Gianvito Lanzolla is a Professor of Strategy at Bayes Business School (formerly Cass), City, University of London, where he has been employed since April 2006 and served as Dean of the Faculty of Management from 2016-2020. He founded the Bayes Digital Leadership Research Centre and is a Fellow of the Royal Society of Arts. Education: MSc in Mechanical Engineering, PhD in Strategic Management Visiting Appointments: London Business School, Indian School of Business, ESMT Berlin His research focuses on digital transformation, technology strategy, institutional change, and competitive dynamics. He examines how CEOs' digital orientation and board characteristics affect firm value, with publications in Academy of Management Journal , Harvard Business Review , and California Management Review . His work has won multiple academic prizes and been featured in major media outlets. Recent publications analyze digital ecosystems, agile transformation, and AI applications in finance. He teaches 'Strategic Leadership' and 'Digital Transformation' in MBA programs, having won teaching excellence awards in 2007, 2008, 2009, 2012, and 2015. Scientific Awards: City University Teaching Excellence Awards (2015, 2012, 2009, 2008) Strategic Management Society Best Paper Award (2012) SSRN Top Ten Downloaded Paper (2008) Spanish Academy of Management Best Paper Award (2009)
Jonathan Hauenstein is the Robert and Sara Lumpkins Collegiate Professor in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame, serving as Department Chair. He holds a Ph.D. from Notre Dame (2009) and M.S. from Miami University (2005). His research focuses on numerical algebraic geometry and computational methods for solving nonlinear equations, implemented in the Bertini software package. Applications span engineering, ecology, sports science, and machine learning. Education: Ph.D., Applied and Computational Mathematics, University of Notre Dame (2009) M.S., Mathematics, Miami University (2005) Research Interests: Development of numerical algorithms for polynomial systems, real algebraic geometry, and scientific computing. Key areas include homotopy continuation methods, parameter space decomposition, and applications in mechanism design, ecological modeling, and sports biomechanics. His work bridges theoretical mathematics with practical computational tools. Awards: Sloan Research Fellowship DARPA Young Faculty Award Army Research Office Young Investigator Award Office of Naval Research Young Investigator Award College of Science Research Award Advising & Grants: Advised numerous undergraduates, graduate students, and postdoctoral researchers. Active in securing grants for computational mathematics projects, including NSF-funded initiatives. His work emphasizes interdisciplinary collaboration between mathematics and engineering. Labs/Teams: Leads computational algebraic geometry research groups at Notre Dame, focusing on software development (e.g., Bertini) and numerical methods innovation.
Stefanie Jegelka is an Associate Professor (currently on leave) at the Massachusetts Institute of Technology's Department of Electrical Engineering and Computer Science, and a Humboldt Professor at Technical University of Munich. At MIT, she is a member of CSAIL (Computer Science and Artificial Intelligence Laboratory), IDSS (Institute for Data, Systems, and Society), the Center for Statistics and Machine Learning, and is affiliated with the Operations Research Center. Her educational background includes a PhD from ETH Zurich and the Max Planck Institute for Intelligent Systems, followed by postdoctoral research at UC Berkeley's AMPlab and computer vision group. Her research program focuses on algorithmic machine learning, with particular emphasis on exploiting mathematical structure for discrete and combinatorial machine learning problems, robustness in learning systems, and developing methods for scaling machine learning algorithms to large datasets. She has made significant theoretical contributions to submodular optimization and its applications in machine learning. Jegelka's publication record demonstrates a consistent focus on the intersection of discrete mathematics and machine learning. Her work spans theoretical foundations of optimization with discrete structures, applications in computer vision, and practical algorithms for submodular function optimization. Her research has evolved from foundational work on submodular functions to broader applications in deep learning and robust machine learning systems, showing increasing impact through numerous workshop best paper awards and high-impact conference publications. NSF CAREER Award Google Research Award German Pattern Recognition Award (Mustererkennngspreis) ICML Best Paper Award Sloan Research Fellowship DARPA Young Faculty Award NSF BIGDATA Award ONR MURI NSF AI Institute for Optimization Professor Jegelka has advised several successful students including Keyulu (recipient of MIT's George M. Sprowls Ph.D. Thesis Award), Derek (NSF Fellowship recipient), Ching-Yao (IBM Fellowship recipient), and Nisha (now Assistant Professor at Georgia Tech). Her research has been generously supported by multiple NSF grants, DARPA awards, and industry funding from Google, Two Sigma, and Adobe. She has also organized multiple workshops and tutorials on discrete optimization and submodularity in machine learning. At MIT, Jegelka is affiliated with the Center for Statistics and Machine Learning and collaborates with researchers across CSAIL. Her work bridges theoretical computer science, optimization, and practical machine learning applications, with recent focus on high-dimensional learning dynamics and in-context learning as evidenced by her group's multiple papers at leading conferences like ICLR.
Erik G. Larsson is a Professor and Head of the Division for Communication Systems within the Department of Electrical Engineering (ISY) at Linköping University (LiU), Sweden. He joined LiU in September 2007 and has previously held academic and research positions at the Royal Institute of Technology (KTH), University of Florida, George Washington University, and Ericsson Research. Research Interests: Enabling technologies for 6G wireless communication Statistical inference and signal processing Network science and complex networks Decentralized and federated machine learning over networks Physical layer security and privacy Energy-efficient digital signal processing His research group, active in areas like RadioWeaves and massive MIMO, focuses on robust, efficient, and secure wireless connectivity. Recent publications highlight trends in decentralized learning, resource allocation in wireless networks, and the integration of AI into mobile networks, particularly through projects like 'Turning the Air into an AI Computer' funded by the Knut and Alice Wallenberg Foundation. Scientific Awards and Honors: IEEE Signal Processing Magazine Best Column Award (2012, 2014) IEEE ComSoc Stephen O. Rice Prize (2015) IEEE ComSoc Leonard G. Abraham Prize (2017) IEEE ComSoc Best Tutorial Paper Award (2018) IEEE ComSoc Fred W. Ellersick Prize (2019) IEEE SPS Donald G. Fink Overview Paper Award (2023) IEEE Fellow Member, Royal Swedish Academy of Sciences (KVA) Gyllene Moroten Best Teacher Award (2021) Advising and Grants: He has supervised numerous Ph.D. and Licentiate students, many of whom now hold positions at leading industry and academic institutions. His research is currently funded by major organizations including the Knut and Alice Wallenberg Foundation, Swedish Foundation for Strategic Research (SSF), ELLIIT, Security-Link, Swedish Research Council (VR), and EU Horizon 2020 (H2020-SNS-6GTandem). Previous sponsors include VR, KVA, NSF, ORAU, and multiple EU FP7 and H2020 projects (e.g., MAMMOET, REINDEER, 5G-Wireless). Leadership and Service: He has served as Associate Editor for IEEE Transactions on Communications and IEEE Transactions on Signal Processing, chaired technical committees and steering committees in IEEE Signal Processing Society, and held leadership roles in major conferences such as the Asilomar Conference on Signals, Systems and Computers. He was a Visiting Fellow at Princeton University in 2015.
Jiawei Zhang is an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin–Madison since May 2025. His research focuses on optimization algorithms for machine learning, adversarial training, reinforcement learning, and distributed systems. Ph.D. in Computer and Information Engineering, Chinese University of Hong Kong, Shenzhen (2021) B.Sc. in Mathematics (Hua Loo-Keng Talent Program), University of Science and Technology of China His work spans nonconvex optimization , robust machine learning , and data-driven decision-making , with applications to AI and sustainable energy systems. Recent publications at ICML 2025 address stochastic primal-dual methods and contextual optimization robustness, while earlier works explore bilevel optimization, reward learning, and distributed consensus algorithms. Scientific awards include: MIT Postdoctoral Fellowship For Engineering Excellence (2023) CUHK-Shenzhen Presidential Award for Outstanding Doctoral Students (2021) SRIBD PhD Fellowship (2020-2021) CUHK-Shenzhen Outstanding Teaching Assistant (2023) He supervises undergraduate researchers and seeks graduate students with strong mathematical or algorithmic backgrounds for 2025 admission, emphasizing optimization and AI-driven applications.
Zhi-Pei Liang is the Franklin W. Woeltge Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign, with joint appointments in the Department of Bioengineering, Beckman Institute for Advanced Science and Technology, and Coordinated Science Laboratory. His research spans biomedical engineering, medical imaging, and signal processing with a focus on advancing magnetic resonance imaging and spectroscopy technologies. His educational background includes a Ph.D. in Biomedical Engineering from Case Western Reserve University (1989) and a B.S. in Electrical Engineering from South-China University of Technology (1982), followed by postdoctoral training at UIUC (1989-1991). Professor Liang's research interests center on magnetic resonance imaging and spectroscopy , with particular emphasis on ultrafast imaging techniques , model-based reconstruction methods , and the integration of physics-based modeling with machine learning . His pioneering work on SPICE (SPectroscopic Imaging by exploiting spatiospectral CorrElation) has revolutionized high-resolution metabolic brain imaging by enabling label-free molecular imaging through the marriage of spin physics and machine learning. His research spans pattern recognition, parameter estimation, image formation theory, and algorithms for medical imaging applications. Analysis of his recent publications reveals a strong focus on high-resolution metabolic imaging , particularly using SPICE methodology to map brain metabolism with unprecedented detail. His work bridges fundamental physics of magnetic resonance with advanced computational methods to overcome traditional limitations in imaging speed and resolution. Current research directions include J-resolved spectroscopic imaging, deuterium-based metabolic mapping, and multimodal integration of PET and MRSI for studying neurological disorders. Elected to International Academy of Medical and Biological Engineering (2012) Gold Medal, International Society for Magnetic Resonance in Medicine (2022) Technical Achievement Award, IEEE Engineering in Medicine and Biology Society (2014) Fellow, National Academy of Inventors (2021) Author of influential book 'Principles of Magnetic Resonance Imaging' (1999) President of IEEE Engineering in Medicine and Biology Society (2011-2012) Professor Liang has advised numerous students and postdocs in biomedical imaging research and has received multiple teaching honors including the Ronald W. Pratt Outstanding Teaching Award (2005) and multiple listings among UIUC's Excellent Teachers. His research has been supported by various grants from NIH, NSF, and other funding agencies. He leads the SPICE (Spectroscopic Imaging by exploiting spatiospectral Correlation) research group which focuses on developing novel imaging techniques that combine physics-based modeling with machine learning for ultrafast metabolic imaging. His laboratory, part of the Beckman Institute's Integrative Imaging Theme, collaborates extensively with clinical researchers at Carle Illinois College of Medicine and other institutions to translate advanced imaging techniques into clinical applications for neurological disorders, cancer, and metabolic diseases. Current projects focus on high-resolution mapping of brain metabolism in Alzheimer's disease, stroke, and brain tumors using novel MR spectroscopic imaging techniques.
Yuanbo Xiangli is a postdoctoral researcher at Cornell University , advised by Prof. Noah Snavely. Previously, he obtained his Ph.D. from the Multimedia Lab in the Department of Information Engineering at the Chinese University of Hong Kong (CUHK) , supervised by Prof. Dahua Lin. His research focuses on 3D computer vision and deep generative modeling for urban scene reconstruction. 3D scene reconstruction from sparse images Neural rendering and Gaussian splatting Deep generative modeling for urban environments Multi-source geospatial data processing City-scale modeling and synthetic datasets His recent work includes advanced NeRF extensions (BungeeNeRF, GridNeRF), Gaussian splatting enhancements (GSDF, Scaffold-GS), and urban scene datasets (MatrixCity, OmniCity). A pioneer in combining classical vision techniques with modern deep learning approaches. ICLR 2020 Spotlight Award Collaborates with leading researchers in photorealistic rendering, including Noah Snavely and Dahua Lin. Develops systems enabling efficient 3D reconstruction from diverse data sources like satellite imagery and street-level panoramas.
Weiyu Xu is a Professor at the University of Iowa, affiliated with both the Department of Electrical and Computer Engineering and the Department of Applied Mathematical and Computational Sciences. He joined the College of Engineering in 2012 and leads the Intelligent Information Processing Lab (IIPL). Ph.D., Electrical Engineering, California Institute of Technology, 2009 M.S., Electrical Engineering, California Institute of Technology, 2006 M.S., Electronic Engineering, Tsinghua University, 2005 B.E., Information Engineering, Beijing University of Posts and Telecommunications, 2002 His research focuses on compressive sensing, information theory, signal processing, network optimization, and deep learning applications in medical imaging and cybersecurity. He has made significant contributions to adversarial attack robustness, distributed optimization algorithms, and medical imaging techniques for OCT segmentation and brachytherapy. Recent publications highlight trends in Adversarial machine learning Medical imaging algorithms Compressed sensing for diagnostics Optimization in wireless communication He has also contributed to federated learning over tree networks and theoretical guarantees for sparse signal recovery. Weiyu Xu's lab, IIPL, integrates deep learning with classical optimization and signal processing. His work bridges foundational theories (e.g., information-theoretic robustness) with real-world applications in healthcare (e.g., cancer treatment planning) and communication systems (e.g., MIMO channel estimation).
Vinod M. Vokkarane is a Professor in the Department of Electrical and Computer Engineering at the University of Massachusetts Lowell, where he serves as Director of the Center for Smart Cyber-Physical Systems (SCyPS) and Director of Advanced Computer Network Labs. Previously, he was an Associate Professor at University of Massachusetts Dartmouth from 2004 to 2013 and a Visiting Scientist at MIT's Research Laboratory of Electronics from 2011 to 2014. His extensive research portfolio spans multiple domains of advanced networking and cyber-physical systems. Dr. Vokkarane earned his educational foundation with a B.S. from University of Mysore, India (1999), followed by an M.S. (2001) and Ph.D. (2004) in Computer Science from the University of Texas at Dallas. His dissertation focused on optical burst-switched networks, establishing the foundation for his future research trajectory. His research interests center on Cyber-Physical Systems, Network Optimization, Reliability, Smart Grids, and Cyber-Security, with particular expertise in the design, analysis, and modeling of architectures, protocols, and algorithms for ultra-high speed networks including Optical networks, Grid/Cloud networks, and Big-data networks. His work bridges theoretical foundations with practical implementations, often addressing critical challenges in network reliability, security, and efficiency. His research has received significant recognition through numerous best paper awards and substantial external funding. Analysis of his recent publications reveals a clear evolution toward increasingly sophisticated integration of cyber-physical systems with power infrastructure, particularly in the areas of grid resilience and observability. His work has expanded from fundamental optical networking research to address critical infrastructure challenges, with a growing emphasis on machine learning applications for network optimization and power system monitoring. The recent focus on PMU networks, disaster resilience, and cyber restoration demonstrates his strategic pivot toward addressing national security and critical infrastructure protection challenges. UMass Dartmouth Scholar of the Year Award (2011) UMass Dartmouth Chancellor's Innovation in Teaching Award (2010-11) University of Texas at Dallas Computer Science Dissertation of the Year Award (2003-04) Multiple Best Paper Awards including IEEE GLOBECOM 2005, IEEE ANTS 2010, ONDM 2015, ONDM 2016, and IEEE ANTS 2016 Texas Telecommunications Engineering Consortium Fellowship (2002-03) Dr. Vokkarane has successfully mentored numerous graduate students who have contributed significantly to his research projects, with several going on to successful careers in academia and industry. His research has been consistently supported by major funding agencies including NSF, DOE, and USMC, with recent projects totaling over $5 million in funding. Current projects include Unified Post-Disaster Restoration Planning for Cyber-Physical Power Distribution Systems (ONR, $550K), CyberCARE: Northeast University Cybersecurity Center (DOE, $3.5M), and Flexible Spectrum Allocation in Next-Generation Optical Networks (NSF, $350K). He leads the Center for Smart Cyber-Physical Systems (SCyPS) and Advanced Computer Network Labs at UMass Lowell, where his research teams work on cutting-edge problems in network architecture, cyber-physical security, and infrastructure resilience. His labs collaborate extensively with national laboratories and industry partners to translate theoretical advances into practical solutions for real-world infrastructure challenges.
Esa Ollila serves as Associate Professor in the Department of Signal Processing and Acoustics at Aalto University, Finland, and holds an adjunct professorship in Statistics at the University of Oulu. His academic appointments include Academy of Finland Research Fellow (2010-2015) and prior senior research/lecturing roles at both institutions. His educational background features: M.Sc. in Mathematics, University of Oulu (1998) Ph.D. in Statistics (with honors), University of Jyväskylä (2002) D.Sc.(Tech) in Signal Processing (with honors), Aalto University (2010) Professor Ollila's research centers on statistical signal processing and robust statistical methodologies , with significant contributions to array processing, high-dimensional data analysis, and covariance matrix estimation. His work bridges theoretical statistics with practical applications in radar systems, wireless communications, and big data analytics, emphasizing robustness against outliers and computational efficiency in modern data-intensive environments. Current focus areas include compressed sensing, sparse approximation, and blind source separation techniques. Analysis of his 15 most recent publications (2024-2025) reveals three dominant trends: (1) robust covariance learning for massive random access systems, (2) integrated sensing and communications (ISAC) for 6G networks using advanced beamforming, and (3) geometric approaches to elliptical distributions in statistical inference. His work increasingly incorporates deep learning (GANs, graph neural networks) while maintaining strong foundations in classical signal processing theory. Key recognitions include: Academy of Finland Postdoctoral Fellowship (2004-2007) Academy of Finland Research Fellowship (2010-2015) His research has been supported through prestigious Academy of Finland grants totaling over a decade of continuous funding. Professor Ollila currently leads an active research group at Aalto University, supervising doctoral candidates and collaborating internationally with institutions including Princeton University (where he served as Visiting Post-doctoral Research Associate during 2010-2011). He maintains strong ties with the University of Oulu through his adjunct professorship and has contributed to EURASIP's Special Area Team on Theoretical and Methodological Trends in Signal Processing. The Esa Ollila Research Group focuses on cutting-edge challenges in statistical signal processing, with current projects spanning robust DOA estimation under non-Gaussian noise, covariance matrix learning for massive MIMO systems, and machine learning-enhanced radar-communication integration. The group actively develops open-source tools like the fitHeavyTail R package for heavy-tailed distribution modeling and maintains collaborations with industry partners in wireless communications.
HaoYu Wang is an Assistant Professor of Computer Science at SUNY Albany. His research focuses on parameter-efficient and data-efficient deep learning, particularly in natural language processing and machine learning, aiming to democratize AI access. He holds a Ph.D. from Purdue University's School of Electrical and Computer Engineering, a B.Eng. from the University of Electronic Science and Technology of China, and an MS from SUNY Buffalo. His work includes innovations like RoseLoRA (sparse low-rank adaptation for knowledge editing), LightLT (lightweight quantization for long-tail data), and FedKC (federated knowledge composition for multilingual NLU). He has received awards such as the Future Leaders in Data Science (2024) and Bilsland Dissertation Fellowship. Key research areas include parameter efficiency, cross-lingual NLU, and model fairness. Recent publications span topics like federated learning optimization, robust retrieval-augmented generation, and mitigating token overfitting in LLMs. He advises students on Ph.D. and intern roles, emphasizing CVs and research interests in applications.
Bruno Volckaert is a Professor in the Department of Information Technology at Ghent University and Senior Researcher at imec. He obtained his Master of Computer Science (2001) and PhD in Grid Computing Resource Management (2006) from Ghent University. His research focuses on distributed cloud systems for Smart Cities and Industry 4.0 applications. Volckaert's expertise spans: Reliable distributed cloud backend systems Autonomous optimization of cloud applications Cybersecurity through machine learning IoT data processing architectures Kubernetes-based container orchestration Edge-to-cloud continuum computing His publications demonstrate strong focus on: cloud-native technologies, Kubernetes optimization, cybersecurity frameworks, and distributed AI systems. Recent work emphasizes reinforcement learning for auto-scaling, secure edge computing, and intrusion detection systems. He has contributed to over 40 national/international research projects and authored 100+ publications. Current affiliations include leadership roles in: IDLab Research Unit (Ghent University) imec Research Center
Sandra González-Bailón is the Carolyn Marvin Professor of Communication at the University of Pennsylvania's Annenberg School for Communication and holds a secondary appointment in Sociology. As Director of the Center for Information Networks and Democracy (CIND), her research examines how communication networks shape information exposure, with implications for political engagement, mobilization dynamics, and news consumption patterns. Her methodological work bridges computational social science and political communication. Education M.S. from University of Oxford (2004) Ph.D. from University of Oxford (2007) Research Focus González-Bailón's research program investigates the intersection of technology and society, with emphasis on how digital networks transform political communication. Key areas include: Information diffusion : Studies how content spreads through social platforms during elections and crises Algorithmic curation : Examines how platform algorithms shape ideological segregation and exposure diversity Network dynamics : Maps communication patterns in online collective action and protest movements Her empirical work employs computational methods to analyze digital trace data at scale. Publication Trends Recent publications demonstrate sustained focus on social media's impact during democratic processes, particularly the 2020 U.S. election. Key thematic threads include: misinformation diffusion patterns, asymmetric polarization in news exposure, experimental analysis of platform deactivation effects, and methodological innovations in computational social science. Notable contributions appear in Science , Nature , and PNAS . Center Leadership As founding director of CIND, González-Bailón oversees research examining how digital technologies impact democratic resilience, with projects spanning misinformation analysis, network mapping of political discourse, and policy interventions for platform governance.
Francis Y. Yan is an Assistant Professor of Computer Science at the University of Illinois Urbana-Champaign (UIUC), holding an affiliate appointment in Electrical & Computer Engineering within the Grainger College of Engineering. He leads the Illinois Networked Systems and AI (NSAI) research group, focusing on building intelligent networked systems that are safe, robust, and performance-optimized through practical machine learning integration. Prior to joining UIUC in January 2025, he served as a Senior Researcher at Microsoft Research Redmond under Victor Bahl. His educational background includes: Ph.D. in Computer Science from Stanford University (2020), advised by Keith Winstein and Philip Levis B.S. in Computer Science (Yao Class) and B.A. in Economics from Tsinghua University (2015) Additional undergraduate studies at MIT Yan's research adopts a holistic approach to practical machine learning for networked systems, emphasizing judicious application rather than indiscriminate use. He builds real-world systems and research platforms to lay ML foundations, devises deployable algorithms using domain insights, and validates performance through extensive empirical evidence. His work consistently addresses operator concerns regarding ML deployment—focusing on safety, robustness, generalization, and efficiency—while strategically combining ML with classical networking and systems techniques. Analysis of his 15 most recent publications (2023-2025) reveals dominant themes in resource allocation for microservices (DeDe, Autothrottle), real-time video optimization (Mowgli, GRACE), and LLM-driven network algorithm design. His work bridges theoretical advances with industrial deployment, evidenced by platforms like Puffer (400,000+ users) and OpenNetLab that have become community standards for validating congestion control algorithms. His research has been recognized with top honors: USENIX NSDI Outstanding Paper Award (2024) for Autothrottle APNet Best Paper Award (2022) IRTF Applied Networking Research Prize (2021) USENIX NSDI Community Award (2020) USENIX ATC Best Paper Award (2018) for Pantheon Yan actively recruits master's and undergraduate researchers for his NSAI group, prioritizing self-motivated students for projects in networked systems and AI. His research is supported by industry collaborations (notably Microsoft) and manifests in deployable platforms like Puffer—which has enabled award-winning research at NSDI and SIGCOMM—and OpenNetLab for real-time communications. His work directly impacts production systems including Microsoft Teams and Bing. He founded and directs the Illinois Networked Systems and AI (NSAI) research group, which operates critical infrastructure including Puffer (a live TV service and research platform) and OpenNetLab. These platforms facilitate community-wide validation of novel algorithms, with Puffer alone supporting multiple best-paper awards at top conferences. Current workstreams span cloud resource management (Teal, Autothrottle, DeDe), low-latency video (Puffer, Tambur, Mowgli), and LLM-augmented systems (Nada, Designing Network Algorithms via LLMs).
Dr. Min Xu is a Courtesy Professor in the Computational Biology Department within the School of Computer Science at Carnegie Mellon University. His research focuses on advancing computer vision and machine learning for biomedical image analysis, particularly cellular cryo-electron tomography (Cryo-ET) and automated science video analysis. He leads a lab developing cutting-edge computational tools for structural biology and medical imaging. Key research directions include: High-resolution 3D Cryo-ET image analysis AI-driven medical image segmentation Few-shot learning for cryo-EM analysis Video analysis frameworks for laboratory automation Notable contributions include the AITom toolkit for Cryo-ET analysis and pioneering work in adapting foundation models for medical imaging tasks. His work has been published in top venues like CVPR, MICCAI, and Nature-associated journals. No academic awards or grants are explicitly listed in the provided text. He maintains an active lab focused on translating computational methods into impactful biomedical research tools.
