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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.

Samantha PettiView profile

Assistant Professor

• Bioinformatics
• Computational Biology
• Machine Learning
• +4 more

Samantha Petti is an Assistant Professor in both the Department of Mathematics (School of Arts and Sciences) and the Department of Computer Science (School of Engineering) at Tufts University. She is based at 177 College Avenue, Medford, MA. Her research focuses on computational biology, bioinformatics, and machine learning, with particular emphasis on protein structure analysis, genotype-phenotype mapping, and algorithm design for biological sequence analysis. She teaches courses such as Master's Thesis supervision, PhD Thesis guidance, and specialized topics in mathematics and computer science. Her work integrates interdisciplinary approaches, combining statistical methods, deep learning, and probabilistic models to address challenges in genomics, structural biology, and network science. Recent projects include developing end-to-end protein alignment tools and exploring sparse graph models for biological systems. Teaching: Supervises graduate thesis work (Master’s/PhD) and advanced courses in mathematics and computer science at Tufts. Lab/Team: Engages in collaborative research at the intersection of computational methods and biological systems.

Yen-Yi HoView profile

Associate Professor

• Computational Biology
• Statistical Genetics
• Genetic Data Analysis
• +6 more

Yen-Yi Ho is an Associate Professor in the Department of Statistics at the University of South Carolina, with a joint appointment in Biological Sciences. She holds a Ph.D. in Biostatistics from Johns Hopkins University. Her research focuses on computational biology, statistical genetics, and the development of methods for analyzing high-throughput genetic data. Key interests include gene pathway enrichment analysis and applications in cancer, frailty mechanisms, and Hirschsprung disease genetics. Her work bridges statistical methodology and biological discovery, with notable contributions to liquid association modeling and modular network construction using eQTL data. She has collaborated on studies involving chemopreventive cancer strategies, frailty biomarkers, and genomic analyses of pancreatic adenocarcinoma. Dr. Ho has pioneered software tools like the LiquidAssociation R package and contributed to the fastLiquidAssociation package. Her publications span topics from Bayesian hierarchical modeling in medical research to single-cell RNA-seq analysis of cellular heterogeneity. Beyond academia, she has applied statistical methods to public health challenges, such as optimizing disability applicant prioritization systems.

Emile Rugamika ChimusaView profile

Professor

• Computational Population Genomics
• Bioinformatics
• Genome-Wide Association Studies
• +5 more

Prof. Emile Rugamika Chimusa is a Professor at Northumbria University , College of Engineering and Science, specializing in Computational Population Genomics and Bioinformatics . Previously, he served as Associate Professor and Programme Director at the University of Cape Town for postgraduate programs in Computational Health Informatics and Human Genetics. His research focuses on genomic methodologies for analyzing large-scale genomic data, including Genome-wide association studies Admixture mapping in mixed-ancestry populations Pharmacogenetics of disease treatments Multi-omics integration for disease-drug associations Development of bioinformatics tools Population-specific disease risk prediction He has received over £17.7 million in research funding from organizations like the NIH , Wellcome Trust , and National Research Foundation . His work spans translational genomics in both non-communicable and infectious diseases .

Istvan RajcanView profile

Professor

• Soybean Breeding
• Plant Genetics
• Seed Quality Traits
• +6 more

Dr. Istvan Rajcan is a Professor and Graduate Coordinator in the Department of Plant Agriculture at the University of Guelph, Ontario Agricultural College. His research focuses on soybean breeding and genetics, particularly developing high-yielding, disease-resistant cultivars for short- and medium-season environments. He leads efforts to enhance seed quality traits, such as protein and isoflavones, using genomic tools like GWAS and genomic selection. Education: B.Sc. (Agr.) from the University of Novi Sad and Ph.D. in Plant Agriculture from the University of Guelph. Research Interests: Soybean seed quality (nutraceuticals), disease resistance (Sclerotinia, soybean cyst nematode), genomic technologies, and breeding innovation. His work integrates molecular markers, quantitative genetics, and phenotyping to address challenges in soybean production. Collaborative projects include the SoyaGen initiative, advancing genomic resources for breeders. Publications highlight advancements in soybean genetics, remote sensing applications, and sustainable breeding strategies. His contributions span trait dissection, disease management, and the application of machine learning in genomics. Advising and Grants: While specific grants are not detailed, his extensive publication record indicates sustained research funding. He mentors graduate students and collaborates widely with institutions like the Ontario Oil & Protein Seed Crop Committee. Labs/Teams: Research activities are based in the Crop Science Building, with involvement in the SoyaGen project and collaborations on soybean diversity panels.

Giovanni DalmassoView profile

Associate Professor

• Computational Biology
• Developmental Biology
• Biomedical Image Analysis
• +4 more

Giovanni Dalmasso is an Associate Professor in the Department of Mathematics and Data Analytics at the IQS School of Engineering, Ramon Llull University. His research focuses on computational modeling of biological systems, including morphogenesis, cellular homeostasis, and biomedical image analysis. He specializes in developing tools for 3D/4D visualization of developmental processes and agent-based models of cellular dynamics. Key research areas include: 3D/4D reconstruction of embryonic development (e.g., limb bud morphogenesis) Biological shape correspondence algorithms (μMatch) Systems biology approaches to mitochondrial function and stress responses Mathematical modeling of calcium signaling dynamics His work contributes to UN Sustainable Development Goals related to innovation and infrastructure through advances in bioimaging and computational tools. Collaborations span institutions globally, including projects with the University of Edinburgh and University Pompeu Fabra. He has developed open-source software like LimbLab and veda for scientific visualization. Research outputs emphasize interdisciplinary methods combining engineering, mathematics, and biology to address fundamental questions in developmental biology and cellular systems. Current efforts focus on integrating moral decision frameworks into AI agents through contractualist models.

Donald BairdView profile

Academic

• Wetland Ecology
• Stress Ecology
• Global Change Processes
• +3 more

Dr. Donald Baird is a Visiting Research Professor in Biology at the University of New Brunswick and Senior Research Scientist at Environment and Climate Change Canada. As a Fellow of the Canadian Rivers Institute, his research focuses on ecological risk assessment and developing innovative methods for biodiversity observation in freshwater ecosystems using ecogenomics approaches. He leads a national initiative applying DNA-based techniques for monitoring Canada's threatened ecosystems, particularly river floodplains and boreal wetlands. His work addresses the complex stressor regimes affecting aquatic ecosystems in the Anthropocene through integrated assessment approaches that combine traditional ecology with molecular tools. With over 150 publications and 4 books, Dr. Baird has conducted research across five continents and serves as a science advisor for international organizations including WHO-Water, Ramsar, and WWF-Canada.

Virendra BhavsarView profile

Research Professor

• Natural Language Processing
• Cognitive Informatics
• Distributed Systems
• +3 more

Virendra Bhavsar is an Honorary Research Professor and Professor Emeritus in the Department of Computer Science at the University of New Brunswick (UNB). He holds a PhD in Computer Science and is affiliated with UNB's Fredericton campus, located in Gillin Hall GE109A. His research focuses on natural language processing (especially for Hindi and other Indian languages), cognitive informatics, distributed systems, and bioinformatics. Bhavsar has also contributed to projects involving virtual reality in education, parallel computing, and machine translation systems. Education: PhD in Computer Science Research Interests: Development of NLP tools for free-word-order languages Optimization of in-memory data processing systems on NUMA architectures Cognitive learning environments using virtual reality Tree-based parsing algorithms for linguistic analysis High-performance spatial and spatio-temporal data systems Recent Work Trends: His articles from 2017–2022 highlight advancements in Hindi language processing, distributed computing optimizations, and cognitive informatics applications. Notable areas include improving data skew resilience in hash joins and developing visualization systems for language learning. Awards: No specific scientific awards listed in available materials. Advising & Grants: No formal advisee list or grant details provided in current data. His collaborations include work on grid computing frameworks like GARUDA and HPGC initiatives. Labs/Teams: Involved in projects related to distributed in-memory systems (e.g., DISTIL) and cognitive informatics research groups.

Lingling JinView profile

Associate Professor

• Bioinformatics
• Genome Evolution
• Comparative Genomics
• +2 more

Dr. Lingling Jin is an Associate Professor in the Department of Computer Science at the University of Saskatchewan. Her research focuses on Bioinformatics, Genome Evolution, Comparative Genomics, Natural Computing, and Mathematical Genomics. She holds a Ph.D. in Computer Science with a specialization in Bioinformatics from the University of Saskatchewan (2017), preceded by an M.Sc. (2010) and B.Eng. (2006) in Computer Science from the same university and Beijing University of Technology, respectively. Dr. Jin’s work integrates computational methods with genomic data analysis, addressing challenges in plant genomics, structural variant detection, and evolutionary biology. Her research explores topics such as polyploid subgenome inference, anti-CRISPR activity modeling, and environmental stress response in organisms like mites and legumes. She also applies machine learning techniques to problems in agriculture, such as wheat kernel defect detection and phenotype prediction from genomic markers. Recent projects include developing tools like SV-JIM and SVPS for structural variant identification, sequencing the Camelina neglecta genome, and advancing self-supervised learning for video object segmentation and dense-pattern analysis. Her interdisciplinary approach bridges computational science with biological applications, contributing to both foundational and applied research in genomics. No scientific awards or grants are explicitly mentioned in the provided text. She currently advises no listed students, though her research likely involves graduate students and collaborators in bioinformatics and computational biology.

• Biomedical Informatics
• Cancer Genomics
• Computational Biology
• +3 more

Dr. Yi Qiao is a researcher in the Department of Biomedical Informatics at the University of Utah School of Medicine. His work focuses on integrating computational methods with genomic data to study cancer evolution, tumor subclonal dynamics, and precision oncology. Key contributions include developing tools like SubcloneSeeker, quickBAM, and Gene.iobio for analyzing genomic variants and clinical data. Education : PhD in Biomedical Informatics from Boston College BS from Zhejiang University Research Interests : Dr. Qiao's research bridges computational biology and clinical oncology. He develops innovative methods to dissect tumor heterogeneity using single-cell and bulk sequencing, with applications in drug response prediction and therapy selection. His work emphasizes understanding how subclonal populations evolve in cancers like leukemia and lung cancer, and how this informs targeted treatments. Key Projects : Framework for combining bulk DNA and single-cell RNA sequencing to study tumor expression (Genome Res, 2024) quickBAM API for high-throughput sequence analysis (Bioinformatics, 2023) ScreenDL deep learning model for drug response prediction (2024) Grants & Collaborations : His research is supported by collaborations with oncology teams and computational biology groups, focusing on translational applications of genomic data. Tools like Taxonomer and bam.iobio have advanced clinical diagnostics through real-time genomic analysis. Labs & Teams : He leads the Computational Oncology Lab, specializing in developing AI-driven tools for precision medicine. Active in cross-disciplinary teams at the University of Utah Health's cancer research initiatives.

Francesco IorioView profile

Researcher

• Computational Biology
• Pharmacogenomics
• Cancer Biology
• +3 more

Francesco Iorio is a Research Group Leader in Computational Biology at Human Technopole (Milan, Italy), leading research in computational cancer pharmacogenomics and therapeutic target discovery. He holds a PhD from the University of Salerno and TIGEM (Naples), focusing on computational methods for drug discovery. Previously, he was a Senior Bioinformatician at EBI, leading CRISPR-Cas9 screen analyses across cancer cell lines to identify synthetic lethalities and therapeutic targets. He led the Cancer Dependency Map Analytics team at Wellcome Sanger Institute, developing algorithms for integrating large-scale cancer datasets. Since 2019, he serves as a Scientific Advisor to the Cancer Research Horizon–AstraZeneca Functional Genomics Centre. His research integrates computational methods with experimental data to uncover cancer dependencies, drug mechanisms, and biomarkers. Key contributions include tools like CRISPRcleanR for bias correction in CRISPR screens, and CELLector for selecting cancer cell line models. His work spans pharmacogenomics, functional genomics, and CRISPR technologies, with a focus on translating findings into clinical applications. Publications highlight advancements in CRISPR screen analysis, cancer dependency mapping, and drug repositioning. Current projects aim to advance precision medicine through computational frameworks for understanding tumor heterogeneity and therapeutic vulnerabilities.

Erin HeinzenView profile

Associate Professor

• Epilepsy Genetics
• Neurodevelopmental Disorders
• Genomics
• +3 more

Erin Heinzen is an Associate Professor at the UNC Eshelman School of Pharmacy, Division of Pharmacotherapy and Experimental Therapeutics, with a joint appointment in the UNC Department of Genetics. She holds a Pharm.D. and Ph.D. from the University of North Carolina at Chapel Hill, followed by postdoctoral training at Duke University. Her research focuses on the genetic and genomic basis of epilepsy disorders, particularly somatic mutations and transcriptomic regulation in neurological diseases. Education: Pharm.D., University of North Carolina at Chapel Hill (2001) Ph.D., Pharmaceutical Sciences, University of North Carolina Chapel Hill (2004) Research Interests: Dr. Heinzen’s lab investigates somatic mutations in epilepsy and brain malformations, using induced pluripotent stem cells and advanced sequencing technologies. Key projects include studying SLC35A2 variants in intractable epilepsy and analyzing transcriptomic changes in brain tissue. Her work has identified 15 epilepsy genes, including the gene for Alternating Hemiplegia of Childhood. Grants & Awards: NIH-funded research, including $7M in collaborative grants to study genetic variants in epilepsy. Active in consortia like Epi4K and ILAE. Labs & Teams: The Heinzen Lab collaborates with neurology, neuropathology, and neurosurgery groups globally, advancing neurogenetics and treatment approaches for intractable seizures.