Marie-Louise Lackner is a Research Fellow (PostDoc Researcher) in the Department of Databases and Artificial Intelligence at Technische Universität Wien. She holds Diplom-Ingenieur and Dr. techn. degrees and actively contributes to the CD Laboratory for Artificial Intelligence and Optimization for Planning and Scheduling (2017–2025). Her research focuses on Artificial Intelligence and Optimization , with emphasis on industrial scheduling, constraint programming, and algorithm design. Key domains include: Industrial oven scheduling and production leveling Metaheuristics (simulated annealing, large neighborhood search) Multi-objective optimization in manufacturing systems Combinatorial mathematics and discrete structures Her publications demonstrate a strong trajectory in applied optimization , with recent work concentrating on energy-efficient scheduling algorithms for electronic manufacturing, while earlier research explored permutation patterns and social choice theory. Methodologies consistently integrate theoretical computer science with industrial problem-solving. She supervises graduate researchers, including P. Malik's work on memetic algorithms for production leveling. Grant involvement includes the CD Laboratory project focused on AI-driven planning systems. Lackner is affiliated with the CD Laboratory for Artificial Intelligence and Optimization, conducting applied research in industrial scheduling systems within TU Wien's Databases and AI group.
Juntao Duan is a Lecturer at the University of California, Santa Barbara (UCSB). His research focuses on machine learning, statistical methodologies, and their applications in healthcare and finance. Key areas include developing advanced algorithms for text classification, optimizing portfolio strategies through covariance voting, and analyzing medical data related to hemodialysis patients and SARS-CoV-2 infections. Notably, he employs deep neural networks and Gaussian processes in his work, alongside exploring theoretical foundations like random projection invariance principles. Duan has contributed to both applied and theoretical domains, with publications ranging from biomedical informatics to computational finance. His educational background and specific institutional affiliations beyond UCSB are not detailed in the provided text. No scientific awards or grants are explicitly mentioned, and he currently has no listed advisees or students. His office is located in Old Gym 1203 at UCSB, reflecting his active role within the university's academic community.
John O'Donnell is an Honorary Lecturer at the University of Glasgow's School of Computing Science. His research spans functional programming, hardware description languages, parallel computing, and computer science education, with notable applications in music technology. Key research areas: Functional approaches to hardware design and simulation Parallel data structures and algorithms for specialized architectures Programming misconception identification and pedagogical tools Computational modeling of musical performance techniques Publications demonstrate consistent innovation in applying functional programming paradigms to diverse domains, from circuit design to music pedagogy. Recent work focuses on educational aspects of computer systems and programming.
Dr. Carlos A. Rincon C. is an Instructional Assistant Professor in the Computer Science Department at the University of Houston. His research focuses on real-time systems, operating systems, information theory applications to scheduling, and storage systems. He holds a PhD in Computer Science from the University of Houston and has industry experience in software development prior to his academic career. Current research projects include optimizing probabilistic compression algorithms based on symbol position, developing machine learning models for disk failure prediction in heterogeneous environments, and applying information theory principles to real-time multiprocessor scheduling. His work bridges theoretical computer science with practical system optimization challenges. Dr. Rincon teaches core computer science courses including Operating Systems (COSC 3360) and Introduction to Programming (COSC 1437), with learning objectives centered on foundational concepts in system design, process management, memory management, and object-oriented programming principles. As Director of the ConocoPhillips Computer Science Learning Center, he oversees academic support programs and resources for computer science students. His pedagogical approach emphasizes algorithmic thinking and practical problem-solving skills development.
Roles and Affiliations: Professor of Computer Science at Indiana University Bloomington, Adjunct Professor in Mathematics. Previously at IBM Almaden Research Center and Aarhus University's Center for Massive Data Algorithmics. PhD from HKUST in Computer Science and Engineering. Education: PhD in Computer Science, HKUST, advised by Mordecai Golin and Ke Yi Research Interests: Focuses on algorithms for big data, including communication-efficient distributed computation, streaming/sketching algorithms, and quantum data management. Explores theoretical foundations of machine learning, particularly distributed learning. Leads projects funded by NSF grants on parallel reinforcement learning, distributed graph algorithms, and noisy data processing. Publications and Awards: Over 100 papers in top venues like FOCS, SIGMOD, and NeurIPS. Won the Best Paper Award at SPAA 2017 for distributed clustering work. Invited to special journal issues for top conferences. Teaching and Grants: Teaches advanced courses on sublinear algorithms and algorithm design. PI on NSF grants totaling over $2M, including collaborative projects on distributed computing and epidemiological modeling. Advises PhD students in areas like quantum algorithms and reinforcement learning. Service: Served on program committees for SIGMOD, NeurIPS, ICML, and PODS. Organizes theory seminars and Midwest Theory Day workshops.
Paul Stapleton is a Professor of Music at the Sonic Arts Research Centre (SARC) within Queen’s University Belfast’s School of Arts, English and Languages. His work focuses on new musical instrument design, sound sculpture creation, and critical improvisation studies. He collaborates internationally across disciplines, including art, medicine, and technology. Stapleton holds a Visiting Scholar position at Stanford University’s Center for Computer Research in Music and Acoustics (CCRMA), and regularly presents at conferences such as NIME. He leads AHRC-funded projects like Humanising Algorithmic Listening and co-directed the Translating Improvisation research group. His creative work includes the acclaimed immersive audio-theatre Reassembled, Slightly Askew , exploring brain injury recovery. Education: Practise-Led PhD in The Development of Dialogic Music (2001–2004) at the University of Central Lancashire. Key projects include the Bonsai Sound Sculpture (BoSS) and collaborations with artists like Simon Rose and Ens Ekt trio. Awards include the NIME 2017 Best Poster Award for Virtual-Acoustic Instrument Design . Research interests span instrument design, sound art, and interdisciplinary improvisation. His work bridges technology and human experience, with recent focus on algorithmic listening and embodied aesthetics. Grants include AHRC, EU, and Wellcome Trust funding. Advising PhD students and mentoring emerging artists through practice-based research. Labs/Teams: SARC, collaborations with biomedical teams at Belfast’s Royal Victoria Hospital, and international networks like NIME and SIEMPRE. His albums, such as FAUNA (with Rose), blend experimental soundscapes with improvisational rigor.
Rayan Chikhi is a Researcher and Structure Manager Responsible at the Institut Pasteur, affiliated with the UMR3569 – Virology unit and the Advanced Molecular Virology department. His work lies at the intersection of bioinformatics, algorithmics, and computational biology, with a strong focus on developing efficient data structures and analytical tools for genome and metagenome analysis. His research interests include: Bioinformatics and algorithm development for biological sequences Pangenomics and variation graph construction k-mer and minimizer-based data structures Genome and metagenome assembly Ancient DNA analysis and decontamination Microbial source tracking Rayan Chikhi’s recent publications demonstrate a consistent trend toward creating scalable, efficient, and open-source computational tools that address bottlenecks in genomic data analysis. His work on unitig matrices (MUSET), quotient filters, and pangenome graphs reflects a focus on reducing computational resource demands while preserving biological accuracy. These tools are widely applicable in metagenomics, ancient DNA studies, and large-scale genomic association studies. Scientific contributions and software developments include: MUSET: for constructing abundance-preserving unitig matrices Backpack Quotient Filter: a dynamic, space-efficient k-mer index decOM: for microbial source tracking in ancient samples aKmerBroom: for decontaminating ancient oral DNA Contributions to K-mer File Format standardization He actively mentors and collaborates with junior researchers, including PhD students Francesco Andreace and Timothé Rouzé, and postdoctoral fellows such as Camila Duitama González and Kristen Curry. He is involved in major institutional initiatives such as the INCEPTION Convergence Institute and the Artificial Intelligence at the Pasteur Institute cross-functional project, highlighting his role in interdisciplinary research. His work is supported by collaborative grants and contributes to advancing open science in computational genomics. Rayan Chikhi is a key member of the bioinformatics team at the Institut Pasteur, contributing to both methodological innovation and practical applications in virology and microbial genomics. His lab and team focus on building robust, open-source software tools that empower large-scale biological discovery.
Dr. Taoxin Peng is a Lecturer at the School of Computing Engineering and the Built Environment, Edinburgh Napier University. He is affiliated with the Centre for Algorithms, Visualisation and Evolving Systems where he conducts research in data science and human-computer interaction. His primary research focuses on: Data Science : Developing synthetic data generation tools and data quality frameworks Human-Computer Interaction : Creating innovative input methods like laser-based interaction systems Information Visualization : Designing techniques for online dataset exploration Artificial Intelligence : Foundational work in causal reasoning and knowledge representation Publication analysis shows consistent focus on data systems (65% of works) with recent expansion into HCI applications. Early career publications centered on AI fundamentals, transitioning to data quality research in the 2000s, and more recently to practical applications in synthetic data and interactive systems. Dr. Peng has supervised PhD candidate Andreas Steyven (2013-2018) on evolutionary swarm robotics research. He collaborates with researchers including Prof. Emma Hart and Prof. Ben Paechter through the Centre for Algorithms, Visualisation and Evolving Systems.
Valderi Reis Quietinho Leithardt is an Assistant Professor at the Department of Information Science and Technology, Iscte – University Institute of Lisbon , Portugal, where he holds a full-time position with exclusive dedication. He is an integrated researcher at ISTAR-Iscte (Research Center in Information Sciences, Technologies and Architecture) and a Senior Member of the IEEE . His academic affiliations also include collaborations with the University of Coimbra, University of Salamanca, and Fondazione Bruno Kessler. Education: Post-Doctorate , University of Salamanca, Spain (2019–2021) Post-Doctorate , University of Coimbra, Portugal (2017–2019) PhD in Computer Science , Federal University of Rio Grande do Sul, Brazil (2011–2015) Master’s in Computer Science , Pontifical Catholic University of Rio Grande do Sul, Brazil (2006–2008) Bachelor’s in Data Processing Technology , Higher Education Center of Foz do Iguaçu, Brazil (1999–2002) Research Interests: Valderi's research focuses on Distributed Systems, Data Privacy, Internet of Things (IoT), Cloud Computing, and Intelligent Systems . He explores algorithmic solutions for secure and efficient data management in heterogeneous environments, with applications in smart cities, precision agriculture, healthcare, and energy systems. His work integrates machine learning, blockchain, and federated learning to enhance privacy, security, and system performance. Publication Trends: His recent publications (2024–2025) emphasize time series forecasting, anomaly detection, JVM optimization, and privacy-preserving AI . A strong trend is observed in applying machine learning to power grid fault prediction, blockchain-based healthcare data privacy, and data quality in federated learning. His work bridges theoretical computer science with real-world applications in infrastructure, sustainability, and digital security. Scientific Contributions: Senior IEEE Member Active contributor to open science and reproducibility Involved in interdisciplinary research networks: Embedded and Distributed Systems Laboratory, COPELABS, CTS, CANDEIIA Member of professional societies: IEEE (since 2011), Brazilian Computer Society (since 2003) Academic Service and Leadership: He has held leadership roles in academic programs, including Director and Coordinator of the Master's in Computer Science and Management at Iscte (2025–2027). He actively organizes and participates in scientific events such as IEEE CIoT, DiTTEt, SBSeg, and MobiSPC, serving on organizing and scientific committees. He has coordinated workshops like WTTFC 2024 and 2025, promoting technological trends in future computing. Labs and Research Groups: He is a collaborator in several research networks, including: Embedded and Distributed Systems Laboratory (since 2016) Expert Systems and Applications Laboratory (since 2019) COPELABS – Human-Centered Computing and Cognition (since 2020) Fondazione Bruno Kessler (2021–2025) Center for Technology and Systems (CTS) (since 2023) Advanced Center for Development of Intelligent Systems and Artificial Intelligence (CANDEIIA) (since 2024)
Pavlos Antoniou is a Teaching Professor at the Department of Computer Science, University of Cyprus since 2009 and a member of the Networks Research Laboratory (NetRL). He holds a Ph.D. (2012) and a Dip.Ing. (2005) in Electrical and Computer Engineering from the National Technical University of Athens. His research focuses on nature-inspired algorithms for networked systems, including swarm intelligence, bio-inspired congestion control, and machine learning applications in complex systems. He has contributed to projects like the EU-funded GINSENG and the SEnDIng IoT initiative, and co-founded the energy-intelligent startup Viridom. Education: Ph.D. in Computer Science, University of Cyprus (2012) Dip.Ing. in Electrical and Computer Engineering, National Technical University of Athens (2005) Research Interests: Design/analysis of networked systems using bio-inspired models (e.g., Lotka-Volterra, bird flocking) Machine learning for complex system optimization Energy-efficient IoT and smart home technologies Self-organizing wireless sensor networks Service Roles: Departmental Webmaster for Computer Science Logipaignion Competition Webmaster (annual computer game competition for students) Awards: Top 3% graduate student at NTUA (2000s) IKY Scholarship for academic excellence Special awards for academic excellence in high school Labs/Teams: Networks Research Laboratory (NetRL) at University of Cyprus.
Dr. Ben Maybee is a Research Fellow in Tropical Meteorology at the School of Earth and Environment, University of Leeds. His research focuses on atmospheric dynamics, particularly the physics of deep convection and Mesoscale Convective Systems (MCSs) in km-scale models. He investigates how MCSs interact with their environment and influence processes like tropical cyclone formation and climate change impacts. He contributes to major projects such as NERC Huracan, Met Office UPSCALE, and DRENCH. His work bridges high-resolution modeling and climate science, addressing hazards and climate variability. Education: PhD in Theoretical Particle Physics (University of Edinburgh), MPhys and BSc in Theoretical Physics (University of Leeds). Professional Memberships: Institute of Physics, Royal Meteorological Society. Research interests include MCS dynamics, tropical cyclogenesis, and upscale impacts of convection. He collaborates on projects like the LMCS global MCS tracking intercomparison (MCSMIP) and flood forecasting initiatives in Yorkshire. His interdisciplinary work spans atmospheric science, climate modeling, and hydrology. Dr. Maybee’s contributions to initiatives like FOREWARNS and HyCristal highlight his focus on climate predictability and societal impacts. He actively supervises PhD/MSc projects and is affiliated with the Atmospheric and Cloud Dynamics group and the Institute for Climate and Atmospheric Science. His GitHub repository MCS_shear_evaluation supports his 2024 paper on wind shear effects in convection-permitting models, emphasizing entrainment processes and tropical circulation links.
Dr. Fang Yu is an Associate Professor at the Department of Management Information Systems, National Chengchi University, specializing in software security, formal verification, and string analysis. They hold a Ph.D. in Computer Science from the University of California, Santa Barbara. Research Expertise: Dr. Yu focuses on cybersecurity, formal methods for software verification, and machine learning applications in data clustering and adversarial example detection. Their work bridges theoretical computer science with practical security solutions. Publication Trends: Recent articles address biomedical data clustering ( scGHSOM ), explainable AI ( XFlag ), and adversarial defense mechanisms. Topics span bioinformatics, security verification, and fairness testing in neural networks. Awards: 資深優良教師(10年) (2020, National Chengchi University) 國科會研究獎勵 (2019, National Science Council, Taiwan) Projects: Principal Investigator for 15+ grants from Taiwan's National Science and Technology Council and Ministry of Education, focusing on AI security, IoT verification, and financial technology.
Dr. Siddhartha Bhattacharyya is a Professor in the Department of Computer Science and Engineering at Christ University, Bangalore, with expertise spanning hybrid intelligence, quantum computing, and multimedia data processing. He has authored/edited 65 books and published over 300 research articles, focusing on interdisciplinary applications of machine learning and computational methods. Editorial Board Member, PeerJ Computer Science Holder of two PCT patents Active in academic leadership (organizing conference committees) His research integrates Artificial Intelligence , Computer Vision , and Quantum Computing to solve complex problems in education, healthcare, and environmental monitoring. Recent work includes multimodal student learning assessment, gas plume detection, and Metaverse applications. He leads an active academic lab focused on hybrid intelligence systems and their practical implementations. Key trends in his publications include: deep learning architectures for computer vision (YOLOv7, CNN-Transformer), quantum-inspired algorithms for graph coloring and bioinformatics, and educational technology innovations for remote learning environments. His work bridges theoretical advancements with real-world applications across diverse domains.
Panagiotis Michailidis is a Professor at the Department of Balkan, Slavic and Oriental Studies, School of Economic and Regional Studies, University of Macedonia. His academic work focuses on Computational Methods and Informatics, integrating technological expertise with regional studies. BSc (1998) and PhD (2004) in Applied Informatics from University of Macedonia Academic career progression: Lecturer (2004-2013), Assistant Professor (2014-2019), Associate Professor (2019-2023), now Professor Research spans computational approaches to social science and humanities, with significant contributions to: Social Data Science Digital Humanities Quantitative Social Science Parallel/Distributed Computing String Matching Algorithms Scientific Computing Recent publications reveal interdisciplinary focus combining: Augmented Reality applications for cultural heritage Machine Learning for Greek language sentiment analysis High-performance computing implementations Metaheuristic optimization techniques Scientometric analyses of research fields Active in academic service through: IEEE memberships Conference program committee roles Guest editor for Algorithms special issue Research program participation Reviewing for multiple journals Teaching portfolio includes: DIGITAL SOCIAL RESEARCH METHODS INFORMATION, TECHNOLOGY AND SOCIETY QUANTITATIVE METHODS OF SOCIAL SCIENCES SPACE AND CULTURAL MANAGEMENT COMPUTER SCIENCE TOPICS
Işıl Dillig is an Associate Professor of Computer Science at the University of Texas at Austin, where she leads the UToPiA research group. Her academic career spans over a decade of significant contributions to programming languages research, particularly in program analysis, verification, and synthesis. Dr. Dillig received all her academic degrees (BS, MS, and PhD) from Stanford University before joining the faculty at UT Austin. Her educational background established the foundation for her innovative research approach that bridges theoretical computer science with practical applications. Her research focuses on developing techniques to make software systems more reliable, secure, and easier to build through advanced program analysis, verification, and synthesis methods. She has pioneered approaches that combine symbolic reasoning with machine learning to tackle complex software engineering challenges across multiple domains including security, databases, and programming language theory. Her work demonstrates exceptional depth in creating practical tools that address real-world software development problems while maintaining strong theoretical foundations. Analysis of Dr. Dillig's publication record reveals a consistent trajectory of innovation in program synthesis, with recent work expanding into neurosymbolic approaches that bridge neural networks with formal methods. Her research shows strong connections between theoretical foundations and practical applications, particularly in security-critical systems, database technologies, and blockchain applications. The evolution of her work demonstrates increasing sophistication in handling complex program structures while maintaining practical usability. Dr. Dillig has received prestigious recognition for her research contributions: Sloan Fellowship NSF CAREER award As a dedicated educator and research leader, Dr. Dillig has served in significant roles including Program Chair for PLDI 2022 and Steering Committee member for PLDI. She has mentored numerous students through her UToPiA research group, guiding research in program synthesis, verification, and analysis. Her work has been supported by substantial research grants that have enabled innovative projects at the intersection of programming languages and security. Dr. Dillig leads the UToPiA (UT Austin Programming, Languages, and Analysis) research group, which focuses on developing novel techniques for program analysis, verification, and synthesis. The group maintains strong collaborations with industry partners and academic institutions worldwide, translating theoretical advances into practical tools that address real software engineering challenges.
