Tilmann Rabl is a Professor affiliated with the Hasso Plattner Institute (HPI) at the University of Potsdam, Germany. His research focuses on database systems, distributed computing, and scalable data processing. He leads projects exploring serverless cloud infrastructure, stream processing, and machine learning integration with databases. Key areas of research include optimizing GPU-based data processing, developing benchmarks like TPCx-IoT and TPCx-AI, and advancing techniques for distributed systems, including RDMA and NVLink-based architectures. His work emphasizes practical systems, such as Skyrise (serverless data processing), Rhino (distributed state management), and PROTEUS (scalable machine learning). Rabl has contributed to foundational tools like BlockJoin for matrix partitioning and has explored performance trade-offs in persistent memory and CXL device memory. His collaborative projects address challenges in real-time data analytics, sensor data coherence, and interoperable data science workflows.
Minos Garofalakis is a Professor at the School of Electronic & Computer Engineering at the Technical University of Crete, specializing in data stream management, complex event processing, and privacy-preserving analytics. His work bridges theoretical and applied computer science, with a focus on scalable algorithms for high-velocity data. Best Paper Award at VLDB 2024 for 'OmniSketch' Leader in sketch-based and distributed stream processing Pioneer in differential privacy for relational data Research interests span stream analytics , probabilistic databases , and interactive query systems . Recent work includes oblivious parallel joins (2025), relational data synthesis under privacy constraints (2024), and cross-platform analytics frameworks (2020). His publications demonstrate a consistent focus on error-controlled approximations and real-time distributed processing. Scientific contributions have been recognized at premier conferences like VLDB, with awards highlighting innovations in multi-dimensional stream analysis and privacy-preserving operations . Collaborations span academia and industry, particularly in bioinformatics and distributed systems.
Amélie Marian is a Professor in the Department of Computer Science at Rutgers University. She maintains an active research program with numerous recent publications and research projects. Her office is located in CoRE 324 on the Busch Campus, and she can be reached at amelie.marian@rutgers.edu or by phone at (848) 445-8324. Dr. Marian's primary research interests focus on Data Management and Algorithms, with specific emphasis on Accountability and Transparency of Algorithms for Decision-Making, Explainable Rankings, Personal Information Management, Data Integration, and Data Corroboration. Her work bridges theoretical computer science with practical applications in decision systems, personal data management, and privacy-preserving technologies. She leads several major research initiatives including YourDigitalSelf (connecting, searching, and understanding personal digital traces), Explainable Rankings (toward transparent ranking functions), and Decentralized Collaborative Filtering (privacy-aware personal recommendations). Analyzing her recent publications reveals a clear trajectory toward increasingly important societal challenges in algorithmic transparency and accountability. Her work spans technical aspects of database systems and information retrieval while addressing critical social implications of algorithmic decision-making. The research shows strong interdisciplinary connections between computer science, social choice theory, human-computer interaction, and public policy. Microsoft Live Labs Award (2006) Google Research Awards (2008, 2010, 2012) NSF CAREER award (2009) NSF MCA Grant Award for Transparent and Accountable Decision Systems (2022) Multiple Google Research Awards for projects including Remembrance of Data Past and PERSEUS Dr. Marian actively mentors graduate students including PhD candidates Yehuda Gale and Shuchang Liu, and MS student Shuyuan Xu. Her research has been supported by significant grants including multiple NSF awards (NSF-SES 2218975, NSF-IIS 0844935, BCS-CDI-Type I 1027801), Google Research Awards, and Microsoft funding. She leads the YourDigitalSelf research group focused on personal information management systems and has established collaborative projects with researchers across multiple institutions.
Artem Barger is a researcher specializing in blockchain technology, distributed systems, and database optimization. With affiliations primarily in blockchain development and academic research, he has contributed extensively to Hyperledger Fabric enhancements and decentralized information systems. Research Interests Optimizing state databases for blockchain platforms Byzantine Fault Tolerance in distributed networks Permissioned blockchain architectures Tokenization of real-world assets AI applications in soft skills evaluation Recent Publications Barger's work focuses on improving blockchain scalability and security through techniques like certification blocks, Patricia Merkle tries, and verifiable randomness. He has also explored tokenization applications in charity and energy sectors.
Abigail Polin is a theoretical and computational astrophysicist and Professor of Physics and Astronomy at Purdue University. Her research focuses on astrophysical transients, particularly Type Ia supernovae and stellar explosions. She utilizes hydrodynamical simulations and radiative transport calculations to bridge theory with observational data. Polin holds a PhD from UC Berkeley (2020), where she worked under Peter Nugent and Dan Kasen, and previously served as a postdoctoral fellow at Carnegie Observatories and Caltech. Her work has been recognized with the NERSC Early Career Award (2025) for modeling sub-Chandrasekhar mass Type Ia supernovae. Polin's research spans observational astronomy, numerical modeling, and instrument design, including proposals for CubeSat missions like UVIa to study ultraviolet signatures of supernovae. She collaborates extensively with international teams on projects like the Carnegie Supernova Project-II and Zwicky Transient Facility surveys. Key research themes include: Explosion mechanisms for calcium-rich transients and Iax/Ia supernovae Stellar progenitor systems and detonation processes Anisotropy studies in supernova remnants Multi-wavelength observations using JWST and ground-based facilities Her recent work emphasizes late-time NIR spectroscopy, forbidden line emission analysis, and the connection between theoretical models and observational data from transient surveys. Polin actively participates in transient classification efforts like POISE and contributes to both observational campaigns and numerical simulation frameworks.
Professor Ajit Singh is a faculty member in the Department of Electrical and Computer Engineering at the University of Waterloo , affiliated with the Faculty of Engineering . His expertise spans Parallel and Distributed Computing , Database Systems , Network-Centric Computing , and Software Architecture and Design . Education: BSc (BIT, India) MPhil (Jawaharlal Nehru University, New Delhi) MSc and PhD (University of Alberta) Research & Industrial Work: Founded SlipStream Data (acquired by Research in Motion/BlackBerry) to accelerate internet content delivery on low-bandwidth networks. Consulted for IBM Canada, Bell Canada, Nortel, and other tech firms. Current projects include Energy Efficient Mobile Computing and Processing Data Streams . Advising & Students: Current advisees: Liton Chakraborty, Mohammad Mursalin Akon, Rajesh Palit, Tan Wang. Past students include Dhrubajyoti Goswami, Afzal Mawji, and others. Research Themes: Focuses on scalable caching, distributed systems, and energy-efficient computing. Explores disk-based optimization for stream joins and parallel architectures.
Ali José Mashtizadeh is an Associate Professor at the Cheriton School of Computer Science, University of Waterloo. His research focuses on operating systems, distributed systems, and storage, with expertise in system reliability, network optimization, and concurrent programming. Education: Ph.D., Computer Science, Stanford University (2017) M.S., Computer Science, Stanford University (2017) M.Eng., Electrical Engineering and Computer Science, MIT (2007) B.S., Electrical Engineering, MIT (2006) His research centers on designing scalable and reliable systems, with recent publications exploring TCP network frameworks, in-memory data persistence, and microsecond-scale scheduling. Key themes include optimizing tail latency, neutralization-based memory reclamation, and fault-tolerant distributed services. His articles consistently demonstrate innovations in low-latency networking, operating system architecture, and cloud infrastructure, with recent emphasis on serverless benchmarks and processor customization. No scientific awards or advising relationships are detailed in the provided materials.
Professor Adrian Barnett is a Professor in the School of Public Health & Social Work at Queensland University of Technology (QUT). He holds a BSc in Statistics from University College London (1994) and a PhD in Mathematics from the University of Queensland (2002). His research focuses on meta-research, research funding, data sharing, and improving the value of health and medical research. He is the past president of the Statistical Society of Australia and current president of the Association for Interdisciplinary Meta-Research and Open Science (AIMOS). His work emphasizes reducing research waste and enhancing research integrity through methodological improvements, policy analysis, and evidence-based practices. He has authored or co-authored over 40 peer-reviewed articles, with recent contributions addressing statistical methodologies, clinical prediction models, and pandemic-related healthcare challenges. Barnett’s leadership includes advocating for open science, transparent reporting, and ethical research practices. He currently oversees the Australian Centre for Health Services and Innovation and is actively involved in supervising postgraduate students across Honours, Masters, and PhD programs.
Reinhard Pichler is a Full Professor at the Vienna University of Technology (TU Wien), affiliated with the Faculty of Informatics and the Department of Databases and Artificial Intelligence . His research focuses on Database Theory , Computational Logic , and Parameterized Complexity . He leads multiple research projects like DeConquer (2023–2027) and HyperTrac (2018–2022), addressing challenges in query optimization and hypergraph decompositions. He holds the prestigious START Prize (2014–2022) for young researchers. His work spans theoretical foundations (e.g., hypertree decompositions) and practical applications (e.g., SPARQL query processing systems like SparqLog). He contributes to academic governance, serving on faculty councils and curriculum commissions. His research innovations bridge algorithmic theory and real-world database systems, emphasizing efficient query evaluation and tractability analysis. Key contributions include advancing fractional hypertree decompositions , SPARQL query optimization , and consistent query answering . His projects often involve collaborations with industry and international funders like the Austrian Science Fund (FWF) and Vienna Science and Technology Fund (WWTF). He actively publishes in top venues like Journal of the ACM , ACM Transactions on Database Systems , and Proceedings of the VLDB Endowment . His academic leadership extends to course design, teaching advanced topics like Complexity Theory and Theoretical Computer Science . He mentors doctoral students and oversees research teams exploring cutting-edge areas like uncertain databases and cloud-based computational social choice .
Michael A. Rasheed is a leading marine ecologist at James Cook University , specializing in seagrass ecosystem monitoring and conservation. His work focuses on seagrass resilience , herbivory impacts , and coastal environmental management across Queensland's Great Barrier Reef and Torres Strait regions. Key research themes include: Seagrass monitoring in industrial ports (Gladstone, Townsville, Weipa) Deep-water seagrass light thresholds for dredging management Herbivore exclusion experiments to study ecosystem structuring Blue carbon stock assessments and climate vulnerability analyses His publications (2016–2025) demonstrate consistent ecosystem monitoring using long-term datasets and spatial analysis to inform marine conservation policies . Collaborations span multi-institutional teams including TropWATER, Reef and Rainforest Research Centre, and Australian Marine Science Association.
Luis Antunes Veiga is an Associate Professor and Senior Researcher at INESC-ID Lisbon, affiliated with the Distributed Systems Group and the Computing Systems and Communication Networks Laboratory. His research focuses on Cloud Computing, Edge Computing, Distributed Systems, and Big Data processing. He teaches courses such as 'Cloud Computing and Virtualization' and 'Operating Systems, Virtualization and Cloud Computing.' His work emphasizes scalable systems, network-aware workflows, and resource-efficient data processing. Research Interests: His primary areas include distributed systems architectures, edge computing frameworks, graph processing algorithms, and software-defined systems. He explores topics like latency-aware network design, resource auction mechanisms for edge environments, and interoperable service workflows. His contributions span theoretical frameworks and practical implementations, such as the RATEE system for edge resource trading and the VeilGraph incremental graph processing framework. Publications: His recent work addresses challenges in distributed systems, such as elastic scaling of stream processing, efficient graph processing in Spark, and latency optimization in internet-scale workflows. These publications reflect a trend toward integrating software-defined approaches with edge and cloud infrastructures. Notable Awards: Best Young Researcher INESC-ID, Excellence in Teaching (IST 2012), and a Best-Paper Award at ACM/IFIP/Usenix Middleware 2007. Labs & Teams: Active in the Computing Systems and Communication Networks Laboratory, leading projects on edge computing and distributed systems.
Nicholas Carnevale is a Senior Research Scientist in Neuroscience at Yale School of Medicine, affiliated with the Department of Neuroscience. His work focuses on computational neuroscience, neuronal modeling, and electrophysiological simulations using the NEURON software. He holds an MD and PhD from Duke University, with postgraduate training at Stanford University and the University of California, San Diego. Carnevale collaborates extensively with Michael Hines on advancing the NEURON simulation environment, including its porting to multiple platforms and integration with real-time dynamic clamp systems. His research emphasizes understanding neuronal structure-function relationships, particularly in hippocampal and olfactory bulb neurons. He has developed educational tools and short courses to disseminate simulation methodologies. Key contributions include the Electrotonic Workbench, ModelDB database, and the Neuroscience Gateway, promoting open-access computational resources. His work bridges theoretical models with experimental neuroscience, advancing drug screening methods and understanding neural network dynamics.
Christoph Janietz is a Research Fellow at the Department of Sociology, University of Groningen. His work focuses on social stratification and labor market inequality, particularly using Dutch administrative data. He completed his PhD at the University of Amsterdam in 2023, researching wage inequality via tax registry data. Currently, he collaborates on the NWO VIDI-funded project “Beyond boardrooms” with Zoltán Lippényi and Sören Tumeltshammer. Education: PhD in Sociology, University of Amsterdam (2023) Research Interests: His research examines wage disparities, occupational hierarchies, and the impact of organizational structures on inequality. He employs advanced quantitative methods to analyze large-scale datasets, including employer-employee linkages and pandemic-era labor market shifts. Awards: None explicitly listed, though his NWO VIDI project indicates competitive research funding. Grants & Projects: Leads the “Beyond boardrooms” project and has contributed to the NIDIO database initiative. His work integrates sociological theory with empirical data analysis. Labs/Teams: Part of the Department of Sociology’s research group, collaborating with international scholars on labor market dynamics.
Jessica Hendy is a Senior Lecturer in Palaeoproteomics at the University of York's Department of Archaeology. Specializing in ancient protein analysis, she explores culinary practices, diets, and disease in prehistoric societies, with a focus on biomolecular methodologies. University of York (2019–present) Max Planck Institute for the Science of Human History (2016–2019) University of York (PhD, 2015) University of Auckland (BA, BSc, 2010) Her research integrates palaeoproteomics and ZooMS to investigate: Dietary biomarkers in archaeological contexts Origins of fermentation and dairying Protein preservation in dental calculus and ceramics Human-animal interactions through biomolecular data Recent publications highlight her work on: Beerstone proteomes for brewing history Multi-taxa dairy identification Viking Age organic artifacts Neolithic food globalization Diagenesis patterns in ancient proteins Salmon fishing and pottery adoption Her scientific awards include: Philip Leverhulme Prize (2019) General Anthropology Division Prize (2023) Max Planck Society Donors Award (2016) Humanities Research Centre Doctoral Fellowship (2015) As a supervisor, Jessica mentors PhD candidates like Charlotte Blacka (rapid proteomics), Eleanor Green (biomolecule substrates), and Jan Dekker (Mesolithic container technology). She co-supervises external students at Cambridge and British Columbia. She leads initiatives such as the BioArCh research group and directs postgraduate research (2021–2022). Currently on a 2023–2024 sabbatical , her work remains pivotal in biomolecular archaeology.
Tao Yang is a Professor in the Department of Computer Science at the University of California, Santa Barbara, where he has been a faculty member since 1993. His research spans web search and mining, database and information systems, machine learning and data mining, parallel and distributed systems, and cloud computing. He serves as an active educator, teaching courses including CS170 Operating Systems (Spring 2024), CS291A Neural Information Retrieval (Fall 2024), and CS140 Parallel Computing (Winter 2025). PhD in Computer Science, Rutgers University ME in Artificial Intelligence, Zhejiang University MS in Computer Science, Rutgers University BS in Computer Science, Zhejiang University Professor Yang's research focuses on advancing the field of information retrieval with particular emphasis on neural approaches to search and ranking. His recent work explores neural document ranking, privacy-aware search systems, and versioned data search. He has led significant projects including Neptune clustering infrastructure, Sorrento self-organizing storage cluster, and TMPI for MPI execution optimization. His research bridges theoretical advances with practical implementations, particularly in scaling search architectures to handle billions of documents while maintaining relevancy, performance, and freshness. His publication record shows a clear evolution from foundational work in parallel and distributed systems toward contemporary research in neural information retrieval. Recent publications demonstrate expertise in optimizing both sparse and dense retrieval methods, with particular focus on efficiency improvements for multi-vector representations. His work consistently addresses real-world challenges in search scalability and privacy preservation. Faculty Research Award, Google Research Research Initiation Award, NSF (1994) UC Regents' Junior Faculty Award (1994) Computer Science Faculty Teacher Award (1995) CAREER Award, NSF (1997) Noble Jeeviant Award, AskJeeves (2002) Professor Yang has supervised numerous graduate students, many of whom have gone on to prominent positions at companies like Google, Apple, and Coursera, or academic positions at universities worldwide. His industry experience as Chief Scientist for Ask.com (2001-2010) and founding Chief Scientist for Teoma (2000-2001) has informed his research direction and provided valuable practical context for his academic work. He has served on program committees for major conferences including WWW, SIGIR, KDD, WSDM, CIKM, ECIR, and EMNLP. His research group maintains active projects in neural information retrieval, privacy-aware search, similarity computing, and parallel computing systems. The group collaborates closely with industry partners, particularly in the search technology space, and has developed systems that power major search engines serving over 100 million users.
