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Sarah M. ColemanView profile

Research Fellow

• Bioinformatics & Computational Biology
• Probability and Statistics
• Metabolic Engineering
• +3 more

Sarah M. Coleman is a Postdoctoral Research Fellow in the Department of Statistics and Data Sciences at the University of Texas at Austin. Her research focuses on integrating computational and experimental approaches to advance metabolic engineering, synthetic biology, and bioprocess optimization. She holds a PhD in Chemical Engineering (2024), MS in Statistics (2024), and BS in Chemical-Biological Engineering (2019) from MIT. Her work emphasizes sustainable biomanufacturing using non-traditional feedstocks and improving microbial strain robustness through systems biology methods. Key research interests include metabolic pathway engineering for omega-3 production, statistical modeling of synthetic gene circuits, and leveraging Yarrowia lipolytica for industrial applications. She explores topics such as extracellular electron transfer for living materials and CFD modeling in bioreactor scale-up. Her interdisciplinary background combines chemical engineering principles with advanced statistical analysis to solve complex bioprocess challenges. Recent publications highlight innovations in microbial tolerance to industrial waste, plant-based photonic systems, and biosensor-driven metabolic pathway optimization. Her research bridges computational modeling with lab-scale experimentation, addressing scalability and sustainability in biotechnology. While no formal awards are listed, her contributions reflect cutting-edge advancements in bioengineering and data-driven bioprocess development.

Ignacio Arganda CarrerasView profile

Associate Professor

• Biomedical Computer Vision
• Deep Learning
• Medical Imaging Analysis
• +3 more

Ignacio Arganda Carreras is an Associate Professor at the Universidad del País Vasco/Euskal Herriko Unibertsitatea (UPV/EHU) and an Ikerbasque Research Associate, affiliated with the Donostia International Physics Center (DIPC). His research focuses on biomedical computer vision, with a strong emphasis on deep learning applications in microscopy and medical imaging. Key areas include bioimage analysis pipelines, domain adaptation for cross-modal image segmentation, and AI-driven solutions for healthcare diagnostics. He has contributed extensively to open-source tools like BiaPy, CartoCell, and DL4MicEverywhere, which advance accessibility to deep learning in bioimaging. His work bridges computational methods with biological and medical challenges, addressing issues like 3D object detection, super-resolution imaging, and automated classification in microscopy and clinical settings. Research highlights include developing the MitoEM and Nucmm datasets for mitochondria and neuronal nuclei segmentation, as well as innovative applications in wound healing modeling and aquaculture monitoring. His methodologies emphasize reproducibility, generalization, and mitigation of overfitting in deep learning models.

Katy GrahamView profile

Assistant Professor

• Environmental Engineering
• Water Quality
• WASH (Water, Sanitation, Hygiene)
• +4 more

Katy Graham is an Assistant Professor at the Georgia Institute of Technology's School of Civil and Environmental Engineering, within the College of Engineering. She joined Georgia Tech in 2023 after completing a President’s Postdoctoral Fellowship there. Her academic background includes a PhD and MS in Environmental Engineering and Science from Stanford University (2015–2021) and a BSE in Chemical Engineering from the University of Michigan (2015). Her research focuses on microbial water quality, water, sanitation, and hygiene (WASH), public health risk assessment, and environmental virology. Her lab employs molecular biology, microbiology, and modeling tools to address water-related health challenges globally. Key research themes include pathogen fate and transport in built/natural environments, wastewater-based epidemiology (WBE), and green stormwater infrastructure efficacy. In 2023, her team received a $2.1M EPA grant to study pathogens and disinfection byproducts in drinking water distribution systems. She leads the WatCH (Water, Climate, and Health) lab at Georgia Tech and collaborates on projects like fecal source tracking and cold-chain-free nucleic acid preservation for wastewater surveillance. Dr. Graham has contributed to high-profile studies on SARS-CoV-2 persistence in wastewater, viral adsorption to sampling tools, and the development of novel microbial source tracking markers such as Tomato Brown Rugose Fruit Virus genes.

Jagroop PandhalView profile

Professor

• Microbial Systems Engineering
• Metabolic Engineering
• Quantitative Proteomics
• +3 more

Professor Jagroop Pandhal is affiliated with the University of Sheffield , serving as a Professor of Microbial Systems Engineering in the Department of Chemical and Biological Engineering . He also holds the role of Engineering Graduate School Deputy Head of Admissions. Research interests include: Metabolic Engineering of Microbial Systems Quantitative Proteomics (metaproteomics, glycosylation) Synthetic Microbial Communities Algae Biotechnology Bioremediation and Resource Recovery His work integrates engineering and biological principles to address global challenges in biomanufacturing and environmental sustainability . Recent articles focus on carbon capture, pollutant biodegradation, and algal-bacterial consortia applications. Scientific awards : NERC Independent Fellowship He supervises an international team of PhD students and postdocs, emphasizing cross-disciplinary collaboration. Contact details include email j.pandhal@sheffield.ac.uk and Room D61, Sir Robert Hadfield Building.

• Protein Synthesis Mechanisms
• Translation Elongation/Termination
• Systems Biology
• +3 more

Professor Ian Stansfield is a Personal Chair in the School of Medicine, Medical Sciences and Nutrition at the University of Aberdeen. He holds a BSc (Hons) in Microbiology and a PhD in Biochemistry from the University of Sheffield. His research focuses on protein synthesis mechanisms in eukaryotes, translation elongation/termination, and systems biology approaches to model these processes. Collaborations with Prof M.Carmen Romano integrate mathematical modeling to study translation dynamics and synthetic biology applications. Teaching roles include coordinating MSc programs in Biotechnology, Bioinformatics, and Biobusiness, as well as undergraduate modules in Microbiology and Molecular Biology. He led successful iGEM teams, achieving Gold and Silver medals for developing diagnostic systems for neglected tropical diseases using synthetic biology. Key research areas include translational stress management during protein expression, bioinformatic analysis of mRNA sequences, and optimizing yeast as a protein expression system. Notable grants include BBSRC funding for synthetic gene circuits and systems biology analysis of translation termination factors. Scientific awards include iGEM Gold Medals (2009, 2014) and a Silver Medal (2010), with the 2014 entry winning Best Health and Medicine and Best Measurement Device awards. His work spans over 40 peer-reviewed publications, exploring topics from ribosomal pausing to tRNA properties in codon usage. Leadership roles include Director of Research in his School, overseeing research strategy and NHS partnerships. External roles include membership on BBSRC panels and the BioProNet management board, reflecting his influence in biotechnology and bioenergy sectors.

Mhaned OubounytView profile

Researcher

• Single-cell transcriptomics
• Spatial single-cell network analysis
• Gene regulatory networks
• +3 more

Dr. Mhaned Oubounyt is a Postdoctoral Researcher at the University of Hamburg, affiliated with the Computational Systems Biology (CoSy.Bio) group within the Faculty of Mathematics, Informatics and Natural Sciences. His work focuses on developing computational methods for analyzing single-cell and spatial transcriptomics data, particularly in the context of disease mechanisms and drug repurposing. He is actively involved in the NetMap project, advancing dimensionality reduction techniques using differential regulatory networks. Research interests include gene co-expression networks, spatial single-cell analysis, and systems medicine applications. His methodologies bridge computational biology with clinical and agricultural challenges, such as vaccine responses in pregnancy, plant disease resistance, and cardiovascular pathophysiology. Key contributions include the SCANet and Drugst platforms for drug candidate identification and network-based analysis. Publications highlight a strong focus on network biology applications across domains: from immune system modeling to plant stress responses, leveraging single-cell multi-omics integration. His work emphasizes translational research, with implications for personalized medicine and crop improvement. Collaborations span academic and clinical institutions, reflecting his interdisciplinary approach. Current projects aim to enhance predictive modeling of disease progression and therapeutic interventions through systems-level insights.

• Genomics
• Transcriptomics
• Network Biology
• +7 more

Pasquale Luca Curci is a Researcher at the Institute of Biosciences and BioResources (IBBR), part of the National Research Council of Italy (CNR) in Bari. His work focuses on integrating large-scale genomic and transcriptomic datasets to uncover biological mechanisms in plants, particularly in crops like durum wheat and artichoke. His research interests span genomics, transcriptomics, and network biology, with a strong emphasis on nitrogen use efficiency, gene regulatory networks, and plant stress responses. He employs systems biology approaches to identify key genes and regulatory elements involved in plant development and adaptation. The analysis of his recent publications reveals a consistent focus on plant genomics, particularly in durum wheat and Cynara species. His work integrates omics data to understand gene function, stress responses, and evolutionary relationships. Key themes include nitrogen metabolism, microRNA regulation, chloroplast genomics, and cross-species network analysis. He has led and coordinated multiple national and international research collaborations with institutions in Belgium, Sweden, Spain, France, and Italy, focusing on gene function discovery, nitrogen efficiency, and germplasm characterization. Integrative network analysis for gene function discovery in wheat (with BASF Agricultural Solutions, Belgium) Cross-species network biology in plants (with SweTree Technologies, Sweden) Transcriptomics and regulatory networks in durum wheat (with Sequentia Biotech SL, Spain) Phenotypization and genomic analysis of durum wheat (with Alma Mater Studiorum, Bologna) Asparagine synthetase genes in durum wheat (with INRA-CNRGV, France) He has contributed to the development of genomic resources for Cynara and has published extensively on chloroplast genomics, transcriptomics under nitrogen stress, and genetic diversity in vegetable crops. His work supports crop improvement and sustainable agriculture through advanced molecular tools.

• Bioinformatics
• Protein Stability
• Molecular Biology
• +3 more

Amelie Stein is an Associate Professor at the Department of Biology, University of Copenhagen, specializing in Bioinformatics and RNA Biology. Her research focuses on protein stability, molecular mechanisms of disease variants, and computational methods for protein design. She is affiliated with the UCPH Quantum Hub, reflecting interdisciplinary interests in biological systems. Her work integrates bioinformatics tools, mutational scanning, and structural biology to understand protein degradation pathways and their relevance to human diseases such as Lynch syndrome and metabolic disorders. Key research areas include analyzing protein variants using deep learning models (e.g., SSEmb), developing web-based tools like MutationExplorer for 3D visualization, and characterizing disease-linked mutations in proteins such as Parkin and MLH1. Her publications highlight breakthroughs in rapid protein stability predictions, degon mapping, and the interplay between protein toxicity and degradation. No scientific awards are explicitly mentioned in the provided texts. Stein’s research also explores the application of computational approaches to biotechnology and therapeutic development, emphasizing translational applications of her findings. Her lab, linked to the SCARB research group (https://www1.bio.ku.dk/english/research/scarb/), focuses on structural and computational biology, with ongoing projects involving protein quality control networks and enzyme variant analysis. Collaborations span molecular biology, bioinformatics, and interdisciplinary quantum-related research through her UCPH Quantum Hub membership.

• Mathematical Physics
• Differential Geometry
• Topology
• +4 more

Prof. Alberto S. Cattaneo is a faculty member at the University of Zurich, affiliated with the Department of Mathematics. He holds the rank of Professor and has been actively involved in teaching and research since at least 1998. His research interests span mathematical physics, differential geometry, topology, and interdisciplinary areas like computational biology, genomics, and digital forensics. He has developed courses on topics such as field theory, quantum mechanics, and differential manifolds, reflecting his expertise in theoretical and applied mathematics. Prof. Cattaneo has contributed to numerous publications, including works on distributed genomic analysis, sensor pattern noise (PNU) in forensics, and algorithm optimization for big data frameworks like Hadoop and Spark. His work bridges pure mathematics with applications in bioinformatics and cybersecurity. Though no awards are explicitly mentioned, his extensive publication record and teaching roles highlight his academic standing. He maintains an active presence through courses and research collaborations, with no indication of part-time roles or retirement.

Nicholas PearceView profile

Assistant Professor

• Structural Biology
• Macromolecular Structure Determination
• X-ray Crystallography
• +7 more

Nicholas Pearce is an Assistant Professor at Linköping University, affiliated with the Department of Physics, Chemistry and Biology (IFM) and the Bioinformatics (BIOIN) division. He leads the Data-Driven Determination of Macromolecular Structures (D3MS) group, also known as PearceLab @ LiU, and is affiliated with SciLifeLab and the Wallenberg Centre for Molecular Medicine (WCMM) through the Data-Driven Life Sciences (DDLS) program. His research focuses on advancing structural biology by developing computational and experimental methods that capture the dynamic nature of proteins. Key interests include protein flexibility, disorder, and functional dynamics, with applications in drug discovery and macromolecular modeling. The group employs techniques such as X-ray crystallography, cryo-EM, machine learning, and statistical modeling to improve the resolution and interpretability of macromolecular structures. The recent publications highlight a strong focus on software and method development for structural biology, particularly in multi-dataset analysis and crystallographic data processing. The work contributes to open-source tools like the CCP4 suite and introduces novel approaches such as PanDDA and PanDEMIC.adp for detecting ligand binding and modeling disorder. While no formal scientific awards are listed in the provided text, his involvement in major collaborative projects and national research initiatives underscores his growing impact in the field. He actively mentors students and welcomes Master’s candidates to join his research group. Nicholas began his independent research career at Linköping University on October 1, 2022, with funding supporting the launch of his lab. His group emphasizes data-driven decision-making in structural analysis and collaborates extensively within Sweden and internationally. Current projects include multi-dataset analysis for fragment screening and decomposition of protein disorder using elastic net models. The D3MS group is active in outreach and scientific communication, maintaining a blog and public presence, including a Twitter feed. They have participated in international research visits, such as a recent trip to the University of Hamburg, and national conferences like Sweprot in Tällberg.

Dorota PorazinskaView profile

Assistant Professor

• Soil Ecology
• Microbial Ecology
• Nematology
• +7 more

Dr. Dorota Porazinska is an Assistant Professor in the Department of Entomology and Nematology at the University of Florida, Gainesville. Her research focuses on soil biodiversity, plant-soil interactions, and microbial ecology, with emphasis on nematodes and extreme environments. She holds a PhD from the University of Florida (1998) and has held research positions at institutions like the University of Colorado and CSIRO Australia. Her work combines classical ecological methods with cutting-edge molecular tools, including metagenomics and mitochondrial metagenomics. Education: PhD, Entomology and Nematology, University of Florida (1994–1998) MS, Ecology, University of Georgia (1992–1994) BS/MS, Biology and Earth Sciences, Nicolaus Copernicus University (1987–1991) Research Interests: Soil microbiomes, nematode ecology, climate change impacts, biosecurity diagnostics, and biodiversity conservation. She investigates microbial community assembly in extreme environments (e.g., Antarctica, high alpine regions) and agricultural systems, leveraging molecular tools for species identification and ecosystem function studies. Key Awards: Science Ambassador, Science Discovery (2015) Distinguished Visiting Scientist, CSIRO (2012) STAMPS Fellowship, Marine Biological Laboratory (2011) Grants & Teaching: Secured over $2M in research funding from NSF, USDA, and others. Active in developing innovative curricula, including digital nematode slide collections and online education tools. Served as Chair of the ENY Seminar Series and Editor of the Journal of Nematology . Labs & Teams: Leads the WORMS ET AL. research group, focusing on soil biodiversity and molecular diagnostics. Collaborates with interdisciplinary teams on projects like the Antarctic cryoconite hole microbiome and nematode gut microbiome assembly.

Zhongyuan ZhaoView profile

Academic

• Wireless Communications and Networking
• Machine Learning
• Signal Processing
• +7 more

Zhongyuan Zhao is a Research Assistant Professor at Rice University's Department of Electrical and Computer Engineering, affiliated with the George R. Brown School of Engineering. He holds a Ph.D. in Computer Engineering from the University of Nebraska-Lincoln and completed his postdoctoral studies at Rice under Prof. Santiago Segarra. His research integrates graph-based neuro-symbolic approaches with domain-specific models to address challenges in wireless communications, edge computing, and networked systems. Dr. Zhao has over a decade of industry experience in 4G base-station development at Ericsson and ArrayComm, followed by academic research roles focusing on distributed algorithms and machine learning applications. His education includes a B.Sc. and M.S. in Electronic Engineering from the University of Electronic Science and Technology of China, where he also worked as a teaching assistant for national design contests. He earned a minor in Finance (15 credits) during his Ph.D. and is completing the CFA Level III program in 2024. Zhao has contributed to large-scale wireless testbeds like NEXTT and authored/co-authored 25+ peer-reviewed publications, 2 patents, and delivered academic service roles including conference session chair and journal reviewer. Research interests span autonomous networking, graph-based machine learning, stochastic optimization, and their applications in infrastructureless wireless systems. His work emphasizes distributed solutions for resource allocation, scheduling, and edge intelligence. Awards include the Future Faculty Fellowship (2023) and IEEE travel grants. He has mentored over 40 students through research internships, hackathons, and undergraduate projects, showcasing his commitment to education. Recent projects focus on scalable computation offloading, generalized backpressure routing in tactical networks, and neuro-symbolic AI frameworks for networked systems. His lab work involves open-source contributions to tools like the Actor-Twin Framework and SelR algorithm, promoting reproducibility and collaboration in the field.

Justin SilvermanView profile

Assistant Professor

• Bayesian Statistics
• Machine Learning
• Genomics
• +7 more

Dr. Justin Silverman is an Assistant Professor of Information Science & Technology, Statistics, and Medicine at Pennsylvania State University, with affiliations in the Department of Statistics and the Institute for Computational and Data Science. He holds an MD from Duke University School of Medicine (2020), a PhD in Computational Biology and Bioinformatics from Duke (2019), and a BS in Physics and Biophysics from Johns Hopkins University (2011). His research integrates Bayesian statistics, machine learning, and genomics to address impactful biomedical questions. Key focuses include microbiome analysis, gene expression studies, and scale-reliant inference methodologies. His work emphasizes methodological rigor and clinical relevance, with applications in personalized medicine, agriculture, and epidemiology. The Silverman Lab develops cutting-edge tools like the fido and philr packages for compositional data analysis. Collaborations span academia and industry, addressing challenges in livestock health, viral surveillance, and precision medicine. He also leads Anarres Analytics LLC, translating statistical methods into practical solutions for clients. His advising includes PhD and MS students working on topics like scalable Bayesian models, microbial co-occurrence networks, and topological data analysis in oncology. Notable projects include studies on ancient microbiomes, dairy cow treatments, and pandemic modeling. Labs and teams: Silverman Lab (PSU), Anarres Analytics. Current positions: PI, startup founder, and collaborator in multi-institutional projects.

• Bioinformatics
• Protein Structure Prediction
• Machine Learning
• +3 more

Gianluca Pollastri is an Associate Professor in the School of Computer Science at University College Dublin (UCD). He leads a research group focused on machine learning applications in bioinformatics, particularly protein structure prediction and analysis. His academic roles include Associate Professor since 2016, Senior Lecturer from 2008, and Lecturer from 2003. He earned an MSc from the University of Florence and a PhD from the University of California, Irvine. His research integrates deep learning and neural networks to address challenges in protein subcellular localization, secondary structure prediction, and intrinsically disordered regions. Key tools developed include SCLpred, PaleAle, Porter, and PUNCH2. He has secured grants from Science Foundation Ireland, the Health Research Board, and UCD. Pollastri’s work emphasizes rigorous validation of machine learning methods in biology, as outlined in the DOME framework. His lab has produced over 100 peer-reviewed articles, with recent focus on leveraging pre-trained language models (PLMs) for protein analysis. Education: MSc, University of Florence PhD, University of California, Irvine Awards: Best M.Sc. Thesis in Artificial Intelligence (1999) Best Student Project in Artificial Intelligence (1997) His teaching includes modules on Bioinformatics, Connectionist Computing, and Programming. He coordinates research collaborations and maintains a lab with postdoctoral fellows and graduate students.

James MurphyView profile

Associate Professor

• Machine Learning
• Harmonic Analysis
• Statistical Learning
• +5 more

James Murphy is an Associate Professor of Mathematics at Tufts University, with affiliate appointments in Computer Science and Electrical and Computer Engineering. His research focuses on the mathematics of artificial intelligence, statistical and machine learning, high-dimensional probability, and their applications to image/signal processing, molecular dynamics, and computational biology. He holds grants from the NSF, NGA, Camille & Henry Dreyfus Foundation, and others. His work integrates theoretical mathematics with computational tools, emphasizing open-source software development. Education: PhD in Mathematics (2015) and MS (2013) from University of Maryland, BS in Mathematics (2011) from University of Chicago. Research interests include machine learning theory (e.g., spectral clustering, unsupervised learning), harmonic analysis, graph theory, and applications to hyperspectral imaging, medical data analysis, and remote sensing. Notable projects involve Wasserstein-based methods for data clustering, diffusion geometry for image processing, and algorithms for anomaly detection in high-dimensional datasets. His research group, HILDA, collaborates on projects like GLIDE (biological network prediction), Fermat distances for clustering, and entropy-regularized optimal transport. He teaches courses in functional analysis and has contributed to open-access educational materials for calculus and algebra.