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Jana ShenView profile

Professor

• Molecular Simulation
• Data Science
• Kinases
• +12 more

Jana Shen is a Professor in the Department of Pharmaceutical Sciences at the University of Maryland School of Pharmacy, where she leads an interdisciplinary research group at the intersection of chemistry, biology, physics, and computer science. Her lab develops and applies advanced simulation and data science tools to understand biomolecular mechanisms and accelerate drug discovery. Education: Postdoc, The Scripps Research Institute (2003–2007) PhD, University of Minnesota at Twin Cities (1999–2003) MS, University of Calgary, Canada (1996–1999) Diplom-Chemie, Bergische Universität Wuppertal, Germany (1991–1995) Her research focuses on molecular simulation , data science , and computational biophysics , with applications in kinases , GPCRs , transmembrane transporters , and pH-responsive materials . She has pioneered the development of continuous constant pH molecular dynamics (CpHMD) methods and their applications in drug design and biomolecular mechanisms. The recent publications highlight a strong trend in computational drug discovery , particularly in covalent inhibitors , opioid receptor mechanisms , antiviral design , and the integration of machine learning with molecular dynamics . These works span high-impact journals such as eLife , JACS , Nature Communications , and ACS journals. Scientific Awards: National Science Foundation CAREER Award American Chemical Society HP Outstanding Junior Faculty Award Junior Faculty Research Award (University of Oklahoma, 2008, 2009) Phi Kappa Phi, University of Minnesota Louise T. Dosdall Graduate Fellowship Nova Graduate Fellowship Dr. Shen has mentored numerous PhD students and postdoctoral fellows, many of whom have gone on to successful careers in academia and industry. Her research is supported by major agencies including the National Institutes of Health , National Science Foundation , and FDA . She leads the Shen Lab, which actively develops open-source tools such as DeepCys , CpHMD , and PKAD-3 , and maintains databases for covalent ligandability and pKa predictions.

• Computer Vision
• Deep Learning
• Semantic Segmentation
• +3 more

Dr. Guangliang Cheng is an Associate Professor in the Department of Computer Science at the University of Liverpool. His research focuses on deep learning, computer vision, and perception algorithms with applications in remote sensing, medical imaging, and autonomous systems. Prior to his current role, he served as a vice research director in the Autonomous Driving Group at SenseTime and completed postdoctoral research at the Aerospace Information Research Institute, Chinese Academy of Sciences. Ph.D. in Pattern Recognition from the National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences (CASIA) Postdoctoral Researcher at Aerospace Information Research Institute, Chinese Academy of Sciences (2017–2019) Dr. Cheng’s research integrates computer vision and deep learning to address challenges in semantic segmentation, domain adaptation, and robust detection. Recent work explores wavelet-based multimodal fusion for remote sensing and attention-guided architectures for medical imaging. His 2025 publications span journals like GIScience & Remote Sensing and Knowledge-Based Systems , emphasizing scalable solutions for geospatial and biomedical applications. In 2025, Dr. Cheng’s article trends highlight remote sensing semantic segmentation, cross-domain medical imaging, and drone-based fire detection. His collaborations span institutions such as SenseTime, Chinese Academy of Sciences, and University of Liverpool teams, focusing on frequency-domain fusion, attention mechanisms, and GPU optimization. As a supervisor, Dr. Cheng seeks highly motivated PhD students to join projects supported by scholarships including the Centres for Doctoral Training (CDT) and Duncan Norman Scholarship. He serves as Module Co-ordinator for COMP338: Computer Vision (2024–2025) and actively reviews for top-tier journals and conferences.

• Image Compression
• JPEG2000
• Signal Processing
• +5 more

Professor David Taubman is a distinguished academic serving as Professor and Deputy Head of School (Research) at the School of Electrical Engineering and Telecommunications (EE&T) at the University of New South Wales (UNSW) in Sydney, Australia. He is also co-director of Kakadu Software Pty. Ltd. and its affiliates Kakadu R&D and Kakadu GPU. With a career spanning over three decades, Professor Taubman has made significant contributions to the field of image and video compression, most notably as the author of the EBCOT coding algorithm adopted in the JPEG2000 international standard. Professor Taubman earned his B.Sc. in Mathematics and Computer Science (1986) and B.E. (Medal) in Electrical Engineering (1988) from the University of Sydney, followed by an M.Sc. (1992) and Ph.D. (1994) in Electrical Engineering from the University of California at Berkeley. His professional journey includes engineering work at the Electricity Commission of N.S.W. (1988-1990), research positions at Hewlett-Packard Laboratories in Palo Alto (1994-1998), and an academic career at UNSW where he progressed from Senior Lecturer (1998-2003) to Associate Professor (2004-2009) and finally to Professor (2009-present). He has held various leadership roles including Head of the EE&T Telecommunications Research Group (2003-2014), Head of the EE&T Signal Processing Research Group (2014-present), Director of Research for the School of EE&T (2011-2016), and Deputy Head of School (Research) since 2017. Professor Taubman's research interests center on image and video compression, with particular expertise in JPEG2000 standards and implementations. His work spans signal processing, wavelet transforms, scalable video coding, motion modeling, and multimedia systems. He has pioneered numerous compression algorithms and frameworks, including the EBCOT coding algorithm that became central to the JPEG2000 standard. His recent research focuses on efficient motion modeling with cuboidal partitioning, learned lifting-based transform structures, and high-throughput implementations of JPEG2000 for video applications. His work bridges theoretical foundations with practical implementations, as evidenced by the commercially successful Kakadu Software tools that have garnered around 500 commercial licensees. Analysis of Professor Taubman's recent publications reveals a consistent focus on advancing compression technologies with particular emphasis on scalability, efficiency, and adaptability. His work spans traditional image compression (JPEG2000 extensions), video coding (cuboid-based partitioning for UHD/360-degree video), and emerging applications (nanopore sequencing data compression). A notable trend is the integration of machine learning techniques with traditional compression frameworks, as seen in his work on learned lifting-based transform structures. His research maintains strong connections to real-world applications across diverse domains including medical imaging, astronomical data processing, and genomic sequencing. IEEE Fellow Engineers Australia Fellow (by invitation) Professor Taubman has served as Associate Editor for the IEEE Transactions on Image Processing for two four-year appointments (2003-2005 and 2010-2013). He has been actively involved in numerous research grants focused on image and video compression technologies, particularly those related to the JPEG2000 standard and its extensions. His work has received significant industry support, reflected in his consultancy with various U.S., Japanese, and Australian corporations. He has also contributed to international standards development as a member of Standards Australia Technical Committee MS-065 (mirroring ISO TC42 on Digital Photography) and as a constitutional member of Standards Australia Technical Committee IT-029 (Coded Representation of Picture, Audio and Multimedia/Hypermedia Information). Professor Taubman co-directs Kakadu Software Pty. Ltd. and its research affiliates Kakadu R&D and Kakadu GPU, which have developed the commercially successful Kakadu Software tools for JPEG2000. His research group at UNSW focuses on advanced image and video compression techniques, with particular expertise in wavelet-based methods, scalable coding, and motion modeling. The group maintains strong industry connections and has contributed significantly to the development and standardization of image compression technologies worldwide.

Stefano MarkidisView profile

Professor

• High-Performance Computing
• Quantum Computing
• Computational Fluid Dynamics
• +5 more

Stefano Markidis is a Professor of Computer Science at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science and the Digital Futures Faculty. He holds a Ph.D. from the University of Illinois at Urbana-Champaign and an MS from Politecnico di Torino. His research focuses on high-performance computing systems, including supercomputers and quantum computers, with expertise in plasma simulations, quantum algorithms, and scalable computational frameworks. Markidis leads the development of the Neko framework for high-fidelity computational fluid dynamics and the iPIC3D particle-in-cell code for plasma physics. He teaches courses such as Quantum Computing for Computer Scientists, High-Performance Computing, and Applied GPU Programming. His work addresses exascale computing challenges, including optimizing algorithms for GPUs, quantum systems, and distributed architectures. Key research interests include: Parallel Programming Models and HPC Frameworks Quantum Computing Applications in Scientific Simulations Physics-Informed Machine Learning Exascale System Optimization Turbulence Modeling and Plasma Dynamics His publications span over 100 articles in journals like Journal of Computational Physics and Scientific Reports , focusing on topics such as scalable CFD, quantum neural networks, and plasma simulation techniques. He has advised numerous students in these areas. Markidis collaborates with institutions like Los Alamos National Laboratory and RISE Research Institutes of Sweden through the Digital Futures initiative, aiming to solve societal challenges via digital technologies.

Riyadh BaghdadiView profile

Assistant Professor

• Compilers
• Programming Languages
• Deep Learning
• +9 more

Riyadh Baghdadi is an Assistant Professor of Computer Science at New York University Abu Dhabi and a Global Network Assistant Professor at the Tandon School of Engineering, NYU. He is also a Research Affiliate at MIT, where he previously completed a postdoctoral fellowship. His academic journey includes a PhD and Master’s from Sorbonne University (INRIA/UPMC) and an engineering degree from Ecole Supérieure d’Informatique in Algiers. Assistant Professor, NYU Abu Dhabi Global Network Assistant Professor, Tandon School of Engineering, NYU Research Affiliate, MIT His research lies at the intersection of compilers, programming languages, and applied machine learning, with a focus on developing advanced compiler techniques for deep learning, high-performance computing, and data-parallel algorithms. He is the lead developer of the Tiramisu compiler , a polyhedral compiler designed to optimize dense and sparse deep learning workloads across diverse architectures including CPUs, GPUs, and FPGAs. Riyadh’s recent publications demonstrate a strong trend toward integrating machine learning into compiler optimization—particularly in cost modeling, loop scheduling, and automatic code generation. His work addresses critical challenges in optimizing sparse neural networks and enabling efficient execution on resource-constrained platforms like smartphones and autonomous vehicles. Outstanding Paper Award, MLSys 2021 He has mentored 18 students and taught core courses such as Computer Systems Organization and Machine Learning at NYUAD. His service to the academic community includes program committee roles at MLSys, IPDPS, ECOOP, and PACT, as well as organizing workshops on polyhedral compilation and machine learning for hardware-software co-design. Riyadh actively contributes to open-source projects and collaborates with industry leaders including Google, Facebook, NVIDIA, and Intel. He leads the development of Tiramisu and collaborates on DSLs like GraphIt and Halide, focusing on performance portability and automation in compiler design.

Andrew Rau-ChaplinView profile

Professor

• High Performance Computing
• Parallel Algorithms
• Data Warehousing
• +14 more

Andrew Rau-Chaplin is a Professor and Dean of the Faculty of Computer Science at Dalhousie University, where he leads the Risk Analytics Lab and contributes significantly to research in high performance computing, parallel algorithms, and risk analytics. He is affiliated with the Institute for Big Data Analytics and has a strong academic and administrative presence. Education: Postdoc - DIMCS (Princeton, Rutgers, Bell Labs) PhD - Carleton University (1993) MCS - Carleton University (1990) BCS - York University (1986) His research focuses on applying parallel and high performance computing to data-intensive domains such as data warehousing, OLAP, catastrophe modeling, and risk analytics. He emphasizes both algorithmic design and practical system implementation, with a strong grounding in experimental evaluation. His work spans theoretical studies and real-world applications in finance, bioinformatics, and geospatial systems. The 15 most recent publications reflect a consistent focus on parallel data processing, OLAP optimization, indexing techniques (e.g., Hilbert curves), and risk modeling. Key themes include scalable data cube computation, view selection, adaptive coding, and spatial analytics, demonstrating expertise in both algorithmic innovation and systems-level performance. He has served on numerous scientific committees and grant panels, including NSERC and Compute Canada, and has been a journal editor for JPDC and DMTCS. Dr. Rau-Chaplin has supervised a wide range of graduate students in areas including risk analytics, GPU computing, text analytics, and parallel algorithms. His lab has received funding for postdoctoral, graduate, and undergraduate research positions. He teaches courses such as Parallel Computing, Software Engineering, Data Structures, and Risk Analytics, and has developed software tools like LaHave, Clustal XP, and Digital Coliseum. His lab, the Risk Analytics Lab, focuses on integrating analytics, risk management, and HPC for challenges in catastrophe modeling and financial risk. The lab leverages technologies such as stochastic simulation, optimization, and spatial OLAP.

Sören LaueView profile

Professor

• Machine Learning
• Optimization Algorithms
• GPU Computing
• +3 more

Prof. Dr. Sören Laue is a Professor of Machine Learning at the University of Hamburg's Department of Informatics. His research focuses on optimization algorithms, machine learning frameworks, and high-performance computing. He leads the Machine Learning research group and developed the GENO optimization framework and the Matrix Calculus toolset. His work emphasizes GPU acceleration, tensor operations, and scalable solutions for classical machine learning problems. Projects: GENO solver (Python-based optimization), Matrix Calculus (derivative computation), and SQL-based tensor operations. Key Research Themes: Optimization frameworks, GPU computing, neural network scalability, and algorithm design. Selected recent publications highlight contributions to tensor calculus benchmarks, GPU-optimized machine learning pipelines, and novel optimization methods. His work bridges theoretical foundations and practical software tools for the machine learning community.

Saman AmarasingheView profile

Professor

• Programming Languages
• Compilers
• Domain-Specific Languages
• +7 more

Professor Saman Amarasinghe is a full Professor in the Department of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT), and Principal Investigator at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). He leads the Commit compiler research group, which focuses on programming languages and compilers that maximize application performance on modern computing platforms. His work spans multiple academic departments and research centers, with strong affiliations to both MIT's School of Engineering and CSAIL. Professor Amarasinghe's research interests center around high-performance domain-specific languages and compiler technology . His work combines language design with sophisticated compilation techniques to deliver unprecedented performance for targeted application domains. His research spans multiple areas including image processing (Halide), sparse tensor algebra (TACO), graph analytics (GraphIt), stream computations (StreamIt), and bioinformatics (Seq). A significant thread throughout his work is the application of machine learning for compiler optimizations, from Meta optimization in 2003 to the OpenTuner autotuner framework. Analysis of Professor Amarasinghe's recent publications reveals a strong focus on sparse computing , compiler vectorization , and domain-specific language implementation . His work consistently bridges theoretical compiler concepts with practical performance gains across diverse application domains. The progression from earlier work on StreamIt and Halide to more recent projects like GraphIt and TACO shows an evolution toward more specialized, high-performance DSLs targeting specific computational patterns. His 2020-2025 publications particularly emphasize sparse tensor operations, GPU acceleration, and machine learning integration with compiler technology. ACM Fellow (2019) Professor Amarasinghe has made significant contributions to academic entrepreneurship and student development. He founded Determina, Inc. (acquired by VMware) based on security research from his MIT lab and co-founded Lanka Internet Services, Ltd., Sri Lanka's first ISP. As faculty director of MIT Global Startup Labs, his programs across 17 countries have helped create over 20 successful startups. His teaching includes the popular Performance Engineering of Software Systems (6.172) course with Professor Charles Leiserson, as well as innovative project-based courses like the Open Source Software Project Lab and Bring Your Own Software Project Lab. His educational approach emphasizes hands-on experience with compiler and language design concepts. Professor Amarasinghe leads the Commit compiler research group at MIT CSAIL, which has produced numerous influential domain-specific languages and compilers including Halide, TACO, Simit, StreamIt, and GraphIt. The lab maintains strong industry connections through projects like OpenTuner and Determina, and collaborates with researchers worldwide on compiler technology. The group's work spans both theoretical compiler research and practical implementation, with a consistent focus on bridging the performance gap between high-level programming abstractions and hardware capabilities.

Marc OlanoView profile

Associate Professor

• Computer Graphics
• Real-Time Rendering
• Graphics Hardware
• +4 more

Marc Olano is an Associate Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC), and serves as the Associate Dean of Academic Programs and Learning in the College of Engineering and Information Technology. He leads the Computer Science Game Development Track and co-directs the VANGOGH lab. His research focuses on interactive 3D computer graphics, programmable shading, graphics hardware, and surface appearance modeling, with contributions to foundational graphics technologies like procedural shading and normal mapping. Research Interests: Olano’s work spans real-time rendering, GPU algorithms, texture compression, and procedural shading. He has pioneered techniques such as LEAN mapping and variable bitrate texture compression, significantly impacting game development and real-time graphics. His research often explores the intersection of hardware capabilities and algorithmic innovation, with applications in medical visualization, visualization of scientific data, and haptic interaction. Key Contributions: Olano’s accomplishments include pioneering procedural shading on graphics hardware, developing homogeneous rendering techniques, and advancing normal mapping. His work on GPU-based curvature estimation and BT volumes for volume rendering exemplifies his focus on leveraging GPU parallelism for real-time visualization challenges. He has also contributed to standards in shading languages and GPU programming. Teaching & Mentorship: Olano teaches courses in computer graphics, game development, and advanced computer architecture. He mentors students in independent studies and has advised numerous MS theses exploring topics like GPU random number generation, volume rendering, and soft shadow algorithms. His students’ work often bridges theoretical research and practical GPU implementations. Labs & Projects: The VANGOGH lab under his co-direction focuses on advanced visualization and graphics research, including real-time rendering techniques, GPU algorithms, and interactive data visualization. His research collaborations span industry partners like Firaxis Games, contributing to titles such as Civilization V through texture compression innovations.

Weiwen JiangView profile

Assistant Professor

• Quantum Computing
• AI Accelerators
• Hardware-Software Co-Design
• +3 more

Weiwen Jiang is a tenure-track Assistant Professor in the Department of Electrical and Computer Engineering at George Mason University (GMU), affiliated with the College of Engineering and Computing (CEC). He leads the JQub lab, focusing on hardware/software co-design for computing systems, spanning classical (FPGAs, ASICs) and quantum computing applications in AI-driven fields like medical imaging and geophysics. Prior to GMU, he held a postdoctoral position at the University of Notre Dame and earned his PhD in Computer Science from Chongqing University with a joint PhD in Electrical and Computer Engineering from the University of Pittsburgh. His research emphasizes quantum computing, AI accelerators, and domain-specific computing. Notable achievements include the 2025 NSF CAREER Award, ACM Sigda Meritorious Service Award (2024), and IEEE QuantumWeek Best Paper Award (2023). His work is funded by NSF, DoE, ARO, Meta, and Leidos. He co-chaired IEEE QuantumWeek (2023–2025) and created workshops like StableQ at ESWEEK 2023. Key contributions include developing frameworks like QuPAD for quantum learning and JQub's AI-driven geophysical and medical imaging tools. His lab graduated Dr. Yi Sheng (now at University of South Florida) and Dr. Zhepeng Wang (Amazon Applied Scientist). Current research explores quantum machine learning, noise mitigation, and fairness in AI for edge devices.

Biresh Kumar JoardarView profile

Assistant Professor

• Deep Learning Accelerators
• In-Memory Computing
• Hardware Security
• +2 more

Biresh Kumar Joardar is an Assistant Professor in the Electrical and Computer Engineering Department at the University of Houston's Cullen College of Engineering. He holds a BE from Jadavpur University (2016) and PhD from Washington State University (2020), with postdoctoral training at Duke University as a Computing Innovation Fellow. His research integrates machine learning with hardware design to develop efficient deep learning accelerators, ReRAM-based architectures, and heterogeneous manycore systems. Current projects focus on enhancing reliability, security, and performance of AI hardware through in-memory computing and 3D integration techniques. Research themes include hardware security (e.g., Rowhammer mitigation), fault-tolerant neural network training, and hardware-software co-design for bioinformatics. Recent articles explore energy-efficient architectures for graph neural networks and cross-layer optimization for AI workloads. Awards: Best Paper Award, International Symposium on Networks-on-Chip (NOCS 2019) Joardar leads the Heterogeneous and In-Memory Computing Lab, seeking PhD students with backgrounds in VLSI, computer architecture, or machine learning. His work has been supported by NSF and industry partnerships.

Guimu GuoView profile

Assistant Professor

• Parallel Computing
• Graph Mining
• GPGPU
• +2 more

Guimu Guo is an Assistant Professor in the Department of Computer Science at Rowan University's College of Science & Mathematics. His research focuses on parallel and distributed computing techniques for large-scale graph mining problems, with applications in bioinformatics and transportation engineering. Ph.D. in Computer Science from University of Alabama at Birmingham M.Sc. in Computer Science from Tongji University Dr. Guo has published extensively in top-tier venues like VLDB, ICDE, and IEEE BigData. His work spans graph mining algorithms, parallel computing, and interdisciplinary applications in transportation and genomics. He actively mentors PhD and Master's students, offering fully funded positions. Key research trends include: Advancing GPU-accelerated graph decomposition techniques Developing distributed frameworks for subgraph querying and task concurrency Exploring parallel algorithms for frequent pattern mining and clique-like subgraphs Scientific Recognition: NSF CRII Award UAB Outstanding PhD Student Award Alabama GRSP Awards (Rounds 15 & 16) Teaching spans from foundational object-oriented programming to advanced graduate courses in parallel programming. His lab group has produced significant contributions to subgraph mining, transportation simulation, and genome assembly systems.

Joe DeviettiView profile

Associate Professor

• Computer Architecture
• Parallel Programming
• Concurrency
• +4 more

Joseph Devietti is an Associate Professor in the Department of Computer & Information Science at the University of Pennsylvania. His research focuses on improving programmability and performance of multiprocessor systems through architectural and programming model innovations. He actively advises PhD students and has supervised numerous graduates now employed at leading tech companies and academic institutions. Education: PhD (2012), MS (2009) in Computer Science and Engineering from University of Washington; BSE (2006) in Computer Science and BA (2006) in English from University of Pennsylvania. Employment: Associate Professor (2020–present), Assistant Professor (2013–2020) at University of Pennsylvania; Principal Scientist & Co-founder at Cloudseal, Inc. (2018–2020). Devietti’s research spans computer architecture, parallel programming, and deterministic execution. Key areas include cache/memory optimization (prefetching, false sharing repair), GPU programming models (race detection, block-size independence), and hardware-software co-design for concurrency safety. His recent work addresses dynamic runtime prefetch tuning (RPG 2 ), online code layout optimization (OCOLOS), and intelligent BTB prefetching (Twig) for data center applications. His publications from 2024–2017 reveal trends in instruction/cache optimization (2024–2020), GPU determinism (2018–2017), and race detection (2018–2016). Awards include the 2024 Penn Engineering Ford Motor Company Award, Radhia Cousot Best Paper (2018), and IEEE Micro Top Picks recognition (2023, 2009, 2008). Scientific Awards : 2024 Penn Engineering Ford Motor Company Award Radhia Cousot Young Researcher Best Paper Award (SAS 2018) IEEE Micro Top Picks (2023, 2009, 2008) Intel Early Career Faculty Honor Program (2013) Intel Ph.D. Fellowship (2011) Advising : Supervised 15+ PhD/Master’s students with placements at Google, Microsoft, Amazon, NYU, and the United States Naval Academy. Collaborations : Works with industry leaders (NVIDIA, Facebook) and academic institutions (University of Washington, Penn).

Dukka KCView profile

Adjunct Professor

• Bioinformatics
• Data Science
• Machine Learning
• +4 more

Dukka KC is an Adjunct Professor in the Department of Computer Science at Michigan Technological University and a member of the Institute of Computing and Cybersystems (ICC). His research focuses on computational data science with applications in bioinformatics, computational biology, and health informatics, particularly leveraging machine learning and high-performance computing to develop predictive tools for protein and nucleic acid modifications. Ph.D., Informatics, Kyoto University, 2006 M.Inf., Informatics, Kyoto University, 2003 B.Eng., Computer Science, Kyoto University, 2001 Research interests include: Developing GPU-accelerated bioinformatics tools (e.g., GPU-I-TASSER) Predicting post-translational modification sites using deep learning (e.g., DeepNGlyPred, DeepRMethylSite) Machine learning approaches for malonylation, succinylation, and sulfenylation site prediction High-throughput analysis of next-generation sequencing data Interdisciplinary projects in biometrics, cybersecurity, and disaster prediction Recent publications highlight a strong trend in applying deep learning to protein structure and function prediction, GPU-parallelization for computational efficiency, and machine learning for both biological and cybersecurity applications. The lab also emphasizes cross-domain collaborations and the development of scalable bioinformatics workflows. Grants and funding include projects like the President's Convergence Science Initiative (PI, $300K), NSF III grants for protein function prediction ($111K), and multi-institutional collaborations on biometric test-beds and synthetic biology research. The KC Lab at Michigan Tech specializes in integrating computational data science with molecular biology, focusing on protein/RNA/DNA modification site prediction and contributing to large-scale proteome analysis through machine learning-driven pipelines.

• 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.