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Leonard HarrisView profile

Assistant Professor

• Cancer systems biology
• Computational modeling
• Multiscale modeling
• +7 more

Leonard Harris is an Assistant Professor in the Department of Biomedical Engineering within the College of Engineering at the University of Arkansas. His research focuses on cancer systems biology, with expertise in computational modeling and simulation of complex intracellular signaling pathways and cell-cell interactions in tumors. Working closely with experimental collaborators, his lab develops comprehensive, mechanistic models of molecular pathways underlying non-genetic mechanisms of drug resistance in cancer cells. Education: Postdoctoral Research Fellow, Biochemistry, Vanderbilt University School of Medicine Postdoctoral Associate, Computational & Systems Biology, University of Pittsburgh School of Medicine Ph.D., Chemical and Biomolecular Engineering, Cornell University B.S., Chemical Engineering, University of Colorado, Boulder Dr. Harris's research centers on cancer systems biology with a focus on phenotypic plasticity and non-genetic heterogeneity in anticancer drug response. His lab develops multiscale models of intracellular signaling pathways and cell-cell interactions to understand tumor heterogeneity. A key aspect of his work examines intrinsic stochasticity in cell fate decision making and its role in therapeutic resistance. His computational approaches aim to create in silico platforms for virtual anticancer drug screens to identify novel molecular targets and improve cancer treatment outcomes. Analysis of Dr. Harris's recent publications reveals a consistent focus on computational approaches to understand tumor heterogeneity and drug resistance mechanisms. His work integrates mathematical modeling with experimental data to dissect genetic, epigenetic, and stochastic sources of variability in cancer cell populations. His research spans multiple cancer types, with particular emphasis on melanoma and lung cancer, examining how non-genetic mechanisms contribute to therapeutic resistance. The publications demonstrate progression from foundational computational methods to increasingly sophisticated models of tumor dynamics and drug response. Scientific Awards: NIH/NCI Transition Career Development Award to Promote Diversity (K22) National Library of Medicine Biomedical Informatics Postdoctoral Fellowship Semiconductor Research Corporation Graduate Fellowship Dr. Harris serves as principal investigator for multiple research grants focused on cancer systems biology and computational oncology. His lab receives funding from the National Institutes of Health, including the NCI K22 award supporting his transition to independence. His research integrates experimental and computational approaches through collaborations with wet-lab researchers at the University of Arkansas and other institutions. Current projects examine tumor-induced bone disease, phenotypic plasticity in small cell lung cancer, and mechanisms of drug tolerance in melanoma. Dr. Harris leads a computational cancer biology lab that develops and applies advanced modeling techniques to understand tumor heterogeneity and drug resistance. His team works at the intersection of computational biology, systems pharmacology, and cancer biology, creating models that bridge molecular, cellular, and population scales. The lab collaborates extensively with experimental groups to validate model predictions and generate new hypotheses about cancer progression and therapeutic response.

Mayur NaikView profile

Professor

• Programming Languages
• Artificial Intelligence
• Neurosymbolic Programming
• +5 more

Mayur Naik is the Misra Family Professor in the Department of Computer and Information Science at the University of Pennsylvania's School of Engineering and Applied Science. He holds office in Room 642B, Amy Gutmann Hall and maintains an active research program focused on the intersection of programming languages and artificial intelligence. Before joining UPenn, he was faculty at Georgia Institute of Technology and a researcher at Intel Labs, Berkeley. Naik received his PhD in Computer Science from Stanford University in 2008 under Alex Aiken, a Masters from Purdue University in 2003 under Jens Palsberg, and a Bachelors from BITS Pilani in 1999. He grew up in Goa, India. His primary research interests center around neurosymbolic programming, which combines symbolic reasoning with machine learning to create more accurate, interpretable, and domain-aware AI systems. His group develops language design, learning algorithms, and compiler optimizations in this space, with their most mature effort being the Scallop neurosymbolic programming language and compiler toolchain. He also conducts research in trustworthy AI for healthcare applications and AI-enabled programming tools that improve programmer productivity. Analysis of his recent publications shows a strong trend toward neurosymbolic programming frameworks (Scallop, TorchQL), LLM-assisted program analysis (IRIS), and applications of these techniques to security, healthcare, and computer vision. His work consistently bridges theoretical foundations with practical implementations, often releasing open-source systems. Misra Family Professor (endowed chair, effective July 2024) Multiple distinguished paper awards (PLDI 2019, FSE 2015, PLDI 2014) Test-of-Time Paper Awards (FSE 2013, FSE 2012, EuroSys 2011) His student Elizabeth Dinella won the 2025 ACM SIGSOFT Outstanding Dissertation award Naik has advised numerous PhD students who have gone on to faculty positions at top institutions including Peking University, University of Toronto, Ashoka University, Bryn Mawr College, and Johns Hopkins University. His research is supported by grants from NSF, Google, Amazon, and other industry partners. His lab maintains active collaborations with clinicians and bioinformatics researchers to apply neurosymbolic programming to healthcare problems. His research group, which includes current PhD students and postdocs, develops practical open-source systems and applies them to diverse domains including computer vision, cybersecurity, medicine, and bioinformatics. The group maintains strong industry connections with Google, Microsoft, Amazon, and other tech companies.

• Medical image processing
• Deep Learning
• Computer Vision
• +5 more

Nicolás Vila Blanco is an Associate Professor at the University of Santiago de Compostela (USC), Spain, conducting research through CiTIUS (Centro de Investigación en Tecnoloxías da Información e as Comunicacións). His academic journey includes a PhD in Information Technology from USC and the University of Coruña (2022), an M.Sc. in Data Science and Computer Engineering from the University of Granada, and a B.Sc. in Computer Science from the University of Vigo. Dr. Vila Blanco's research focuses on medical image processing using Deep Learning techniques, with particular expertise in dental panoramic radiograph analysis, age and sex estimation, oral microbiome research, and medical imaging applications for conditions like COVID-19. His work bridges computer science and clinical dentistry/medicine, developing practical tools that address real clinical challenges. His publication record demonstrates consistent output in high-impact journals and conferences, with numerous Q1 publications across medical imaging, dentistry, and bioinformatics fields. His research shows a clear trajectory from theoretical AI development to practical clinical implementation, with emphasis on tools that can be readily adopted in medical practice. Dr. Vila Blanco has developed several important research tools including DenTiUS Deep Plaque software for automated dental plaque quantification, IDALib for efficient image data augmentation, and has contributed to PrimerEvalPy for microbiome primer evaluation. His clinical research has produced evidence on antimicrobial efficacy, dental plaque analysis methods, and diagnostic systems with real-world applicability. His collaborative approach is evident in his numerous co-authored publications spanning dentistry, microbiology, cardiology, and computer science departments. This interdisciplinary work positions him as a bridge between technical AI development and clinical healthcare applications.

Gang MeiView profile

Associate Professor

• Numerical Simulation and Computational Modeling
• GPU Computing
• Machine Learning
• +5 more

Dr. Gang Mei is an Associate Professor in Scientific Computing within the School of Engineering and Technology at China University of Geosciences (Beijing), where he has held academic positions since 2014. His career progression includes Postdoctoral Researcher (2014-2016), Lecturer (Oct-Dec 2016), and current Associate Professor (since Jan 2017). His research bridges computational science and engineering applications with significant editorial contributions to computer science literature. Education: Ph.D. in Computer Science, University of Freiburg, Germany (2014) Research Interests: Dr. Mei specializes in Numerical Simulation and Computational Modeling, GPU Computing, Machine Learning, and Data Mining, with strong applications in Network Science and Spatial Information Systems. His work integrates Distributed and Parallel Computing techniques for large-scale scientific simulations, particularly in geospatial modeling and network analysis. The research demonstrates consistent focus on computational efficiency through hardware acceleration and algorithmic optimization across diverse domains including satellite imagery processing, financial event detection, and medical image classification. Publication Trends: His editorial portfolio reveals strong interdisciplinary patterns connecting computer science fundamentals with domain-specific applications. Recent works emphasize GPU-accelerated methods for data-intensive problems (2020-2022), spatial-temporal modeling (2019-2020), and network science applications (2021). The publications consistently address computational scalability challenges while maintaining practical relevance across geospatial, financial, medical, and engineering contexts. Professional Recognition: As an IEEE Member, Dr. Mei serves on editorial boards for IEEE Access and PeerJ Computer Science, reflecting peer recognition in computational fields. His editorial contributions span 15+ publications demonstrating expertise in evaluating cutting-edge computer science research. Academic Service: Beyond editorial work, Dr. Mei's service includes advising on computational methodology across multiple disciplines. His role as Academic Editor demonstrates commitment to scholarly communication, particularly in bridging theoretical computer science with practical engineering applications. No grant funding details were specified in available materials.

• Bioinformatics
• Genomics
• Algorithm Design
• +5 more

Jean-Stéphane VARRÉ is a Professor in the Computer Science Department at the University of Lille and a core member of the BONSAI bioinformatics research team. His administrative leadership includes: Vice-head of the Computer Science Department (since 2018) In charge of the B.Sc. in Computer Science (since 2015) Former head of the MOCAD master's degree in Computer Science (2010-2015) His research centers on algorithmic solutions for genomic challenges, specializing in genomic rearrangements with duplicated markers, transcription prediction (splicing/orthology), third-generation sequencing assembly, and GPU-accelerated bioinformatics. Key contributions include ProCARs for ancestral genome reconstruction and the TFM suite for sequence comparison using position weight matrices. Recent publications (2019) demonstrate dual expertise in microbial genomics (bacterial assembly graph analysis) and clinical virology (coronavirus OC43 sequencing protocols), reflecting his team's capacity to bridge theoretical algorithm design with high-impact biomedical applications through scalable computational frameworks. Professor VARRÉ has co-supervised six doctoral candidates across 17 years: Pierre Marijon: Assembly graph analysis (current) Antoine Thomas: Genomic rearrangements with duplicates Tuan Tu Tran (2009-2012): Manycore architecture data structures Aude Darracq (2006-2010): Plant mitochondrial genome rearrangements Aude Liefooghe (2004-2008): Transcription factor binding site algorithms Martin Figeac (2002-2005): Constrained genomic rearrangement scenarios The BONSAI team maintains an active software repository for genomic analysis tools, including GPU-optimized string matching implementations and breakpoint region analyzers, supporting both academic research and clinical applications through open-source computational pipelines.

• Biophysics
• Biochemistry
• Chemical Biology
• +3 more

Nikos Hatzakis is a Professor in the Department of Chemistry at the University of Copenhagen, Denmark, based at Universitetsparken 5, 2100 København Ø. He maintains an active research profile through the Hatzakis Lab (https://www.hatzakislab.com) and contributes to interdisciplinary biophysical research with international collaborations. His research spans critical areas in modern biophysics and biochemistry: Single-molecule dynamics and diffusion analysis Protein aggregation in neurodegenerative diseases and diabetes Super-resolution microscopy development and application Deep learning for biological data interpretation Drug delivery mechanisms across biological barriers Recent publications reveal a strong trend toward computational innovation in experimental biophysics, particularly GPU-accelerated tools for single-particle tracking and real-time observation of molecular processes. His work bridges physics, computer science, and biology to decode protein function-aggregation relationships. The Hatzakis Lab drives methodological advances in chemical biology, utilizing cutting-edge imaging to address fundamental questions in cellular biophysics and therapeutic development, with significant cross-institutional partnerships evident in high-impact publications.

Shayan ShamsView profile

Assistant Professor

• Artificial Intelligence
• Deep Learning
• Machine Learning
• +6 more

Dr. Shayan Shams is an Assistant Professor in the Department of Health Data Science and Artificial Intelligence at UTHealth Houston's McWilliams School of Biomedical Informatics, where he has been faculty since 2019. He holds a PhD in Computer Science from Louisiana State University and an MEng in Robotics from University of Malaya. His research group is affiliated with the Center for Secure Artificial Intelligence for Healthcare (SAFE). Dr. Shams' research integrates artificial intelligence, high-performance computing, and big data analytics to address healthcare challenges. Key focus areas include: Unsupervised/semi-supervised deep learning for medical computer vision High-performance computing infrastructure for genomic data Hardware acceleration (GPU/FPGA) for medical AI Knowledge transfer across clinical domains (radiology, pathology, neurology) Edge device implementation for personalized medicine His recent publications (2016-2019) demonstrate evolving work in deep learning frameworks for healthcare applications, spanning medical imaging diagnostics, genomics pipelines, disaster response analytics, and distributed computing infrastructure. Research consistently leverages convolutional networks and generative models for biomedical problem-solving. Dr. Shams actively seeks graduate students and postdoctoral researchers for projects at the intersection of AI and healthcare. He has served as reviewer for premier conferences including MICCAI, IEEE ICDCS, and ACM-BCB.

Maura JohnView profile

Researcher

• Bioinformatics
• Genome-wide Association Studies
• Machine Learning in Biology
• +4 more

Maura John serves as a Research Associate at the Chair of Bioinformatics at Hochschule Weihenstephan-Triesdorf's Straubing Campus for Sustainable Resource Use. Her research focuses on developing advanced computational methods for biological data analysis, with particular expertise in genome-wide association studies and protein structure prediction. Her primary research interests include: Genome-wide association studies with permutation-based significance thresholds that preserve population structure Development of bioinformatics tools like permGWAS2 and easyPheno Protein thermostability prediction using machine learning approaches Genomic selection methodologies for crop breeding applications Dr. John's recent publications demonstrate a strong focus on methodological improvements in computational biology, particularly addressing limitations of traditional approaches in handling skewed phenotype distributions and population structure. Her work bridges theoretical statistical methods with practical biological applications across plant genomics and protein science. Notable contributions include: permGWAS2: An improved method that maintains population structure during permutations ProLaTherm: A protein language model-based thermophilicity predictor outperforming existing methods easyPheno: A comprehensive Python framework for phenotype prediction model comparison Her research program demonstrates strong collaborative efforts with Dominik Grimm's group and other bioinformatics researchers, focusing on developing open-source tools that address critical challenges in genomic data analysis. The work has practical applications in plant breeding, protein engineering, and understanding genotype-phenotype relationships.