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Michael LandisView profile

Assistant Professor

• Computational Biology
• Statistical Inference
• Phylogenetics
• +9 more

Michael Landis is an Assistant Professor in the Department of Biology at Washington University in St. Louis, with a courtesy appointment in the Department of Computer Science & Engineering. He obtained his PhD in Biology from UC Berkeley, followed by postdoctoral fellowships at Iowa State University and Yale University. His research focuses on computational and statistical approaches to understand evolutionary processes, particularly phylogenetics, biogeography, and trait evolution. He develops probabilistic models and open-source software (e.g., RevBayes) to analyze evolutionary patterns in biological datasets. His work spans topics like historical biogeography, phylogenetic biome shifts, and the evolution of ecological interactions. Landis also teaches courses in bioinformatics and computational biology. His lab actively collaborates on projects involving plant radiations (e.g., Hawaiian plants) and viral phylogeography. Funded by NSF and other grants, his research integrates methods from computer science, statistics, and evolutionary biology. Education: PhD in Biology, UC Berkeley; Postdocs at Iowa State and Yale. Research interests include phylogenetic modeling, Bayesian inference, and deep learning applications in evolutionary studies. His lab emphasizes open-source tools and community-driven science. Current students and postdocs work on topics like chromosome evolution, viral spread dynamics, and phenotypic diversification. Lab values prioritize collaboration, mentorship, and balancing personal-professional life.

Grace YiView profile

Professor

• Statistical Methodology
• Measurement Error
• Causal Inference
• +3 more

Grace Yi is a Professor at the University of Western Ontario and holds a Tier I Canada Research Chair in Data Science. She is affiliated with the Departments of Statistical and Actuarial Sciences and Computer Science. Her research focuses on statistical methodology addressing challenges in measurement error, causal inference, missing data, and machine learning. Yi has authored influential works, including the monograph Statistical Analysis with Measurement Error or Misclassification and co-edited Handbook of Measurement Error Models . She has served as Co-Editor-in-Chief of The Electronic Journal of Statistics and President of the Statistical Society of Canada. Her accolades include the CRM-SSC Prize (2010), Fellowships from the IMS and ASA, and leadership roles in professional societies. Education: Ph.D. in Statistics (University of Toronto, 2000), M.A. in Statistics (York University, 1996), M.Sc. and B.Sc. in Mathematics (Sichuan University, China). Research Interests: Measurement error models, causal inference, high-dimensional data analysis, statistical machine learning. Yi’s work bridges theoretical advancements and practical applications, particularly in handling noisy data across disciplines like epidemiology and public health. Her recent studies include analysis of COVID-19 data dynamics and quarantine strategies. She has supervised numerous students and contributed to software development, including R packages like augSIMEX and swgee .

Zhen LiView profile

Assistant Professor

• Multiscale/Multiphysics modeling
• Soft matter and Smart materials
• Biophysics and Collective dynamics
• +7 more

Dr. Zhen Li is an Assistant Professor in the Department of Mechanical Engineering at Clemson University's College of Engineering, Computing and Applied Sciences. He joined Clemson in August 2019 after serving as a research associate professor at Brown University and a postdoctoral research associate at University of California, Merced. Education: Ph.D. in Fluid Mechanics, Shanghai University, 2012 MS in Fluid Mechanics, Shanghai University, 2008 BS in Engineering Mechanics, Wuhan University, 2005 Dr. Li's research focuses on multiscale modeling of soft matter, complex fluids, biophysics, and collective dynamics using both bottom-up (coarse-grained molecular modeling) and top-down (from continuum descriptions to fluctuating hydrodynamics) approaches, along with high-performance computing. His work spans mathematical theory for coarse-graining and model reduction, statistical methods and machine-learning approaches applied to multiscale modeling, memory effects in complex fluids, and concurrent coupling of heterogeneous solvers for scale-bridging. Analysis of Dr. Li's recent publications reveals a strong trend toward integrating machine learning with traditional computational methods, particularly neural operators for multiscale problems. His work spans diverse applications from bubble dynamics and blood flow to materials science and bioprinting, demonstrating the versatility of his computational approaches across multiple disciplines in engineering and physics. Awards and Recognition: CECAS Dean's Professor Award (2024) Award of Excellence - Junior Faculty (2021-2022) Best Research Poster Award at SC19 (2019) 2nd Place Award of Best Poster Presentation at DOE/EFRC AIM for Composites meeting (2024) Dr. Li actively mentors PhD students including Miles Lu, Ryan Wan, Haizhou Wen, and Ali Mohammadi, who have published significant research in computational mechanics. His research is supported by multiple grants including an NSF Elements grant as PI for 'SciMem: Enabling High Performance Multi-Scale Simulation on Big Memory Platforms', an NSF CDS&E grant as co-PI for 'HAM3R: Heterogeneous Automated Management of Multiscale Methods and Resources', a DOE/EFRC grant as Thrust lead co-PI for 'AIM for Composites', and a NASA EPSCoR grant as Science-PI. Dr. Li leads the MuthComp (Multiscale theory and Computation) research group, which focuses on developing interfaces between Engineering, Applied Mathematics, Physics-based Machine Learning, and High Performance Scientific Computing. The group has active collaborations with institutions including Idaho National Laboratory, University of Tokyo, and Brown University, and has developed open-source software including USERMESO for GPU-accelerated DPD simulations.

Luca Di GasperoView profile

Associate Professor

• Artificial Intelligence
• Combinatorial Optimization
• Metaheuristics
• +1 more

Luca Di Gaspero is an Associate Professor of Information Technology at the University of Udine, specializing in metaheuristic optimization techniques. His research enhances combinatorial optimization through hybridization of algorithms for scheduling, routing, and industrial applications. Research spans artificial intelligence in optimization, scheduling algorithms for manufacturing/healthcare, and metaheuristic framework development. Recent publications focus on LLMs in optimization, parallel batch scheduling, and energy-efficient manufacturing. Key Contributions: Developed EasyLocal++ framework for local search algorithms Advanced multi-neighborhood simulated annealing techniques Applied metaheuristics to healthcare logistics and emergency services

Christen FlemingView profile

Assistant Professor

• Animal Movement
• Species Distribution
• Phylogenetic Evolution
• +3 more

Christen Fleming is an Assistant Professor at the University of Central Florida who leads the Ecoinformatics Lab. The lab specializes in developing and applying analytic methods for ecological, environmental, and evolutionary data with a focus on conservation needs. Develops continuous-time movement modeling (ctmm) software Collaborates with institutions like CASUS, UMD, UBC, and UA Focuses on interdisciplinary approaches combining mathematics, statistics, and computational methods Research interests emphasize animal movement analysis, species distribution modeling, and population dynamics. Fleming’s work involves complex data analysis and stochastic processes to improve conservation strategies through better understanding of animal space use and movement patterns.

• Network Optimization
• Infrastructure Resilience
• Supply Chain Risk
• +3 more

Nafiseh Ghorbani-Renani is a Teaching Professor in the Department of Systems and Enterprises at Stevens Institute of Technology. Her research develops mathematical frameworks for managing risk in interconnected systems such as critical infrastructure, supply chains, and energy networks. Education includes: PhD in Industrial and Systems Engineering (University of Oklahoma) MS in Industrial Engineering (University Technology Malaysia) BS in Industrial Engineering (Yazd University) Research integrates optimization techniques with machine learning for resilience planning in interdependent networks. Current projects examine microgrid energy trading, supply chain disruption management, and infrastructure restoration scheduling. Recent publications focus on tri-level optimization models for critical infrastructure protection, energy equity in community microgrids, and machine learning applications for network resilience. Articles demonstrate consistent advancement in modeling complex interdependencies across infrastructure systems. As Faculty Advisor for the Software Division, she guides student projects bridging theoretical models with practical implementation. Course instruction spans software engineering fundamentals, agile methodologies, and systems development lifecycle management.

Daniel TauritzView profile

Professor

• Artificial Intelligence for National Security
• Cyber Security
• Critical Infrastructure Protection
• +5 more

Daniel Tauritz is a Professor in the Department of Computer Science and Software Engineering at Auburn University and serves as Director for National Laboratory Relationships. He holds a Ph.D. and M.S. in Computer Science from Leiden University, along with propaedeutic studies in Computer Science and Mathematics. His research focuses on AI-driven cybersecurity, automated algorithm design using hyper-heuristics, computational game theory, and evolutionary computation. Tauritz leads initiatives such as the Auburn Cyber Research Center and collaborates with institutions like Los Alamos National Laboratory on critical infrastructure protection and satellite network security. His work bridges academia and national security through projects like the Cyber Fire Puzzles competition and the Satellite Tycoon economic simulation game. His educational background includes advanced studies at Leiden University, with a strong foundation in computer science and mathematics. Research contributions span evolutionary algorithms for molecular evolution, coevolutionary defense strategies, and generative hyper-heuristics. Tauritz has secured grants including an NSF award for AI-cybersecurity education and contributed to Auburn’s partnerships with national laboratories. He is actively involved in fostering student engagement through ethical hacking clubs and experiential learning programs. Notable achievements include moderating panels on AI in cybersecurity and AI workforce development, as well as developing frameworks like Galaxy for network emulation and DCAFE for automated cyber experiments. His publications emphasize applying evolutionary computation to real-world challenges, including satellite constellation economics and adversarial network defense strategies.

Sergio Gómez JiménezView profile

Associate Professor

• Complex Networks
• Complex Systems
• Neural Networks
• +4 more

Sergio Gómez Jiménez is an Associate Professor in the Department of Computer Engineering and Mathematics at Rovira i Virgili University (URV), Tarragona, Spain. He joined URV in 1995 and has held his current position since 1997. He obtained degrees in Physics (1990) and Mathematics (1995) and a PhD in Physics (1994) from the Universitat de Barcelona. His research focuses on complex networks, including community structure analysis, epidemic spreading, urban congestion, and applications to biology, medicine, and social systems. He has authored over 100 publications in high-impact journals like Nature Methods and Physical Review Letters. He coordinates the interuniversity Master's in Biomedical Data Science and the PhD Program in Bioinformatics. His editorial roles include Associate Editor of Complexity and Review Editor of Frontiers in Physics. Notable awards include the American Physical Society's Outstanding Referee (2015) and the Web Science Trust's Test of Time Award (2024). His work on modeling the spatiotemporal spread of epidemics, such as the 2020 COVID-19 pandemic, has received significant attention. He also contributed to urban traffic congestion analysis and developed algorithms for hierarchical clustering (e.g., MultiDendrograms). Collaborations span institutions like the University of Oxford and CERN, reflecting his interdisciplinary approach to complex systems.

Carson LeungView profile

Professor

• Big Data Science
• Data Mining
• Machine Learning
• +5 more

Dr. Carson Kai-Sang Leung is a Full Professor in Computer Science at the University of Manitoba's Faculty of Science. He founded and directs the Database & Data Mining Lab. His research focuses on big data science, data mining, machine learning, health informatics, and visual analytics. He holds SMIEEE and SMACM fellowships, reflecting his contributions to the field. Education: B.Sc., M.Sc., and Ph.D. from the University of British Columbia (UBC). Research interests include human-centered exploratory data mining, image databases, and scalable algorithms. He emphasizes user-driven constraints in mining processes and has developed techniques like the segment support map and OSSM for optimized frequency counting. His work on subimage queries in large image databases addresses real-world challenges in visual data retrieval. Publications span data mining, healthcare analytics, and transportation systems. His lab collaborates on projects like visual analytics for motor vehicle accidents and environmental data science for smart cities. He is affiliated with institutions such as the Institute of Industrial Mathematical Sciences (IIMS) and TRLabs. Key awards: Senior Member of IEEE (SMIEEE) and Senior Member of ACM (SMACM).

Jun YanView profile

Professor

• Survival Analysis
• Spatial Statistics
• Network Analytics
• +2 more

Dr. Jun Yan is a Professor in the Department of Statistics at the University of Connecticut. His research spans network analytics, spatial extremes, survival analysis, and statistical computing with applications in public health, finance, and environmental science. His core research interests include: network modeling and analysis, spatial statistics for climate extremes, survival analysis methodologies, statistical computing frameworks, and applications in interdisciplinary domains including sports analytics. Dr. Yan has developed significant statistical methodologies for network analysis, climate change detection, financial modeling, and health analytics. His recent publications demonstrate innovation in modeling complex network structures, analyzing climate extremes, developing computational approaches for massive datasets, and creating specialized statistical methods for health and finance applications. He maintains active collaborations across disciplines and contributes to open-source statistical software. Honors include: Guggenheim Fellowship, multiple Fromm Foundation commissions, and Barlow Endowment recognition.

Audris MockusView profile

Professor

• Digital Archaeology
• Software Engineering
• Open Source Development
• +3 more

Audris Mockus is the Ericsson-Harlan D. Mills Chair Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville. He holds a PhD in Statistics from Carnegie Mellon University and has extensive experience in software engineering research, particularly in Digital Archaeology and Open Source Software analysis. His work focuses on understanding developer behavior, software evolution, and global software development challenges. Education: PhD in Statistics, Carnegie Mellon University, 1994 MS in Applied Mathematics, Moscow Institute of Physics and Technology, 1991 BS in Applied Mathematics, Moscow Institute of Physics and Technology, 1988 Research Interests: Mockus specializes in recovering and analyzing digital traces of software development to study developer behavior, software quality, and project dynamics. His research includes quantifying open-source supply chains, measuring developer expertise, and improving software defect prediction. He has pioneered methods for analyzing version control systems and global software teams. Awards and Grants: Recipient of multiple best paper awards at ICSE, FSE, and other conferences. Lead PI on NSF grants totaling over $2M for projects like the World of Code (WoC) infrastructure and forensic image curation. Patents in data visualization and software defect prediction. Advising and Labs: Mockus has advised PhD students on topics like npm ecosystem analysis and forensic image clustering. His research group collaborates with institutions like the National Institute of Justice on projects like Image Cloud Platform for forensic data curation.

Tracy CampView profile

Professor

• Ad Hoc Networks
• Wireless Sensor Networks
• Machine Learning
• +3 more

Tracy Camp is a Professor and Founding Department Head of Computer Science at the Colorado School of Mines. She leads the Toilers research group, focusing on ad hoc networks and wireless sensor systems for geosystems. With over 20 NSF grants and $20M in funding, her work has produced 12 software tools used globally. She holds ACM and IEEE Fellowships, a Fulbright Scholarship, and the Mines Outstanding Faculty Award. Education: B.S. Mathematics, Kalamazoo College (1987) M.S. Computer Science, Michigan State University (1989) Ph.D. Computer Science, The College of William & Mary (1993) Research Interests: Her work bridges machine learning and geosystems, including dam integrity monitoring via seismic data, UAV communication protocols, and secure encrypted traffic classification. She emphasizes interdisciplinary approaches for real-world challenges like disaster response and environmental safety. Awards: ACM Fellow (2017) IEEE Fellow (2015) NSF CAREER Award (2007) Fulbright Scholar (2006) Grants & Impact: Over 80 refereed publications and 12 invited articles, cited ~7,000 times. Her grants include initiatives to broaden participation in computing, such as the S-STEM scholarship program. Software tools developed under her grants have been adopted by 3,000+ researchers in 86 countries. Labs & Teams: Directs the Toilers group, advancing ad hoc network evaluation and geophysical monitoring. Collaborates on projects like the ADMIRE dam monitoring system and the DREAM master’s program for underrepresented students.

Bruce JacobView profile

Professor

• Memory Systems
• Computer Architecture
• Memory Devices
• +7 more

Bruce Jacob is a Professor in the Cyber Science Department within the School of Engineering, Computing, and Weapons at the United States Naval Academy, where he joined in Fall 2022. Prior to USNA, he served as a professor of Electrical & Computer Engineering at the University of Maryland for 25 years, establishing himself as a leading expert in memory systems and computer architecture. Dr. Jacob received his A.B. in Mathematics from Harvard in 1988 and his Ph.D. in Computer Science & Engineering from The University of Michigan in 1997. Before graduate school, he worked in Boston start-ups as a software engineer at Boston Technology and later as chief engineer and system architect at Priority Call Management, which was successfully acquired in the late 1990s. His research focuses on memory systems, computer architecture, and memory devices, with significant contributions to memory system design across industry and government sectors. Jacob has designed computer-system architectures and memory-system architectures for major organizations including Micron (Hybrid Memory Cube DRAM architecture), Cray (Black Widow memory system), Northrop Grumman (experimental ultra-low-power datacenter), and the European Commission (1024-core Teraflux chip). His work spans resistive memory systems, non-volatile memory, memory simulation, and hardware security. His publication record demonstrates consistent leadership in memory systems research, with recent work focusing on ReRAM development, trusted execution environments for in-storage computing, and advanced memory simulation techniques. His research shows a clear trajectory toward monolithic memory integration, 3D stacking, and addressing the semantic gap between software and memory systems. Fellow of the IEEE Patent in memory-systems design Three patents in electric guitar circuit design Featured in Washington Post, Los Angeles Times, Chronicle of Higher Education, and NPR for guitar-related innovations Jacob has written two textbooks on computer memory systems and numerous articles spanning memory systems, computer design, embedded systems, operating system design, and even ventured into astrophysics and algorithmic composition. His DRAMsim memory simulator has become an industry standard tool. His practical industry experience complements his academic work, having consulted for major technology companies on memory system design challenges.

• Computer Science
• Software Engineering
• Formal Methods
• +6 more

Thomas Henzinger is a Professor at the Institute of Science and Technology Austria (IST Austria) since 2009, serving as its President from 2009 to 2022. He holds honorary doctorates from Fourier University and Masaryk University. His research focuses on formal methods for improving software reliability in concurrent and embedded systems, with contributions to quantitative verification, quantum computing, and algorithmic fairness. He leads the Henzinger Group , which addresses challenges in software correctness, safety-critical systems, and AI ethics. Education: PhD from Stanford University (1991), followed by postdoctoral work at Université Joseph Fourier (1991). Research Interests: Concurrent and embedded systems, formal methods, quantum algorithms, fairness in AI, and runtime verification. His team develops mathematical foundations and tools to ensure software robustness in complex systems. Recent Trends in Publications: Focus on advancing formal verification techniques for neural networks, quantum computing, and ethical AI. Key contributions include fairness shields for decision systems and middleware for third-party monitoring (VAMOS). Awards: Royal Society Fellow (2024), US National Academy of Sciences Member (2020), Wittgenstein Award (2012), and numerous test-of-time awards. Advising & Grants: Advised over 20 PhD students. Secured ERC Advanced Grants (VAMOS, QUAREM) and FWF grants (Wittgenstein Award). Labs/Teams: Leads the Henzinger Group , collaborating on projects like SPyCoDe (semantic security) and VAMOS (third-party monitoring middleware).

Daniel H. JarvisView profile

Professor

• Mathematics Education
• Technology Integration
• STEAM Education
• +2 more

Dr. Daniel H. Jarvis is a professor at the Schulich School of Education, Nipissing University, where he teaches in both undergraduate (BEd) and graduate (MEd/PhD) programs. His research focuses on mathematics education, technology integration, curriculum development, and STEAM education initiatives. He has led several collaborative research projects including a SSHRC-funded international study on Computer Algebra Systems (CAS) technology in undergraduate mathematics instruction. Dr. Jarvis's research interests span mathematics pedagogy, workplace technology applications, interdisciplinary curriculum integration, and innovative educational approaches. His work consistently explores the intersection of mathematical concepts with technology-enhanced learning environments. His publications demonstrate a strong focus on educational technology integration, mathematics pedagogy, and curriculum innovation. Recent work examines exergames in wellbeing education, communities of practice for language teachers, and technology-enhanced assessment methods in mathematics education. Dr. Jarvis has held significant administrative roles including Chair of Graduate Studies in Education (2011-2013) and Associate Dean (Interim) of the Bachelor of Education programs (2022-2024). He has collaborated on cross-disciplinary projects with colleagues in Geography, Computer Science, Nursing, and Physical Education.