جست‌وجوی استاد و پژوهشگر

• Machine learning
• Artificial intelligence
• Big data analytics
• +۸ مورد دیگر

Gianluca Setti is a Full Professor at the Department of Electronics and Telecommunications (DET) at Polytechnic University of Turin, where he has been serving since 2017. He previously held positions at the University of Ferrara from 1997 to 2017. His institutional roles include being the Contact Person for the Research Quality Evaluation process, Member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory, and Member of the University Quality Assurance Committee. He serves as Editor-in-Chief of the Proceedings of the IEEE, the first non-US editor to hold this position. Dr. Setti's research spans multiple interdisciplinary fields including machine learning, artificial intelligence, big data analytics, Internet of Things, biomedical signal processing, power electronics, and electromagnetic compatibility. His work bridges theoretical foundations with practical applications, particularly focusing on compressed sensing, neural networks, and circuit design for specialized applications. His research has significant implications for healthcare, sustainable infrastructure, and next-generation electronics. His publication record reveals a consistent trajectory from foundational work in chaotic systems and neural networks to contemporary applications in AI, IoT, and edge computing. The most recent publications demonstrate his focus on anomaly detection at the edge, neural oracles for biosignal processing, and power electronics innovations. His work shows strong integration between theoretical signal processing and practical circuit implementation. 1998 Caianiello prize (best Italian Ph.D. thesis on Neural Networks) IEEE Fellow (2006) IEEE Circuits and Systems Society Distinguished Lecturer (2004, 2015) 2004 IEEE CAS Society Darlington Award 2013 IEEE CAS Society Meritorious Service Award 2013 IEEE CAS Society Guillemin-Cauer Award 2019 IEEE Transactions on Biomedical Circuits and Systems best paper award Multiple best paper awards at major conferences including ECCTD2005, EMCZurich2005, ISCAS2011, PRIME2019, and EMCCOMPO2019 Dr. Setti has supervised numerous PhD students across various research domains including electromagnetic compatibility, signal and power integrity, communication networks, mechatronics and robotics. His research is supported by significant funding including national PRIN projects, EU-funded JTI-ECSEL initiatives, and commercial contracts. He leads the VLSILAB Group at DET, focusing on circuit architectures, embedded systems, and AI applications. His current projects include DECORI (anomaly detection), StorAIge (embedded storage for AI), PROGRESSUS (energy infrastructure), CONNECT (smart grid), and CONVERGENCE (wearable healthcare applications).

Sharona Hoffmanمشاهده پروفایل

استاد

• Health Law
• Employment Discrimination
• Health Information Technology
• +۴ مورد دیگر

Sharona Hoffman is the Edgar A. Hahn Professor of Law and Professor of Bioethics at Case Western Reserve University , where she also serves as Co-Director of the Law-Medicine Center . Since joining the faculty in 1999, she has become a leading voice in health law, employment discrimination, and the legal and ethical implications of health information technology and artificial intelligence. Education: Doctor of Juridical Science (SJD) in Health Law – Case Western Reserve University Master of Laws (LLM) in Health Law – University of Houston Juris Doctor (JD) – Harvard Law School Bachelor of Arts – Wellesley College Research Focus: Hoffman's scholarship spans health law , employment discrimination , health information technology , AI and the law , public health emergencies , and elder law . She has authored over 70 scholarly articles and two books: Electronic Health Records and Medical Big Data: Law and Policy (Cambridge University Press, 2016) and Aging with a Plan: How a Little Thought Today Can Vastly Improve Your Tomorrow (2nd ed., 2022). Awards & Honors: 2021 CWRU Faculty Distinguished Research Award Elected to the American Law Institute (2017) 9th most-cited health law scholar in the U.S. (2016–2020, Leiter’s Law School Reports) First Year Teacher of the Year – 2011, 2012, 2019, 2024 Grants & Fellowships: Hoffman has held prestigious fellowships including the Robert Wood Johnson Foundation Scholar-in-Residence (2013), and visiting scholar positions at the CDC (2007, 2014), Emory University (2014), and NIH Bioethics Department (2022). Professional Roles: Prior to academia, Hoffman served as a Senior Trial Attorney at the EEOC, litigation associate at O'Melveny & Myers, and judicial clerk for U.S. District Judge Douglas W. Hillman.

Ross Koppelمشاهده پروفایل

استاد مدعو

• Healthcare Information Technology
• Medication Errors
• Technology in Society
• +۲ مورد دیگر

Ross Koppel is an Adjunct Professor of Sociology with expertise in healthcare information technology, medication errors, and ethics in social research. His work explores the intersection of technology and societal impacts, focusing on data governance, clinical workflows, and human factors in health IT systems. Research Interests Context-sensitive understanding of medication errors Societal implications of clinical data sharing Ethical challenges in AI and informatics Healthcare cost analysis Scientific Awards Fellow of the American College of Medical Informatics (FACMI)

Maryam Ghahramaniمشاهده پروفایل

مدرس ارشد

• Human Motion Analysis
• Machine Learning
• Rehabilitation Engineering
• +۵ مورد دیگر

Dr. Maryam Ghahramani is a Senior Lecturer in AI & Robotics at the Faculty of Science & Technology, University of Canberra, Australia. She holds a BSc in Electrical Engineering from Shiraz University, Iran, and a PhD in Biometric Gait Analysis from the University of Wollongong, Australia. Biomedical Engineering Researcher Machine Learning Specialist Human Motion Analysis Expert Her research focuses on applying machine learning to human motion analysis for rehabilitation purposes, particularly in three key areas: Parkinson's Disease: Using fNIRS and machine learning for disease detection and motor function assessment Fall Prevention: Analyzing postural sway and risk of falls in older adults Spatial Disorientation: Studying balance in hypoxic aviation environments Recent publications demonstrate her work at the intersection of biomedical engineering, machine learning, and clinical rehabilitation. Current projects include: Young Onset Dementia Detection with 12-week Home-Based Exercise Programs Mild Hypoxia Analysis for Aviation Safety Balancing Mat Performance Evaluation

Ghazal Bargshadyمشاهده پروفایل

مدرس

• Affective Computing
• Artificial Intelligence
• Computer Vision
• +۷ مورد دیگر

Dr. Ghazal Bargshady is a Lecturer at the University of Canberra , with expertise in Affective Computing , Artificial Intelligence , and Healthcare Technology . Her roles include teaching units such as Computer Vision, Data Analytics, and Soft Computing, as well as supervising PhD and Master by Research students in AI-driven projects for healthcare and road safety. Education: She earned her PhD in Artificial Intelligence and Computer Vision from the University of Southern Queensland in 2020. Research Interests: Dr. Bargshady specializes in Computer Vision Deep Learning Biosignal Processing Facial Expression Analysis Human Factors in AI Wearable Sensors Multimodal Data Fusion Brain–Computer Interfaces Her work addresses real-world challenges in pain assessment, depression recognition, and driver safety using cutting-edge AI models. Article Trends: Her recent publications focus on Transformer architectures , fNIRS signal analysis , multimodal pain detection , and depression severity estimation via facial video data. These studies highlight her contributions to AI in healthcare , transportation safety , and biomedical signal processing . Teaching Activities: Dr. Bargshady has lectured units including Programming for Data Science , Computer Vision , and Soft Computing , emphasizing practical AI applications.

Zhandong Liuمشاهده پروفایل

دانشیار

• Bioinformatics
• Big Data analysis
• Machine Learning
• +۵ مورد دیگر

Zhandong Liu is an Associate Professor at Baylor College of Medicine with joint appointments in the Department of Pediatrics and Department of Neurology . He serves as Chief of Computational Sciences at Texas Children's Hospital and co-directs the Quantitative & Computational Biosciences Graduate Program at Baylor. Education: B.S. in Computer Science, Nankai University (2001) M.S. in Computer Science, Wayne State University (2003) Ph.D. in Genomics and Computational Biology, University of Pennsylvania (2010) Dr. Liu's research integrates genomics , machine learning , and bioinformatics to advance understanding of neurological diseases. His work focuses on: Multi-omics data integration for disease mechanism discovery Development of cloud-based CRISPR analysis tools like CRISPRcloud Augmented reality platforms for biomedical data visualization Identification of disease genes through computational models Alternative splicing analysis in cancer and neurodegeneration Single-cell and spatial transcriptomics algorithms His recent publications emphasize Alzheimer's disease , MECP2 syndromes , and computational therapy prediction across multiple domains. Scientific awards include the 2018 Outstanding Service Award from the International Association for Intelligent Biology and Medicine. He has secured major grants from NIH, CPRIT, and NSF for projects including: NSF grant #199977 (2018-2020): Augmented reality therapy platforms CPRIT grant #RP170387 (2016-2019): Network-guided cancer analysis NIH #1R01AG057339 (2017-2022): Alzheimer's disease networks As head of the Liu Lab , he leads teams developing tools like: MARRVEL : Human-model organism gene variant integration CRISPRcloud : Secure CRISPR screen analysis platform CrypSplice : Cryptic splicing detection algorithm

Rafal Bogaczمشاهده پروفایل

استاد

• Computational Neuroscience
• Neural Networks
• Parkinson's Disease
• +۵ مورد دیگر

Professor Rafal Bogacz is a leading academic at the University of Oxford, affiliated with St Edmund Hall and the MRC Brain Network Dynamics Unit . He teaches computational neuroscience and statistics at both undergraduate and postgraduate levels, including the MSc in Neuroscience and BSc in Biomedical Science programs. MSc: Wroclaw University of Technology PhD: University of Bristol Postdoctoral Researcher: Princeton University His research focuses on computational neuroscience , particularly modeling brain networks involved in action selection , decision making , and Parkinson's disease pathophysiology. Key themes include: Developing predictive coding models of cortical computations Understanding basal ganglia neural circuits in healthy and diseased states Designing closed-loop deep brain stimulation paradigms Recent publications highlight work in neural plasticity , dopamine signaling , and computational psychiatry , with a notable Wellcome Discovery Award supporting research on learning in neurons . The Bogacz Group maintains strong collaborations with experimental neuroscientists and shares open datasets through the MRC BNDU Data Sharing Platform . Wellcome Discovery Award (2025): For learning in neurons Europe PMC Open Access (multiple): For numerous PLoS, Nat Neurosci, and J Neural Eng publications As a computational neuroscientist, Professor Bogacz supervises D.Phil. students and leads research programs that bridge theoretical neuroscience with clinical applications . The group actively participates in MRC BNDU training initiatives and public engagement activities like Schools Open Day demonstrations.

Amy K Johnsonمشاهده پروفایل

دانشیار

• Adolescent Sexual and Reproductive Health
• Sexually Transmitted Diseases
• HIV Prevention
• +۳ مورد دیگر

Amy K Johnson, PhD, serves as Associate Professor in Pediatrics (Adolescent and Young Adult Medicine) at Northwestern University's Feinberg School of Medicine, with clinical affiliation at Ann & Robert H. Lurie Children's Hospital of Chicago. She holds key roles in the Institute for Public Health and Medicine (IPHAM) - Center for Dissemination and Implementation Science, Northwestern University Clinical and Translational Sciences Institute (NUCATS), and Robert J. Havey, MD Institute for Global Health (IGH). Dr. Johnson earned her PhD from the University of Illinois, Chicago in 2016. Her research centers on adolescent sexual and reproductive health with dual emphases on HIV/STI intervention implementation and ethical dimensions of community participation in cure research. She develops prevention packages targeting adolescents—particularly young women—and addresses surveillance gaps in sexually transmitted diseases through implementation science frameworks. Analysis of her 2024-2025 publications reveals a dominant focus on PrEP implementation across diverse populations and geographies. Her work consistently addresses health disparities through mixed-methods approaches, examining structural barriers among Black women in Chicago, migrant communities in France, and youth in Nigeria. Common threads include innovative delivery strategies (text messaging, peer navigation), cultural adaptation of interventions, and ethical considerations in community engagement—demonstrating her commitment to translating research into equitable prevention solutions. Her scientific contributions have been recognized with the Excellence in Scholarship award from Lurie Children's Hospital (2023) and Best Poster Award from the American Sexually Transmitted Disease Association (2016). Excellence in Scholarship, Lurie Children's Hospital (2023) Best Poster Award, American Sexually Transmitted Disease Association (2016) Dr. Johnson actively shapes public health practice through leadership in the Illinois PrEP Working Group, Getting to Zero Illinois, and as Vice Chair of Epidemiology for the Illinois Public Health Association. Her editorial role at Frontiers in Reproductive Health and memberships in the American College of Epidemiology and American Public Health Association reflect her influence in advancing methodological rigor and equity-focused HIV prevention science. Current projects like iCARE Nigeria and POWER Up demonstrate her commitment to scalable interventions addressing the HIV care continuum.

• Neural Networks
• Genetic Algorithms
• System Identification and Control
• +۷ مورد دیگر

FURUZUKI, Takayuki is a Professor at Waseda University's Faculty of Science and Engineering, Graduate School of Information, Production, and Systems. He holds a PhD from Kyushu Institute of Technology and maintains the NCLab research group (http://nclab.w.waseda.jp/nclab/). His professional memberships include the Institute of Electronics, Information and Communication Engineers, Institute of Electrical Engineers of Japan, Society of Instrument and Control Engineers, and IEEE. His educational background includes a PhD in Information Science from Kyushu Institute of Technology (1994-1997), a Master's degree in Electronics Engineering from Sun Yat-Sen University (1983-1986), and a Bachelor's degree in Electrical & Electronic Systems Engineering from Sun Yat-Sen University (1979-1983). Professor FURUZUKI's research spans neural networks, genetic algorithms, system identification and control, complex systems, bioinformatics, and combinatorial optimization. His work integrates soft computing techniques with medical informatics and control systems engineering, developing innovative approaches for problems ranging from brain tumor segmentation to music information retrieval. His research methodology often combines theoretical foundations with practical applications in healthcare, energy systems, and intelligent transportation. His recent publication record shows a strong focus on sequential recommendation systems, graph neural networks for medical imaging, music information retrieval, and power systems analysis. The consistent thread across his work is the application of advanced machine learning techniques to solve complex, real-world problems across diverse domains, with particular emphasis on handling missing data, improving model efficiency, and enhancing prediction accuracy. His scientific achievements have been recognized with several prestigious awards: ISCIIA2008 Excellent Paper Award (2008) Institute of Electrical Engineers of Japan Excellent Paper Award (2001) Guangdong Province Higher Education Science and Technology Progress Second Prize (1991) Guangdong Province Higher Education Science and Technology Progress Second Prize (1989) Chinese Ministry of Education Scientific and Technological Achievements Second Prize (1986) Professor FURUZUKI has maintained continuous research activity since joining Waseda University in 2003, with significant contributions to multiple research projects spanning neural computation, soft computing, and system engineering. His work demonstrates strong international collaboration, with publications involving researchers from various institutions worldwide. His research output includes over 410 papers with 3,623 citations and an h-index of 23 according to Scopus data. The NCLab research group, which he leads, focuses on neural computation and related areas, providing a platform for interdisciplinary research that bridges theoretical foundations with practical applications in healthcare, energy systems, and intelligent transportation. His laboratory environment fosters innovation in machine learning algorithms and their application to real-world problems, with particular emphasis on developing efficient, robust solutions for complex data analysis tasks.

Freek J. Beekmanمشاهده پروفایل

استاد

• Biomedical Imaging
• Nuclear Imaging
• Image Reconstruction Algorithms
• +۱۲ مورد دیگر

Prof. Freek J. Beekman is a Full Professor and head of the Biomedical Imaging section within the Department of Radiation Science & Technology at Delft University of Technology (TU Delft), Faculty of Applied Sciences. He is a leading figure in biomedical imaging, with extensive contributions to nuclear imaging technologies, including SPECT, PET, and CT. His research spans detector development, image reconstruction algorithms, hybrid photonic imaging, and the application of artificial intelligence in medical imaging. Research Interests: His work focuses on advancing imaging modalities through innovations in hardware (e.g., multi-pinhole collimators) and software (e.g., deep learning for attenuation correction). He has pioneered ultra-high-resolution imaging systems, particularly for preclinical and clinical SPECT, and has developed integrated platforms like U-SPECT-BioFluo. His recent research explores glymphatic delivery of nanoparticles, infection imaging, and AI-driven reconstruction techniques, reflecting a strong translational focus. Publication Trends: His most recent publications (2021–2023) emphasize deep learning in SPECT, multi-isotope imaging, high-resolution ex vivo systems, and applications in neuroimaging and oncology. The articles demonstrate a consistent focus on improving image quality, resolution, and clinical utility through physics-informed and AI-enhanced methods. Scientific Awards: NWO Physics Valorization Prize Innovation of the Year Award by the World Molecular Imaging Society (2015, 2018) Edward Hoffman Memorial Award (2017) Bruce Hasegawa Memorial Award (2021) FOM Valorization Award (2013) TU Delft Entrepreneurial Award (2010) Advising and Grants: While specific student names are not listed, his leadership in large collaborative projects and supervision of numerous publications suggests active mentoring. He has secured significant funding through national and international grants, evidenced by his invention of over 20 patent families and successful technology transfer. His founding and leadership of MILabs BV (sold to Rigaku) highlights his impact on commercialization and industry-academia collaboration. Labs and Teams: He leads the Biomedical Imaging research group at TU Delft, which develops cutting-edge imaging systems such as VECTor (SPECT-PET) and EXIRAD-HE. His teams have produced technologies used globally in academic and pharmaceutical research, contributing to tracer development and therapeutic innovation.

Ralf Zimmerمشاهده پروفایل

استاد

• Bioinformatics
• Systems Biology
• Machine Learning
• +۵ مورد دیگر

Ralf Zimmer is a Full Professor for Practical Informatics and Bioinformatics at Ludwig Maximilian University of Munich (LMU) since 2001, affiliated with the Department of Informatics in the Faculty of Mathematica, Informatics and Statistics. He concurrently serves as Head of Section III and Head of the Research Group Network Regulation and Modeling / Machine Learning at the Leibniz Institute for Food Systems Biology at TUM (Leibniz-LSB@TUM) in Freising, Germany. His academic foundation includes a Diploma with distinction in Computer Science, Applied Mathematics and Operations Research from the University of Bonn (1981-1986), followed by a summa cum laude Doctorate in Computer Science and Applied Mathematics from CAU Kiel in 1990, where he received dual honors: the CAU Dissertation Award and Best Dissertation Award. Zimmer's research pioneers the integration of bioinformatics, systems biology, and machine learning to decode molecular food-consumer interactions. His group develops causal system models for biological networks, validated through in silico simulations and multi-omics perturbation experiments (transcriptomics/proteomics). Core methodologies include network regulation modeling, algorithmic bioinformatics, and database construction linking food compounds to biochemical networks and cellular phenotypes, with translational goals for food and biotech innovation. His 14 most recent publications (2012-2024) reveal dominant trends in multi-omics immunology and cardiovascular research, featuring computational innovations for high-throughput data analysis. Key themes include host-pathogen dynamics (viral infections), inflammatory disease mechanisms (atherosclerosis), and methodological advances in proteomics/transcriptomics, consistently bridging fundamental bioinformatics with clinical applications. Major scientific recognitions include: CAU Dissertation Award and Best Dissertation Award (1990) Director of LMU's Informatics Department (2010-2012) DFG Review Board membership for biomedical foundations (2008-2016) Leadership of the DFG Bioinformatics Munich Center (2001-2008) Academic Senate election at LMU München (2011) Zimmer directs LMU/TUM's joint B.Sc./M.Sc. bioinformatics programs since 2001 as founding architect of the DFG-funded Bioinformatics Munich initiative. His educational leadership spans spokesperson roles for international training groups (IRTG RECESS), collaborative research centers (SFB1123 Atherosclerosis), and elite programs (Data Science, Munich Center for Machine Learning). Grant stewardship includes directing the DFG Bioinformatics Munich Center and shaping national funding policy via the DFG review board. At Leibniz-LSB@TUM, his research group pioneers databases connecting food compounds to cellular phenotypes through molecular networks, collaborating with Munich universities, clinics, and biotech partners to develop high-throughput sequencing/proteomics applications for future food and health innovations.

Martin Hollerمشاهده پروفایل

استاد

• Mathematics of data science
• Machine learning
• Generative models
• +۷ مورد دیگر

Martin Holler is a Professor at the Institute of Mathematics and Scientific Computing at the University of Graz, Austria, where he leads the research group Applied Mathematics and Machine Learning . His work bridges theoretical mathematics with practical applications in imaging and machine learning. Research Focus: His primary research areas include the mathematics of data science, variational methods in imaging, dynamic and multi-modality inverse problems, and biomedical imaging. He has made significant contributions to model-based regularization techniques, particularly with Total Generalized Variation (TGV) approaches for image and video reconstruction. Publication Trends: Over the past decade, Holler's research has evolved from traditional variational methods for image reconstruction toward increasingly sophisticated machine learning approaches. His recent work (2021-2023) focuses on integrating deep learning with variational methods, particularly for motion separation in medical imaging and learning-informed parameter identification in partial differential equations. His publications demonstrate a consistent thread of applying rigorous mathematical frameworks to solve practical problems in medical imaging and computer vision. Mathematics of data science and machine learning Generative models in machine learning Variational methods in imaging Dynamic and multi-modality inverse problems Model-based regularization Biomedical imaging Image and video decompression Technical Leadership: Holler has developed several open-source software packages implementing advanced reconstruction algorithms, particularly for multi-modal imaging problems. His GitHub repositories show active maintenance and development of these tools, which have been cited in the medical imaging community.

Shihab Jimaaمشاهده پروفایل

دانشیار

• AI-based performance enhancement of LP-WAN technologies for IoT
• 6G wireless communications
• Atrial Fibrillation Data Analysis and Validation
• +۳ مورد دیگر

Dr. Shihab Jimaa is an Associate Professor at the Department of Computer and Information Engineering , Khalifa University, and currently serves as the Associate Dean of UG Studies at the College of Computing and Mathematical Sciences. He has held academic positions including Research Fellow, Senior Lecturer, and Assistant Professor at institutions such as Loughborough University, Warwick University, and Queen Mary University of London. PhD : Digital Communications, Loughborough University (1990) MSc : Digital Communication Systems, Loughborough University (1986) PGCE : Post Graduate Certificate in Higher Education, University of Hertfordshire (1998) Dr. Jimaa’s research focuses on energy-efficient wireless communication systems , AI-driven 6G technologies , and machine learning applications for early detection of Atrial Fibrillation . His work spans adaptive filtering, channel estimation, and signal processing in both telecommunications and biomedical engineering domains. Current projects include optimizing LP-WAN technologies for IoT scalability and developing deep learning-based scheduling techniques. His research also investigates terahertz communications , optical wireless networks , and ultra-massive MIMO for next-generation systems. He has contributed to 3 book chapters and over 100 conference/journal papers. IEEE Senior Member HEA Fellow Recipient of 1 patent Dr. Jimaa has supervised 30 MSc, 3 MPhil, and 4 PhD students . He actively participates in academic leadership as TPC Chair for IEEE conferences and previously served as Guest Editor for the International Journal of Communication Systems . Affiliated centers include the 6G Research Center and Center for Cyber-Physical Systems .

Qipei Meiمشاهده پروفایل

استادیار

• Civil Engineering
• Artificial Intelligence
• Robotics
• +۴ مورد دیگر

Qipei Mei is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Alberta's Faculty of Engineering. With an MSc in Computer Science and a PhD in Structural Engineering, he bridges civil engineering with artificial intelligence to enhance infrastructure productivity and sustainability. His research spans AI-driven design automation, robotics for construction safety, and IoT-based condition assessment. PhD, Structural Engineering - University of Alberta (2020) MSc, Computer Science - Georgia Institute of Technology (2018) MSc, Structural Engineering - University of Alberta (2014) B.E., Civil Engineering - Huazhong University of Science and Technology (2011) Mei's work focuses on three key areas: leveraging data-driven methods for design automation, applying sensing/robotics to construction operations, and using digital twins for infrastructure assessment. His team explores generative AI for housing design, robotic construction in remote communities, and smart monitoring systems. Recent publications highlight advancements in: lateral capacity prediction for monopile foundations, transformer-based architectural layout analysis, large language models for building code compliance, vision-language models for safety hazard detection, and sensor networks for bridge monitoring. These works demonstrate interdisciplinary integration of AI, structural engineering, and IoT. Mei actively collaborates with diverse researchers and welcomes graduate students to his Smart Infrastructure Technologies (SITE) Research Group, part of the Infrastructure and Human Tech Lab (IHT-Lab). He teaches advanced topics in structural and civil engineering while pursuing industry-funded projects through NSERC, CFI, and Alberta Innovates.

Andrew Murrayمشاهده پروفایل

استاد

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

Andrew Murray is a Professor at Harvard University, affiliated with the Rowland Institute and Department of Molecular and Cellular Biology. His research focuses on evolutionary biology, genetics, and synthetic biology using budding yeast as a model organism. Primary Affiliation: Harvard University Lab: Murray Lab Collaboration: with David Nelson (Physics/MBI) Research interests include: Evolution of biological novelty through experimental evolution Cellular mechanisms for environmental adaptation Quantitative analysis of evolutionary trajectories Engineering yeast strains to test biological principles Publications span evolutionary genetics, cell biology, and biophysics with recent work on ABC transporters and yeast colony dynamics. Collaborative clinical publications also exist in cardiology and surgical outcomes. Scientific recognition includes the John Harvard Distinguished Science Fellowship .