Find Academic Supervisors

• Computational Biology
• Genomics
• Cancer Research
• +5 more

Adam Olshen, PhD, MA is a Professor in the Department of Epidemiology & Biostatistics at the University of California, San Francisco School of Medicine. His research program bridges advanced statistical methodology with cancer genomics and clinical applications, focusing on developing computational approaches to analyze complex genomic data and understand cancer biology and patient outcomes. Dr. Olshen's research spans multiple critical areas in computational biology and cancer genomics. He is best known for developing the circular binary segmentation algorithm, a foundational method for analyzing DNA copy number data that has become standard in cancer genomics research. His work consistently applies sophisticated statistical models to address biological questions, particularly in cancer research where he examines tumor biology, treatment response mechanisms, and patient symptomatology. Recent work demonstrates expertise in multi-omic analyses, single-cell sequencing methodologies, and machine learning applications for understanding cancer stem cells, treatment resistance mechanisms, and symptom biology. His research has particular strength in translating complex genomic data into clinically relevant insights. Analysis of Dr. Olshen's recent publications reveals a strong focus on applying cutting-edge computational methods to understand cancer biology at increasingly granular levels. His work spans multiple cancer types including breast cancer, leukemia, prostate cancer, and others, with particular attention to genomic alterations, epigenetic changes, and their relationship to clinical outcomes. A notable trend is the application of advanced computational techniques to analyze single-cell and multi-omic data to uncover novel biological insights about cancer heterogeneity, progression, and symptom biology. His publications frequently appear in high-impact journals across computational biology, oncology, and clinical medicine. Dr. Olshen maintains an active collaborative research program across UCSF, working extensively with clinicians and basic scientists to apply sophisticated statistical methods to complex biological questions. His work has contributed significantly to the development of novel biomarkers, improved understanding of cancer biology, and better methods for analyzing genomic data in clinical contexts. He has been instrumental in numerous studies examining the relationship between genomic alterations and clinical outcomes, as well as the development of novel computational methods for genomic data analysis that have been widely adopted by the research community.

Hari SundarView profile

Associate Professor

• Parallel Algorithms
• High Performance Computing
• Computational Sciences
• +3 more

Hari Sundar is an Associate Professor in the Department of Computer Science at Tufts University, holding the Ada Lovelace Associate Professorship. Previously, he served as an Associate Professor at the Kahlert School of Computing, University of Utah. His research focuses on developing parallel algorithms for computational sciences and high-performance computing, addressing challenges in biosciences, geophysics, computational fluid dynamics, and computational relativity. He leads efforts in adaptive mesh refinement, geometric multigrid methods, and scalable scientific computing frameworks like Dendro-GR for numerical relativity. Education: Ph.D. in Computer Science from the University of Pennsylvania (2009), and a Bachelor of Engineering from the University of Delhi (2000). Postdoctoral work at the Oden Institute, University of Texas at Austin. Research Interests: Parallel algorithms, high-performance computing architectures, computational relativity (binary black hole simulations), multiphase flow modeling, and domain-specific languages for scientific computing. His work emphasizes scalability and efficiency on modern supercomputers. Key Contributions: Development of the Dendro-GR platform for gravitational wave simulations, scalable PDE solvers, and GPU-optimized algorithms for phonon transport and genomic sequence alignment. His recent work includes advancements in gravitational waveform modeling for LISA space missions and thermodynamically consistent two-phase flow simulations. Grants & Collaborations: Active in NSF-funded projects on computational relativity, multiphase flow algorithms, and scalable PDE solvers. Collaborates across disciplines in astrophysics, materials science, and bioinformatics.

• GPU Computing
• Parallel Computing
• Domain-Specific Languages
• +5 more

Prof. Dr.-Ing. Richard Membarth is a Research Professor for System-on-a-Chip and AI at the Edge Computing at Technische Hochschule Ingolstadt (THI). He is affiliated with the Hardware-Software Co-Design group and holds a secondary position at the German Research Center for Artificial Intelligence (DFKI) Saarbrücken. Co-creator of DSL frameworks like AnyDSL and Hipacc Key contributor to MetaDL (AI metaprogramming) and PRIME (predictive rendering) His research bridges GPU computing , domain-specific languages , and compiler technology , with recent work on Vulkan SPIR-V compilation and device-driven SpMV algorithms . Notable awards include the HiPEAC Paper Award (2018) and multiple Best Paper Awards for his compiler frameworks.

• AI Hardware
• Digital Chip Design
• Efficient AI Algorithms
• +6 more

Deliang Fan is an Associate Professor at the School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ. His research focuses on AI hardware, in-memory computing, and neuromorphic systems. He received his MS and PhD from Purdue University under Prof. Kaushik Roy. Education: PhD, Purdue University (2015) Research Interests: AI Hardware, In-Memory Computing, Adversarial AI, Neuromorphic Computing His work spans cross-layer co-design for AI applications, including deep learning, bioinformatics, and graph processing. He has authored 170+ peer-reviewed papers and developed hardware solutions for spintronic and memristor-based systems. Recent publications emphasize efficient architectures for transformers, federated learning, and robust neural networks. Awards include the NSF Career Award and multiple best paper recognitions. He serves in editorial and organizational roles for leading conferences like DAC, ISQED, and GLSVLSI.

Debajyoti MondalView profile

Associate Professor

• Information visualization
• Graph drawing
• Computational geometry
• +6 more

Dr. Debajyoti Mondal is an Associate Professor in the Department of Computer Science at the University of Saskatchewan. His research focuses on algorithms, network visualization, computational geometry, and visual analytics. He holds a PhD from the University of Manitoba and has held postdoctoral positions at the University of Waterloo and Microsoft Research. Mondal's work spans interdisciplinary applications, including collaborations with Saskatoon Transit and academic medicine. He has authored over 100 peer-reviewed publications and secured grants such as NSERC Discovery, CFI, and Canada First Research Excellence grants. His awards include the 2023 New Scholar RSAW Award. Education: Ph.D. in Computer Science, University of Manitoba, 2016 MSc in Computer Science, University of Manitoba, 2012 BSc. Engg. in Computer Science, Bangladesh University of Engineering and Technology, 2009 Research Interests : Algorithms, graph drawing, computational geometry, visual analytics, and interdisciplinary applications in software engineering, transportation, and bioinformatics. His lab (VGA Lab) develops visualization systems for big data analysis. Key Contributions : Advanced theoretical foundations in computational geometry and graph drawing, developed practical visualization tools, and contributed to climate-related projects like Global Water Futures. Grants & Awards : NSERC Discovery Grant (2018-2024) CFI Grant (2021-2025) Microsoft Research Internship (2015-2016) New Scholar RSAW Award (2023) Labs/Teams : Leads the VGA Lab, collaborating with interdisciplinary teams on projects like Clone-World (software clone visualization) and SET-STAT-MAP (mixed data visualization).

Michel SannerView profile

Professor

• Molecular Interactions
• Molecular Graphics
• Component-Based Software
• +3 more

MICHEL SANNER is a Professor of Molecular Biology at the Department of Integrative Structural and Computational Biology at Scripps Research. He holds a PhD in Computer Science from the University of Haute Alsace, France (1992). His research focuses on computational methods for molecular interactions, molecular graphics, and component-based software development. Notable contributions include the AutoDock suite (for molecular docking), PMV (a molecular visualization environment), and Vision (a visual programming tool). His research group develops tools like AutoDock CrankPep for peptide docking and F2Dock for protein-protein interactions. These tools are widely used in drug discovery and structural biology. His work emphasizes software engineering principles to create adaptable computational pipelines for analyzing macromolecular structures and simulating interactions. Publications span topics like peptide-docking methodologies, ligand-binding site prediction, and GPU-accelerated docking algorithms. His articles highlight advancements in computational methods for understanding protein-ligand interactions, with applications in anticoagulant research and HIV/FIV protease inhibition. Collaborations include work with Arthur J. Olson and David S. Goodsell on docking methodologies.

James R. GreenView profile

Professor

• Machine Learning
• Biomedical Informatics
• Computational Biology
• +9 more

James R. Green is a Professor in the Department of Systems and Computer Engineering at Carleton University , where he has been a faculty member since 2005. He holds a PhD from Queen's University and is a licensed Professional Engineer (P.Eng.) and Senior Member of IEEE. His work integrates machine learning, biomedical informatics, and high-performance computing. His educational background includes: B.A.Sc. in Systems Design Engineering, University of Waterloo (1998) M.Sc.(Eng.), Queen's University (2000) PhD, Queen's University (2005) Dr. Green's research focuses on machine learning challenges in biomedical informatics , particularly class imbalance and rare event prediction. Key areas include protein structure, function, and interaction prediction; microRNA detection in unique species; non-contact neonatal monitoring; and accelerating scientific computing via parallel architectures like the Cell BE processor. His lab has developed several widely used bioinformatics tools such as PIPE, ProtDCal, and PCI-SUMO. His recent publications reflect a strong trend in computational biology and machine learning , with applications in proteomics, genomics, and medical diagnostics. He has published over 100 peer-reviewed papers and secured funding from NSERC, CIHR, CFI, ORF, OCE, MITACS, and IBM. Scientific and teaching recognitions include: Three teaching awards NSERC Best Project Award (twice: 2006-2007 and 2007-2008) Multiple student projects resulting in conference papers (e.g., CMBEC) He has supervised numerous undergraduate capstone projects in areas such as assistive technologies, robotic systems, and bioinformatics. His teaching portfolio includes courses in Pattern Classification, Machine Learning, Computer Architecture, and Biomedical Engineering. He leads an active research group that bridges computer engineering and life sciences, fostering interdisciplinary collaboration. Lab and research team initiatives include: Development of open-access web servers for protein analysis Collaborations with biologists and clinicians Integration of hardware and software for medical applications

Dollas ApostolosView profile

Professor

• Reconfigurable Computing
• Hardware Architecture
• Embedded Systems
• +4 more

Professor Dollas Apostolos serves as a Professor in the School of Electrical and Computer Engineering at the Technical University of Crete (TUC), where he has held leadership roles such as Department Chairman. He directs the Microprocessor and Hardware Laboratory, focusing on reconfigurable computing, embedded systems, and high-performance digital systems. His work emphasizes rapid prototyping and real-world implementation of computational solutions. Education: Ph.D., Computer Science, University of Illinois at Urbana-Champaign (1987) M.Sc., Computer Science, University of Illinois at Urbana-Champaign (1984) B.Sc., Computer Science, University of Illinois at Urbana-Champaign (1982) Research Interests: Reconfigurable computing architectures FPGA-based acceleration for bioinformatics and genomics Embedded systems and real-time processing Hardware-software co-design for high-performance computing His research bridges theoretical innovation with practical applications, such as FPGA implementations for genome assembly and aquaculture monitoring systems. Publications: Recent work highlights FPGA-based solutions for bioinformatics (e.g., genome assembly acceleration), real-time embedded systems (e.g., fish cage net monitoring), and scalable data processing frameworks. His articles often explore the intersection of FPGA technology with computational biology, embedded vision, and distributed systems. Awards and Affiliations: Senior Member, IEEE and IEEE Computer Society Recipient of IEEE Computer Society Golden Core and Meritorious Service Awards Twice honored with the University of Illinois Teaching Excellence Award He is a co-founder of IEEE conferences like FCCM and RSP, reflecting his leadership in the reconfigurable computing community. Teaching and Labs: Teaches courses on computer architecture, logic design, and VLSI design. The Microprocessor and Hardware Lab under his direction drives advancements in FPGA-based systems, with projects ranging from bioinformatics hardware accelerators to embedded vision systems.

Giulia GuidiView profile

Assistant Professor

• High-Performance Computing
• Parallel Computing
• Sparse Linear Algebra
• +4 more

Giulia Guidi is an Assistant Professor of Computer Science at Cornell University, affiliated with the Cornell Ann S. Bowers College of Computing and Information Science. She leads the Cornell High-Performance Computing (HPC) Group and is an Affiliate Faculty at Lawrence Berkeley National Laboratory’s Performance and Algorithms Research Group. Her research focuses on high-performance computing for computational sciences, sparse linear algebra, and scalable software infrastructure for parallel systems. She holds a PhD in Computer Science from UC Berkeley (2022) and has been recognized with awards including the 2024 SIAG/Supercomputing Early Career Prize and the 2023 ISSNAF Young Investigator Award. Her work addresses challenges in genomics, population genetics, and scalable computational methods through collaborations like the NSF-funded 'ACED' project with April Wei’s Lab. Guidi mentors a diverse group of PhD, MEng, and undergraduate students, emphasizing parallel programming and HPC applications. Her lab’s research spans GPU-accelerated algorithms, sparse matrix computations, and bioinformatics tools like the Popcorn and BELLA aligners. She is also a Graduate Field Faculty in Computational Biology and Applied Mathematics at Cornell.

• Graph Representation Learning
• Deep Learning
• Reinforcement Learning
• +7 more

Jian Tang is an Assistant Professor at HEC Montreal and the Montreal Institute for Learning Algorithms (MILA), as well as an Associate Professor at the Department of Computer Science and Operations Research (DIRO) at Université de Montréal. He is also affiliated with IVADO (Institut de valorisation des données) as a member. His research spans multiple institutions including collaborations with leading biology labs worldwide and access to extensive computational resources through industry partners. Ph.D. in Computer Science, Peking University (2009-2014) Visiting Ph.D. student, University of Michigan (2011.10-2013.8) B.S. in Mathematics, Beijing Normal University (2005-2009) Professor Tang's research focuses on the intersection of deep learning and graph theory, with particular emphasis on geometric deep learning, knowledge graph reasoning, and applications in drug discovery. His work bridges symbolic and neural approaches to create robust reasoning systems that can handle complex structured data. He has pioneered techniques in graph representation learning that have significantly advanced the field of molecular property prediction and protein design. His publication record shows a clear trajectory toward applying geometric deep learning to biological problems, with a growing emphasis on protein design, molecular conformation generation, and multi-omics analysis. Recent work demonstrates sophisticated integration of 3D geometry with deep learning architectures to model complex biomolecular interactions. Canada CIFAR Artificial Intelligence Chairs (CCAI Chair) Tencent AI Lab Rhino-Bird Gift Fund Amazon Faculty Research Award Microsoft-Mila collaboration grant National Research Council Canada (NRC) Collaborative Research and Development Grant Professor Tang actively mentors doctoral and master's students, with six recent graduates working on cutting-edge topics including graph neural networks for reasoning, protein design, and molecular representation learning. His research is supported by substantial funding from industry partners including Microsoft, Amazon, and Tencent, as well as government agencies like NRC. He collaborates extensively with biology labs worldwide, applying AI to solve real-world biomedical challenges. He leads a research group focused on geometric deep learning for drug discovery, with active projects in protein design using geometric-aware models and large language models for multi-omics analysis. The group has access to thousands of GPUs through industry collaborations, enabling large-scale experiments in molecular simulation and generative modeling.

• Data Mining
• Bioinformatics
• Machine Learning
• +3 more

Domingo Savio Rodríguez Baena is a Professor at Pablo de Olavide University, affiliated with the Department of Computer Languages and Systems. His research focuses on data mining, bioinformatics, and computational biology, with a particular emphasis on biclustering algorithms, gene co-expression networks, and high-performance computing applications. PhD in Engineering, Data Science, and Bioinformatics (2012) from Pablo de Olavide University His work spans interdisciplinary domains, including recommender systems , livestock behavior analysis , and biological data interpretation . Recent articles highlight his contributions to multi-GPU optimization , ensemble learning , and historical database construction . Key collaborations include the DATAi Intelligent Data Analysis and DASE Data Analytics Science & Engineering research groups. He has developed tools like the CyEnGNet–App for gene network visualization and BIGO for gene enrichment analysis. Contact: dsrodbae@upo.es

Min ShinView profile

Professor

• Computer Vision
• Multiple Object Tracking
• Automated Visual Inspection
• +4 more

Min Shin is a Professor and Chair of the Computer Science Department at the University of North Carolina at Charlotte (UNC Charlotte), where he also serves as Assistant Dean for Research in the College of Computing and Informatics. He earned his Ph.D. in Computer Science & Engineering from the University of South Florida in 2001. His research focuses on Computer Vision, particularly multiple object tracking and automated visual inspection of nuclear power plants. He leads the Video and Image Analysis Lab, developing user-friendly tracking software funded by NSF, NIH, DARPA, and industry grants. Notable projects include the ABCTracker.org platform, which deploys algorithms refined over 10+ years of research. Dr. Shin’s work spans tracking applications in diverse domains such as ants, bees, cells, termites, robots, and vehicles. He has mentored Ph.D. and undergraduate students extensively, emphasizing computational methods in biology and engineering. His service includes roles as Associate Editor for IEEE Transactions on Systems, Man, and Cybernetics-B, and program committees for top conferences (ICCV, CVPR, ECCV). His grants and collaborations reflect interdisciplinary strengths in vision algorithms, usability, and real-world deployment.

Mai ElshehalyView profile

Lecturer

• Information Visualisation
• Human-Computer Interaction
• Visual Analytics
• +7 more

Mai Elshehaly is a Lecturer in Computer Science at City, University of London, where she is affiliated with the giCentre in the Department of Computer Science, School of Science and Technology. She focuses on designing, developing, and evaluating visualisation solutions that support data-driven decision-making, particularly for non-technical users in healthcare and public sectors. Her work involves close collaboration with healthcare practitioners, local authorities, and schools to co-design tools grounded in real-world needs. Her research interests center on information visualisation and human-computer interaction , with a strong emphasis on user-centered and co-design methodologies . She investigates how lived experiences of stakeholders can shape visual analytics systems, especially in healthcare quality improvement, population health management, and digital transformation initiatives. Her approach bridges technical innovation with social impact, promoting equitable access to data insights. Her recent publications highlight trends in narrative visualization, integration of lived experience with electronic records (e.g., CLEVER framework), and adaptable dashboards for healthcare (QualDash). These works are published in top venues such as IEEE VIS, IEEE TVCG, and Computer Graphics Forum, reflecting a consistent focus on usability, evaluation, and real-world deployment of visual analytics tools. Department-level achievement award during PhD at Virginia Tech University-level achievement award during PhD at Virginia Tech Mai has secured research funding from the Bradford Institute for Health Research (BIHR), NIHR, and internal university sources including EPSRC doctoral training funds. She has led and contributed to interdisciplinary projects such as the Digital Makers Programme and Connected Bradford, which involve partnerships across academia, healthcare, and local government. While no formal PhD students are listed, she mentors through collaborative research and advisory roles. She is actively involved in applied research through leadership positions: Co-Director of the Digital Makers Programme, Deputy Director of the Workforce Observatory for West Yorkshire, member of the advisory board at the Wolfson Centre for Applied Health Research, and cross-theme visualisation expert at the Yorkshire and Humber Patient Safety Research Centre. These roles underscore her commitment to translating research into practice within health and education systems.

Saman AmarasingheView profile

Professor

• Programming Languages
• Compiler Design
• High-Performance Computing
• +5 more

Professor Saman Amarasinghe is a faculty member in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT), where he leads the Commit compiler research group at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). His research focuses on programming languages and compilers that maximize application performance on modern computing platforms, with a particular emphasis on high-performance domain-specific languages. Professor Amarasinghe received his bachelor's degree in electrical engineering and computer science from Cornell University in 1988, followed by master's and PhD degrees in electrical engineering from Stanford University in 1990 and 1997, respectively. He joined the MIT faculty as an assistant professor in 1997 and has since become a world leader in his field. Professor Amarasinghe's research interests span programming languages, compiler design, and high-performance computing, with a particular focus on domain-specific languages. His group has developed numerous influential languages and compilers including Halide, TACO, Simit, StreamIt, StreamJIT, PetaBricks, MILK, Cimple, and GraphIt, which deliver unprecedented performance for application domains such as image processing, stream computations, and graph analytics. He has also pioneered the application of machine learning for compiler optimizations, from Meta optimization in 2003 to the OpenTuner autotuner framework. Professor Amarasinghe's publication history reveals a consistent research trajectory toward creating specialized language and compiler solutions that address performance challenges in specific domains while hiding complexity from application developers. His recent work focuses heavily on sparse computing, tensor algebra, graph processing, and the integration of machine learning techniques into compiler technology, demonstrating his ability to identify and address emerging computational challenges. ACM Fellow (2019) As an educator, Professor Amarasinghe has developed the popular Performance Engineering of Software Systems (6.172) class with Professor Charles Leiserson and created innovative project-based courses including the Open Source Software Project Lab, the Open Source Entrepreneurship Lab, and the Bring Your Own Software Project Lab. He also serves as the faculty director of MIT Global Startup Labs, which has helped create more than 20 startups across 17 countries. His research has translated into practical applications through startups like Determina, Inc. (acquired by VMware), demonstrating the real-world impact of his academic work. Professor Amarasinghe co-led the Raw architecture project with Professor Anant Agarwal, which did pioneering work on scalable multicores. His entrepreneurial activities include founding Determina, Inc. based on computer security research from his MIT lab and co-founding Lanka Internet Services, Ltd., the first Internet Service Provider in Sri Lanka, showcasing his ability to bridge academic research with commercial applications.

Daniel TauritzView profile

Professor

• Artificial Intelligence for National Security
• Cyber Security
• Critical Infrastructure Protection
• +5 more

Daniel Tauritz is a Professor in the Department of Computer Science and Software Engineering at Auburn University and serves as Director for National Laboratory Relationships. He holds a Ph.D. and M.S. in Computer Science from Leiden University, along with propaedeutic studies in Computer Science and Mathematics. His research focuses on AI-driven cybersecurity, automated algorithm design using hyper-heuristics, computational game theory, and evolutionary computation. Tauritz leads initiatives such as the Auburn Cyber Research Center and collaborates with institutions like Los Alamos National Laboratory on critical infrastructure protection and satellite network security. His work bridges academia and national security through projects like the Cyber Fire Puzzles competition and the Satellite Tycoon economic simulation game. His educational background includes advanced studies at Leiden University, with a strong foundation in computer science and mathematics. Research contributions span evolutionary algorithms for molecular evolution, coevolutionary defense strategies, and generative hyper-heuristics. Tauritz has secured grants including an NSF award for AI-cybersecurity education and contributed to Auburn’s partnerships with national laboratories. He is actively involved in fostering student engagement through ethical hacking clubs and experiential learning programs. Notable achievements include moderating panels on AI in cybersecurity and AI workforce development, as well as developing frameworks like Galaxy for network emulation and DCAFE for automated cyber experiments. His publications emphasize applying evolutionary computation to real-world challenges, including satellite constellation economics and adversarial network defense strategies.