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 .
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.
Martin Burke is the May and Ving Lee Professor for Chemical Innovation and Professor of Chemistry at the University of Illinois Urbana-Champaign , with additional appointments in Biochemistry, Biomedical & Translational Sciences, and multiple campus institutes including the Beckman Institute and the Carl R. Woese Institute for Genomic Biology. Education B.S. Johns Hopkins University , 1998 Ph.D. Harvard University , 2003 M.D. Harvard Medical School , 2003 Research Interests Burke’s program centers on molecular prosthetics : the design, synthesis and application of small molecules that replicate or replace missing or dysfunctional proteins. His group pioneered iterative cross-coupling (ICC) using MIDA-protected haloboronic acids to automate the construction of complex natural products and function-oriented small molecules. Current projects target ion-channel replacement in cystic fibrosis, iron-transport restoration in anemia, and non-toxic antifungals that overcome drug resistance. Scientific Awards & Honors National Academy of Medicine (2021) AAAS Fellow (2021) ASCI Member (2021) iCON Award (2019) Mukaiyama Award, Japan (2019) ACS Nobel Laureate Award for Graduate Education (2017) Thieme-IUPAC Prize in Synthetic Organic Chemistry (2014) Elias J. Corey Award (2013) Arthur C. Cope Scholar Award (2011) Research Output & Impact Burke has authored >120 peer-reviewed articles, >30 patents, and his work has been cited >20,000 times. High-impact publications in Nature , Science , and Angewandte Chemie have advanced automated synthesis, molecular prosthetics, and cystic fibrosis therapeutics. Laboratory & Training The Burke Laboratories house a multidisciplinary team of graduate students, post-doctoral researchers, and physician-scientists developing next-generation molecular prosthetics. The group is supported by NIH, NSF, private foundations, and industry partnerships aimed at democratizing molecular innovation.
Prof. Hans van Lint is a Professor of Traffic Simulation and Computing at Delft University of Technology (TU Delft), where he holds the Anthony van Leeuwenhoek Chair since 2013. He is affiliated with the Department of Transport & Planning within the Faculty of Civil Engineering and Geosciences. His research focuses on the intersection of traffic flow theory, data analytics, and traffic simulation, with applications in estimating and predicting traffic states in networks. He has supervised numerous PhD students and contributed to valorization projects translating research into practical solutions. Van Lint earned his MSc in Civil Engineering in 1997 and returned to TU Delft for his PhD, which he completed in 2004 on 'Freeway Travel Time Prediction.' He has held roles including Assistant Professor (until 2009), Associate Professor, and has served as Director of Education for the MSc Transport, Infrastructure and Logistics program from 2010–2016. His research interests include traffic simulation frameworks, data assimilation techniques, and the development of tools for traffic state estimation. He has authored influential papers on topics such as microscopic traffic modeling, congestion pattern analysis, and macroscopic fundamental diagrams. His work emphasizes bridging theoretical models with real-world applications, enhancing traffic management and infrastructure planning. Van Lint teaches courses like 'Transport & Planning' and 'Interdisciplinary Fundamentals,' reflecting his commitment to both research and education. He actively contributes to TU Delft's labs, including the Traffic Dynamics, Modelling and Control Lab, advancing interdisciplinary approaches to mobility challenges.
Martin Willis Monroe is an Assistant Professor in the Department of Classics and Ancient History at the University of New Brunswick. His research focuses on the ancient Middle East, particularly cuneiform cultures, with an emphasis on the history of scholarly knowledge in Babylonian and Assyrian societies. He specializes in Mesopotamian astronomy and astrology, as well as quantitative approaches to historical data. Dr. Monroe holds a PhD in Assyriology from Brown University (2016), an MPhil and BA in Ancient Near Eastern Studies from the School of Oriental and African Studies (2008, 2007). Previously, he served as a postdoctoral fellow and research associate at the University of British Columbia (2016–2023). His current projects include the publication of his Hellenistic astrology research under contract with Brill, titled Celestial Schemata: A Series of Astrological Tables from Seleucid Babylonia , and his role as associate director of the Database of Religious History , a global initiative to quantitatively analyze religious and cultural data. He has contributed to excavations at the Neo-Assyrian site of Tušhan in southeastern Turkey and advocates for responsible qualitative-to-quantitative data conversion in historical scholarship. Dr. Monroe’s research bridges traditional philological approaches with digital humanities, focusing on topics such as cuneiform astral diagrams, the intersection of ‘scientific’ and ‘religious’ texts in antiquity, and the application of computational methods to undeciphered scripts. He teaches courses on ancient civilizations, Near Eastern history, and archaeology.
Qimin Liu is an Assistant Professor at Boston University, serving as Lab Director of the Quantitative Psychopathology Laboratory. He holds a PhD in Psychological Sciences from Vanderbilt University with specializations in Clinical Science and Quantitative Methods. His research focuses on emotional disturbances across development, statistical methodology development, and health equity with an emphasis on intersectional marginalization. He has expertise in analyzing intensive longitudinal data and has published extensively on topics like irritability, suicidality, and mental health disparities among sexual and gender minority populations. Dr. Liu’s work frequently integrates advanced statistical techniques such as latent variable modeling, network analysis, and machine learning. His recent studies explore the temporal dynamics of affect, the impact of stigma on mental health, and the role of childhood adversity in psychiatric outcomes. Notable contributions include developing methods for analyzing zero-inflated longitudinal data and creating algorithms for digital phenotyping of mood disorders through mobile device usage patterns. His scholarship emphasizes bridging clinical phenomena with rigorous quantitative approaches, addressing gaps in understanding how social determinants and individual differences shape mental health trajectories. He has collaborated on large-scale datasets like the Collaborative Psychiatric Epidemiological Surveys and contributed to interdisciplinary research on public health outcomes among aging sexual minority men. Dr. Liu’s methodological innovations include the DACF framework for ceiling/floor effect data and the lamme package for log-analytic multiplicative effects modeling. He actively publishes in high-impact journals such as Psychological Methods and Journal of Abnormal Psychology , focusing on both empirical findings and statistical best practices.
Flavio P. Calmon is the Thomas D. Cabot Associate Professor of Electrical Engineering at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS). He holds a Ph.D. in Electrical Engineering and Computer Science from MIT, an M.Sc. from the Universidade Estadual de Campinas (Brazil), and a B.Sc. from the Universidade de Brasília (Brazil). His research focuses on the intersection of information theory, machine learning, and statistics, with applications to privacy, fairness, and trustworthy AI systems. He has received prestigious awards, including the NSF CAREER Award (2018) and the James L. Massey Award (2024). Research interests include developing theoretical foundations for fair and private machine learning, understanding algorithmic bias, and designing systems with provable guarantees. His work spans information-theoretic tools for responsible AI, distributed privacy mechanisms, and understanding the limits of fairness interventions. He has advised numerous students, including Hao Wang, Hsiang Hsu, and Lucas Monteiro Paes, many of whom now hold prominent roles in academia and industry. Calmon's research is supported by grants from NSF, Amazon, Google, and IBM. He has organized workshops on AI in Brazil and leads initiatives to broaden participation in STEM from underrepresented groups. His lab collaborates with institutions globally and emphasizes both foundational theory and practical applications of machine learning.
Dr. Peter J.F. Lucas is a Full Professor specializing in Datamanagement & Biometrics with over 35 years of experience in artificial intelligence, probabilistic graphical models, and clinical decision support systems. His research spans intelligent systems, machine learning, and eHealth, with a focus on applying Bayesian networks and probabilistic logic to medical and non-medical domains.
Michael U. Gutmann is a Senior Lecturer in Machine Learning at the School of Informatics, University of Edinburgh, and a member of the Institute for Adaptive and Neural Computation. His research lies at the intersection of machine learning, statistics, and scientific applications, with a focus on developing inference methods for complex and implicit models. Education: PhD in Computational Neuroscience, University of Tokyo MSc in Engineering and Applied Mathematics, Swiss Federal Institute of Technology (ETH) Zurich MSc, Ecole Centrale Paris His primary research interests include Bayesian inference, likelihood-free inference, optimal experimental design, unsupervised learning, and applications in computational biology and neuroscience. He is best known for introducing Noise-Contrastive Estimation (NCE), a foundational technique for training unnormalized statistical models. His recent work spans variational inference, density ratio estimation, flow models for missing data, and AI-driven experimental design in behavioral and biological sciences. His publications, including in NeurIPS , ICML , JMLR , and eLife , demonstrate a strong emphasis on methodological innovation for scientific discovery. He has contributed to open-source tools such as ELFI (Engine for Likelihood-Free Inference) and developed practical implementations of robust inference algorithms. Scientific Awards: No specific awards listed in the provided texts. Michael Gutmann actively supervises students and collaborates with leading researchers in machine learning and computational biology. He has secured research funding from EPSRC and BBSRC for projects in generative modeling and infectious disease epidemiology. He teaches advanced courses such as Probabilistic Modelling and Reasoning and Data Mining, reflecting his deep engagement with both theoretical and applied aspects of machine learning. Labs and Research Groups: Institute for Adaptive and Neural Computation (ANC), University of Edinburgh Former affiliations with Department of Mathematics and Statistics and Department of Computer Science at the University of Helsinki and Aalto University
