Jan Fostier is a researcher at Ghent University, affiliated with the Internet Technology and Data Science Lab (IDLab). His work focuses on High Performance Computing (HPC) and Big Data Analytics in life sciences and bioinformatics, leveraging supercomputers and GPUs to accelerate genomic data processing. He collaborates across disciplines with bioscience engineers, biologists, medical doctors, and industry partners like Janssen Pharmaceutica and Agilent. His research interests include: Bioinformatics Sequencing Data Analysis High-Performance Computing Stochastic Modelling Genomic Data Compression Recent publications highlight advancements in approximate pattern matching, de Bruijn graph construction, and nanopore signal analysis. Awards include an Outstanding Presentation Award (2015) and an Outstanding Oral Poster Presentation Award (2015). He has developed tools like Halvade , Jabba , and BLSSpeller for genomic applications.
Dr. Ahmed Hassan is an Assistant Professor in the Department of Computer Science and Engineering at Lehigh University, part of the P.C. Rossin College of Engineering and Applied Science. He is a core member of the Scalable Systems Software (SSS) research group. Previously, he held an Assistant Professor position at Alexandria University and a Postdoctoral Research Associate role at Virginia Tech. His research focuses on distributed computing, concurrent and transactional data structures, multiprocessor programming, and NUMA-aware software design. He teaches courses including Analysis and Design of Algorithms, Advanced Programming Techniques, and Distributed Systems. Education: Ph.D., Computer Engineering, Virginia Tech, 2015 M.S., Computer Engineering, Alexandria University, Egypt, 2011 B.S., Computer Science, Alexandria University, Egypt, 2006 Research Interests: Hassan’s work spans distributed systems, transactional memory, and high-performance computing. He explores synchronization protocols, concurrent data structures, and optimizing software for multi-core architectures. His projects often address challenges in scalability, consistency, and efficiency in modern computing environments. Advising & Grants: While specific student advisees or grant details are not listed, his involvement with the SSS group suggests active participation in collaborative research projects. His work has been disseminated through top-tier conferences and technical reports, reflecting a strong focus on applied and theoretical systems research. Labs/Teams: Member of the Scalable Systems Software (SSS) research group at Lehigh University, focused on advancing scalable systems software and distributed computing solutions.
Adonis Bogris is a Professor at the Department of Informatics, University of West Attica (formerly part of the Technological Educational Institute of Athens). His research focuses on all-optical signal processing, high-speed transmission systems, neuromorphic computing, and nonlinear optics. He holds a B.S. in Informatics, M.Sc. in Telecommunications, and Ph.D. from the National and Kapodistrian University of Athens (1997, 1999, 2005). He has authored/co-authored over 200 articles, cited over 3,000 times. His work spans optical fiber networks, mid-infrared photonics, and physical-layer security. He serves as an Associate Editor for Optica Optics Continuum (since 2018) and IEEE Journal of Lightwave Technology (since 2022). He is a Senior Member of Optica and actively reviews for top journals like IEEE, Elsevier, and Nature. Research interests include: All-optical networking and transmission systems Neuromorphic photonic accelerators Nonlinear effects in optical fibers/waveguides Earthquake detection via fiber-optic sensing (e.g., DAS) Recent work emphasizes photonic neuromorphic processors for imaging cytometry and secure communication systems. He has led EU/national projects as Principal Investigator and contributed to standards through roles in GUnet, GRNET, and UNESCO committees. His lab explores cutting-edge applications like fiber-based seismology and high-throughput convolutional neural networks using integrated photonics.
Yifan Cheng is an Assistant Professor at the Department of Food Science and Technology, Virginia Tech. Their research focuses on improving food safety, quality, and sustainability through advanced material engineering. Key areas include antimicrobial nanostructures for packaging, food waste valorization via immobilized enzymes, AI-driven packaging design, and active/intelligent packaging systems. The lab, known as the Cheng Nano Lab, emphasizes interdisciplinary approaches to surface engineering and material-microbe interactions. Educational Background Ph.D., Food Science, Cornell University, 2017 B.S., Food Science, Cornell University, 2012 B.S., Plant Science & Technology, Shanghai Jiao Tong University, 2012 Research Interests Cheng’s work integrates nanotechnology, AI, and material science to address critical challenges in food packaging and safety. The lab’s mission is to leverage material-microbe-food interface science to reduce food loss, enhance packaging performance, and valorize food waste. Current projects include developing antimicrobial coatings, optimizing enzyme immobilization for waste conversion, and AI-guided material discovery. Laboratory and Team The Cheng Nano Lab fosters a diverse, inclusive environment for innovation. Open to postdocs, graduate students, and undergraduates, the lab emphasizes collaborative research and professional development. Funding sponsors include USDA NIFA and industry partnerships.
Joseph Poon is the William Barton Rogers Professor of Physics at the University of Virginia, with a courtesy appointment in Materials Science and Engineering. He leads a research group focused on experimental and computational studies of condensed matter physics, particularly in thermoelectric materials, magnetic skyrmions, and high-entropy alloys. His work spans advanced materials design, spintronic applications, and data-driven materials discovery. Education: Ph.D. and B.S. from California Institute of Technology (1978 and 1974). Research Interests: Thermoelectric properties of semiconductors/semimetals near topological phase transitions Skyrmionic states in amorphous ferrimagnetic heterostructures High-entropy alloy design in high-dimensional composition space His recent work emphasizes machine learning for alloy phase prediction, magnetic skyrmion dynamics, and optimizing thermoelectric performance in half-Heusler compounds. Recent Research Trends: Focus on multi-functional materials combining mechanical strength, corrosion resistance, and electronic/magnetic properties. Key contributions include demonstrating ultrafast skyrmion switching in Mn4N-based systems and designing refractory B2 high-entropy alloys with exceptional strength-ductility synergy. Grants/Advising: While specific grants are not listed, his research group's activities imply significant funding in materials science and spintronics. No advisee names are explicitly provided in the text. Labs/Teams: Operates a multidisciplinary lab integrating experimental synthesis, computational modeling, and data science for materials innovation. Collaborates extensively on projects involving skyrmionics and high-entropy alloys.
Cory Merkel is an Associate Professor in the Department of Computer Engineering at Rochester Institute of Technology (RIT), part of the Kate Gleason College of Engineering. He holds a BS, MS, and Ph.D. in engineering from RIT, completing his Ph.D. in Microsystems Engineering in 2015. Prior to joining RIT in 2018, he worked as a research electronics engineer at the Air Force Research Lab's Information Directorate. His research focuses on neuromorphic computing, AI hardware design, and energy-efficient systems using memristors and mixed-signal architectures. Key areas include mapping neural networks to novel hardware, brain-inspired computing, and optimizing neuromorphic systems for edge devices. Merkel's work emphasizes practical applications in defense, healthcare, and energy-constrained environments. Recent articles highlight advancements in quantum computing integration, neuromorphic device scalability, and security against hardware vulnerabilities. His research has been published in Neurocomputing, IEEE journals, and conferences like International Conference on Neuromorphic Systems. Merkel also leads the Brain Lab at RIT, advancing interdisciplinary AI research. Teaching responsibilities include Digital System Design II, Machine Intelligence, and Special Topics in Computer Engineering. His scholarly contributions bridge theoretical computer science and practical hardware implementation, with a focus on sustainability and performance.
Dr Vincent Lim is a Researcher in the Department of Fluid Science and Resources at the University of Western Australia. His research focuses on probabilistic modeling of gas hydrate formation, particularly in oil and gas production systems. He specializes in nucleation dynamics, inhibitor efficacy evaluation, and stochastic phase equilibrium analysis. Lim completed his First Class Honours in Chemical & Process Engineering at UWA in 2015, with a project on hydrate nucleation probability distributions. His work develops automated apparatus for high-throughput hydrate nucleation measurements, enabling data-driven design and management of subsea pipelines. Key areas include kinetic inhibitor performance assessment, hybrid inhibition strategies, and eco-friendly alternatives like cyclodextrins. Lim's contributions bridge fundamental thermodynamics with applied engineering solutions for energy infrastructure reliability. Recent research highlights include quantifying nucleation rates under varying conditions, evaluating minimum miscibility pressures for CO₂-EOR applications, and validating probabilistic models against experimental data from advanced measurement systems like the HPS-Alta apparatus. His interdisciplinary approach integrates fluid mechanics, materials science, and statistical analysis to address challenges in multiphase flow systems.
Pramod Viswanath is the Forrest G. Hamrick Professor in Engineering at Princeton University , with affiliations in the Department of Electrical and Computer Engineering and the Computer Science Department . His research spans blockchain technologies, wireless communication, and deep learning applications in coding theory. Ph.D. in Electrical Engineering and Computer Science, University of California-Berkeley (2000) Current research focuses on first-principles understanding and design of blockchains and communication algorithms via deep learning . He has pioneered innovations like the Trifecta consensus algorithm and Coded Merkle Tree , achieving significant improvements in blockchain throughput and security. Past work in wireless communication includes co-authoring a foundational book on the subject and designing Flash-OFDM , the first OFDM-based cellular system. His recent projects include the Prism consensus algorithm for Bitcoin scaling and Spider networking stack for blockchain efficiency. Best Paper Award, Sigmetrics (2015) Xerox Faculty Research Award, UIUC (2010) NSF CAREER Award (2002) Eliahu Jury Award, UC Berkeley EECS (2000) Bernard Friedman Prize, UC Berkeley Mathematics (2000) He has advised numerous graduate students and leads research projects at the intersection of computing, networking, data science, and security & privacy . His work includes both theoretical advances and practical implementations, with applications in decentralized finance (DeFi) and blockchain governance.
Michael A. Webb is an Assistant Professor in the Department of Chemical and Biological Engineering at Princeton University, where he leads the Webb Research Group focused on computational materials design. His research integrates theory, simulation, and machine learning to develop soft materials for sustainability and health applications, including batteries, water treatment, and drug delivery systems. Education Ph.D., Chemical Engineering, California Institute of Technology, 2016 M.S., Chemical Engineering, California Institute of Technology, 2015 B.S., Chemical and Biomolecular Engineering, University of California – Berkeley, 2011 Research Focus The Webb Group develops predictive computational frameworks to study charge transport in polymers, stimuli-responsive biopolymers, and interfacial phenomena. Key methodologies include coarse-grained molecular dynamics, machine learning, and multiscale modeling, with applications spanning energy storage, environmental remediation, and biomedical engineering. Current efforts emphasize data-driven design of architecturally complex polymers and biomimetic systems. Recent publication trends highlight three core themes: (1) Physics-guided machine learning for polymer property prediction, (2) Phase behavior of biomolecular condensates and disordered proteins, and (3) Sustainable material design through computational optimization. Methodological innovations in coarse-graining and neural networks underpin much of this work. Awards and Honors ACS COMP OpenEye Cadence Molecular Sciences Outstanding Junior Faculty Award (2025) NSF CAREER Award (2023) Howard B. Wentz, Jr. SEAS Junior Faculty Award (2022) Herbert Newby McCoy Award, Caltech (2016) Chemical Computing Group Excellence Award (2016) Resnick Sustainability Institute Fellowship (2012–2014) Research Group and Advising Professor Webb advises 10 graduate students working on polymer informatics, biomolecular simulations, and energy materials. The Webb Research Group collaborates extensively with experimentalists and employs high-throughput computational screening. Recent group achievements include machine-learning frameworks for enzyme-polymer hybrids and awards for poster presentations at major conferences.
Maricel Kann is an Associate Professor in the Department of Biological Sciences at the University of Maryland, Baltimore County (UMBC), with an affiliate appointment in the Computational Sciences and Engineering Department. Her research integrates computational biology, bioinformatics, and systems biology to understand protein networks, domain-level functional impacts of genetic variants, and molecular mechanisms in diseases such as cancer. Ph.D., University of Michigan, Ann Arbor, 2001 Postdoctorate, National Center for Biotechnology Information, NIH, 2007 Her research focuses on developing computational methodologies to analyze protein domain interactions, prioritize disease-associated variants, and derive molecular signatures of cancers like prostate and breast cancer. She leads projects such as DMDM (Domain Mapping of Disease Mutations) and EMU (Extractor of Mutations), which enable domain-level mutation analysis and text-mining of disease-related variants. Her work emphasizes interdisciplinary collaboration with experimentalists and the integration of genomic, functional, and evolutionary data. Her recent publications reflect a strong trend in domain-centric cancer genomics, statistical modeling of mutation hotspots, and computational frameworks for interpreting non-coding variants and personal genomes. She has contributed significantly to benchmarking in computational biology and the development of tools for translational bioinformatics. NCI Transition Career Development Award (K22) 2009-2012 NIH Intramural Research Training Award 2002-2007 NIH Fellows Award for Research Excellence 2003 Susan Lipschutz Award for Women Graduate Students 1999 Sloan Foundation Summer Graduate Fellowship 1998 Graduate Fellowship of the Organization of the American States 1996-1998 Dr. Kann mentors numerous graduate and undergraduate students through projects like EMU and PINTT (Protein INteraction Text-mining Tool), fostering interdisciplinary training. She has served on editorial boards (e.g., Journal of Biomedical Informatics), advisory committees (PubMedCentral, UniProt), and as organizer for major conferences in bioinformatics. She currently teaches BIOL 495: Seminar in Bioinformatics and is actively recruiting Ph.D. students in computational biology and bioinformatics. Her lab collaborates with researchers across institutions and is part of several interdisciplinary programs including the Chemistry and Biology Interface Program at UMBC and the University of Maryland Greenebaum Cancer Center. She is also involved with the University of Maryland School of Medicine’s Program in Biochemistry and Molecular Biology.
Phil Carns is a computer scientist at Argonne National Laboratory's Mathematics and Computer Science Division and an Adjunct Associate Professor in the Department of Electrical and Computer Engineering at Clemson University. He is also a fellow of the Northwestern-Argonne Institute for Science and Engineering and serves as deputy director of the Software Tools Ecosystem Project. Education: Ph.D. in Computer Engineering, Clemson University, 2005 His research focuses on high-performance computing (HPC), particularly in the areas of HPC storage architectures, system software, and I/O workload analysis. He has pioneered advancements in scalable data storage and management through projects such as Mochi (composable data services), Darshan (I/O characterization), PVFS (parallel file system), and CODES (storage simulation). His work supports exascale computing initiatives and scientific data infrastructure. The 15 most recent publications reflect a consistent focus on HPC storage, I/O analysis, and system software design. Broad keywords include Computer Science, High-Performance Computing, Distributed Systems, and Simulation. These works explore subfields such as composable data services, I/O profiling, parallel file systems, performance monitoring, and exascale data libraries, demonstrating a trajectory toward modular, scalable, and efficient data management solutions for scientific computing. Scientific Awards: R&D 100 Award R&D 100 Award R&D 100 Award Phil Carns has served as principal investigator, technical lead, and developer on numerous federally funded HPC projects, including the Exascale Computing Project. While no formal students are listed, his leadership roles imply mentorship and collaboration within research teams. He is actively involved in advancing software tools and data ecosystems for large-scale scientific computing. He leads and contributes to several key research initiatives, including the Mochi Project , Darshan Project , and the Software Tools Ecosystem Project (STEP) , all aimed at improving data services, monitoring, and software sustainability in HPC environments.
Shyam Parekh serves as an Adjunct Associate Professor at the University of California, Berkeley, specializing in communication networks architecture and performance optimization for wired and wireless systems. He actively teaches EE 290-10 (Wireless Networks) and EE 122 (Introduction to Communication Networks), engaging students at both graduate and undergraduate levels in cutting-edge networking concepts. Professor Parekh earned his Ph.D. in Electrical Engineering and Computer Sciences from UC Berkeley, establishing the foundation for his three-decade career in networking research. His academic journey reflects deep institutional ties and continuous contribution to the field. His research focuses on communication networks architecture , optimization , and performance analysis , with recent emphasis on Software Defined Networking (SDN) , Network Function Virtualization (NFV) , and data-plane programming . Current projects include the Intelligent RAN Controller (developing machine learning algorithms for 5G/6G network slicing) and the Quantum Internet initiative (addressing fundamental challenges in interconnecting quantum computers). These efforts bridge theoretical analysis with practical implementations, as evidenced by multiple patents and his widely adopted textbook. Over his extensive career spanning from 1986 to 2017, Professor Parekh has demonstrated evolving research focus—from foundational wireless/video streaming work to modern SDN/NFV innovations. His publication pattern shows consistent output with increasing industry relevance, highlighted by seven patents between 2013-2015 and the second edition of his textbook. The research consistently addresses real-world implementation challenges while advancing theoretical understanding. Professor Parekh mentors students through active research projects, currently involving Angela Wang and Zhechang Xu on the Intelligent RAN Controller. His teaching responsibilities in core networking courses demonstrate commitment to education, while his WiMAX Forum co-chair role (2008-09) indicates industry engagement. Though specific grant details aren't listed, ongoing projects suggest sustained research support. Operating without a formally named laboratory, Professor Parekh leads research through two primary initiatives: the Intelligent RAN Controller team focusing on machine learning for network control, and the Quantum Internet group exploring quantum network architectures. These projects foster collaboration between students and industry partners to advance networking frontiers in 5G/6G and quantum communication domains.
Bruce R. Childers is a Professor of Computer Science and the Interim Dean of the School of Computing and Information at the University of Pittsburgh. His research focuses on cybersecurity, high performance computing, and reproducibility in computationally-driven science, with a strong emphasis on open science and distributed governance of information. His work spans GPU memory management, hybrid memory compression, and computational epidemiology. Publications highlight innovations in unified memory systems, fine-grained sharing for quality of service, and frameworks for reproducibility in infectious disease modeling, reflecting his interdisciplinary approach bridging computer systems design and public health applications. As Dean, he advocates for collaboration, transparency, and cross-disciplinary initiatives like Pitt's Data Science Task Force, which he led to develop coordinated strategies for data science integration across multiple disciplines. His research trends emphasize optimizing hardware-software interactions, advancing reproducibility standards, and applying computational methods to societal challenges such as pandemic modeling.
Professor Carolin Körner serves as Head of the Chair WTM (Materials and Technology Management) at Friedrich Alexander University Erlangen-Nuremberg's Faculty of Engineering, Department of Materials Science and Engineering. She also holds significant leadership positions as Member of the control board of the Central Institute of New Materials and Processes (ZMP) in Fürth and Scientific head of the division Additive Manufacturing at New Materials Fürth, Ltd. Her research focuses on additive manufacturing technologies, particularly electron beam powder bed fusion (EB-PBF), with emphasis on process optimization, microstructure evolution, and development of advanced metallic materials. Her work spans fundamental process understanding to industrial applications, addressing challenges in thermal management, material development, and process control for high-performance components. She has pioneered approaches for in-situ alloying, complex geometry fabrication, and microstructure tailoring through innovative scanning strategies. Analysis of her recent publication record reveals consistent leadership in electron beam additive manufacturing research, with particular emphasis on process simulation, novel material development (including high-temperature alloys, copper-based systems, and shape memory alloys), and advanced characterization techniques. Her work demonstrates strong industry relevance while maintaining rigorous scientific foundations, with applications spanning aerospace, medical devices, and energy sectors. As Head of the Chair WTM, Professor Körner leads a substantial research team and collaborates extensively with industry partners. Her leadership extends to directing research initiatives at the Central Institute of New Materials and Processes, where she oversees significant resources for advanced manufacturing development. While specific grant details aren't provided in the source material, her extensive publication record and leadership roles indicate successful acquisition of substantial research funding. Professor Körner's laboratory infrastructure is closely tied to the Central Institute of New Materials and Processes in Fürth, which houses state-of-the-art electron beam powder bed fusion systems and complementary characterization equipment. Her research group forms part of a larger collaborative ecosystem focused on advancing additive manufacturing technologies from fundamental research to industrial implementation.
Mark Robinson is a Professor at the Department of Molecular Life Sciences, University of Zurich, and affiliated with the Swiss Institute of Bioinformatics. He leads the Robinson Research Group, focusing on Computational Biology Bioinformatics Single-Cell RNA Sequencing Statistical Genomics His work bridges computational method development with applications in cancer immunology, epigenetics, and developmental genetics. Key research contributions include Development of bioinformatics tools like pubassistant.ch, scDblFinder, and DESpace Advancements in spatial transcriptomics and single-cell data analysis Studies on epigenetic aging and tumor microenvironment dynamics Notable collaborations span institutions in Switzerland, Germany, and international agricultural pest research groups. His recent publications (2023-2025) emphasize Spatial omics data interpretation Interdisciplinary collaboration frameworks Optimized tissue processing methods Computational benchmarks for reproducible research While no specific scientific awards are mentioned in the data, his software tools and methodological papers demonstrate significant impact on open science and bioinformatics communities.
