Find Academic Supervisors

• Machine Learning
• Medical Image Analysis
• Multimodal Learning
• +3 more

Mohammod Naimul Islam Suvon serves as a Research Assistant in Machine Learning for Medical Image Analysis at the University of Sheffield's School of Computer Science within the Department of Computer Science. He is an active member of the Machine Learning research group focusing on healthcare applications. His research spans Medical Image Analysis , Cardiac Hemodynamics , and Multimodal Learning with emphasis on cost-effective diagnostic solutions. Key projects include pulmonary hypertension detection using cardiac MRI and ECG analysis, leveraging tensor-based multimodal fusion and variational autoencoders. His work bridges clinical cardiology needs with advanced AI techniques. Analysis of his 15 most recent publications reveals strong specialization in medical multimodal AI (80% of works), particularly for cardiovascular diagnostics. Secondary interests include pandemic response systems (CT-scan analysis for COVID-19), agricultural AI, and educational technology applications. His methodology consistently employs deep learning architectures adapted for specific clinical constraints. Suvon maintains active collaborations with clinical researchers at Sheffield, evidenced by co-authorship with medical professionals like Swift AJ and Alabed S. His publication pattern shows increasing focus on low-cost diagnostic solutions since 2022, suggesting strategic alignment with healthcare accessibility goals.

Francis YanView profile

Assistant Professor

• Networked Systems
• Computer Networks
• Machine Learning for Systems
• +3 more

Francis Yan is an Assistant Professor of Computer Science at the University of Illinois Urbana-Champaign (UIUC), affiliated with the Siebel School of Computing and Data Science and the Electrical & Computer Engineering department. He leads the Illinois Networked Systems and AI (NSAI) Lab, focusing on integrating practical machine learning into networked systems. Previously, he was a Senior Researcher at Microsoft Research Redmond, working on systems and networking research. His work emphasizes deployable ML solutions, validated through large-scale platforms like Puffer and Pantheon. Education: Ph.D. in Computer Science (Stanford University, 2020); B.S. in Computer Science (Tsinghua University, 2015) and B.A. in Economics (Tsinghua University, 2015). Research Interests: Networked systems, machine learning for systems and networking, intelligent video systems, and cloud resource management. His lab develops frameworks like DeDe, Mowgli, and Autothrottle to enhance system performance and robustness. Awards: USENIX NSDI Outstanding Paper Award (2024), Applied Networking Research Prize (2021), and USENIX ATC Best Paper Award (2018). He serves on program committees for NSDI, SIGCOMM, and ACM workshops. Grants & Service: Co-chair of SIGCOMM 2024 Artifact Evaluation. Active in open-source projects and research platforms supporting reproducible networking experiments. Labs/Teams: Directs the NSAI Lab, collaborating on projects such as Puffer (400k-user video service), Teal (WAN optimization), and Genet (curriculum learning for networking).

Nishanth SastryView profile

Professor

• Internet Data Science
• Social Networks
• Computational Social Science
• +9 more

Professor Nishanth Sastry is a distinguished academic in the Department of Computer Science at the University of Surrey , serving as Associate Head of Research and Innovation. He holds a PhD from the University of Cambridge and has extensive industry experience at Cisco Systems, IBM, and MIT. His research focuses on social networks, computer networks, online harms, edge computing, and privacy , with notable projects analyzing hate speech on Twitter, optimizing LEO satellite networks, and developing privacy-preserving technologies. He has received multiple awards including a SIGCOMM Best Paper Award and nine US patents. Education: Bachelor’s (Distinction) – RV College of Engineering Master’s – University of Texas at Austin PhD – University of Cambridge Research Interests: Nishanth’s work bridges computer networks and social dynamics , applying Internet Data Science to improve system efficiency. Key areas include: Peer-assisted content delivery and edge caching Hate speech detection and mitigation Blockchain-based pervasive systems Satellite network optimization Privacy in social platforms Articles & Impact: His recent work explores conversation kernels for context analysis , LEO satellite gateway placement , and emotion-aware speech synthesis . These studies address real-world challenges like reducing video traffic load and enhancing decentralized web accessibility. Awards & Recognition: Best Paper Award (SIGCOMM 2017) Yunus Innovation Challenge Award (MIT 2016) Multiple IBM and industry accolades Advising & Grants: Supervises PhD students in distributed systems and cybersecurity. Active in securing grants for projects like Transition Guardian (vulnerable user privacy) and EdgeMart (edge network economies). Labs & Collaborations: Leads the Distributed and Networked Systems Group and co-leads the Surrey Security Network . Collaborates with organizations like the Alan Turing Institute, BBC R&D, and Wikimedia Foundation.

Hui GuanView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Programming Systems
• +3 more

Hui Guan is an Assistant Professor at the Manning College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst. Currently on leave, she works at Amazon AWS developing LLM systems. She holds a PhD in Electrical Engineering from North Carolina State University (2020) and is a member of the PLASMA lab at UMass. Education: PhD in Electrical Engineering, North Carolina State University, 2020 Research Interests: Focuses on machine learning systems, optimizing deep multitask and graph learning. Aims to improve efficiency, scalability, and reliability of ML through system innovations. Leverages principles like composability and locality awareness to democratize ML applications. Awards: NSF CAREER Award (2024) Amazon Research Award (2022) NCSU ECE Distinguished Dissertation Award (2020) IBM PhD Fellowship (2015-2018) Grants and Projects: Includes NSF support for projects like Adaptive Deep Learning Systems and Memory-Driven Collaboration for Embedded Systems. Received grants from Adobe and Dolby. Students and Advising: Advises PhD students including Lijun Zhang, Kunjal Panchal, and Qizheng Yang. Collaborates with students from other groups like Sohaib Ahmad. Labs and Teams: Active member of the PLASMA lab, focusing on programming languages and systems research.

• Computer Vision
• Networking Protocols
• Machine Learning
• +3 more

Maria Ito is a Visiting Professor at Queen Mary University of London's School of Electronic Engineering and Computer Science, focusing on interdisciplinary research at the intersection of computer vision, networking protocols, and adaptive media streaming. Her work emphasizes video stabilization techniques, network performance optimization, and machine learning-driven solutions for real-time media delivery systems. Her research interests span video processing, deep learning applications in computer vision, and the design of efficient adaptive streaming protocols. Notable contributions include frameworks for distortion-free video stabilization and performance evaluations of QUIC vs. TCP protocols. Publications highlight trends in combining control theory with adaptive streaming systems and developing benchmark datasets for deep learning models. While no specific awards or grants are listed, her work demonstrates significant contributions to both theoretical and applied aspects of media streaming technologies. Advising and lab affiliations are not explicitly detailed in the provided materials.

Niklas CarlssonView profile

Professor

• Distributed Systems
• Network Security
• Streaming Media Technologies
• +4 more

Niklas Carlsson is a Professor at Linköping University's Department of Computer and Information Science (IDA), where he leads research in distributed systems, network security, and multimedia streaming. He holds a M.Sc. in Engineering Physics from Umeå University and a Ph.D. in Computer Science from the University of Saskatchewan, with postdoctoral experience in Canada. His research spans adaptive streaming technologies, privacy-preserving systems, and sports analytics. Notable projects include the ASTECC initiative on edge-cloud systems and collaborations with Linköping Ice Hockey Club on strategy analysis. He has authored over 150 publications, including works on video fingerprinting, homomorphic encryption, and social media dynamics. Carlsson actively serves on editorial boards and conference committees (e.g., ACM SIGMETRICS, MASCOTS) and has received awards for best papers (2022, 2023) and innovative frameworks like AnonFACES. His lab focuses on real-world applications of theoretical insights, emphasizing measurable performance improvements.

Fred Harris, Jr.View profile

Professor

• Virtual Reality
• Data Analysis
• Machine Learning
• +4 more

Roles & Affiliations: Associate Dean for Faculty and Academic Affairs, College of Engineering Foundation Professor of Computer Science & Engineering, University of Nevada, Reno Nevada State EPSCoR Director, Project Director of Nevada NSF EPSCoR Education: PhD in Computer Science, Clemson University (1994) Bachelor's/Master's Degrees, Greenville, SC (1986) Research Interests: - Virtual Reality applications in education, healthcare, and mining safety - Data-driven solutions for smart cities and environmental monitoring - Machine learning for multi-omics analysis and wave height prediction - Personalized learning frameworks in robotics education - GPU-accelerated algorithms for high-performance computing Publications Overview: Recent work focuses on VR frameworks (e.g., ScryVR), AI-driven education tools (FORE), and smart infrastructure monitoring. Key domains include mining safety simulators (METS VR), EEG analysis systems, and spatial data pipelines for IoT. Grants & Awards: Nevada NSF EPSCoR Project Director EPSCoR Director role highlights sustained federal/state funding leadership Advising & Labs: Current advisees focus on VR input fatigue reduction, multi-omics pathway analysis, and LoRaWAN data systems. Collaborates with Sergiu Dascalu on interdisciplinary projects.

• Multimedia Communication
• Signal Processing
• Image and Video Communication
• +3 more

Dr. Shadan Khan Khattak is a Senior Lecturer at the Cardiff School of Technologies, Cardiff Metropolitan University since 2020. His expertise lies in Multimedia Communication and Signal Processing. Prior roles include Assistant Professorships at King Faisal University (Saudi Arabia) and COMSATS University Islamabad (Pakistan), and an R&D Engineer at Panasonic R&D Centre Singapore (2017–2018). His research focuses on image/video communication, cybersecurity in multimedia, and hardware design for signal processing. He has over 10 peer-reviewed publications in journals/conferences like IEEE Transactions on Circuits and Systems, IEEE Signal Processing Letters, and Picture Coding Symposium. He is an active IEEE member and serves as a reviewer for several journals. Recent work includes advancements in ensemble deep learning for medical imaging, privacy in metaverse environments, and FPGA-based TCAM emulation. His contributions span both theoretical research and applied engineering solutions, emphasizing interdisciplinary innovation in technology and healthcare domains.

Mohamed SedkyView profile

Professor

• Computer Vision
• Video Analytics
• Cyber-Physical Systems
• +4 more

Professor Mohamed Sedky holds the position of Professor of Cyber-Physical Systems at Staffordshire University. He previously founded SKM Communication Systems and served as a lecturer at the Arab Academy for Science and Technology before joining Staffordshire in 2003. His roles include MSc Computer Science Deputy Award Leader and BSc Internet of Things Award Leader. He leads research at AVA Technologies Ltd, focusing on real-time video analytics algorithms such as Spectral-360®, which addresses security, safety, and business intelligence applications. His patented work in object detection and change detection has been globally recognized. He teaches modules like Fundamentals of Computer Networks and Router Security Technologies. Education: BEng (Hons) Electro-Physics and Communications, Alexandria University (1996) MSc Communications and Electronics, Arab Academy for Science and Technology (2002) PhD Computer Vision, Staffordshire University (2009) PGC Research Supervision, Staffordshire University PGC Higher and Professional Education, Staffordshire University Research Interests: Object recognition/segmentation Video surveillance systems IoT forensic processes AI in healthcare and smart environments Commercial Ventures: Founder of Spectral-360® technology Lead Researcher at AVA Technologies Ltd Consultant for Serco, Centralweighing, and WMTN Labs & Projects: Developed AVA Technologies' Detect/Protect/Count/Summarise solutions Designed OpenSHS (Open Smart Home Simulator) Conducted feasibility studies for buried utility detection and industrial inspection His research bridges academia and industry through patented innovations in video analytics and IoT systems, addressing challenges in security, healthcare, and smart infrastructure.

Ramesh JohariView profile

Professor

• Online Platforms Design
• Economic Analysis
• Machine Learning
• +3 more

Ramesh Johari is a Professor of Management Science and Engineering and Professor (by courtesy) of Electrical Engineering at Stanford University. He holds affiliations with the Institute for Human-Centered Artificial Intelligence (HAI), Precourt Institute for Energy, and the Institute for Computational and Mathematical Engineering (ICME). As Associate Director of Stanford Data Science and Co-Director of the Stanford Causal Science Center, he leads initiatives in data-driven research and causal inference. His research focuses on online platforms, statistical/machine learning, and healthcare technology, with contributions to diabetes care optimization and algorithmic decision-making. Education: PhD in Electrical Engineering and Computer Science from MIT (2004). His work integrates economics, operations research, and computer science to address challenges in platform design and healthcare systems. Notable achievements include developing the Timely Interventions for Diabetes Excellence (TIDE) platform, improving outcomes for type 1 diabetes patients through remote monitoring and algorithmic prioritization. Key research areas include: design of digital health platforms, economic analysis of online markets, and scalable healthcare solutions. His recent studies emphasize equitable access to CGM technology and precision medicine in diabetes care. Scientific Awards: Over 11 honors, including the George E. Nicholson Student Paper Competition (2003), ACM Doctoral Dissertation Award (2004), and NSF CAREER Award (2007). Advising: Mentors doctoral and master’s students in healthcare technology, data science, and operations research. Serves on editorial boards and program committees for top conferences in his field. Current roles include partial leave from January 2025 to June 2025. Labs/Teams: Active in the Society and Algorithms Lab (SOAL) and collaborates with Stanford’s interdisciplinary healthcare initiatives. His work bridges academic research and real-world applications in technology and medicine.

• Machine Learning
• Deep Learning
• Data Science
• +3 more

Dr Ryan Cunningham serves as a Lecturer in Data Science within the Department of Computing and Mathematics at Manchester Metropolitan University. His academic profile centers on developing advanced deep learning systems for medical image analysis, with particular focus on real-time skeletal muscle assessment via ultrasound and facial expression recognition in video sequences. His work bridges computer science and healthcare to address neurological disorders including motor neuron disease and dystonia. Education: Ph.D in Computing, Manchester Metropolitan University (2012-2015) BSc in Artificial Intelligence, Manchester Metropolitan University (2009-2011) HND in Computing, The Manchester College (2007-2009) Dr Cunningham's research spans machine learning, deep learning, computer vision, and medical image analysis with consistent application to healthcare challenges. His primary focus involves developing convolutional neural networks for ultrasound-based muscle segmentation and real-time analysis to enable early disease diagnosis. Recent work extends to facial expression recognition using 3D-CNNs and generative models for both macro and micro-expressions in long-duration videos, demonstrating interdisciplinary innovation at the intersection of AI and clinical medicine. His technical expertise includes MATLAB, Python, Java, and C/C++ for implementing complex algorithms into practical software solutions. Analysis of his 15 most recent publications reveals a strong trajectory in medical AI applications, with 60% focused on ultrasound-based muscle analysis for neurological disorders and 40% on facial expression recognition systems. Key technical trends include progression from foundational segmentation models to efficient lightweight architectures (2021), integration of temporal modeling for video sequences (2021), and recent exploration of generative approaches for expression synthesis (2023). The research consistently targets real-world clinical utility through real-time processing capabilities and automated diagnostic tools. Scientific Awards: No scientific awards or fellowships are documented in the provided materials. Dr Cunningham currently supervises a PhD candidate investigating deep learning applications for macro and micro facial expressions in high spatiotemporal resolution videos. His teaching responsibilities include postgraduate instruction in High Performance Computing and Big Data, specifically covering TensorFlow as a deep learning framework. Previous teaching experience includes advanced undergraduate programming courses, demonstrating commitment to both research and pedagogy. While no specific grants are mentioned, his research output suggests active engagement with medical imaging and AI funding streams. His work is conducted within Manchester Metropolitan University's Department of Computing and Mathematics, leveraging institutional resources including the Dalton Building facilities. The research direction indicates collaboration with medical professionals and neurology specialists, though specific lab affiliations or research teams are not explicitly documented. Current projects focus on extending deep learning capabilities for real-time ultrasound analysis and advancing facial expression recognition systems for clinical applications in neurological assessment.

Clemens WesterkampView profile

Professor

• Computer Science
• Intelligent Distributed Systems
• Industry 4.0
• +5 more

Prof. Dr.-Ing. Clemens Westerkamp is a full-time professor in the Faculty of Engineering and Computer Science at Osnabrück University of Applied Sciences . With a Ph.D. in Electrical Engineering from Leibniz University Hannover (1996), his career spans academia and industry, including roles as R&D VP at Algovision Systems GmbH and development leader at Controlware GmbH. Research Focus: Applied research in intelligent distributed systems , Industry 4.0 , and IoT , particularly in agricultural contexts. Key projects include PACE (Ambient Communication Systems) and collaborations with Airbus, Stanford University, and agritech firms. Selected Scientific Contributions: Co-author of DIN SPEC 16593 for Industry 4.0 service architectures EU reviewer for Aeronautics and Innovative Training Networks Key member of Industrial Informatics (INDIN) research network Recent Publications analyze satellite networks for agriculture, friction modeling for autonomous systems, and energy-aware embedded software. His work bridges theoretical models with practical implementations in smart farming and remote engineering.

Md Baharul IslamView profile

Associate Professor

• Image Processing
• Computer Vision
• Augmented and Virtual Reality (AR/VR)
• +5 more

Dr. Md Baharul Islam is an Associate Professor in the Department of Computing & Software Engineering at Florida Gulf Coast University (FGCU), affiliated with the U.A. Whitaker College of Engineering. He holds a Ph.D. from Multimedia University (2018), an MS from Nanyang Technological University (2012), and a BS from Rajshahi University of Engineering & Technology (2007). His research focuses on image processing, computer vision, AR/VR for vision rehabilitation, and human-centered computing. He has authored over 80 peer-reviewed publications and secured prestigious grants like the TUBITAK 2232 Outstanding Young Researchers Grant, supporting seven postgraduate students. Education : Ph.D., Multimedia University, 2018 MS, Nanyang Technological University, 2012 BS, Rajshahi University of Engineering & Technology, 2007 His work spans medical imaging applications, including retinal segmentation, acne detection, and Parkinson’s disease analysis. He has received four best paper awards and the IEEE SPS Research Excellence Award (2018). His ARVA system enhances mobility for visually impaired users, while his teams pioneered solutions for heatstroke prediction and wearable health monitoring. He actively contributes to IEEE and ACM, serving on technical committees for international conferences. Dr. Islam’s research bridges technical innovation with societal impact, addressing challenges in healthcare, accessibility, and multimedia systems through interdisciplinary approaches.

Jürgen SchönwälderView profile

Professor

• Computer Networks
• Distributed Systems
• Embedded Systems
• +1 more

Prof. Jürgen Schönwälder is a Professor of Computer Science at the School of Computer Science and Engineering, Constructor University Bremen gGmbH. His research focuses on computer networks, distributed systems, embedded systems, and computer security. He has held positions at TU Braunschweig, University of Twente, and Bell Labs. He leads the Computer Networks and Distributed Systems research group, which addresses challenges in robust network infrastructure and distributed systems resilience. Education: Doctoral Degree in Computer Science, Technical University Braunschweig (1996) Diploma in Computer Science, Technical University Braunschweig (1990) Research Interests: Design of scalable and resilient network services Security in distributed systems Measurement of network performance and behavior IoT and constrained device management Standardization of network protocols (e.g., NETCONF, YANG) Funded Projects: EU Horizon 2020 Concordia (2019-2023) EU FP7 Flamingo (2012-2016) Industry-funded projects in network management and security Key Contributions: Over 100 publications in top venues (IEEE/ACM Transactions, SIGCOMM) and co-chair roles in IETF working groups (NETMOD, ISMS). His work on network configuration (NETCONF), flow analysis, and IPv6 transition mechanisms has shaped modern network management practices.

• Causal Inference
• Machine Learning
• Social Media Analysis
• +3 more

Prof. Drew Dimmery is the Professor of Data Science for the Common Good at the Hertie School's Data Science Lab. He holds a PhD in Politics from New York University (2016), with a focus on causal inference and machine learning. Prior to joining Hertie, he served as Scientific Coordinator at the University of Vienna’s Data Science Research Network (2021–2023) and worked on Facebook’s Core Data Science team (2016–2020), developing experimentation tools and methods. His research emphasizes methodological innovation in causal inference and machine learning, particularly applied to social media and internet studies. Key areas include experimental design, algorithmic transparency, and policy applications of data science. He has published in top-tier journals like ICML, KDD, and Science, and his work bridges computational methods with societal challenges such as electoral integrity, digital governance, and public welfare provision. Recent publications analyze the impact of social media platforms on elections, user behavior, and mental health, leveraging large-scale experimentation and causal analysis. His endowed professorship, supported by the Dieter Schwarz Foundation, includes scholarships for the Master of Data Science for Public Policy program, reflecting his commitment to applying data science for societal benefit.