جست‌وجوی استاد و پژوهشگر

• Behavioral Economics
• Decision Theory
• Game Theory
• +۸ مورد دیگر

Jose Apesteguia is an ICREA Research Professor at the Department of Economics and Business, Universitat Pompeu Fabra, Barcelona, Spain. His research focuses on behavioral economics, decision theory, experimental economics, and game theory, with particular emphasis on topics such as behavioral heterogeneity, rationality measures, stochastic choice models, and social preferences. Apesteguia has collaborated extensively with scholars like Miguel A. Ballester and Jörg Oechssler, producing influential work on topics ranging from imitation dynamics to the impact of language on moral decisions. His research integrates theoretical frameworks with experimental methods, exploring how individuals make decisions under uncertainty, time preferences, and social contexts. Key contributions include the development of measures of rationality and welfare, as well as analyses of behavioral adaptation and the role of information in competitive environments. His work frequently bridges economic theory with empirical validation, addressing real-world issues such as rule compliance in public institutions and team performance in organizations. Apesteguia’s publications span top journals including the American Economic Journal: Microeconomics , Journal of Economic Theory , and Econometrica . He teaches advanced courses on bounded rationality and behavioral decision theory at the undergraduate and graduate levels. His research has been applied to fields like finance (e.g., copy trading behavior) and public policy (e.g., promoting compliance in libraries).

• Hematopathology
• Molecular Diagnostics
• Flow Cytometry
• +۷ مورد دیگر

Alexa Siddon, MD, is an Assistant Professor in the Department of Laboratory Medicine at Yale School of Medicine, with secondary appointments in Pathology. She serves as Hematopathology Fellowship Director, Director of the Molecular Diagnostics Laboratory, Director of Flow Cytometry, and Associate Director of the Hematology Laboratory. She is also affiliated with the Yale Cancer Center, Clinical Flow Cytometry Laboratory, and the Hematopathology Laboratory at the Veteran Affairs Connecticut Healthcare System. Dr. Siddon's research focuses on hematopathology, particularly myeloid malignancies, acute leukemias, and post-transplant monitoring. Her work integrates molecular diagnostics, flow cytometry, and genomic analysis to improve patient diagnosis and outcomes. She has co-authored over 10 peer-reviewed articles in recent years, frequently collaborating with leading hematologists and pathologists such as Christopher Tormey, Henry Rinder, and Rory Shallis. Her publications reveal a strong trend in translational research, emphasizing the clinical implications of genetic mutations (e.g., TP53, NPM1, IDH1/2), chimerism testing post-transplant, and immunophenotypic characterization of hematologic malignancies. The research spans disciplines including oncology, molecular biology, immunology, and clinical pathology, with a clear focus on precision diagnostics and prognostic modeling. Scientific Awards: ASCP Distinguished Educator Award Paul J. Strandjord Young Investigator Award Donald H. Buchholz Research Prize Dr. Siddon is actively involved in medical education, directing fellowship programs and mentoring trainees. She has no reported grants listed in the text, but her leadership in diagnostic laboratories and active publication record suggest ongoing research funding. She is not part-time, not retired, and remains a current, active faculty member. She is affiliated with multiple research entities including the Cancer Immunology Center, Center for Biomedical Data Science, and the Yale Cancer Center, contributing to interdisciplinary teams focused on hematologic malignancies and diagnostic innovation.

Ana Maria Velezمشاهده پروفایل

دانشیار

• Insect Toxicology
• Environmental Toxicology
• Resistance Management
• +۷ مورد دیگر

Ana Maria Velez is an Associate Professor at the Department of Entomology, University of Nebraska-Lincoln. Her research focuses on insect responses to chemical stressors, particularly RNA interference (RNAi) and Bt toxins for pest management. With a 80% research and 20% teaching appointment, she leads the Insect Toxicology Lab and teaches courses like 'Toxins in the Environment' and 'Insecticide Toxicology.' Education: Ph.D. in Entomology, University of Nebraska-Lincoln, 2013 M.S. in Entomology, Universidad Nacional de Colombia, 2009 B.S. in Biology, Pontificia Universidad Javeriana, Colombia, 2006 Her research spans molecular, organismal, and population levels to evaluate transgenic crops and RNAi technologies. Key areas include resistance mechanisms, non-target effects, and risk assessment frameworks. She has extensive publications on western corn rootworm and fall armyworm, emphasizing sustainable pest control. Her work also addresses sublethal impacts on non-target species like monarch butterflies and honeybees. Recent articles highlight RNAi delivery optimization, Bt resistance dynamics, and ecological impacts of insecticides. Her lab collaborates on patents for RNAi-based pest suppression methods targeting chromatin remodeling and developmental genes. Scientific Awards Distinguished Multicultural Alumni (2019) DuPont Young Professor Award (2016) International Congress of Entomology Travel Awards (2016) Widaman Trust Distinguished Graduate Assistant (2011) Milton E. Mohr Teaching Fellowship (2012) The Vélez Arango Lab investigates durability and safety of insect control technologies, with emphasis on RNAi and Bt crops. Their work informs integrated pest management (IPM) systems and regulatory frameworks.

Laurie Williamsمشاهده پروفایل

استاد

• Software Security
• Agile Software Development
• Software Supply Chain Security
• +۴ مورد دیگر

Laurie Williams serves as a Goodnight Distinguished University Professor in the Computer Science Department within the College of Engineering at North Carolina State University. She co-directs both the NCSU Secure Computing Institute and the NC State Science of Security Lablet, demonstrating deep institutional leadership in cybersecurity research. With over 260 refereed publications, her work establishes her as a prominent figure in software security academia. Her research spans critical areas including software security, agile development practices (particularly continuous deployment), software reliability, and software supply chain security. Williams focuses on practical security solutions addressing modern challenges like malicious dependencies in open-source ecosystems, AI-generated code vulnerabilities, and runtime protection mechanisms. Her work bridges theoretical security principles with industry-relevant applications. Recent publications reveal strong trends toward software supply chain security, with multiple 2024-2025 papers addressing vulnerability exploitability, malicious commit detection, and metrics-driven security control selection. Her research increasingly incorporates machine learning for threat detection while maintaining focus on human factors in secure development practices. IEEE Fellow (2018) National Science Foundation CAREER Award (2004) ACM SIGSOFT Influential Educator Award (2009) Multiple IBM Faculty Awards (2002-2012) NCSU Alumni Association Outstanding Research Award (2015-2016) Williams leads multiple major NSF-funded projects including the $5.7M SaTC Frontiers grant on secure software supply chains and the Science of Security Lablet with $3.6M in DoD funding. Her research emphasizes practical industry impact through collaborations with Cisco and Laboratory for Analytic Sciences. She actively mentors through the NCSU Research Leadership Academy and maintains significant educational outreach in software security. Her laboratory work centers on the Secure Computing Institute and Science of Security Lablet, where her team develops frameworks for vulnerability prediction, supply chain risk assessment, and secure development methodologies. Current projects focus on machine learning integrity, cognitive modeling for security decisions, and empirical analysis of build/deployment logs for anomaly detection.

• Autism Spectrum Disorders
• Medical Genetics
• Genomic Analysis
• +۳ مورد دیگر

Deqiong Ma is an Assistant Professor in the Department of Genetics at Yale University School of Medicine and Associate Director of the DNA Diagnostic Laboratory. She holds an MD from Tongji Medical University (1991), a PhD from the University of Tasmania (2003), and completed a postdoctoral fellowship at Duke University and a clinical fellowship at Albert Einstein College of Medicine. Her research focuses on genetic and genomic mechanisms underlying autism spectrum disorders, particularly copy number variants (CNVs), structural variation analysis, and clinical diagnostic methodologies. Key research interests include identifying novel genetic risk factors for autism using advanced genomic techniques, such as homozygosity mapping and fine-scale structural variation analysis. Her work bridges clinical genetics and molecular biology, with applications in diagnostic testing and understanding neurodevelopmental disorders. She collaborates extensively on studies involving autism candidate genes (e.g., MBD5, TBL1X) and genomic pathway analysis. Publications emphasize translational research, including diagnostic improvements for pediatric patients and elucidating genetic architecture in autism. She leads efforts in the DNA Diagnostic Lab to integrate genomic data into clinical practice, focusing on regions of homozygosity and uniparental disomy.

Philip Booneمشاهده پروفایل

عضو هیئت علمی

• Medical Genetics
• Genomics
• Pediatric Genetics
• +۴ مورد دیگر

Philip Boone, MD, PhD, is an Attending Physician in the Division of Genetics and Genomics at Boston Children's Hospital and an Instructor of Pediatrics at Harvard Medical School. He specializes in medical genetics with particular expertise in rare disorders, medical mysteries, deletion and duplication syndromes, and Cornelia de Lange syndrome. Dr. Boone sees patients at Boston Children's Brookline location (2 Brookline Place, 7th Floor) and provides comprehensive genetic care including diagnostics, counseling, and individualized management. Stanford University (Undergraduate, 2006) Baylor College of Medicine (Graduate & Medical School, 2013-2014) Boston Combined Residency Program (Internship & Residency, 2016-2020) Harvard Medical School Genetics Training Program (Fellowship, 2020) Dr. Boone's research focuses on neurodevelopmental disorders, chromatin regulation, and genetic diagnostics. His work spans from fundamental genetic mechanisms to clinical applications, with particular emphasis on cohesinopathies including Cornelia de Lange syndrome. He has contributed significantly to understanding genetic variants associated with growth disorders, developmental features, and structural chromosomal abnormalities. His research combines advanced genomic technologies with clinical insights to improve diagnosis and management of rare genetic conditions. Analysis of Dr. Boone's publication record reveals a strong focus on medical genetics with emphasis on neurodevelopmental disorders, chromatin regulation, and genetic diagnostics. His work spans basic research on gene function and regulation to clinical applications in rare disease diagnosis. A notable trend is his investigation of cohesin complex disorders, particularly SMC3 variants and their relationship to Cornelia de Lange syndrome. His publications demonstrate expertise in both traditional genetic analysis and cutting-edge genomic technologies including long-read sequencing and telomere-to-telomere assembly. Dr. Boone actively contributes to medical education through publications on genetic diagnostics and distance learning resources for medical genetics. He has co-authored educational materials that help advance the field's knowledge base and training capabilities. As an attending physician in the Division of Genetics and Genomics at Boston Children's Hospital and a research fellow in the Center for Genomic Medicine at Massachusetts General Hospital, Dr. Boone works within one of the largest pediatric genetics practices in the country. The division includes over 30 board-certified clinical geneticists, genetic counselors, dieticians, and nursing staff who provide comprehensive care for patients with both common and extremely rare genetic conditions.

Ben Raphaelمشاهده پروفایل

استاد

• Bioinformatics
• Computational Biology
• Cancer Genomics
• +۶ مورد دیگر

Ben Raphael is a Professor in the Department of Computer Science at Princeton University, with affiliations at the Lewis-Sigler Institute for Integrative Genomics, Omenn-Darling Bioengineering Institute, and Center for Statistics and Machine Learning. He is also an Affiliate Faculty member at the Rutgers Cancer Institute of New Jersey, Irving Institute for Cancer Dynamics at Columbia University, and New York Genome Center. His research focuses on computational methods for analyzing large-scale biological data, emphasizing cancer evolution, network/pathway analysis, and structural variation in genomes. Research Trends: His recent work spans cancer lineage trees, spatial transcriptomics, optimal transport for developmental models, and network analysis of mutations. Articles highlight applications in prostate cancer, pancreatic cancer, and single-cell genomics. Scientific Awards: 2024 ACM Fellow 2023 RECOMB Test of Time Award 2022 RECOMB Test of Time Runner-Up 2021 ISCB Innovator Award 2021 RECOMB Best Paper Runner-Up 2020 ISCB Fellow 2020 AACR Team Science Award 2011 NSF CAREER Award 2013 RECOMB Best Paper 2010-2012 Sloan Research Fellowship Advising: He has mentored numerous Ph.D. students and postdoctoral fellows, many of whom have transitioned to academic and industry roles. Current advisees include Uthsav Chitra, Gillian Chu, and Alexander Strzalkowski. Labs & Teams: Raphael leads the Raphael Lab at Princeton, developing tools like HotNet2, CHISEL, and HATCHet for cancer genomics and network analysis.

• Systems Biology
• Cancer Research
• Drug Resistance
• +۷ مورد دیگر

Sampsa Hautaniemi is a Professor at the Department of Biochemistry and Developmental Biology within the Faculty of Medicine at the University of Helsinki. He serves as Principal Investigator of the Systems Biology of Drug Resistance in Cancer research group and holds docentship in the Faculty of Medicine. Professor Hautaniemi actively supervises doctoral students across multiple programs including the Doctoral Programme in Biomedicine, Doctoral Programme in Clinical Research, and Doctoral Programme in Integrative Life Science. His research focuses on systems biology approaches to understand drug resistance mechanisms in cancer, particularly ovarian cancer. His work integrates computational biology, genomics, epigenetics, and bioinformatics to develop precision medicine approaches for cancer treatment. His laboratory develops innovative computational methods and experimental models including patient-derived organoids to study tumor evolution, identify therapeutic targets, and predict treatment responses. Analysis of his recent publications reveals a strong emphasis on understanding tumor heterogeneity, clonal evolution during therapy, and the development of computational tools for cancer genomics. His work spans multiple disciplines including cancer biology, computational biology, immunology, and precision medicine, with a particular focus on high-grade serous ovarian cancer as a model system. Scientific Awards: The Anders Jahre Medical Prize to young medical scientists (2014) for outstanding research on systems biology and cancer The Finnish Medical Foundation 50-years jubileum award (2010) Professor Hautaniemi has supervised numerous doctoral students and early-career researchers, contributing significantly to cancer research education. His research is supported by multiple active projects including the DECIDER project (Clinical Decision via Integrating Multiple Data Levels to Overcome Chemotherapy Resistance in High-Grade Serous Ovarian Cancer) funded until 2026, as well as projects from the Academy of Finland, Cancer Foundation, and industry partners like Orion Corporation. His laboratory maintains a patient-derived organoid biobank for high-grade serous ovarian cancer and develops computational tools like Jellyfish for visualizing tumor evolution.

Stephen Bustinمشاهده پروفایل

استاد

• Cancer biomarkers
• Molecular genetics
• Reverse transcription
• +۵ مورد دیگر

Stephen Bustin is a Professor of Molecular Medicine at Anglia Ruskin University , UK. He has held multiple academic positions, including Professor of Molecular Science at Queen Mary University of London (2004-2012) and Senior Lecturer/Reader at Barts and The London School of Medicine & Dentistry. Academic Ranks: Professor (Molecular Medicine, Anglia Ruskin University, 2014-present) Education: PhD in Molecular Genetics, Trinity College Dublin Research Interests: Bustin's work focuses on improving molecular diagnostic techniques, particularly PCR, with applications in infectious disease testing (e.g., SARS-CoV-2) and cancer biomarkers (e.g., colorectal cancer). He pioneered the MIQE guidelines for standardized qPCR reporting and developed rapid diagnostic assays like CoV2-ID for point-of-care testing. Publication Trends: Recent articles emphasize PCR standardization, reproducibility challenges, and rapid diagnostic solutions for SARS-CoV-2. His work spans molecular diagnostics, assay design, and scientific integrity, with a strong focus on clinical applications. Awards: 2010 : Fellow of the Society of Biology (FSB) 2006-2013 : Visiting Professor of Molecular Biology, University of Middlesex Expert Testimony: Bustin served as an expert witness in high-profile trials, including the MMR vaccine-autism class action (2003-2007) and the Lundy murders (2015), where he critiqued flawed molecular evidence. Grants & Collaborations: While no specific grants are listed, he led international consortia for MIQE guidelines and collaborated with institutions like TATAA Biocenter and Kim-Il-Sung University. His work includes developing rapid PCR assays and debunking misinformation about diagnostic techniques. Labs & Teams: Bustin's research has been conducted at Anglia Ruskin University, with collaborations in the UK, Switzerland, and New Zealand. He actively promotes transparency in molecular research through his publications and advocacy.

David R. Raleighمشاهده پروفایل

استادیار

• Neuro-oncology
• Radiation Oncology
• Molecular Mechanisms of Brain Tumors
• +۳ مورد دیگر

David R. Raleigh, MD, PhD, is an Assistant Professor in the Departments of Radiation Oncology and Neurological Surgery at the University of California San Francisco (UCSF). He serves as a Principal Investigator at the Brain Tumor Center and Director of the Preclinical Therapeutics Core. Education: BA in Molecular and Cell Biology and Cognitive Science (UC Berkeley, 2004), MD and PhD in Pathology (University of Chicago, 2012), Residency in Radiation Oncology (UCSF, 2017) His research focuses on the molecular mechanisms of brain tumor growth, particularly meningiomas, integrating developmental biology with oncology to identify novel treatments. Methodologies include biochemistry, mouse genetics, genomics, and pharmacology. Dr. Raleigh's recent publications highlight molecular classification of meningiomas, genomics, and targeted therapies. Awards include Phi Beta Kappa, multiple travel grants, and the Robert and Ruth Halperin Endowed Chair in Meningioma Research.

Jie Pengمشاهده پروفایل

استاد

• Graphical Models
• High Dimensional Inference
• Functional Data Analysis
• +۴ مورد دیگر

Jie Peng, Ph.D., is a Professor and Vice-Chair for Graduate Affairs in the Department of Statistics at the University of California, Davis. Her research focuses on statistical methodologies with applications in genomics, neuroimaging, and functional data analysis. She holds a Ph.D. from Stanford University and is actively involved in advancing statistical theory and its practical implementations in biomedical and computational sciences. Education Ph.D. in Statistics, Stanford University Research Interests Dr. Peng’s work spans several key areas including graphical models, high-dimensional inference, and neuroimaging analysis. She develops innovative statistical tools for analyzing complex biological data, such as tumor genomics and brain connectivity patterns derived from diffusion MRI. Her research emphasizes the interplay between theoretical statistics and real-world applications in healthcare and precision medicine. Professional Contributions She serves as an Associate Editor for the Journal of Computational and Graphical Statistics and has contributed to numerous high-impact publications. Her methods have been applied to identify biomarkers in ovarian cancer and to study brain lateralization using Human Connectome Project (HCP) data. Dr. Peng also leads efforts in developing computational frameworks for spatial transcriptomics and dynamic network modeling.

• Natural Language Processing
• Computational Linguistics
• Language Models
• +۳ مورد دیگر

Kentaro Inui is a distinguished researcher at Tohoku University , specializing in Natural Language Processing , Computational Linguistics , and Machine Learning . His work focuses on advancing language model behavior through rigorous empirical analysis, including mechanisms for detokenization , entity identification , and numerical reasoning . Inui has pioneered methods to rectify spurious beliefs in LLMs via unlearning techniques and explored the dynamics of reasoning strategies in neural models. His research addresses chat translation quality through metrics like MQM-Chat and investigates repetition neurons responsible for text generation patterns. Inui also contributes to argumentation analysis with annotation frameworks like LPAttack and develops resources such as COPA-SSE for commonsense reasoning. His work on universal graph-based relation extraction and cross-stitching architectures has established new benchmarks in NLP task performance. Inui's publications span top-tier conferences including ACL , EMNLP , and LREC , often involving collaborations with researchers like Benjamin Heinzerling and Jun Suzuki. His methodological innovations in semi-structured explanation generation , position embedding (e.g., SHAPE), and zero pronoun resolution demonstrate his focus on both theoretical and practical NLP challenges. While no direct awards or student mentorship data appear in the provided corpus, his extensive publication record (over 20 papers between 2021-2025) underscores significant contributions to NLP education tools , knowledge base integration , and dialogue system consistency . Current projects like ReCall mechanisms and numerical property encoding directions highlight his ongoing impact on model interpretability and reasoning accuracy.

Hao Chenمشاهده پروفایل

دانشیار

• Anomaly/signal detection for streaming data
• Graph-based methods
• Two-sample test for high-dimensional and non-Euclidean data
• +۷ مورد دیگر

Hao Chen, Ph.D. is an Associate Professor in the Department of Statistics at the University of California, Davis. His research focuses on statistical methodology for high-dimensional and non-Euclidean data, including anomaly detection, graph-based methods, and change-point analysis. He also explores AI security, multimodal models, and geospatial applications. His work bridges statistical theory and practical machine learning challenges. Education: Ph.D., Graduate Group in Biostatistics, Stanford University Research Interests: Dr. Chen’s expertise spans statistical methods for streaming data, categorical data analysis, and allele-specific copy number variation. He has pioneered work in detecting signals in complex datasets and developing robust AI systems. His recent focus includes mitigating modality interference in LLMs, enhancing model safety via guardrails, and advancing geospatial AI through projects like GeoLM. Publications: His recent work addresses cutting-edge topics such as multimodal model vulnerabilities, unlearning algorithms, and clinical radiology applications. Key themes include improving model robustness, ethical AI design, and interdisciplinary data integration. Labs/Teams: Engages in collaborative projects at UC Davis, though specific lab names are not listed in the provided information.

Ahmad Ghafarianمشاهده پروفایل

استاد

• Information Security
• Computer Forensics
• Cybersecurity
• +۵ مورد دیگر

Dr. Ahmad Ghafarian is a Professor of Computer Science in the Mike Cottrell College of Business at the University of North Georgia (UNG), where he has served for over 20 years since 1996. He is a dedicated educator who emphasizes positive teacher-student relationships and has taught a wide range of undergraduate and graduate courses in computer science and cybersecurity. His educational background includes: Ph.D. in Computer Science, University of Glasgow, UK, 1982 M.S. in Computer Science, University of Glasgow, UK, 1974 B.S. in Mathematics, Ferdowsi University of Mashhad, Iran, 1970 Postdoctoral Studies in Information Security, University of Maryland University College, 2007 Graduate Certificate in Information Security, Purdue University, 2003 Dr. Ghafarian's research focuses on digital forensics and cybersecurity, with expertise in computer forensics, malware detection, cloud security, social media privacy, and SQL injection attacks. His work involves empirical studies of security tools and protocols, advancing forensic methodologies and security practices through rigorous experimentation and analysis. His publication record demonstrates a clear evolution from foundational computer science education topics in the 1990s to specialized cybersecurity research since the mid-2000s. Current work emphasizes memory forensics for social media platforms, ransomware detection mechanisms, connected vehicle security, and VoIP system vulnerabilities, reflecting his adaptation to emerging technological threats. No scientific awards were explicitly mentioned in the source material. Dr. Ghafarian has developed comprehensive curricula across 15+ graduate and undergraduate courses including cybersecurity capstones, network security, and computer forensics. His teaching innovations include semester-long projects, ethical case studies, and practical lab components using tools like Kali Linux. While specific grant funding isn't documented, his extensive publication record and curriculum development demonstrate sustained research engagement and academic leadership.

• Machine Learning
• Bioinformatics
• Medical Imaging
• +۵ مورد دیگر

Paul J. Kennedy is a Professor at the University of Technology Sydney's Centre for Artificial Intelligence. He holds a PhD from the same institution (1999). His research focuses on machine learning applications in healthcare, bioinformatics, medical imaging, and data mining. Key areas include developing algorithms for genomic data analysis, healthcare pathway modeling, and edge-cloud frameworks for omics data. Education: PhD in Artificial Intelligence (1999, UTS). Research interests span machine learning, health informatics, and data compression. Notable work includes studies on administrative health records, lung nodule detection, and virtual reality-based cancer cohort analysis. He has co-authored over 100 publications across journals like BMC Bioinformatics, IEEE Transactions, and Artificial Intelligence in Medicine. Advising: Collaborates extensively with students/researchers but no explicit student list provided. Grants and labs: Active in interdisciplinary projects involving medical and computational teams, though specific grants are not detailed here.