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Patrick W. C. HoView profile

Lecturer

• Memristor
• Non-volatile memories
• VLSI
• +7 more

Dr. Patrick W. C. Ho is a Lecturer in the Department of Electrical & Computer Systems Engineering (ECSE) at Monash University Malaysia School of Engineering. He holds a PhD in Electronics Engineering from the University of Nottingham Malaysia Campus (2016), with research focusing on non-volatile FPGA architectures using memristors. His academic journey includes roles as a Scholarly Teaching Fellow and unit coordinator for courses like ECE2131 Electrical Circuits and ECE4063 Large Scale Digital Design. He has industry experience with Intel Microelectronics and Altera Corporation, alongside teaching A-level Physics at Methodist College Kuala Lumpur. Education: BEng (First Class Honours) in Engineering (2009) MSc in Science (2012) PhD in Electronics Engineering (2016) Research Interests: Dr. Ho specializes in memristor-based non-volatile memory systems, VLSI design, and FPGA architectures. His work bridges hardware design with emerging materials, as seen in his Q1 journal article on memristive LUTs. Collaborations with CAD-IT expand his focus into AI, image processing, and object recognition. Recent projects include studies on memristor substrate performance (2023–2026) and UAV communication reliability (2021–2024). Teaching and Industry Engagement: As ECSE’s Industrial Training Advisor and IAP representative, he actively connects academic curricula with industry needs. His teaching spans foundational engineering courses and advanced digital design modules. Labs and Collaborations: Active in CAD-IT partnerships for student FYP co-sponsorship. Research groups focus on nanotechnology, machine learning integration in UAV systems, and memristor material analysis.

• Finance
• Macroeconomics
• Labor Economics
• +4 more

Lawrence D. W. Schmidt is the Victor J. Menezes Career Development Assistant Professor of Finance at the MIT Sloan School of Management. He holds a PhD and MA in Economics from the University of California, San Diego, and previously served as an Assistant Professor at the University of Chicago’s Kenneth C. Griffin Department of Economics. His research bridges finance and macro-labor economics, focusing on household risk exposures, institutional investor behavior, and labor market dynamics. Key areas include human capital valuation, asset pricing implications of labor market risks, and the design of retirement savings institutions. Education: BA, University of California, Santa Barbara; PhD and MA, University of California, San Diego. Research Interests: Human capital risk and asset pricing Income inequality and retirement savings disparities Big data analysis of labor and financial markets Policy design for financial welfare Recent Trends in Publications: Schmidt’s work increasingly emphasizes the interplay between technological change and labor market outcomes, particularly in quantifying risks faced by workers and the implications for financial markets. His studies on retirement savings inequities highlight racial and socioeconomic disparities in wealth accumulation, informed by large-scale administrative datasets. Awards: 2024 Dimensional Fund Advisors Prize 2015 AQR Top Finance Graduate Award Advising & Grants: Collaborations with institutions like NBER and MIT Sloan reflect his focus on policy-relevant research. His work often involves interdisciplinary teams, as seen in co-authored papers on employer 401(k) practices and technology-driven labor displacement. Labs/Teams: Active contributor to the MIT Sloan Consumer Finance Initiative, which explores household finance and policy solutions for economic stability.

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.

FENG MenglingView profile

Associate Professor

• Biostatistics
• Healthcare Data Analytics
• Medical AI
• +3 more

**FENG Mengling** is an Associate Professor at the National University of Singapore (NUS) and holds primary affiliation with the Saw Swee Hock School of Public Health. She serves as the Domain Leader for the Biostatistics, Modelling, AI and Data Analytics (B.MAD) Domain and Director of the AI for Public Health (AI4PH) Program. Her academic credentials include a Senior Post-doc from Harvard-MIT Health Science Technology Division, a PhD from Nanyang Technological University (2009), and a Bachelor's degree (2003) from NTU. Research & Teaching: Her research focuses on causal inference for evidence-based medicine, generative models for medical time-series analysis, and healthcare data analytics. She teaches courses on big data technologies for healthcare problems and healthcare data analytics. Professional Roles & Awards: She has led the Biomedical and Healthcare Analytics Lab at the Institute for Infocomm Research (2014–2015) and currently serves as an Affiliate Scientist at Harvard-MIT. Notable accolades include the MIT Teaching & Learning Laboratory Kaufman Teaching Certificate and recognition as a finalist in MIT’s 2013 Innovation Showcase. Her work has been featured in prominent media outlets like The Straits Times and Channel NewsAsia, highlighting breakthroughs such as AI nurses and Singlish-speaking healthcare assistants. Publications & Impact: Over 50 peer-reviewed publications span AI-driven clinical decision support, medical imaging analysis, and predictive modeling in critical care. Key contributions include frameworks like MedDreamer (reinforcement learning for EHR analysis) and DivScore (LLM-generated text detection). Her research bridges causal inference, generative AI, and scalable healthcare solutions. Labs & Initiatives: As a leader in NUS’s Public Health AI Innovation Center (launching early 2025), she drives initiatives like FxMammo (AI for breast cancer screening) and the Biomedical and Healthcare Analytics Lab. Her work emphasizes ethical AI deployment and cross-disciplinary collaboration in healthcare.

• Psychometrics
• Log Data Analysis
• Missing Data Methods
• +4 more

Prof. Dr. Steffi Pohl holds the Chair of Methods and Evaluation/Quality Assurance at the Faculty of Education and Psychology, Freie Universität Berlin since 2019. Previously, she was a Junior Professor (2013-2019) and researcher at institutions including Friedrich-Schiller University Jena and University of Bamberg. She earned her PhD in Psychometrics from Friedrich-Schiller University Jena (2010) and holds a Diplom in Psychology (2004) from Freie Universität Berlin. Her research focuses on advanced statistical methods in educational and psychological testing, including response time modeling, missing data mechanisms, and causal inference in assessment. She has pioneered work on test engagement detection via response patterns and log数据分析. Awards include the 2020 Psychometric Society Early Career Award and 2011 Gustav A. Lienert Dissertation Prize. Pohl serves on editorial boards of Psychometrika , Journal of Educational and Behavioral Statistics , and Zeitschrift für Psychologie . She chairs the Berlin School of Mind and Brain faculty and holds governance roles in academic senates. Her research projects include the National Educational Panel Study (NEPS) and collaborations on test design innovations. Current teaching includes advanced courses in empirical research methods, multivariate statistics, and educational measurement. She actively develops methodologies for analyzing log数据 from digital testing platforms and improving assessment reliability in large-scale studies.

Ioan RaicuView profile

Professor

• Distributed Systems
• Cloud Computing
• Grid Computing
• +3 more

Ioan Raicu is a Professor in the Department of Computer Science at Illinois Institute of Technology (IIT) and a guest research faculty at Argonne National Laboratory's Math and Computer Science Division. He leads the Data-Intensive Distributed Systems Laboratory (DataSys) at IIT, focusing on distributed systems, cloud computing, and high-performance computing. His work is primarily funded by the NSF and DOE. Research interests include distributed systems, many-task computing, and data-intensive applications. Over 140 peer-reviewed publications have yielded a H-index of 46, with top papers addressing cloud vs. grid computing comparisons, Globus GridFTP, Swift workflow systems, and Falkon frameworks. Recent projects explore fine-grained parallelism, scalable indexing, and energy-efficient blockchain algorithms. Awards include NSF grants and recognition for lab innovations. Advised PhD students include Alexandru Orhean and Poornima Nookala. The DataSys lab has won the Grainger Computing Innovation Prize and leads initiatives like the BigDataX REU program. Active in conferences such as SC and IEEE IPDPS, Raicu's work bridges theory and practice in extreme-scale computing systems.

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.

Cheng HanView profile

Assistant Professor

• Adaptable and Sustainable Intelligence
• Efficient Multimodality
• Parameter-Efficient Fine-Tuning
• +7 more

Cheng Han is a tenure-track Assistant Professor in the School of Science and Engineering at the University of Missouri -- Kansas City (UMKC), where he conducts research in adaptable and sustainable intelligence, focusing on efficient AI systems and parameter-efficient fine-tuning methods for large-scale models. Ph.D., Rochester Institute of Technology (RIT) M.S., Pennsylvania State University (PSU) B.S., Tianjin University (TJU) His research interests center on creating energy-wise AI systems that empower communities and address environmental and social challenges. He focuses on multimodal and visual prompt tuning, transfer learning, and robust AI. His work bridges theoretical innovation with real-world deployment, particularly in efficient adaptation of vision and language models. His recent publications span top venues like NeurIPS, ICCV, CVPR, ICLR, EMNLP, and IEEE TPAMI. The research trends highlight a strong focus on parameter efficiency , prompt engineering , model robustness , and multimodal understanding . He investigates when and why prompt tuning outperforms full fine-tuning and develops novel frameworks like E^2VPT and M^2PT for efficient adaptation. Cheng Han actively contributes to the academic community as a reviewer and committee member. Program Committee, AAAI (2023–present) Program Committee, SIAM SDM (2024) Reviewer for NeurIPS, ICLR, CVPR, ICML, ICCV, TPAMI, TMLR, and others He advises Ph.D. students and teaches courses such as Deep Learning (COMP-SCI 5567). He has given invited talks at ICLR, ICCV, and seminars at NSF and Naval Research Laboratory. His research is supported by academic collaborations and likely grant funding, given his active publication and service profile. He leads a research group focused on sustainable and efficient AI, with code available on GitHub.

Gérard Ben ArousView profile

Professor

• Probability Theory
• Stochastic Analysis
• Large Deviations
• +6 more

Gérard Ben Arous is a Silver Professor of Mathematics at New York University's Courant Institute of Mathematical Sciences, where he has served as Director and Vice Provost for Science and Engineering Development since 2011. He holds a PhD in Mathematics from the University of Paris VII (1981) and has previously taught at the University of Paris-Sud, École Normale Supérieure, and the Swiss Federal Institute of Technology in Lausanne. His research focuses on probability theory, stochastic analysis, and their applications to physics and industrial problems, particularly exploring complex systems' long-time behavior and aging phenomena in disordered media. Education: PhD in Mathematics, University Paris 7, France (1981) M.Sc. in Statistics, University Paris-Sud Orsay, France (1979) B.S. in Mathematics, École Normale Supérieure (Paris), France (1978) His research interests bridge probability with partial differential equations, dynamical systems, and statistical mechanics. Key contributions include studies on random media, random matrices, and the interplay between complexity, disorder, and aging in physical systems. He has held leadership roles in academic institutions, including directing the mathematics departments at Orsay and École Normale Supérieure, and founded Lausanne's Bernoulli Center. Notable awards include Fellow of the Institute of Mathematical Statistics and the Montyon Prize from the French Academy of Sciences. His work is published in top journals like Annals of Probability and Communications in Pure and Applied Mathematics , and he co-edits Probability Theory and Related Fields . Ben Arous has advised numerous researchers and contributed to interdisciplinary projects, including studies on machine learning landscapes and financial mathematics. His lab focuses on stochastic modeling and its applications across disciplines.

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.