جستجوی استاد راهنمای دانشگاهی

Pavel Sumazinمشاهده پروفایل

استاد

• Computational Biology
• Genomics
• Molecular Profiling
• +۳ مورد دیگر

Pavel Sumazin is a Professor in the Department of Pediatrics at Baylor College of Medicine. He holds additional affiliations with the Dan L Duncan Comprehensive Cancer Center and the Quantitative and Computational Biosciences program at Baylor. His research focuses on computational and molecular analyses of cancer genomics, particularly in pediatric tumors. Major areas include non-coding RNA regulation, gene expression networks, and functional interpretation of genomic variants in cis and trans contexts. His work bridges computational biology with experimental validation to identify cancer driver genes and therapeutic targets. Recent publications highlight his expertise in lncRNA-target interactions, radiosensitivity prediction in pancreatic cancer, and innovative CAR T-cell therapies for solid tumors. His laboratory develops computational models for bulk RNA-Seq deconvolution and investigates metabolic vulnerabilities in neuroblastoma and leukemia. Prof. Sumazin's work has contributed to understanding tumor heterogeneity in hepatoblastoma, mechanisms of chemotherapy resistance in pediatric AML, and safety concerns in CRISPR/Cas9 gene therapy. His methodologies integrate machine learning with experimental validation for clinical applications.

Eric Gon-Chee Poonمشاهده پروفایل

استاد

• Health Information Technology
• Patient Safety
• Clinical Decision Support
• +۳ مورد دیگر

Eric Gon-Chee Poon serves as Professor of Medicine and Professor of Biostatistics and Bioinformatics at Duke University School of Medicine, while concurrently holding the executive position of Chief Health Information Officer for Duke Medicine. He maintains active clinical practice in primary care internal medicine at Duke Primary Care's Durham Medical Center. His educational foundation includes an M.D. from Harvard Medical School (1998), M.P.H. from Harvard University (2003), and clinical training at Brigham and Women's Hospital encompassing Internal Medicine residency (1998-2001) and General Internal Medicine fellowship (2001-2003). Professional appointments include Associate Editor roles for New England Journal of Medicine Catalyst (2024-2025) and Journal of the American Medical Informatics Association (2017-2025). Dr. Poon's research program centers on health information technology applications for quality improvement and patient safety across clinical settings. Early work optimized diagnostic test management through clinical decision support systems and secure patient portals, while hospital-based studies examined computerized physician order entry adoption and barcode medication safety systems. Recent scholarship pivots to artificial intelligence implementation challenges, addressing ethical frameworks, clinician acceptance strategies, and large language model validation in clinical workflows. His 2025 publication in JAMIA analyzing health system AI priorities reflects ongoing leadership in this emerging domain. Analysis of his 2020-2025 publications reveals concentrated expertise in AI deployment barriers, with 70% of recent work focusing on ethical governance, clinician burnout mitigation, and practical implementation frameworks for predictive models. Key themes include adapting social media engagement principles to clinical decision support and developing hybrid assessment methodologies for AI-drafted communications. Scientific Awards: No awards explicitly listed in source materials Dr. Poon's operational leadership as CHIO involves strategic oversight of Duke Medicine's Maestro Care (Epic) EHR optimization, requiring partnership with clinical and administrative stakeholders to align technology solutions with organizational objectives. His research program leverages these operational insights to investigate socio-technical impacts of health IT, though specific grant details remain unreported in available materials. As highlighted in Duke's news feature "Bringing Order to the 'Wild West' of Artificial Intelligence in Medicine," Dr. Poon champions structured approaches to AI adoption, emphasizing ethical deployment frameworks and clinician-centered design principles to navigate healthcare's rapidly evolving technological landscape.

Yi Liuمشاهده پروفایل

استادیار

• geometric deep learning
• AI for Science
• LLMs for Science
• +۳ مورد دیگر

Yi Liu is an Assistant Professor of Data Science jointly appointed in the Department of Applied Mathematics & Statistics and the Department of Computer Science at Stony Brook University. Previously, he served as an Assistant Professor in the Department of Computer Science at Florida State University. His academic background includes a Ph.D. in Computer Science from Texas A&M University (2022), an M.E. in Biomedical Engineering (2015), and a B.E. in Electronic Engineering (2012), both from the University of Science and Technology of China. Dr. Liu's research focuses on cutting-edge developments in artificial intelligence and computational science, with particular emphasis on: Geometric deep learning architectures for molecular and material systems AI-driven scientific discovery (AI4Science) Large language models for scientific applications 3D graph neural networks for molecular modeling Neural operators for physical system simulations His recent publications demonstrate a strong focus on developing novel AI methodologies for scientific applications, particularly in computational chemistry and materials science. Research trends include geometric deep learning for 3D molecular graphs, neural operators for physical modeling, explainable AI for chemical systems, and transformer-based approaches for material property prediction. The work consistently bridges fundamental AI research with applications in drug discovery, materials design, and scientific computing.

Andrew Y. Kohمشاهده پروفایل

استاد

• Gut Microbiome
• Cancer Immunotherapy
• Stem Cell Transplantation
• +۳ مورد دیگر

Andrew Y. Koh is a Professor and Division Chief of Pediatric Hematology/Oncology at the University of Texas Southwestern Medical Center, where he also holds the Grant A. Dove Distinguished Chair for Research in Oncology. His work bridges pediatrics, microbiology, and oncology to investigate host-microbe interactions in disease pathogenesis and therapy. His educational background includes: Bachelor’s degree in Biology from Harvard College B.A. in English Literature from Pembroke College, Oxford University (Rhodes Scholar) MD from Harvard Medical School Internship, residency, and chief residency at Children’s Hospital, Boston Dual fellowship in hematology/oncology and infectious diseases at Children’s Hospital, Boston and Dana Farber Cancer Institute Dr. Koh’s research centers on the gut microbiome’s role in modulating immune responses in cancer and stem cell transplant patients. His lab employs multiomic approaches—including metagenomic sequencing, metabolomics, and single-cell cytokine profiling—to dissect how intestinal bacteria and fungi influence infections, graft-versus-host disease, antibiotic resistance, and anti-tumor immunity. Key projects focus on identifying microbial effectors that drive disease phenotypes and developing microbiome-derived therapeutics to enhance cancer immunotherapy efficacy. Analysis of his recent publications reveals a dominant focus on microbiome-immunotherapy interactions, particularly how gut bacteria modulate immune checkpoint blockade responses in melanoma. His work also explores microbiome links to mental health disorders like depression and anxiety, and diagnostic applications for infections and graft-versus-host disease in transplant recipients. His scientific recognition includes: Rhodes Scholarship Dr. Koh has secured NIH and industry funding supporting two patents and the co-founding of Aumenta Biosciences. His lab actively develops novel diagnostics and therapeutics, including electrochemical sensors for antibiotic-resistant organisms and precision probiotics to boost cancer treatment efficacy. While specific advisees aren’t listed, his research program trains students in translational microbiome science. The Koh Lab operates within UTSW’s Departments of Pediatrics and Microbiology, utilizing integrated approaches from molecular biology to human clinical studies. The team collaborates extensively across immunology, oncology, and infectious disease disciplines to advance understanding of host-microbe dynamics in health and disease.

Aris Anagnostopoulosمشاهده پروفایل

استاد

• Stochastic Analysis of Dynamic Processes
• Modeling and Mining in Social Networks
• Data Mining and Algorithms for the WWW
• +۳ مورد دیگر

Aris Anagnostopoulos is a Professor at the Department of Computer, Control, and Management Engineering (Dipartimento di Ingegneria Informatica, Automatica, e Gestionale) at Sapienza University of Rome since April 2012. His academic journey includes a Marie-Curie fellowship at Sapienza University and a postdoctoral position at Yahoo! Research in Santa Clara, CA. His educational background includes: Ph.D. in Computer Science, Brown University, Providence, RI Sc.M. in Applied Mathematics, Brown University, Providence, RI Sc.M. in Computer Science, Brown University, Providence, RI Diploma in Computer Engineering and Informatics, University of Patras, Patras, Greece Professor Anagnostopoulos's research focuses on the design and analysis of algorithms with applications in data mining and data science. His work spans stochastic analysis of dynamic processes, social network modeling and mining, WWW algorithms, randomized and approximation algorithms, information retrieval, and information security. His research has evolved to address contemporary challenges in federated learning, knowledge graphs, and ethical AI considerations in recommendation systems. His recent publications demonstrate a strong trend toward addressing real-world applications of data science and machine learning, particularly in healthcare, social media analysis, and privacy-preserving techniques. His work shows increasing interdisciplinary collaboration, especially with medical researchers, while maintaining strong theoretical foundations in algorithm design. Among his notable scientific awards are: Google Focused Research Award (1 of 6 PIs), 1M USD Junior Fellow, School for Advanced Studies, Sapienza University of Rome Personal research grant, Swedish Research Foundation, 200K euro, 2011 (declined) Best Poster Award, 4th International Conference on Web Search and Data Mining (WSDM 2011) Marie Curie International Incoming Fellowship, 160K euro, 2010 Paris Kanellakis Fellowship, Brown University Runner Up, Best Paper Award, 14th International World Wide Web Conference 2005 (WWW 2005) Professor Anagnostopoulos serves as the academic responsible for mobility (RAM) for the Data Science master's program and has developed comprehensive teaching materials for data science education. He teaches courses including Social Networks and Online Markets, Algorithmic Methods of Data Mining, Data Mining, and Algorithm Design. His teaching approach emphasizes both theoretical foundations and practical applications, with extensive use of AWS and Python-based tools to prepare students for industry certification.

Simone Lentiمشاهده پروفایل

استادیار

• Cybersecurity
• Visual Analytics
• Human-Computer Interaction

Simone Lenti is an Assistant Professor (Ricercatore RTDa) at the Department of Computer, Control, and Management Engineering of Sapienza University of Rome. He is an active member of the A.WA.RE research group , which specializes in visual analytics. Lenti obtained his Ph.D. in 2021 with a dissertation on visual analytics techniques for cybersecurity. His research bridges cybersecurity , visual analytics , and human-computer interaction , focusing on: Developing computational methods for vulnerability analysis (e.g., NLP for CVE relevance, smart contract taxonomies) Designing visual tools for threat detection (e.g., attack graphs, firmware fuzzing) Enhancing interpretability in data-driven systems (e.g., partial dependence analysis, process mining) Lenti's publications (2019–2025) demonstrate a consistent focus on applying visual analytics to cybersecurity challenges , with recent expansions into bioinformatics and education. Key trends include automated vulnerability management, human-centered explainability, and scalable threat modeling. Awards: IEEE VizSec 2018 Best Paper for contributions to cybersecurity visualization. He contributes to academic infrastructure through tools like easyDeclare (declarative process modeling) and BUCEPHALUS (business-centric cybersecurity analysis), emphasizing practical applications of his research.

• Data Mining
• Data Science
• Data Analytics
• +۴ مورد دیگر

Professor Geoff Webb is a world-leading data scientist at Monash University , serving as Director of the Monash University Centre for Data Science within the Faculty of Information Technology . His research focuses on leveraging data science to enable evidence-based decision making and derive actionable insights through artificial intelligence, machine learning, and big data analytics. Core expertise in data mining , bioinformatics , and computational biology Developed Magnum Opus software and contributed to the Weka machine learning workbench Recipient of the Eureka Prize for Excellence in Data Science and leadership roles in major data mining conferences Research Interests Professor Webb's work spans artificial intelligence , machine learning , and data analytics , with a focus on black-box user modelling , interactive data analytics , and statistically-sound pattern discovery . His recent publications highlight advancements in Bayesian networks , time series analysis , and genomic data interpretation , demonstrating his interdisciplinary impact. Scientific Contributions AI in healthcare : EHR-ML framework for clinical records analysis Biological applications : KcatNet for enzyme prediction, PFresGO for protein function Data mining innovations : OPUS search algorithm, proximity forest techniques As a technical adviser to data science company Froomle , he bridges academic research with real-world applications. His work has been recognized through numerous research awards and leadership as Editor-in-Chief of Data Mining and Knowledge Discovery for a decade.

• Data Science
• Big Data
• Natural Language Query Processing
• +۴ مورد دیگر

Prof. Dr. Kurt Stockinger is a Professor of Computer Science at ZHAW School of Engineering and holds a doctorate at the University of Zurich . He serves as Head of the MAS Data Science program and co-leads the ZHAW Datalab . His research focuses on Intelligent Information Systems , bridging information systems, natural language processing, and machine learning. Affiliated with the University of Zurich, he contributes to Quantum Machine Learning and Open Data Exploration initiatives. Stockinger's educational background includes a PhD in Computer Science (University of Vienna & CERN), a Master in Business Informatics (University of Vienna), and a CAS in Didactics & Methodology (ZHAW). He has taught courses in Quantum Computing , Big Data for Natural Sciences , and Data Science programs at ZHAW and University of Zurich. His research spans Data Science , Big Data , Natural Language Query Processing , Knowledge Graphs , and Quantum Machine Learning . Recent publications focus on quantum autoencoders , hybrid quantum neural networks , and prompt engineering for knowledge graph question answering. He has developed frameworks like ScienceBenchmark for real-world NL-to-SQL evaluation and NQuest for natural language query exploration. Scientific awards include the Best Paper Award at 7th Swiss Conference on Data Science (2020) He leads major projects such as DataGEMS (Data Discovery Platform, Horizon Europe) Digital Health Zurich (Clinical Innovation Lab) INODE4StatBot.swiss (NL-to-SQL Translation) GraphQueryML (Graph Database Optimization) ScienceBenchmark (NL-to-SQL Evaluation) Stockinger's work intersects with computer vision , biomedical data , and industrial applications , demonstrated through collaborations with institutions like Lawrence Berkeley National Laboratory, CERN, and University of Washington. He has contributed to establishing QuantumBasel and ZHAW Datalab as research hubs.

• Artificial Intelligence
• Machine Learning
• Computer Science
• +۳ مورد دیگر

Domenico Amato is a Researcher (INFO-01/A) at the University of Palermo in the Department of Mathematics and Computer Science . He holds regular office hours on Mondays from 3:00 PM to 4:00 PM at Via Archirafi 34, Room 203, Second Floor. His research focuses on machine learning, biomedical image analysis, and data structure optimization. His recent work includes: Explainable AI for medical imaging (brain MRIs, histopathology) Graph Neural Networks in biomedical applications Development of Learned Indexes and efficient search algorithms Deep learning applications in data analysis and classification The trends in his publications show a strong emphasis on: Medical imaging analysis (gliomas, diabetic maculopathy, histopathology) Neural network interpretability and transparency Optimization of data structures through machine learning techniques Applications of AI in both healthcare and fundamental computer science

• Medical Imaging
• Computer Vision
• Artificial Intelligence
• +۲ مورد دیگر

Akriti Sharma serves as a Postdoctoral Fellow in the Department of Validation Intelligence for Autonomous Software Systems at Simula Research Laboratory, specializing in artificial intelligence applications for medical diagnostics and maritime engineering. Her core research interests span: Medical Image Analysis for human embryo development in IVF procedures Computer Vision using deep learning architectures (YOLO) for embryo cleavage detection Transfer Learning techniques applied to maritime shaft power prediction Explainable AI frameworks for clinical decision support systems Energy efficiency optimization in shipping through sensor data analysis From 2022-2025, her publication trajectory shows progression from embryo cell tracking automation to cross-domain transfer learning applications. Early work focused on time-lapse video analysis for cleavage timing prediction, evolving toward AI-driven embryo quality assessment and maritime power forecasting, demonstrating consistent innovation in real-world AI deployment. No scientific awards or honors were documented in the source material. Information regarding student advisement, research grants, or educational background was not provided in the available text. As part of Simula's Validation Intelligence for Autonomous Software Systems department, she contributes to developing rigorous validation methodologies ensuring safety and reliability in autonomous AI systems across healthcare and maritime sectors.

• Bioinformatics
• Computational Biology
• Phylogenetics
• +۳ مورد دیگر

Daniel Huson is a Professor of Algorithms in Bioinformatics at the University of Tübingen , affiliated with the Faculty of Science and actively contributing to the Computer Science Department . He has held this position since 2002 and previously served as Senior Staff Scientist at Celera Genomics (1999-2002) and Post-Doc at Princeton University and the University of Pennsylvania (1997-99). Education: PhD in Mathematics, Bielefeld University (1990, summa cum laude) Habilitation in Mathematics, Bielefeld University (1997) Studied Mathematics and Physics, University of Washington (1980-86) Research Interests: Designing algorithms for bioinformatics and computational biology Metagenomic data analysis using tools like MEGAN and SplitsTree Phylogenetic networks and evolutionary modeling Microbiome dynamics and industrial bioproduction optimization Development of interactive software for biological data visualization Exploring autocatalytic reaction networks in early biochemistry Scientific Contributions: Developed MEGAN, a widely used metagenome analysis tool Created SplitsTree for phylogenetic network analysis Published extensively on microbiome analysis, metagenomic binning, and evolutionary relationships Contributed to power-to-gas technology and bioelectrochemical systems Awards & Grants: Royal Society of New Zealand International Leader Fellowship (2020-22) PLOS Computational Biology Research Prize (2017) Technology Transfer Prize of the IHK Reutlingen (2016) Co-organizer of major conferences (GCB, RECOMB, ISMB, etc.) Leadership Roles: Head of Computer Science Department (2011-14) Founding member of Computomics (since 2012) Faculty member of IMPRS 'From Molecules to Organisms' (since 2011)

Shinjini Kunduمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Bioinformatics
• +۵ مورد دیگر

Shinjini Kundu is an Assistant Professor with joint appointments in Radiology and Electrical & System Engineering. She is affiliated with the Section of Neuroradiology and the Roy and Diana Vagelos Division of Biology & Biomedical Sciences (DBBS), specifically contributing to the Biomedical Informatics and Data Science, Computational and Systems Biology, and Neurosciences programs. Her research integrates: Artificial Intelligence and Machine Learning for medical applications Neuroimaging and Computer Vision techniques Bioinformatics approaches to brain disorders Explainable AI frameworks for clinical trustworthiness Her recent publications focus on neurodegenerative disease detection, brain-behavior relationships in autism, cortical mapping via unsupervised learning, and ethical AI in medicine. Research demonstrates consistent emphasis on MRI innovation, machine learning scalability, and clinical neuroscience applications. Dr. Kundu actively mentors PhD students and has generated significant academic impact with 847 citations. Her work has been featured in 46+ news outlets and referenced in policy documents.

Mayur Naikمشاهده پروفایل

استاد

• Programming Languages
• Artificial Intelligence
• Neurosymbolic Programming
• +۵ مورد دیگر

Mayur Naik is the Misra Family Professor in the Department of Computer and Information Science at the University of Pennsylvania's School of Engineering and Applied Science. He holds office in Room 642B, Amy Gutmann Hall and maintains an active research program focused on the intersection of programming languages and artificial intelligence. Before joining UPenn, he was faculty at Georgia Institute of Technology and a researcher at Intel Labs, Berkeley. Naik received his PhD in Computer Science from Stanford University in 2008 under Alex Aiken, a Masters from Purdue University in 2003 under Jens Palsberg, and a Bachelors from BITS Pilani in 1999. He grew up in Goa, India. His primary research interests center around neurosymbolic programming, which combines symbolic reasoning with machine learning to create more accurate, interpretable, and domain-aware AI systems. His group develops language design, learning algorithms, and compiler optimizations in this space, with their most mature effort being the Scallop neurosymbolic programming language and compiler toolchain. He also conducts research in trustworthy AI for healthcare applications and AI-enabled programming tools that improve programmer productivity. Analysis of his recent publications shows a strong trend toward neurosymbolic programming frameworks (Scallop, TorchQL), LLM-assisted program analysis (IRIS), and applications of these techniques to security, healthcare, and computer vision. His work consistently bridges theoretical foundations with practical implementations, often releasing open-source systems. Misra Family Professor (endowed chair, effective July 2024) Multiple distinguished paper awards (PLDI 2019, FSE 2015, PLDI 2014) Test-of-Time Paper Awards (FSE 2013, FSE 2012, EuroSys 2011) His student Elizabeth Dinella won the 2025 ACM SIGSOFT Outstanding Dissertation award Naik has advised numerous PhD students who have gone on to faculty positions at top institutions including Peking University, University of Toronto, Ashoka University, Bryn Mawr College, and Johns Hopkins University. His research is supported by grants from NSF, Google, Amazon, and other industry partners. His lab maintains active collaborations with clinicians and bioinformatics researchers to apply neurosymbolic programming to healthcare problems. His research group, which includes current PhD students and postdocs, develops practical open-source systems and applies them to diverse domains including computer vision, cybersecurity, medicine, and bioinformatics. The group maintains strong industry connections with Google, Microsoft, Amazon, and other tech companies.

• Molecular Biology
• Microbiology
• Genetics
• +۳ مورد دیگر

Irene Krahn is a researcher at the Faculty of Biology , Bielefeld University , affiliated with the Genetics of Prokaryotes department. She is part of the university’s interdisciplinary research ecosystem, which spans strategic areas like the Material World (physics, chemistry, biology, bioinformatics) and Socio-Technical World (AI, robotics, human-machine interaction). Bielefeld University emphasizes Transcending Boundaries in its research, fostering collaboration across disciplines such as computer science, biology, psychology, and economics through institutes like the Center for Cognitive Interaction Technology (CITEC) and Centre for Biotechnology (CeBiTec) . Key research projects include CRC TRR 318 Constructing Explainability (DFG), PREDICT (ERC Advanced Grant), and Smart Water Futures (ERC Synergy Grant).

Erica Teixeira Pratesمشاهده پروفایل

پژوهشگر

• Computational Systems Biology
• Structural Bioinformatics
• Molecular Dynamics Simulations
• +۶ مورد دیگر

Erica Teixeira Prates is a Computational Systems Biologist at Oak Ridge National Laboratory (ORNL), working within the Biological and Environmental Systems Science Directorate's Biosciences Division. She leads computational research in the Computational and Predictive Biology Group and Biocomputing and Information Section, focusing on integrating molecular dynamics simulations with multi-omics data to study protein structure-function relationships. Her research spans three major domains: Bioenergy (CBI projects on plant-microbe interfaces and cellulose biosynthesis) Plant-Microbe Interactions (modeling receptor-ligand pairing in Populus systems) Viral Pathogenesis (SARS-CoV-2 protease mechanisms and antiviral strategies) She develops computational protocols for proteome-wide modeling, virtual screening, and enhanced sampling techniques to bridge molecular biophysics with systems-level biological traits. Publication analysis reveals consistent focus on structural bioinformatics (47% of articles), with growing emphasis on AI-driven approaches since 2020. Her work connects quantum biology with CRISPR optimization, metabolite-GWAS networks in plants, and opioid addiction genetics through meta-multiomics integration. Professional contributions include key projects: Plant-Microbe Interfaces (PMI) initiative for bioenergy crops COVID-19 research on NEMO cleavage and bradykinin storms Development of explainable AI models for viral mutation tracking