Find Academic Supervisors

• Theoretical Computer Science
• Formal Program Verification
• Parametrised Algorithms
• +4 more

Amir Kafshdar Goharshady is an Associate Professor of Computer Science at the University of Oxford and a Tutorial Fellow at St Catherine's College. His research focuses on theoretical computer science, including formal program verification, parametrised algorithms, compiler optimisation, and blockchain technology. He leads the ALPACAS research group, which explores algorithms, logic, program analysis, cryptocurrencies, and smart contracts. Education: PhD in Theoretical Computer Science from IST Austria (supervised by Prof. Krishnendu Chatterjee), MSc in Computer Science (Systems) from Georgia Tech, and undergraduate studies in Maths and Computing at the Universities of London and Yazd. Prior to Oxford, he was an Assistant Professor at HKUST and maintains a visiting role at IIT Bombay. Research Interests: Formal methods for program verification Algorithmic efficiency in compiler optimisation Decentralized systems and blockchain applications Game-theoretic approaches to secure protocols Advising and Grants: Supervises over 20 PhD and Master’s students, with a rigorous selection process emphasizing problem-solving skills. Leads an international research group with multiple grants and collaborations. Former roles include organizing high school Olympiads and founding the Sundar STEM School in Pakistan. Labs/Teams: ALPACAS group, known for its work on smart contract optimization, blockchain security, and parametrised algorithm design. Regularly hosts visiting researchers such as Petr Novotný and Thomas Henzinger.

Massimiliano PontilView profile

Professor

• Machine Learning
• Statistical Learning Theory
• Kernel Methods
• +5 more

Massimiliano Pontil is a **part-time Professor of Computational Statistics and Machine Learning** in the Department of Computer Science at University College London (UCL) and a Senior Researcher at the **Istituto Italiano di Tecnologia (IIT)**, leading the **Computational Statistics and Machine Learning (CSML)** research group. He holds an MSc and PhD in Physics from the University of Genova (1994 and 1999) and has held postdoctoral positions at MIT and research roles at institutions like the University of Siena and City University of Hong Kong before joining UCL in 2003. **Research Interests**: Focuses on machine learning, including statistical learning theory, kernel methods, multitask and transfer learning, online learning, sparsity regularization, and applications in computer vision, bioinformatics, and user modeling. His work bridges theoretical foundations (e.g., regularization, optimization) with practical applications. **Publications**: Over 100 papers in top venues like NIPS/NeurIPS, ICML, COLT, and JMLR, covering topics such as structured sparsity, multitask learning, and optimization frameworks. Recent work emphasizes meta-learning, fair algorithms, and dynamical systems learning. **Grants & Awards**: EPSRC Advanced Research Fellowship. His research has been applied to diverse domains, including bioinformatics (e.g., protein-protein interface prediction) and social network analysis (e.g., mobility patterns). **Labs/Teams**: Leads the CSML group at IIT, collaborating on projects like Koopman operator regression and conditional meta-learning. Active in organizing workshops on multi-task learning and structured outputs (e.g., PASCAL thematic programs).

Samantha PettiView profile

Assistant Professor

• Bioinformatics
• Computational Biology
• Machine Learning
• +4 more

Samantha Petti is an Assistant Professor in both the Department of Mathematics (School of Arts and Sciences) and the Department of Computer Science (School of Engineering) at Tufts University. She is based at 177 College Avenue, Medford, MA. Her research focuses on computational biology, bioinformatics, and machine learning, with particular emphasis on protein structure analysis, genotype-phenotype mapping, and algorithm design for biological sequence analysis. She teaches courses such as Master's Thesis supervision, PhD Thesis guidance, and specialized topics in mathematics and computer science. Her work integrates interdisciplinary approaches, combining statistical methods, deep learning, and probabilistic models to address challenges in genomics, structural biology, and network science. Recent projects include developing end-to-end protein alignment tools and exploring sparse graph models for biological systems. Teaching: Supervises graduate thesis work (Master’s/PhD) and advanced courses in mathematics and computer science at Tufts. Lab/Team: Engages in collaborative research at the intersection of computational methods and biological systems.

Marco GrangettoView profile

Professor

• Computer Vision
• Image Processing
• Artificial Intelligence
• +5 more

Marco Grangetto serves as Full Professor in the Department of Computer Science at the University of Turin, coordinating research in image processing and computer vision. His expertise spans wavelets, image/video coding, data compression, error resilient video coding, and biomedical image processing, with significant contributions to ISO JPEG2000 standardization and editorial roles in IEEE Transactions on Multimedia. His educational background includes a PhD in Electrical and Communications Engineering (2003) and MSc in Telecommunications Engineering (1999), both from Politecnico di Torino. His research integrates Artificial Intelligence and Deep Learning with medical imaging and fundamental compression theory , producing innovations in neural network pruning, capsule networks, and entropy-based models. Recent work focuses on Covid-19 diagnosis from chest X-rays and efficient 3D scene modeling. His publication trends reveal dual trajectories: applied medical AI (Covid-19 diagnostics, lung cancer segmentation) and theoretical advances (learned compression, contrastive learning, bias mitigation). This bridges clinical validation with information-theoretic foundations, particularly in resource-constrained environments. Scientific recognition includes: Premio Optime by Unione Industriale di Torino (2000) Fulbright Grant for research at UC San Diego (2001) He maintains leadership through IEEE editorial positions, ISO standardization participation, and MPAI membership. His research group develops medical datasets (UniToChest, UniToPatho) while advancing neural network efficiency for clinical deployment. Current projects focus on entropy minimization techniques and unbiased representation learning for healthcare applications.

Yannick SalaminView profile

Assistant Professor

• Quantum Optics
• Nonlinear Optics
• Photonics
• +3 more

Dr. Yannick Salamin is an Assistant Professor at CREOL, The College of Optics and Photonics at the University of Central Florida (UCF). His research focuses on quantum properties of light and nonlinear optical systems, particularly exploring quantum states of light, macroscopic quantum phenomena, and applications in quantum technology. His work utilizes optical parametric oscillators (OPOs) to generate and characterize quantum states, aiming to advance computing and metrology. Dr. Salamin holds a B.S. in Electrical Engineering from the University of Applied Sciences of Western Switzerland (2010), an M.S. from Zhejiang University (2014), and a Dr.Sc. from ETH Zurich (2019). His postdoctoral research at MIT under Prof. Marin Soljačić furthered his expertise in quantum nonlinear photonics. His research group develops systems to harness quantum vacuum noise for probabilistic computing and machine learning. Key areas include controlling nonlinear driven-dissipative systems, biasing quantum vacuums, and creating macroscopic probability distributions. Recent work includes generating large Fock states and squeezed states via nonlinear bound states in the continuum. Research Themes: Quantum vacuum engineering, nonlinear photonics, stochastic computing, and quantum metrology. Applications: Quantum computing, advanced sensors, and probabilistic machine learning. Dr. Salamin has received prestigious awards including the 2025 Ralph E. Powe Award, ABB Research Prize (2021), and the ETH Medal (2020). His lab includes three Ph.D. advisees and multiple undergraduate researchers. He collaborates widely, with publications in Nature Photonics , Science , and Proceedings of the National Academy of Sciences . Current projects emphasize scalable quantum systems and novel photonic devices.

• Unsupervised Learning
• Semi-supervised Learning
• Probabilistic Generative Modeling
• +4 more

Tian Han is an Assistant Professor at the Department of Computer Science within the Charles V. Schaefer, Jr. School of Engineering and Science at Stevens Institute of Technology. His research focuses on artificial intelligence (AI) and machine learning, particularly in developing statistical learning methods for probabilistic models and building explainable, controllable AI systems. He holds a PhD in Statistics from UCLA (2019) and a degree in Computer Science from HKUST (2013). His research interests span unsupervised/semi-supervised learning, probabilistic generative modeling, explainable AI, and computer vision. Notable contributions include work on latent space energy-based models, hierarchical feature learning, and robust representation techniques. Han has served as an Area Chair/Senior Program Committee member at conferences like CVPR, NeurIPS, and AAAI. Education: PhD in Statistics, UCLA (2019) MSc/BS in Computer Science, HKUST (2013) His publications emphasize advancements in energy-based models, latent space hierarchies, and generative AI. Recent work includes enforcing sparsity in latent representations for robust AI systems (WACV 2024), molecule design via latent space modeling (UAI 2023), and context-aware health prediction (AAAI 2022). He received the NSF CAREER Award (2024) for his research. Han teaches courses on machine learning fundamentals, deep learning, and computing foundations at Stevens.

Isaac Kofi NtiView profile

Assistant Professor

• Applied Machine Learning
• Cybersecurity
• Health Informatics
• +4 more

Dr. Isaac Kofi Nti is an Assistant Professor in the School of Information Technology at the University of Cincinnati, with over 16 years of interdisciplinary research experience in applied machine learning, health informatics, cybersecurity, and data governance. His work emphasizes responsible AI deployment in high-impact domains including healthcare, finance, and education. He concurrently holds affiliations with Ghanaian institutions (University of Energy and Natural Resources and Sunyani Technical University) and serves as a Visiting Assistant Professor at Cincinnati until 2024. Education PhD in Computer Science, University of Energy and Natural Resources, Ghana (2021) MSc in Information Technology, Kwame Nkrumah University of Science and Technology, Ghana (2016) BSc Computer Science, Catholic University of Ghana (2011) Higher National Diploma in Electrical & Electronic Engineering, Sunyani Technical University, Ghana (2007) Research Focus Dr. Nti develops lightweight, explainable AI systems for cybersecurity, healthcare diagnostics, agricultural optimization, and educational technology. His research integrates secure data analytics, ethical AI frameworks, and cloud-based learning infrastructures. Current projects explore generative AI in education, predictive modeling for disease outbreaks, and blockchain applications for health data verification. Publication Trends His 60+ publications (2022-2024) demonstrate strong emphasis on ensemble learning methods, healthcare AI applications, and cybersecurity innovations. Recent works frequently utilize hybrid models (LSTM/XGBoost/StackNet) for fraud detection, disease prediction, and renewable energy forecasting. Awards & Grants Provost Office Strategic Collaborative Faculty Team Award (2024-2025): $19,200 for AI integration in higher education Faculty Development Grant (2023-2024): $10,038 for emerging technology training Faculty Incentive Awards for Research (CECH, UC 2022-2024) Teaching & Service Teaches graduate/undergraduate courses in machine learning, cybersecurity, and data technologies. Leads multiple university committees including PhD Admissions Review and Applied Research teams. Professional memberships include ACM, IEEE, and AAAI.

Thomas GrafView profile

Associate Professor

• Theoretical Linguistics
• Computational Linguistics
• Syntax
• +5 more

Thomas Graf is an Associate Professor in the Department of Linguistics and an Affiliate Professor at the Institute for Advanced Computational Science (IACS) at Stony Brook University. He holds a Ph.D. from UCLA (2013) and an M.A. from the University of Vienna. His research focuses on the intersection of theoretical linguistics and computer science, exploring structural complexity in syntax, morphology, and phonology, and its implications for language processing and acquisition. Graf’s work emphasizes computational constraints on natural language, such as subregular complexity and parsing efficiency, and involves empirical studies across languages like English, Icelandic, and American Sign Language. Education: Ph.D., University of California, Los Angeles (2013) M.A., University of Vienna Research interests include computational syntax and phonology, parsing theory, and the formal characterization of linguistic universals. Key themes involve the computational power of linguistic frameworks (e.g., Minimalist grammars), the subregular hierarchy, and the typological implications of computational constraints. Graf’s recent work investigates how empirical phenomena like syntactic islands arise from complexity limits in natural language. His contributions to computational linguistics include Python tools for Minimalist grammar processing and collaborations in labs like the Computational Linguistics Lab (CompLab) at Stony Brook. He has also received a NSF CAREER grant for research on abstract universals in morphosyntax. Labs/Teams: Active member of the Computational Linguistics Lab (CompLab) at Stony Brook University. Leads the MathLing Reading Group and contributes to NLP research initiatives.

Frank WoodView profile

Associate Professor

• Probabilistic Programming
• Deep Generative Models
• Reinforcement Learning
• +4 more

Frank Wood is an Associate Professor of Computer Science at the University of British Columbia and a Canada CIFAR AI Chair at AMII. He directs the Pacific Laboratory for Artificial Intelligence (PLAI) research group and co-founded Inverted AI, a spin-out focused on advanced simulation for autonomous vehicles. His research focuses on probabilistic programming, deep generative models, and reinforcement learning, with applications in autonomous driving, robotics, and vision. Wood teaches courses such as Machine Learning and Data Mining (CPSC 340) and Topics in Artificial Intelligence (CPSC 532W). He has been on academic leave from January to December 2025. His work integrates theoretical advancements with practical applications, including projects like PLAICraft, a large-scale embodied AI dataset. Key awards include the ICML Best Paper Honourable Mention and contributions to probabilistic programming systems like Anglican. His research group collaborates on tools such as TorchDriveEnv for autonomous driving benchmarking and explores cutting-edge methods in diffusion models and continual learning.

• Computational Biology
• Cardiac Electrophysiology
• In Silico Trials
• +2 more

Blanca Rodriguez is Professor of Computational Medicine at the University of Oxford, heading the Computational Biology and Health Informatics theme. Her research develops computational methods to accelerate therapy development through human-based modeling and simulation, reducing animal testing via digital twins and in silico trials. Key innovations include the PRISM probabilistic model checker, virtual human heart models, and quantitative verification tools for assessing drug cardiotoxicity. Current work focuses on personalized cardiac models, AI-driven therapy optimization, and multi-scale physiological simulations. Wellcome Trust Senior Research Fellow (2013-2025) NC3Rs International Prize (2017) Impact Award, MPLS Division (2018) Professor Rodriguez mentors graduate students in computational medicine and collaborates extensively with pharmaceutical companies and regulators. Her work has generated 150+ patents licensed to 31 companies, and she co-founded two biotechnology companies.

Hanwen XingView profile

Academic

• Computational Statistics
• Bayesian Statistical Modelling
• Health Informatics
• +3 more

Hanwen Xing is a Research Associate at St Peter's College and a Postdoctoral Researcher in Artificial Intelligence at the Nuffield Department of Women's & Reproductive Health, University of Oxford. He holds a DPhil in Statistics (2022) and MSc in Statistical Science (2018) from Oxford, following a Bachelor of Mathematics from the University of Waterloo (2017). Affiliations: University of Oxford, St Peter's College Roles: Academic support for MSc/DPhil students, Bayesian methodology development His research focuses on computational statistics and Bayesian modelling, particularly applying approximate Bayesian inference methods to healthcare and medical science challenges. He has developed novel Bayesian approaches for integrating drug response and protein profiling data to identify tumor-specific cancer dependencies, demonstrated through projects like DepInfeR-GP. His work bridges statistical theory with practical applications in precision oncology. Key research outputs include advancements in Gaussian process modelling for single-cell perturbation data, continual learning frameworks using probabilistic methods, and improved bridge estimators via f-GAN techniques. These contributions highlight his expertise in both foundational statistical theory and applied computational methods. No scientific awards explicitly mentioned. He advises students in statistics programs and contributes to collaborative projects involving ex-vivo drug sensitivity analysis. His GitHub repository hosts implementations of his Bayesian models, reflecting a commitment to open-source scientific software development.

Gabriella BoscoView profile

Professor

• Optical Communications
• Fiber Optics Transmission Systems
• Digital Signal Processing (DSP)
• +3 more

Gabriella Bosco is a Full Professor at Politecnico di Torino in the Department of Electronics and Telecommunications. She specializes in optical communication systems, focusing on fiber optics transmission, digital signal processing (DSP), and nonlinear effects mitigation. Her roles include Editor-in-Chief of the IEEE/OSA Journal of Lightwave Technology and Board member of the IEEE Photonics Society. She earned her Telecommunication Engineering degree (1998) and PhD (2002) from Politecnico di Torino. Research Interests: Performance analysis and design of optical transmission systems Application of DSP in optical links Fiber-optic sensor networks and anomaly detection Nonlinear interference mitigation techniques Awards: OSA Fellow (2017) IEEE Fellow (2019) 2014 & 2015 Best Paper Award in Journal of Lightwave Technology Professional Service: Program Chair/General Chair of OFC 2017-2019 Editorial roles in major photonics journals EU-funded projects: E-Photon/One, Nobel, BONE Labs & Groups: Leading the OptCom Group, a pioneer in optical communications research since 2000. Collaborates with industry leaders including Huawei and Cisco.

• Machine Learning
• Neural Networks
• Uncertainty Quantification
• +4 more

Dr. Matias Valdenegro Toro is an Assistant Professor of Machine Learning at the University of Groningen within the Faculty of Science and Engineering and the Artificial Intelligence department of the Bernoulli Institute. He holds a PhD from Heriot-Watt University (2019) and a Master's in Autonomous Systems from Bonn-Rhein-Sieg University of Applied Sciences (2014). His research focuses on trustworthy machine learning models , particularly in uncertainty quantification , medical AI , and robotics , with applications in computer vision and explainable AI. He teaches courses like Introduction to Machine Learning and Deep Learning at the Bachelor and Master levels. His work emphasizes robustness in AI systems, including uncertainty estimation for medical applications, super-resolution techniques, and neuromorphic robotics. He has published widely on topics like Bayesian neural networks, prompt tuning, and sanity checks for explanations. Notable awards include Best Reviewer at ICML (2024) and Highlighted Reviewer at ICLR (2022). He collaborates with institutions like the German Research Center for Artificial Intelligence and actively contributes to open-source datasets (e.g., the Japanese Uncertain Scenes Dataset ). Key grants and activities include organizing the ENLIGHT BIP Course on Deep Learning for Forestry and teaching at the European Summer School on AI . His research also addresses regulatory challenges like the EU AI Act's implications for uncertainty quantification in general-purpose AI.

• Visual Media Compression
• Machine Learning
• End-to-End Optimization
• +5 more

Jona Ballé is an Associate Professor in the Department of Electrical and Computer Engineering at New York University Tandon School of Engineering. His research focuses on visual media compression, leveraging machine learning and end-to-end optimization to advance compression techniques for traditional and emerging modalities (e.g., AR, plenoptic imaging). He holds a PhD in signal processing from RWTH Aachen University (2012), followed by postdoctoral work at NYU’s Center for Neural Science and a Research Scientist role at Google (2017–2024). His contributions include foundational work on JPEG AI and leadership in conferences such as CLIC and DCC. Education: PhD in Signal Processing, RWTH Aachen University (2012) Master’s in Signal Processing, RWTH Aachen University (2007) Research: Ballé bridges machine learning and perceptual science to improve compression efficiency. Key areas include perceptual metrics, end-to-end optimization, and distributed coding. His work on Wasserstein distortion and Fourier basis models reflects cutting-edge advancements in compression theory. Impact: As co-organizer of the Challenge on Learned Image Compression (CLIC) and program committee member of the Data Compression Conference (DCC), he drives community progress. His industry collaborations and leadership in the JPEG AI standard highlight his translational research.

Michael BurkeView profile

Senior Lecturer

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

Dr. Michael Burke is a Senior Lecturer and Deputy Graduate School Coordinator at the Department of Electrical and Computer Systems Engineering, Monash University. He specializes in robotics, focusing on probabilistic machine learning and computer vision for autonomous systems. His roles include research supervision, curriculum development, and fostering graduate culture. Education: PhD in Statistical Signal Processing, University of Cambridge (2012–2016) MSc in Electronic Engineering, Stellenbosch University (2009–2011) BEng in Electronic Engineering, University of Pretoria (2005–2008) Research Interests: Robot learning and control Probabilistic machine learning for robotics Interpretable robotics systems and hybrid control architectures Robot perception and visuomotor control His work bridges robotics with machine learning to develop safe, adaptive autonomous systems for applications like surgical robotics and field robotics. Awards and Recognition: Best Paper Award at AMDO 2014 Runner-Up for Best Paper at CoRL 2019 Advising & Grants: Supervising over 20 PhD and Master’s students in robotics and related fields Principal Investigator for ARC-funded projects on robotic pharmaceutical formulation and human-robot interaction (2024–2027) Labs and Teams: Past leadership of the Mobile Intelligent Autonomous Systems group at CSIR, South Africa (2009–2018) Current collaborations with researchers at the University of Edinburgh and University of Melbourne