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Haozhe WangView profile

Assistant Professor

• Materials Science
• Nanotechnology
• Two-dimensional Materials
• +3 more

Haozhe Wang serves as Assistant Professor of Electrical and Computer Engineering within Duke University's Pratt School of Engineering. He joined Duke in July 2023 and leads the Wang Lab, focusing on advanced nanomaterials research with applications spanning quantum systems, robotics, and electronic devices. His research centers on two-dimensional quantum materials—particularly MXene—with emphasis on synthesis techniques, atomic-scale properties, and device integration. Current investigations explore nanofabrication methods for quantum systems, MXene-based robotics, and autonomous experimentation frameworks for accelerated materials discovery, leveraging collaborations with institutions like MIT and Caltech. Recent publication trends reveal concentrated advancement in MXene applications across three domains: atomic layer processing for semiconductor manufacturing, soft robotics actuation, and AI-driven materials experimentation, indicating strategic convergence of nanomaterials science with autonomous systems engineering. Key recognitions include: NSF CAREER Award (2025) Early Career Distinguished Presenter at Materials Research Society Fall Meetings (2023) Dr. Wang secured the NSF CAREER grant to develop autonomous materials experimentation systems, mentoring lab members including postdoctoral researchers in MXene synthesis and robotics applications. His team actively participates in national conferences while building industry-academic partnerships for nanofabrication technology transfer. The Wang Lab maintains active research thrusts in MXene atomic layer etching, quantum material characterization, and soft robotics development, operating through collaborations with NNCI network facilities and hosting annual graduate recruitment events since 2024.

Stefano CurtaroloView profile

Professor

• Artificial Intelligence Materials Science
• Autonomous Materials Design
• Computational Materials Science
• +4 more

Stefano Curtarolo is the Edmund T. Pratt Jr. School Distinguished Professor of Mechanical Engineering and Materials Science at Duke University’s Pratt School of Engineering. He also holds professorships in the Department of Physics and Electrical and Computer Engineering, and serves as Director of the Center for Extreme Materials (2025–present). Education: M.S. in Physics, University of Padua (1995, 1998) M.S. in Physics, Pennsylvania State University (1999) Sc.D. in Physics, Massachusetts Institute of Technology (2003) Research Interests: Curtarolo’s work bridges Materials Science , Physics , and Artificial Intelligence , focusing on Autonomous Materials Design , High-Entropy Disordered Systems , and Materials for Extreme Environments . His methodologies integrate Quantum Mechanics , Machine Learning , and High-Throughput Computing . Recent Research Trends: His 2024–2025 publications highlight advancements in High-Entropy Ceramics , including Soliquidy (a geometric descriptor for disorder), Dual-Phase Ceramic Synthesis , and Machine Learning Interatomic Potentials . These works emphasize disorder engineering , plasticity enhancement , and AI-driven material discovery for Aerospace and Deep Space Exploration applications. Scientific Awards: European Academy of Sciences (2024) Clarivate Highly Cited Researcher (2021) Friedrich Wilhelm Bessel Research Award (2016) DOD MURI Awards (2013, 2015) NSF CAREER (2008) ONR Young Investigator (2008) PECASE (2007) Grants & Leadership: Current projects include DETER (2024–2027, Missouri S&T) and Transition States Algorithms (2024–2027, ONR). He co-leads NRT-HDR (2020–2026, NSF). Labs & Teams: Curtarolo directs the Center for Extreme Materials and leads the AFLOW consortium , a global materials design ecosystem. His lab combines machine learning , quantum simulations , and experimental validation for multiscale materials discovery .

Daniel BoleyView profile

Professor

• Numerical Linear Algebra
• Data Mining
• Machine Learning
• +5 more

Daniel Boley is a Professor and Distinguished University Teaching Professor at the University of Minnesota, within the College of Science and Engineering, Department of Computer Science and Engineering. He serves as the Director of Graduate Studies for the Graduate Program in Data Science, which offers a Master's of Science and a Post-Baccalaureate Certificate. His office is located in Kenneth H. Keller Hall at 4-225C. Professor Boley's research spans computational methods in linear algebra, scalable data mining algorithms, and applications in systems biology and bioinformatics. His work focuses on scalable algorithms for convex optimization in machine learning, analysis of networks and graphs from metabolic biochemical networks, and wireless device networks. He has made significant contributions to numerical linear algebra methods for control problems, parallel algorithms, and iterative methods for matrix eigenproblems. His research interests also include algebraic models in systems and evolutionary biology, and biochemical metabolic networks. His recent publications demonstrate a strong focus on applying graph theory and network analysis to diverse domains including robot swarms, medical imaging (particularly for glioblastoma and COVID-19 diagnosis), and metabolic network analysis. His work bridges theoretical computer science with practical applications in biology and medicine, with a consistent emphasis on developing scalable computational methods. The trend shows increasing interdisciplinary collaboration, particularly with medical researchers. Distinguished Member by the ACM Top university award for post baccalaureate, graduate and professional education Distinguished University Teaching Professor title Professor Boley has advised numerous PhD students including Tatiana Lenskaia (2021), Shaozhe Tao (2018), Ham Ching Lam (2014), and others dating back to 1994. His research has been supported by various grants enabling work on scalable computation of elementary pathways through metabolic networks, Markov models of viral evolution, and scalable data mining algorithms for text analysis. He has developed software tools for clustering, dot plot visualization, and educational graphics. Professor Boley directs the Graduate Program in Data Science and has been involved in projects such as the Principal Direction Divisive Partitioning (PDDP) Project. His research group develops practical implementations of theoretical advances, including the PDDP clustering algorithm, Dot.py genome viewer, and various educational graphics tools for introductory programming courses. He maintains active collaborations across disciplines, particularly in bioinformatics and medical imaging applications.

Fabian BarthelsView profile

Researcher

• RNA Biology
• Medicinal Chemistry
• Epitranscriptomics
• +6 more

Dr. Fabian Barthels is a Researcher and Research Group Leader at the Institute of Pharmaceutical and Biomedical Sciences of Johannes Gutenberg-Universität Mainz , Germany. His work focuses on RNA methyltransferases, epitranscriptomics, and medicinal chemistry technologies. Ph.D. in Medicinal Chemistry (University of Mainz, 2018) Biochemistry M.Sc. (University of Tübingen, 2016) Biochemistry B.Sc. (University of Tübingen, 2013–2016) Dr. Barthels' research explores dysregulation of RNA modifications in disease pathology, developing selective covalent/non-covalent inhibitors for RNA methyltransferases using DNA-encoded libraries and PROTACs. His group created microscale thermophoresis assays and 3D-printed lab equipment like FINDUS and a differential scanning fluorometer. His group's publications highlight advancements in RNA-MST protocols, METTL1/DNMT2 inhibitor discovery, and photoaffinity probes for viral proteases. Recent work includes fluorescent MTase probes (Angewandte Chemie 2024) and RNA-ligand interaction studies (Chemical Science 2023). 2023 Boehringer-Ingelheim Foundation Dissertation Award 2022 Walter-Schunack Prize in Medicinal Chemistry 2022 Phoenix Pharmacy Award (co-awarded) 2016–2018 Deutschlandstipendium for academic potential Dr. Barthels' lab includes researchers like Ariane Frey (Poster Award winner) and Annabelle Weldert (best presentation awardee). His DFG-funded Transregio 319 project on RNA Modification and Processing and CZS MAINCE initiative on Medical AI demonstrate his grant leadership.

• Viral Genomics
• Virology
• Transcriptomics
• +5 more

Dr. Ana Da Silva Filipe is a molecular virologist and Head of CVR Viral Genomics at the University of Glasgow's Centre for Virus Research. She has been with the university since 2008, accumulating extensive experience leading research in viral genomics and transcriptomics. Currently, she leads the CVR Genomics team, which provides expertise in high-throughput sequencing to contribute to the understanding of viruses and their impact on human health. Dr. Da Silva Filipe's research focuses on viral hepatitis and respiratory viruses, with particular emphasis on developing sequencing solutions for viral emergence and discovery. Her work bridges fundamental virology with clinical applications, applying genomic approaches to address critical questions in viral infections. Her research has significant public health implications, particularly in understanding viral evolution, transmission dynamics, and pathogenesis across diverse settings. Analysis of her recent publications reveals a strong focus on viral genomics applied to emerging and re-emerging viral threats, including SARS-CoV-2, influenza viruses, hepatitis viruses, and various zoonotic pathogens. Her work often involves international collaborations across Africa, Europe, and South America, leveraging advanced sequencing technologies to understand viral evolution, transmission patterns, and host-pathogen interactions. National Institutes of Health: Beyond discovery: bat behavior and virus shedding as drivers of spillover risk (2024-2025) Biotechnology and Biological Sciences Research Council: MonkeyPox - Rapid Response (2022-2023) UK Research and Innovation: Identification of viruses causing undiagnosed febrile illness in returning travellers to the UK (2022-2023) Medical Research Council: Interim support for ISARIC4C Clinical Characterisation Protocol (2022-2023) Biotechnology and Biological Sciences Research Council: SARS-CoV-2 infections in cats (2021-2022) UK Research and Innovation: Genotype to Phenotype Consortium (2021-2022) Dr. Da Silva Filipe leads the CVR Genomics team at the Centre for Virus Research, which serves as a hub for viral genomic analysis supporting both internal research and external collaborations. Her team's work has contributed significantly to understanding viral evolution, transmission dynamics, and pathogenesis across multiple viral families and hosts, with particular relevance to public health responses during outbreaks and pandemics.

Alexander MeeskeView profile

Assistant Professor

• CRISPR-Cas Systems
• Bacterial Immunity
• Bacteriophage Interactions
• +4 more

Alexander Meeske is an Assistant Professor at the University of Washington in the Department of Microbiology . His research focuses on the mechanistic relationships between CRISPR-Cas systems, bacterial hosts, and bacteriophages , utilizing high-throughput genetics, biochemical reconstitution, and microscopy. Established Listeria seeligeri as a model for RNA-targeting CRISPR-Cas13 immunity during postdoctoral work at Rockefeller University. Current work explores regulation of CRISPR immunity, phage resistance mechanisms, and non-immune roles of CRISPR systems. Research interests include: CRISPR-Cas Systems Anti-CRISPR Proteins Bacterial Cell Wall Synthesis Phage-Bacteria Coevolution RNA-Guided Immunity High-Throughput Genetic Screens Recent publications highlight discoveries in: CRISPR-Cas13 dormancy and resuscitation pathways (2019–2023). Anti-CRISPR protein mechanisms (2020, 2022). Phage-encoded escape strategies (2016, 2021). Cell wall synthesis in Bacillus subtilis (2013–2016). Lab members include: Postdocs: Shally Margolis, Shoshanna Kahne. PhD Students: Marshall Godsil, Sarina Kao, Amanda Antoch, Edith Sawyer, Luke Oriolt, Alison Ciling. Research Scientists: Maddie Williams, Mark Katz. Collaborations extend to the Guo Lab, focusing on joint PhD training programs.

Taylor WeissView profile

Assistant Professor

• Algae Technology
• Carbon Capture
• Sustainable Bioproduction
• +7 more

Taylor Weiss is an Assistant Professor in Environmental and Resource Management at The Polytechnic School, Arizona State University (ASU), and a Senior Global Futures Scientist in the Global Futures Scientists and Scholars initiative. He is also affiliated with the Arizona Center for Algae Technology and Innovation (AzCATI) and the Sustainability Initiative at ASU. Weiss specializes in algae biotechnology for carbon capture, sustainable bioproduction, and Food/Energy/Water Nexus innovation. His research includes engineering algae-bacteria consortia, developing sensors for algal applications, and exploring bio-enhanced materials for environmental and health benefits. Key research keywords include: Algae Technology Carbon Capture Renewable Energy Synthetic Biology Environmental Engineering Bioplastic Production Scientific awards include the National Institutes of Health (NIH) Training Fellowship. His work has been cited over 1152 times with 16 scholarly works and 8 grants.

James SchnableView profile

Professor

• Plant Genomics
• Crop Phenomics
• Quantitative Genetics
• +3 more

James Schnable serves as the Charles O. Gardner Professor of Agronomy in the Department of Agronomy and Horticulture at the University of Nebraska-Lincoln. His research integrates genomic, phenomic, and environmental data to advance crop breeding methodologies for maize and sorghum, with particular emphasis on climate-resilient varieties. Dr. Schnable's research program spans plant genomics , high-throughput phenomics , and quantitative genetics , focusing on genetic dissection of nitrogen use efficiency, photosynthetic traits, and stress tolerance mechanisms. His laboratory pioneers UAV- and satellite-based phenotyping systems, machine learning applications for image analysis, and genomic prediction models that bridge genotype-phenotype gaps under variable environmental conditions. Key innovations include nighttime fluorescence phenotyping to reduce environmental noise and spectral feature extraction for accelerated trait assessment. Analysis of his 2021-2025 publications reveals consistent leadership in genotype-environment interaction studies and computational phenotyping , with dominant themes including transcription factor binding site variation explaining heritability, nonphotochemical quenching kinetics in stress responses, and scalable satellite-based yield prediction methods. His work frequently employs the Genomes to Fields Initiative infrastructure for multi-state field validation. Charles O. Gardner Professorship Dr. Schnable directs an active research program involving advanced sensor networks, genomic selection pipelines, and multi-institutional field trials. His team develops computational frameworks like PlantSegNet for 3D plant reconstruction and SPARC-LoRa for agricultural IoT applications. While specific grant details aren't provided in source materials, his extensive publication record in high-impact journals indicates sustained funding from major agricultural research programs. The laboratory maintains cutting-edge phenotyping infrastructure including UAV fleets, hyperspectral imaging systems, and gas sensor networks for early stress detection. Current projects focus on nitrogen-responsive growth trajectories, chilling tolerance mechanisms in panicoid grasses, and gut microbiome interactions with grain composition, reflecting an integrative approach from molecular mechanisms to field performance.

William S. BushView profile

Professor

• Bioinformatics
• Genomics
• Genetic Epidemiology
• +4 more

William S. Bush, PhD, is Professor in the Department of Population and Quantitative Health Sciences and Department of Genetics and Genome Sciences at Case Western Reserve University School of Medicine, serving as Associate Director for Computational Genomics at the Institute for Computational Biology. His research integrates functional genomics knowledge into genetic analysis and leverages electronic medical records for translational applications in neurocognitive disorders. Education: PhD, Vanderbilt University, 2008 MS, Vanderbilt University, 2008 Dr. Bush specializes in bioinformatics and statistical methods development for detecting complex genetic effects, knowledge-guided genomic analysis using structured biological hypotheses, and genetic epidemiology of Alzheimer's Disease and HIV-associated neurocognitive disorders. His work emphasizes nuclear and mitochondrial genetic interactions and pathway-based analytical approaches. His 2019-2020 publications reveal strong focus on Alzheimer's disease genomics through large consortia like the Alzheimer's Disease Sequencing Project, with significant contributions to software engineering for reproducible bioinformatics, pathway analysis methodologies, and multi-ethnic genomic studies. Key themes include immune pathway involvement in neurodegeneration and translational applications of genomic data. Scientific Awards: None listed in source materials. Dr. Bush mentors Master's students, PhD candidates, and postdoctoral fellows while leading major NIH-funded initiatives: NIH/NIA RO1: Genetic drivers of resilience to Alzheimer’s disease NIH/NIGMS RO1: Atlas of clinical associations mapping to protein structures NIH/NIA UO1: Alzheimer’s Disease sequence analysis collaborative USDA/Foundation for Food and Agriculture Research: Modeling neighborhood food futures NIH/NIDDK U2: Cleveland KPMP recruitment network NIH/NIAID R56: Genetics of TB resistance in HIV+ subjects NIH/NIA U54: Alzheimer’s Disease Sequencing Project coordination NIH/NIA U01: Replication of ADSP discoveries in African-Americans NIH/NIA R01: Genetics of Alzheimer Disease in Puerto Ricans He co-chairs the ADSP Annotation Working Group, develops widely used genomic annotation resources, and holds external appointments at Cleveland Clinic Genomic Medicine Institute and Cleveland Institute for Computational Biology.

Gerald DownesView profile

Professor

• Neuroscience
• Genetics
• Neurobiology
• +5 more

Gerald Downes is a Professor in the Department of Biology at the University of Massachusetts Amherst, where he leads the Downes Laboratory. His research focuses on the genetic and neural mechanisms underlying motor behavior and epilepsy, utilizing zebrafish as a model organism. He earned his Ph.D. from Washington University, St. Louis, and completed a postdoctoral fellowship at the University of Pennsylvania. Education: B.S. from Johnson C. Smith University (1992), Ph.D. from Washington University, St. Louis (1999), Postdoctoral fellowship at the University of Pennsylvania (1999–2005). Downes’s research integrates genetics, microscopic imaging, and behavioral analysis to study neural networks in zebrafish. His lab has established high-throughput platforms for epilepsy research and therapeutic development, including the open-source web app Marigold for behavioral analysis. Recent work includes collaborations on environmental neurotoxins linked to ALS. His lab has received grants such as the TBCK Foundation Pilot Grant (2024) and has mentored students including Wayne (Ph.D.), Colin, Gregory, Madison, and others. The lab’s publications span topics like genetic compensation in zebrafish mutants and neurotoxin effects on motor neurons. Scientific Awards: TBCK Foundation Pilot Grant (2024). The Downes Laboratory actively trains students and collaborates with institutions like Wesleyan University. Current projects focus on modeling genetic epilepsies and identifying novel therapeutics through zebrafish studies.

• Catalysis
• CO₂ Valorization
• Kinetic Modeling
• +4 more

Dr. Sofia Angeli is a Group Leader at the Institute of Catalysis Research and Technology (IKFT), Karlsruhe Institute of Technology (KIT), Germany, since March 2024. Previously, she held roles as Senior Scientist/Group Leader (2021–2024) and Postdoctoral Researcher (2017–2019) at KIT. Her research focuses on catalysis for energy transition, CO₂ valorization, kinetic modeling, and digitalization of catalytic processes. She leads interdisciplinary projects integrating experimental and computational methods to advance sustainable chemical processes. PhD in Chemical Engineering (2016): Aristotle University of Thessaloniki, Greece. Thesis: Hydrogen production via intensified methane steam reforming processes. M.Sc. in Advanced Materials (2012): Aristotle University of Thessaloniki. Diploma in Chemical Engineering (2009): Aristotle University of Thessaloniki. Research Interests : Sofia’s work spans kinetic modeling, CO₂ conversion, catalytic pollutant removal, and digital tools for catalysis research. She develops novel catalysts for methane reforming and designs data management systems like Adacta and CaRMeN to streamline reaction mechanism analysis. Her projects often address industrial challenges in emissions reduction and renewable energy. Her recent publications emphasize catalytic processes for energy sustainability (e.g., methane oxidative coupling) and automated modeling frameworks. She collaborates widely, contributing to journals like Chemical Engineering Journal and ACS Catalysis . Advising & Teams : As a Group Leader, she oversees researchers in catalysis and data-driven processes. Her team works on cutting-edge projects supported by KIT’s infrastructure and interdisciplinary networks. Labs/Teams : Active in the IKFT and the Deutschmann Group, focusing on catalyst design and process intensification.

• Cybersecurity
• Machine Learning
• Network Security
• +6 more

Dr. Zhiyuan Tan is an Associate Professor in the School of Computing at Edinburgh Napier University (ENU), specializing in cybersecurity research. He holds a PhD in Computer Systems from the University of Technology Sydney (UTS), Australia (2014), an MEng from Beijing University of Technology, China (2008), and a BEng with high distinction from North-eastern University, China (2005). Before joining ENU in 2016, Dr. Tan held research positions at the University of Twente (Netherlands), University of Technology Sydney (Australia), and La Trobe University (Australia). Dr. Tan's research focuses on cybersecurity, machine learning, data analytics, virtualisation, and cyber-physical systems. His work has resulted in over 44 scholarly publications with an H-Index of 13 and more than 830 citations according to Google Scholar. His recent publications demonstrate a continued focus on network security, intrusion detection systems, and the application of machine learning techniques to cybersecurity challenges, with publications spanning from 2022-2025 in top venues including IEEE Transactions and international conferences. Dr. Tan has received significant research funding, including AUD 27,800 from CSIRO and UTS for autonomous network intrusion detection research and £6,987 from ENU for securing future 5G health care systems. His research has been recognized with awards including the National Research Award 2017 from the Research Council of the Sultanate of Oman, a Best Paper Award, and the Kaspersky Lab's Annual Student Cyber Security Conference Finalist Award. National Research Award 2017 from the Research Council of the Sultanate of Oman Best Paper Award Kaspersky Lab's Annual Student Cyber Security Conference Finalist Award Dr. Tan has mentored 9 PhD students over the past 5 years, with 6 successfully completing their studies. His students have produced 12 journal and 10 conference publications. He has also served as an editorial board member for international journals, organized special issues, and participated as a technical program committee member for major international conferences. Dr. Tan is currently recruiting PhD students for research projects on network security, adversarial machine learning for anomaly/malware detection, virtualization security, and IoT security.

Alán Aspuru-GuzikView profile

Professor

• Quantum Computing
• Energy Materials
• AI for Discovery
• +5 more

Alán Aspuru-Guzik is a Professor in the Department of Chemistry at the University of Toronto, with a cross-appointment in the Department of Computer Science. His research focuses on interdisciplinary approaches at the intersection of theoretical chemistry, quantum computing, and artificial intelligence. He leads the development of self-driving laboratories and materials acceleration platforms, combining robotics, AI, and quantum chemistry for accelerated molecular discovery. His mission is to advance technologies like quantum computing, machine learning, and automation to discover new chemicals and materials beneficial to society. The group collaborates across disciplines, including computer science, physics, and applied mathematics. Key research areas include quantum algorithms for molecular simulation, energy materials, and biophysical systems. Notable projects involve creating autonomous laboratories that integrate experimental and computational methods. While no awards are explicitly listed in the provided texts, his work has attracted significant academic and industrial attention. His team operates from the Lash Miller Chemical Laboratories in downtown Toronto.

Emily MeversView profile

Assistant Professor

• Natural Products
• Drug Discovery
• Chemical Biology
• +3 more

Emily Mevers is an Assistant Professor in the Department of Chemistry at Virginia Tech's College of Science, where she established her research program in August 2020. Her lab investigates ecological metabolomes to discover bioactive natural products with therapeutic potential, focusing on underexplored niches like marine egg masses and millipede secretions. Education: B.S. in Chemistry, University of South Florida (2009) M.S. in Organic Chemistry, University of California San Diego (2011) Ph.D. in Organic Chemistry, University of California San Diego (2014) Postdoctoral Research Fellow, Harvard Medical School (2014-2016) Research Associate, Harvard Medical School (2017-2020) Research Focus: Dr. Mevers employs a function-first discovery approach to isolate natural products evolved for specific ecological roles. Her work centers on marine egg mass microbiomes (seeking predation-deterrent compounds for cancer/infection applications) and deep-sea hydrothermal vent systems (studying electron shuttles for mineral respiration). This integrates chemical ecology , organic synthesis , and microbial physiology to bridge ecological function with drug discovery. Publication Trends: Analysis of her 15 most recent publications reveals dominant themes in millipede defensive chemistry (5 articles), marine egg mass microbiomes (4 articles), and bacterial electron transfer (3 articles). Her work consistently links ecological function to bioactive compound discovery, with increasing focus on computational prediction of redox properties and host-microbe chemical interactions since 2023. Scientific Recognition: American Society of Pharmacognosy Postdoctoral Research Fellow Travel Award (2015) Teddy Traylor Award (2013) David Carew Graduate Student Travel Award (2013) Biochemistry of Growth Regulation and Oncogenesis NIH Trainee (2011-2014) Research Leadership: Dr. Mevers directs the Mevers Lab at Virginia Tech, mentoring graduate students in natural product discovery. Her prior NIH-funded training and current institutional support enable access to advanced instrumentation for metabolomics and bioassay development. She actively collaborates with marine biologists for sample collection and ecologists for functional validation. Research Infrastructure: The lab operates from 3103 Hahn Hall South, utilizing NMR, mass spectrometry, and microbial culturing facilities. Current projects include chemical profiling of marine invertebrate microbiomes, millipede alkaloid biosynthesis studies, and high-throughput screening of redox mediators – all aligned with Virginia Tech's strategic focus on One Health initiatives.

Lani WuView profile

Professor

• Systems Biology
• Cell Signaling
• Cancer Biology
• +7 more

Dr. Lani Wu is a Professor in the Department of Pharmaceutical Chemistry at the University of California, San Francisco (UCSF), affiliated with the School of Pharmacy. She leads the Altschuler & Wu Laboratories , focusing on systems biology, cell signaling, and drug discovery. Her work integrates experimental biology, mathematics, and computational methods to study cellular individuality, collective behaviors, and drug resistance mechanisms. Key projects include high-throughput screening platforms, cancer persister cell mechanisms, and mathematical modeling of biological networks. Education: PhD in Mathematics, University of California, San Diego (1990) Masters in Mathematics, University of California, San Diego (1987) Bachelor of Arts in Mathematics, Taiwan University (1985) Research Interests: Dr. Wu’s research spans systems biology, cell polarity, and computational approaches to drug discovery. She investigates how biological networks rewire during disease, leveraging machine learning and image analysis to uncover design principles of biological systems. Current projects include modeling cancer drug resistance, identifying compounds for acute hypoxic stress, and developing tools for spatial transcriptomics. Publications: Her work emphasizes drug discovery, cancer biology, and cell signaling. Recent efforts highlight techniques like phenopushing platforms, high-content screening, and spatial analysis of cell morphologies. These studies aim to accelerate therapeutic development and understand cellular adaptation under stress. Affiliations: She is part of the Biological and Medical Informatics Graduate Program and the Quantitative Biosciences Institute (QBI) at UCSF, reflecting her interdisciplinary approach. Labs/Teams: The Altschuler & Wu Laboratories combine experimental and computational expertise to address complex biological questions, with particular emphasis on translational research in oncology and systems biology.