Francesca ORSO is an Associate Professor at the Department of Translational Medicine , Universita' degli Studi del Piemonte Orientale 'Amedeo Avogadro'. Her research focuses on MicroRNA mechanisms in cancer progression, particularly in Melanoma , Breast Cancer , and Chronic Myeloid Leukemia . She explores how miRNAs regulate Tumor Metabolism , Metastasis , and Drug Resistance , with a strong emphasis on mTORC2 , Angiogenesis , and Tumor Microenvironment interactions. Key Research Areas : MicroRNA, Cancer Biology, Tumor Metabolism, Molecular Biology, Medical Biotechnology, Genetics Recent Trends : Her 2024 publications highlight miR-15a in Leukemia , RICTOR/mTORC2 in Melanoma Resistance , and miR-214 in Lung Cancer Metabolism . Earlier studies (2023-2008) address 3D Culture Systems , AXL-miRNA Sponges , and Transcription Factor Networks in tumor dissemination. Awards : Premio Chiara D'Onofrio Giovani Riercatori (2009)
Vincent Tollon is a Lecturer-Researcher at ISARA's Agroecology and Environment Research Unit, where he integrates data science with agroecological research. His work focuses on biodiversity analysis, GIS applications (QGIS/ARCGIS), and statistical modeling (ADE4/R software) to enhance sustainable food systems. PhD in Ecology from Université de Savoie (2010) Habilitation from University of Greifswald (2004) Doctorate in Animal Ecology from University of Hohenheim (1998) Geoecology MSc from University of Karlsruhe (1993) His research explores agroecological principles through projects like: INNOVAB: Innovative cultivation systems for organic crops PERMEAGRI: Agricultural space permeability and ecological planning ECOSTACK: Biodiversity-driven crop protection tools Current publications analyze: Carbon dynamics in aquaculture systems Agroecological practices enhancing pollinator diversity Landscape-scale impacts on carabid beetle communities Telemetry-based wildlife behavior studies He teaches statistics (ANOVA, linear models) and GIS to agroecology students, while supervising field research applications in sustainable food production.
Thomas Tradler is a Professor of Mathematics at New York City College of Technology, a constituent college of the City University of New York. He received his Ph.D. in Mathematics from The Graduate Center, CUNY. His research focuses on Algebraic Topology, with applications spanning operads, K-theory, homotopy theory, and mathematical physics. Education: Ph.D. in Mathematics, The Graduate Center, City University of New York His work explores advanced mathematical structures including operads, K-theory extensions, factorization algebras, and connections between topology and quantum field theory. Research employs techniques from homological algebra, differential geometry, and combinatorial methods to investigate algebraic and topological invariants. Recent publications (15 most recent from 2006-2018) primarily appear in specialized mathematics journals. The articles demonstrate consistent focus on algebraic topology, K-theory, and mathematical physics, with evolving applications to quantum backgrounds, factorization algebras, and geometric structures. Later works show increased emphasis on differential extensions of K-theory and interactions with physics.
Wan-Yee Tang is an Associate Professor in the Department of Environmental and Occupational Health at the University of Pittsburgh. Her research focuses on understanding how environmental pollutants, allergens, and dietary factors alter the epigenome through DNA methylation/demethylation and chromatin remodeling, contributing to respiratory disorders, cardiovascular diseases, and cancer. Education BSc in Biochemistry (2000), The Chinese University of Hong Kong PhD in Biochemistry (2004), The Chinese University of Hong Kong Her work utilizes experimental asthma models and methylation profiling to uncover novel roles of DNA hydroxymethylation in airway hyperresponsiveness. She bridges laboratory discoveries with population studies, investigating how inter-individual epigenetic variations relate to disease risks like diabetes and coronary heart disease in human cohorts. Key article trends include: Epigenetic effects of pollutants (e.g., bisphenol A, PAHs) Transgenerational epigenetic inheritance Metal exposure and DNA methylation Metabolic-epigenetic crosstalk in asthma and aging Sex-based differences in epigenetic responses Public health implications for preventive strategies Her teaching includes courses on Epigenetics and Epigenomics of Environmental Health and Principles of Toxicology . Collaborative projects span environmental epigenomics consortia like NIEHS TaRGET II, with applications to chronic disease prevention and therapeutic development.
James Cross is an Associate Professor in the School of Politics and International Relations at University College Dublin (UCD) and serves as the Director and co-founder of the Connected_Politics Lab @ UCD. His position places him at the intersection of political science and data analytics, where he applies computational methods to study EU political processes and decision-making. Dr. Cross received his BA from University College Cork, an MSc in International Relations from the University of Bristol, and completed his PhD at Trinity College Dublin. His postdoctoral work included positions at the ETH in Zurich and as a Max Weber and Jean Monnet Fellow at the European University Institute in Florence. Cross's research agenda focuses on international and comparative politics, with particular emphasis on EU policymaking processes. He has pioneered the application of natural language processing techniques to analyze large corpora of legislative texts in the EU, developing innovative methods to examine patterns of conflict and cooperation between EU institutions. His work extends beyond the EU context, with methodological approaches adaptable to other legislative bodies worldwide. His research spans several interconnected themes: data science applications in EU politics, legislative amendment tracking, transparency in EU decision-making, and the Decision-Making in the EU (DEU) project. Cross frequently employs computational methods including text analysis, network analysis, and machine learning to extract meaningful insights from political texts and communications. His publication record demonstrates a consistent trajectory of innovative research applying computational methods to political science questions, particularly concerning EU institutions. Cross's work shows a strong emphasis on methodological innovation alongside substantive contributions to understanding EU legislative processes, transparency mechanisms, and institutional dynamics. His recent publications increasingly focus on central banking communications, algorithmic bias in political analysis, and the application of network approaches to understanding policy agenda dynamics. PSAI Teaching and Learning prize (2021) European Union Politics/SAGE Award for best article (2018) Nomination for best conference paper (2015) Visiting Scholar fellowship at the University of Milan (2015) Jean Monnet Chair (2019-2022) Cross has secured multiple research grants including the Jean Monnet Chair, the EUR_data project integrating EU studies with data analytics, the ParliView project bridging information divides, the SDG_EU project tracking sustainable development goals' influence, and the EU Data Laboratory project. He actively engages in teaching innovation, with course syllabi openly shared on GitHub, and coordinates modules including Advanced Seminar in Politics, AI and Large Language Models, EU Foreign Policy, Introduction to EU Politics, and Politics of (mis-)information. As Director of the Connected_Politics Lab, Cross leads a research environment at the intersection of political science and data analytics. The lab collaborates with the Insight Centre for Data Analytics at UCD and focuses on applying computational methods to political texts and processes. His work has established him as a key figure in the application of data science to EU politics, with particular expertise in analyzing legislative processes and political communications through computational approaches.
Dr. Aditya Devarakonda is an Assistant Professor in the Department of Computer Science at Wake Forest University, where he teaches courses in algorithms, data structures, and parallel numerical optimization. Prior to joining Wake Forest, he was an Assistant Research Scientist in the Department of Physics and Astronomy at Johns Hopkins University. His educational background includes: Ph.D. in Computer Science from the University of California, Berkeley (2018) B.S. in Electrical & Computer Engineering from Rutgers University, New Brunswick (2012) Dr. Devarakonda's research focuses on high performance computing and machine learning , with particular emphasis on communication-avoiding algorithms for parallel systems. His work spans several key areas: Redesigning machine learning algorithms to reduce communication bottlenecks in distributed systems Developing communication-avoiding variants of optimization methods like block coordinate descent Exploring novel training techniques for deep learning models that improve performance on multi-GPU systems Implementing matrix factorization techniques that scale efficiently across distributed architectures His publication record shows a consistent focus on communication efficiency in parallel computing, with numerous papers on avoiding communication in various optimization methods. His research has evolved from foundational work on communication-avoiding Krylov methods to applications in machine learning and deep learning. Recent publications indicate expanding interests in graph neural networks and distributed Shapley values. Dr. Devarakonda has received notable recognition for his work: NSF Graduate Research Fellowship EECS Department Fellowship While specific advising information isn't detailed in the available materials, Dr. Devarakonda teaches graduate courses including CSC 721 (Theory of Algorithms) and CSC 790 (Parallel Numerical Optimization), suggesting he likely mentors graduate students in high performance computing and machine learning. His research appears to be supported by grants related to high-performance computing and machine learning optimization. His work leverages parallel computing infrastructure, including Cray supercomputers and NVIDIA GPU clusters, to develop and test communication-avoiding algorithms. His research group likely focuses on implementing and benchmarking these algorithms across various distributed computing platforms.
Christina Guttmann-Gruber is a Researcher at the Department of Dermatology and Allergology, Faculty of Medicine, Paracelsus Medical University. She leads the Research Program for Molecular Therapy in Genodermatoses and has established herself as a prominent researcher in the field of rare skin disorders, particularly epidermolysis bullosa. Her research interests span across dermatology, molecular therapy, gene editing, skin cancer research, wound healing, drug repositioning, transcriptomics, stem cell research, and antisense therapy. She has focused extensively on epidermolysis bullosa, investigating molecular mechanisms, developing therapeutic approaches, and studying complications such as squamous cell carcinoma development in these patients. Analysis of her recent publications reveals a strong focus on innovative therapeutic strategies for epidermolysis bullosa, including gene editing techniques, antisense oligonucleotide therapies, drug repositioning approaches, and stem cell-based models. Her work bridges basic molecular research with clinical applications, aiming to translate scientific discoveries into tangible treatments for patients with rare skin disorders. Anton Luger Dissertations Preis (2009) Isidor Neumann Poster Preis (2011) Wissenschaftspreis in Bronze 2010 (2011) Non-Melanoma Skin Cancer Forschungspreis der Firma MEDA Pharma (2013) Sanofi Preis (2014) Dr. Guttmann-Gruber has successfully led multiple research projects focusing on epidermolysis bullosa, including investigations into wound healing mechanisms, squamous cell carcinoma development, and bacterial contributions to disease progression. She serves as a peer reviewer for prominent journals including PLOS ONE and NEOPLASIA, contributing to the scientific community through editorial activities. Her research program represents a comprehensive approach to understanding and treating genodermatoses, with particular emphasis on molecular therapeutic interventions.
Dr. Sampriti Mukherjee is an Assistant Professor in the Department of Molecular Genetics and Cell Biology at the University of Chicago. Her research focuses on understanding how bacteria decode and integrate self-generated and environmentally-derived stimuli to control transitions between individual and collective behaviors, particularly in the context of biofilm formation and bacterial pathogenesis. Dr. Mukherjee received her BS and MS in Microbiology from the University of Calcutta (2007 and 2009), followed by a PhD in Microbiology from Indiana University (2014). She completed her postdoctoral training in Molecular Biology at Princeton University in 2020. Her laboratory employs a multidisciplinary approach combining bacterial genetics, molecular biology, biochemistry, microfluidics, fluorescence microscopy, and genome-scale studies to investigate bacterial signal detection, relay, and integration mechanisms. Her research has significant implications for developing strategies to combat antibiotic-resistant infections by targeting bacterial communication systems rather than bacterial growth directly. The work has revealed novel mechanisms of bacterial communication, including the discovery of new quorum-sensing autoinducers and their receptors, the integration of photosensing with quorum sensing, and the regulation of biofilm development through combinatorial control of multiple signaling pathways. Dr. Mukherjee's research has been recognized with several prestigious awards: Searle Scholars Program (2022-2025) NIH Pathway to Independence Award (K99/R00, 2018-2023) Life Sciences Research Foundation Postdoctoral Fellowship (2016-2019) She currently leads multiple NIH-funded research projects as Principal Investigator, including R35GM150803 "Probing the role of sensory cues in the regulation of bacterial biofilm development" (2023-2028) and R00GM129424 "A New Quorum-Sensing Autoinducer Acts with the RhlR Receptor to Control Virulence and Biofilms in Pseudomonas Aeruginosa" (2018-2023), demonstrating her significant contributions to the field of bacterial signal transduction and pathogenesis. Dr. Mukherjee's lab is part of the vibrant research community in the Department of Molecular Genetics and Cell Biology at the University of Chicago, collaborating with researchers studying bacterial pathogenesis, signal transduction, and microbial communities to advance our understanding of bacterial collective behaviors and their implications for human health.
Volodymyr Aushev is Professor in the Department of Nuclear Physics, Faculty of Physics, Taras Shevchenko National University of Kyiv, Ukraine. He simultaneously maintains a senior-researcher affiliation with the Institute for Nuclear Research of the National Academy of Sciences of Ukraine. His career spans four decades, beginning in 1978 at the Institute for Nuclear Research (Kyiv) and expanding through long-term visiting-scientist engagements at DESY (Germany), the Max-Planck Institutes, and Fermilab (USA). Education & Training: PhD studies focused on polarised recoil β-active nuclei and nuclear spin physics at low energies. Extensive specialised training in Quantum Chromodynamics, heavy-flavour physics, top-quark physics and neutrino physics through participation in HERA-B, ZEUS, D0, LHCb, Belle II, DUNE, WA105 and FCAL collaborations. Research Interests: Professor Aushev leads the Kyiv high-energy physics group whose principal goal is understanding the fundamental constituents of matter and their interactions at the highest energies. His work centres on experimental particle physics , with major themes including: Quantum Chromodynamics and parton distribution functions; Production and decay of charm, beauty and top quarks; Neutrino oscillations and astrophysical neutrinos; Development of radiation-hard detectors and advanced instrumentation for collider and neutrino experiments. Recent Publications Overview (2010-2016): The twelve highlighted papers reflect an intensive focus on HERA ep/γp data (ZEUS), Tevatron pp̅ data (D0) and early DUNE/Belle-II planning . Analyses span precision measurements of structure functions, heavy-quark cross-sections, top-quark properties and searches for exotic hadrons, underlining a commitment to multi-TeV energy-frontier physics and long-baseline neutrino science. Scientific Awards & Recognition: Author of ≈120 peer-reviewed papers in high-energy physics and ≈30 in nuclear physics, averaging 33.4 citations each. Group leader for Ukrainian participation in ZEUS, D0, Belle II, DUNE, WA105 and FCAL collaborations. Advising & Outreach: Professor Aushev mentors a vibrant team of post-doctoral researchers, PhD and master’s students within the Kyiv high-energy physics group. He teaches lecture courses on High-Energy Physics , Nuclear Astrophysics , Dark Matter , Neutrino Physics and Modern Experiments in High-Energy Physics , and leads outreach programmes to inspire school pupils and undergraduates. Laboratories & Teams: He heads the Kyiv High-Energy Elementary Particle Physics Group , comprising three faculty members, post-docs, PhD students and undergraduates. The group is responsible for detector R&D (radiation-hard calorimeters, micro-strip monitors), software development and physics analyses for international experiments in Europe, the United States and Japan.
Alexandre Hippert-Ferrer is an Associate Professor at Université Gustave Eiffel, where he is a permanent member of the LASTIG lab working in the Strudel team. He primarily teaches at the National School of Geographic Sciences (École Nationale des Sciences Géographiques). Dr. Hippert-Ferrer received his PhD from University Savoie Mont Blanc, Annecy, France in 2020, supervised by Philippe Bolon and Yajing Yan. His doctoral research focused on reconstruction of missing data in displacement time series by remote sensing. Following his PhD, he was a post-doctoral researcher at L2S (Université Paris-Saclay) working with Florent Bouchard and Frédéric Pascal. He maintains active collaborations with researchers including Arnaud Breloy and Nabil El Korso from LEME at University of Paris Nanterre. Hippert-Ferrer's research focuses on robust statistical methods for signal processing, particularly for geospatial data with missing values. His work bridges the fields of remote sensing, signal processing, and statistics, with a strong emphasis on practical applications in earth observation and geodesy. He has developed innovative approaches using Empirical Orthogonal Function (EOF) analysis, Expectation-Maximization (EM) algorithms, and Riemannian geometry for handling incomplete data in displacement measurement time series. His methods have been applied to Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) data for monitoring ground displacement. His publication record shows a clear progression from basic gap-filling techniques to sophisticated statistical models. Starting with EOF-based approaches, his work has evolved to incorporate advanced methods including elliptical distributions, low-rank covariance estimation, and graphical factor models with Riemannian optimization. The consistent theme across his publications is developing mathematically sound yet practical algorithms for real-world geospatial data challenges, particularly when data is incomplete or corrupted. Dr. Hippert-Ferrer has made significant contributions through publications in top journals including IEEE Transactions on Signal Processing and IEEE Transactions on Geoscience and Remote Sensing. His open-source implementation of the EM-EOF algorithm on GitHub demonstrates his commitment to reproducible research. As an educator at the National School of Geographic Sciences, he trains the next generation of geospatial professionals, bridging theoretical statistical methods with practical applications in earth observation.
Prof. Ondřej Kalenda is a faculty member at the Department of Mathematical Analysis, Faculty of Mathematics and Physics, Charles University, Prague. His research focuses on functional analysis, topology, and Banach space theory, with significant contributions to Valdivia compacta, JB*-triples, and weak non-compactness measures. Education : DSc (Doctoral Thesis, 2011), Habilitation (2004), PhD (1997), all from Charles University. Research Interests : Functional analysis, topological properties of Banach spaces, operator algebras, measure theory, and descriptive topology. Publications : Over 40 research articles on topics like Valdivia compact spaces, projectional skeletons, JB*-triples, and quantitative versions of classical theorems (Schur, Dunford-Pettis). Academic Roles : Guarantor of the Mathematical Analysis master’s program since 2020/21; participant in seminars on Real and Abstract Analysis and Winter Schools. Contact : Email: Ondrej.Kalenda@mff.cuni.cz , kalenda@karlin.mff.cuni.cz Office: K 254 (3025), Karlín, Sokolovská 83, Prague 8
Jianing (Jenny) Gao is a Postdoctoral Fellow conducting cutting-edge research in vascular biology and cardiovascular diseases, with a focus on molecular mechanisms underlying atherosclerosis, lymphatic development, and metabolite-mediated intercellular communication. Her work bridges experimental biology and computational approaches to address critical questions in vascular pathophysiology. Her primary research interests include: Vascular Biology Atherosclerosis Single-Cell Genomics Machine Learning in Biomedicine Cardiovascular Metabolism Lymphatic Development Analysis of her publication record (2021-2025) reveals consistent emphasis on endothelial cell identity, lymphatic valve formation, and gut microbiota-derived metabolites' roles in cardiovascular pathologies. Key trends include the application of machine learning to decode vascular cell identity genes, mechanistic studies of S1PR1 signaling in lymphatic development, and clinical investigations linking metabolites like trimethylamine N-oxide to heart failure and pulmonary hypertension outcomes.
Dr. Stig Hellebust is a Lecturer in Physical Chemistry at the School of Chemistry, University College Cork (UCC), Ireland. Based in Room 206B of the Kane Building, he can be contacted at s.hellebust@ucc.ie or +353 214902680. His research focuses on atmospheric chemistry, environmental monitoring, and advanced data analysis techniques for understanding air quality and pollution sources across Ireland. Dr. Hellebust's research interests span several key areas of environmental chemistry and data science: Atmospheric observational data analysis, particularly high-dimensional datasets collected over extended time periods Application of multivariate statistical methods and machine learning for environmental data interpretation Source apportionment of atmospheric pollutants using receptor modeling techniques Development of algorithms for processing large environmental datasets Application of clustering and classification techniques to identify pollution sources Fourier-transform infrared spectroscopy data analysis His extensive publication record demonstrates expertise in air quality monitoring, particularly focusing on PM2.5 sources, urban pollution dynamics, and health impacts. He frequently employs advanced statistical methods including principal component analysis (PCA), positive matrix factorization (PMF), and various machine learning approaches to extract meaningful information from complex environmental datasets. His work bridges atmospheric science, public health, and data analytics, with significant contributions to understanding Ireland's air quality challenges. Dr. Hellebust has secured substantial research funding from multiple sources including the Environmental Protection Agency (EPA), Health Research Board, Science Foundation Ireland, and European Union programs. His current major projects include "Sources of PM2.5 in the Air of Irish Towns" (2024-2027, €233,796.00) and "Impact of Agricultural Emissions on Rural and Urban Air Quality" (2022-2025, €119,700.00), demonstrating his leadership in addressing critical environmental challenges. He currently supervises doctoral student Rósín Eileen Byrne and has previously supervised Eimear Heffernan who completed her PhD in 2022 on "Spatial and temporal variation of ambient carbonaceous aerosol in Ireland and strategies for effective monitoring of source contributions." His mentorship extends to interdisciplinary research connecting chemistry, environmental science, and public health. Dr. Hellebust is an active member of UCC's Atmospheric and Environmental Chemistry research group, collaborating with colleagues across Ireland and internationally on air quality monitoring and pollution source identification projects. His work has significant policy implications for urban planning, public health interventions, and environmental regulation in Ireland and beyond.
Dr. Yasu Kawakami is a Professor in the Department of Genetics, Cell Biology and Development (TMED) at the University of Minnesota Medical School. With over 30 years of research activity, his work focuses on the genetic and molecular mechanisms underlying developmental biology, particularly in limb and skeletal formation, cancer, and regenerative medicine. University: University of Minnesota Department: Genetics, Cell Biology and Development (TMED) Academic Rank: Professor His research spans developmental biology , genetics , and regenerative medicine , with a strong emphasis on zebrafish and mouse models. Key projects include the genetic regulation of progenitor cells in appendicular skeletal development, SALL4 function in posterior trunk mesoderm, and the role of TRPS1 in cis-regulatory control. Recent publications highlight his contributions to understanding the GDF5–RUNX–extracellular matrix gene axis in limb development, mesodermal gene expression in trunk development, and the FGF-AKT pathway in cardiomyocyte survival. His work aligns with UN Sustainable Development Goals, particularly in health and well-being through biomedical research. Dr. Kawakami has received continuous funding from institutions including the NIH NIAMS and National Institutes of Health, with grants spanning from 2012 to 2024. Collaborations include partnerships with researchers at Johns Hopkins University and contributions to open-access journals like Nature Communications and Development (Cambridge).
Dr. Catherine Van Raamsdonk serves as Associate Professor in the Department of Medical Genetics within the Faculty of Medicine at the University of British Columbia. She leads an active research laboratory at the Life Sciences Institute in Vancouver, BC, specializing in melanoma pathogenesis using genetically engineered mouse models. Her work is funded by the Canadian Institutes of Health Research (CIHR), and she supervises graduate students in UBC's Medical Genetics MSc and PhD programs. Her research focuses on the molecular mechanisms transforming normal melanocytes into melanoma, with particular emphasis on uveal melanoma and the role of GNAQ/GNA11 oncogenes. Key projects investigate the developmental origin of ocular melanocytes, transcriptional/proteomic signatures in uveal melanoma, and why GNAQ specifically transforms internal melanocytes (in eyes, dermis, CNS) but not epidermal melanocytes. The lab employs transgenic mouse models to dissect neural crest development, melanocyte migration, and oncogenic signaling pathways. Analysis of her recent publications reveals a consistent trajectory in melanoma genetics, particularly the interplay between GNAQ mutations, neurofibromin loss, and BAP1 deficiency in tumor progression. Her work bridges developmental biology with cancer mechanisms, using mouse-human comparative approaches to identify conserved pathways in uveal and cutaneous melanoma. Publications increasingly address therapeutic implications including MEK inhibition and tumor microenvironment interactions. Dr. Van Raamsdonk is an active member of the Pigment Cell and Melanoma Research Society (PASPCR), contributing to the melanocyte research community. Her laboratory receives primary funding from CIHR, supporting investigations into melanoma initiation, progression, and metastasis using sophisticated genetic models. The lab maintains strong collaborations with clinical researchers to translate basic findings to human melanoma contexts. The Van Raamsdonk Lab operates within UBC's Life Sciences Institute, providing advanced facilities for mouse genetics, molecular biology, and histopathological analysis. Current work integrates transcriptomic, proteomic, and in vivo modeling approaches to understand melanoma heterogeneity and identify novel therapeutic targets, particularly for treatment-resistant uveal melanoma.
