Find Academic Supervisors

• Computer Graphics
• Computer Vision
• Multimedia Communications
• +7 more

Anup Basu is a Professor in the Department of Computing Science at the University of Alberta. His research focuses on computer graphics, computer vision, and multimedia communications. He holds an B.S. in Math & Statistics from the Indian Statistical Institute (1980), an M.E. in Computer Science from the Indian Statistical Institute (1983), and a Ph.D. in Computing Science from the University of Maryland (1990). His work emphasizes Quality of Service (QoS) in multimedia delivery for e-commerce and telelearning, adaptive bandwidth monitoring, and 3D visualization tools. He pioneered foveated image compression and stereo visualization techniques, contributing to MPEG-4 coding standards. He leads major initiatives like the ASRA/TelePhotogenics/IBM 3D Medical Imaging project ($2M+ funding) and developed patented SHR Stereo/3D scanning technologies. Awards include the American Neurological Association Fellowship. He has held leadership roles as General Chair for IEEE International Conferences on SMC (2017), Multimedia & Expo (2013), and SMC (2014). His research integrates interdisciplinary collaborations across universities and industry partners, leveraging advanced equipment like the CAVE system for immersive visualization.

• Machine Learning
• Medical Imaging
• Biomedical Engineering
• +3 more

Dr. Cheng Ouyang is a Departmental Lecturer at the University of Oxford's Institute of Biomedical Engineering, part of the Department of Engineering Science. Affiliated with St. Peter's College, his research focuses on developing data-efficient, robust machine learning approaches for medical imaging and signal analysis. Key interests include domain generalization, few-/zero-shot learning, uncertainty modeling, and multimodal learning applied to medical data such as ultrasound and MRI. Prior to Oxford, he conducted postdoctoral research in cardiac imaging at Imperial College London, where he also earned his PhD in Computing. His work emphasizes practical medical applications, such as accelerating MRI reconstruction and enhancing ECG classification through multimodal techniques. Recent contributions include the CMRxRecon2024 dataset for cardiac MRI and federated learning approaches for low-dose CT denoising. His methods address challenges in generalizability, stability, and user interaction in clinical AI systems. Awards and recognitions are pending explicit mentions in the text. Dr. Ouyang's research spans foundational machine learning theory and applied biomedical engineering, with a focus on bridging gaps between algorithmic innovation and clinical utility. His lab collaborates across disciplines to advance medical imaging analysis and decision support systems.

Xihong LinView profile

Professor

• Statistical Machine Learning and AI
• Statistical Genetics and Genomics
• Causal Inference and Mediation Analysis
• +4 more

Xihong Lin is a Professor of Statistics at Harvard University and a Professor of Biostatistics at the Harvard T.H. Chan School of Public Health. She is a distinguished academic, holding membership in both the National Academy of Sciences and the National Academy of Medicine. Her research focuses on scalable statistical inference for big data, statistical machine learning, causal inference, and integrative data analysis, with applications in genomics, public health, and precision medicine. Lin’s work addresses challenges in analyzing large-scale genomic and multi-ancestry data, including methods for rare variant association testing, ancestry-adjusted sample analysis, and scalable computing frameworks. Her contributions span biobank studies (e.g., UK Biobank, TOPMed) and clinical applications in lung cancer, cardiovascular health, and smoking cessation. Her scientific awards reflect her leadership in statistical genetics and public health. Key research trends include leveraging single-cell sequencing for functional genomics, developing ensemble machine learning methods for health subtyping, and enhancing polygenic risk prediction across diverse populations. Lin’s methodologies prioritize interpretability and scalability, enabling impactful analyses of complex observational and genomic datasets. Awards: Member, National Academy of Sciences; Member, National Academy of Medicine Her grants and advising efforts focus on interdisciplinary collaborations, bridging statistics, AI, and domain sciences. Lin leads initiatives to improve genomic data management and ethical use of federated data (e.g., FADI framework). She is affiliated with labs advancing statistical genetics and cloud-based workflows (e.g., STAAR workflow).

Abdullah MuzahidView profile

Associate Professor

• Computer Architecture
• Systems
• Artificial Intelligence
• +5 more

Abdullah Muzahid is an Associate Professor in the Department of Computer Science and Engineering at Texas A&M University (since August 2024), previously serving as an Assistant Professor there since August 2018. Prior, he held an Assistant Professor role at the University of Texas at San Antonio (2012-2018). He earned his Ph.D. from the University of Illinois at Urbana-Champaign (2012), focusing on architectural support for debugging concurrency bugs under Prof. Josep Torrellas. Education: Ph.D., Computer Science, University of Illinois at Urbana-Champaign (2012) M.S., Computer Science, University of Illinois at Urbana-Champaign (2009) B.S., Computer Science and Engineering, Bangladesh University of Engineering and Technology (2005) Research Interests: His work spans Computer Architecture , Systems , and Artificial Intelligence , with focus on multiprocessor architecture, parallel programming, debugging, and applying machine learning to system optimization. Recent projects include cache indexing via entropy estimation, DNN training acceleration, and hardware-software co-design for security. Awards: NSF CAREER Award (2017) Excellence in Research Award (UTSA, 2015 & 2017) W. J. Poppelbaum Award (UIUC, 2012) Intel Ph.D. Fellowship (2011) Grants & Advising: He leads NSF-funded projects on robust deep learning and stream processing systems. Advised 5 PhD graduates and currently mentors 4 PhD students. Served on program committees for ISCA, HPCA, MICRO, and as NSF panelist. Labs/Teams: Active in Texas A&M’s Computer Architecture group, collaborating on machine programming, hardware security, and AI-driven systems optimization.

• Artificial Intelligence
• Optimization
• Machine Learning
• +7 more

Sebastian Pokutta is a Professor at Technische Universität Berlin, Vice President at the Zuse Institute Berlin (ZIB), and Chair of the Cluster of Excellence MATH+ and MODAL. His research lies at the intersection of Artificial Intelligence, Optimization, and Machine Learning, with applications in sustainability, quantum computing, and mathematical discovery. Research Interests: Development of novel optimization algorithms, particularly Frank-Wolfe and Conditional Gradient methods. Integration of machine learning with decision-making and combinatorial optimization. AI for Science (AI4Science), including applications in quantum mechanics and ecology. AI and creativity, human-AI co-creativity, and social science modeling using multi-agent LLMs. His recent publications (2025) demonstrate a strong focus on scalable optimization, interpretability, and algorithmic foundations. The work spans theoretical advances in convergence analysis, practical implementations in Julia (FrankWolfe.jl), and real-world deployments in biomass estimation and quantum certification. Scientific Awards: Gödel Prize (2023) STOC Test of Time Award (2022) Science Prize of the Association for Pediatric Orthopedics (2025) Google Research Awards (2021, 2020) NSF CAREER Award (2015) He advises a vibrant research group, with former students and postdocs securing faculty positions at institutions like Inria, Carlos III University, and James Madison University. His group has received funding from Google, DFG, and Math+, and he leads major collaborative efforts such as the Thematic Einstein Semester on Mathematical Optimization for Machine Learning. Labs and Teams: Interactive Optimization and Learning Lab at TU Berlin and ZIB. Leadership in MODAL and MATH+ research clusters, fostering interdisciplinary collaboration in mathematical optimization and AI.

Chicheng ZhangView profile

Assistant Professor

• Interactive Machine Learning
• Reinforcement Learning
• Contextual Bandits
• +15 more

Chicheng Zhang is an Assistant Professor in the Computer Science Department at the University of Arizona, where he conducts research in the theory and applications of interactive machine learning. He earned his Ph.D. in Computer Science from the University of California, San Diego (UCSD) in 2017 under the supervision of Professor Kamalika Chaudhuri, and was previously an undergraduate student at Peking University working with Professor Liwei Wang. From 2017 to 2019, he was a postdoctoral researcher at the Machine Learning Group at Microsoft Research NYC. His research lies at the intersection of learning theory and practical algorithm design, focusing on interactive machine learning paradigms such as reinforcement learning, contextual bandits, active learning, and imitation learning. He aims to develop algorithms that are data-efficient, computationally tractable, and robust, with applications in healthcare, wireless communication, and fair AI systems. His work emphasizes principled algorithm design with theoretical guarantees and empirical validation. The most recent publications reflect a strong trend in developing efficient, theoretically grounded methods for sequential decision-making and interactive learning. Key themes include sample efficiency, robustness to noise, fairness in algorithmic decisions, and application-driven research in domains like oral cancer detection and mmWave network optimization. His work frequently bridges theoretical analysis with real-world deployment considerations. While no scientific awards are mentioned in the provided text, Dr. Zhang actively mentors prospective PhD students and encourages collaboration. He has contributed to interdisciplinary projects involving fairness-aware bandit algorithms for network coexistence, interpretable classifiers for cancer detection, and LLM-based initialization for reinforcement learning. His lab focuses on developing intelligent agents that actively learn from environments and human experts. He can be reached at chichengz@arizona.edu .

Lu ShijianView profile

Associate Professor

• Computer Vision
• Deep Learning
• Image and Video Analytics
• +14 more

Lu Shijian is an Associate Professor (tenured) at the School of Computer Science and Engineering , Nanyang Technological University (NTU) , Singapore. He holds a PhD in Electrical and Computer Engineering from the National University of Singapore and leads the Visual Intelligence Lab (VILab) , focusing on humanlike visual perception, understanding, and creation. University: Nanyang Technological University School: School of Computer Science and Engineering Academic Rank: Associate Professor (tenured) Email: Shijian.Lu@ntu.edu.sg Office: N4-02C-101, NTU, Singapore His research spans computer vision, deep learning, image and video analytics, visual intelligence, and machine learning , with key topics including scene text detection, unsupervised domain adaptation, image synthesis, satellite image analytics, and facial expression recognition. His work integrates supervised, semi-supervised, and self-supervised learning across 2D images, 3D point clouds, and multi-spectral data. The recent publications highlight a strong trend in domain adaptation, generative modeling, 3D vision, and multimodal AI . His lab produces high-impact work accepted at top venues like CVPR, ICCV, ECCV, NeurIPS, and TPAMI, with applications in autonomous systems, image editing, and robust AI. Top winner of ICFHR2014 Competition on Word Recognition from Historical Documents Top winner of ICDAR 2013 Robust Reading Competition (scene text segmentation) Top winner of ICDAR 2013 Document Image Binarization Contest (DIBCO 2013) Top winner of H-DIBCO 2010 – Handwritten Document Image Binarization Competition Top winner of ICDAR 2009 Document Image Binarization Contest (DIBCO 2009) Lu advises several PhD students and serves as an Associate Editor for Pattern Recognition and Neurocomputing . He has held leadership roles in top conferences as Senior Program Committee member (IJCAI, AAAI) and Area Chair (ICDAR, WACV). His lab, the Visual Intelligence Lab , is actively recruiting PhD students and conducting cutting-edge research in visual intelligence, with recent work on 3D Gaussian splatting, backdoor attacks, and vision-language models.

Peter AlvaroView profile

Assistant Professor

• Distributed Systems
• Databases
• Programming Languages
• +7 more

Peter Alvaro is an Assistant Professor in the Department of Computer Science and Engineering at the Baskin School of Engineering, University of California, Santa Cruz. He joined the faculty in 2015 after earning his PhD from UC Berkeley under Professor Joe Hellerstein. His research lies at the intersection of databases, distributed systems, and programming languages , with a strong emphasis on data-centric approaches to building robust, scalable, and predictable distributed systems. He is the creator of the Dedalus language and co-creator of the Bloom language, both designed to simplify reasoning about distributed computation. Peter's recent work focuses on non-volatile memory (NVM) , computational storage , and data-centric operating systems , as seen in the Twizzler OS project. His publications span top venues such as USENIX ATC, HotNets, and Communications of the ACM, showing trends toward system resilience, efficient data management, and novel abstractions for modern hardware. Best Presentation award at USENIX ATC 2020 Peter advises graduate students, including Daniel Bittman, and is a key contributor to the Storage Systems Research Center (SSRC), now succeeded by the Center for Research in Storage Systems (CRSS). His work is supported by ongoing collaborations with researchers at UC Santa Cruz and beyond, particularly in the areas of storage, operating systems, and distributed computing.

Amir RahmatiView profile

Assistant Professor

• System Security
• Internet of Things (IoT) Security
• Augmented Reality (AR) Security
• +3 more

Amir Rahmati is an Assistant Professor in the Department of Computer Science at Stony Brook University , where he directs the Ethos Security and Privacy Lab and contributes to the Stony Brook National Security Institute . His research focuses on system security , with specific emphasis on the security and privacy challenges of emerging technologies such as IoT , AR , and ML systems . Teaching: Instructor of SBU102: Computer Security (Spring 2025) Collaborations: Frequent collaborations with institutions like University of Michigan, University of Toronto, and IEEE/USENIX conferences. Rahmati’s research addresses security vulnerabilities in resource-constrained devices and real-world ML applications. His work includes adversarial robustness in neural networks, attack synthesis on medical devices, and privacy-preserving frameworks for IoT ecosystems. Trends in his publications reveal a focus on practical system design to mitigate security threats in cyber-physical systems , augmented reality , and blockchain technologies . Prospective students: Rahmati seeks researchers with expertise in hardware/software, machine learning, network protocols, and security to tackle system-stack challenges in his lab.

Deepa KundurView profile

Professor

• Cybersecurity
• Signal Processing
• Complex Dynamical Networks
• +4 more

Deepa Kundur is the Professor & Chair of The Edward S. Rogers Sr. Department of Electrical & Computer Engineering at the University of Toronto. She earned her BASc, MASc, and PhD in Electrical and Computer Engineering from the same institution in 1993, 1995, and 1999, respectively. Current roles: IEEE Spectrum Advisory Board Conference leadership: General Chair of 2018 GlobalSIP Symposium, TPC Co-Chair for IEEE SmartGridComm 2018, among others Her research focuses on cybersecurity , signal processing , and complex dynamical networks , particularly in smart grid applications. She has authored over 200 publications and pioneered techniques for detecting false data injection attacks, enhancing grid resilience, and integrating machine learning into power systems. Her recent work spans quantum learning for grid security , LLM-based mental health prediction , and resilient control systems . She has received 14 best paper recognitions, including IEEE SmartGridComm (2015) and IEEE INFOCOM Workshop (2008). Fellowships: IEEE Fellow (2015), Canadian Academy of Engineering Fellow (2016), Massey College Senior Fellow (2019) Teaching awards: Tenneco Meritorious Teaching Award (2005), Gordon Slemon Teaching of Design Award (2002) Early career honors: NSERC Scholarships (PGS A/B), Canada Scholarship She leads the Kundur Research Group , developing models for cyber-physical systems in smart grids and autonomous vehicle networks. Her team explores reinforcement learning for grid defense , transmissibility-based fault detection , and privacy-preserving smart grid analytics .

Vatsal SharanView profile

Assistant Professor

• Machine Learning
• Theoretical Computer Science
• Statistics
• +6 more

Vatsal Sharan is an Assistant Professor in the Thomas Lord Department of Computer Science at the University of Southern California's Viterbi School of Engineering. He maintains affiliations with the Theory Group, Machine Learning Center, and the Center for AI in Society at USC. Education: Ph.D. in Computer Science from Stanford University, advised by Greg Valiant Postdoctoral research at MIT, hosted by Ankur Moitra Vatsal Sharan's research centers on the theoretical foundations of machine learning, positioned at the intersection of machine learning, theoretical computer science, and statistics. His work investigates fundamental limits for solving learning and estimation tasks under computational and information-theoretic constraints, with the goal of developing practical algorithms that are efficient, fair, and robust. His research spans memory-efficient learning, algorithmic fairness, robustness in deep learning, and the theoretical underpinnings of transformers and large language models. A significant portion of his work explores how memory constraints affect learning algorithms and whether memory can serve as a distinguishing factor between 'efficient' and 'expensive' techniques in machine learning. His recent publications demonstrate a strong focus on multicalibration, transformer interpretability, and trustworthy AI systems. Scientific Awards: Amazon Research Award (2021 and 2023) SoCal NLP Symposium 2023 Best Paper Award COLT 2022 Best Paper Award Vatsal Sharan advises a diverse group of Ph.D. students including Siddartha Devic, Bhavya Vasudeva, Julian Asilis, Deqing Fu, Devansh Gupta, Spandan Senapati, and Tianyi Zhou. His research is supported by multiple prestigious grants from the NSF, Amazon Research, Google Research, and the Okawa Foundation. He is an active participant in the Learning Theory Alliance (LeT-All), a community-building and mentorship initiative for the learning theory community. His teaching portfolio includes advanced courses on machine learning theory and trustworthy machine learning at USC, where he shapes the next generation of researchers in theoretical aspects of artificial intelligence.

• Computer Vision
• Machine Learning
• Deep Learning
• +5 more

Abhinav Shrivastava is an Associate Professor in the Department of Computer Science at University of Maryland, College Park, with a joint appointment in the Institute of Advanced Computer Studies (UMIACS). Previously, he served as an Assistant Professor at the same institution from August 2018 to June 2024, and spent one year as a Visiting Research Scientist at Google Research from September 2017 to August 2018. His educational background includes: PhD in Robotics and Artificial Intelligence from Carnegie Mellon University (2017), advised by Abhinav Gupta, with thesis titled 'Discovering and Leveraging Visual Structure for Large-scale Recognition' MS in Artificial Intelligence from Carnegie Mellon University (2011), supervised by Alyosha Efros and Martial Hebert BTech in Computer Science and Engineering from Jaypee Institute of Information Technology (2010) Professor Shrivastava's research focuses on computer vision and machine learning, with particular expertise in object detection, image recognition, and neural representations. His work bridges theoretical advances with practical applications, exploring how visual systems can discover and leverage structure in large-scale recognition problems. He has made significant contributions to understanding the role of supervision in vision transformers, developing novel approaches for object-state composition recognition, and creating efficient neural representations for videos and 3D scenes. His research often addresses fundamental challenges in visual recognition, including handling novelty in open-world environments and improving the efficiency of visual systems. An analysis of his recent publications reveals a strong emphasis on neural representations, particularly for dynamic content like videos and 3D scenes. His work demonstrates increasing sophistication in handling open-world vision problems, with research spanning object discovery, localization, and representation learning. The publications show a clear progression toward more efficient and scalable models, with recent work focusing on model compression, sparse representations, and addressing the challenges of working with limited annotations. His scientific contributions have been recognized with several prestigious awards: Best Paper Award (Applications) at IEEE Winter Conference on Applications of Computer Vision (2020) Microsoft Research PhD Fellowship (2014-2016) Best Student Paper Award at IEEE Winter Conference on Applications of Computer Vision (2014) Outstanding Reviewer Award at IEEE CVPR (2015) Professor Shrivastava has successfully mentored numerous graduate students, many of whom have become prominent researchers in computer vision. His Amazon Research Awards (2020 and 2023) have supported innovative projects including 'The pursuit of knowledge: discovering and localizing new concepts using dual memory' and 'Audio-conditioned Diffusion Models for Generating Lip-synchronized Videos.' He has served as Area Chair for major conferences including ICCV, CVPR, and AAAI, demonstrating his leadership in the computer vision community. His research has attracted significant funding from both academic and industry sources, supporting his exploration of fundamental questions in visual recognition and representation learning.

Chao LiuView profile

Researcher

• Surgical Robotics
• Teleoperation
• Haptics
• +2 more

Chao Liu is a Research Scientist at CNRS (French National Center for Scientific Research) since 2008, affiliated with the DEXTER team and the Department of Robotics, LIRMM at University of Montpellier, France. He earned his Ph.D. in Electrical & Electronic Engineering from Nanyang Technological University, Singapore (2006). Current research focuses on surgical robotics , haptics , teleoperation , and nonlinear control theory with applications in computer vision. His work addresses challenges in robotic-assisted telesurgery, including: Stable and transparent human-robot interaction through wave variable compensators and passivity filters Physiological motion compensation using spatio-temporal LSTM and dual Kalman filters EMG-based motion recognition for surgical skill assessment 3D soft-tissue reconstruction with stereo-endoscopes and deep learning Dr. Liu leads European and French projects like: TS2RT (CNRS-funded): Safer teleoperation with motion compensation ROBACUS (ANR-funded): Needle positioning with MPC control HaTUMoCo (CNRS-funded): Haptic teleoperation with uncertainty handling ARAKNES (EU-funded): Microrobotic systems for endoluminal surgery Scientific honors include Senior Member of IEEE and Member of Sigma Xi . He supervises Ph.D. and Master's students working on topics such as concentric tube robot optimization, haptic teleoperation, and EMG-based force estimation. Dr. Liu serves on IEEE Technical Committees for Telerobotics and Haptics , and as Technical Editor of IEEE/ASME Transactions on Mechatronics.

Garrett WarnellView profile

Researcher

• Artificial Intelligence
• Machine Learning
• Robotics
• +2 more

Garrett Warnell is a Visiting Researcher in the Department of Computer Science at The University of Texas at Austin, specializing in artificial intelligence, computer vision, and robotics with applications in autonomous navigation systems. Education: PhD in Electrical Engineering, University of Maryland Master's in Electrical Engineering, University of Maryland B.S. in Computer Engineering, Michigan State University Research Interests: Dr. Warnell's work focuses on machine learning for robotic control , computer vision for scene understanding , and autonomous navigation in challenging environments . His contributions span imitation learning with limited demonstrations, preference-aware path planning, and off-road mobility. Recent research integrates vision-language models and transformer architectures for social navigation and terrain adaptation, emphasizing human-robot collaboration and robustness in constrained spaces. Publication Trends: Analysis of Dr. Warnell's 2023-2025 publications reveals dominant themes in off-road navigation robustness, with emphasis on particle filtering, diffusion models, and transformer networks for geo-localization and terrain adaptation. A significant trend involves human preference alignment through extrapolation techniques and open-vocabulary models for costmap generation, reflecting growing integration of natural language understanding in robotic systems. Scientific Awards: No awards specified in available documentation. Advising and Grants: Public records indicate no listed advisees or grant funding details. Labs and Teams: Affiliated with UT Austin's Computer Science Department, though specific research group affiliations remain undocumented in provided materials.

Neil ShahView profile

Academic

• Graph Neural Networks
• Machine Learning
• Data Mining
• +5 more

Neil Shah is a Lead Research Scientist at Snap Inc., leading initiatives in user modeling, personalization, and trust and safety across Snapchat. His research focuses on advancing machine learning algorithms for large-scale structured data, including graph and sequential representations, with applications to recommendation systems and social platform security. PhD in Computer Science, Carnegie Mellon University (2017), advised by Christos Faloutsos B.S. in Computer Science, North Carolina State University Current research interests span: Graph Neural Networks (GNNs) for real-time inference and scalable training Cross-domain recommendation systems and generative modeling Test-time augmentation and hyperbolic geometry in representation learning Explainability methods for GNNs and fairness-aware outlier detection Recent publications highlight productionized GNN frameworks (GiGL), multimodal graph benchmarks, and novel approaches to link prediction and collaborative filtering. His work has appeared at top venues like KDD, ICLR, NeurIPS, and WWW. Scientific recognition includes: Outstanding Service Award at WSDM 2022 Best Paper Honorable Mention at CHI 2019