Christos Faloutsos is the Fredkin Professor of Computer Science at Carnegie Mellon University, with a courtesy appointment in Electrical and Computer Engineering. He holds a B.Sc. from the National Technical University of Athens and M.Sc./Ph.D. from the University of Toronto. His research focuses on data mining, graph analysis, fractals, and database systems. Notable contributions include foundational work on R-trees, graph mining laws (e.g., Kronecker graphs), and applications in medical imaging, network security, and fraud detection. Key projects include PEGASUS (petascale graph mining), fraud detection in online auctions (NetProbe), and tools for human trafficking analysis (TrafficVis). He has led NSF-funded projects on tensor mining, network anomaly detection, and bioinformatics. Over 300 refereed publications highlight his contributions across databases, data mining, and networks. Awards include the KDD Best Paper (2005, 2016), SIGMOD Test-of-Time Award, and recognition as a top nurturer in IT. His lab collaborations span the Parallel Data Lab (PDL), Machine Learning Department, and Computational Biology.
Anamaria Crisan is an Assistant Professor at the University of Waterloo, affiliated with the Insight Lab. Her research focuses on interdisciplinary work at the intersection of Human-Computer Interaction (HCI), Data Visualization, and Applied AI/ML. She explores human-centered approaches to AI/ML systems, visualization design for decision-making, and healthcare data science applications. Dr. Crisan holds a PhD in Computer Science from the University of British Columbia (2019), an MSc in Bioinformatics (2010), and a BComp in Biomedical Computing from Queen’s University (2008). Her educational background bridges computer science, biology, and healthcare informatics. Her research interests include responsible AI/ML systems, interactive visualization for data-driven decisions, and leveraging visualization in healthcare to improve outcomes. She emphasizes transparency, trustworthiness, and human alignment in AI technologies. Her work spans diverse applications such as genomic epidemiology, dashboard design, and ethical AI evaluation. Notable contributions include studies on human-AI collaboration, visualization linters, and scalable dashboard census methodologies. She has published widely in top-tier venues like IEEE VIS and ACM CHI. Dr. Crisan’s lab (UW Insight Lab) focuses on human-centered approaches to automating data science and improving visualization practices in critical domains like healthcare and public health.
Jonas Fischer is the head of the Explainable Machine Learning group at the Max Planck Institute for Informatics, Department of Computer Vision and Machine Learning. His research focuses on interpreting complex machine learning models, particularly in genomics and healthcare, aiming to enhance robustness and alignment with human decision-making. Prior to his role at MPI, he was a postdoctoral fellow at Harvard University's Department of Biostatistics, where he worked on interpretable models for gene regulatory systems in cancer. Education: PhD in Computer Science from Saarland University (2022), with a thesis titled More than the sum of its parts , exploring the intersection of pattern mining and deep learning. He has contributed to advancing methods in neural network pruning, federated learning, and low-dimensional embeddings (e.g., dtSNE, Mercat). His work bridges computational biology, data mining, and machine learning, with applications in DNA methylation analysis, graph-based differential networks, and biomedical informatics. Key research areas include: (1) Explainable AI and neural network interpretability, (2) Biomedical applications of machine learning (e.g., gene regulatory networks, cancer genomics), (3) Low-dimensional embeddings and visualization techniques, (4) Federated learning for privacy-preserving collaborative models, and (5) Pattern mining for error analysis in NLP and classification tasks. Publications span top venues like NeurIPS, ICLR, Bioinformatics, and Genome Biology. His group develops tools such as BONOBO for omics data integration and node2vec2rank for scalable graph analysis. He actively collaborates with biomedical researchers to address challenges in data-driven healthcare and precision medicine.
Adam Runions is a researcher in the Department of Computer Science at the University of Calgary, leading the MPG Partner Group in computational analysis of leaf development through collaborative work with Miltos Tsiantis. His group is embedded in the Graphics Cluster, focusing on interdisciplinary problems at the intersection of computer science and developmental biology. University of Calgary - Department of Computer Science MPG Partner Group (2022) Graphics Cluster affiliation His research explores computational modeling and analysis of plant form and development across multiple scales, integrating geometric modeling, physically-based simulation, and computer-aided design. Key themes include plant morphogenesis, self-organization of natural forms, and cross-disciplinary applications in computer graphics and animation. Recent publications emphasize plant development (leaf shape, bark patterning), mathematical modeling (auxin-driven patterning), and geometric techniques (subdivision surfaces, PUPs). Collaborations span institutions like the Max Planck Institute for Plant Breeding Research. Scientific Awards Marie Sklodowska-Curie Fellowship Best Paper Award (International Conference on Cyberworlds 2015) Best Student Paper Award (Computer Graphics International 2011) The group actively recruits BSc, MSc, and PhD students with backgrounds in computer science and mathematics for projects on plant form simulation and digital content creation. Research integrates evolutionary biology, biomechanical modeling, and computational techniques.
Michael A Osborne is Professor of Machine Learning at the University of Oxford and leads the Bayesian Exploration Lab . He serves as Director of the EPSRC Centre for Doctoral Training in Autonomous Intelligent Machines and Systems and co-directs the Oxford Martin AI Governance Initiative. His research focuses on Bayesian optimization, Gaussian processes, and probabilistic numerics with applications in quantum devices, battery modeling, and AI governance. Key Positions: Professor of Machine Learning, University of Oxford Official Fellow, Exeter College Co-founder of Mind Foundry Lead Researcher, Oxford Martin Programme on Technology and Employment Research Themes: Probabilistic modeling for quantum systems Uncertainty quantification in energy storage AI safety and societal impact analysis Automated experimental design Quantum device calibration Probabilistic numerical methods Technical Contributions: Bridging reality gap in quantum devices Efficient Bayesian quadrature techniques Personalized neurostimulation algorithms Automated measurement protocols Quantum-classical hybrid ML
Bei Wang Phillips is an Associate Professor in the School of Computing and a faculty member at the Scientific Computing and Imaging (SCI) Institute at the University of Utah. She holds a Ph.D. in Computer Science from Duke University and an undergraduate degree from the University of Bridgeport. Her research focuses on Topological Data Analysis (TDA), data visualization, computational topology, and machine learning, with applications in scientific data exploration and analysis. She has received prestigious awards including the NSF CAREER Award (2022) and the PECASE Award (2025). Her work spans projects funded by NSF, NIH, and DOE, including multiparameter TDA and topology-aware data compression. She advises numerous students and collaborates on interdisciplinary initiatives in astrophysics, climate science, and AI fairness. Education: Ph.D. in Computer Science, Duke University (2010) B.S. in Computer Science and Mathematics, University of Bridgeport (2003) Research Interests: Topological techniques for large-scale data analysis Integration of topological, geometric, and machine learning methods Applications in visualization, bioinformatics, and network analysis Key Projects: NSF-funded TDA research (DMS-2301361, OAC-2313124) DOE project on topology-preserving data compression Collaborations with NASA, Argonne National Lab, and Carnegie Institution of Washington Awards: Presidential Early Career Award for Scientists and Engineers (2025) NSF CAREER Award (2022) DOE Early Career Research Program (2020) Advising and Grants: Mentored over 30 students and postdocs Recipient of multiple NSF and DOE grants totaling millions
Alexis Battle is an Associate Professor at Johns Hopkins University with appointments in Biomedical Engineering , Computer Science , and Genetic Medicine (secondary). She directs the Malone Center for Engineering in Healthcare and serves as Deputy Director of the Data Science and AI Institute . Educated at Stanford University (PhD in Computer Science, 2013), Battle transitioned to academia after leadership roles at Google. Research Focus: Battle’s work bridges genomics and machine learning , emphasizing the impact of genetic variation on human health. Her lab develops tools like Watershed to predict functional effects of rare variants, aiming to enhance rare disease diagnosis. Key themes include non-coding DNA analysis , personalized genomics , and systems biology , with applications in cardiovascular disease and neurodegenerative disorders . Publications & Awards: Over 60 peer-reviewed articles in journals like Nature , Science , and Genome Biology , with recent emphasis on single-cell transcriptomics , multiomics integration , and telomere biology . Recipient of the President’s Frontier Award (2022), Microsoft Investigator Fellowship (2019), and Searle Scholar (2016). Scientific Awards: 2022 President’s Frontier Award 2019 Microsoft Investigator Fellowship 2019 Johns Hopkins Discovery Award 2017 Johns Hopkins Catalyst Award 2016 Searle Scholar Advising & Funding: Mentors 11 PhD students, 3 undergraduates, and postdoctoral fellows. Her research is funded by NIH, Searle Scholars, and institutional grants. The Battle Lab collaborates on projects like the GTEx Consortium , focusing on gene regulation and clinical genomics .
Pierre Vandergheynst is a Full Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) in the Department of Electrical Engineering, with a courtesy appointment in Computer and Communication Sciences. He serves as EPFL’s Vice-Provost for Education since 2015 and leads the Signal Processing Laboratory 2 (LTS2). His research spans harmonic analysis, sparse approximations, mathematical data processing, and applications in signal/image processing, computer vision, machine learning, and graph-based data analysis. PhD in Mathematical Physics (1998), Université catholique de Louvain Postdoctoral Researcher at EPFL (1998-2001) Assistant Professor at EPFL (2002-2007) His research explores geometry/symmetry in high-dimensional data, redundant dictionaries for dimensionality reduction, and computational harmonic analysis on manifolds. Recent work focuses on protein structure modeling, geometric deep learning, and graph-based signal processing. Key article trends include graph neural networks for protein analysis, geometric deep learning in neuroscience, and structured knowledge priors in neural models. His 2023-2025 publications emphasize interpretable AI, long-range dependencies in graphs, and molecular representation learning. Scientific Awards: IEEE Signal Processing Magazine Best Paper Award (2023) Signal Processing Society Best Paper Award (2022) Apple ARTS Award (2007) De Boelpaepe Prize, Royal Academy of Sciences of Belgium (2009-2010) He has supervised over 30 PhD theses and contributed to foundational work in graph signal processing, compressive sensing, and geometric deep learning. His lab develops tools for data science on non-Euclidean structures, with applications in medicine, astronomy, and wireless systems.
Pieter Abbeel is a Professor in the Department of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley. He leads the Berkeley Robot Learning Lab and co-directs the Berkeley Artificial Intelligence Research (BAIR) Lab. His work focuses on advancing AI and robotics through deep reinforcement learning, imitation learning, and unsupervised learning, with applications in automation, healthcare, and education. Abbeel's research also explores the societal implications of AI and its potential to revolutionize other scientific and engineering fields. Education: Ph.D. in Computer Science, Stanford University (2008) M.S. in Electrical Engineering, KU Leuven, Belgium (2000) Research Interests: Robotics, AI, Machine Learning, Reinforcement Learning, Autonomous Systems, and Applications in Surgery, Manufacturing, and Education. Recent Article Trends: Focus on multimodal learning, robot manipulation, protein structure prediction, and scalable AI systems. Key areas include sim-to-real transfer, embodied AI, and foundation models for decision-making. Awards & Honors: IEEE Kiyo Tomiyasu Award (2022) ACM Prize in Computing (2021) IEEE Fellow (2018) MIT Tech Review TR35 (2011) Advising & Grants: Advises startups and has received grants from NSF, DARPA, and industry partnerships. Notable students include those advancing robotics, reinforcement learning, and bioAI. Labs & Initiatives: Berkeley Robot Learning Lab, BAIR Lab, and collaborations with the Center for Human-Compatible AI (CHAI). Founded companies include Gradescope, Covariant, and Berkeley Open Arms.
Qianwen Wang is a tenure-track Assistant Professor in the Computer Science department at the University of Minnesota, Twin Cities. Her research combines interactive visualization with interpretable machine learning to foster intuitive, efficient, and reliable Human-AI collaboration. She actively seeks motivated students, research assistants, and interns to join her dynamic research team at UMN CS. Dr. Wang's research focuses on three primary themes: Human-AI Collaboration, where she designs tools to facilitate Human-AI interaction; Automatic & Intelligent Visualization, where she develops techniques to make visualizations accurately interpreted and easily used; and VIS+(X)AI in Biomed/Healthcare, where she studies how visualization and explainable AI can promote scientific discoveries in biomedicine and healthcare. Her work has made significant contributions to visualization, human-computer interaction, and bioinformatics, with particular applications in biomedical knowledge graphs and single-cell omics analysis. Her recent publications demonstrate a strong trend toward integrating visualization with large language models and graph neural networks for biomedical applications. She has published extensively at top venues including IEEE VIS, ACM CHI, and Nature Medicine, with a focus on making AI systems more interpretable and useful for domain experts, particularly in healthcare contexts. Her work often bridges theoretical advances in visualization with practical applications in genomics and healthcare. Two IEEE VIS Honorable Mention Awards (2022, 2024) Best Paper Award from IMLH@ICML 2021 Two Best Abstract Awards from BioVis ISMB (2021, 2022) HDSI Postdoctoral Research Fund Award Research covered by MIT News and Nature Technology Features Dr. Wang actively contributes to the academic community through service roles including General Chair for ISMB BioVis, VisNotes and Poster Chair for IEEE PacificVis, and Program Committee member for IEEE VIS, ACM CHI, and ACM IUI. Her research has been supported by various grants that enable her team to develop innovative visualization techniques and explore their practical applications in biomedical domains. Her research group maintains an active presence in the visualization and HCI communities, with members participating in major conferences and workshops. The lab focuses on creating tools that bridge the gap between complex AI models and human understanding, with particular emphasis on making these technologies accessible and useful for domain experts in biomedical research.
Gail E. Kaiser is a Professor of Computer Science and the Director of the Programming Systems Laboratory (PSL) in the Computer Science Department at Columbia University. She has been with Columbia University since 1985, becoming a full Professor in 1998. Prof. Kaiser's research spans software engineering, program analysis, software testing, and software security, with recent focus on addressing challenges in AI/ML systems testing and security. Prof. Kaiser received her PhD in Computer Science from Carnegie Mellon University in 1985 and her ScB in Computer Science and Engineering from MIT in 1979. Her dissertation at CMU was titled "Semantics for Structure Editing Environments" under advisor Nico Habermann, and at MIT she completed "Automatic Extension of an Augmented Transition Network Grammar for Morse Code Conversations" under advisor Al Vezza. Prof. Kaiser's research interests primarily focus on software engineering following a systems building approach, with recent emphasis on static and dynamic program analysis techniques to improve software reliability and security. Since 2005, she has investigated testing "non-testable" programs, particularly in machine learning, data mining, and scientific computing applications where traditional testing oracles are insufficient. She has developed novel techniques and tools for detecting bugs and verifying repairs in complex systems. Concurrently, she has worked on collaboration environments for computational scientists, creating knowledge sharing and domain-aware environments to support scientific workflows. Prof. Kaiser's recent publications demonstrate a strong focus on the intersection of software engineering and artificial intelligence. Her work addresses critical challenges in testing AI systems, code understanding through deep learning, vulnerability detection, and educational tools for computational thinking. There's a clear evolution from traditional software engineering topics toward AI/ML applications, with particular emphasis on metamorphic testing for non-testable systems, code similarity analysis, and educational applications. Prof. Kaiser has received numerous prestigious awards throughout her career: Distinguished Journal Award (10 Years) from 18th IEEE International Conference on Software Testing, Verification and Validation (ICST), April 2025 Best Research Paper Award at 24th IEEE International Conference on Source Code Analysis & Manipulation (SCAM), October 2024 Distinguished Reviewer Awards for ASE 2024 and FSE 2024 ACM SIGSOFT Distinguished Paper Award for "CONCORD: Clone-aware Contrastive Learning for Source Code", July 2023 Best Student Paper Award at ICCE 2021 Multiple ACM SIGSOFT Distinguished Paper Awards dating back to 2014 Presidential Young Investigator in Software Engineering and Software Systems from NSF (1988-1993) Prof. Kaiser has chaired Columbia's doctoral program since 1997 and served on editorial boards including IEEE Internet Computing and as a founding associate editor of ACM Transactions on Software Engineering and Methodology. Her lab has been continuously funded by major agencies including NSF, NIH, DARPA, ONR, NASA, and numerous companies. Current grants include significant NSF funding for secure containers architecture, learning semantics of code for software assurance, and finding semantic security bugs. As Director of the Programming Systems Laboratory (PSL), Prof. Kaiser leads research in software systems, program analysis, and software testing. The lab has developed numerous tools and techniques for software reliability and security, with recent focus on challenges in AI/ML systems. Her work bridges theoretical foundations with practical applications, often resulting in deployable tools that address real-world software engineering challenges.
Wengong Jin is an Assistant Professor at the Khoury College of Computer Sciences, Northeastern University, and a visiting research scientist at the Eric and Wendy Schmidt Center at the Broad Institute. He holds a PhD from MIT CSAIL, advised by Prof. Regina Barzilay and Prof. Tommi Jaakkola. Research Interests: His work focuses on geometric and generative AI models for drug discovery, biology, and chemical engineering. Key areas include equivariant neural networks (e.g., FAFormer), diffusion models for binding energy prediction, antibody/enzyme design (RefineGNN, SurfPro), and molecular design through graph neural networks (Junction Tree VAE). He also explores domain generalization and systems for autonomous molecular discovery. Publications: His research has been published in top venues like NeurIPS, ICLR, ICML, Nature, Science, and Cell. Recent breakthroughs include discovering novel antibiotics using explainable AI and designing synergistic drug combinations for cancer treatment. Awards: He has received the BroadIgnite Award, Dimitris N. Chorafas Prize, and MIT EECS Outstanding Thesis Award for his contributions to computational biology and AI-driven drug discovery. Teaching: Currently teaches a PhD seminar on AI for Science, focusing on integrating machine learning into scientific discovery processes.
John Paisley is an Associate Professor of Electrical Engineering at Columbia University's Fu Foundation School of Engineering and Applied Science, and a member of Columbia's Data Science Institute (DSI). He holds a B.S., M.S., and Ph.D. in Electrical and Computer Engineering from Duke University (2004-2010), followed by postdoctoral research in Computer Science at Princeton University and UC Berkeley. His research focuses on Bayesian models, posterior inference techniques for Big Data, and applications in data analysis, recommendation systems, information retrieval, and compressed sensing. He has pioneered methods like Bayesian Gaussian Process ODEs and Double Normalizing Flows, with recent work emphasizing uncertainty quantification in environmental modeling and neuroimaging analysis. His collaborative workflows (e.g., bneR ) address air pollution exposure and PM2.5 concentration uncertainties, combining Bayesian nonparametric ensembles with geospatial data. He has also developed frameworks for neural network interpretability, image denoising, and compressed sensing MRI. Paisley's work bridges statistical theory and applied machine learning, with applications in healthcare, environmental science, and geophysics. His academic contributions include over 50 publications since 2016, spanning topics like deep metric learning, adversarial learning, and variational inference optimization. He maintains an active research group and serves on editorial boards for machine learning and signal processing journals.
Itsik Pe'er is a Full Professor and Vice-Chair in the Department of Computer Science at Columbia University's Fu Foundation School of Engineering & Applied Science, and holds a joint appointment as Professor of Systems Biology at the Vagelos College of Physicians and Surgeons. His research focuses on computational methods in human genetics, including genetic variation analysis, disease association studies, and algorithm development for genomic data. He leads the Itsik Pe'er Lab of Computational Genomics, which develops tools like Xplorigin, Germline, and SEACells to address challenges in genomics and medical research. His work spans machine learning applications in healthcare, microbiome analysis, and cancer genomics. Notable contributions include studies on hypertensive disorders in pregnancy, bias correction in predictive models, and the development of non-Euclidean learning libraries like Manify. Pe'er has advised students including Vladimir Vacic, Anat Kreimer, and Arthi Ramachandran, and collaborates on grants addressing genetic epidemiology and computational biology. His lab's location is in the Computer Science Building at Columbia's Morningside Campus.
Owen R. White is a Professor in the Department of Epidemiology & Public Health at the University of Maryland School of Medicine, serving as Associate Director of the Institute for Genome Sciences and Associate Director of Research Collaboration & Development. He leads a team of 25 scientists and engineers developing genomic annotation pipelines and data analysis tools for state-of-the-art research in microbiome and multi-omic studies. His academic background includes: BS in Biotechnology from the University of Massachusetts (1985) PhD in Molecular Biology from New Mexico State University (1992) Postdoctoral Fellowship in Genome Informatics at the Institute for Genomic Research (TIGR) (1994) Dr. White's research spans bioinformatics, genomics, transcriptomics, and metagenomics with emphasis on data management, metadata standards, ontologies, and cloud systems. His work has been foundational for large-scale initiatives like the Human Microbiome Project (HMP) and Integrative Human Microbiome Project (iHMP), generating over 50,000 datasets totaling 10 terabytes of multi-omic data. Analysis of his recent publications reveals a strong trend toward neuroscience multi-omics (BRAIN Initiative), cloud-based data infrastructure, and ethical data sharing frameworks. His work consistently bridges microbiome research with emerging fields like single-cell analysis and Alzheimer's disease biomarker discovery through integrated data platforms. Notable awards include: Benjamin Franklin Award for Open Access in the Life Sciences (2015) Kumho Science International Award in Plant Molecular Biology and Biotechnology (2001) As Principal Investigator for major NIH-funded centers, he has secured sustained support for the HMP Data Analysis and Coordination Center and iHMP Data Coordination Center. His team's work combines fee-for-service models with collaborative research funding to maintain cutting-edge genomic analysis capabilities. The Institute for Genome Sciences houses his computational team responsible for developing production annotation pipelines, database systems, and visualization tools that serve researchers across the University of Maryland School of Medicine and national consortia.
