Kamesh Madduri is an Associate Professor in the Department of Computer Science and Engineering at Pennsylvania State University, with affiliations to the Huck Institutes of the Life Sciences. His research focuses on graph analytics, parallel algorithms, and high-performance computing for large-scale data analysis. NSF CAREER Award (2013) His work contributes to the development of scalable graph partitioning algorithms, extreme-scale sparse data analytics, and heterogeneous computing frameworks. Recent projects include multilayer network analysis (NetSplicer) and GPU-accelerated graph processing (Jet). Key research areas include network science, computational biology, and distributed-memory graph algorithms. His publications highlight applications in genomic workflows, advertising keyphrase recommendation (Graphite/BroadGen), and large-scale hydrology data management. Collaborative Research: CCRI (2021-2023) SHF: Medium: NetSplicer (2020-2024) PPoSS: Extreme-scale Sparse Data Analytics (2018-2022) XPS: Genomic Workflows Acceleration (2014-2020) EAGER: SME Manufacturing Integration (2024-2026)
Stephen A. Edwards is an Associate Professor at the Computer Science Department of Columbia University , where he explores automating software for embedded systems and real-time control . His work focuses on compiler techniques for languages like Esterel and domain-specific solutions for device drivers and communication protocols . Education : PhD in Electrical Engineering, University of California, Berkeley (1997) MS in Electrical Engineering, UC Berkeley (1994) BS in Electrical Engineering, California Institute of Technology (1992) Research Interests span hardware synthesis , functional programming , and synchronous languages . He develops domain-specific languages to bridge software and hardware, emphasizing determinism and timing precision in embedded systems. His group's work includes projects like the FHW compiler and the Sparse Synchronous Model . Article Trends show a focus on FPGA-based systems , garbage collection for accelerators, and parallel functional programming . These reflect his broader interests in hardware-software co-design and compiler optimization for real-time applications. Students : John Hui (2021-2024, Apple) Max Levatich (2020-) Richard Townsend (2013-2019, Tufts) Nalini Vasudevan (2007-2011, Google) Marcio Buss (2004-2008) Jia Zeng (2002-2008) Cristian Soviani (2002-2007, Synopsys) Consulting & Expert Services : He provides litigation support and expert witness services in patent cases involving software architecture and computer design , including ITC cases. Labs & Projects : Leads research on sparse synchronous systems and hardware synthesis , with initiatives like the GAPS CLOSURE Project and FHW Compiler for translating Haskell to System Verilog.
Tyler McCormick is a Professor in both the Department of Statistics and Department of Sociology at the University of Washington. He also serves as a Senior Data Science Fellow at the eScience Institute and maintains affiliations with the Center for Statistics and the Social Sciences, the Center for Studies in Demography and Ecology, and the Responsible AI Systems & Experiences (RAISE) initiative. Dr. McCormick earned his Ph.D. in Statistics from Columbia University in 2011. His academic journey has established him as a leading researcher at the intersection of statistical methodology and social science applications. McCormick's research program focuses on developing innovative statistical approaches to address complex societal challenges: Bayesian methods for modeling high-dimensional dependence structures in social networks Estimating vital demographic rates from sparse data sources Developing interpretable predictive models with proper uncertainty quantification Creating methodological frameworks for verbal autopsy analysis in global health His publication record reveals a consistent trajectory of methodological innovation with practical impact. Recent work demonstrates increasing sophistication in handling network interference, integrating machine learning with statistical theory, and addressing data scarcity challenges in global health contexts. His research bridges theoretical advances with applications that inform public health policy and social science understanding. McCormick has received significant recognition for his scholarly contributions: NIH Director's New Innovator Award (2019) Election as Fellow of the American Statistical Association (2023) As an educator, McCormick teaches advanced graduate courses including Hierarchical Modeling for the Social Sciences and Quantitative Techniques in Sociology. His research has been supported by competitive grants from NICHD (2015-2020) focused on vital rate estimation in developing countries and NSF (2016-2018) funding for compact Bayesian models of social networks. His work has influenced policy discussions through media coverage in the Wall Street Journal and Washington Post. McCormick leads the OpenVA initiative, providing open-source tools for verbal autopsy analysis, and has developed multiple R packages implementing his methodological contributions to network analysis and causal inference. His research continues to address critical challenges at the intersection of statistical theory, computational methods, and societal impact.
Maurice van Keulen is an Associate Professor affiliated with the University of Twente's research institutes including Datamanagement & Biometrics, Digital Society Institute, and TechMed Centre. His multidisciplinary work bridges computer science, healthcare, and social systems. Research Focus: Van Keulen specializes in artificial intelligence applications with emphasis on: Data management (quality, integration, probabilistic databases) Explainable AI and interpretable machine learning models Healthcare informatics (cancer prediction, medical imaging, outcome analysis) Natural language processing and social media analytics His recent work explores dynamic sparse training, meta-learning for data imputation, and ethical AI frameworks. Publication Trends: Recent articles (2023-2025) show strong focus on: Interpretable AI methods in healthcare diagnostics Robust machine learning under data corruption Meta-learning approaches for data preprocessing 3D medical imaging and reconstruction techniques Awards: Beste paper award (2018) for work on probabilistic data conditioning Supervision & Activities: Has supervised 14 research projects and serves on executive boards including EDBT (Extending DataBase Technology) and IFIP WG 2.6. Leads research on ethical dimensions of AI systems.
Professor Saman Amarasinghe is a faculty member in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT), where he leads the Commit compiler research group at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). His research focuses on programming languages and compilers that maximize application performance on modern computing platforms, with a particular emphasis on high-performance domain-specific languages. Professor Amarasinghe received his bachelor's degree in electrical engineering and computer science from Cornell University in 1988, followed by master's and PhD degrees in electrical engineering from Stanford University in 1990 and 1997, respectively. He joined the MIT faculty as an assistant professor in 1997 and has since become a world leader in his field. Professor Amarasinghe's research interests span programming languages, compiler design, and high-performance computing, with a particular focus on domain-specific languages. His group has developed numerous influential languages and compilers including Halide, TACO, Simit, StreamIt, StreamJIT, PetaBricks, MILK, Cimple, and GraphIt, which deliver unprecedented performance for application domains such as image processing, stream computations, and graph analytics. He has also pioneered the application of machine learning for compiler optimizations, from Meta optimization in 2003 to the OpenTuner autotuner framework. Professor Amarasinghe's publication history reveals a consistent research trajectory toward creating specialized language and compiler solutions that address performance challenges in specific domains while hiding complexity from application developers. His recent work focuses heavily on sparse computing, tensor algebra, graph processing, and the integration of machine learning techniques into compiler technology, demonstrating his ability to identify and address emerging computational challenges. ACM Fellow (2019) As an educator, Professor Amarasinghe has developed the popular Performance Engineering of Software Systems (6.172) class with Professor Charles Leiserson and created innovative project-based courses including the Open Source Software Project Lab, the Open Source Entrepreneurship Lab, and the Bring Your Own Software Project Lab. He also serves as the faculty director of MIT Global Startup Labs, which has helped create more than 20 startups across 17 countries. His research has translated into practical applications through startups like Determina, Inc. (acquired by VMware), demonstrating the real-world impact of his academic work. Professor Amarasinghe co-led the Raw architecture project with Professor Anant Agarwal, which did pioneering work on scalable multicores. His entrepreneurial activities include founding Determina, Inc. based on computer security research from his MIT lab and co-founding Lanka Internet Services, Ltd., the first Internet Service Provider in Sri Lanka, showcasing his ability to bridge academic research with commercial applications.
Mehtaab Sawhney is a Clay Research Fellow and a tenure-track assistant professor at Columbia University specializing in combinatorics, probability, analytic number theory, and theoretical computer science. His academic journey began at the University of Pennsylvania where he enrolled in a Bachelor of Engineering in Computer Science (2016-2017), then continued at MIT where he earned a Bachelor of Science in Mathematics with Minor in Computer Science (2017-2020), followed by a Doctor of Philosophy in Mathematics (2020-2024) under the advisorship of Yufei Zhao. His research spans probabilistic combinatorics, random matrix theory, additive number theory, and theoretical computer science. Sawhney's work bridges theoretical mathematics with computational applications, focusing on random structures, additive combinatorics, and spectral properties of discrete objects. His publications demonstrate a strong interdisciplinary approach that connects number theory with probabilistic methods to solve complex combinatorial problems. The analysis of his publication record reveals a consistent focus on foundational mathematical structures with applications across multiple domains. His work on random graphs, additive bases, and arithmetic progressions has established him as a leading researcher in modern combinatorics, often collaborating with prominent mathematicians including Ashwin Sah, Yufei Zhao, and Vishesh Jain. His research output shows remarkable depth and breadth, with contributions to both pure mathematics and theoretical computer science. 2024 Clay Research Fellow 2021 Frank and Brennie Morgan Prize for Outstanding Research in Mathematics by an Undergraduate Student (joint with Ashwin Sah) Churchill Scholar 2020 Best Student Paper STOC 2021 (Joint with Ryan Alweiss, Yang Liu) Best Student Paper ITCS 2022 (Joint with Yang Liu, Ashwin Sah) 2023 Hartley Rogers Jr. Prize 2022 Charles W. and Jennifer C. Johnson Prize (joint with Ashwin Sah) NSF Graduate Fellowship Sawhney has established a robust research program with significant contributions across multiple mathematical disciplines. His frequent collaborations with top researchers worldwide indicate an active and influential research network. While specific advisees aren't listed in available information, his extensive publication record with numerous co-authors suggests active mentorship of junior researchers through collaborative projects.
Yoann Altmann is Professor in the School of Engineering & Physical Sciences at Heriot-Watt University and a member of the Institute of Sensors, Signals & Systems. Since 2024 he holds the Chair in Electrical, Electronic & Computer Engineering (EECE), directing a research programme that bridges statistical signal processing, computational imaging and quantum & neuromorphic sensing. Education & career: 2010 – Eng. degree (Electrical Engineering), ENSEEIHT, Toulouse, France 2010 – M.Sc. (Signal Processing), National Polytechnic Institute of Toulouse 2013 – Ph.D. (Signal & Communications), IRIT Laboratory, Toulouse 2014-2017 – Post-doctoral Research Fellow, Heriot-Watt University 2017 – Royal Academy of Engineering Research Fellow & Assistant Professor, HWU 2024 – promoted to Professor, School of Engineering & Physical Sciences, HWU Research interests: Prof. Altmann develops mathematical and algorithmic tools for Bayesian inverse problems, with emphasis on single-photon LiDAR, low-illumination imaging, neuromorphic computational sensing, variational inference and sparse reconstruction. His work combines principled statistical modelling with efficient computational schemes to enable imaging in extreme scenarios such as underwater scattering, photon-starved environments, quantum metrology and real-time 3-D scene reconstruction. Publication trends: Across 160 outputs (2011-2025) his recent articles reveal a clear trajectory toward integrating modern machine-learning paradigms—variational autoencoders, diffusion generative models, spiking neural networks—with rigorous physics-based forward models. Applications span quantum parameter estimation, multimode-fiber endoscopy, hyperspectral & Compton imaging, nuclear safeguards and cultural-heritage spectroscopy, demonstrating both methodological breadth and high-impact interdisciplinary deployment. Honours & recognition: Royal Academy of Engineering Research Fellowship – competitively awarded (2017) Grants & datasets: He has generated four open datasets supporting reproducible research in quantum sensing, variational autoencoders, underwater single-photon LiDAR and multispectral fluorescence imaging, reflecting sustained funding and commitment to open science. Continuous peer-review service for IEEE and Elsevier journals since 2013 underlines his standing within the signal-processing community. Labs & teams: He leads the Bayesian Imaging & Sensing Computing (BISC) group ( https://bisc.site.hw.ac.uk ) which hosts post-docs, PhD researchers and international visitors working on statistical machine-learning for imaging, sensing and quantum technologies.
Gaurav Rattan is an Assistant Professor in the Department of Applied Mathematics at the University of Twente's Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), where he joined in May 2024. His research focuses on the mathematical foundations of machine learning on graphs and discrete structures, with particular emphasis on theoretical aspects of graph neural networks. University of Twente, Department of Applied Mathematics (May 2024-present) TU Darmstadt, Postdoctoral Researcher in Pascal Schweitzer's group RWTH Aachen, DFG Eigene Stelle Researcher in Martin Grohe's group Dr. Rattan completed his PhD at IMSc Chennai under V. Arvind and earned his B. Tech. from IIT Bombay, establishing a strong foundation in theoretical computer science and mathematics. His research spans graph theory, algorithms, and machine learning on graphs, with specific expertise in graph isomorphism, graph homomorphisms, and the theoretical underpinnings of graph neural networks. He applies mathematical techniques from logic and algebra to develop theory-driven approaches for graph learning systems, with practical applications in optimization, bioinformatics, and databases. Dr. Rattan's publication record reveals a consistent focus on the intersection of theoretical computer science and machine learning. His recent work explores Weisfeiler-Leman algorithms, symmetry breaking techniques, and parameterized complexity of graph problems, demonstrating how classical graph algorithms connect with modern graph learning methodologies. His research provides crucial theoretical foundations for understanding the capabilities and limitations of graph neural networks. Active in the academic community, Dr. Rattan regularly presents at conferences including the Netherlands Mathematical Congress, SIGAlgo, LOGAMS, and specialized workshops on graph learning. Recent presentations include "From Graph Homomorphisms Densities to Graph Learning" at the Graph Learning Workshop at NITMB Chicago and "Color Refinement: One Algorithm, Many Facets" at SIGAlgo 2024.
Dr. Anthony J. Parolari is an Associate Professor in the Department of Civil, Construction and Environmental Engineering at Marquette University. He holds a Ph.D. in Civil and Environmental Engineering from MIT (2012) and has previously taught courses in engineering computing, urban hydrology, and ecohydrology. Education: Ph.D., 2012, Civil and Environmental Engineering, Massachusetts Institute of Technology M.S.E., 2005, Environmental Engineering, University of Michigan B.S.E., 2004, Civil Engineering, University of Michigan His research focuses on the interactions between hydrology and ecosystems, particularly in urban and engineered environments. Key areas include watershed hydrology, ecohydrology, stormwater management, nutrient cycling, and climate change impacts on urban water systems. He develops quantitative models to link hydrologic variability to ecosystem processes, aiming to improve urban water infrastructure design and real-time control technologies. Analysis of his recent publications reveals trends in machine learning for flood prediction, nutrient management in green infrastructure, and ecohydrological responses to climate variability. His work often integrates experimental data with theoretical models to address water quality and quantity challenges. Dr. Parolari leads grants from the National Science Foundation (NSF), Minnesota Department of Transportation (MnDOT), and Milwaukee Metropolitan Sewerage District (MMSD), focusing on sustainable stormwater solutions and real-time control systems. He collaborates with institutions like MIT, University of Michigan, and Duke University on research initiatives. His team at Marquette’s Water Quality Center investigates soil biogeochemistry in engineered ecosystems, leveraging field studies and simulations to optimize stormwater wetlands and green roofs.
Bryan Daniels is an Assistant Professor at Arizona State University's School of Complex Adaptive Systems . He is affiliated with the ASU-SFI Center for Biosocial Complex Systems , Biosocial Complexity Initiative , and holds the title of Senior Global Futures Scientist . Educational Background: Ph.D. in Theoretical Physics from Cornell University. Research Interests: Daniels focuses on predictive modeling of collective behavior in biological systems, exploring how functional aggregates emerge from heterogeneous networks. His work spans computational biology, network science, and dynamical systems. Key Article Trends: Recent publications emphasize collective behavior in biological systems, network dynamics, criticality, and control strategies. Topics range from neural networks and Boolean models to animal group behavior and conflict analysis. Teaching: He teaches courses like Fundamentals of CAS Science and Applied Complex Adaptive Systems at the graduate level. Labs & Affiliations: Leads the Collective Logic Lab and contributes to interdisciplinary research through multiple ASU-SFI collaborations.
Mario Annunziato is a Researcher in Mathematics at the Department of Physics, University of Salerno, since 2004. His work focuses on numerical methods for stochastic processes and optimal control. Institution: University of Salerno Department: Department of Physics Academic Rank: Researcher Research Interests include numerical solutions of PDEs and integral equations for stochastic processes, probability density function optimization, and modeling random phenomena. His work addresses positivity, monotonicity, and conservation in discrete PDFs. Article Trends span stochastic control frameworks, computational finance, biophysics applications, and numerical methods for jump-diffusion processes. Key topics involve Fokker-Planck equations, Hamilton-Jacobi-Bellman formulations, and splitting methods. Advising and Grants include teaching Numerical Analysis until 2013 and securing funding from the University of Salerno's FARB program, INdAM-GNCS, and the European Science Foundation's OPTPDE grants. He participated in the STRIKE Marie Curie ITN network. Labs & Teams : Collaborated with Prof. Alfio Borzì at Würzburg University and contributed to open-source tools like MATLAB Central File Exchange for PDP solvers.
Dr. He Xu is a Visiting Professor in the Department of Engineering Science at the University of Oxford, with a focus on Biomaterials , Tissue Engineering , and Biomechanics . She previously worked at Shanghai Normal University, rising from lecturer (2014) to associate professor (2018) and full professor (2024). Education: BEng in Materials Science and Engineering (China University of Geosciences), DPhil in Biomedical Engineering (Shanghai Jiao Tong University, 2014) Her research explores: Biomaterials : Smart hydrogels, piezoelectric systems, and nanogenerators for therapeutic applications. Tissue Engineering : Innovations in intervertebral disc and tendon regeneration. Drug Delivery : Targeted activation, nitric oxide therapy, and bioelectronic systems. Her publications span 2021–2025 , combining Biomaterials , Nanotechnology , and Medical Imaging to address challenges in Diabetes , Cancer , and Cardiovascular Disease . Key collaborations include the 3DMed Interreg 2 Seas Consortium and work on rapid Covid-19 testing .
Prof. Mehdi Dastani is a Professor and chair of the Intelligent Systems group within the Department of Information and Computing Sciences at Utrecht University's Faculty of Science. He leads the Master's program in Artificial Intelligence and focuses on formal and computational models in AI, particularly multi-agent systems. His research integrates insights from philosophy, psychology, and law to develop autonomous agents that reason about social and cognitive concepts like norms, emotions, and responsibility. Dastani has held academic roles at Utrecht University since 2001, including postdoctoral research and faculty positions. Education: M.Sc. Computer Science (University of Amsterdam, 1991), M.Sc. Philosophy (University of Amsterdam, 1992), Ph.D. in Humanities (University of Amsterdam, 1998). His work spans theoretical and applied projects, including grants for initiatives like Golden Agents (simulating Golden Age creative industries) and traffic control systems using virtual organizations. He is actively involved in academic committees, editorial boards, and organizing international conferences like AAMAS and PRIMA. Research Interests: Multi-Agent Programming, Normative Systems, Autonomous Agents, Cognitive Robotics, and Human-Centered AI. His projects address challenges like norm enforcement, decision-making in complex systems, and ethical AI integration with societal needs. Advising & Grants: Supervised numerous PhD students (e.g., Birna van Riemsdijk, Bas Testerink) and secured grants for projects such as 'Controllable AI: Human-Centered Approach'. His work includes collaborations on urban governance, autonomous driving, and AI tools for literacy support in children. Labs & Teams: Leads the Intelligent Systems group, contributing to agent-based simulations, ethical AI frameworks, and interdisciplinary collaborations with social scientists and urban planners.
Professor Perumal Nithiarasu is a Professor and Director of Research in the College of Engineering at Swansea University. He also serves as Deputy Head of the College and Dean of Academic Leadership (Research Impact). His expertise spans computational engineering, biomedical engineering, and artificial intelligence, with a focus on blood flow dynamics and finite element methods. He has held leadership roles, including directing the Zienkiewicz Center for Computational Engineering and co-chairing international conferences like the Computational Biomedical Engineering series. Notable awards include the Zienkiewicz ICE Silver Medal (2002) and an EPSRC Senior Fellowship (2006). Research Interests: Computational Fluid Dynamics (CFD) Biomedical Engineering Applications Finite Element Method (FEM) Digital Twin Technology Artificial Intelligence in Biomedical Systems Scientific Contributions: Professor Nithiarasu has pioneered numerical methods like the Locally Conservative Galerkin (LCG) and developed influential tools such as the Zienkiewicz Lecture series. His work bridges computational models with clinical applications, including cardiovascular simulations and patient-specific coronary analysis. He leads a 30+ member research group and edits the International Journal for Numerical Methods in Biomedical Engineering . Grants & Impact: His research has been funded by EPSRC and other bodies, with applications in medical devices (e.g., glaucoma treatment shunts) and thermal systems. Collaborations include RAEng, IACM, and industry partners.
Xuan Zhang serves as Associate Professor in Electrical and Computer Engineering at Northeastern University, leading the Sensory AI Lab since joining in January 2024. Her research bridges computer architecture, integrated circuits, and artificial intelligence to develop miniaturized AI systems for autonomous physical platforms. She earned her PhD in Electrical and Computer Engineering from Cornell University in 2012. Her educational background forms the foundation for her interdisciplinary work spanning hardware and software co-design. Dr. Zhang's research focuses on artificial intelligence hardware, machine vision sensors, and security for autonomous systems. She pioneers techniques for efficient in-sensor computing, analog circuit optimization via machine learning, and hardware-level privacy preservation. Her work addresses critical challenges in energy efficiency, robustness, and security for edge AI deployment, particularly in resource-constrained environments like medical devices and autonomous vehicles. Analysis of her 2023-2025 publications reveals three dominant trends: (1) hardware-accelerated privacy mechanisms for sensors, (2) machine learning-driven analog circuit design automation, and (3) energy-efficient architectures for neural network inference. These works consistently target real-world applications in healthcare, autonomous systems, and semiconductor design. Her accolades include the prestigious NSF CAREER Award (2020) and leadership in a $10 million federal semiconductor initiative. She contributes to national efforts in AI-powered chip design through the National Center for the Advancement of Semiconductor Technology. Dr. Zhang advises graduate researchers in the Sensory AI Lab, securing significant funding for projects spanning hardware security, in-sensor computing, and autonomous system assurance. Her lab collaborates with federal agencies and industry partners on cutting-edge semiconductor research. The Sensory AI Lab operates at the hardware-software interface, developing novel architectures for intelligent edge devices. Current projects include optical privacy preservation, robust analog design tools, and energy modeling frameworks for in-sensor visual computing systems.
