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Sudhir KumarView profile

Professor

• Molecular Evolution
• Phylomedicine
• Functional Genomics
• +5 more

Sudhir Kumar is a Professor and Principal Investigator at Temple University, leading a research laboratory focused on molecular evolution, phylomedicine, and functional genomics. His lab develops mathematical methods, computational algorithms, and software packages for analyzing genomic variation across populations, pathogens, tumors, and species. Key contributions include the widely used MEGA software (www.megasoftware.net) for molecular evolutionary analysis and the TimeTree knowledge-base (www.timetree.org) that synthesizes evolutionary knowledge on species divergence times. Dr. Kumar's research interests center on integrating mathematical and computational techniques into evolutionary biology and biomedicine. His lab pursues a holistic paradigm where evolutionary and genomic patterns are discovered through comparative analysis of big datasets, then used to reveal underlying biological processes and develop predictive models. His work spans phylomedicine of genetic diseases, molecular phylogenomics, and the timetree of life, with recent innovations including Bayesian methods, machine learning algorithms, and statistical approaches for inferring molecular phylogenies, divergence times, and pathogenic mutations. Analysis of his recent publications reveals a strong trend toward applying artificial intelligence and machine learning to evolutionary genetics, with multiple 2025 papers focused on sparse learning techniques, transformer-based models, and AI-assisted analytical protocols. His work increasingly bridges evolutionary biology with cancer genomics and precision medicine applications. His scientific achievements have been recognized with the prestigious 2025 George W. Beadle Award from the Genetics Society of America, which honors his "efforts to democratize evolutionary genetics." Dr. Kumar has mentored numerous doctoral candidates, postdoctoral researchers, and graduate students, many of whom have gone on to faculty positions at institutions including Oakland University and universities in Brazil. His lab includes current doctoral candidates working in bioinformatics and statistical molecular evolution, supported by technical staff including programmers, genome tech specialists, and informatics specialists. The Kumar Laboratory operates as an interdisciplinary research hub with multiple projects including MEGA (Molecular Evolutionary Genetics Analysis), TimeTree, myPEG (web-based evolutionary tools), and FlyExpress (a knowledge base for Drosophila melanogaster embryo images). The lab emphasizes green computing efforts aimed at democratizing scientific practice and making big data analytics more accessible.

Seyed Mojtaba HoseyniView profile

Lecturer

• Process Safety
• Hydrogen Safety
• Risk Assessment
• +6 more

Dr. Seyed Mojtaba Hoseyni is a Lecturer in Process Safety and Loss Prevention at the School of Chemical, Materials and Biological Engineering, University of Sheffield. Previously a Postdoctoral Research Associate at the same institution (2022-2024), he holds a PhD in Energy Engineering from Politecnico di Milano (2021). His research focuses on enhancing system resilience, risk assessment, and decision-making under uncertainty in engineering systems, particularly for decarbonization applications. Royal Academy of Engineering Global Talent (Exceptional Promise) in Chemical and Process Engineering His work spans hydrogen safety, climate change risk, nuclear engineering safety, and predictive maintenance. Recent publications emphasize integrating resilience metrics into HAZOP analysis and optimizing sensor placement for risk-informed decision-making. Teaching activities include the Hazards and Protections module (CPE61020). Specializes in RAMS (Reliability, Availability, Maintainability, and Safety) analysis Develops safety frameworks for hydrogen energy systems Applies advanced computational techniques to nuclear and industrial safety

Lianne LefsrudView profile

Associate Professor

• Risk Management
• Sustainability
• Organizational Theory
• +5 more

Lianne Lefsrud serves as Associate Professor and Risk, Innovation, and Sustainability Chair (RISC) in the Department of Chemical and Materials Engineering at the University of Alberta's Faculty of Engineering. Her interdisciplinary research bridges engineering, social sciences, and policy to transform risk management practices across energy, mining, construction, and railroading industries, directly influencing regulations, building codes, and industry operations for sustainable development. Her academic credentials include: BSc in Civil Engineering (Cooperative Program), University of Alberta (1994) MSc in Interdisciplinary Civil & Environmental Engineering and Sociology, University of Alberta (1996) PhD in Strategic Management and Organization, Alberta School of Business (2014) Dr. Lefsrud's research centers on risk management frameworks for sustainability challenges. She examines hazard identification, social license to operate, and technology adoption drivers in high-hazard industries, with emphasis on prospective risk assessment (e.g., hydrogen infrastructure design) and retrospective analysis (e.g., microplastic pollution impacts). Her work integrates circular economy principles into energy systems while addressing unintended consequences across UN Sustainable Development Goals. Recent publications (2024-2025) demonstrate heavy focus on machine learning applications for rail and construction safety, hydrogen infrastructure risk analysis, and science denial mitigation. Key patterns show cross-industry adaptation of AI for incident prediction, regulatory gap analysis for emerging energy systems, and socio-technical approaches to reconcile sustainability goals with operational realities. Scientific recognition includes: Erb Post-Doctoral Fellowship (University of Michigan) Dow Sustainability Research Fellowship (Ross School of Business) Dr. Lefsrud mentors graduate students through industry-integrated projects like her Sustainable Design course where teams generated patents and city solutions. Her research secures Alberta Innovates funding with 1:4 industrial-to-federal matching, collaborating with Suncor, Transport Canada, and Canadian Standards Association. Grants target practical implementations including railcar inspection systems and hydrogen safety protocols. She co-founded Insight Risk Systems and leads the Lefsrud Lab, prioritizing inclusive teams with under-represented groups (women, Indigenous, LGBTQ2S+, neurodiverse) to tackle 'wicked problems' in sustainability. The lab leverages interdisciplinary partnerships across engineering, computer science, psychology, and environmental sociology for real-world risk management solutions.

Le-Nam TranView profile

Researcher

• Wireless Communications
• 5G/6G Network Optimization
• Signal Processing
• +4 more

Dr. Le-Nam Tran is a researcher at the UCD School of Electrical & Electronic Engineering , University College Dublin. His work focuses on optimizing the last hop of 5G/6G wireless networks through mathematical programming, with emphasis on energy efficiency, interference management, and security against eavesdropping. Develops low-cost, low-complexity transmission techniques Projects supported by Science Foundation Ireland Career Development Award Author of over 80 peer-reviewed publications Research Keywords: Wireless Communications Network Security Signal Processing Energy-Efficient Systems Beamforming Optimization Interference Mitigation

Xiaotian ZhengView profile

Assistant Professor

• Spatial Statistics
• Spatio-Temporal Statistics
• Point Process Modeling
• +5 more

Xiaotian Zheng is an Assistant Professor of Statistics at the University of Georgia. Previously, they were a Postdoctoral Research Fellow with the Australian Research Council Special Research Initiative Securing Antarctica's Environmental Future at the University of Wollongong, working under Professor Noel Cressie and Associate Professor Andrew Zammit-Mangion. They earned their Ph.D. in Statistical Science from the University of California, Santa Cruz, advised by Professors Athanasios Kottas and Bruno Sansó. Their research focuses on developing statistical and machine learning methods for analyzing complex, dependent data, particularly in ecological and environmental contexts. Key areas include spatial/spatio-temporal statistics, probabilistic downscaling, data integration, transfer learning, and statistical deep learning. Xiaotian's publications reflect their work on mixture transition distribution models, nearest-neighbor mixture models, and geostatistical frameworks for discrete-valued processes. These contributions emphasize Bayesian inference, computational efficiency, and real-world applications in environmental science and biodiversity modeling.

Qi YuView profile

Professor

• Artificial Intelligence
• Machine Learning
• Data Science
• +6 more

Qi Yu is a Professor in the School of Information at the Golisano College of Computing and Information Sciences at Rochester Institute of Technology (RIT). He serves as the Graduate Program Director and directs the Machine Learning and Data Intensive Computing Lab. His research focuses on machine learning, deep learning, and data-driven knowledge discovery, particularly in knowledge-rich domains like medicine and bioinformatics. He holds a B.E. from Zhejiang University, an M.E. from the National University of Singapore, and a Ph.D. from Virginia Tech. His work emphasizes interpretable models, multimodal data fusion, and human-in-the-loop learning. He has secured significant grants, including a $500K NSF award and a $1.6M ONR grant, supporting projects on Bayesian learning frameworks and decision-making under uncertainty. His lab actively explores active learning, few-shot learning, and uncertainty quantification. He advises a vibrant group of Ph.D. and MS students and teaches courses such as Data-Driven Knowledge Discovery and Thesis/Project Capstones. Education: B.E., Electrical Engineering, Zhejiang University (2001) M.E., Computer Engineering, National University of Singapore (2003) Ph.D., Computer Science, Virginia Tech (2008) Research Interests: Machine Learning, Deep Learning, Vision-Language Models, Uncertainty Quantification, Active Learning, Multimodal Data Fusion, Bayesian Methods, and Applications in Healthcare and Cybersecurity. Recent Work Trends: His articles emphasize label-efficient learning, interactive systems, and applying ML to complex domains like medical imaging and anomaly detection. Notable projects include Bayesian learning for dynamic decision-making and evidential optimization for robust models. Awards/Grants: NSF IIS Award ($500K, 2018–2023); DoD/ONR Award ($1.6M, 2018–2023); multiple conference recognitions (NeurIPS, ICML, CVPR). Advising spans over 20 students, many securing roles at Amazon, Samsung, and academia. Labs/Teams: Leads the Mining Lab, collaborating on interdisciplinary projects with domain experts in medicine, cybersecurity, and material science.

Syed Ejaz AhmedView profile

Professor

• Big Data Analytics
• Statistical Machine Learning
• Predictive Modeling
• +6 more

Syed Ejaz Ahmed is a distinguished Professor of Mathematics and Statistics at Brock University , with a career spanning multiple institutions including the University of Windsor, University of Regina, and University of Western Ontario. He serves as a Review Editor for Technometrics and holds editorial roles for several journals. PhD, Carleton University MSc, University of Guelph MSc in Statistics, University of Karachi BSc (Honors), University of Karachi His research focuses on big data analytics , statistical machine learning , and shrinkage estimation , with applications in healthcare, economics, and environmental science. He has organized international workshops on high-dimensional data analysis since 2011. Recent scientific contributions highlight advanced methods for high-dimensional regression, censored data analysis, and predictive modeling. His work has received international recognition through awards and fellowships. Fellow, American Statistical Association Fellow, Royal Statistical Society Elected Member, International Statistical Institute NSERC Discovery Grant (2017-2022) ISOSS Gold Medal A dedicated educator, he has supervised numerous PhD/MSc students and postdoctoral fellows. Currently, he leads the Centre for Business Analytics at Brock and collaborates with institutions worldwide through honorary professorships and visiting roles.

• High-dimensional statistics
• Bayesian statistics
• Statistical analysis of complex data
• +4 more

David Rossell is an Associate Professor at the Department of Economics, Universitat Pompeu Fabra (UPF) in Barcelona, Spain. He is affiliated with the Statistics@UPF research group and directs the Master in Data Science at the Barcelona School of Economics (BSE). Previously, he held positions at IRB Barcelona as head of the Biostatistics Unit and at the University of Warwick's Statistics Department. He obtained his PhD in Statistics from Rice University, Houston (USA), and conducted postdoctoral research at M.D. Anderson Cancer Center under Professors Valen Johnson and Veera Baladandayuthapani. Research Interests: Rossell specializes in high-dimensional statistical inference, Bayesian methods, computational statistics, and applications in biomedicine and social sciences. His work emphasizes methodology for complex data integration, variable selection, graphical models, and experimental design. Key areas include non-local priors, scalable Bayesian computation, and the development of R packages for statistical analysis (e.g., casper , chroGPS , gaga ). Publications: His recent work focuses on advancing Bayesian variable selection, graphical models with external data, and causal inference. Themes include leveraging external datasets for improved model accuracy, robustness to model misspecification, and applications in healthcare and complex mixture analysis. His contributions span methodological innovation and computational tools for high-dimensional problems. Funding & Grants: Rossell has secured funding through Spanish and European grants, including Juan de la Cierva Fellowships, AGAUR fellowships, and Marie Slodowska-Curie Actions. He supports PhD and postdoctoral researchers through programs like La Caixa InPhD and Beca Beautriu de Pinós. Labs & Teams: He leads the BSE Data Science Center and contributes to interdisciplinary collaborations at UPF and IRB Barcelona, bridging statistical theory and practical applications in genomics, epigenomics, and health data analysis.

Xiajun JiangView profile

Assistant Professor

• Adaptive AI Computing
• Physics-Informed Deep Learning
• AI for Healthcare
• +3 more

Xiajun Jiang is an Assistant Professor in the Department of Computer Science at the University of Memphis, joining in Fall 2024. He holds a PhD in Computing and Information Sciences from Rochester Institute of Technology (2024), an M.S. in Computer Science from the University of Southern California (2018), and a B.S. in Electrical Engineering and Automation from Zhejiang University (2016). His research focuses on adaptive AI computing, physics-informed deep learning, and their applications in healthcare, particularly in medical imaging and cardiac simulation. Key contributions include hybrid neural state-space modeling for electrocardiographic imaging and physics-informed frameworks for bi-ventricular electrophysiological simulations. Education: PhD, Rochester Institute of Technology, 2024 M.S., University of Southern California, 2018 B.S., Zhejiang University, 2016 Research Interests: Machine learning for healthcare Adaptive computing in AI models Physics-informed deep learning His work bridges machine learning and biomedical engineering, with applications in cardiac imaging and electrophysiology. Recent articles highlight advancements in hybrid models for ECGI and meta-learning approaches for personalized cardiac simulations. He has reviewed for top conferences like ICLR, NeurIPS, and MICCAI, and contributed to projects like the Computational Biomedical Lab (CBL).

Zezhou ChengView profile

Assistant Professor

• Computer Vision
• Machine Learning
• AI for Science
• +6 more

Zezhou Cheng is an Assistant Professor of Computer Science at the University of Virginia, leading the Computer Vision Lab. He holds a Ph.D. from UMass Amherst (2023), a postdoctoral position at Caltech, and a Bachelor's degree from Sichuan University (2015). His research focuses on computer vision, machine learning, and their applications in ecology, materials science, and autonomous systems. Key areas include 3D understanding, self-supervised learning, and AI-driven ecological monitoring. He has received awards such as the Best Synthesis Award (2020) and Outstanding Reviewer (CVPR 2021). His work spans publications in top venues like CVPR, ICCV, and ECCV, addressing challenges in 3D reconstruction, generative models, and ecological data analysis. Education: Ph.D. in Computer Science, UMass Amherst (2023) Bachelor's Degree, Sichuan University (2015) Postdoctoral Researcher, Caltech (advised by Georgia Gkioxari) Research Highlights: Developed LU-NeRF for unposed scene reconstruction and camera pose estimation Contributed to AI for ecology via bird roost detection using weather radar data Advanced 3D representation learning through procedural programs and self-supervised techniques Awards & Recognition: Outstanding Reviewer, CVPR 2021 Best Poster Award, New England Computer Vision Workshop 2019 National Scholarship (China, 2014 and 2016) His lab explores cutting-edge topics in computer vision, with a focus on interdisciplinary applications. Teaching roles include leading Caltech's AI Bootcamp and serving as a Teaching Assistant at UMass Amherst. Industry collaborations include internships at Google Research, Snap, and Amazon.

• Wireless Networks
• Signal Processing
• 5G/6G Communications
• +3 more

Dr. Wibowo Hardjawana is a Senior Lecturer in Telecommunications Engineering at the School of Electrical & Computer Engineering , University of Sydney. He holds a PhD from the University of Sydney and serves as an ARC DECRA Research Fellow. His research focuses on wireless network softwarisation, enabling programmable radio interfaces to address traffic elasticity in 5G/6G systems. Education : PhD (University of Sydney) Grants : ARC DP210100744 (2021), ARC DECRA DE140101114 (2014) His work spans 5G/6G network architectures , machine learning for wireless systems , and open radio interfaces . Key contributions include graph representation learning for interference management, Bayesian neural network detectors for OTFS modulation, and NOMA decoding techniques . Recent publications analyze ultra-reliable low-latency communications , UAV-enabled networks , and stochastic geometry in wireless systems . He has collaborated with institutions in China, Indonesia, and UAE, and engaged with industry partners like Telstra and Ausgrid.

Xujia ZHUView profile

Associate Professor

• Uncertainty Quantification
• Surrogate Modeling
• Stochastic Simulators
• +4 more

Xujia ZHU is an Associate Professor at CentraleSupélec, Paris-Saclay University, affiliated with the Laboratory of Signals and Systems (L2S). His research focuses on uncertainty quantification, surrogate modeling, stochastic simulators, and reliability analysis. He holds an engineer’s degree in Mechanics from École Polytechnique (2015), a Master’s in Computational Mechanics from TU Munich (2017), and a Ph.D. from ETH Zurich (2022). He was a postdoctoral researcher at ETH Zurich until 2023. Education: Ph.D., Chair of Risk, Safety, and Uncertainty Quantification, ETH Zurich, 2022 Master’s (high distinction), Computational Mechanics, Technical University of Munich, 2017 Engineer’s Degree, Mechanics, École Polytechnique, 2015 Research Interests: Xujia’s work bridges numerical simulations and statistics, addressing topics like uncertainty propagation, sensitivity analysis, and surrogate modeling for stochastic systems. Key areas include polynomial chaos expansions, Bayesian active learning, and applications in seismic fragility analysis. Publications: His recent work emphasizes emulation techniques for stochastic simulators, multi-fidelity methodologies, and Bayesian active learning strategies in reliability analysis. Key themes include sparse polynomial chaos expansions and latent variable modeling. Labs/Teams: Affiliated with L2S, he collaborates on transversal projects in energy, industry, and health, leveraging interdisciplinary approaches in uncertainty quantification and computational modeling.

David HuberView profile

Professor

• Cognitive Psychology
• Neuroscience
• Memory Processes
• +7 more

David Huber is a Professor in Psychology and Neuroscience at the University of Colorado. He holds a PhD from Indiana University (2000). His research focuses on human perception, memory, and decision-making from a computational perspective, integrating behavioral studies with neuroimaging techniques (fMRI, ERP) and computational models (Bayesian, neural networks). Key areas include visual cognition, spatial navigation models, memory retrieval dynamics, and priming effects. Education: PhD in Psychology from Indiana University (2000). Research emphasizes mechanisms underlying memory processes, perceptual adaptation, and the neural basis of cognitive functions. Recent work challenges traditional roles of hippocampal place/grid cells, proposing memory-centric models. He explores how perceptual fluency influences decision-making and juror cognition, with studies on serial dependence and gestalt principles in binocular rivalry. His neurocomputational methods include fMRI-based tuning function analysis and virtual electrophysiology modeling. Publications span over two decades, focusing on memory models (SAM-RI), neural habituation, and Bayesian modeling of brain activity. His work advocates for rigorous model-driven approaches in cognitive neuroscience.

Lirong XiaView profile

Professor

• Artificial Intelligence
• Machine Learning
• Multi-Agent Systems
• +9 more

Lirong Xia is a Professor of Computer Science at Rutgers University - New Brunswick and Deputy Director of DIMACS (Center for Discrete Mathematics and Theoretical Computer Science). He holds a Ph.D. in Computer Science from Duke University, an M.A. in Economics from Duke, and a B.E. in Computer Science and Technology from Tsinghua University. His research focuses on the intersection of artificial intelligence, machine learning, and social choice theory, addressing challenges in voting systems, fair division, privacy, and multi-agent systems. Key research areas include algorithmic fairness, computational social choice, and mechanism design. Recent work explores equitable voting rules, privacy-preserving mechanisms, and strategic behavior analysis. Notable publications include advancements in computational social choice and privacy in voting systems. Xia has been recognized with prestigious awards such as the NSF CAREER Award and IEEE’s “AI’s 10 to Watch.” Education: Ph.D. Computer Science, Duke University (2011) M.A. Economics, Duke University (2010) B.E. Computer Science and Technology, Tsinghua University (2004) Awards: NSF CAREER Award Simons-Berkeley Research Fellowship 2018 Rensselaer James M. Tien’66 Early Career Award IEEE Intelligent Systems “AI’s 10 to Watch” Advising: Supervised over 30 students, including PhDs and master’s candidates in AI, algorithms, and social choice theory.

Alex KaleView profile

Assistant Professor

• Human Computer Interaction
• Data Visualization
• Data Science
• +4 more

Alex Kale is an Assistant Professor of Computer Science at the University of Chicago and a core member of the Data Science Institute. His research focuses on data visualization and human-computer interaction, emphasizing tools that explicitly represent users' cognitive processes during data analysis. He leads the Data Cognition Lab, exploring software for uncertainty visualization, causal inference, and decision-making support. Kale holds a PhD in Information Science from the University of Washington (2022), an MSc from UW (2020), and a BSc in Psychology with minors in Music and Philosophy (2015). Affiliations: University of Chicago, Data Science Institute, Data Cognition Lab Education: PhD, UW (2022); MSc, UW (2020); BSc, UW (2015) Research interests include human-computer interaction, statistical reasoning interfaces, and systems for managing large-scale data. He has developed tools like MetaExplorer for meta-analysis and EVM for exploratory visual modeling. Key awards include the Best Paper Honorable Mention at CHI 2023 and VIS 2021, and the Best Paper Award at VIS 2020. His work bridges visualization design, decision theory, and cognitive science, with applications in participatory budgeting, causal inference, and reproducible research. Courses taught include Visualization for Data Science and Statistical Rethinking.