Find Academic Supervisors

Konstantinos P. TriantisView profile

Professor

• Management Systems Engineering
• Data Envelopment Analysis
• Critical Infrastructure Systems
• +7 more

Konstantinos P. Triantis is the John Lawrence Professor of Industrial and Systems Engineering at Virginia Tech's College of Engineering, based at the Northern Virginia Center. He serves as Director of the Industrial and Systems Engineering (ISE) program in the National Capital Region (NCR) and co-director of the System Performance Lab (SPL). His roles include leadership on the NCR College of Engineering executive committee and former directorship of the Systems Engineering Program. Education: Ph.D., Industrial Engineering and Operations Research, Columbia University (1984) M. Phil., Industrial Engineering and Operations Research, Columbia University (1980) M.S., Industrial and Management Engineering, Columbia University (1977) B.S., Industrial and Management Engineering, Columbia University (1976) Research Interests: Dr. Triantis focuses on Management Systems Engineering , Data Envelopment Analysis (DEA) , and Critical Infrastructure Resilience . His work bridges engineering with social sciences, emphasizing dynamic performance evaluation (DPEM), complex adaptive systems, and efficiency measurement in transportation, healthcare, and disaster response systems. Recent themes include automation reliance in safety-critical roles and policy evaluation for infrastructure sustainability. Grants & Funding: He has led over $6.6M in funded projects from NSF, NASA, DOD, and others. Notable areas include resilience of interdependent infrastructures and traffic congestion mitigation strategies. Awards: John L. Lawrence Professorship (2013) Ralph H. Bogle Professor Fellow (2008) Advising: Advised over 60 graduate students, with six current advisees. Pioneered TV/compressed video course delivery in the NCR. Labs & Outreach: Co-directs the System Performance Lab (SPL), focusing on interdisciplinary solutions for engineered systems. Active in NSF-funded initiatives like the Resilient Infrastructure Processes and Systems (RIPS) program.

Sanja FidlerView profile

Associate Professor

• Computer Vision
• Machine Learning
• 3D Reconstruction
• +5 more

Sanja Fidler is an Associate Professor at the University of Toronto (Department of Computer Science) within the Mathematical and Computational Sciences School. She is also a co-founder and affiliated faculty at the Vector Institute and serves as VP of AI Research at NVIDIA. Her research focuses on the intersection of Computer Vision, Machine Learning, and Graphics, with emphasis on 3D reconstruction, generative models, and interactive methods. She previously held a Research Assistant Professor position at the Toyota Technological Institute at Chicago. Research Interests: Dr. Fidler specializes in Computer Vision and Machine Learning, particularly in areas such as 3D vision, 3D reconstruction/synthesis, object detection, and interactive scene understanding. Her work bridges graphics and vision, addressing challenges in geometric representations, generative models, and real-world applications like medical imaging and autonomous systems. Awards: She has received notable awards including the Canada CIFAR AI Chair (2018), Connaught Innovation Award (2020), NVIDIA Pioneer of AI Award (2016), and multiple outstanding reviewer distinctions at top conferences. Her teaching excellence was recognized with the Professor of the Year award by UofT's CS Student Union in 2014/2015. Collaborations & Labs: Leading the NVIDIA Toronto AI Research Lab and contributing to initiatives at the Vector Institute, her work integrates academic and industrial research. She co-organized workshops on topics like 'Geometry Meets Deep Learning' and '3D Indoor Scene Understanding' at major conferences.

Bo ShenView profile

Assistant Professor

• Machine Learning
• Deep Learning
• Additive Manufacturing
• +4 more

Bo Shen is an Assistant Professor in the Department of Mechanical and Industrial Engineering at New Jersey Institute of Technology (NJIT). His research focuses on machine learning, deep learning, additive manufacturing, and data analytics with applications in solar physics, healthcare, and materials science. He holds a Ph.D. in Industrial and Systems Engineering from Virginia Tech (2022) and a B.S. in Statistics from the University of Science and Technology of China (2017). His work bridges theoretical advancements in AI with practical industrial and scientific challenges. Education: Ph.D., Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, 2022 B.S., Statistics, University of Science and Technology of China, 2017 Research Interests: Prof. Shen’s research spans deep learning , optimization algorithms , additive manufacturing process analytics , and solar physics data modeling . He develops novel methods for model explainability, anomaly detection in manufacturing, and high-dimensional data analysis. His work often integrates physics-based principles with machine learning frameworks. Publications: His recent articles address topics like neural operator learning for fatigue prediction, Bayesian optimization in additive manufacturing, and deep learning-driven solar image reconstruction. These reflect a trend toward applying advanced AI techniques to solve interdisciplinary problems in engineering and science. Awards/Grants: No awards explicitly listed, but his prolific publication record suggests active research engagement. Details on grants or lab affiliations are not provided in the text. Labs/Teams: While specific lab affiliations are not mentioned, his work aligns with NJIT’s strategic research focus areas in data analytics and advanced manufacturing.

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.

Cangxiong ChenView profile

Researcher

• Machine Learning
• Artificial Intelligence
• Data Science
• +3 more

Cangxiong Chen is a Researcher at the Institute for Mathematical Innovation (IMI), University of Bath. Prior to this, he worked in startups in Cambridge, UK, and Beijing, China, applying machine learning and statistical methods to consumer behavior modeling, pricing strategies, and recommendation systems. He holds a PhD in Number Theory from the University of Cambridge (2017), an MPhil in Mathematics from the University of Hong Kong (2011), and a Bachelor’s degree from the same institution. His research focuses on advancing the mathematics of machine learning, particularly in designing algorithms with privacy, fairness, robustness, and generalisability. He explores topics like differential privacy in image/video processing, training data leakage prevention, and collective intelligence systems. Education: • Doctor of Philosophy in Mathematics, University of Cambridge (2011–2016) • Master of Philosophy in Mathematics, University of Hong Kong (2009–2011) • Bachelor’s degree in Mathematics, University of Hong Kong His research interests span the theoretical foundations of machine learning, with a focus on privacy-preserving techniques such as differential privacy. He investigates robust algorithms for image and video processing, leveraging compressed sensing and dynamical systems. Additionally, he contributes to collective intelligence research, exploring how AI can enhance large-scale problem-solving and interdisciplinary collaboration. His work bridges pure mathematics (e.g., number theory) with applied domains like cybersecurity and social computing. In his recent projects, he has analyzed vulnerabilities in models like CLIP, studied leakage via gradients in neural networks, and developed strategies for AI-driven collective intelligence. These studies highlight his commitment to advancing both ML theory and its practical applications in diverse fields. Grants and Collaborations: Chen has contributed to seven research projects, including those funded by UKRI, EPSRC, and the Advanced Research and Inventions Agency. Notable roles include Researcher Co-Investigator (CoI) in projects like 'A New Analytical Framework for Dexterous Soft Robotic Manipulators' and 'AI for Collective Intelligence (AI4CI)’. He collaborates with interdisciplinary teams on topics ranging from soft robotics to policy strategy, emphasizing real-world impact through UK and international partnerships. Labs and Teams: Affiliated with the Institute for Mathematical Innovation at the University of Bath, he engages with collaborative teams across multiple disciplines, including artificial intelligence, data science, and mathematical modeling. His work often intersects with industry needs, as evidenced by his startup experience and ongoing projects in applied ML.

Iris HuijbenView profile

Researcher

• Representation Learning
• Sleep Medicine
• Medical Signal Processing
• +3 more

Iris Huijben is a Researcher and Doctoral Candidate in the Signal Processing Systems group at Eindhoven University of Technology (TU/e). Her work bridges signal processing and deep learning in sleep medicine and fetal health monitoring. She holds an MSc in Electrical Engineering (cum laude, 2019) from TU/e and is pursuing a PhD under Prof. Ruud van Sloun, focusing on discrete representation learning for sleep recordings. Research Interests: Representation learning for biomedical signals, fetal health assessment using cardiotocograms, sleep stage analysis, and machine learning-driven data compression. Key Projects: Developed attention mechanisms for sleep stage classification, improved fetal health anomaly detection with contrastive predictive coding, and analyzed temporal dynamics of parasomnia awakenings. Collaborations: Research internship at Qualcomm AI Research (Amsterdam) on data compression. Publications highlight advancements in medical AI, including real-time fetal monitoring systems and sleep dynamics analysis. Awards include TU/e’s MSc Thesis Award 2020 for work on sub-Nyquist sampling in medical ultrasound.

Kristian KerstingView profile

Professor

• Statistical Relational Learning
• Machine Learning
• Data Mining
• +3 more

Kristian Kersting is a Professor at the University of Bonn, Germany, and affiliated with Fraunhofer IAIS. His research focuses on Statistical Relational Learning, Machine Learning, and their applications in Robotics and Computational Biology. He earned his PhD from Albert-Ludwigs-University of Freiburg under Luc De Raedt and was a postdoc at MIT's CSAIL. Award highlights include the ECCAI 2006 Dissertation Award and ECML-06 Best Paper Award. He has supervised numerous students, including those working on relational sequence alignments and robotics navigation policies. He co-organized key workshops like MLG-2007 and served on program committees for ECML/PKDD, ICML, and AAAI. His contributions span probabilistic logic learning, including Bayesian Logic Programs and Logical Hidden Markov Models. Recent work includes neuro-symbolic systems, causal reasoning, and benchmarks in emotion detection and AI safety.

James J ClarkView profile

Professor

• Computer Vision
• Machine Learning
• Neural Networks
• +7 more

James J Clark is a Professor at McGill University's Centre for Intelligent Machines (CIM). His research focuses on advancing machine vision, neural networks, and deep learning applications in areas such as visual attention modeling, 3D environments, and edge computing. He specializes in developing efficient neural network architectures and optimization techniques for real-world systems. Key research interests include computer vision, machine learning, and their intersections with fields like image processing and natural language processing. Recent work emphasizes model compression, knowledge distillation, and hardware-aware neural architecture search for resource-constrained devices. His publications span topics such as gaze field modeling in 3D environments, dataset pruning for transformers, and adversarial correction in domain adaptation. Notable contributions include frameworks for efficient BERT inference on multi-core processors and methods for visual attention prediction in retail scenarios. No scientific awards or student advisees are explicitly listed. His research is anchored in the CIM, reflecting a strong focus on interdisciplinary machine intelligence applications.

• 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.

Stuart CunninghamView profile

Senior Lecturer

• Affective Technologies
• User Experience (UX)
• Human-Computer Interaction
• +6 more

Dr. Stuart Cunningham is a Senior Lecturer in Computer Science at the University of Chester, where he also serves as Programme Leader for the MSc Advanced Computer Science, REF Unit of Assessment 11 co-ordinator, and Senior Postgraduate Research Tutor. Previously, he held roles at Manchester Metropolitan University (Visiting Research Fellow and Senior Lecturer) and Glyndwr University (Reader in Audio and Affective Computing, Head of Department, and Lecturer/Senior Lecturer). With 20+ years in higher education, his expertise spans academic leadership, teaching, and research in computing and creative technology hybrids. He holds a PhD in Computer Science from the University of Wales (2009), an MSc in Multimedia Communications from the University of Paisley (2003), and a BSc in Computer Networks (2001). Cunningham’s research focuses on affective technologies, audio compression (e.g., ACER codec), and interdisciplinary applications of sound design in fields like healthcare, gaming, and film. He chairs the Audio Mostly steering committee and contributed to the MPEG-SMR working group developing ISO/IEC 14496-23:2008. His work bridges technical and creative domains, exploring biofeedback sensors for emotional state analysis, Foley artistry in 3D animation, and auditory alerts in safety systems. He advocates for practice-led research methodologies that combine creative outputs with empirical validation. Cunningham has authored numerous publications, including research articles, book chapters, and creative works, and actively participates in peer reviewing for journals like IEEE Transactions and Personal and Ubiquitous Computing . In teaching, he emphasizes interactive/collaborative learning and portfolio-based assessment across undergraduate and postgraduate courses. He has designed and delivered degree apprenticeship programs, particularly during the pandemic, and teaches courses like Web Systems, Software Development, and User Experience Design. His research also extends to UX education, esports analytics, and assistive technologies for dementia care.

Kwanghee WonView profile

Associate Professor

• Computer Vision
• Deep Convolutional Neural Networks
• Deep Reinforcement Learning
• +4 more

Kwanghee Won is an Associate Professor at the McComish Department of Electrical Engineering and Computer Science, South Dakota State University. His research focuses on computer vision, deep learning, and their applications in autonomous systems, precision agriculture, and structural health monitoring. He holds a Ph.D. from Kyungpook National University, Korea. Education: B.S., M.S., and Ph.D. in Computer Engineering from Kyungpook National University, Daegu, Korea Research Interests: Deep convolutional neural networks for anomaly detection and classification Autonomous vehicle systems and augmented reality applications Computer vision in smart agriculture and infrastructure assessment Model compression techniques for efficient neural networks Professional Contributions: Upsilon Pi Epsilon honor society faculty advisor (2021–present) ACM member and SIG Applied Computing participant Labs and Teams: Director of the Intelligent Vision Systems Laboratory , focusing on advanced vision-based solutions for real-world challenges.

Eric XingView profile

Professor

• Artificial Intelligence
• Machine Learning
• Bioinformatics
• +3 more

Eric Xing is a Professor in the Machine Learning Department at Carnegie Mellon University, with an affiliation to the Computer Science Department. His research focuses on advancing artificial intelligence, particularly in machine learning, bioinformatics, and interdisciplinary applications. He advises three students: Aviv Bick, Lingjing Kong, and Yonghao Zhuang. His recent work spans cutting-edge topics such as graph neural networks, generative AI, biosecurity safeguards, and scalable language model evaluation. Notable contributions include foundational models for RNA prediction, efficient video diffusion systems, and ethical frameworks for AI deployment. He actively bridges AI with fields like genomics, healthcare, and urban planning through collaborative projects. Xing’s research often emphasizes practical applications, such as edge computing-optimized inference systems and open mathematical corpora. His work has implications for both technical advancements and societal challenges, reflecting a commitment to impactful, interdisciplinary AI research.

• Machine Learning
• Inference Acceleration
• World Models
• +4 more

Hongyang Zhang is a tenure-track Assistant Professor at the University of Waterloo's David R. Cheriton School of Computer Science and Faculty of Mathematics, affiliated with the Vector Institute for AI. His research bridges theoretical and applied aspects of machine learning, including world modeling, AI inference acceleration, and security. He leads the SafeAI Lab and holds memberships in the AI Institute and Cybersecurity and Privacy Institute. Dr. Zhang completed his Ph.D. at Carnegie Mellon University's Machine Learning Department and conducted postdoctoral research at Toyota Technological Institute at Chicago. His educational background includes a degree from Peking University. Research interests focus on: Developing generative world models for robotics and autonomous systems Creating efficient algorithms for accelerating LLM inference Building System-2 LLMs for enhanced reasoning Advancing AI security against adversarial threats Publications demonstrate strong focus on efficient AI systems and security, with recent work on EAGLE series acceleration techniques and The Matrix world generation framework dominating top conferences like ICML, NeurIPS, and CVPR. Awards and honors: 1st place in CVPR 2021 Security AI Challenger Multiple NeurIPS challenge wins in adversarial vision AAAI New Faculty Highlights awardee IEEE Senior Member Leads SafeAI Lab focusing on trustworthy AI systems and regularly serves as area chair for top machine learning conferences including NeurIPS, ICML, and ICLR.

Fredo DurandView profile

Professor

• Computer Graphics
• Computer Vision
• Computational Photography
• +5 more

Fredo Durand is the Amar Bose Professor of Computing at MIT's Electrical Engineering and Computer Science Department, affiliated with CSAIL. He earned his PhD from Grenoble University (1999), followed by a postdoc at MIT under Julie Dorsey. His work spans computational photography, computer graphics, and signal processing, emphasizing perceptual principles. Durand leads research groups in Computer Graphics and Vision, co-developed Halide, and pioneered imaging privacy studies via ambient light sensors. Education: PhD in Computer Science, Grenoble University (1999) Research Focus: Realistic rendering, non-photorealistic techniques, and AI-driven visual computing Notable awards include the ACM SIGGRAPH Achievement Award (2016) and Eurographics Young Researcher Award (2004). He advises over 40 students and collaborates on projects like Bounce Flash photography systems and neural light field reconstruction. Durand also co-founded the IEEE International Conference on Computational Photography. Labs/Teams: CSAIL's Computer Graphics Group, Vision Group, and Visual Computing Initiative

• Machine Learning
• Computer Science
• Data Science
• +8 more

Haichao Wang is a researcher affiliated with multiple institutions, including Shanghai University of Electric Power, Henan Polytechnic University, Army Engineering University of PLA, and Huazhong University of Science and Technology . His work spans interdisciplinary applications in Machine Learning, Embedded Systems, Environmental Science, and Biomedical Engineering , with a focus on k nowledge distillation, rough set theory, and signal processing. Research interests include sleep staging using neural networks, defect detection in industrial settings, acoustic analysis of wind turbines , and environmental monitoring . Recent publications highlight trends in multi-modal learning, adaptive thresholding, and 5G-enabled geospatial modeling . The 15 most recent articles reflect a blend of deep learning architectures, remote sensing, and biomedical applications . His collaborations extend to institutions in China and international scholars, emphasizing interdisciplinary solutions for technical and environmental challenges.