جست‌وجوی استاد و پژوهشگر

• Statistical Network Analysis
• Machine Learning
• Data Privacy
• +۷ مورد دیگر

Tianxi Li is an Assistant Professor in the Department of Statistics at the University of Minnesota, Twin Cities, within the College of Science and Engineering. Their research integrates statistical methodology with applications in network science, data privacy, and biomedical data analysis. Their research interests lie at the intersection of statistics and network science, focusing on statistical modeling of complex networks , data privacy , network security , and biomedical applications such as neuroimaging and genomics. They develop adaptive and scalable methods for network estimation, community detection, and differential correlation analysis. The recent publications demonstrate a consistent focus on advancing statistical tools for network-structured data, with increasing applications in neuroscience and cancer genomics. The work spans theoretical development (e.g., network growth models) and practical applications (e.g., glioblastoma gene modules), reflecting a balance between methodology and real-world impact. Tianxi Li leads an active research program funded by the National Science Foundation, indicating recognition and support for their innovative work. Principal Investigator, Statistical tools for network security protection: from data privacy to threat detection , NSF (2024–2025) They advise graduate students in statistics and data science, though specific advisees are not listed. Their collaborative network includes researchers in biostatistics, computer science, and machine learning, as evidenced by co-authorships and interdisciplinary projects. Li's work contributes to the UN Sustainable Development Goals, particularly through advancements in data-driven solutions for secure and ethical data analysis.

Moncef Gabboujمشاهده پروفایل

استاد

• Machine learning
• Artificial intelligence
• Signal processing
• +۹ مورد دیگر

Professor Moncef Gabbouj is a distinguished academic and researcher currently serving as Professor of Signal Processing at the Department of Computing Sciences, Faculty of Information Technology and Communication Sciences, Tampere University, Finland. Previously, he held the same position at Tampere University of Technology before the merger in 2019. He has also held visiting professorships at prestigious institutions including Hong Kong University of Technology and Science, University of Southern California, and Purdue University. Ph.D. and MSc. in Electrical Engineering from Purdue University, USA (1989 and 1986) B.Sc. in Electrical Engineering from Oklahoma State University, USA (1985) Prof. Gabbouj's research spans multiple domains within signal and image processing, with a strong focus on machine learning applications. His primary research interests include artificial intelligence, machine learning, Big Data analytics, multimedia content-based analysis, indexing and retrieval, nonlinear signal and image processing, voice conversion, and video processing and coding. His work bridges theoretical advancements with practical applications across various industries, particularly in multimedia communications and biomedical applications. His extensive publication record demonstrates a clear evolution from traditional signal processing techniques toward more sophisticated machine learning and deep learning approaches. Recent work shows increasing focus on convolutional neural networks for various applications including ECG classification, video processing, financial time-series analysis, and image recognition tasks, reflecting the broader trend in the field toward deep learning methodologies while maintaining strong foundations in signal processing theory. IEEE Fellow (2011) Member, Finnish Academy of Science and Letters (2014) Knight, First Class, of the Order of the White Rose of Finland (2006) Nokia Foundation Recognition Award (2005) Nokia Foundation Visiting Professor Award (2012) Finnish Cultural Foundation for Art and Science Award (2017) TUT Foundation Grand Award (2015) Prof. Gabbouj has supervised 64 doctoral and 72 Master's theses, demonstrating his significant contribution to academic mentoring. His research has been supported by substantial funding, including research grants totaling 8.5 million Euro (2001-2015). He has served as Academy of Finland Professor during 2011-2015 and has been involved in numerous EU research projects including Horizon, ESPRIT, HCM, IST, COST, Tempus and Erasmus programs. As Editor, Guest Editor or member of the Editorial Board of 6 international scientific journals, he has significantly influenced the academic discourse in his field. He leads the Signal Analysis and Machine Intelligence (SAMI) research group at Tampere University and serves as the Finland Site Director of the NSF IUCRC funded Center for Visual and Decision Informatics. His research unit focuses on applying advanced machine learning techniques to solve complex problems in signal processing, computer vision, and multimedia analytics, with applications ranging from healthcare to multimedia communications and financial analysis.

Ali Ghodsiمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Natural Language Processing
• +۳ مورد دیگر

Ali Ghodsi is a Professor at the University of Waterloo and Director of the Data Science Lab, with affiliations at the Vector Institute. His research spans machine learning, deep learning, and artificial intelligence, with applications in natural language processing, bioinformatics, and computer vision. His group develops theoretical frameworks and algorithms for analyzing large-scale datasets, focusing on neural network architectures, knowledge distillation, and model efficiency. Current projects include deep learning for identity control, computational antibody design, and generative AI/large language models. Ghodsi has authored influential tutorials on diffusion models, graph neural networks, and large language models. Notable research contributions include computational methods for de novo peptide sequencing from mass spectrometry data, green simulation-assisted reinforcement learning, and efficient natural language processing models. His lab maintains collaborations with industry partners including Google, Amazon, and Roche.

Ragib Hasanمشاهده پروفایل

استاد

• Cybersecurity
• Cloud Security
• Internet of Things (IoT) Security
• +۸ مورد دیگر

Ragib Hasan is a Professor in the Department of Computer Science at the University of Alabama at Birmingham (UAB), affiliated with the College of Arts and Sciences. His research focuses on cybersecurity, with specialties in cloud security, IoT systems, digital forensics, and biomedical device security. He leads the Secure and Trustworthy Computing Lab (SECRETLab) and contributes to the UAB Center for Cyber Security and NIST Cloud Forensics Working Group. Education: M.S. and Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign, followed by a postdoctoral fellowship at Johns Hopkins University. Affiliations: NIST Cloud Forensics Working Group, UAB Center for Cyber Security. His research addresses threats in smart cities, autonomous vehicles, and healthcare technologies. Key interests include securing IoT networks, mitigating cyberattacks on critical infrastructure, and advancing forensic methodologies in cloud environments. Recent work emphasizes threat modeling for connected vehicles, medical devices, and AI-driven systems. Dr. Hasan’s funding comes from the Department of Homeland Security, NSF, ONR, and industry partners like Facebook, Google, and Amazon. His awards include the NSF CAREER Award (2014), Google RISE Award (2013), and Deutsche-Welle Best of Blogs (2014) for his BanglaBraille initiative. Grants & Projects: Supported by DHS, NSF, and corporate collaborations. Outreach: Founded Wikimedia Bangladesh, Shikkhok.com (STEM education platform), and contributed to Bangla and English Wikipedia. His lab develops frameworks like StreetBit for pedestrian safety and InSight for emergency alert systems, integrating Bluetooth beacon technology to enhance urban security and sustainability.

• Computer Vision
• Machine Learning
• Biomedical Informatics
• +۴ مورد دیگر

Charless Fowlkes is a Professor in the Department of Computer Science at the University of California, Irvine (UCI). His research focuses on computational vision, spanning human visual system understanding, machine vision systems, and applications in biomedical informatics and forensic science. He holds a Ph.D. from UC Berkeley (2005). His work integrates techniques from computer vision, AI, and applied mathematics to address challenges in automated biological data analysis, morphology, and spatial gene expression. Key research areas include forensic science (e.g., shoeprint matching via 3D reconstruction), biomedical applications (e.g., heart function mapping and pollen classification), and AI-driven systems for scene understanding. Recent projects include a $20M forensic science center funded by the National Institute of Justice. His publications emphasize geometric reasoning, 3D reconstruction, and adaptive learning algorithms. Notable contributions include developing algorithms for 3D human pose estimation with scene constraints, automated pollen identification via CNNs, and frameworks for cross-domain forensic analysis. His work bridges theoretical computer vision with real-world applications in forensics, healthcare, and environmental science.

Abraham Silberschatzمشاهده پروفایل

استاد

• Database Systems
• Operating Systems
• Storage Systems
• +۳ مورد دیگر

Abraham Silberschatz is the Sidney J. Weinberg Professor of Computer Science at Yale University. He previously served as Vice President of the Information Sciences Research Center at Bell Laboratories and held a chaired professorship at the University of Texas at Austin. His research focuses on database systems, operating systems, and network management. Silberschatz has advised over a dozen PhD students, many now in academia and industry. Education: Ph.D., Computer Science, Stony Brook University (SUNY) Research Interests: His work spans database systems, operating systems, storage systems, and network management. Notable contributions include foundational textbooks like Operating System Concepts and Database System Concepts , which have become industry standards. He has also developed innovative systems like DataPlay and contributed to projects such as NetInventory. Publications: His 15+ years of research include influential papers on database architecture, network routing, and distributed systems. Recent work explores leveraging non-volatile memory technologies in systems design. Awards: ACM Karl V. Karlstrom Outstanding Educator Award (1998) IEEE Taylor L. Booth Education Award (2002) VLDB Test of Time Award (2019) Multiple Bell Laboratories President's Awards for innovation Grants & Patents: Recipient of over two dozen grants and over four dozen patents, including foundational IP in multimedia storage and distributed systems. His team's HadoopDB project merged MapReduce and DBMS technologies. Labs/Teams: Collaborates with Prof. Robert Soulé on projects in database systems and networking, focusing on next-gen memory technologies. Active in mentoring graduate students and postdocs in their research group.

Bonnie Bergerمشاهده پروفایل

استاد

• Compressive Genomics
• Network Inference
• Structural Bioinformatics
• +۴ مورد دیگر

Bonnie Berger is the Simons Professor of Mathematics at the Massachusetts Institute of Technology and head of the Computation and Biology group at MIT's Computer Science and AI Lab. She holds additional appointments as an Associate Member of the Broad Institute, Faculty member of Harvard/MIT Health Science & Technology, and Affiliated Faculty of Harvard Medical School. Her career has been dedicated to pioneering computational approaches in molecular biology, where she has been instrumental in defining the field. Professor Berger's research focuses on designing algorithms to extract biological insights from large-scale data sets. Her work spans Compressive Genomics, Network Inference, Structural Bioinformatics, Genomic Privacy, and Medical Genomics. She actively collaborates with experimental biologists to maximize the power of computation for biological discovery, developing methods that address the challenges of modern high-throughput biological data. Her recent publications demonstrate a strong trend toward integrating machine learning with structural biology and genomic privacy. The articles show increasing sophistication in using deep learning for protein structure prediction, developing privacy-preserving techniques for genomic data sharing, and creating efficient algorithms for massive biological data sets. Her work bridges theoretical computer science with practical biological applications. Professor Berger's scientific recognition includes: Election to the National Academy of Sciences (2021) ISCB Accomplishments by a Senior Scientist Award SIAM Sonya Kovalevsky Lecture Prize Fellowships in ACM, ISCB, AMS, and other prestigious societies Multiple RECOMB Test of Time Awards NIH Margaret Pittman Director's Award She has mentored numerous students who have gone on to make significant contributions in computational biology, including Ellen Zhong, Yun William Yu, and Hyunghoon Cho. Her lab receives substantial research funding supporting projects in genomic privacy, structural bioinformatics, and compressive algorithms for biological data. Professor Berger serves on the Executive Editorial Board of the Journal of Computational Biology and multiple other editorial boards. The Computation and Biology group at MIT CSAIL, which she leads, is at the forefront of developing computational methods for biological discovery. The group combines expertise in algorithms, machine learning, and biology to tackle fundamental challenges in genomics and structural biology. They are currently organizing the Machine Learning in Structural Biology workshop at NeurIPS 2025, highlighting their leadership in this rapidly evolving interdisciplinary field.

Shawn Gomezمشاهده پروفایل

استاد

• Systems Biology
• Network Pharmacology
• Translational AI
• +۷ مورد دیگر

Dr. Shawn Gomez is a Professor in the Lampe Joint Department of Biomedical Engineering at UNC-Chapel Hill and North Carolina State University and in the Department of Pharmacology at UNC-Chapel Hill. He serves as the Executive Director of FastTraCS, a component of the NC TraCS Institute funded through the NIH CTSA Program, and is a UNC Lineberger Comprehensive Cancer Center member. His educational background includes a PhD in Biomedical Engineering from Columbia University (1999), an MS in Aerospace Engineering Sciences from the University of Colorado, Boulder (1993), and a BS in Aerospace Engineering Sciences from the same institution (1990). He completed postdoctoral training in Bioinformatics and Computational Biology at the Judith P. Sulzberger Columbia Genome Center and Institut Pasteur in Paris. Dr. Gomez's research spans systems biology , network pharmacology , and translational AI , with a focus on understanding cell signaling architecture in human disease. His lab develops computational approaches for network pharmacology and targeted cancer therapies, along with machine learning methodologies to address clinical needs and enhance clinical decision making. The research integrates computational modeling with experimental approaches to improve diagnostic and therapeutic interventions. His recent publications reveal a strong focus on kinome research, particularly in pancreatic cancer and understudied kinases, with increasing integration of machine learning techniques for predicting clinical outcomes and kinase-substrate relationships. The work spans from fundamental systems biology to translational applications in cancer therapeutics and surgical outcomes prediction. Scientific Awards: Leadership Advanced Program, UNC-Chapel Hill 2017 Chancellor's Entrepreneurship Boot Camp 2015 ACCLAIM Scholar (Academic Career Leadership Academy in Medicine) 2013-2014 UNC Research Council Award 2011 Carl Storm URM Fellowship 2008 UNC Junior Faculty Development Award 2006 Florence Gould Scholar 2005 Pasteur Foundation Fellow 2002-2005 Dr. Gomez directs the Gomez Lab, which focuses on systems biology, network pharmacology, and translational AI. The lab maintains several key resources including Darkkinome.org, FAAS, and IAS servers for kinome research. His work bridges computational biology with clinical applications, particularly in cancer therapeutics and surgical outcomes prediction through machine learning approaches.

Huiyan Liمشاهده پروفایل

دانشیار

• Biosensors
• Lab-on-a-chip Technologies
• Cancer Diagnostics
• +۴ مورد دیگر

Huiyan Li, PhD, P.Eng., is an Associate Professor in the Department of Biomedical Engineering at the University of Guelph. Her research focuses on developing micro/nanoscale biosensors and lab-on-a-chip technologies for cancer diagnostics and personalized medicine. Dr. Li holds a Ph.D. in Biomedical Engineering from McGill University and completed postdoctoral training at Harvard Medical School/Massachusetts General Hospital. Her multidisciplinary research integrates biosensing, micro/nanofabrication, bio-optics/electronics, and computational tools to study cancer molecular complexity. Current openings exist for M.A.Sc. students interested in biosensing research. Key research areas include extracellular vesicle analysis, multiplexed immunoassays, magnetic/nanoparticle-enhanced bioassays, and graphene-based biomedical sensors. Recent work emphasizes point-of-care diagnostics and enhanced protein detection via novel material integration. Teaching responsibilities include ENGG 6301 (Advanced Micro/Nano Biotechnology) and undergraduate courses in bio-instrumentation and biomedical signal processing. Her work spans biomaterials classification, microfluidic systems, and antimicrobial nanocomposite development. Research outputs emphasize scalable microarray formats, EV-based biomarker discovery, and sensor sensitivity enhancement through nanomaterial innovations. Current projects address EV concentration measurement, 3D antibody microarrays, and magnetic-responsive hydrogel discs for bioassay improvements.

Erwin Schoofمشاهده پروفایل

دانشیار

• Proteomics
• Mass Spectrometry
• Single-Cell Analysis
• +۷ مورد دیگر

Erwin Schoof is an Associate Professor at the Department of Biotechnology and Biomedicine , Technical University of Denmark. He leads the Cell Diversity Lab and focuses on advancing proteomics and mass spectrometry technologies. Expertise in single-cell proteomics , stem cell niches , and bioinformatics . Active in myelofibrosis and leukemia research , with applications in UN Sustainable Development Goals . Research Trends from 2025–2024 include: Machine learning-driven peptide sequencing (InstaNovo, InstaNexus). Single-cell resolution tools for mapping hematopoietic stem cells and tumor microenvironments . Biomarker discovery in chronic diseases and respiratory conditions . Supervision : Mentors multiple PhD students on single-cell proteomics , omics data analysis , and biotherapeutic production . Labs & Collaborations : Collaborates with international teams on plasma proteomics , 3D bioengineering , and advanced mass spectrometry workflows .

Wolfgang Nejdlمشاهده پروفایل

استاد

• Web Science
• Information Retrieval
• Artificial Intelligence
• +۴ مورد دیگر

Prof. Dr. Wolfgang Nejdl is a Professor at the Institute for Data Science within the Faculty of Electrical Engineering and Computer Science at Leibniz University Hannover. He serves as Executive Director of the L3S Research Centre and Leibniz Forschungszentrum Inclusive Citizenship. Web Science Information Retrieval Artificial Intelligence Deep Learning His recent research focuses on AI applications in medicine , multimodal data fusion , and ethical AI systems . Projects include CAIMed (AI in Causal Medicine) and DAISEC (AI & Cybersecurity). His publications span conferences like AAMAS, WWW, and SIGIR. Notable awards include membership in the National Academy of Science and Engineering (acatech) . Former students hold positions at institutions like Stanford, TU Dresden, and ETH Zürich. Current projects involve climate resilience AI , federated learning for healthcare , and quantum-inspired data science .

Chelsey R. Carterمشاهده پروفایل

استادیار

• Black feminist anthropology
• Health equity
• Neurodegenerative diseases
• +۸ مورد دیگر

Chelsey R. Carter is an Assistant Professor of Public Health in the Department of Social and Behavioral Sciences at Yale School of Public Health with a secondary appointment in the Department of Anthropology. A Black feminist anthropologist of medicine, public health, and race from St. Louis, Missouri, Dr. Carter's interdisciplinary work bridges public health, anthropology, and critical race studies to address health inequities, particularly focusing on neurodegenerative diseases like ALS in Black communities. Dr. Carter received her BA in Anthropology from Emory University (2012), followed by an MPH and PhD from Washington University in St. Louis (2021), and completed a Presidential Postdoctoral Fellowship at Princeton University (2022). Her educational background reflects her commitment to both anthropological theory and public health practice. Her research examines the relationship between social determinants of health (particularly anti-Black racism, socioeconomic status, and gender) and neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS) and motor neuron diseases. Dr. Carter employs ethnographic research and qualitative methodologies to investigate how embodied inequality impacts diagnosis, treatment, and engagement in clinical trials for Black individuals with ALS. She also leads research on precision medicine and genomic research in Black communities through The Black Genome Project and studies caregiving among persons impacted by ALS. Her scholarship has been funded by the National Science Foundation, the Andrew Mellon Foundation, and the Wenner Gren Foundation. Analysis of Dr. Carter's recent publication trends reveals a strong focus on health equity, particularly examining how systemic racism manifests in medical settings and contributes to health disparities. Her work spans multiple domains including neurodegenerative diseases, HIV/AIDS care, genomics, and mental health, with consistent attention to intersectional approaches that consider race, gender, and socioeconomic status. A notable pattern in her scholarship is the use of qualitative and mixed methods to center the lived experiences of marginalized communities, challenging dominant biomedical paradigms through a Black feminist lens. Principal's Development Fund Visiting Scholar (2024) Distinguished Teaching Award from Yale School of Public Health (2023) Funding from the National Science Foundation Support from the Andrew Mellon Foundation Wenner Gren Foundation grant Dr. Carter actively mentors students and has developed innovative teaching approaches, including her course 'Biomedical Justice: Public Health Critiques and Praxis,' which encourages students to bring their full selves into the classroom. Her research has been supported by significant grants that enable community-engaged work addressing health disparities. She emphasizes the importance of community partnerships in her research, ensuring that academic work serves and is accountable to the communities being studied. Dr. Carter is Founder and Director of The LEITH (Lived Experiences Igniting Transformations in Health) Lab, which addresses Black invisibility and misdiagnosis for rare neurodegenerative and genetic diseases. The lab brings together persons and caregivers impacted by rare diseases, researchers, clinical providers, educators, students, and activists to develop more humanizing and critical approaches in the rare neurodegenerative field. The LEITH Lab's vision centers on advancing anti-racist research, fostering awareness through education, creating palliative networks for support, and developing a pipeline of health equity scholars.

Gianmarco Pintonمشاهده پروفایل

دانشیار

• Biomedical Imaging
• Ultrasound Imaging
• Traumatic Brain Injury
• +۴ مورد دیگر

Gianmarco Pinton is an Associate Professor in the Department of Biomedical Engineering at the University of North Carolina at Chapel Hill. His research focuses on nonlinear ultrasound and mechanical wave propagation, with applications to medical imaging and therapy. He specializes in traumatic brain injury, shear shock waves, and ultrasound therapy. Ph.D., M.S., and B.S.E. in Biomedical Engineering/Physics from Duke University His lab develops physics and simulation tools for nonlinear wave propagation, aiming to create advanced diagnostic ultrasound methods. Key areas include traumatic brain injury, transcranial imaging, and therapeutic ultrasound. His recent work explores super-resolution imaging, brain motor circuits, and Alzheimer's disease vascular mapping using ultrasound. Article trends highlight innovations in transcranial ultrasound, super-resolution techniques, lung imaging, and neuromodulation. His publications address image degradation, contrast agents, and shear wave dynamics in neurological contexts.

• Microbiology
• Immunology
• Medicinal Chemistry
• +۴ مورد دیگر

Yan Liu is a Senior Lecturer in Glycosciences at Imperial College's Department of Metabolism, Digestion and Reproduction (Faculty of Medicine). She leads the Wellcome Trust-funded Carbohydrate Microarray Facility and co-leads the GlycoTWINNG Network. Her research focuses on glycan-mediated interactions in host-microbiota interfaces, with applications in infectious diseases and reproductive health. She holds a B.Sc. from Peking University and a Ph.D. from the University of Bristol, followed by postdoctoral work at Imperial College under Professor Ten Feizi FRS. Her academic journey includes roles as a Microarray Project Leader and co-investigator in the March of Dimes European Preterm Birth Research Centre. She is PI of a £1.34M Wellcome Trust grant maintaining Imperial's Carbohydrate Microarray Facility, advancing glycan array technologies. Research interests span microbiology, immunology, and biomolecular chemistry, with a focus on glycobiology. Key contributions include defining glycan interactions in pathogen-host systems (e.g., Candida albicans, adenoviruses), developing glycan probes, and analyzing vaginal microbiota impacts on preterm birth. Grants and collaborations include EPSRC Translational funding and the UK 'FluTrialMAP' consortium. Labs affiliations include the Glycosciences Laboratory and Imperial's Institute of Infection.

Joel Greenhouseمشاهده پروفایل

استاد

• Statistical Methodology
• Biostatistics
• Meta-Analysis
• +۴ مورد دیگر

Joel Greenhouse is a Professor of Statistics at Carnegie Mellon University (CMU), affiliated with the Department of Statistics & Data Science. He has been on the faculty since 1983 and held leadership roles, including serving as Associate Dean of the College of Humanities and Social Sciences from 1997 to 2002. He also holds an adjunct appointment as Professor of Epidemiology and Psychiatry at the University of Pittsburgh. His expertise spans statistical methodology, clinical trial design, and meta-analysis, with a focus on integrating data from multiple sources to address complex healthcare and public health challenges. Greenhouse earned his Ph.D. in Biostatistics from the University of Michigan and completed a postdoctoral fellowship at CMU. His research emphasizes developing statistical tools for observational studies, clinical trials, and meta-analytic frameworks, particularly in neurology, mental health, and public policy contexts. Notable contributions include analyzing the impact of media on youth suicide rates, improving aphasia classification through automated speech analysis, and evaluating highway safety through driver health data. Education: Ph.D. in Biostatistics, University of Michigan Affiliations: Adjunct Professor at University of Pittsburgh, Member of National Academy of Sciences’ committees Professional Service: Data and safety monitoring boards for NIH/VA studies, co-chair of Federal Motor Carrier Safety Administration review panels His awards include CMU’s Doherty Award for Education, Ryan Teaching Award, and E. Dunlop Smith Award for teaching excellence. His work bridges theoretical statistics with real-world applications, particularly in interdisciplinary collaborations across medicine, psychology, and public policy. Greenhouse’s recent articles highlight trends in leveraging large datasets for clinical insights (e.g., aphasiaBank), re-evaluating environmental and behavioral health associations, and advancing causal inference methods. His interdisciplinary approach ensures statistical rigor addresses societal challenges, from suicide prevention to highway safety.