Mohammad Mehrali is an Assistant Professor specializing in Thermal Engineering, with an ORCID identifier and extensive research contributions. He maintains an h-index of 50 with over 7,000 citations, demonstrating significant academic impact since his first publication in 2012. Research Interests: Graphene-based nanomaterials Thermal conductivity optimization Phase change material innovation Nanofluid dynamics Composite material development His recent publications (2018-2019) focus on hybrid nanofluids for thermal systems, waste-derived biomaterials, and advanced solar energy conversion technologies. Key trends show integration of nanotechnology with both energy systems and biomedical applications.
Ozgur Aktunc is a Professor in the Department of Engineering at St. Mary's University , San Antonio, Texas. He joined the university in 2009 and specializes in software engineering education, agile methodologies, and human-centered design. Ph.D. in Computer Engineering (University of Alabama at Birmingham, 2007) M.S.E.E. (University of Alabama at Birmingham, 2002) B.S. in Electrical Engineering (Istanbul Technical University, 1999) His research focuses on agile software development , human-centered design , and computing education . He has pioneered outreach initiatives like the St. Mary's Pre-Engineering Summer Program to engage middle and high school students. His work spans software complexity metrics , educational frameworks , and accessibility tools , with publications in IEEE, ASEE, and SDPS venues. Aktunc also authored the book An Entropy-Based Measurement Framework for Component-Based Systems . He is a member of the American Society for Engineering Education (ASEE) and has collaborated on projects related to GitHub integration in programming courses , web accessibility , and seismic monitoring systems . His teaching methodology emphasizes interactive learning and practical applications.
Alistair Moffat is a prominent faculty member at the University of Melbourne's School of Computing and Information Systems, with an extensive publication record spanning over four decades from 1980 to the present. His career demonstrates sustained contributions to information retrieval, data compression, and evaluation metrics within the field of computer science. Moffat's primary research interests encompass: Information Retrieval and Search Engine Technologies Data Compression Algorithms and Indexing Techniques Development and Analysis of Evaluation Metrics Process Mining and Event Log Analysis User Modeling in Information Seeking Behavior Theoretical Foundations of Search Effectiveness His recent work (2023-2025) reveals a continued focus on refining search evaluation methodologies, with particular attention to user-oriented metrics, rank-biased quality measurement, and the relationship between query variations and experimental consistency. Moffat has also expanded into process mining applications, developing entropy-based metrics like Entropia for measuring log representativeness. His publications consistently appear in top-tier venues including SIGIR, ACM Transactions on Information Systems, and Information Processing & Management. Moffat maintains extensive research collaborations, most notably with Justin Zobel (70 joint publications), J. Shane Culpepper (35), and Matthias Petri (32), demonstrating a strong network within the information retrieval research community. His work bridges theoretical computer science with practical applications in search technology, medical information retrieval, and process analysis. As evidenced by his continuous publication output through 2025, Moffat remains an active and influential researcher in his fields of expertise, contributing both to foundational theories and practical implementations in information access systems.
Holger Pirk is a researcher at Imperial College London , UK, focusing on database systems and data science. His work bridges hardware-aware query optimization, in-memory processing, and machine learning integration. He collaborates with institutions like MIT, TU Delft, and VU Amsterdam. Affiliation: Imperial College London, UK Key Collaborators: Samuel Madden (MIT), Martin Kersten (CWI), Georgios Theodorakis (Imperial), Stefan Manegold (CWI) Research Interests include: Hardware-conscious database optimization Stream/window aggregation algorithms Portable execution models via homoiconicity Compiler-database system integration Efficient tree/index structures Recent Publications (2023-2025) address topics like database kernel composition (BOSS), LLM-generated text compression, hardware-efficient data imputation, and fault-tolerant stream processing. His work emphasizes CPU/cache efficiency, parallelism, and cross-domain system design.
Berkay Anahtarcı is an Assistant Professor in the Department of Mathematical Engineering at Özyeğin University. He received his B.Sc. from Boğaziçi University in 2008, followed by his M.Sc. and Ph.D. from Sabancı University in 2011 and 2015, respectively, under the supervision of Professor Plamen Djakov. His educational background includes: B.Sc. in Mathematics, Boğaziçi University, 2008 M.Sc. in Mathematics, Sabancı University, 2011 Ph.D. in Mathematics, Sabancı University, 2015 Dr. Anahtarcı's research spans both theoretical mathematics and applied interdisciplinary fields. His early work focused on Functional Analysis and Spectral Theory of Differential Operators, particularly spectral gaps of Dirac and Mathieu operators. More recently, his research has evolved toward Game Theory with a focus on Mean-Field Games and Machine Learning, especially Reinforcement Learning. This transition demonstrates his ability to bridge pure mathematics with cutting-edge applications in artificial intelligence. His publication record shows a clear progression from theoretical mathematics (2012-2018) to increasingly applied work in game theory and machine learning (2020-2025). The majority of his recent publications center on learning algorithms for mean-field games, inverse reinforcement learning, and related computational frameworks. Among his notable achievements is the Faculty Teaching and Learning Excellence Award from Özyeğin University in 2024. He is also the principal investigator for the TUBITAK 1001 research grant "Inverse Reinforcement Learning for Mean-Field Games" (2024-2027), one of Turkey's most competitive research funding programs. Dr. Anahtarcı has been teaching courses in both Mathematics and Industrial Engineering departments at Özyeğin University and Sabancı University since 2014, demonstrating his interdisciplinary approach to education and research. His work has been published in prestigious venues including the Journal of Machine Learning Research, Dynamic Games and Applications, and IEEE Conference on Decision and Control.
Zico Kolter is a Professor and Director of the Machine Learning Department at Carnegie Mellon University . He also serves on the OpenAI Board of Directors as chair of the safety and security committee, co-founded Gray Swan AI (an AI security company), and acts as a Chief Expert at Robert Bosch, LLC . Research Focus: AI safety and robustness, LLM security, data impact on models, implicit models, and adversarial defense. Teaching: Offers graduate courses in artificial intelligence and deep learning systems. His work investigates robustness in deep learning, constraints in optimization, and security in foundation models. Recent publications address adversarial compression, diffusion models, and prompt engineering. Notable Awards: DARPA Young Faculty Award Sloan Fellowship Best Paper at NeurIPS, ICML (honorable mention), AISTATS (test of time), IJCAI, KDD, and PESGM.
Professor Shrikant Joshi holds a Doctor of Philosophy in Chemical Engineering at University West's Department of Engineering Science, Division of Mechanical engineering. His research spans Additive Manufacturing (AM) and Surface Engineering with significant industrial collaborations including GKN, Siemens, Sandvik, Arcam, Quintus, and Exova. Funded by KK.Stiftelsen and Vinnova, his work integrates academic research with industrial applications in high-performance materials development. His research interests focus on Additive Manufacturing of Ni-based superalloys (particularly post-treatment effects on build properties) and advanced thermal spray technologies including solution precursor plasma spraying (SPPS), suspension plasma spraying (SPS), hybrid coatings, and HVAF coatings. Key objectives include enhancing functional properties (thermal barrier, wear, corrosion resistance) beyond conventional powder-based methods, with applications in power plant boilers, hydrogen production electrolyzers, and high-temperature environments. Current projects involve luminescent coatings development in collaboration with ESS, University of Oslo, and Stony Brook University. Professor Joshi developed and taught a Surface Engineering course in Autumn 2016 under the Produktion 2030 initiative. His research is supported by multiple grants from KK.Stiftelsen and Vinnova, focusing on coating chemistries for boiler environments and advanced material systems. Industrial partnerships form a critical component of his research ecosystem, enabling direct translation of findings to real-world applications. KK.Stiftelsen-funded project on coating chemistries for boiler environments Vinnova-supported Additive Manufacturing research International collaboration on luminescent coatings (ESS, University of Oslo, Stony Brook University) His laboratory work centers on thermal spray technologies and additive manufacturing, with specialized capabilities in solution-based plasma spraying, hybrid coating systems, and high-velocity air-fuel processes. The research environment leverages University West's strengths in allied AM areas and complementary industrial partner capabilities for comprehensive materials development and testing.
Mario Stipčević is a senior scientist at the Ruđer Bošković Institute (RBI) in Zagreb, Croatia, where he heads the Photonics and Quantum Optics Laboratory within the Division of Experimental Physics. Holding the Croatian rank of scientific advisor in permanent position —equivalent to full professor—he has led national and international research efforts spanning quantum optics, quantum-information science, and high-energy neutrino physics. Education 2011–2012: Visiting Specialization in Experimental Quantum Information, University of California, Santa Barbara, USA 2010–2011: Fulbright Scholar, Experimental Quantum Information, University of California, Santa Barbara, USA 1994: PhD in Particle Physics, Université de Savoie, Chambéry, France 1991: BSc in Theoretical Nuclear Physics, Faculty of Science, University of Zagreb Research Interests His work focuses on quantum communication , quantum randomness , single-photon detection , quantum key distribution , and quantum holography . Early in his career he contributed to neutrino oscillation experiments (NOMAD, OPERA) and calorimetry R&D for the LHC. Scientific Awards Fulbright Scholarship (2010–2011) Golden Medal ARCA 2005, Zagreb International Autumn Fair Golden Medal, Salon International de Inventions, Geneva 2005 Young Investigator Award, Ministry of Science and Technology, Croatia (1998) Projects & Leadership Since 2014 he has been Head of the Photonics and Quantum Optics Research Unit at the RBI Centre of Excellence for Advanced Materials and Sensing Devices. Earlier projects include the Croatian Ministry of Science grant Experiments in Quantum Communication and Quantum Information (2007–2014) and World-Bank-funded development of a Quantum Random Bit Generator (2004–2005). Laboratory & Teams At RBI he directs the Photonics and Quantum Optics Laboratory , a multi-disciplinary group developing photon-counting detectors, entangled-photon sources, and quantum-network testbeds. The lab participates in European initiatives such as the European Quantum Internet Alliance and the ESSnuSB neutrino super-beam design study .
Dr. Salvatore Grasso is a Senior Lecturer in Ceramics at the School of Engineering and Materials Science, Queen Mary University of London . He serves as Editor of the European Ceramic Society and Associate Editor for the American Ceramic Society and International Journal of Applied Ceramic Technology. With over 250 publications and 28 patents, his work focuses on sustainable ceramics processing. Royal Society Industry Fellow (2025-2028, £157,805) Developed MagMat for electromagnetic material processing Research Interests include: Ultra-fast high-temperature sintering (UHS) with heating rates up to 10⁴°C/min Cold sintering processes for room-temperature joining Magnetic field alignment (9-15 Tesla) for textured ceramics High-entropy oxides and perovskites for energy storage Thermal shock synthesis of advanced ceramics Scientific Awards include: Pfeil Award Best paper in Asian Ceramic Society Journal Best PhD thesis (Japan) Research Trends in his 15 most recent articles show focus on: Reducing energy consumption in ceramic manufacturing Developing novel multi-field-assisted processing techniques Exploring high-entropy systems for enhanced stability Creating sustainable routes for functional ceramics Current Funding supports his project Revolutionizing fuel and electrolysis cell production for NET-Zero: Sustainable, Efficient, and Rapid ceramics processing (SER) . He leads international collaborations with groups in Europe, Asia, and America using custom equipment and multi-physics FEM simulations.
Naoki Masuda is a Professor at the Department of Mathematics and the Gilbert S. Omenn Department of Computational Medicine and Bioinformatics at the University of Michigan. His research lies at the intersection of network science and mathematical biology , with a focus on dynamic networks, time-varying systems, and data-driven analysis of complex systems. B.Sc., University of Tokyo (1998) M.Sc., University of Tokyo (2000) Ph.D., University of Tokyo (2002) Masuda's theoretical work includes temporal networks , multilayer networks , hypergraphs , and random walk algorithms . His applications span genomics , fMRI neuroimaging , animal behavioral biology , and social networks in public health . He develops methods for analyzing time-varying network structures and has created open-source tools like MATLAB and Python implementations for network analysis. Recent publications focus on epidemic dynamics on temporal networks (2017-2020), energy landscape analysis of brain activity (2013-2020), and evolutionary cooperation mechanisms (2011-2016). His work often involves interdisciplinary collaborations with neuroscientists, biologists, and public health researchers. Masuda maintains active research groups with students like Alber Aqil (PhD candidate in biological sciences) and Yanyan Li (former undergraduate math student). He welcomes researchers interested in complex networks and mathematical biology , particularly those working with temporal network data or neuroimaging datasets .
Florian De Vuyst is a Full Professor at the Department of Computational Engineering, University of Technology of Compiègne (UTC), where he conducts research at the Biomechanical Bioengineering Laboratory (BMBI UMR 7338). Previously affiliated with the Applied Mathematics Laboratory (LMAC), his work spans Fluid-Structure Interaction (FSI) , Scientific Machine Learning , GPU Computing , and Reduced-Order Modeling (ROM) . His research focuses on multimaterial flows , automotive crash dynamics , and bioengineering applications like microcapsule deformation in Stokes flows. His recent publications highlight machine learning integration with FSI simulations and GPU-accelerated solvers for compressible flows. Collaborations include Renault , Michelin , and ENS Paris-Saclay on projects like tsunami coastal impact modeling (DIGISCOPE EquipEx) and vehicle drag estimation . He has supervised 15+ PhD students, including Azzedine Tiba (non-intrusive ROM) and Vincent Mahy (multimaterial methods). Scientific Awards: Recipient of the Best Applied Paper Award at EGC 2008 Grants & Collaborations: Involved in CNRS Editions' interdisciplinary volume on tsunamis, ERCOFTAC symposia, and industrial partnerships
Jona Ballé is an Associate Professor in the Electrical and Computer Engineering department at New York University's Tandon School of Engineering. His research focuses on developing efficient representations of visual media through machine learning and end-to-end optimization techniques. Dr. Ballé's research interests center on visual media compression, spanning still images, video, augmented reality, virtual reality, plenoptic imaging, and holographic imaging. His work bridges information theory, computer vision, and machine learning to develop perceptually optimized compression algorithms. He has made significant contributions to understanding the relationship between human visual perception and image statistics, which has led to improved compression results and ultimately contributed to the JPEG AI standard finalized in 2025. His recent publications demonstrate a strong trend toward Wasserstein distortion metrics, neural compression architectures, and rate-distortion optimization. These works span computer vision, information theory, and signal processing domains, with applications in both traditional and emerging visual media formats. His research shows consistent innovation in developing perceptually relevant metrics that balance fidelity and realism in compressed media. Contributed to JPEG AI standard (2025) Co-organizer of Challenge on Learned Image Compression (CLIC) since 2018 Program committee member of Data Compression Conference (DCC) since 2022 Reviewer for top-tier publications including NeurIPS, ICLR, ICML, and IEEE Transactions journals Dr. Ballé has advised numerous graduate students who have co-authored significant publications with him, particularly in the areas of neural compression and perceptual metrics. His research has been supported by institutions including the Simons Foundation. He maintains active collaborations across academia and industry, with his work at Google (2017-2024) directly informing his current academic research. His laboratory focuses on developing open-source implementations of advanced compression techniques, with notable GitHub repositories including Wasserstein Distortion implementation in PyTorch and CoDeX (Learned data compression in JAX), demonstrating his commitment to reproducible research and community engagement.
Tyler Bell, PhD, is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Iowa, where he also serves as a faculty member of the Public Digital Arts cluster and a research affiliate of the Center for Bioinformatics and Computational Biology. He directs the Holo Reality Lab, focusing on immersive 3-D technologies. Education PhD in Computer Engineering, Purdue University, 2018 MS in Human Computer Interaction, Iowa State University, 2014 BS in Computer Science, Iowa State University, 2012 Research Interests Bell’s research spans high-quality 3-D video communications, high-speed high-resolution 3-D imaging, virtual and augmented reality, human-computer interaction, and multimedia compression on mobile devices. His work integrates advanced sensing, real-time compression, and user-centric design to create immersive, bandwidth-efficient experiences. Publication Trends From 2024 back to 2016, Bell’s publications reveal a steady trajectory in 3-D data compression, neural encoding methods, VR/AR training systems, and assistive technologies. Recent 2024 papers emphasize AI-driven compression and educational VR, while 2022–2020 works focus on foveated and variable-precision depth encoding, holographic conferencing, and mobile 3-D capture. Earlier studies concentrate on structured-light calibration and high-dynamic-range 3-D sensing. Awards & Honors Research Excellence Award, Iowa State University Innovation of the Year Finalist, TechPoint Mira Awards (2018) Labs & Teams Bell leads the Holo Reality Lab , which develops real-time 3-D imaging and streaming technologies for mobile AR/VR applications. His interdisciplinary collaborations include the Public Digital Arts cluster and the Center for Bioinformatics and Computational Biology, fostering cross-cutting research in digital arts and computational biology.
Rubén Cuevas Rumin is an Associate Professor at the Telematics Engineering Department of Carlos III University of Madrid. He serves as Deputy Director of Human Resources for his department and co-leads the Joint Institute of Big Data in Finance UC3M-Banco Santander , focusing on interdisciplinary applications of network and data science. Academic Affiliation: Carlos III University of Madrid Department: Telematics Engineering Department Institute: UC3M-Banco Santander Big Data in Finance His research bridges network performance analysis and digital advertising , with emphasis on user privacy and anonymity in web applications. He explores time-series classification , browser fingerprinting , and geolocation accuracy in adTech ecosystems. Recent work includes quantifying carbon footprints of online ads and designing zero-knowledge advertising frameworks. Scientific awards include I-COM Datascience Hackathon Winner (2016) . His publications reveal expertise in large-scale behavioral analytics , ad fraud detection , and energy-efficient networking . He actively investigates filter bubbles , contact tracing , and gender inequality through Facebook advertisement data.
Daniel Díaz Sánchez is an Associate Professor at the Telematics Engineering Department of Carlos III University of Madrid , where he serves as Deputy Director of Laboratories . His research focuses on IoT security , Post-Quantum Cryptography , and network protocols . Email: daniel.diaz@uc3m.es Office: 4.0.F04 - Quevedo Towers (Leganés) His recent work explores DNSSEC soft delegation for microservices, quantum random number generators , and machine learning applications in cybersecurity. Key publication themes include IoT credential management , secure communication protocols , and control system optimization .
