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Youngchul RaView profile

Associate Professor

• Combustion Modeling
• Computational Fluid Dynamics (CFD)
• Internal Combustion Engines
• +4 more

Dr. Youngchul Ra is an Associate Professor in the Department of Mechanical and Aerospace Engineering at Michigan Technological University. He holds a PhD from MIT (1999) and degrees from Seoul National University. His expertise includes computational fluid dynamics (CFD), combustion modeling, chemical kinetics, and alternative fuel research. His work focuses on advanced combustion strategies like Gasoline Compression Ignition (GCI), engine CFD code development, and high-performance computing. Education: PhD in Mechanical Engineering, Massachusetts Institute of Technology (1999) Masters and Bachelors in Mechanical Engineering, Seoul National University Research Interests: Developing multi-component fuel models for real-world applications Optimizing six-stroke GCI engines with advanced valve technologies Reducing emissions via combustion control and injection strategies Parallel computing techniques for large-scale engine simulations Recent work emphasizes oxygenated fuels in GCI engines and parametric studies of combustion efficiency. His CFD models are validated against experimental data for accuracy. His research has led to advancements in low-temperature combustion and emission reduction without explicit awards listed. He collaborates on engine design optimization and fuel formulation projects.

Fabio PierellaView profile

Associate Professor

• Offshore Wind Energy
• Fluid Dynamics
• Structural Engineering
• +3 more

Fabio Pierella is an Associate Professor at the Technical University of Denmark (DTU), affiliated with the Department of Wind and Energy Systems Flows, specializing in Wind Turbine Design Division. His research focuses on offshore wind energy systems, fluid dynamics, and structural engineering. He has contributed to projects like OC6 Phase IV and the DeRisk database, validating numerical models for floating offshore wind structures and extreme wave loads. Key research interests include computational fluid dynamics (CFD), hydrodynamic load modeling, and the design of large-scale floating wind turbines. His work spans numerical simulations, experimental validation, and database development for extreme sea states. Pierella has presented at international conferences on topics like wave-structure interaction and turbine control systems. He received the Best Poster Presentation Award (2024) and contributed to datasets such as the DeRisk Database, which provides critical wave data for offshore wind turbine design. His research emphasizes practical applications, including monopile structural integrity under extreme loads and control strategies for floating platforms. Pierella's activities include conference presentations on ultra-large floating turbines (EMULF2 project) and the impact of wave shape on 15MW turbine loads. His interdisciplinary approach integrates computational models with experimental results to address challenges in offshore renewable energy systems.

Abolfazl HashemiView profile

Assistant Professor

• Large-Scale Optimization
• Machine Learning
• Artificial Intelligence
• +6 more

Abolfazl Hashemi is an Assistant Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University, directing the MINDS Group. He holds a B.Sc. from Sharif University of Technology (2014), and M.S.E. and Ph.D. degrees from The University of Texas at Austin (2016, 2020). His research focuses on Large-Scale Optimization for AI/ML, Learning at the Edge, and Decision-Making under Uncertainty, with applications in Federated Learning, Medical Image Analysis, and Cyber-Physical Systems. He leads the MINDS Group and collaborates with EnCORE and ICON centers. Key research areas include optimizing algorithms for machine learning, robustness in distributed systems, and adversarial learning. He has developed algorithms with mathematical guarantees for efficient deployment under resource constraints. Teaching includes Optimization for Deep Learning (graduate) and undergraduate courses like ECE 20001. He advises the Purdue RoboMaster robotics team and has mentored students through programs like SURF and Summer Stay Scholars. Outreach activities include fostering diversity through robotics competitions and research fellowships. His work bridges theoretical optimization with practical AI applications, emphasizing equitable and robust solutions in federated and decentralized learning.

• Mobile and cloud computing
• Energy-efficient computing
• Image and video processing
• +2 more

Yung-Hsiang Lu is a Professor of Electrical and Computer Engineering at Purdue University's Elmore Family School of Electrical and Computer Engineering. His research focuses on mobile/cloud computing, energy-efficient computing, and image/video processing. He holds a BSEE from National Taiwan University (1992), an MSEE (1996), and a PhD (2002) from Stanford University. Dr. Lu's academic background includes significant contributions to VLSI and circuit design, with primary emphasis on computer engineering. His work spans theoretical and applied domains, including optimizing neural networks for edge devices, securing deep learning models, and leveraging large language models for software development. Recent research trends in his articles emphasize energy efficiency in AI systems, interdisciplinary applications of transformers (e.g., music analysis), and challenges in model interoperability and security. His publications also highlight innovations in global camera networks and real-time visual data analysis. While no specific grants or awards are explicitly mentioned, his extensive list of publications reflects sustained academic engagement. His educational contributions include developing C programming resources and teaching large-scale image processing using global camera networks. Dr. Lu's professional address is at Purdue's Materials and Electrical Engineering Building in West Lafayette, Indiana, where he maintains an active research lab focused on embedded systems and low-power computing innovations.

• Data Mining for Business
• Data Visualization
• Large-Scale Experimental Design
• +3 more

Ben Collier is an Assistant Teaching Professor of Business Analytics at the Tepper School of Business , Carnegie Mellon University. He holds a PhD in Information Systems and Organizational Behavior from Carnegie Mellon and has extensive experience in data science leadership roles in industry. PhD, Information Systems and Organizational Behavior (2012), Carnegie Mellon University MS, Information Systems and Organizational Behavior (2009), Carnegie Mellon University MBA, Information Systems (2007), University of Wisconsin-Madison BBA, Management Computer Systems and Mathematics (2004), University of Wisconsin-Whitewater His research focuses on data mining for business , data visualization , and large-scale experimental design , with applications in healthcare analytics, online community dynamics, and gender equity in technology. His recent work includes monetization data science for Duolingo's $6.5 billion IPO and developing UPMC's CognitiveRx analytics engine. Ben's publications span topics including gender gaps in Wikipedia , leadership in open collaboration communities , and conflict resolution in crowdsourced platforms . He has served on CMU committees for curriculum review and summer summit planning, and actively advises MSBA capstone projects.

Nora AyanianView profile

Associate Professor

• Robotics
• Artificial Intelligence
• Multi-Agent Systems
• +4 more

Nora Ayanian is an Associate Professor of Computer Science and Engineering at Brown University, leading the Automatic Coordination of Teams Lab. She holds a PhD and M.S. in Mechanical Engineering from the University of Pennsylvania, followed by postdoctoral research at MIT’s CSAIL. Her work focuses on multi-robot systems, integrating task assignment, path planning, and control to enable coordinated teams of robots for applications in manufacturing, environmental monitoring, and emergency response. Affiliations: Brown University School of Engineering, Department of Computer Science. Former roles include Andrew and Erna Viterbi Associate Professor at USC and WiSE Gabilan Chair holder. Research Interests: Aerial robotics, multi-agent systems, swarm intelligence, and control systems. Notable projects include the Crazyswarm nano-quadcopter swarm and downwash dynamics research for large-scale UAV teams. Awards: 2016 MIT TR35 Innovator, NSF CAREER Award, Okawa Foundation Grant, and recognition as one of IEEE's 'AI’s 10 to Watch'. Teaching: Courses on coordinated robotics and robotics as an artistic medium. Active mentorship with awards for inspiring students and fostering diversity in STEM. Grants & Labs: Director of the Automatic Coordination of Teams Lab, with funding from NSF and industry partnerships. Research has been featured in USA Today, Discovery Channel, and Tech Insider.

Fernando CamelliView profile

Associate Professor

• Computational Fluid Dynamics (CFD)
• Urban Environmental Modeling
• Contamination Dispersion Modeling
• +3 more

Fernando Camelli is an Associate Professor in the Physics & Astronomy Department at George Mason University, holding dual roles as Instructional Faculty and Faculty. His research focuses on computational fluid dynamics (CFD), urban environmental modeling, and high-performance computing. He specializes in simulating complex fluid flows in urban environments, subway systems, and industrial applications, with particular emphasis on turbulence modeling, fluid-structure interaction, and GPU-accelerated algorithms. Key research areas include: CFD for urban airflow and contamination dispersion Meshless and immersed boundary methods Integration of geographic information systems (GIS) with CFD Large-scale simulations using parallel computing His work addresses practical challenges such as subway ventilation optimization, emergency contaminant dispersion prediction, and urban infrastructure design. Recent studies emphasize scalability improvements for fluid-structure interaction simulations and GPU-based code modernization.

Roman SamulyakView profile

Professor

• Computational Fluid Dynamics
• Magnetohydrodynamics
• Plasma Physics
• +2 more

Roman Samulyak is a Professor in the Department of Applied Mathematics and Statistics at Stony Brook University. He holds a Ph.D. from NJIT in Applied and Computational Mathematics with specializations in Hydro- and Electrodynamics. His research develops advanced numerical algorithms for modeling complex physical systems in high-energy physics and fusion energy. Research spans computational methods for magnetohydrodynamics, plasma physics, nuclear fusion/fission systems, and particle accelerator design. Current applications include disruption mitigation in tokamaks and laser-driven particle acceleration. Recent publications demonstrate strong focus on plasma-based accelerators and fusion reactor modeling, particularly pellet ablation dynamics, laser wakefield acceleration, and MHD simulations of tokamak plasmas. Research utilizes high-performance computing resources for large-scale simulations. Office location is Math Tower 1-108 at Stony Brook University.

Rajen ShahView profile

Professor

• Statistical Inference
• High-Dimensional Data Analysis
• Machine Learning
• +3 more

Professor Rajen Shah is a faculty member in the Statistical Laboratory at the University of Cambridge, part of the Department of Pure Mathematics and Mathematical Statistics (DPMMS) within the Faculty of Mathematics. His research focuses on statistical methodology, particularly in high-dimensional data analysis, machine learning, and robust statistical inference. He is known for contributions to areas such as change-point regression, inverse propensity score weighting, and efficient estimation techniques in complex models. Key research interests include developing novel methods for variable selection, robust hypothesis testing, and scalable algorithms for large-scale data. He has collaborated on interdisciplinary projects, such as functional genomics studies (e.g., screening conserved genes of unknown function). His work often emphasizes theoretical rigor alongside practical applications in fields like causal inference and computational statistics. Prof. Shah has published extensively in top-tier journals like The Annals of Statistics , Bernoulli , and Journal of the Royal Statistical Society Series B . His recent articles address challenges in cross-validation for change-point detection, rank-transformed subsampling, and sandwich boosting methods. He is actively involved in the academic community, contributing to the Cambridge Statistics Clinic and supervising research in statistical methodology. His research group is affiliated with the Statistical Laboratory at the Centre for Mathematical Sciences, Cambridge. The lab focuses on advancing statistical theory and applications, with a strong emphasis on high-dimensional and assumption-lean methods.

Chun WangView profile

Professor

• Quantitative Research Methods
• Item Response Theory (IRT)
• Computerized Adaptive Testing (CAT)
• +3 more

Chun Wang is a Professor in the Department of Measurement & Statistics at the University of Washington's College of Education. His research focuses on advancing quantitative methods in educational and psychological measurement, with expertise in item response theory (IRT), computerized adaptive testing (CAT), and cognitive diagnostic modeling. He holds affiliate faculty status at the Center for Statistics and the Social Sciences. Education: B.S. in Psychology, Peking University (China) M.S. and Ph.D. in Quantitative Psychology, University of Illinois at Urbana-Champaign Research Interests: Development and validation of multidimensional/mixture IRT models Computerized adaptive testing optimization Cognitive diagnostic modeling for classroom applications Health measurement and bias detection in assessments Recent Trends in Articles: His work bridges statistical innovation with practical applications, emphasizing fairness and efficiency in assessments. Notable areas include: - Healthcare : Predictive models for discharge disposition and functional outcomes - Education Technology : Adaptive learning systems and diagnostic feedback mechanisms - Methodology : Bias detection (DIF), Bayesian estimation techniques, and computational efficiency Scientific Awards : Includes the Anne Anastasi Award (2020), McKnight Presidential Fellowship (2017), and multiple best reviewer recognitions from leading psychometrics journals. Advising & Grants: Supervised students including Xiao J., Zhu R., and Lu J.* in high-impact projects. Co-led a $10M NIH grant (AmplifyGAIN Center) to advance Gen AI in STEM education. Published extensively in Psychometrika , Journal of Educational and Behavioral Statistics , and other top outlets. Labs/Teams: Directs the Pmetrics Lab ( https://sites.uw.edu/pmetrics/ ), collaborating on cutting-edge measurement tools for education and healthcare.

Daniela CalvettiView profile

Professor

• Large Scale Scientific Computing
• Computational Inverse Problems
• Uncertainty Quantification
• +4 more

Daniela Calvetti is the James Wood Williamson Professor in the Department of Mathematics, Applied Mathematics, and Statistics at Case Western Reserve University. Her research focuses on large-scale scientific computing, computational inverse problems, uncertainty quantification, and predictive modeling in neuroscience, metabolism, and cellular physiology. She holds a PhD from the University of North Carolina-Chapel Hill. Her work integrates advanced mathematical techniques with biomedical applications, including brain energy metabolism modeling, MEG/EEG source reconstruction, and computational methods for medical imaging. Notable contributions include Bayesian hierarchical algorithms for inverse problems and interdisciplinary collaborations bridging mathematics with neuroscience and physiology. Recent research highlights include developing sparsity-promoting Bayesian models for tomography, computational frameworks for neuromuscular control variability, and predictive models of disease dynamics like post-pandemic COVID-19 recurrence. Her methodologies emphasize statistically inspired preconditioning and adaptive meshing techniques to enhance computational efficiency in solving complex inverse problems. Dr. Calvetti has published extensively across computational science, inverse problems, and biomedical applications. She leads a research group advancing interdisciplinary computational methods with applications in neuroscience, virology, and metabolic systems.

Shrimai PrabhumoyeView profile

Adjunct Assistant Professor

• Large Language Models (LLMs)
• Machine Learning
• Natural Language Processing (NLP)
• +4 more

Shrimai Prabhumoye is a Senior Research Scientist at NVIDIA's Applied Deep Learning Research group and an Adjunct Assistant Professor at Boston University. Her research focuses on advancing large language models (LLMs), particularly in enhancing their reasoning capabilities and ensuring safety through toxicity and bias reduction. She is a core contributor to the Nemotron family of LLMs, including the state-of-the-art Nemotron4-15B. Previously, she earned her PhD from Carnegie Mellon University (2021), advised by Prof. Ruslan Salakhutdinov and Prof. Alan W. Black, with a thesis on controllable text generation and computational ethics in NLP. Education: PhD in Machine Learning (CMU, 2021), MS in Language Technologies (CMU, 2017), Undergraduate at National Institute of Technology Karnataka, India. Research Interests: LLMs, controllable generation, ethical AI, bias mitigation, data curation, and model safety. Her work emphasizes rigorous evaluation frameworks and practical applications of NLP systems. Publications span key venues like EACL, ACL, NAACL, and EMNLP, with notable contributions to bias testing, toxicity control, and model scalability. She has been recognized as a Rising Star finalist in the VentureBeat Women in AI Awards 2022 and has led teams in competitions like the Alexa Prize. Labs/Teams: Part of NVIDIA's Applied Deep Learning Research Group and contributed to CMU's Computational Ethics for NLP initiatives. Active in mentoring students on projects like politeness transfer and dataset creation.

Xuhui FanView profile

Lecturer

• Artificial Intelligence
• Machine Learning
• Bayesian Methods
• +5 more

Dr. Xuhui Fan is a Lecturer in Artificial Intelligence at the School of Computing, Macquarie University. He holds a PhD in Computer Science from the University of Technology Sydney (Australia) and a bachelor's degree in Mathematical Statistics from China. Prior to his current role, he worked as a project engineer at Data61 (formerly NICTA), a postdoc fellow at the University of New South Wales, and a lecturer at the University of Newcastle. His research focuses on Bayesian methods, federated learning, temporal point processes, and neural network architectures. He is affiliated with the Data Horizons Research Centre and the Frontier AI Research Centre at Macquarie University. Key research interests include developing interpretable AI models, advancing federated learning for privacy-sensitive applications, and applying Bayesian techniques to complex data analysis. His work bridges theoretical advancements in machine learning with practical applications in areas such as anomaly detection, generative models, and spatio-temporal data analysis. Dr. Fan’s publications span top-tier conferences like NeurIPS, ICML, and IJCAI, covering topics such as diffusion models, nonstationary processes, and scalable relational models. He has contributed to surveys on Bayesian federated learning and developed novel frameworks for dynamic customer segmentation and network sustainability. His research collaborations span institutions in Australia and internationally, reflecting his expertise in interdisciplinary AI applications. Current projects emphasize ethical AI practices, efficient uncertainty quantification, and scalable inference techniques for large-scale datasets.

Vikrant VazeView profile

Associate Professor

• Transportation Optimization
• Aviation Systems
• Healthcare Analytics
• +3 more

Vikrant Vaze is the Stata Family Career Development Associate Professor and Executive Director of the Master of Engineering Management Program at Dartmouth's Thayer School of Engineering. He leads research in transportation systems, aviation optimization, and healthcare analytics, developing data-driven solutions for complex logistics challenges. His work integrates game theory, statistical modeling, and large-scale optimization. Research spans sustainable urban mobility, airline disruption recovery, multimodal pricing alliances, and healthcare operations. Articles consistently focus on optimization algorithms for real-world transportation and healthcare systems, with recent emphasis on electric aerial mobility and pandemic-responsive logistics. Major Awards: INFORMS Aviation Applications Best Paper (2024, 2023) AGIFORS Best Innovation Award (2024) NSF CAREER Award (2018) President of India Gold Medal As founding co-director of the Operations Research Group, he collaborates with industry partners like Multivariate Systems to translate academic research into deployable solutions.

Samson ZhouView profile

Assistant Professor

• Algorithms and theory
• Data science
• Machine learning
• +7 more

Samson Zhou is an Assistant Professor in the Department of Computer Science & Engineering at Texas A&M University, part of the College of Engineering. He holds a Ph.D. from Purdue University and dual B.S. and M.Eng. degrees from MIT in Computer Science and Mathematics. His research focuses on theoretical computer science, data science, and machine learning, with specializations in numerical linear algebra, streaming algorithms, and differential privacy. He has held postdoctoral positions at institutions including Carnegie Mellon University and Rice University. Education: Ph.D., Computer Science, Purdue University (2018) M.Eng., Computer Science, MIT (2011) B.S., Computer Science & Mathematics, MIT (2010-2011) Research Interests: Dr. Zhou explores intersections of algorithms, data science, and machine learning, emphasizing numerical linear algebra, streaming algorithms, differential privacy, and adversarial robustness. He designs efficient algorithms for large-scale data processing with provable guarantees. Key Contributions: His work includes advancements in sliding window clustering, adversarially robust streaming algorithms, and privacy-preserving techniques. Notable achievements include the Silver Best Paper Award at ICML 2021 and spotlight presentations at ICLR 2025. Service & Leadership: He organizes workshops (e.g., TTIC 2024 on Learning-Augmented Algorithms) and serves on program committees for SOSA, NeurIPS, and COLT. He also co-leads the TAMU Math Circle's problem-solving sessions and the Algorithms & Data Science Reading Group. Labs/Teams: He collaborates on projects involving co-hosted postdocs (e.g., Chen Wang) and masters students (e.g., Shenghao Xie), focusing on topics like coresets, streaming algorithms, and fair clustering.