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Wittung Stafshede PernillaView profile

Professor

• Protein Biology
• Biophysics
• Biochemical Engineering
• +7 more

**Pernilla Wittung Stafshede** is a Full Professor with Tenure and Head of the Chemical Biology division at Chalmers University of Technology (Gothenburg, Sweden). She previously held professorships at Umeå University (Sweden) and Rice University (USA). Her research focuses on protein folding, metalloproteins, biophysical methods, and applications to diseases like cancer and Parkinson’s. She leads the Wittung-Stafshede Lab , part of Chalmers’ Chemical Biology infrastructure. **Research Interests**: Protein folding mechanisms, metal transfer proteins, spectroscopic techniques, amyloid formation, and biophysical studies of macromolecules. Her work bridges fundamental biochemistry with disease mechanisms, leveraging advanced experimental and computational methods. **Awards**: Notable honors include the Arrhenius Medal (2016), membership in the Swedish Royal Academy of Sciences (2016), and sustained Wallenberg Scholar funding (2010–2022). She is recognized for pioneering contributions to chemical biology. **Key Contributions**: Her lab’s work spans protein dynamics, metallomics, and disease-related protein misfolding. Recent publications highlight systems biology approaches to yeast metabolism and drought stress responses in plants.

Willy ZwaenepoelView profile

Professor

• Operating Systems
• Distributed Systems
• Database Replication
• +4 more

Willy Zwaenepoel is a Professor and Dean of the Faculty of Engineering at the University of Sydney. He holds a B.S. from the University of Gent and M.S./Ph.D. from Stanford University. Previously, he served as Dean of the School of Computer and Communication Sciences at EPFL and was a faculty member at Rice University. His expertise spans operating systems, distributed systems, and high-performance computing. Education: B.S., University of Ghent, Belgium (1979) M.S., Stanford University (1980) Ph.D., Stanford University (1984) Research Interests: Dr. Zwaenepoel focuses on distributed systems, operating systems, and their applications in database replication, virtual machine performance, and software update mechanisms. His work includes foundational contributions to distributed shared memory (e.g., Treadmarks) and startups like iMimic Networking. Awards: ACM Fellow (2000) IEEE Fellow (1998) Fellow of the Australian Academy of Technical Sciences and Engineering (2020) Recipient of the IEEE Tsutomu Kanai Award (2007) Key Contributions: His research addresses challenges in distributed systems performance, such as latency reduction in key-value stores and efficient graph processing. Current projects explore I/O optimization in virtualized environments and causal consistency for geo-replicated systems. Students/Advising: Advises Ph.D. students and postdocs, including William in database replication. His mentorship led to the Rice University Teaching Award (2000).

Francisco ChiclanaView profile

Professor

• Fuzzy preference modelling
• Decision making with heterogeneous fuzzy information
• Decision support systems
• +7 more

Professor Francisco Chiclana is a leading academic in Computational Intelligence and Decision Making at the School of Computer Science and Informatics, De Montfort University (UK). As founder of DIGITS (De Montfort University Interdisciplinary Group in Intelligent Transport Systems), he pioneered research in trust-driven decision frameworks and fuzzy systems. PhD and BSc in Mathematics from University of Granada Coordinated DMU's REF 2014 submission in Computer Science and Informatics Co-developed DMU's Doctoral Training Programme in Intelligent Systems His research focuses on fuzzy preference modelling , consensus reaching processes , and social network analysis for decision support systems. Recent work explores AI applications in trust propagation, power-asymmetric conflict resolution, and large-scale group decision frameworks. Key publications include 15+ peer-reviewed articles in journals like IEEE Transactions on Fuzzy Systems and Information Sciences , covering topics from type-2 fuzzy logic to Nash bargaining compensation mechanisms . His Greenfield-Chiclana Collapsing Defuzzifier won third prize at DMU's Creative Thinking Awards 2010. Outstanding PhD Award (University of Granada, 2000) Finalist in DMU-THE OSCAR AWARDS for Research Excellence (2012) As supervisor of 8+ current and completed PhD students, including Sarah Greenfield and Sergio Alonso Burgos, he has shaped next-generation researchers. His £145K EPSRC-funded project (2006-2009) extended fuzzy logic applications in consensus modelling.

Maryam Mehri DehnaviView profile

Associate Professor

• High-Performance Computing
• Cloud Computing
• Machine Learning
• +3 more

Maryam Mehri Dehnavi is an Associate Professor in the Department of Computer Science at the University of Toronto and a Principal Research Scientist at NVIDIA. She holds the Canada Research Chair in Parallel and Distributed Computing and leads the ParaMathics research group. Research focuses on high-performance computing , machine learning , sparse matrix optimizations , and compiler design for heterogeneous systems. Her work develops domain-specific languages , scalable numerical libraries , and auto-vectorization techniques for cloud and GPU platforms. Recent publications address LLM compression , sparse code translation , GPU kernel synchronization , and control flow optimization . Scientific recognition: Ontario Early Researcher Award (2021), NSF CRII Grant, NSERC New Frontiers in Research Fund. Current students: Mushegh Shahinyan , Martin Phan , Maryam Haghifam , and others. Former advisees: Kazem Cheshmi (NJIT), Zachary Blanco (MIT Lincoln Lab), Yuanxi Li (Amazon).

Giacomo LanzaniView profile

Assistant Professor

• Economic Theory
• Learning
• Decision Theory
• +3 more

Giacomo Lanzani is an Assistant Professor in the Department of Economics at the University of California, Berkeley, starting July 2025. He currently holds a Postdoctoral Fellowship at the Harvard Department of Economics. He earned his PhD in Economics from MIT in June 2023. His research focuses on economic theory , with a dual emphasis on learning mechanisms under nonstandard decision criteria and the impact of social networks on economic interactions . A key theme in his work is the interplay between learning dynamics and decision-making processes in complex environments. His academic journey includes significant contributions to decision theory, particularly in analyzing how agents learn when faced with misspecifications or bounded rationality. He explores how these factors influence long-run stability, disagreement in opinion dynamics, and strategic interactions within networks. His work frequently bridges economic theory with insights from computer science and probability theory. Lanzani’s research has been recognized with prestigious awards, including the Aliprantis Prize 2025 and the Best Paper Award at the European Winter Meeting of the Econometric Society 2024. His publications appear in top journals such as Econometrica , Journal of Political Economy , and Review of Economic Studies . Before joining Berkeley, he was a visiting scholar at MIT and has collaborated with leading institutions like Harvard and UC Berkeley. His current research agenda includes exploring nonlinear fixed points in economic systems and analyzing heterogeneous learning in product markets.

Kevin T. TurnerView profile

Professor

• Mechanics
• Materials Science
• Manufacturing
• +6 more

Kevin T. Turner is the John Henry Towne Department Chair and Professor of Mechanical Engineering and Applied Mechanics at the University of Pennsylvania's School of Engineering and Applied Science, with a secondary affiliation in Materials Science and Engineering. He leads the Turner Research Group, which investigates mechanics, materials, and manufacturing challenges, specializing in micro/nano-systems, adhesion, fracture mechanics, and advanced manufacturing. His research focuses on three primary thrusts: Materials with programmable mechanical properties (e.g., electroadhesives for robotics) Fracture and adhesion in structured/heterogeneous materials Printed and flexible sensors (including biodegradable cellulose-based variants) Key projects include tunable adhesion surfaces, architected materials for damage tolerance, and additive manufacturing stress control. Turner's recent publications (2022-2023) demonstrate a strong emphasis on adhesion mechanics, robotics applications, and nanomaterial design. Trends include bio-inspired structures, machine learning optimization, and interdisciplinary approaches bridging mechanics with biomedicine and agriculture. Computational methods like physics-informed neural networks are increasingly utilized for material property analysis. He directs an active research laboratory developing novel sensor technologies and materials systems, collaborating widely across engineering and applied science disciplines.

Xingye QiaoView profile

Professor

• Statistics
• Machine Learning
• Causal Inference
• +3 more

Xingye Qiao is a Professor and Chair in the Department of Mathematics and Statistics at Binghamton University, State University of New York . He serves as Chair of the Data Science Transdisciplinary Area of Excellence steering committee and has been affiliated with Binghamton since 2010. Education : Ph.D. in Statistics (2010), University of North Carolina at Chapel Hill M.S. in Statistics (2007), University of North Carolina at Chapel Hill B.S. in Mathematics and Applied Mathematics (2005), Fudan University His research focuses on Statistics, Machine Learning, and Causal Inference , with recent work exploring conformal prediction methods, treatment effect estimation, and set-valued classification techniques. His publications span journals like Transactions on Machine Learning Research , NeurIPS , and AAAI . His 15 most recent articles (2025-2020) demonstrate expertise in areas including: bandit feedback systems, treatment effect heterogeneity analysis, spectral clustering for neuroscience, and conformal prediction under distribution shifts. These works blend statistical theory with real-world applications in healthcare, ecology, and data science. He mentors Ph.D. students in mathematical sciences and has supervised research topics such as: machine learning in precision medicine, goodness-of-fit tests for spatial processes, and high-dimensional data analysis. Courses he teaches include Math 605: Theory of Machine Learning and Data 501: Predictive & Inferential Analytics .

• Flow in underground porous media
• Geoenergy
• Underground energy storage
• +12 more

Dario Viberti is a Full Professor at the Polytechnic University of Turin, affiliated with the Department of Environmental, Land and Infrastructure Engineering (DIATI). He is a member of the Interdepartmental Center Ec-L - Energy Center Lab and actively contributes to the Master's and Continuing Education School. He serves as Coordinator for both Master’s levels I and II of the Natural Resources Development and Storage program and participates in doctoral colleges for Civil and Environmental Engineering and Materials, Sustainable Processes, and Systems for the Transition. His research focuses on flow in underground porous media , geoenergy , and underground energy storage , particularly hydrogen and CO₂ storage. His expertise spans numerical modeling, reservoir engineering, fluid mechanics, well testing, and energy transition systems. He is involved in commercial research projects with ENI Corporate University and leads initiatives related to sustainable energy applications. His recent publications reveal a strong trend in underground hydrogen storage , biogeochemical modeling , pore-scale simulation , and PVT analysis of gas mixtures . These works emphasize safety, efficiency, and long-term integrity of subsurface storage systems using advanced computational and experimental methods. Scientific Manager, Underground CO2 Storage Project (2022) Scientific Manager, Reservoir Modeling Project (2022) Coordinator, Master’s Program in Natural Resources Development and Storage Member, SEASTAR Competence Center Committee (2020–2025) He mentors several PhD students, including Michel Tawil, Marialuna Loffredo, Alice Raeli, and Alice Massimiani, supporting research in microfluidics, Lattice Boltzmann simulations, and thermodynamic characterization. He teaches courses such as Numerical Modeling for Multiphase Flow , Underground Energy Storage , and Well Logging and Testing across multiple degree programs. His work aligns with UN SDGs 7 (Affordable and Clean Energy), 9 (Industry, Innovation, and Infrastructure), and 13 (Climate Action).

• hp-Finite Element Method (hp-FEM)
• Fractional Diffusion Equations
• Boundary Element Method (BEM)
• +3 more

Prof. Jens Markus Melenk is a Professor at TU Wien's Faculty of Mathematics and Geoinformation, leading the Computational Mathematics Research Group (E101-02-1). His research focuses on advanced numerical methods for partial differential equations, with a strong emphasis on hp-FEM (hp-Finite Element Method), fractional diffusion equations, and wave propagation problems. He has contributed significantly to the development of robust and efficient algorithms for complex domains and heterogeneous media. Key research interests include the application of hp-FEM to fractional operators (e.g., integral fractional Laplacian), boundary element methods (BEM), and the analysis of wavenumber-explicit convergence for Maxwell's equations and elastic wave equations. His work bridges theoretical analysis and computational implementation, addressing challenges in multiscale problems and singular perturbations. Notable contributions: Exponential convergence of hp-FEM for fractional Laplacian problems, wavenumber-explicit error estimates for wave equations in heterogeneous media. Collaborations: Active involvement with researchers like Markus Faustmann, Christoph Schwab, and Dirk Praetorius. Supervised theses: Includes works on hp-FEM for fractional operators, RBF interpolation, and error estimators for elliptic PDEs. His research group develops and analyzes numerical schemes for challenging PDE scenarios, with applications in electromagnetism, acoustics, and computational mechanics.

Ganesh Lalitha GopalakrishnanView profile

Professor

• Computer Science
• Formal Methods
• High-Performance Computing (HPC)
• +6 more

Ganesh Gopalakrishnan is a Professor of Computer Science at the University of Utah's School of Computing. His research focuses on software correctness, floating-point arithmetic, and formal methods for HPC applications. He leads the ComPort DOE project and NSF REU Site on Trust and Reproducibility. He has advised 26 PhD students and holds seven active NSF grants and a DOE award. Education: B.Sc.(EE), National Institute of Technology Calicut (1978) M.Tech (EE), Indian Institute of Technology Kanpur (1980) PhD in Computer Science, Stony Brook University (1986) Research Interests: Formal methods, HPC correctness, GPU debugging, floating-point error analysis, neural network compression, and reproducible scientific computing. His tools include FPBoxer, HiRace, and FPDetect. Awards & Recognition: ACM Distinguished Scientist IEEE Senior Member University of Utah Beacon of Excellence Award (2012) NSF and DOE funding leadership Labs & Projects: Principal Investigator of the ComPort project (DOE) and NSF REU Site. Co-developed the Jove system for automata education and the Flit floating-point testing framework.

• Parallel Computing
• Compiler Optimization
• Performance Tuning
• +3 more

Dr. Mary Hall is a Professor in the School of Computing at the University of Utah, specializing in compiler optimization, parallel computing, and high-performance computing (HPC). Her work focuses on autotuning techniques, compiler-driven performance optimization, and minimizing data movement in computations to enhance efficiency. She has contributed significantly to frameworks like Bricks and Peak , advancing code generation for GPUs and block-structured grids. Her research also addresses educational initiatives, such as improving student retention in introductory computing courses and fostering diversity in the computing workforce through NSF-funded programs. Her research interests span compiler technology, stencil computations, and energy-efficient HPC applications. Key projects include optimizing geometric multigrid methods, developing communication-avoiding algorithms, and integrating machine learning into autotuning. She has led efforts to streamline performance portability across heterogeneous architectures and has published extensively on scheduling languages and compiler-driven optimizations. Mary Hall’s contributions include advancing data layout strategies for sparse tensors and DNNs, as well as fostering reproducibility in computational research through collaborative NSF REU programs. Her work emphasizes practical tools like ytopt and Rigel , which automate performance tuning for scientific applications. She remains active in both academic and industrial HPC communities, addressing challenges in extreme heterogeneity and scalable computing.

Hadi El-AmineView profile

Associate Professor

• Operations Research
• Healthcare Optimization
• Public Policy
• +6 more

Hadi El-Amine is an Associate Professor at George Mason University's Department of Systems Engineering and Operations Research, focusing on applying operations research to healthcare and public policy challenges. His methodological expertise spans stochastic and robust optimization, probability theory, and resource allocation under uncertainty. PhD in Operations Research from Virginia Tech (2012) MS in Engineering Management and BS in Electrical and Computer Engineering from American University of Beirut, Lebanon His research addresses critical healthcare issues through mathematical modeling, including blood bank safety, pandemic control, organ transplantation logistics, and surgical infection risk assessment. Recent work explores fair clustering algorithms, risk-based quarantine policies, and incentive design for absenteeism reduction. Scientific awards include the 2015 INFORMS Bonder Scholarship and finalist recognitions for the 2015 Pierskalla Award and 2014 Washington DC Student Excellence Competition. Collaborations with the American Red Cross have produced impactful blood screening strategies balancing safety and budget constraints.

Michael ThorneView profile

Associate Professor

• Global seismic wave propagation
• Global seismic structure and dynamics
• Core-mantle boundary structure
• +9 more

Michael Thorne is an Associate Professor in the Department of Geology & Geophysics at the University of Utah, where he has been faculty since July 2014. His research focuses on using seismology to investigate Earth's structure, with specialization in mapping seismic structure of the deep mantle and developing numerical techniques for seismic wave propagation modeling. He teaches courses in geophysics, the dynamic Earth, and seismology. Dr. Thorne's educational background includes: B.S. in Physics from Indiana University Bloomington (1991-1996) Ph.D. in Geological Sciences from Arizona State University (2000-2005) His primary research interests span global seismic wave propagation, Earth's deep interior structure, and the dynamics of the core-mantle boundary region. Dr. Thorne specializes in studying ultra-low velocity zones (ULVZs), D" discontinuity structure, and seismic array processing techniques. His work combines advanced waveform modeling with seismic observations to understand Earth's deep structure and dynamics, including connections between deep mantle features and surface geology. Dr. Thorne's recent publications demonstrate a consistent focus on ultra-low velocity zones at the core-mantle boundary, with increasing sophistication in modeling techniques. His work has evolved from basic detection of ULVZs to detailed characterization of their morphology, elastic properties, and potential origins. Recent papers incorporate advanced Bayesian inversion methods and multidimensional modeling to better understand these enigmatic features and their implications for Earth's thermal and chemical evolution. Dr. Thorne actively mentors graduate students through thesis research courses and has secured numerous research grants to support his work. His current projects include: "Mapping the Lateral Variability of Groundwater Input into the Great Salt Lake Using Electrical Methods" (2024-2025) "NSFGEO-NERC: Advancing Capabilities to Model Ultra-Low Velocity Zone Properties Through Full Waveform Bayesian Inversion" (2024-2027) "The Future of Glaciers Using a Novel, Interdisciplinary Approach" (2024-2026) "Global Search for D Discontinuity Structure" (2022-2026) His research group utilizes advanced computational methods and collaborates with institutions worldwide to investigate Earth's deep interior structure using seismic observations and modeling.

Francesca IevaView profile

Associate Professor

• Biostatistics
• Health Data Science
• Statistical Learning
• +5 more

Francesca Ieva serves as Associate Head of the Health Data Science Centre at Human Technopole and Associate Professor of Statistics at the Politecnico di Milano, where she leads the MOX - Modeling and Scientific Computing laboratory within the Department of Mathematics. She also co-leads the Di Angelantonio & Ieva Group, a collaborative research team focused on integrating molecular data with clinical information to advance precision medicine. Dr. Ieva received her PhD in Mathematical Models and Methods for Engineering in 2012. Her academic journey has positioned her at the forefront of health data science, bridging statistical methodology with biomedical applications. Her research focuses on statistical learning in biomedical sciences, with particular emphasis on developing advanced models for integrating complex clinical data to inform predictions in clinical decision-making. Francesca's work spans multiple domains including: Development of novel survival analysis techniques for healthcare applications Integration of DNA methylation data for cardiovascular risk prediction Application of machine learning to clinical pathway analysis in mental health Innovative approaches to polygenic risk scoring incorporating SNP interactions Development of radiomics models for cancer prognosis and treatment response Analysis of Dr. Ieva's recent publications reveals a strong trend toward federated learning approaches in healthcare data analysis, reflecting growing concerns about data privacy while maintaining analytical power. Her work increasingly integrates multiple data types (genomic, epigenetic, imaging, and clinical records) to create more comprehensive patient profiles for precision medicine applications. A notable pattern is her focus on translating complex statistical models into clinically actionable tools that can directly inform patient care and healthcare policy decisions. As Associate Head of the Health Data Science Centre at Human Technopole, Dr. Ieva oversees a research group comprising epidemiologists, statisticians, and data scientists working collaboratively to bridge the gap between genotype and phenotype. The Di Angelantonio & Ieva Group develops innovative studies that integrate biomolecular data with medical records, imaging, and portable medical device data. Her team utilizes both existing healthcare data and newly generated population-based studies, applying novel analytical methods that integrate clinical epidemiology with health research to improve data interpretation. Dr. Ieva actively mentors several PhD students including Andrea Lampis, Katherine Marie Logan, Alessia Mapelli, Michela Carlotta Massi, and Andrea Mario Vergani. Her research has been supported by collaborations with major healthcare institutions and likely receives funding from research councils and health technology initiatives, though specific grant details are not provided in the available information.

Sara BallouzView profile

Senior Lecturer

• Transcriptomics (single-cell and bulk)
• Co-expression
• X-linked disorders
• +3 more

Dr. Sara Ballouz is a Senior Lecturer at the University of New South Wales (UNSW) , affiliated with the Garvan Institute of Medical Research and the Garvan-Weizmann Centre for Cellular Genomics . Her research integrates computational and experimental approaches to understand the genetic architecture of disease , with a focus on X-linked disorders , sex differences in disease , and personalized medicine . 2013 - PhD, Victor Chang Cardiac Research Institute and UNSW 2008 - BE Bioinformatics (First Class Honours), UNSW 2008 - BSc Genetics, UNSW Her work addresses challenges in analyzing multi-omics data (genomes, transcriptomes, epigenomes, proteomes) to develop robust tools for personalized medicine. Key contributions include improving RNA-seq accuracy through pan-human consensus genomes and creating computational methods like AuPairWise and CoCoCoNet for co-expression analysis. She explores developmental stochasticity and its transcriptional legacy, alongside cross-tissue X-chromosome inactivation patterns. Recent publications emphasize single-cell transcriptomics applications in autoimmune diseases (e.g., X-linked chronic granulomatous disease ), long COVID immune dysregulation , and cardiometabolic disease treatment. Her group at Garvan-Weizmann Centre leverages computational genomics and meta-analysis to address reproducibility in biomedical research. Dr. Ballouz supervises students including Lachlan Gray , who investigates X chromosome roles in female autoimmune disease . She collaborates with institutions like Cold Spring Harbor Laboratory and Victor Chang Cardiac Research Institute . Email: s.ballouz@unsw.edu.au