جست‌وجوی استاد و پژوهشگر

• Formal methods in computer science
• Safety engineering
• Computational intelligence
• +۳ مورد دیگر

Prof. Jan Magott holds a research position at the Faculty of Information and Communication Technology of Wrocław University of Science and Technology , specifically within the Department of Computer Engineering . His work bridges formal methods in computer science with safety engineering applications. Focus on safety-critical systems across railway and aviation domains Expertise in computational intelligence and dependability analysis Research interests span: Formal verification of time-dependent systems Fault tree modeling with temporal constraints Functional Resonance Analysis Method (FRAM) applications Hospital safety and medical diagnostics optimization Urban transport reliability analysis Human factors in safety systems Recent publications show increasing focus on: Railway safety protocols and traffic management (2023) Medical error prevention in primary care (2021-2020) Formal timing analysis in software engineering (2016) Aviation incident modeling with fuzzy logic (2016) Key methodological contributions include: Time-dependent fault tree analysis Execution time modeling for real-time systems Fuzzy probability applications in safety engineering FRAM framework for complex system analysis

• Breeding blankets
• Controlled thermonuclear fusion
• Thermal-hydraulic analysis
• +۷ مورد دیگر

Antonio Froio is an Associate Professor at the Department of Energy (DENERG) at Politecnico di Torino. His academic career focuses on Industrial and Information Engineering (Area 0009), particularly in Nuclear Power Plants (IIND-07/D). He is an active member of the American Nuclear Society (2021-), Nuclear and Reason Committee (2021-), and Italian Nuclear Association (2020-). Research Interests: Antonio's work centers on breeding blankets , controlled thermonuclear fusion , and thermal-hydraulic analysis . He applies computational engineering (ERC PE8_4) to advance affordable and clean energy (SDG 7). His research includes Design of tritium extraction systems CFD co-simulation for nuclear plants Multiphysics modeling of fusion reactors Uncertainty quantification in nuclear data Recent Publications: His academic output spans thermal-hydraulic assessments, limiter system designs, and computational tools for fusion reactors. Notable contributions include work on EU DEMO systems, PbLi loops, and transient accident simulations. Scientific Awards: Effective member - American Nuclear Society (2021-) Effective member - Nuclear and Reason Committee (2021-) Effective member - Italian Nuclear Association (2020-) Teaching and Supervision: Antonio teaches Computational heat and mass transfer , Monte Carlo methods , and Nuclear fusion reactor engineering courses. He supervises PhD students in the NEMO Research Group including Mauro Spro', Antonio Zurzolo, Marco Caravello, Fabrizio Lisanti, and Alex Aimetta.

Shounak Roychowdhuryمشاهده پروفایل

دانشیار

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۴ مورد دیگر

Shounak Roychowdhury is an Associate Professor of Practice at the School of Information, University of Texas at Austin. He holds a PhD in Computer Engineering from the University of Texas at Austin (2013) and an MS in Computer Science from the University of Tulsa (1997). His teaching focuses on foundational and advanced topics such as Computer Vision, Database Management, Programming, and Data Science. Recent courses include INF 385T: Special Topics in Information Science: Computer Vision (2025) and I 320D: Topics in Human-Centered Data Science: Database Design (2024). Roychowdhury's research spans Computer Science , Machine Learning , and Software Engineering , with a focus on probabilistic methods, fuzzy systems, and medical informatics. His work explores areas like fault localization in software, data clustering optimization, and applying generative models to healthcare analytics. He has also contributed to fuzzy logic applications in decision-making and pattern recognition. His publications highlight interdisciplinary approaches, including Alzheimer’s disease modeling with GANs and color space analysis in art through probabilistic divergences. While no scientific awards are listed, his work reflects sustained contributions to theoretical and applied computing. Roychowdhury advises no listed students but maintains an active teaching role. No specific grants or labs are mentioned, though his research aligns with computational and data-driven methodologies.

• Theoretical Computer Science
• Formal Program Verification
• Parametrised Algorithms
• +۴ مورد دیگر

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 Pontilمشاهده پروفایل

استاد

• Machine Learning
• Statistical Learning Theory
• Kernel Methods
• +۵ مورد دیگر

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 Pettiمشاهده پروفایل

استادیار

• Bioinformatics
• Computational Biology
• Machine Learning
• +۴ مورد دیگر

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 Grangettoمشاهده پروفایل

استاد

• Computer Vision
• Image Processing
• Artificial Intelligence
• +۵ مورد دیگر

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 Salaminمشاهده پروفایل

استادیار

• Quantum Optics
• Nonlinear Optics
• Photonics
• +۳ مورد دیگر

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
• +۴ مورد دیگر

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 Ntiمشاهده پروفایل

استادیار

• Applied Machine Learning
• Cybersecurity
• Health Informatics
• +۴ مورد دیگر

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 Grafمشاهده پروفایل

دانشیار

• Theoretical Linguistics
• Computational Linguistics
• Syntax
• +۵ مورد دیگر

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.

Shamil Asgarliمشاهده پروفایل

استادیار

• Algebraic Geometry
• Arithmetic Geometry
• Finite Fields
• +۳ مورد دیگر

Shamil Asgarli is an Assistant Professor in the Department of Mathematics and Computer Science at Santa Clara University (SCU), College of Arts and Sciences. He holds a B.Sc. from the University of British Columbia (UBC) and a Ph.D. from Brown University (2019), advised by Brendan Hassett. Previously, he was a postdoctoral fellow at UBC under mentors Dragos Ghioca and Zinovy Reichstein. His research focuses on algebraic and arithmetic geometry, with recent explorations into algebraic graph theory. Core themes include geometry and combinatorics over finite fields, transverse hypersurfaces, blocking sets, and maximum cliques in Cayley graphs. Notable contributions involve Bertini-type theorems, Picard group analysis of quadric intersections, and studies of Frobenius nonclassical hypersurfaces. Teaching spans courses at SCU (e.g., Cryptography, Calculus), UBC, and Brown, including roles as instructor and teaching assistant for calculus, linear algebra, and number theory. He completed the Sheridan Center's Reflective Teaching Program (Certificate I). His work bridges pure mathematics with combinatorial applications, often leveraging finite field structures. Key collaborations include projects with Dragos Ghioca, Chi Hoi Yip, and Kuan-Wen Lai on transverse subspaces, blocking curves, and geometric irreducibility. His publications frequently address problems at the intersection of algebraic geometry and finite field theory, with applications to coding and cryptography.

Frank Woodمشاهده پروفایل

دانشیار

• Probabilistic Programming
• Deep Generative Models
• Reinforcement Learning
• +۴ مورد دیگر

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
• +۲ مورد دیگر

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 Xingمشاهده پروفایل

عضو هیئت علمی

• Computational Statistics
• Bayesian Statistical Modelling
• Health Informatics
• +۳ مورد دیگر

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.