of Life Sciences (Silwood Park) and the Natural History Museum, London. His research focuses on the evolutionary and genetic mechanisms underlying insect biodiversity, particularly in beetles (Coleoptera), using advanced genomic techniques like metagenomics and environmental DNA (eDNA). He has held academic positions since 1995, becoming a Full Professor in 2006. Vogler is affiliated with the Georgina Mace Centre for the Living Planet, the Grantham Institute, and the Microbiome Network, contributing to interdisciplinary projects in ecology and conservation. Education: PhD in Bacterial Genetics (1988), University of Osnabrück, Germany MSc in Taxonomy and Biodiversity Biology degree (1984), University of Regensburg, Germany Research Interests: Vogler combines phylogenetics, population genomics, and bioinformatics to study species diversity across hierarchical levels (populations to insect orders). He explores community dynamics in complex environments, including tropical rainforests and soil ecosystems, using cutting-edge methods like metabarcoding and qPCR. His work emphasizes understanding trait evolution, dispersal constraints, and ecological interactions in biodiversity hotspots. Recent projects investigate the role of mitochondrial metagenomics in reconstructing evolutionary histories and predicting climate change impacts on communities. Academic Leadership: Since 1996, Vogler has directed the MSc in Taxonomy and Biodiversity and the MRes in Biosystematics programs, overseeing academic content and student research supervision. He also contributes to the Biodiversity, Evolution and Conservation MRes (UCL/NHM) and has been a module organizer for courses like Tree-of-Life and molecular systematics. Labs & Collaborations: Vogler leads the Molecular Systematics research group at the Natural History Museum, collaborating with Imperial College's Silwood Park campus. His work integrates with the SITE-100 project, aiming to build a global genomic framework for biodiversity synthesis. He also participates in DNAqua-Net, advancing genetic tools for aquatic ecosystem assessment.

• Machine Learning
• Data Mining
• Computer Vision
• +۳ مورد دیگر

Professor Xue Li is a faculty member in the School of Electrical Engineering and Computer Science at the University of Queensland. His research focuses on machine learning, data mining, and their applications in healthcare, materials science, and computer vision. He has authored over 300 publications, including seminal works on knowledge graph completion, video quality enhancement, and alloy design using machine learning. His work bridges theoretical advancements with real-world applications, such as clinical diagnosis andTinyML systems. Key research interests include graph representation learning, medical informatics, and efficient algorithms for multimedia data. Notable contributions include developing commonsense-enhanced relation extraction models and frameworks for compressed video reconstruction. His research also addresses challenges in federated learning and privacy-preserving genomics. Prof. Li has collaborated extensively with industry and academia, contributing to projects in RFID systems, electronic nose pattern recognition, and cybersecurity. His work is published in top-tier venues like IEEE Transactions and ACM conferences. Despite no listed awards, his prolific output underscores academic impact.

• Data Science
• Bioinformatics
• Open Science
• +۴ مورد دیگر

Philippe Rocca-Serra is a Researcher at the Oxford e-Research Centre (OeRC), University of Oxford, and an Associate Member of the Engineering Science Department. Affiliated with Kellogg College, his work focuses on advancing open science through FAIR data principles, interoperable metadata standards, and translational biomedical data systems. He holds a DPhil in Molecular Genetics from the University of Bordeaux, supported by an EMBO Fellowship, and has contributed to major initiatives like the ISA-Tab format, the FAIR Cookbook, and the Translational Data Catalog. His research spans data standards for omics technologies, semantic validation frameworks (e.g., BioValidator), and infrastructure for reproducible research. Key contributions include the ISA API platform, metabolomics standards (nmrML, mzTab-M), and FAIRification frameworks. He actively collaborates with global initiatives such as ELIXIR, the Common Fund, and the Precision Toxicology Initiative. Rocca-Serra’s work emphasizes bridging data producers and consumers through machine-actionable metadata, fostering interdisciplinary research and policy compliance. He leads projects on clinical trial metadata profiling and has published extensively on data governance, computational workflows, and the role of FAIR principles in drug discovery and pandemic preparedness. Education: Ecole Nationale Supérieure d'Agronomie de Rennes (Diplôme d'Ingénieur), University of Bordeaux (PhD, Molecular Genetics) Key Roles: Group Coordinator at OeRC, Co-Investigator on international grants Tools Developed: ISAcreator, COPO, FAIR Cookbook, Data Tags Suite (DATS) Awards: EMBO Fellowship (2001) Labs/Teams: Oxford Data Readiness Initiative, FAIR Implementation Network