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Jessica ZhangView profile

Professor

• Computational Geometry
• Isogeometric Analysis
• Finite Element Method
• +12 more

Dr. Jessica Zhang is the George Tallman Ladd and Florence Barrett Ladd Professor of Mechanical Engineering at Carnegie Mellon University, with a courtesy appointment in Biomedical Engineering. She holds prestigious fellowships from ASME, SIAM, and other institutions. Her research focuses on computational geometry, isogeometric analysis, and applications in biomedicine and materials science. She has published over 200 technical articles and received numerous awards, including the PECASE and ASME Van C. Mow Medal. Education: PhD in Computational Engineering and Sciences from the University of Texas at Austin (2005), M.Eng. in Aerospace Engineering (UT Austin), and degrees from Tsinghua University in China. Research Interests: Computational geometry, mesh generation, finite element methods, and their applications in biomedical engineering, materials science, and mechanical engineering. Her lab develops algorithms for biomodeling at molecular to organ scales. Recent Achievements: In 2025, she was selected for the AWM-SIAM Kovalevsky Lecture and the ASME Van C. Mow Medal. Her work on neuron growth modeling and machine learning frameworks has advanced computational biomedical applications. Awards: Includes Simons Visiting Professorship, NSF CAREER Award, and multiple best paper awards. She chairs committees for conferences like Solid and Physical Modeling Symposium. Grants and Mentorship: Advised over 50 students and postdocs. Her lab collaborates across disciplines, including NextManufacturing Center and Materials Research Science and Engineering Center. Labs/Teams: Computational Bio-Modeling Lab, focusing on interdisciplinary projects in mechanical, biomedical, and materials engineering. Active in initiatives like Bioengineered Organs and NextManufacturing.

• Machine Learning
• Artificial Intelligence
• Bioinformatics
• +7 more

Eric Poe Xing is a Professor at Carnegie Mellon University's School of Computer Science, holding joint affiliations with the Machine Learning Department, Language Technology Institute, and Computer Science Department. He also serves as President of the Mohamed bin Zayed University of Artificial Intelligence. His research focuses on machine learning methodology, statistical systems, and large-scale computational architectures, with recent work on foundation models for biology (AIDO), world/agent models (PAN), and open-source LLM initiatives (LLM360). He advises numerous students and postdocs in areas like AI, NLP, and computational biology. He teaches graduate courses in Machine Learning and Probabilistic Graphical Models, and actively contributes to academic leadership roles, including ICML program chairs and editorial boards. Research interests span automated reasoning, AI ethics, and scalable computing. His lab, SAILING, develops cutting-edge models for vision-language tasks, bioinformatics, and multi-modal learning. Notable projects include LLM360's open-source AGI efforts and innovations in distributed ML systems. His work emphasizes interdisciplinary applications, from healthcare decision-support (PetuumMed) to climate modeling (ClimSatDiff).

Eugen PircalabeluView profile

Lecturer

• High-dimensional statistics
• Probabilistic graphical models
• Social network models
• +6 more

Eugen Pircalabelu is a Lecturer at UCLouvain (Université catholique de Louvain) working at the Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA) within LIDAM at the Faculty of Science. Prior to his current position, he held a Visiting Professor position at Ghent University and a Postdoctoral position at KU Leuven. Dr. Pircalabelu's research focuses on high-dimensional statistics, with particular emphasis on probabilistic graphical models, social network analysis, copula models, and information criteria. His work bridges theoretical statistics with practical applications in fields such as neuroscience (fMRI data analysis) and epidemiology (COVID-19 modeling). His recent publications reveal a strong trend toward distributed and federated learning approaches for high-dimensional graphical models, with applications spanning from financial time series to brain connectivity networks. His methodological contributions include innovations in model selection criteria, sparse estimation techniques, and time-varying network models that have advanced the field of high-dimensional statistical inference. Dr. Pircalabelu actively supervises multiple PhD students including Mengxue Li, Lise Léonard, and Lara Wautier, and has recently guided Ensiyeh Nezakati and Alexandre Jacquemain to completion of their doctoral studies. He teaches courses in nonparametric statistics, numerical methods for statistics, and statistical learning. He serves the statistical community through organizing regular Statistical/Econometrics seminars at ISBA and contributing to the RShiny@UCLouvain platform for open educational resources. Notably, during the COVID-19 pandemic, he developed a SHINY app for predicting hospitalizations and ICU admissions in Belgium.

• Transport Network Resilience
• Civil Engineering
• Probability Analysis
• +7 more

Beatriz Martinez-Pastor is an Assistant Professor in the School of Civil Engineering at University College Dublin (UCD). Her roles include teaching and coordinating modules such as Transport Modelling, Transportation Engineering, and Civil Engineering Design Graphics. She holds a PhD from Trinity College Dublin and an MSc from the University of Cantabria, complemented by a Professional Certificate in University Teaching and Learning from UCD. Her research focuses on transport network resilience, particularly in quantifying system behavior under disruptions like natural disasters and cyber-physical attacks. Key areas include probabilistic analysis, data-driven modeling, and infrastructure vulnerability assessment. She has contributed to frameworks for evacuation planning, cyber-physical system robustness, and flood adaptation strategies. Her articles highlight advancements in accident analysis methods, cyber-physical resilience, and multi-objective optimization for disaster scenarios. Notable grants include the Horizon Europe-funded SETO project (2023–2026) and CAPABLE (2023–2024), addressing transport enforcement and community resilience. Martinez-Pastor has received the Excellence Award (2019) and serves on committees like the TRB Committee on Critical Infrastructures. Her work bridges academic research with practical applications in disaster management, cybersecurity, and sustainable infrastructure.

Ceylan Talu YozgatlıgilView profile

Professor

• Time Series Analysis
• Machine Learning
• Data Science
• +4 more

Dr. Ceylan Talu Yozgatlıgil is a Professor at the Department of Statistics, Middle East Technical University (METU), where she has served since 2007. She earned her Ph.D. in Statistics from Temple University (2007), M.S. (1999) and B.S. (1996) from METU. Her roles include Vice Chairperson of the Department (2020–present) and former Associate Director of the Institute of Applied Mathematics (2017–2020). Education: Ph.D. in Statistics, Temple University (2007) M.S. in Statistics, METU (1999) B.S. in Statistics, METU (1996) Her research spans Time Series Analysis , Machine Learning , and Applied Statistics , with recent work on drought forecasting, comorbidity modeling, and Industry 4.0 production efficiency. Her 15 most recent articles reflect expertise in hybrid forecasting methods, copula modeling, and spatio-temporal analysis. Scientific Awards include the THD Clinical Hematology Paper Award (2007), TÜBİTAK Publication Support (2004, 2009), and a Full Time Doctorate Scholarship (2001–2006). She has developed advanced courses in time series and statistical computing, and her collaborations span actuarial science, climate research, and biomedical studies.

Akshat KUMARView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Multiagent Systems
• +4 more

Akshat Kumar is an Associate Professor (Research Track) in the School of Information Systems at Singapore Management University (SMU). He holds a PhD in Computer Science from the University of Massachusetts Amherst (2013) and was previously a Research Scientist at IBM Research India. Research Focus : His work lies at the intersection of AI, Machine Learning, and Multiagent Systems, with applications in urban mobility optimization (e.g., autonomous vehicles, maritime traffic management), decision-making under uncertainty, and computational sustainability. He leads the Representation, Learning, and Reasoning (RLR) Lab, which develops algorithms for large-scale multiagent coordination using reinforcement learning and probabilistic inference. Awards & Grants : Recipient of the Lee Kong Chian Fellowship (2020-22), Best Paper Awards at AAAI (2017) and ICAPS (2014), and featured in IEEE’s 'AI’s Ten to Watch' (2018). Principal Investigator (PI) of Singapore MOE Tier-2 grants totaling S$1.1M for projects like 'Data Driven Collective Decision Making For Urban System Optimization' (2019–2021). Education & Teaching : Taught courses on computational thinking, artificial intelligence, and spreadsheet modeling. Advises PhD students and research engineers on topics like reinforcement learning, maritime traffic simulation, and cloud security policy design. Impact : His research addresses challenges in urban systems, such as optimizing autonomous taxi fleets, reducing maritime congestion, and improving smart grid management. Collaborations include Fujitsu-SMU Urban Computing Lab and Singapore port authorities.

Yan TongView profile

Professor

• Computer Vision
• Human-Computer Interaction
• Affective Computing
• +3 more

Yan Tong is Thomas E. Hattrick Chair Professor in Computer Science and Engineering at the University of South Carolina's Molinaroli College of Engineering and Computing. Her research focuses on computer vision, pattern recognition, and human-computer interaction, with applications in affective computing, healthcare, and multi-robot systems. Her research integrates computer vision with machine learning to solve problems in human behavior analysis and robotic perception. Key areas include facial action recognition, eye gaze tracking, and probabilistic graphical models for uncertainty management. Recent work extends to gait analysis systems combining wearable sensors and robotics for clinical applications. Tong's publication trends show strong integration of deep learning with robotics, particularly in navigation systems, human-robot interaction, and biomedical engineering. Her research consistently bridges theoretical machine learning with practical applications in healthcare and assistive technologies. She leads a substantial research group with 11 current PhD students and has graduated 6 PhDs. Professional service includes editorship of IEEE Robotics and Automation Magazine and leadership roles in major conferences including ICRA and IROS.

• Production Systems
• Business Processes
• Supply Chain Optimization
• +4 more

George Ioannou is a Professor of Production Management and Business Processes at the Athens University of Economics and Business, where he leads the MBA International Postgraduate Program and the Center for Business Processes and ERP Systems at the Laboratory of Management Science. Previously, he served as Assistant Professor at Virginia Tech's Department of Production and Systems Engineering. His academic journey includes a B.Sc. in Mechanical Engineering from National Technical University of Athens, an M.Sc./DIC in Industrial Robotics/Production Automation from Imperial College London, and a Ph.D. in Mechanical Engineering from the University of Maryland. Current Academic Roles: Professor, Director of MBA Program, Head of ERP Systems Center Prior Academic Roles: Assistant Professor at Virginia Tech Education: B.Sc., M.Sc./DIC, Ph.D. His research focuses on integrating web-based technologies with operational research to optimize production systems, business processes, and supply chains. This spans applications in plant spatial planning, ERP systems, and cultural heritage digitization. He has secured research funding from global organizations including NSF, European Commission, and private sector leaders like Microsoft. Scientific awards include Microsoft's Excellence in Education Award and multiple teaching excellence recognitions for MBA programs. Ioannou serves on the Technical Chamber of Greece (TEE) and the editorial board of Production Planning & Control , with extensive consulting experience for public and private sector entities.

• Machine Learning
• Artificial Intelligence
• Graph Neural Networks
• +2 more

Maoying Qiao is a Lecturer at Australian Catholic University's Peter Faber Business School, specializing in machine learning and artificial intelligence. Her research spans graph neural networks, computer vision, and probabilistic modeling, with applications in network analysis and 3D vision. Key publications focus on improving graph convolutional networks through negative sampling, adapting stochastic block models for power-law networks, and developing conditional graphical lasso methods for multi-label image classification. Marine science applications include automated catch detection systems for fisheries.

Adam C. SiepelView profile

Professor

• Comparative Genomics
• Molecular Evolution
• Population Genetics
• +4 more

Adam C. Siepel is a Professor in the Watson School of Biological Sciences and Chair of the Simons Center for Quantitative Biology at Cold Spring Harbor Laboratory (CSHL). He also holds an adjunct appointment as Adjunct Associate Professor in the Computer Science Department at Stony Brook University. His research focuses on computational genomics, evolutionary biology, and statistical methods, with contributions to understanding human population genetics, gene regulation, and HIV evolution. Education: Bachelor's in Engineering from Cornell University (1990-1994) Master's in Computer Science, University of New Mexico (2001) Ph.D. in Computer Science, UC Santa Cruz (2005) Research Interests: Siepel's work bridges computer science, statistics, and biology. Key areas include comparative genomics, molecular evolution, and algorithm development for analyzing large genomic datasets. He has addressed topics such as HIV recombinant strain identification and estimation of human population divergence timelines. Awards: Guggenheim Fellowship Microsoft Research New Faculty Fellowship Packard Fellowship NSF CAREER Award Sloan Research Fellowship Advising & Leadership: Directed Cornell's Computational Biology Ph.D. program (2006–2014) and served on editorial boards for Genome Research and PLoS Computational Biology . Currently chairs the Simons Center for Quantitative Biology at CSHL. Labs/Teams: Leads research groups in computational biology at CSHL, focusing on quantitative approaches to genomic and evolutionary problems.

• Biomedical Informatics
• Genomics
• Artificial Intelligence
• +5 more

Edgar J. Hernandez is a Research Assistant Professor in the Department of Biomedical Informatics at the University of Utah School of Medicine. He holds a PhD in Ecology, Systematics, and Evolution from the University of Missouri and a Bachelor's in Biology from Universidad Nacional de Colombia. His research bridges biostatistics, artificial intelligence, and clinical data to improve healthcare outcomes, particularly in personalized medicine and genomic diagnostics. He leads computational efforts in the Yandell lab, focusing on predictive modeling of genetic-clinical interactions, multi-omics analysis, and developing tools for diagnosing rare diseases using next-generation sequencing. His expertise spans applications in pediatric cardiology, nephrology, oncology, and metabolic disorders. Collaborations with consortia and industry partners emphasize translating genomic insights into clinical practice. Notable contributions include AI-driven risk stratification for perinatal mortality and cardiovascular outcomes, as well as genomic forecasting tools for congenital heart surgery outcomes. His work integrates electronic health records (EHR) and machine learning to address complex health challenges, such as identifying novel molecular signatures in multisystem inflammatory syndrome in children (MIS-C). Hernandez’s research portfolio reflects interdisciplinary innovation, with a focus on improving genomic information utilization for therapeutic advancements. His computational methods include probabilistic graphical models, natural language processing, and explainable AI frameworks to enhance diagnostic precision and predictive accuracy in healthcare.

Manuele LeonelliView profile

Assistant Professor

• Statistics
• Machine Learning
• Decision Sciences
• +5 more

Manuele Leonelli is an Assistant Professor at the School of Science and Technology , IE University in Madrid, Spain. He holds a PhD in Statistics from the University of Warwick (UK) and has held roles including CAPES Postdoctoral Fellow at the Federal University of Rio de Janeiro (Brazil) and Lecturer at the University of Glasgow (UK). His research bridges Statistics, Machine Learning, and Decision Sciences , with a focus on probabilistic graphical models such as Bayesian networks and staged tree models. Dr. Leonelli’s work emphasizes practical applications in healthcare analytics, sports performance analysis, and environmental risk management . He has authored over 30 peer-reviewed publications and developed open-source tools like the bnmonitor R package for Bayesian network analysis. He currently serves on the editorial board of Bernoulli News and is a member of the ELLIS Society, reflecting his commitment to collaborative research and advancing statistical science. His recent research trends include contextualized causal discovery, robust model learning from incomplete data, and interdisciplinary applications of staged trees in healthcare and transportation systems. Though no formal awards are listed, his contributions to open-source software and methodological advancements highlight his impactful academic trajectory.

Markos A. KatsoulakisView profile

Professor

• Uncertainty Quantification
• Stochastic Processes
• Statistical Mechanics
• +5 more

Markos A. Katsoulakis is a Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. He holds a Ph.D. from Brown University (1993) and a B.Sc. from the National and Kapodistrian University of Athens (1987). His research focuses on Uncertainty Quantification, Stochastic Analysis, and Multi-scale Modeling, with applications in molecular systems, hybrid models, and computational physics. He has contributed to developing methods like Information-theoretic Sensitivity Analysis (ISAP) and hierarchical coarse-graining techniques. His work bridges theoretical mathematics with computational tools, addressing challenges in complex systems, rare event analysis, and machine learning integration. Recent research emphasizes robustness in generative models, optimal transport divergences, and data-driven uncertainty quantification. He has taught courses such as STAT 515: Introduction to Statistics I and has been supported by grants from the Simons Foundation. His publications span topics including stochastic dynamics, variational representations, and graph-informed neural networks, reflecting his interdisciplinary approach to mathematical sciences.

• Machine Learning
• Causal Modeling
• Artificial Intelligence
• +2 more

Michèle Sebag is a Principal Scientist (Directrice de recherche) at CNRS and member of the Académie des Technologies, affiliated with Université Paris Saclay. She heads the Équipe A-O at Laboratoire de Recherche en Informatique (LRI) and co-leads Project-Team TAU. Her research spans causal modeling, machine learning, optimization, and AI applications in social sciences. Leadership: Head of Steering Committee for ECML PKDD (2015-2019); Editorial board member for Machine Learning Journal; Board member of DataIA Institute and INS2I Council. Current PhD Students: Armand Lacombe Eléonore Bartenlian Victor Berger Roman Bresson Héri Rakotoarison Nilo Schwencke

Neill CampbellView profile

Professor

• Visual Computing
• Machine Learning
• Computer Vision
• +6 more

Neill D. F. Campbell is a Professor of Visual Computing and Machine Learning at the University of Bath, Department of Computer Science. He holds an honorary Associate Professor position at University College London. His research focuses on shape modeling, machine learning, and their applications in computer vision and graphics. Campbell directs the CAMERA research centre and co-leads the Centre for Mathematics and Algorithms for Data (MAD). He leads the MyWorld project, a £46M creative hub in the Bath-Bristol region. Education: PhD in Engineering (Automatic 3D Model Acquisition from Uncalibrated Images) from the University of Cambridge (2011), MEng in Engineering from the University of Cambridge (2006). Research Interests: Modeling shape and appearance using machine learning, Bayesian nonparametric models, generative models, uncertainty quantification, and applications in entertainment, health, and sports science. Active in developing structured uncertainty prediction networks (SUPN) and exploring generative models for inverse problems. Key Projects: MyWorld (Strength in Places Fund), CAMERA, EPSRC CDT in Statistical Applied Mathematics, and collaborations with industry partners. Recent work includes anomaly detection, depth estimation, and conditional sampling techniques. Awards: Royal Society Industry Fellow (2017–present). Over 54 research outputs spanning CVPR, ECCV, SIGGRAPH, and NeurIPS, focusing on structured uncertainty, generative models, and vision applications. Labs/Teams: Director of CAMERA, co-director of MAD, and collaborator in UKRI CDT in Responsible AI. Active in organizing workshops on uncertainty quantification in computer vision (UNCV).