Find Academic Supervisors

• 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.

SHEN LeiView profile

Researcher

• Multiscale Modeling and Simulation
• Materials Informatics
• Computational Physics
• +3 more

SHEN Lei is a researcher at the National University of Singapore (NUS), affiliated with the Department of Physics. With a PhD in Physics from NUS, he specializes in Multiscale Modeling and Simulation and Materials Informatics , leveraging machine learning and computational methods for advanced materials discovery. Research Focus: Density functional theory, molecular dynamics, finite element analysis, and data-driven design of materials. Teaching: Modules include Mechanics and Waves (PC1433), Applied Quantum Mechanics (PC2130B), and Mechanical Properties of Materials (ESP2109). His work spans spintronics, ferroelectricity, and energy storage materials, with recent publications on interatomic potentials, sliding heterostructures, and battery anodes. He has received the Teaching Commendation Award and declined the Lee Kuan Yew Postdoctoral Fellowship . Notable Trends: Recent articles emphasize machine learning in materials science, van der Waals heterostructures, quantum transport, and medical image analysis. Subfields include Rashba spin-orbit coupling, piezoelectric tensor modeling, and defect-informed neural networks. Scientific Awards: Teaching Commendation Award (AY15/16; AY16/17) Lee Kuan Yew Postdoctoral Fellowship (2014) (declined)

• 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.

Paul RuvoloView profile

Professor

• Machine Learning
• Robotics
• Computer Vision
• +3 more

Dr. Paul Ruvolo is a Professor of Computer Science at Olin College in Needham, MA. His research focuses on developing assistive technologies for people with sensory and motor impairments, leveraging machine learning, robotics, and computer vision. He holds a Ph.D. and M.S. in Computer Science and Engineering from the University of California San Diego, and a B.S. in Computer Science from Harvey Mudd College. Key research areas include creating systems that learn through imitation and experience, such as navigation aids for the visually impaired and educational tools for orientation and mobility. He leads projects like Co-Designing Assistive Apps with Students Who Are Blind, emphasizing participatory design. His work integrates Bayesian statistics, numerical optimization, and linear algebra to solve complex sensorimotor tasks. Education: Ph.D., Computer Science and Engineering, UC San Diego M.S., Computer Science and Engineering, UC San Diego B.S., Computer Science, Harvey Mudd College Awards: NSF IGERT Fellowship for 'Learning and Vision in Humans and Machines' Recent publications highlight innovations in AR navigation systems, smartphone-based SLAM for indoor environments, and educational tools for blind users. His work bridges computational methods with real-world accessibility challenges, emphasizing interdisciplinary collaboration and user-centric design. Dr. Ruvolo’s lab, linked at occam.olin.edu , focuses on assistive technologies. He actively contributes to Teach Access and other initiatives promoting inclusive technology education. His research has applications in robotics, healthcare, and educational technology.

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.

Ruben PauwelsView profile

Associate Professor

• Artificial Intelligence
• Medical Physics
• Radiation Protection
• +4 more

Ruben Pauwels is an Associate Professor in the Department of Dentistry and Oral Health at Aarhus University, Denmark. As the strategic research coordinator of the 'Intelligent Systems' research theme, his work focuses on integrating artificial intelligence (AI) and deep learning into clinical workflows, particularly in medical imaging, radiation protection, and dental data science. His research spans applications like image enhancement, automated segmentation, lesion detection, and risk assessment for treatment planning, with a strong emphasis on cone-beam computed tomography (CBCT) and medical physics in dentistry. His educational background includes a PhD and MSc, and he actively contributes to interdisciplinary research involving multiple departments. Teaching activities align with his expertise, covering digital workflows and novel technologies in dental practice. Key research areas include biomedical image processing, machine learning in odontology, and biophysics. He has authored 118 publications, with recent work focusing on AI-driven medical imaging solutions and radiation protection standards. Notable contributions include the European consensus on patient contact shielding and organ-specific deep learning models for radiation dose calculation. Received awards such as the Bagger-Sørensen Young Researcher Award (2024) and ECMP Best Radiation Protection Presentation (2022). Active in professional networks: EMRA, EFOMP, and ITU/WHO/WIPO initiatives on AI in healthcare. Supervised students like B. N. de Freitas and R. J. Gonçalves da Motta in projects involving digital twin technology and mandibular canal labeling.

• Machine Learning
• Social Media Analysis
• Public Health
• +3 more

Lydia Manikonda is an Assistant Professor in the Department of Information Systems and Web Science at the Lally School of Management, Rensselaer Polytechnic Institute (RPI). She is also affiliated with the AIRC (Artificial Intelligence Research Center) at RPI. Her research focuses on developing intelligent decision-making models using data from social media, online forums, and news articles to address challenges in business, public health, and technology. Her work has been recognized with awards, including the Best Reviewer Award at ICWSM 2016 and the Outstanding Demonstration Award at ICAPS 2014. Dr. Manikonda holds a PhD in Computer Science from Arizona State University (2019). Her research interests span interpretable machine learning, privacy in AI systems, social media analytics, and the societal impacts of technology. She has published extensively on topics like misinformation detection, ethical AI design, and pandemic-related behavioral shifts. Education: PhD in Computer Science, Arizona State University (2019) Her recent work emphasizes interdisciplinary approaches to tackle real-world problems, such as predicting intimate partner violence using machine learning and analyzing anti-Asian hate discourse on Reddit during the pandemic. She collaborates with interdisciplinary teams to bridge gaps between technical innovation and societal needs. Key Awards: Best Reviewer Award at ICWSM 2016 Outstanding Demonstration Award at ICAPS 2014 Dr. Manikonda's research also explores anthropomorphism in voice assistants, temporal privacy concerns, and the role of emotional cues in medical crowdfunding platforms. She actively participates in academic leadership roles, such as organizing the Web Science 2024 conference. Her lab focuses on actionable domain models and data-driven decision-making, with applications in healthcare, technology ethics, and public policy. She mentors students in these areas and is committed to advancing AI for societal good.

Jeff HeflinView profile

Associate Professor

• Semantic Web
• Ontology Development
• Artificial Intelligence
• +3 more

Jeff Heflin is an Associate Professor in the Department of Computer Science and Engineering at Lehigh University, and Director of the Cognitive Science Program. He holds a PhD from the University of Maryland, College Park, and a B.S. from The College of William and Mary. His research focuses on AI for Data Management, Scalable Reasoning, and Data Exploration, with significant contributions to Semantic Web technologies like SHOE, DAML+OIL, and OWL. Education: Ph.D. in Computer Science, University of Maryland, 2001 M.S. in Computer Science, University of Maryland, 1999 B.S. in Computer Science, The College of William and Mary, 1992 Research Interests: Heflin’s work advances Semantic Web infrastructure, including ontology development, scalable reasoning systems, and data integration. His contributions include foundational benchmarks like LUBM and innovations in entity matching (DAME), table search (StruBERT), and neural-augmented logical reasoning. Awards: Winner of the Billion Triple Challenge (ISWC 2012) Best Paper Award, ISWC 2004 Ruth and Joel Spira Award for Excellence in Teaching (2017) Service & Leadership: Served as Vice-President of the Semantic Web Science Association (SWSA), Editorial Board member of the Artificial Intelligence Journal , and General Chair of ISWC 2017. Active in conference organization (CIKM 2020, COLING 2022) and journal special issues on Semantic Web evaluation. Labs & Collaborations: Leads the Semantic Web and Agent Technologies (SWAT) Lab at Lehigh, focusing on AI-driven data management and scalable reasoning systems. Collaborates on NSF-funded projects like CRISPS (cell-centric image similarity search) and Domain-Agnostic Dataset Search.

• Cancer Biomarkers
• Translational Cancer Research
• Digital Pathology
• +3 more

Lill-Tove Rasmussen Busund is a Professor and Consultant Pathologist at the Department of Medical Biology, UiT The Arctic University of Norway. Her research focuses on translational cancer studies with emphasis on cancer biomarkers in tissue and blood, combining molecular biology with artificial intelligence applications in pathology. She leads the Translational Cancer Research Group (TCRG) and contributes to projects like the TNM-Immune Cell Score Trial and AI–Pathology initiatives. Role: Professor/Consultant Pathologist University: UiT The Arctic University of Norway Department: Department of Medical Biology Email: lill.tove.rasmussen.busund@uit.no Rasmussen Busund's work spans epidemiological cancer research , multi-omic data integration , and machine learning applications for cancer diagnostics. She develops prognostic and predictive biomarkers using population-based cohorts like the NOWAC study, with particular attention to hormone-dependent cancers and immune microenvironment analysis . Recent publications highlight her expertise in digital pathology and AI-driven tumor classification , including studies on microRNA expression patterns in breast and colon cancer, immune cell score development for NSCLC, and computational approaches to lymphocytic infiltration assessment. Her research directly impacts precision oncology and immunotherapy response prediction . She contributes to teaching as leader of the Musculoskeletal System course at the Medical School, and maintains active participation in scientific knowledge migration projects like the transition to the National Science Archive (NVA). Her ORCID identifier is 0000-0001-8235-6163 .

• Statistics
• Machine Learning
• Bioinformatics
• +4 more

Ping Ma is a Professor of Statistics with a courtesy appointment in Computer Science at the University of Georgia. His research focuses on developing innovative statistical and machine learning methodologies for complex high-dimensional data, with applications spanning bioinformatics, computational biology, social network analysis, and anomaly detection in power systems. Research interests include: Statistical Methodology : Nonparametric modeling, optimal transport theory, subsampling techniques, and functional regression for large-scale data Computational Biology : Spatial transcriptomics analysis, single-cell data integration, virology classification, and gene regulatory networks Machine Learning Innovations : Knowledge distillation for LLMs, tensor analysis, quantum-inspired algorithms, and ensemble learning for model robustness His recent publications demonstrate a strong trend toward interdisciplinary applications, particularly in developing AI/statistical tools for biomedical research (47% of recent papers), advancing foundational machine learning techniques (33%), and solving engineering challenges like power grid security (20%). Methodologically, 67% focus on novel algorithm development while 33% refine existing techniques for scalability.

• Intelligent Transportation Systems
• Wireless Sensing
• Deep Learning
• +3 more

Hadi EL ZEIN is a Researcher affiliated with the Université Technologique de Troyes (UTT). His work focuses on intelligent transportation systems, wireless sensing technologies, and healthcare applications leveraging machine learning. He specializes in human activity recognition using Wi-Fi signals and deep learning models, with notable contributions to fall detection and driver drowsiness alert systems. His research integrates real-time processing, signal analysis, and lightweight neural networks for practical deployment in smart environments. Recent publications highlight advancements in applying convolutional neural networks (CNN) to analyze wireless channel state information (CSI) for health monitoring and transportation safety. Contact: hadi.el_zein@utt.fr

Anton AkusokView profile

Lecturer

• Machine Learning
• Big Data Analytics
• Mobile Edge Computing
• +3 more

Anton Akusok is a Part-time Lecturer in the Big Data Analytics Master's program at Arcada University of Applied Sciences. He holds a BSc in IT from Moscow (2011), MSc in ML and Data from Aalto University (2014), and a DSc in ML from the University of Iowa, USA (2016). His research focuses on Extreme Learning Machines (ELM), hardware acceleration for ML on mobile devices, and real-time geospatial predictions. He has developed libraries like HPELM and Scikit-ELM, and created the HaSuRiski app for acid sulfate soil prediction in Finland. Research Interests: ELM applications in environmental modeling, federated learning security, mobile edge computing, and geospatial visualization. Key projects include real-time mapping apps with iOS integration and open-source ML tools. Publications (2021-2024) highlight work on federated learning privacy, acid sulfate soil detection, signature verification, and distributed ELM algorithms.

• Machine Learning
• Computer Vision
• Human Behavior Analysis
• +5 more

Dr. Harshala Gammulle is a Research Fellow at Queensland University of Technology (QUT), School of Electrical Engineering & Robotics. She holds a PhD in Computer Vision from QUT (2019), receiving the QUT Executive Dean's Commendation for Outstanding Doctoral Thesis. Her expertise spans machine learning, computer vision, and spatio-temporal modeling for human behavior understanding. She leads interdisciplinary projects with funding from DST Group, SmartSat CRC, QLD DESI, and others. Research focuses include: human action recognition, medical anomaly detection, satellite image analysis, and AI for environmental monitoring. Key projects involve quantum-classical hybrid ML for biomedical signal analysis, disaster forecasting via hyperspectral data, and autonomous combat vision systems. She has supervised PhD/MPhil candidates in ML and quantum hybrid ML. Education: PhD (Computer Vision, QUT 2019), BSc (University of Peradeniya, Sri Lanka). Awards: WiT Emerging Achiever Technology Award finalist (2021), University Award for Academic Excellence (2015). Teaching includes units like Digital Signals and Image Processing (EGH444), and Computing & Data for Engineers (EGB103). Current grants involve QLD DESI, SmartSat CRC, and Rheinmetall Defence Australia collaborations. Active in labs like SAIVT and QUT's Early Career Research schemes.

Andrea SaracinoView profile

Associate Professor

• Cybersecurity
• Privacy-Preserving Technologies
• Machine Learning Applications
• +3 more

Andrea Saracino is an Associate Professor specializing in cybersecurity, privacy-preserving technologies, and machine learning applications. His research focuses on enhancing security in IoT systems, smart homes, and mobile devices, with a particular emphasis on Android malware detection and usage control frameworks. He has received the IEEE TCCPS Early-Career Award 2023 for his contributions. Key projects include the SIFIS-Home initiative for privacy in globalized smart homes and the ACE framework for access control. His work addresses challenges in balancing privacy, utility, and explainability in machine learning models, particularly in image and tabular data analysis. He actively explores cybersecurity in emerging domains like software-defined vehicles and industrial control systems.