Todd A. Alonzo is a Professor of Research in the Department of Preventive Medicine at the University of Southern California . As Group Statistician for the Children's Oncology Group , he focuses on statistical methods for biomarker analysis, medical diagnostic testing, and clinical trial design in pediatric acute myeloid leukemia (AML). Education: B.S. in Statistics, California State Polytechnic University (1994) MS and PhD in Biostatistics, University of Washington (1997, 2000) Research Interests include: Development of statistical frameworks for diagnostic accuracy Genomic and proteomic profiling in AML Pharmacogenomic score systems for chemotherapy response Non-inferiority trial design in low-event-rate settings Health disparities in pediatric oncology Scientific Awards : Fellow, American Statistical Association (2018) Outstanding Teacher Award, International Society for Magnetic Resonance in Medicine (2017) NIH Predoctoral Cardiovascular Biostatistics Training Grant (1995) ENAR Biometrics Society Distinguished Student Paper Award (1999) WNAR Biometrics Society Best Student Oral Presentation (1999) Leadership & Service includes editorial board memberships (Biometrics, Pediatric Blood & Cancer, Biometrical Journal), reviewer for 30+ scientific journals, and roles on multiple Data Safety and Monitoring Boards. He served as President of the International Biometric Society Western Northern America Region (WNAR) in 2009.
Raphael Franzini serves as Associate Professor of Medicinal Chemistry at the University of Utah, actively contributing to the Biological Chemistry PhD Program. His research pioneers innovative chemical approaches for therapeutic development, with dual focus on DNA-encoded library technologies and bioorthogonal drug delivery systems. His educational foundation includes an M.S. from the Swiss Federal Institute of Technology (Lausanne) and a Ph.D. from Stanford University. This training underpins his group's multidisciplinary methodology combining organic synthesis, bioconjugation, computational modeling, and advanced imaging techniques. Dr. Franzini's research program centers on two transformative areas: First, advancing DNA-encoded library screening through computational integration to identify leads for challenging targets like Tankyrase and Sirtuin 6, with recent work addressing false negatives in machine learning prediction. Second, developing novel bioorthogonal release chemistry using isonitrile-tetrazine reactions for spatiotemporally controlled drug activation, validated in zebrafish models. His group emphasizes both technological innovation and therapeutic translation, with chemistry designed to minimize off-target effects in solid tumors. Analysis of his 15 most recent publications reveals escalating integration of computational methods with experimental library screening, alongside refinement of bioorthogonal release kinetics. The work spans chemical biology, medicinal chemistry, and pharmaceutical sciences, with growing emphasis on machine learning for library data interpretation and in vivo validation of drug-release systems. Dr. Franzini maintains an active research laboratory that provides comprehensive training in cutting-edge drug discovery methodologies. His group culture prioritizes both scientific innovation and researcher development, with projects spanning from fundamental reaction kinetics to therapeutic applications. The lab's infrastructure supports organic synthesis, molecular imaging, and computational analysis for advancing precision therapeutics.
Anders Krogh is a Professor at the Department of Computer Science, University of Copenhagen, and also holds a position at the Department of Public Health in the Section for Health Data Science and AI. He serves as the head of the Center for Health Data Science (HeaDS) in the Faculty of Health and Medical Sciences. Previously, he was affiliated with the Department of Biology at the University of Copenhagen until 2020. Dr. Krogh earned his PhD in theoretical physics but transitioned into machine learning and bioinformatics during his doctoral studies. His research spans both theoretical foundations and practical applications in these fields. He is particularly renowned for his pioneering work on hidden Markov models for biological sequences, which has had significant impact in computational biology. In recent years, Krogh's research has focused on deep generative models applied to gene expression data and other biomedical applications. His work bridges computer science with healthcare, developing AI-driven approaches for precision medicine, cancer diagnostics, and analysis of complex biological systems. His current research integrates machine learning with quantum computing applications in biomolecular modeling. Analysis of his recent publications reveals a strong trend toward applying artificial intelligence to healthcare challenges, particularly in rare diseases, cancer diagnostics, and personalized medicine. His work increasingly incorporates federated learning approaches to address privacy concerns while enabling collaborative research across institutions. There's also a growing emphasis on quantum computing applications in biomolecular modeling and drug discovery. As head of the Center for Health Data Science, Krogh leads interdisciplinary research efforts that bring together computer scientists, medical researchers, and clinicians. His team develops novel computational frameworks like MOSAIC for multimodal analysis of rare cancers and multiDGD for multi-omics data integration. These tools are designed to translate AI innovations into clinical practice while addressing the unique challenges of medical data.
Darryl Overby is a Professor of Mechanobiology in the Department of Bioengineering at Imperial College London's Faculty of Engineering. His academic affiliations include the CRUK Convergence Science Centre, Cancer Technology Network, and multiple interdisciplinary networks focused on ocular biomechanics, vascular science, and regenerative medicine. He holds an Orcid identifier (0000-0001-9894-7515) and was previously affiliated with Tulane University and Harvard Medical School. Education: Ph.D. in Mechanical Engineering (2002) from MIT under Prof. Roger Kamm, followed by postdoctoral research at Harvard Medical School under Prof. Donald Ingber. His work focuses on cellular biomechanics, mechanotransduction, and the role of mechanical forces in intraocular pressure regulation and glaucoma pathophysiology. Research interests span mechanobiology of Schlemm’s canal endothelial cells, nitric oxide signaling in ocular tissues, and development of organ-on-chip models for studying aqueous humor outflow dynamics. His lab explores how mechanical signals drive transcellular pore formation and how these mechanisms fail in glaucomatous conditions. Key contributions include consensus guidelines for Brillouin microscopy measurements, ex vivo perfusion studies, and the role of TRPV4 channels in mechanosensing. His work integrates engineering techniques with clinical ophthalmology to advance therapies targeting glaucoma and ocular hypertension. Professional activities include leadership roles in the Ocular Biomechanics Network and Wound Healing and Regeneration Network. His research has led to patents for devices enhancing aqueous humor drainage and RNAi-based therapies.
Christopher J. Chang is the Edward and Virginia Taylor Professor of Bioorganic Chemistry at Princeton University's Department of Chemistry. His research focuses on chemical biology, catalysis, and inorganic chemistry, with an emphasis on transition metal signaling, activity-based sensing, and drug discovery. He leads the Chang Lab, which develops innovative chemical tools to study metal-dependent biological processes, including copper's role in neurobiology and cancer, formaldehyde's role in epigenetic regulation, and redox-driven protein function. His work integrates organic, inorganic, and biological chemistry, enabling discoveries in imaging, proteomics, and precision medicine. Notable achievements include pioneering activity-based sensing platforms for copper and reactive metabolites, revealing metalloplasia in cancer, and developing copper-specific therapies. Christopher Chang has received over 50 prestigious awards, including the Guggenheim Fellowship and the Howard Hughes Medical Institute Investigatorship. His lab's infrastructure includes advanced analytical instruments, synthetic chemistry facilities, and cell culture capabilities, supported by grants from NIH, NSF, and industry partnerships. Awards: ACS Bader Award (2024), Ivano Bertini Award (2022), Blavatnik National Award (2015) Lab Focus Areas: Transition metal signaling, copper-dependent biology, formaldehyde metabolism, redox drug discovery Key Technologies: Activity-based sensors, imaging probes, bioconjugation methods
Li Tang is an Associate Professor with tenure at École polytechnique fédérale de Lausanne (EPFL), affiliated with the Institute of Bioengineering (IBI) and the Institute of Materials Science and Engineering (IMX) within the School of Engineering (STI). She leads the Laboratory of Biomaterials for Immunoengineering, focusing on developing innovative strategies at the intersection of immunology, materials science, and cancer therapy. Her work bridges fundamental research and clinical translation, with multiple ongoing clinical trials based on CAR-T cell therapies developed in her lab. B.S. in Chemistry, Peking University (2003–2007) Ph.D. in Materials Science and Engineering, University of Illinois at Urbana-Champaign (2007–2012) Postdoctoral Fellow, MIT (2013–2016) Her research lies at the forefront of immunoengineering, integrating chemical, metabolic, and mechanical approaches to modulate immune responses. Key areas include cancer immunotherapy, immune metabolism, mechano-immunology, and biomaterials. She investigates how physical and biochemical cues can reprogram T cells, overcome exhaustion, and enhance tumor targeting. Her work emphasizes multidimensional immunity-disease interactions, aiming to develop safer and more effective therapies for cancer and autoimmune diseases. The recent publications highlight a strong trend in engineering immune cells (especially CAR-T) for enhanced durability and function, using advanced biomaterials and metabolic reprogramming. There is a clear focus on overcoming challenges in solid tumors, modulating the tumor microenvironment, and translating findings into clinical applications. The use of nanoparticle delivery, single-cell analysis, and biomechanical cues are recurring themes across her work. Notable scientific awards include: Friedrich Miescher Award (2025) ERC Starting Grant (2018) MIT TR35 Innovators Under 35 (China Region, 2020) Nano Research Young Innovator Award (2018) Biomaterials Science Emerging Investigator (2019) Materials Horizons Emerging Investigator (2020) Li Tang actively mentors PhD students across multiple doctoral programs (EDBB, EDMS, EDMX) and has advised numerous graduates who have gone on to prestigious postdoctoral and faculty positions. She is involved in significant research grants, including an Innosuisse project with Novochizol SA, and her lab is supported by competitive funding. She teaches core courses such as Immunoengineering and Next-Generation Biomaterials, shaping the next generation of scientists. Her lab fosters interdisciplinary collaboration and innovation, with active projects in chemical, metabolic, and mechanical immunoengineering, as well as CAR-T cell development. She is the Principal Investigator of the Tang Lab, which includes postdoctoral fellows, PhD students, and technical staff. The lab is actively recruiting and has a strong publication and clinical translation record. Tang Lab is also involved in multiple MA/BA training projects and promotes student engagement in cutting-edge research. The lab’s discoveries are being translated into clinical trials, reflecting a strong commitment to translational science.
Vikas Singh is a Professor in the Department of Biostatistics at the University of Wisconsin-Madison, with appointments in Computer Sciences and Statistics. He also serves as a part-time Faculty Researcher at Google DeepMind. His research focuses on image analysis, machine learning, and medical imaging applications, particularly in neuroimaging and Alzheimer's disease studies. Singh holds a Ph.D. in Computer Science from SUNY Buffalo and has taught courses such as BMI/CS 767 (Medical Image Analysis) and CS 766 (Computer Vision). Affiliations: UW Computer Vision Group, Wisconsin Alzheimer's Disease Research Center (W-ADRC), Machine Learning@UW. Research: Develops algorithms for medical image analysis, including tools for neuroimaging and longitudinal biomarker studies. Grants: Collaborates on grants related to Alzheimer's progression modeling and imaging techniques. His work emphasizes interdisciplinary applications, bridging statistics, geometry, and optimization to solve real-world problems in healthcare and engineering.
Cathy Wu is a distinguished academic holding the Unidel Edward G. Jefferson Chair in Engineering and Computer Science at the University of Delaware. She serves as Director of the Center for Bioinformatics & Computational Biology (CBCB), Data Science Institute (DSI), and Protein Information Resource (PIR). Her roles include professorships in the Departments of Computer & Information Sciences and Biological Sciences. Education: BS in Plant Pathology (National Taiwan University, 1978), MS and PhD in Plant Pathology (Purdue University, 1982–1984), and a second MS in Computer Science (University of Texas at Tyler, 1989). She completed postdoctoral training in Molecular Biology at Michigan State University (1985–1986). Research interests focus on computational biology, bioinformatics, and data science with emphasis on protein informatics, biological text mining, ontology development, gene-disease-drug networks, and machine learning applications. She leads initiatives in integrating FAIR principles into biological databases like UniProt and InterPro. Her work bridges computational methods with biomedical challenges, including cancer genomics, epigenetic regulation, and proteomic analyses. She has spearheaded educational programs such as the Online Graduate Certificates in Applied Bioinformatics and Biomedical Informatics and Data Science. Her contributions include over 290 peer-reviewed publications (48,000+ citations, h-index 71) and authored/co-authored four books on bioinformatics. She directs multidisciplinary research teams and collaborates internationally on projects like the HALO study on ovarian cancer genetics. Awards and recognition are implied through her leadership roles and academic appointments, though specific prizes are not listed here. Her grants and funding support large-scale initiatives in bioinformatics infrastructure and translational research.
James Zou is an Associate Professor of Biomedical Data Science at Stanford University, with courtesy appointments in Computer Science and Electrical Engineering. His research focuses on advancing machine learning methodologies for healthcare applications, emphasizing reliability, fairness, and statistical rigor. He holds a Ph.D. from Harvard University and has held positions at Microsoft Research, Cambridge University (as a Gates Scholar), and UC Berkeley (Simons Fellow). Zou leads the Stanford Data4Health hub and is a Chan-Zuckerberg Investigator. His work spans AI-driven diagnostics, spatial transcriptomics, and ethical AI frameworks. Key achievements include the EchoNet AI system for echocardiography and foundational contributions to data valuation (e.g., Data Shapley). Awards include the Sloan Fellowship, NSF CAREER Award, and Google/Tencent AI awards. Education: Ph.D., Harvard University (2014); Postdoctoral roles at Microsoft Research, Cambridge, and Berkeley. Research Interests: Machine learning for healthcare, algorithmic fairness, interpretable AI, spatial omics, and translational bioinformatics. His lab develops tools like TextGrad (PyTorch for text agents) and frameworks for evaluating medical AI systems. Recent work addresses LLMs in peer review and clinical decision-making. Grants/Grants: Supported by NSF, Sloan Foundation, Chan-Zuckerberg Initiative, and industry partnerships (Google, Amazon, Adobe). Advises on over 20 doctoral students, many contributing to high-impact papers in Nature , Science , and top conferences (NeurIPS, ICML). Leads collaborations in cardiology, oncology, and veterinary medicine. Labs/Teams: Stanford AI Lab, Stanford Data4Health, and interdisciplinary groups in precision medicine. Active in open-source projects like FrugalML and MetaViz.
Associate Professor Arnold Lining Ju is a biomedical engineer at the University of Sydney's School of Biomedical Engineering, affiliated with multiple institutes including the Heart Research Institute and Sydney Nano Institute. He holds academic positions in both the Faculty of Engineering and Faculty of Medicine & Health. Education: BSc from Peking University, PhD from Georgia Tech and Emory University (USA). Honors include Snow Fellowship, Heart Foundation Future Leader Fellowship, and multiple awards for cardiovascular research innovation. Research focuses on mechanobiology and biomechanics of thrombosis, developing microfluidic devices and organ-on-chip systems. Key projects include AI-driven single-cell nanotools, 3D biofabrication, and anti-thrombotic peptide design. Leads interdisciplinary teams and collaborates internationally with institutions like Harvard and University of Texas. Teaching roles include coordinating advanced cellular biomechanics courses and supervising PhD/Masters students in biomedical engineering and physiology. Over 50 peer-reviewed publications, with contributions to Nature Materials, Nature Communications, and other top journals.
Dr. Christina Leslie is a Research Professor and Member of the Computational & Systems Biology Program at Memorial Sloan Kettering Cancer Center (MSK). She leads an active research laboratory focused on developing computational approaches to understand complex biological systems. Dr. Leslie earned her PhD from the University of California, Berkeley and has established herself as a leading computational biologist in cancer research and immunology. Computational & Systems Biology Program, Memorial Sloan Kettering Cancer Center Gerstner Sloan Kettering Graduate School of Biomedical Sciences Dr. Leslie's research focuses on developing novel computational methods to study cellular biological systems from a global and data-driven perspective. Her lab exploits diverse high-throughput functional and genomic data to understand molecular networks underlying fundamental cellular processes, including transcription regulation, pre-mRNA processing, signaling, and post-transcriptional gene silencing. Her algorithmic methods draw heavily on machine learning to build accurate predictive models from noisy and high-dimensional biological data. Key areas of interest include modeling cell-type specific transcriptional programs and dissecting co- and post-transcriptional regulation, particularly microRNA-mediated gene regulation. Analysis of Dr. Leslie's publication record over the last five years reveals a strong focus on computational approaches to cancer genomics, immunology, and epigenetics. Her work bridges multiple disciplines, with a particular emphasis on developing machine learning methods to interpret complex biological data. The publications demonstrate increasing sophistication in integrating multiple data types (genomic, transcriptomic, epigenomic) to understand cancer biology and immune responses. Recent work shows a growing emphasis on single-cell technologies and spatial analysis of tumor microenvironments. Introduction of string kernel methodology for SVM classification of biological sequences Development of algorithms for predictive modeling of gene regulation First systems-level analyses of competition between microRNAs and between target transcripts Dr. Leslie actively mentors numerous graduate students and research associates, with current lab members including Vianne Gao, Alireza Karbalaghareh, Erik Ladewig, and several others. Her lab has received significant research funding to support their work on computational approaches to cancer biology and immunology. The Leslie Lab maintains close collaborations with multiple experimental groups at MSK, facilitating the translation of computational insights into biological understanding. The Leslie Lab operates within the Computational & Systems Biology Program at MSK, with strong ties to both the research and clinical missions of the institution. The lab maintains state-of-the-art computational infrastructure for analyzing large-scale genomic and proteomic datasets and collaborates extensively with wet-lab researchers to validate computational predictions experimentally.
Monica N. Fornier is a physician scientist at Memorial Sloan Kettering Cancer Center (MSK) specializing in breast medical oncology . With a clinical focus on improving outcomes for breast cancer patients, she has particular expertise in bone health related to cancer therapies. Fluent in English, Italian, and French, Dr. Fornier treats international patients and contributes to multidisciplinary care teams. Education: MD from University of Milan (Italy), residency and fellowship at University Hospital of Milan and MSK Certifications: Board-certified in Internal Medicine and Medical Oncology (Europe) Her research spans neoadjuvant endocrine therapy , bone loss prevention in cancer patients, and HER2-positive breast cancer treatment beyond trastuzumab. Recent publications address ER+/HER2- breast cancer immune responses, alopecia management in survivors, and regulatory T cell mechanisms . Dr. Fornier has received multiple Castle Connolly Top Doctor awards (2023-2025) and participates in MSK clinical trials for novel cancer treatments.
Maria Timofeeva is an Associate Professor in the Epidemiology, Biostatistics and Biodemography (EBB) department at the University of Southern Denmark (SDU), with additional affiliation at the Danish Institute for Advanced Study (DIAS). She holds an Honorary Fellow position at the University of Edinburgh since December 2019. Her research focuses on cancer prevention and prediction, particularly studying the effects of environmental and genetic factors on cancer risk and progression. Dr. Timofeeva earned her Dr.sc.hum in Epidemiology from Heidelberg University (2005-2009), with a dissertation on genetic polymorphisms as risk factors for early onset lung cancer. Prior to her current position, she worked as a Statistical Geneticist at the University of Edinburgh (2013-2019) and as a Postdoctoral Fellow at the International Agency for Research on Cancer (2009-2013). Her research interests center around understanding the genetics of cancer risk through multi-omic analysis. She leads several significant projects, including the Interdisciplinary Project on Adherence to Colorectal Cancer Screening, meta-analysis of factors associated with false-positive and false-negative FOBT results (registered in PROSPERO ID: CRD42022315767), and the COlorectal Cancer screening Among RElatives (CoCARE) twin-family study in Denmark. Her methodological expertise spans observational epidemiological studies (case-control, population-based cohort studies, twin studies), meta-analysis, umbrella reviews, and multi-omics data analysis. Analysis of her recent publications reveals a strong focus on colorectal cancer genetics, with particular emphasis on genome-wide association studies, Mendelian randomization approaches, and trans-ancestry analyses. Her work frequently leverages large datasets including the UK Biobank and international consortia, with applications in cancer risk prediction and understanding gene-environment interactions. Dr. Timofeeva has an extensive publication record with 73 publications listed in her profile. Her research has been cited across multiple platforms, with mentions in news outlets, social media, and academic readership platforms like Mendeley. She is actively involved in academic service, serving as a peer reviewer for journals including BMC Cancer and Scientific Reports, and participating in conferences such as the 26th Nordic Congress of Gerontology. She also serves on evaluation committees, including with the World Cancer Research Fund International (April-May 2024). Her teaching activities include courses on evidence-based drug utilization and biostatistics, as well as supervision of research projects on gene expression in twins. Dr. Timofeeva has engaged with the public through media contributions, including an interview titled 'Jeg vil forstå, hvorfor vi får kræft' (November 15, 2021), where she discussed understanding why we get cancer.
Jeremy Wang, PhD is an Assistant Professor in the Department of Genetics at the UNC School of Medicine . His research focuses on applying high-performance computational methods and machine learning to analyze high-throughput sequence data using long-read technologies (e.g., Oxford Nanopore) to advance precision personalized medicine . Key disease areas include Inflammatory Bowel Diseases (IBD) Respiratory Infectious Diseases His lab specializes in microbiome analysis , host-pathogen interactions , and computational genomics , working with collaborators in clinical, translational, and computational domains. His publications demonstrate expertise in long-read sequencing applications for Pediatric cancer classification SARS-CoV-2 genomic epidemiology Microbiome spatiotemporal dynamics Murine disease models Drosophilid genome assemblies Metagenomic bias analysis Collaborations span UNC and global institutions, with current work extending to clinical laboratory partnerships for pathogen sequencing and oral microbiome sampling methodology.
Pedro A Hermida de Viveiros, MD serves as an Assistant Professor in the Department of Medicine, Division of Hematology and Oncology at Northwestern University's Feinberg School of Medicine. His clinical practice focuses on solid tumor oncology with specialized expertise in sarcomas and precision medicine approaches. His research interests center on clinical development of novel therapeutic strategies for sarcomas and gastrointestinal stromal tumors (GIST), emphasizing biomarker-driven treatments and molecularly targeted therapies. Key areas include IDH1-mutant chondrosarcoma management, neoadjuvant response assessment in sarcomas, and immunotherapy-radiotherapy combinations for metastatic disease. Analysis of his recent publications reveals a consistent focus on translating molecular targets into clinical applications, particularly through phase 1-3 trials of targeted agents like ivosidenib and IDRX-42. His work bridges radiological assessment, pathological response, and precision oncology frameworks for rare solid tumors. Professional engagements include active membership in major oncology societies: American Society of Clinical Oncology (ASCO) Connective Tissue Oncology Society (CTOS) Sociedade Brasileira de Oncologia Clínica (SBOC) European Society for Medical Oncology (ESMO) He currently leads four active clinical trials through the Robert H. Lurie Comprehensive Cancer Center, including the CHONQUER phase 3 study of ivosidenib for IDH1-mutant chondrosarcoma and first-in-human trials of novel agents for advanced solid tumors and GIST. His work integrates translational research with clinical trial design to advance personalized treatment paradigms.
