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Jayson BoubinView profile

Assistant Professor

• Autonomous Systems
• Unmanned Aerial Vehicles (UAVs)
• Edge Computing
• +5 more

Jayson Boubin is an Assistant Professor of Computer Science at Binghamton University's School of Computing. He joined in 2022 and focuses on autonomous systems, particularly UAVs, edge computing, and machine learning applications in agriculture and infrastructure. His work emphasizes solving real-world challenges through innovative engineering and software solutions. Education: PhD in Computer Science (Ohio State University), BA (Miami University) Research Interests: Autonomous systems, UAVs, edge computing, robotics, and machine learning. Projects include SoftwarePilot (an open-source UAV testing platform), Fleet Computer (Kubernetes-based edge architecture), and PROWESS (a testbed for constrained edge workloads). Key Achievements: NSF Graduate Research Fellowship Developed open-source tools like SoftwarePilot and PROWESS Focus on UAV applications in precision agriculture, search-and-rescue, and infrastructure inspection Labs/Teams: Active in edge computing and UAV research groups, contributing to both academic and open-source communities.

• Condensed Matter Physics
• Materials Discovery
• Ab Initio Calculations
• +4 more

Fernando Manuel da Silva Nogueira is an Associate Professor at the Department of Physics, Faculty of Sciences and Technology, University of Coimbra. He holds a PhD in Theoretical Physics from the same institution (1999) and has been a faculty member since 1990. His research focuses on materials discovery using ab-initio methods , computational physics, and development of scientific software tools like Octopus and APE . He leads the Condensed Matter Physics group at CFisUC (Centro de Física da Universidade de Coimbra) and has been Director of the Portuguese Physics Olympiad (2007-2018). Education: PhD in Theoretical Physics (1999), University of Coimbra MSc in Theoretical Physics (1993), University of Coimbra BSc in Physics (1990), University of Coimbra Research interests include computational materials science , nonlinear optics , density functional theory , and electronic structure calculations . He has authored 40+ peer-reviewed articles, 3 books, and directed over 25 research projects. His work spans topics like carbon nanotube properties, firefly bioluminescence mechanisms, and high-throughput materials discovery. He has organized 25+ conferences, advised 3 PhD students and 17 MSc students, and contributed extensively to open-source computational physics software. His ORCID is 0000-0003-3125-3660 .

• Artificial Intelligence
• Cloud Computing
• Fog Computing
• +3 more

Sundas Iftikhar is a Teaching Professor at the School of Electronic Engineering and Computer Science, Queen Mary University of London. She specializes in software engineering education and research focused on artificial intelligence, fog/cloud computing, and task scheduling optimization. Her research spans AI applications in distributed systems , including Energy-efficient computing Quality of Service (QoS) optimization Deep learning for healthcare Cloud-fog hybrid architectures Recent publications analyze AI-based fog/edge computing trends , with emphasis on systematic reviews, taxonomy development, and sustainability. She also explores machine learning for serverless computing and smart home applications through fog infrastructure. Teaching duties include the Software Engineering Project module, where students work in teams to solve real-world problems.

Alberto DainottiView profile

Associate Professor

• Internet Measurement
• Cybersecurity
• Data Science
• +6 more

Alberto Dainotti is an Associate Professor at the School of Computer Science, College of Computing, Georgia Institute of Technology (Georgia Tech), and Director of the Internet Intelligence Lab. His research focuses on Internet measurement, data science, and cybersecurity, with a particular emphasis on infrastructure reliability and socio-political implications of cyber events. Before joining Georgia Tech in 2014, he served as an Associate Research Scientist and Principal Investigator at CAIDA (Center for Applied Internet Data Analysis) at UC San Diego. He holds a Ph.D. in Computer Engineering and Systems from the University of Napoli 'Federico II', Italy (2008). He teaches courses such as Internet Data Science (CS 8803), Computer Networking I (CS 3251), and Computer Networks/Networking II (CS 6250/4251). Current students include Feng Zhu, Iliana Xygkou, Olivier Bemba, Weili Wu, Zahra Yazdani, and Zhiyi Chen. Former advisees have transitioned to roles at Amazon AWS Research, Northeastern University, UC Davis, Microsoft Azure, and VMware, among others. Research Interests: Internet Connectivity Outages & Mitigation BGP Routing Security & Monitoring Cybersecurity in Political Contexts Address Space Utilization DDoS/Botnet Analysis Network Measurement Tools Scientific Contributions: Developed systems like IODA, BGPStream, and GRIP for real-time Internet monitoring Recipient of the IRTF Applied Networking Research Prize (2012, 2017) Awarded Best Dataset (PAM 2022) and Best Community Artifact (PAM 2023) Authored over 50 publications in top venues including SIGCOMM, IMC, and NSDI Grants & Funding: National Science Foundation (NSF) grants CNS-1705024 / 2202288 and CNS-1703592 US Department of Homeland Security Cisco Systems, Comcast, ISOC, Google, and Open Technology Fund Labs & Collaborations: Internet Intelligence Lab (Georgia Tech) CAIDA collaboration (UC San Diego) MAPKIT project with University of Wisconsin-Madison Contributor to OECD and BITAG reports on routing security

Ingrid MoermanView profile

Professor

• Wireless Networking
• Cognitive Radio Networks
• Internet of Things
• +4 more

Ingrid Moerman is a part-time Professor at Ghent University and a staff member at the Internet Technology and Data Science Lab (IDLab), a core research group of imec embedded within Ghent University and the University of Antwerp. She coordinates mobile and wireless networking research and leads a team of over 30 researchers at Ghent University, with extensive involvement in European and national funding initiatives. She received her Electrical Engineering degree (1987) and Ph.D. (1992) from Ghent University. Her research spans collaborative networks, cognitive radio, software-defined radio, IoT, LPWAN, and high-density wireless access, emphasizing experimentally-supported development of next-generation wireless systems with practical implementations in spectrum management and real-time control. Recent publications (2024-2025) reveal a strong pivot toward AI-integrated wireless networking, featuring OFDMA scheduling innovations, Wi-Fi 6/7 interference mitigation, and time-sensitive networking for industrial applications. Key trends include 5G/6G convergence, vehicular communication enhancements, and digital twin frameworks for network observability, reflecting her focus on mission-critical industrial use cases. Her accolades include: 9 Best Paper Awards 2 FWO Prizes (Research Foundation - Flanders) IMEC Prize of Excellence 2001 MSc Thesis Award (as promoter) Best Demo/Exhibit Award at ICT 2013 DARPA Spectrum Collaboration Challenge Prize ($750,000) She has coordinated major EU projects (FP7/H2020: CREW, WiSHFUL, eWINE, ORCA) with industry partners, securing substantial funding for experimental wireless research. Her grant portfolio emphasizes collaborative innovation in spectrum sharing and neutral-host architectures for multi-operator environments. At IDLab, she directs advanced wireless testbeds supporting real-world validation of technologies like openwifi and White Rabbit, with active experimentation in time-sensitive networking and spectrum collaboration for industrial IoT deployments.

Tiziano De MatteisView profile

Assistant Professor

• Parallel and Distributed Computing
• High-Performance Computing
• Energy Efficiency in Computing
• +4 more

Tiziano De Matteis is an Assistant Professor in the @Large Research group at Vrije Universiteit Amsterdam's Faculty of Science, Department of Computer Systems. He also holds an affiliation with the Network Institute. His research focuses on overcoming post-Moore architecture challenges through parallel and distributed computing, high-performance systems, energy efficiency, and FPGA applications. Previously, he was a PostDoc at ETH Zurich's SPCL Group and earned his MSc/PhD from the University of Pisa. Education PhD in Computer Science, University of Pisa MSc in Computer Science, University of Pisa Research Interests Post-Moore architectures for distributed ecosystems Energy-aware parallel computing High-level abstractions for parallel software development FPGA-based hardware acceleration Data stream processing and distributed systems Recent Research Trends Recent work emphasizes: Data center risk analysis and sustainability Optimizing microservices and distributed scheduling LLM model offloading to NVMe storage Python-based data-centric programming productivity GPU interconnect performance in supercomputing Grants & Projects Participates in the EU-funded 'Extreme and Sustainable Graph Processing' project (2023-2025), exploring scalable graph algorithms and energy-efficient computing systems. Teaching Accelerator-Centric Computing Ecosystems Computer Organization Distributed Systems Systems Seminar

Xie LitianView profile

Professor

• Transport Planning
• Transport Modelling & Simulation
• Mobility Strategy & Solutions
• +3 more

Professor Xie Litian is a part-time faculty member at the National University of Singapore (NUS), teaching postgraduate programmes since January 2014. He holds a PhD in Transport Modelling from Nanyang Technological University (2006) and has over 20 years of experience in transport planning and policy across public and private sectors. His primary affiliation is with the Urban Redevelopment Authority (URA) of Singapore, where he leads innovative solutions for Land Use & Transport Integrated Planning. He previously served as Vice President (mobilityX) and Deputy Head (Transport Planning) at SMRT (2018–2021), and at Singapore Land Transport Authority (LTA) as a senior transport modeller (2008–2018). His research focuses on Transport Modelling & Simulation , Mobility Strategy & Solutions , and Transport Impact Assessment . Key projects include leading the Singapore Transport Impact Assessment (TIA) Guidelines (2011 & 2017), which established technical standards for car-lite developments. His work spans international consultancy projects in China, India, UAE, and others. Xie’s publications emphasize Smart City Planning and Transport Policy Implementation , with contributions to journals like Transportation Research Part A . His recent work explores Mobility-as-a-Service (MaaS) solutions and integrates data analytics into transport systems. Despite no explicit awards mentioned, his leadership roles and technical guidelines reflect industry recognition. He advises on transport networks and development strategies for Singapore’s districts, leveraging simulation tools and interdisciplinary approaches. His part-time academic role allows him to bridge research and real-world policy, maintaining active engagement with academic communities through paper reviews and conference participations.

Eduardo EyrasView profile

Professor

• RNA Biology
• Cancer Research
• Long-read Sequencing Technologies
• +4 more

Eduardo Eyras is a Professor at the Australian National University (ANU) and EMBL Australia Group Leader, leading research in computational RNA biology and cancer genomics. He directs the Centre for Computational Biomedical Sciences and is part of the Shine-Dalgarno Centre for RNA Innovation. His work focuses on transcriptome and epitranscriptome analysis using long-read sequencing, machine learning, and computational methods to study cancer mechanisms. Eyras holds a PhD in Mathematics from the University of Groningen (1999) and previously led research at the Sanger Institute and Pompeu Fabra University. Affiliations: Director, Centre for Computational Biomedical Sciences Researcher, Shine-Dalgarno Centre for RNA Innovation Member, Division of Genome Sciences and Cancer Leader, The Eyras Group - Computational RNA Biology Research Interests: Development of algorithms for long-read sequencing Machine learning applications in RNA biology Epitranscriptomic modifications and cancer Therapeutic mRNA platform steering Key Projects: Novel algorithms for transcriptome variation analysis Predictive models of RNA modifications in disease Ribosomal DNA variation analysis Advisees & Grants: Supervises PhD students (e.g., Favour Oyelami, Stefan Prodic) and leads ARC-funded projects on mRNA diagnostics and epitranscriptomic therapies. Collaborates with global teams on forensic genomics, cancer drug resistance, and AI-driven translational research. Labs/Teams: Leads the Eyras Group, collaborating with the Hannan Group (Cancer Therapeutics) and Shirokikh Group (Protein Biosynthesis).

• Machine Learning
• Deep Learning
• Video Classification
• +3 more

Reza Bosagh Zadeh is an Adjunct Professor at the Institute for Computational and Mathematical Engineering (ICME) at Stanford University. His research focuses on machine learning, deep learning, and their applications in video classification, healthcare analytics, and distributed algorithms. He specializes in developing scalable computational methods for real-time data processing and has contributed to advancements in neural networks and optimization techniques. Reza's work spans theoretical and applied domains, with notable contributions to TensorFlow frameworks, video summarization systems, and medical imaging analysis. His research often integrates interdisciplinary approaches, leveraging both academic and industrial collaborations. Notable projects include developing machine learning models for glaucoma detection and creating efficient algorithms for large-scale data processing in environments like Apache Spark. His publications emphasize real-time video stream analysis, distributed computing architectures, and practical implementations of deep learning. Reza holds a strong presence in both academic and tech sectors, with contributions to platforms like Twitter's Who-to-Follow system and innovations in edge computing for video surveillance.

Bruce WigginsView profile

Associate Professor

• Audio Engineering
• 3D Audio
• Ambisonics
• +5 more

Bruce Wiggins is an Associate Professor in Audio Engineering at the College of Science and Engineering. His research focuses on spatial audio technologies, including Ambisonics, binaural auralization, and 3D audio systems. Notable projects include the GASP guitar system and WHAM webcam-based head-tracked audio solutions. He has contributed to advancements in microphone array calibration, speaker array modeling, and virtual reality audio integration. His work bridges theory with practical applications in music technology and acoustic engineering. Education: PhD in Audio Engineering (2004). Research Interests: Ambisonics, spatial audio capture and reproduction, 3D audio for virtual reality, binaural rendering, and innovative musical instrument design. His work emphasizes practical implementations such as the GASP guitar system and calibration tools for low-cost microphone arrays. Article Trends: Recent publications address virtual stereo microphone techniques (2024), dynamic electrical systems (2024), and browser-based 3D audio (2023). Earlier work explores head-tracking algorithms (2016–2020) and acoustic modeling for domestic environments (2017). Grants/Advising: No explicit grants listed. Advising details unavailable but has collaborated with numerous researchers on projects like WHAM and GASP. Labs/Teams: Active in interdisciplinary teams developing spatial audio tools and instruments, including collaborations on virtual reality auralization and ambisonic guitar systems.

Raman AdaikkalavanView profile

Professor

• Information Security (IoT, Android)
• Data Streaming
• Computer Science Education
• +3 more

Dr. Raman Adaikkalavan is a Professor in the Department of Computer and Information Sciences at Indiana University South Bend (IUSB), and serves as Associate Vice Chancellor for Enrollment Management. He holds a Ph.D. in Computer Science and Engineering from the University of Texas at Arlington (2006), and has extensive academic leadership experience. His research focuses on information security (particularly IoT and Android), data streaming, and computer science education. Notable contributions include developing the IU Test web-based assessment tool and advancing secure data stream processing architectures. Education: B.E. (1999) from Bharathidasan University, M.S. and Ph.D. (2002/2006) from University of Texas at Arlington, with additional certificates in online teaching (2013). Research emphasizes practical applications like secure stream processing in cloud environments and improving pedagogical methods through active learning. His work has been supported by NSF grants and institutional funding. Awards include the IU Trustees' Teaching Award (2011) and recognition as a University Scholar (UT Arlington). He advises students on topics like secure data stream processing and software engineering. Collaborations include projects with Dr. Indrakshi Ray (Colorado State) and Dr. Sharma Chakravarthy (UT Arlington). His IU Test system aids in program assessment and accreditation reporting for ABET.

Khandaker Mamun AhmedView profile

Assistant Professor

• Computer Vision
• Federated Learning
• Cybersecurity
• +4 more

Dr. Khandaker Mamun Ahmed is an Assistant Professor at The Beacom College of Computer & Cyber Sciences, Dakota State University. He teaches undergraduate and graduate courses in artificial intelligence, algorithms, and data structures. He holds a Ph.D. in Computer Science from Florida International University (2024), an M.Sc. from the same institution (2023), and a B.Sc. in Software Engineering from the University of Dhaka (2016). His research focuses on computer vision, federated learning, cybersecurity, explainable AI, vision-language models, and optimization algorithms. He has contributed to peer-reviewed publications and conference presentations, with notable work in federated learning for IoT, anomaly detection in videos, and AI applications in healthcare and agriculture. Recent articles highlight advancements in federated learning frameworks, AI-driven healthcare systems, and real-time object detection using neural networks. His work also addresses cybersecurity challenges in DevOps pipelines and generative AI for educational datasets. Recipient of the 'Best graduate student in research award' (2022), Dr. Ahmed advises on AI ethics and mentors students through academic-industry collaborations. His research bridges theoretical computer science with practical applications in agriculture, healthcare, and infrastructure monitoring.

• Human-Machine Interaction (HMI)
• Augmented Reality (AR)
• Virtual Reality (VR)
• +4 more

Overview ASSILA Ahlem is a Researcher-Lecturer at CESI, specializing in Human-Machine Interaction (HMI), Augmented Reality (AR), and Virtual Reality (VR). She holds a PhD in Computer Science from Université de Valenciennes (2016) and a postdoctoral position at Institut Image ARTS ET METIERS PARISTECH (2017). Her research focuses on usability evaluation, digital twin technology, and BIM-integrated XR systems. She has supervised multiple engineering and master’s projects, including AR application development for network management. Research Contributions Developed frameworks for integrating subjective/objective usability metrics using ISO standards Proposed maturity models for BIM-based AR/VR systems Explored digital twin applications in manufacturing and construction industries Education & Responsibilities Teaches computer science at all engineering levels (L1-M2) at CESI Reims, including algorithmics, HMI design, and project-based learning. Served as pilot for engineering program cycles (2017–2020). Active in organizing international conferences (e.g., HCI 2020, Flexible Automation 2018) and peer review for journals like IJISE and IEEE VR. Awards & Recognition No specific awards listed, but recognized for contributions to HCI and industry-relevant research. Advising & Grants Supervised over 10 student projects including PFEs and internships. Actively participates in jury panels for engineering thesis defenses and academic promotions across multiple institutions. Labs & Collaborations Member of the CESI Chair for Industry and Services of Tomorrow, focusing on technology integration in construction and manufacturing sectors.

Antonio KungView profile

Lecturer

• Embedded Systems
• Real-Time Systems
• Privacy in Intelligent Transportation Systems (ITS)
• +6 more

Antonio Kung is a Lecturer with 30 years of experience in embedded systems. He co-founded Trialog in 1987 and currently serves as its Chief Technology Officer (CTO), leading product development and collaborative projects in embedded systems, privacy for Intelligent Transportation Systems (ITS), ICT for aging populations, and smart grids. He holds a Master's degree from Harvard University and an Engineering degree from École Centrale Paris. His research focuses on privacy-preserving technologies in transportation systems, ambient assisted living (AAL) platforms, and real-time embedded systems. Key areas include securing vehicular communication networks, designing flexible distributed systems (Flex-eWare), and creating interoperable solutions for aging-in-place technologies. He has contributed to projects like the Sevecom initiative for secure vehicle networks and the TEAHA framework for open home networks. Dr. Kung has presented at major conferences such as the Transport Research Arena 2008, ACM Computers, Freedom, and Privacy (CFP) 2010, and the European Innovation Partnership on Active and Healthy Ageing. His work bridges industry and academia, emphasizing practical implementations of privacy-by-design principles in embedded and smart systems. He has been actively involved in interdisciplinary initiatives, including the European Commission’s AAL Interoperability Days, where he addressed complexities in managing AAL systems. His publications span topics from Ada run-time libraries (1980s) to modern challenges in IoT and aging technologies, reflecting a career-long commitment to advancing embedded systems security and usability.

Lasse HeikkinenView profile

Senior Lecturer

• Physics
• Applied Physics
• Process Tomography
• +5 more

Lasse Heikkinen is a Senior Lecturer at the Department of Technical Physics, Faculty of Science, Forestry and Technology, University of Eastern Finland. His work spans applied physics, process tomography, and educational technology, with a focus on innovative teaching methods like flipped classrooms during the pandemic. Current Role: Deputy Head of Department, Senior University Lecturer Research Themes: Electrical impedance tomography for industrial processes, flipped teaching frameworks, and physics education adaptation to remote learning. Recent publications highlight his dual expertise in process tomography (gas-solid flows, pharmaceutical monitoring) and pedagogical innovation (learning analytics, flipped classrooms). He has contributed to educational manuals and toolkits for teacher training. Key Projects: Technology education infrastructure development (2015–2024), pandemic-era teaching adjustments (2022). Collaborations: Active in international process tomography conferences and multidisciplinary teams like the Ameba project.