Emma Pierson is an Assistant Professor of Computer Science at the University of California, Berkeley, affiliated with the Berkeley Artificial Intelligence Research Lab (BAIR) , Computational Precision Health , and the Center for Human-Compatible AI . She focuses on developing data science and machine learning methods to address issues in healthcare equity and social inequality . Her work includes studies on race adjustments in clinical algorithms, migration patterns, and leveraging LLMs for health equity. Education: Ph.D. in Computer Science from Stanford University (2020), Master’s in Statistics from the University of Oxford. Prior roles include Assistant Professor at Cornell Tech, Senior Researcher at Microsoft Research, and data scientist at 23andMe and Coursera. Research Interests: Her research spans fair clinical prediction , sparse autoencoders , health disparities , and algorithmic fairness . Notable projects include the MIGRATE dataset for granular migration analysis and studies on policing disparities. Awards: NSF CAREER Award, Rhodes Scholarship, Hertz Fellowship, MIT Technology Review 35 Innovators Under 35, and Samsung AI Researcher of the Year. She writes a statistics blog ( Obsession with Regression ) and contributes to media outlets like The New York Times and FiveThirtyEight . Labs/Teams: Leads the MIGRATE project, a collaboration to analyze fine-grained migration data. Engages in interdisciplinary work across AI, healthcare, and social science.
Tanya P. Garcia, PhD is an Associate Professor of Biostatistics at the Gillings School of Public Health and Research Faculty in the UNC Neurology Huntington Disease Program at the University of North Carolina at Chapel Hill . She leads the Methods for INcomplete Data (MIND) Lab , focusing on statistical methods for handling censored, missing, or incomplete data in neurodegenerative disease progression studies. Education: PhD in Statistics, Texas A&M University MS in Statistics, University of Western Ohio MS in Industrial Engineering and Operations Research, UC Berkeley Research Interests: Specializing in High-Dimensional Variable Selection , Longitudinal Data Analysis , and Neurodegenerative Disease Modeling , her work develops reproducible statistical methods for Huntington's disease progression, improving clinical trial design and biomarker identification. Scientific Awards: American Statistical Association Fellow (2024) Landis Award for Outstanding Mentorship (2024) Roy R. Kuebler Award (2024) Gertrude M. Cox Award (2024) Leadership & Mentorship: Director of the MIND Lab, Chair-Elect of the Biometrics Section of ASA, and Tyson Academic Leadership Fellow (2023–2024). Her lab alumni have secured prestigious positions at institutions like Wake Forest University and Baylor University.
Kathryn Roeder is the UPMC University Professor of Statistics and Life Sciences at Carnegie Mellon University (CMU), affiliated with the Dietrich College of Humanities and Social Sciences and the Departments of Statistics & Data Science and Computational Biology. Her research focuses on developing statistical methods for genetic and genomic data, particularly in identifying autism risk genes and analyzing single-cell multi-omic data. She earned her Ph.D. in Statistics from Penn State University and has been at CMU since 1994, previously serving as Vice Provost for Faculty (2015–2019). Education: Ph.D. in Statistics, Penn State University (1988) B.S. in Wildlife Resources, University of Idaho (1982) Research Interests: Her work integrates modern statistical techniques (high-dimensional statistics, machine learning, networks) to study complex diseases like autism and schizophrenia. Recent efforts include tools for analyzing single-cell RNA-seq and proteomic data, such as UNICORN, DAWN, and SCEPTRE. Key Awards: COPSS Distinguished Achievement Award (2020) National Academy of Sciences Member (2019) COPSS Presidents’ Award (1997) AAAS Fellow (2020) Advising & Grants: She has advised over 20 Ph.D. students, many contributing to landmark studies in autism genetics. Her grants include NIH funding for projects like the Autism Sequencing Consortium. Current research teams focus on computational biology and statistical genetics. Labs & Collaborations: Her lab develops software tools (e.g., TADA, MIND) and collaborates with the Autism Sequencing Consortium and iPSYCH-BROAD Consortium on large-scale genomic studies.
Dr. Xiaoxiao Li is an Assistant Professor in the Electrical and Computer Engineering Department at the University of British Columbia (UBC), with joint appointments in Computer Science (Associate Member) and the School of Medicine at Yale University (Adjunct Assistant Professor). She is also a Canada CIFAR AI Chair and Canada Research Chair (Tier II) in Responsible AI. Her research focuses on enhancing trustworthiness, fairness, and efficiency in AI algorithms and foundation models, particularly in healthcare applications. Education: B.S. (Honors) in Zhejiang University (2015), Ph.D. in Biomedical Engineering from Yale University (2020), Postdoc at Princeton University (2020-2021). She leads the Trusted and Efficient AI (TEA) Lab at UBC, which develops algorithms for federated learning, medical imaging analysis, and interpretable AI systems. Research interests include federated learning, generative models, medical image analysis, AI fairness, and graph-based methods for neuroimaging. Recent projects include GMValuator (data valuation for generative models), FairMedFM (fairness benchmarking in medical AI), and FedTextGrad (textual gradient-based FL optimization). Grants: Canada Foundation for Innovation Grant (2023), UBC Green Lab Fund (2023), Vector Institute funding Teaching: Courses on machine learning, federated learning, and AI ethics at UBC Awards & Recognition: Best Paper Award at FL@FM WWW 2024, Editorial Board Member of Medical Image Analysis , multiple top-tier conference acceptances (NeurIPS, ICLR, CVPR, MICCAI). Lab & Teams: TEA Lab collaborates with industry and hospitals to translate AI research into clinical tools. Current projects address AI fairness in healthcare, federated learning for medical data, and multimodal medical analytics.
Maria Apostolaki is an Assistant Professor of Electrical and Computer Engineering at Princeton University. Her research focuses on designing secure, reliable, and high-performance networked systems, integrating expertise in networking, security, blockchain, and machine learning. She holds a Ph.D. from ETH Zurich (2021) and an M.Eng. from the National Technical University of Athens (2015). Education: Ph.D., ETH Zurich 2021 | M.Eng., NTUA 2015 Her work investigates challenges in network security, distributed systems, and adversarial resilience. Notable contributions include SABRE (protecting Bitcoin from routing attacks) and TANGO (collaborative route control). She has advised multiple graduate students in computer science and engineering. Key honors include the NSF CAREER Award and the IRTF/IETF Applied Networking Research Prize (2018). Her lab explores cutting-edge topics like contextual robustness in ML-driven network functions and formal methods for secure systems. Advising: 6 advisees in COS/ECE domains Grants: NSF CAREER Award, innovation grants for AI/robotics Her TANGO framework enables secure cross-domain routing, while recent work addresses vulnerabilities in Ethereum PoS and BGP routing protocols.
Christopher M. Overall is a Full Professor at the University of British Columbia in the Faculty of Dentistry, Department of Oral Biological and Medical Sciences . He is also a Principal Scientist at the Centre for Blood Research and holds associate memberships in UBC's Biochemistry & Molecular Biology , Obstetrics and Gynecology , and Bioinformatics Graduate Program departments. As a Canada Research Chair Laureate , he pioneered the field of degradomics to study proteases in vivo. B.D.S., University of Adelaide Ph.D., University of Toronto Postdoctoral Fellowship, UBC (with Nobel Laureate Michael Smith) Dr. Overall’s research focuses on protease proteomics and systems biology , particularly degradomics to analyze protease substrates in diseases like COVID-19 and immunodeficiency . His work on matrix metalloproteinases has revealed new therapeutic strategies for inflammatory diseases and cancer . His 15 most recent articles (2015–2008) demonstrate expertise in TAILS proteomics , protein terminomics , and protease network analysis with applications in arthritis , antiviral immunity , and precision medicine . Scientific Awards 2022 Helmut Holzer Award 2018 Royal Society of Canada Fellow 2014 Tony Pawson Canadian Proteomics Award 2013 IADR Distinguished Scientist Award Dr. Overall has mentored 61 trainees , including 9 full professors with department chairs, and received the UBC John McNeill Mentorship Award (2023). He leads the HUPO Chromosome-centric Human Proteome Project and consults for Genentech and Novartis .
Soumya Dutta is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur (IITK), where he leads the INSIGHT: Intelligent Scientific and Visual Computing of Big Data Research Group. He joined IIT Kanpur in October 2022 after working as a Scientist II at Los Alamos National Laboratory (LANL) from July 2019 to August 2022, and previously as a Postdoctoral Research Associate at LANL from June 2018 to July 2019. His educational background includes a Ph.D. and M.S. in Computer Science and Engineering from The Ohio State University (2011-2018), where he was part of the GRAVITY research group, and a B.Tech. in Electronics and Communication Engineering from West Bengal University of Technology, India (2005-2009). Research Interests: Dr. Dutta's research focuses on the intersection of machine learning, visual computing, big data, and high-performance computing. His primary research areas include Machine Learning for Visual Computing and Image Analysis, Big Data Visualization and Analytics, Data Science and HPC, Machine Learning for Scientific Computing, and Explainability and Interpretability of AI Models. His work addresses various big data characteristics including the 5 Vs: Volume, Velocity, Variety, Veracity, and Value. He develops techniques that make complex machine learning models more interpretable and explainable, enabling their effective adoption in real-life applications across scientific domains, social media, IoT, healthcare, and industry applications. Dr. Dutta's research group has secured multiple funded projects including: DAVi: An Intelligent Data Analytics and Visualization Framework (funded by ISRO), Intelligent Visual Computing of Extreme-scale Data for Accelerating Scientific Discovery (IIT Kanpur Initiation Grant), Enabling Interactive Big Data Analytics and Visualization at Exascale (SERB), Development of AI-Enabled National Portal for Efficient Search of Missing People (C3iHub), and Proactive and Generalized Deepfake Defense Mechanisms (C3iHub). Best Reviewer, Honorary Mention Award for IEEE Transactions on Visualization & Computer Graphics (TVCG), 2021 Best Paper Award at ISAV 2021, co-located with Supercomputing (SC) LAAP Award at Los Alamos National Laboratory, 2021 Best Paper Award at TopoInVis 2019 Best Paper Award at ISAV 2018, co-located with Supercomputing (SC) Best Poster Award in 12th Annual CSE Student Poster Exhibition, The Ohio State University, 2018 Best Poster Award in 11th Annual CSE Student Poster Exhibition, The Ohio State University, 2017 Best Paper Honorable Mention Award at IEEE Visualization Conference (IEEE VIS) 2016 Dr. Dutta actively mentors a large group of students including Ph.D., M.Tech., and B.Tech. students. His current Ph.D. students include Shanu Saklani, Sankhadeep Bhowmick, Ananya Chaturvedi, Arpita Santra, Anubhav Dixit (co-supervised), and Robin Shah. He has supervised numerous M.Tech. students with thesis topics ranging from uncertainty-aware neural networks to deepfake detection. Dr. Dutta currently teaches courses including CS360 - Introduction to Computer Graphics and CS661 - Big Data Visual Analytics. The INSIGHT research group collaborates internationally with researchers from Meta, Oak Ridge National Laboratory, and National Taiwan Normal University. The group's work focuses on building machine learning and data science-based solutions to analyze large-scale multifaceted data in a scalable way, enabling interactive and interpretable analytics of complex data from scientific simulations, social media, IoT, healthcare, and other application domains.
Paolo Ienne is a Professor at the Swiss Federal Institute of Technology in Lausanne (EPFL), where he leads the Processor Architecture Laboratory (LAP) within the School of Computer and Communication Sciences. His research focuses on advancing reconfigurable computing systems through innovative FPGA architectures and high-level synthesis methodologies. His primary research domains include reconfigurable computing, FPGA architecture design, dynamically scheduled dataflow circuits, and hardware acceleration techniques. Recent work emphasizes memory system optimization for FPGAs, formal verification of circuit transformations, and rapid C-to-hardware compilation flows. He has pioneered approaches for handling thousands of outstanding memory misses in FPGA accelerators and developed novel techniques for switch-block exploration without explicit pattern enumeration. Analysis of his 2023-2025 publications reveals a strong trend toward practical FPGA deployment challenges, with increasing focus on HBM integration, virtual memory systems for PCIe-attached devices, and formally verified circuit transformations. His work consistently targets real-world bottlenecks in high-level synthesis toolchains while maintaining theoretical rigor in dataflow architecture design. Professor Ienne's laboratory receives support from the Swiss National Science Foundation and industry partners including Huawei, enabling cutting-edge research in FPGA-based acceleration. His collaborative network spans major semiconductor companies and academic institutions worldwide, with frequent co-authorship on conference proceedings and journal publications in IEEE and ACM venues.
Shu Yang is an Associate Professor of Statistics at North Carolina State University (NC State), specializing in causal inference, missing data analysis, and biostatistics. She holds a Ph.D. in Applied Mathematics and Statistics from Iowa State University and has held roles including Postdoctoral Fellow at Harvard University and Assistant Professor at NC State. Her research focuses on developing statistical methods for observational and clinical studies, particularly in healthcare and environmental applications. Education: Ph.D. in Applied Mathematics and Statistics from Iowa State University (2014) B.Sc. in Mathematics and Applied Mathematics from Beijing Normal University (2009) Research Interests: Dr. Yang’s work addresses challenges in causal inference, including longitudinal data analysis, missing data imputation, and high-dimensional statistics. She applies these methods to environmental health, cardiovascular diseases, HIV infection, and cancer research. Her team also explores spatial statistics and data integration techniques. Awards: 2025: Think, Collaborate & Do Ideation Award 2024: COPSS Emerging Leader Award, Cavell Brownie Mentoring Award 2022: University Faculty Scholar 2018: Ralph E. Powe Junior Faculty Enhancement Award Grants & Advising: She leads funded projects on causal inference methods in environmental health, sepsis detection, and marine protected areas. She advises over 20 Ph.D. students and postdocs, focusing on causal methods, data integration, and healthcare analytics.
Harri Lähdesmäki is an Associate Professor (tenured) at the Department of Computer Science, Aalto University, where he leads the Computational Systems Biology research group. His work focuses on probabilistic machine learning and deep generative models with applications in biomedicine and molecular biology. Key Research Interests: Probabilistic machine learning, deep generative models, computational biology, bioinformatics, longitudinal data modeling Contact: harri.lahdesmaki@aalto.fi | Konemiehentie 2, 02150 Espoo, Finland His recent publications highlight advancements in: Gaussian process priors for scalable deep generative models Single-cell analysis of immune repertoires in leukemia and diabetes Probabilistic deconvolution methods for RNA-seq data Epigenetic analysis using hidden Markov and mixed models Transformer-based survival prediction and missing data handling Harri’s work integrates mechanistic modeling with Bayesian inference, particularly applied to immunology, cancer biology, and early disease prediction.
Aapo Hyvärinen is a Professor of Computer Science at the University of Helsinki , affiliated with the Helsinki Institute for Information Technology and the Helsinki Probabilistic Machine Learning Lab . He previously held the position of Professor of Machine Learning at the Gatsby Computational Neuroscience Unit, University College London (2016-2019). Education : Undergraduate Mathematics at University of Helsinki, Vienna, and Paris; Ph.D. in Information Science from Helsinki University of Technology (1997) His research focuses on machine learning and computational neuroscience , particularly: Independent Component Analysis (ICA) Natural Image Statistics Causal Representation Learning Neural Signal Processing Applications to brain imaging (MEG, CryoEM) Recent publications emphasize causal discovery , identifiable machine learning , and nonlinear ICA . Key projects include: VETURI (AI for health) DIGIMIND (AI in mental health) CIFAR grants (2022-2025) Scientific awards : Highly Cited Researcher (2010) He serves as Action Editor for the Journal of Machine Learning Research and Neural Computation , and has held Area Chair roles at NeurIPS, ICML, ICLR, AISTATS, and UAI conferences. His work bridges theoretical machine learning with neuroscience and philosophical implications of artificial intelligence .
Katia Bianchini is a Research Fellow at the Max Planck Institute for Social Anthropology in Halle, Germany, with a focus on the intersection of law and anthropology in refugee and immigration contexts. She holds a PhD in Law (University of York), an LLM in Comparative Laws (University of San Diego), and a Law degree (Università di Pavia). Her work bridges empirical legal analysis with anthropological methodologies to address issues such as statelessness, asylum adjudication, and the rule of law in migration policies. Education PhD in Law, University of York (2011–2016) LLM in Comparative Laws, University of San Diego (1999–2000) Law degree, Università di Pavia (1992–1998) Bianchini’s research interests include refugee law , immigration law , statelessness , EU law , and human rights law . Her current project examines legislation and responses to missing and deceased sea migrants in Italy , analyzing legal frameworks for search, identification, and burial practices, particularly along the Central Mediterranean Route. She emphasizes the importance of legal anthropology to understand gaps between abstract law and its real-world application. The trends in her publications reflect a focus on statelessness determination procedures , asylum law (notably witchcraft-based claims), humanitarian visas , and rule of law challenges in migration contexts. Her work spans journal articles , book chapters , and monographs , often combining legal analysis with empirical data from interviews and fieldwork. Scientific Awards Erasmus scholarship, University of Leuven, Belgium (1997–1998) Grants and Professional Activities include visiting researcher roles at Fordham University and the University of Oxford, as well as editorial work for the Statelessness and Citizenship Review and the CUREDI Database . She has presented extensively on topics like statelessness in the UK , cultural diversity in asylum adjudication , and NGO criminalization in Mediterranean rescue operations .
Manuel Kaufmann is a Lecturer in the Department of Computer Science at ETH Zürich. His work focuses on advanced 3D human motion capture, sensor-based systems, and computer vision applications. He is affiliated with the Institute of Informatics (inf.ethz.ch) and contributes to research in real-time motion tracking, dataset development, and machine learning integration for human-robot interaction. Research interests include holistic human-scene reconstruction from monocular videos, gaze estimation using EEG signals, and expressive avatar creation. His projects emphasize practical applications in robotics, sports analytics, and biomedical engineering, often leveraging electromagnetic and inertial sensors for high-precision data acquisition. His publications reflect a trend toward multi-modal data fusion, real-world dataset creation (e.g., WorldPose, ARCTIC), and addressing challenges in loose garment modeling (Reloo). These efforts aim to improve markerless motion capture, crowd analysis, and human-robot collaboration. No scientific awards or grants are explicitly listed. He has no documented advisees, though his research may involve collaborations with students or teams. His office is located at OAT X 23, Andreasstrasse 5, Zürich, Switzerland, and contact details include a phone number and professional email.
Padhraic Smyth is a Distinguished Professor and Hasso Plattner Endowed Chair in Artificial Intelligence at the University of California, Irvine (UCI), holding joint appointments in the Department of Computer Science and Department of Statistics. He leads the DataLab research group, focusing on machine learning, AI, and their applications in climate science, healthcare, and education. His research spans probabilistic modeling, deep learning, and human-AI collaboration. Education: PhD in Electrical Engineering from the California Institute of Technology (1988), MSEE (1985), and BEng (1984). Prior to UCI, he worked at NASA's Jet Propulsion Laboratory (1988–1996). Research Interests: Machine learning, AI, pattern recognition, Bayesian methods, climate science applications, algorithmic fairness, and human-AI interaction. He has published over 200 papers and co-authored textbooks like Modeling the Internet and the Web . Awards: ACM Fellow, IEEE Fellow, AAAI Fellow, AAAS Fellow, and ACM SIGKDD Innovation Award recipient. He has held leadership roles in UCI's Center for Machine Learning and Data Science. Key Projects: Human-AI collaboration frameworks, robustness in deep learning, climate modeling using spatio-temporal data, and AI fairness with missing attributes. Collaborates with institutions like NASA and industry partners (e.g., Google, eBay). Labs/Teams: Director of UCI’s Data Science Initiative and HPI Research Center in Machine Learning. Supervises a vibrant PhD program with over 30 alumni in academia and industry.
Joey W Huston is a Professor at the Department of Physics & Astronomy, Michigan State University, and a Visiting Professor at the Institute for Particle Physics Phenomenology, Durham University. He has over 600 publications with >30,000 citations, including 9 papers with >500 cites each. Positions: MSU Research Foundation Professor (1998-present), Visiting Professor at Durham (2003-present) Education: Ph.D. (1983) and B.S. (1976) from University of Rochester and Carnegie-Mellon University His research focuses on Quantum Chromodynamics (QCD) , Parton Distribution Functions , and Jet Physics . He contributes to Higgs Boson studies, Supersymmetry , and Dark Matter Searches via the ATLAS experiment at the LHC. Recent articles (2025) highlight advancements in Jet Flavour Tagging , Top-Quark Mass Measurement , and Exotic Higgs Decays , alongside computational innovations like Neural Simulation-Based Inference and Cloud Resource Optimization . Scientific Awards: APS Fellow (2025), Distinguished Visitor by Scottish Universities Physics Alliance (SUPA) Prof. Huston has co-spoken for the CTEQ collaboration and organized workshops at Les Houches, Kavli Institute, and Fermilab. He authored the Handbook on Perturbative QCD and is writing a book on QCD at the LHC for Oxford University Press.
