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Fabian DennerView profile

Associate Professor

• Numerical analysis
• Modeling and simulation
• Multiphase systems
• +5 more

Fabian Denner is an Associate Professor at the Department of Mechanical Engineering, Polytechnique Montréal. His work focuses on modeling multiphase flows and related physical phenomena, including cavitation, acoustic wave dynamics, and high-performance computing (HPC). He develops advanced numerical methods and software tools for simulating incompressible and compressible flows, with applications in medicine, chemical engineering, and aerospace. His research group addresses challenges in predicting cavitation effects, liquid jet breakup, and acoustic modulation in accelerating flows. Education Dipl.-Eng. in Automotive Engineering, University of Stuttgart (2009) Ph.D. in Mechanical Engineering, Imperial College London (2013) Research Interests His expertise spans: Numerical modeling of multiphase flows Acoustic wave propagation and modulation Cavitation dynamics and biomedical applications High-performance computing (HPC) for fluid dynamics Liquid jet atomization and particle-laden flows Surface tension and interface reconstruction techniques Publications and Software Fabian has published extensively in leading journals like Physics of Fluids , Journal of Computational Physics , and Journal of Fluid Mechanics . He co-developed software frameworks such as Wave-DNA and APECSS for simulating complex fluid dynamics and acoustic phenomena. Scientific Recognition Margaret Fishenden Centenary Memorial Prize (2015) Leadership Roles Vice-director, Canadian Society for Mechanical Engineering - Fluid Engineering Technical Committee Co-director, Editorial Advisory Committee of Canadian Journal of Chemical Engineering Active contributor to international conferences (APS DFD, ICMF, IUTAM)

John R. LangeView profile

Associate Professor

• High Performance Computing
• Operating Systems
• Virtualization
• +3 more

John R. Lange is an Associate Professor in the Computer Science Department at the University of Pittsburgh's School of Computing and Information. Currently on leave at Oak Ridge National Laboratory, he has established himself as a leading researcher in high-performance computing and systems software. His work bridges academic research and practical supercomputing applications, with extensive collaborations across national laboratories and research institutions. PhD in Computer Science from Northwestern University (2006) M.S. in Computer Science from Northwestern University (2010) B.S. in Computer Engineering from Northwestern University (2003) B.S. in Computer Science from Northwestern University (2003) Dr. Lange's research centers on High Performance Computing systems, with particular expertise in operating systems for supercomputing environments, virtualization technologies, and network optimization. His work explores how specialized operating systems can enhance performance in large-scale computing infrastructures, addressing challenges in resource management, fault tolerance, and system composition. He investigates the intersection of traditional HPC architectures with emerging virtualization techniques to create more flexible and efficient computing environments. His publication record demonstrates a consistent focus on advancing the state of the art in high-performance systems, with recent work emphasizing exascale computing challenges, network optimization for leadership-class supercomputers, and innovative approaches to system composition. His research shows an evolution from foundational virtualization techniques toward more sophisticated solutions for next-generation supercomputing architectures, with increasing emphasis on practical implementations in real-world leadership computing facilities. Best Paper Finalist at SC 2024 for network cost optimization research Best Paper Finalist at SC 2023 for Frontier exascale supercomputer architecture Best Paper Nominee at HPDC 2012 for VNET/P overlay networking As an educator, Dr. Lange has taught numerous systems courses including Introduction to Systems Software (CS 0449), Computer and Network Security (CS 1652), and Operating Systems (CS 2510 and CS 3510). His teaching spans both undergraduate and graduate levels, focusing on practical systems knowledge with hands-on projects. His research has been supported by collaborations with Oak Ridge National Laboratory and other national computing facilities, enabling his work to directly impact real-world supercomputing infrastructure. Dr. Lange is actively involved with The Prognostic Lab and maintains strong connections with the Oak Ridge Leadership Computing Facility (OLCF), where his current leave appointment allows him to work at the forefront of exascale computing research and development.

Urvij SaroliyaView profile

Researcher

• Computer Architecture
• High Performance Computing (HPC)
• Performance Analysis and Modeling
• +4 more

Urvij Saroliya is a doctoral candidate at the Technical University of Munich , affiliated with the Chair of Computer Architecture and Parallel Systems under Prof. Martin Schulz. He focuses on high-performance computing (HPC) and computer architecture, with expertise in resource management, heterogeneous accelerators, and energy-aware systems. Research Interests: Computer Architecture, HPC, Performance Modeling, Reinforcement Learning His recent publications analyze reinforcement learning applications for HPC resource management, including NUMA systems and GPU partitioning. His work emphasizes performance portability and energy efficiency in heterogeneous architectures. He contributes to ongoing projects like PDexa and participates in seminars on quantum computing integration and AI hardware development.

• Low-Level Programming
• Heterogeneous Computing
• Machine Learning Accelerators
• +2 more

Dirk Stober is a researcher at the Chair of Computer Architecture and Parallel Systems at the Technical University of Munich . As a Ph.D. candidate, his work focuses on low-level programming, heterogeneous computing, and novel computer architectures, with a strong emphasis on FPGA programming and machine learning accelerators. His research explores the intersection of high-performance computing (HPC), quantum computing, and AI hardware development. Research Interests: Low-Level Programming Heterogeneous Computing Machine Learning Accelerators FPGA Programming Novel Computer Architectures His work contributes to advancing programming frameworks and performance modeling techniques for emerging architectures like quantum accelerators and AI-specific hardware. While no explicit scientific awards are listed, his involvement in courses such as Efficient Programming of HPC Systems and projects like SEANERGYS (EuroHPC) and Q-DESSI (MQV) highlights his engagement with cutting-edge computational technologies.

Jonas WinklmannView profile

Researcher

• Quantum Computing Architecture
• Efficient Algorithms on Heterogeneous Hardware
• Control Algorithms for Quantum Systems
• +3 more

Jonas Winklmann is a researcher at the Chair of Computer Architecture and Parallel Systems at Technische Universität München (TUM), focusing on quantum computing architectures and hardware acceleration. He contributes to projects like SEANERGYS and MUNIQC-ATOMS, integrating quantum systems with high-performance computing (HPC) frameworks. Research: Quantum hardware design, FPGA-based acceleration, and parallel algorithms Collaborations: EuroHPC initiatives, interdisciplinary quantum computing efforts Teaching: Mentoring lab courses on HPC systems and quantum integration His work spans neutral atom quantum computing, algorithm optimization for heterogeneous platforms, and control systems for quantum devices. Publications highlight FPGA applications in quantum simulation and atom detection techniques. He collaborates on software projects like QMPI and SWEET, advancing quantum-HPC hybridization.

• Vectorization
• Parallel Programming
• High-Performance Computing
• +4 more

Sylvain JUBERTIE serves as a Lecturer at the University of Orleans, affiliated with the LIFO (Laboratoire d'Informatique Fondamentale d'Orléans) research laboratory. His academic career spans over two decades with continuous contributions to high-performance computing, particularly in vectorization techniques and seismic simulation methodologies. His research interests demonstrate deep specialization in: Hardware-specific vectorization (ARM NEON/SVE, SIMD) for seismic kernels GPU acceleration and code portability across architectures High-order spectral finite element methods (EFISPEC3D) Energy efficiency optimization in parallel computing Algorithmic skeleton libraries (OSL, NSIMD) for parallel programming Memory layout reorganization for numerical kernels Analysis of his 15 most recent publications (2013-2025) reveals a consistent research trajectory focused on seismic wave propagation simulation. His work systematically addresses vectorization challenges across ARM architectures, GPU offloading, and memory access patterns, with EFISPEC3D serving as the primary application framework. Notable trends include the development of the NSIMD abstraction layer and rigorous energy-performance trade-off studies on embedded platforms like Jetson boards. Dr. Jubertie maintains active collaboration with key researchers including Fabrice DUPROS, Florent DE MARTIN, and Guillaume QUINTIN through the LIFO laboratory, contributing to France's geophysics research infrastructure while advancing compiler-level optimizations for emerging processor architectures.

• Mass Spectrometry
• Supramolecular Chemistry
• Structural Chemistry
• +5 more

Antonello Filippi is an Associate Professor at the Department of Chemistry and Pharmaceutical Technologies, Sapienza University of Rome. His research focuses on structural and supramolecular chemistry, combining experimental mass spectrometry (IRMPD, IM) with theoretical modeling via high-performance computing (HPC). He also applies GC-MS to characterize food matrices for analytical and biomedical purposes. Graduated with honors in Chemistry from Sapienza University of Rome (1992) Researcher at CNR’s Institute of Nuclear Chemistry (1986–1996) University Researcher (1996–2006) His research projects include structural studies of DNA adducts and non-covalent interactions in supramolecular systems. Recent publications highlight applications in food chemistry, pharmaceutical analysis, and chiral recognition mechanisms. Current teaching activities involve courses in General and Inorganic Chemistry, Analytical Chemistry, and laboratory sessions. He has authored textbooks in general chemistry and stoichiometry.

Uroš LotričView profile

Associate Professor

• Soft Computing
• Distributed Processing
• High Performance Computing

Uroš Lotrič is an Associate Professor at the Faculty of Computer and Information Science, University of Ljubljana, Slovenia. His academic and professional work spans research in soft computing methods, distributed processing, and high-performance computing applications. Education: BSc in Physics (1994), MSc in Computer Science (1997), PhD in Computer Science (2000), all from the University of Ljubljana. Research Interests He focuses on soft computing techniques, distributed systems, and their applications in industrial and computational domains. His work integrates neural networks, wavelet transforms, and predictive modeling to solve complex problems in fields like rubber processing and time series analysis. Scientific Awards Best Assistant 2007 Best Professor 2009 Projects and Laboratory He is involved in national and European research programs such as P2-0241, EUMaster4HPC, and ARISA. Additionally, he is a member of the Adaptive Systems and Parallel Processing laboratory, contributing to advancements in adaptive algorithms and parallel computing.

Alexandre FournierView profile

Professor

• Earth's magnetic field
• Planetary dynamos
• Fluid dynamics
• +4 more

Alexandre Fournier is a Professor of Geophysics at the Institut de Physique du Globe de Paris (IPGP), where he leads the Geologic Fluid Dynamics research group. He serves as the scientific manager of IPGP's shared HPC and data processing service (S-CAPAD) and coordinates the MOOC 'Our Planet'. From 2016 to 2020, he headed the STPE and GGG master's programs. His research spans Earth's and planetary dynamos, fluid dynamics in planetary settings, inverse problems, data science, and numerical methods for geophysical applications. He completed his Accreditation to Supervise Research at Paris-Diderot University (2012), PhD in Geosciences at Princeton University (2003), DEA in internal geophysics at IPGP (1998), and Master's in Physics at ENS de Lyon (1998). His students include Elisabeth Canet, Sabrina Sanchez, Marie Bocher, Guillaume Pichon, Venkatesh Gopinath, Thijs Franken, Marie Troyano, and Théo Tassin. His recent articles focus on geodynamo simulations, archaeomagnetic studies, and computational methods for planetary fluid dynamics. Notable collaborations include Julien Aubert, Thomas Gastine, and Yves Gallet. His work emphasizes high-performance computing, data assimilation, and applications to Earth and planetary systems.

Mahmoud AlzoubiView profile

Assistant Professor

• Transport phenomena in porous media
• Phase-change heat and mass transfer
• Artificial ground freezing
• +5 more

Dr. Mahmoud Alzoubi is an Assistant Professor at Queen's University , cross-appointed between the Robert M. Buchan Department of Mining Engineering and the Department of Mechanical and Materials Engineering . He leads an interdisciplinary research program that couples advanced transport phenomena with energy-efficient technologies for mining and renewable energy applications. Education: Ph.D. in Mining & Mechanical Engineering, McGill University (2018) M.Sc. in Engineering Systems & Management, Masdar Institute of Khalifa University in collaboration with MIT (2014) B.Sc. in Mechanical Engineering, Jordan University of Science and Technology (2005) Research Interests: His work centers on transport phenomena in porous media , with emphasis on phase-change heat and mass transfer , artificial ground freezing , thermal energy storage , microfluidic devices , and renewable HVAC cycles . By integrating high-fidelity experiments with large-scale numerical simulations performed on high-performance clusters, he advances sustainable solutions for energy-intensive mining operations and green building technologies. Publication Impact: Across 32 peer-reviewed articles (2013-2024), a dominant theme emerges: developing computationally efficient models for coupled thermo-hydraulic processes in freezing, storage and ventilation systems. Studies range from Stefan-problem analytical solutions for phase-change materials to large-eddy simulations of cough-jet dispersion for indoor-air safety, underscoring a methodological breadth that spans pure mathematics, experimental heat transfer, and applied computational fluid dynamics. Funding & Recognition: Total research funding secured: CAD 466,000+ (direct cash CAD 381,000 + high-performance computing allocation CAD 85,000) Former member, Canadian Hydrogen in Mining Advisory Committee , Natural Resources Canada Laboratory & Teams: Dr. Alzoubi directs a research laboratory at Queen’s University equipped with state-of-the-art instrumentation for multiphysics experimentation and access to national HPC facilities. The group collaborates closely with industry partners (mining, HVAC) and government laboratories to translate fundamental findings into scalable, energy-efficient technologies for northern mining and cold-region infrastructure.

Mahesh M SView profile

Associate Professor

• Vibroacoustics
• Aeroelasticity
• Computational Mechanics
• +9 more

Mahesh M S is an Associate Professor in the Mechanical & Aerospace Engineering department at the Indian Institute of Technology Hyderabad . He holds a Ph.D. in Aerospace Engineering from the University of Illinois at Urbana-Champaign. Education Ph.D. (2013), University of Illinois at Urbana-Champaign M.Tech. (2004), Indian Institute of Technology Madras B.Tech. (2004), Indian Institute of Technology Madras Research Interests include Vibroacoustics , Aeroelasticity , Computational Mechanics , Aerodynamics , Aeroacoustics , and High Performance Computing . His group explores compressible multiphase flows, radar cross-section prediction, and shock wave interactions. Current Projects include Internal Ballistics (ARMREB, 2024–2027) and Hydroacoustics (NSTL, DRDO, 2024–2025). Past projects span underwater supersonic jets (DRDO, 2019–2021), cavity aeroacoustics (ADA, 2020–2021), and terminal ballistics (ARB, 2016–2018). Scientific awards : None listed. Students : Dr. Amartya Jana (Ph.D. candidate), Dubba Bhuvana Sai Charan Nath (Ph.D. scholar), Shikshit Nawani (M.Tech), Guruprasad Arya (M.Tech), Akshay Khare (M.Tech), Swapna Yalamanchili (Ph.D. scholar), Vishnu S. B. (Project Scientist), Chele Rajesh (Junior Research Fellow), Sharad Verma (M.Tech), Mangesh Dholwade (M.Tech). Contact : Room C-515, Academic Block C, IIT Hyderabad, Telangana, India. Email: mahesh@mae.iith.ac.in .

Ramakrishna UpadrastaView profile

Associate Professor

• Domain Specific Programming Languages for Parallelization
• LLVM Optimizations
• Polyhedral Compilation
• +6 more

Ramakrishna Upadrasta is an Associate Professor in the Department of Computer Science & Engineering and Heritage Science & Technology at Indian Institute of Technology Hyderabad. He earned his PhD from University of Paris-Sud and INRIA, Paris, with a focus on improving scalability in polyhedral compilation tools. His academic journey includes an M.S. from Colorado State University, M.Tech from Indian Institute of Science (IISc), and B.E. in Electrical and Electronics Engineering from Andhra University. His research expertise includes: Domain Specific Programming Languages for Parallelization LLVM Optimizations Polyhedral Compilation Abstract Interpretation Sub-polyhedral Approximations High-Performance Computing (HPC) He leads the IITH-LLVM group and collaborates with industries on compiler technologies. His work addresses scalability vs. precision trade-offs in static analysis tools and extends polyhedral compilation frameworks like Pluto and Polly. Scientific Recognition : HiPEAC paper award for his POPL-2013 publication As an educator, he has taught advanced compiler courses (e.g., CS6250, CS5260) and coordinated interdisciplinary initiatives, including Sanskrit language courses via Samskrita Bharati. His students span PhD, M.Tech, and B.Tech levels, focusing on compiler design and parallelization.

Saswata BhattacharyaView profile

Professor

• Phase-field modeling
• Microstructural evolution in alloys and oxides
• Phase transformations
• +5 more

Saswata Bhattacharya is the Head (MS) & Professor in the Department of Materials Science and Metallurgical Engineering at the Indian Institute of Technology Hyderabad. He earned his Ph.D. from the Indian Institute of Science in Bengaluru and focuses on computational materials science and data-driven modeling. Education: Ph.D., Indian Institute of Science, Bengaluru Research Interests Phase-field modeling of microstructural evolution in alloys and oxides Phase transformations Micromechanical modeling Multiscale modeling AI/ML integration in materials science High Performance Computing (HPC) applications Materials Processing Office Address: Room MSME-402, MSME Block, Indian Institute of Technology Hyderabad, Kandi-502284, Sangareddy, Telangana, India.

Shantanu DesaiView profile

Professor

• Cosmology
• Galaxy clusters
• Pulsars and pulsar timing arrays
• +6 more

Shantanu Desai is a full-time Professor of Physics (and jointly of Artificial Intelligence) at the Indian Institute of Technology Hyderabad , a position he has held since October 2023 after serving as Associate Professor from 2016-2023. He earned his Ph.D. in Physics from Boston University in 2004 and is a member of several high-profile international collaborations, most notably the Super-Kamiokande Collaboration —sharing the 2016 Breakthrough Prize in Fundamental Physics for the discovery of atmospheric neutrino oscillations and solving the solar neutrino puzzle. Education Ph.D. in Physics, Boston University, May 2004 M.A. in Astronomy, Boston University, May 1997 B.Tech in Physics, Indian Institute of Technology Bombay, May 1995 Research Interests Desai’s work straddles observational cosmology, high-energy astrophysics, and data-driven methodologies. His core research areas include: Cosmology and Galaxy Clusters: probing dark energy, cluster abundances, and large-scale structure. Pulsar Timing & Gravitational Waves: member of the Indian Pulsar Timing Array searching for nano-Hertz gravitational waves. Neutrino Astrophysics: utilizing Super-Kamiokande data to study solar and atmospheric neutrinos. Gravitational Lensing: strong and weak lensing analyses within the Dark Energy Survey and Euclid. Machine Learning & Astrostatistics: developing AI/ML techniques for transient detection, lens finding, and survey optimization. High-Performance Computing: leveraging HPC resources for large-scale simulations and data processing. Publication & Research Trends Across 2024-2025, Desai’s publications reveal a focused synergy between multi-wavelength observations and advanced statistical methods. Topics include joint analyses of DES Y3 weak-lensing and ACT SZ data, targeted gamma-ray searches for dark matter signals in galaxy clusters, cosmological constraints on neutrino masses, and machine-learning-driven lens discoveries. These works consistently integrate large survey datasets (DES, ACT, Fermi-LAT, InPTA) with rigorous profile-likelihood techniques to address tensions in the standard cosmological model. Scientific Awards 2016 Breakthrough Prize in Fundamental Physics – shared as a member of the Super-Kamiokande Collaboration for the discovery of atmospheric neutrino oscillations and resolution of the solar neutrino problem. Student Supervision & Mentorship At IIT Hyderabad, Desai has mentored over 60 students ranging from B.Tech to Ph.D. levels, many proceeding to prestigious graduate programs worldwide (e.g., Penn State, University of Utah, Southern Methodist University, IUCAA, Swinburne, Florida Tech). Current advisees include Ph.D. students Siddhant Manna, Kamal Bora, Aman Srivastava, Gopika K., and Srinadh Reddy (co-supervised). Laboratory & Team Affiliations Desai leads a vibrant research group within the Physics Department at IIT Hyderabad. The group is actively involved in the Indian Pulsar Timing Array (InPTA) , Dark Energy Survey (DES) , Euclid Consortium , and Atacama Cosmology Telescope (ACT) collaborations, maintaining dedicated computational resources and close ties with international partners for joint observations and data analysis.

• Machine Learning for Visual Computing
• Big Data Visualization and Analytics
• Data Science and High-Performance Computing (HPC)
• +4 more

Soumya Dutta is an Assistant Professor in the Department of Computer Science and Engineering (CSE) at the Indian Institute of Technology Kanpur (IITK) since 2022. He previously held positions at Los Alamos National Laboratory as a Postdoctoral Researcher (2018-2019) and Scientist II (2019-2022). Dr. Dutta earned his Ph.D. and M.S. in Computer Science from The Ohio State University (2011-2018) and a B.Tech in Electronics and Communication Engineering from the West Bengal University of Technology (2005-2009). His research lies at the intersection of Machine Learning , Visual Computing , Big Data Analytics , and High-Performance Computing (HPC) . He focuses on developing scalable solutions for extreme-scale data, such as exascale simulations, social media, IoT, and healthcare. His work emphasizes uncertainty quantification in AI models and interactive visualization techniques. Dr. Dutta’s recent publications highlight his expertise in in situ visualization for climate modeling, implicit neural representations for uncertainty-aware rendering, and statistical sampling for exascale systems. His funded projects include AI-driven data analytics frameworks and deepfake defense mechanisms supported by ISRO, SERB, and C3iHub. Scientific Awards include Best Reviewer (TVCG), Best Paper (ISAV, TopoInVis), and LAAP Award (LANL).