Kelly Arnold is an Associate Professor in the Department of Biomedical Engineering at the University of Michigan. Her research integrates systems engineering principles with immunology to investigate variability in immune responses across infection, vaccination, and injury, with a focus on computational modeling and clinical translation. Research Focus Systems-level immune response modeling Vaccination and antibody functionality Vaginal microbiome-host interactions Chronic lung disease progression Computational serology and proteomics Recent Work Her 2025 studies examine SARS-CoV-2 vaccination responses in cancer patients and computational frameworks for vaginal probiotics. Earlier works (2024-2007) span COPD progression, lupus fibrosis, HIV susceptibility, and tissue engineering for fertility preservation. Methodologies include proteomic profiling, network modeling, and microfluidic systems.
Antti Honkela is a Professor of Data Science at the University of Helsinki's Department of Computer Science, within the Faculty of Science. He also serves as the Coordinating Professor for the Privacy-preserving and Secure AI Research Programme at the Finnish Center for Artificial Intelligence (FCAI), and as Deputy Director of the Master's Programme in Data Science. His roles include membership in the Health and Social Data Permit Authority (Findata) and as an Action Editor for Transactions on Machine Learning Research. Honkela's research focuses on privacy-preserving machine learning, differential privacy, Bayesian methods, and their applications in computational biology and healthcare. He leads projects such as the European Lighthouse in Secure and Safe AI (ELSA) and the Data Literacy for Responsible Decision-making initiative. His work emphasizes developing robust frameworks for privacy-aware AI, including differentially private synthetic data and federated learning. Honkela has advised numerous PhD and Master's students, and his contributions span theoretical advancements and practical implementations, such as the D3p Python package for differentially private probabilistic programming. Key contributions include advancements in Bayesian inference from synthetic data, privacy accounting mechanisms, and computational methods for genomic epidemiology. His interdisciplinary approach bridges machine learning, statistics, and healthcare, addressing challenges in data privacy and secure AI deployment.
Manuel R. Amieva is a Professor at Stanford University School of Medicine , holding joint appointments in Pediatrics - Infectious Diseases and Microbiology & Immunology . He is also a member of the Maternal & Child Health Research Institute (MCHRI) . His clinical practice at Stanford Medicine Children's Health focuses on pediatric infectious diseases. Education: Medical Education: Stanford University School of Medicine (1997) Fellowship: Stanford University Pediatric Infectious Disease Fellowship (2004) Internship & Residency: Stanford Health Care at Lucile Packard Children's Hospital (1998-1999) Dr. Amieva's research investigates host-pathogen interactions at epithelial barriers, with specific expertise in Helicobacter pylori , Listeria monocytogenes , Salmonella enterica , and Staphylococcus aureus . His lab develops innovative organoid culture systems with controlled polarity to study microbial colonization and oncogenic mechanisms. Key discoveries include: H. pylori's manipulation of epithelial junctions via the CagA protein Listeria's exploitation of cell extrusion sites for invasion Staphylococcus toxin interactions with adherens junctions Gastric stem cell activation by pathogens Recent publication trends show continued leadership in infectious disease mechanisms (2020-2025), with a focus on: Pathogen-specific epithelial breach strategies Organoid modeling of viral/bacterial interactions Redox-dependent host factor regulation Single-cell spatial transcriptomic analyses Multi-institutional educational frameworks His scientific collaborations span disciplines including: Gastric cancer genomics initiatives COVID-19 lung infection models Stem cell-microbe interactions Medical education reform projects Dr. Amieva maintains active clinical research while mentoring students in both the Microbiology & Immunology and Pediatrics programs. His lab at Stanford employs advanced 3D confocal microscopy and organ-on-a-chip technologies to visualize epithelial colonization dynamics.
Dr. Lourdes Pena-Castillo is a Professor jointly appointed in the Departments of Computer Science and Biology at Memorial University of Newfoundland's Faculty of Science. Her research focuses on applying machine learning and bioinformatics to study bacterial gene regulation, with emphasis on transcriptomics, gene expression pathways, and microbiology. She leads the Bioinformatics Lab at MUN, developing computational tools like Promotech for promoter prediction and sRNARFTarget for sRNA target identification. Education: BSc in Information Systems Engineering, ITESM-Mexico MSc in Computer Science, University of Alberta PhD in Computer Science (Doktoringenieurin), Otto-von-Guericke Universität Magdeburg Postdoc in Bioinformatics, University of Toronto Research Interests: Bioinformatics, Genomics, Machine Learning, Artificial Intelligence, Transcriptomics, Gene Regulation, Microbiology Her work integrates computational methods with biological data to address challenges in molecular biology, including analyzing bacterial sRNA functions, promoter recognition, and disease diagnostics using machine learning. She has advised numerous graduate students, including PhD candidates Purvikalyan Pallegar and Bonita McCuaig, and MSc students like Ruben Chevez-Guardado and Kratika Naskulwar. Her lab focuses on translational research with applications in both basic science and clinical contexts. Publications span computational methods for bacterial gene regulation, bioinformatics tool development, and interdisciplinary projects in VR and healthcare informatics. Her research has contributed to understanding symbiotic relationships in marine organisms, inflammatory bowel disease diagnostics, and clavulanic acid production in Streptomyces. Grants & Collaborations: Works with interdisciplinary teams across computer science and biology, supported by grants enabling projects in bacterial genomics and computational tool development. Labs & Teams: Leads the Bioinformatics Lab at MUN, fostering collaborations with researchers in microbiology, computer science, and healthcare.
Prof. Knut Drescher is an Associate Professor at the Biozentrum, University of Basel , leading a research group focused on bacterial biofilms , swarming , and microbial multicellularity . Previously, he served as a Professor of Biophysics and Max Planck Research Group Leader at Philipps-Universität Marburg (2015-2021) and conducted postdoctoral research at Princeton University. Research Interests: Physical and biological mechanisms of biofilm formation Cell-cell interactions in microbial communities Antibiotic resistance in biofilms Hydrodynamics of bacterial swarms Evolution of cooperation in multispecies biofilms Development of bioimaging software (BiofilmQ, BacStalk) Scientific Awards: 2023: SNSF Consolidator Grant 2019: Heinz Maier-Leibnitz Prize (DFG), VAAM Research Prize, IUPAP Young Scientist Prize 2016: ERC Starting Grant Advising & Grants: Advises PhD and Master's students in microbiology, biophysics, and bioinformatics Secured major grants from ERC , HFSP , and DFG
Prof. Dr. Alex Hall is an Associate Professor at the Department of Environmental Systems Science and Head of the Institute of Integrative Biology at ETH Zürich , Switzerland. He holds a PhD from the University of Edinburgh (2008), followed by postdoctoral work at the University of Oxford and ETH Zürich, including prestigious SNSF Ambizione and Marie Curie Intra-European fellowships. Research Focus: The Hall lab investigates Microbial interactions in human microbiomes Antibiotic resistance evolution Horizontal gene transfer dynamics Computational and in vitro modeling of microbial communities Evolutionary medicine applications Scientific Contributions: His recent studies analyze probiotic treatments for Staphylococcus aureus decolonization, strain-specific ecological success in gut microbiomes, and CRISPR-Cas system roles in plasmid conflicts. The lab employs computational frameworks combined with experimental validations. Scientific Awards: SNSF Ambizione fellowship Marie Curie Intra-European fellowship He also serves on the SNSF PRIMA fellowship evaluation panel and the ETH Zürich Coordination Council Medicine .
Alfred O. Hero, III is the John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science and the R. Jamison and Betty Williams Professor of Engineering at the University of Michigan, Ann Arbor. He is currently on leave from the University of Michigan as a Program Director in the CISE Directorate at the National Science Foundation. His primary appointment is in the Department of Electrical Engineering and Computer Science (EECS), with secondary appointments in the Department of Biomedical Engineering and the Department of Statistics. He is also affiliated with the UM Center for Computational Medicine and Bioinformatics (CCMB), the UM Graduate Program in Applied and Interdisciplinary Mathematics (AIM), the UM Applied Physics Program, and the Michigan Institute for Data Science (MIDAS), which he co-founded from 2015-2018. Hero's research focuses on data science, developing theory and algorithms for multimodality data collection, fusion, analysis and visualization that use statistical machine learning and distributed optimization. His work has applications in wearable technologies for personalized health and predictive medicine, spatio-temporal networks in biology, climate, and social discourse, anomaly detection, and data analysis for international security. His recent research interests include high dimensional spatio-temporal data analysis, multimodal data integration, statistical signal processing, and machine learning, with particular emphasis on predictive mathematical models for biological and physical sciences, social networks, network security and forensics, and personalized health and disease. His recent publications demonstrate a strong focus on high-dimensional statistical methods, contrastive learning, neural network optimization, change detection in temporal graphs, and applications in microbiome analysis and epidemic modeling. The research spans theoretical foundations in information theory and statistical learning while addressing practical applications across multiple domains. Scientific Awards: IEEE Signal Processing Society Best Paper Award (1998) Best Original Paper Award from Journal of Flow Cytometry (2008) Best Magazine Paper Award from IEEE Signal Processing Society (2010) SPIE Best Student Paper Award (2011) IEEE ICASSP Best Student Paper Award (2011) IEEE Signal Processing Society Technical Achievement Award (2014) IEEE Signal Processing Society Society Award (2015) IEEE Fourier Award (2020) University of Michigan Distinguished Faculty Achievement Award (2011) Hero has advised over 60 PhD students and 30 postdocs in areas including modeling, computation, and inference for large scale time varying data in the biosciences. He has received significant research funding from the Department of Energy, Army Research Office, Air Force Office of Scientific Research, and National Science Foundation. He has held leadership positions including President of the IEEE Signal Processing Society (2006-2007), Director of Division IX (Signals and Applications) on the IEEE Board of Directors (2009-2011), and Chair of the Committee on Applied and Theoretical Statistics of the US National Academies (2018-2020).
Jennifer L. Clarke is a Professor in the Department of Statistics at the University of Nebraska–Lincoln and Director of the Quantitative Life Science Initiative. She holds leadership roles in enabling big data integration across the University of Nebraska system through collaborative research programs. Her affiliations include the Institute of Agriculture and Natural Resources (IANR) and the College of Agriculture and Natural Resources. Dr. Clarke's research focuses on statistical methodology for high-dimensional data, computational biology, bioinformatics, and bacterial genomics. Her work bridges statistical innovation with applications in oncology, microbiome analysis, and agricultural phenomics. Key areas include predictive modeling, machine learning, and genomic/metagenomic data integration. Her recent publications span cancer biomarker discovery, plant phenotyping methodologies, and microbial community analysis, reflecting her interdisciplinary approach. Articles emphasize translational applications like therapeutic target identification and precision agriculture. Dr. Clarke leads initiatives fostering collaboration between statisticians and domain scientists, including the Quantitative Life Science Initiative and contributions to the Agricultural Genome-to-Phenome Initiative (AG2PI). Her work advances data-driven solutions for healthcare and food security challenges. Notable projects include developing statistical tools for microbiome studies, analyzing root architecture via 3D imaging, and investigating cranberry-derived compounds' cancer-inhibitory mechanisms. Her methodological contributions include hybrid clustering techniques and predictive model validation frameworks.
Xiang Ji is an Assistant Professor in the Department of Mathematics at Tulane University, affiliated with the School of Science & Engineering. His research focuses on statistical phylogenetics, computational biology, and bioinformatics, particularly in viral evolution and genomic epidemiology. He collaborates with Dr. Wu-Min Deng on cancer biology research from a bioinformatics perspective. Education: Ph.D., 2017: Bioinformatics and Statistics (Co-Major), North Carolina State University M.S., 2013: Material Science and Engineering, North Carolina State University B.S., 2011: Economics (Double Major) and Physics, Peking University Research Interests: Dr. Ji develops statistical models and computational tools for phylogenetic analysis, including scalable algorithms for large-scale genomic data. His work spans viral evolution, zoonotic disease surveillance, and parallel computing libraries for Bayesian inference. He emphasizes practical implementations such as Torchtree and TreeFlow . Articles Trends: Recent publications emphasize viral evolution dynamics (e.g., SARS-CoV-2, avian influenza), genomic surveillance strategies, and computational methods for phylogenetic inference. His work often bridges statistical theory with real-world applications in public health and epidemiology. Advising & Grants: While specific grant details are not listed, his active research program indicates involvement in funding initiatives related to computational biology and viral evolution. He teaches advanced courses in data analysis, linear models, and probability theory. Labs & Teams: Collaborates with Tulane’s Cancer Biology group and maintains partnerships with institutions globally, focusing on genomic epidemiology and phylogenetic software development.
Professor Hala Zreiqat AM is a leading biomedical engineer at The University of Sydney , serving as the Director of the ARC Training Centre for Innovative BioEngineering . A Fellow of all major Australian academies (AAS, ATSE, FAHMS, FRSN), she develops 3D printed bioceramics for bone regeneration while championing diversity through initiatives like the IDEAL Society and BIOTech Futures mentorship program. Her work bridges academia, clinical practice, and industry in musculoskeletal research . Research Focus: Her lab creates synthetic bone scaffolds that mimic natural bone architecture, strength, and porosity, enabling non-rejected bone regeneration via patient-matched implants. Key applications include orthopaedic, dental, and maxillofacial repair , with over $18M in competitive funding and multiple patents. Current projects explore AI-driven scaffold performance prediction and anti-senescence strategies for aging-related bone loss. Scientific Trends: Recent publications highlight 3D printed nanovoxelated ceramics , antisenescence biomaterials , and multifunctional theranostic platforms . Her team integrates machine learning for scaffold design, atom probe tomography for interface analysis, and two-photon imaging for cellular monitoring in 3D environments. 2021-2022 Fulbright Senior Scholar 2018 NSW Premier's Woman of the Year 2019 Eureka Prize for Innovative Use of Technology Fellow of Australian Academy of Science (2021) Over $18M in research funding Teaching & Leadership: She designed core courses like Tissue Engineering and Nanomaterials in Medicine , mentoring 158 students in 2020 alone. As Chair of CAAR (2020-2023), she strengthens Australia-Arab collaborations. Her lab trains early-career researchers , with alumni now in academia and industry.
Lucas Paoli is a Researcher and Course Instructor at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He leads the Paoli Lab (Microbiome Immunity and Ecology) within the Global Health Institute (GHI) under the School of Life Sciences (SV). His roles include scientific collaboration and teaching responsibilities in life sciences engineering. Paoli holds a PhD in Microbiome Research from ETH Zürich (2018-2023), an M.Phil. in Environmental Policy from the University of Cambridge (2017-2018), and an M.Sc. in Ecology and Evolution from École normale supérieure (2015-2017). Research Focus: His work bridges microbial immunity and ecology, investigating how microbes defend against viral infections across ecosystems like oceans and human microbiomes. Techniques include global-scale metagenomics and functional genomics to study immune strategies and ecological interactions. Key themes involve microbial community dynamics, viral defense mechanisms, and the impact of environmental factors on microbial immunity. Lab & Education: The Paoli Lab explores microbiome-immunity interactions with a focus on ecological contexts. Paoli supervises PhD students in Life Sciences Engineering. His research outputs span microbial ecology, metagenomic tool development, and marine microbiome studies. Contact: lucas.paoli@epfl.ch, AAB 1 39, +41 21 693 16 99.
Dr. Michael Baym is an Associate Professor of Biomedical Informatics at Harvard Medical School with affiliate appointments in Microbiology and the Laboratory of Systems Pharmacology, and as an Associate Member of the Broad Institute. He leads the Baym Lab, which studies microbial evolutionary genomics and antibiotic resistance through a hybrid of experimental, computational, and theoretical approaches. His research focuses on: Antibiotic Resistance Evolution and practical interventions Mobile Genetic Elements (plasmids, phages, transposons) Computational Genomic Algorithms for big data analysis Synthetic Biology tools and technologies Key recent publications explore phage discovery systems , phylogenetic compression of microbial genomes, and RNA-guided gene drives in plasmids. His work is supported by multiple NIH/NIGMS and NSF grants including a MIRA award. Scientific honors include: Packard Fellowship (2018) Pew Biomedical Scholarship (2020) Sloan Research Fellowship (2020) A. Clifford Barger Excellence in Mentoring Award (2021) SSQBio Mentorship Award (2022) The lab actively trains PhD students and postdoctoral fellows with alumni occupying academic and industry positions globally. Current team members include researchers from interdisciplinary backgrounds working at the intersection of experiment, computation, and theory .
Jonathan Conway is an Assistant Professor in the Department of Chemical and Biological Engineering at Princeton University and an associated faculty member of the High Meadows Environmental Institute (HMEI). He leads the Conway Lab, which focuses on engineering plant-microbe interactions for applications in bioagriculture, bioenergy, and biochemical industries. Education: B.S. Chemical Engineering, University of Notre Dame (2011) M.S. Chemical Engineering, North Carolina State University (2013) Ph.D. Chemical Engineering, North Carolina State University (2017) Postdoctoral Fellow, University of North Carolina Chapel Hill & Howard Hughes Medical Institute (2017-2021) Research Interests: The Conway Lab develops genetic engineering approaches for non-model bacteria at plant-microbe interfaces. Key research areas include: chemical signaling between plants and microbes, microbiome impacts on plant immunity, environmental stress responses in agricultural systems, and enzymatic degradation of lignocellulosic biomass using thermophilic bacteria. The lab employs bacterial genetics, systems biology, and biomolecular engineering to create technologies for sustainable bioindustries. Publication Trends: Recent work demonstrates strong emphasis on molecular mechanisms of plant-microbe communication (2020-2024), enzyme characterization in biomass degradation (2024-2025), and development of synthetic microbial communities for climate resilience (2024). Earlier research focused on extremophile enzymology and metabolic engineering (2012-2019). Student Advising: Currently mentors 4 graduate students and 8 undergraduates. Alumni include 9 former advisees who graduated between 2022-2024. The lab actively recruits students through Princeton's Chemical Engineering graduate program and undergraduate research initiatives. Laboratory: The Conway Lab develops microfluidic systems for root microbiome studies and genetic tools for engineering plant-associated bacteria. Current projects include designing thermophilic microbial consortia for consolidated bioprocessing and characterizing bacterial immune evasion strategies.
Prof. Veronika Somoza is a leading academic in Nutritional Systems Biology, currently affiliated with the University of Vienna and Technical University of Munich (TUM). She holds a professorship in Molecular Food Science and has led key research groups such as the Institute of Physiological Chemistry and the Christian Doppler Laboratory for Bioactive Aromatics. Her career includes roles at institutions like the German Research Institute for Food Chemistry (Garching) and the University of Wisconsin-Madison. Education: Diplom (Justus Liebig University Giessen, 1991), PhD (University of Vienna, 1995), Habilitation (Kiel University, 2002) Research Focus: Bioactive food compounds, flavor chemistry, taste receptor signaling, and gastrointestinal physiology Her work bridges food science and human health, particularly in understanding how food ingredients influence digestion, inflammation, and disease. Notable contributions include discoveries on bitter peptide effects on gastric acid secretion and flavor perception modulation. Awards: FEMA Excellence in Flavor Science (2016), ACS AGFD Fellow (2020), Hans Adolf Krebs Prize (2004) Prof. Somoza has pioneered methodologies in atomic force microscopy for foodborne virus detection and developed bitterness-masking compounds for pharmaceuticals. Her interdisciplinary approach integrates nanobiophysics with nutrition to advance functional food design and clinical applications.
Marcos Cruz is Professor of Innovative Environments at The Bartlett School of Architecture, University College London (UCL), where he leads research in bio-integrated design. He runs Bio-ID with Dr. Brenda Parker, a multidisciplinary research platform investigating design driven by biotechnology, computation, materials, and fabrication. Previously, he served as Director of The Bartlett from 2010-2014 and founded the BiotA Lab (2014-2018). His academic career spans multiple institutions including University College London (where he ran MArch Unit 20 for 19 years), University of Westminster (2008-2010), UCLA (2010), and IAAC (2014-present). Professor Cruz holds a Licenciatura from ESAP Porto, a Masters with distinction from UCL, and a PhD from UCL (2007), sponsored by the Portuguese Foundation for Science and Technology. His doctoral research on 'Neoplasmatic Architecture' earned him the RIBA President's Research Award in 2008. He is a registered architect with both the Architects Registration Board (ARB) and the Portuguese Architecture Chamber. His primary research area is Bio-Integrated Design, which explores how biotechnology and computation can reshape our built environment in response to climate change. This work goes beyond using nature as inspiration; instead, it treats nature as the medium for a multi-layered design approach. His key research project, Poikilohydric Living Walls, investigates integrating growth systems directly on building facades using algae and mosses that can switch photosynthetic activity on and off without additional maintenance. Another significant research area is The Body in Architecture, which examines the relationship between human flesh and architectural flesh, proposing a 'thick embodied flesh' that creates truly inhabitable architectural interfaces. His recent publications demonstrate a strong progression toward integrating living systems directly into building materials and facades. The research spans from microbial to tectonic scales, with increasing focus on biomaterials, robotic fabrication, and sustainable design approaches that actively participate in urban ecosystems rather than merely responding to them. RIBA President's Research Award (2008) for 'Neoplasmatic Architecture' Multiple Best Unit awards at Bartlett Summer Show (awarded by Thom Mayne, Paul Finch, Richard Rogers, Claude Parent, and Ross Lovegrove) Work part of permanent collection at FRAC Orleans Exhibitions at Venice and São Paulo Biennales Professor Cruz has supervised numerous PhD students through UCL's Research-by-Design programme and currently directs the MArch/MSc in Bio-Integrated Design. His research has been supported by EPSRC and involves industrial partners including Laing O'Rourke, Pennine Stone Limited, and Amorim, with academic partners at UCL Biochemical Engineering, University of Coimbra, and IST Tomar. He co-founded MAM-ARCH London (formerly marcosandmarjan) in 2000, whose work has built buildings and pavilions, won international competitions including the Kunsthaus Graz, and been exhibited globally. His practice represents a significant bridge between architectural design and biological systems, positioning him at the forefront of bio-integrated architectural research.
