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Justus ThiesView profile

Professor

• Computer Graphics
• Computer Vision
• Neural Rendering
• +3 more

Prof. Justus Thies is Full Professor for 3D Graphics & Vision at the Technical University of Darmstadt and leads the Neural Capture & Synthesis research group at the Max Planck Institute for Intelligent Systems. His research develops AI methods to capture and synthesize the real world using commodity hardware, focusing on markerless motion capture of faces and bodies, and photorealistic neural rendering. His work has been recognized with the German Pattern Recognition Award, Eurographics Young Researcher Award, and an ERC Starting Grant (all 2024). Recent publications focus on Gaussian-based avatars, neural human reconstruction, and diffusion models for scene synthesis.

Melanie ZeilingerView profile

Associate Professor

• Learning-based Control
• Distributed Control and Optimization
• Robotics
• +4 more

Prof. Dr. Melanie Zeilinger is an Associate Professor at the Department of Mechanical and Process Engineering at ETH Zurich, leading the Intelligent Control Systems group at the Institute for Dynamic Systems and Control. She holds a diploma in Engineering Cybernetics from the University of Stuttgart (2006) and a Ph.D. in Electrical Engineering from ETH Zurich (2011). Her postdoctoral research included stints at EPFL (2011–2012), a Marie Curie fellowship at UC Berkeley and the Max Planck Institute (2012–2015), and a professorship at the University of Freiburg (2018–2019). Her research focuses on learning-based control, distributed control systems, and robotics , with applications to medical devices (e.g., hydrocephalus shunts) and human-in-the-loop systems. She organizes the Conference on Learning for Dynamics and Control (L4DC) and contributes to initiatives like the "Algorithm on My Team" project. Her awards include the ETH Medal for her PhD thesis, a Marie-Curie IO Fellowship , and an SNF Assistant Professorship grant . She serves as an Associate Editor for IEEE Control Systems Letters and actively reviews for top journals/conferences like IEEE TAC, Automatica, and NeurIPS. Key projects include: VIEshunt: A smart ventricular shunt for hydrocephalus treatment, combining control systems and medical engineering. Autonomous Racing: Contextual tuning and safety-certified learning-based MPC for real-time obstacle avoidance. Data-Driven Control: Integrating Gaussian processes and state-space models into MPC frameworks for uncertain systems. Her work bridges control theory, machine learning, and robotics, addressing societal challenges such as healthcare and energy efficiency.

• Machine Learning
• Probabilistic Modeling
• Bayesian Optimization
• +3 more

Dr. José Miguel Hernández-Lobato is a Professor of Machine Learning at the Department of Engineering, University of Cambridge . He also serves as Chief AI Officer at Angstrom AI and Director of the Cambridge ELLIS unit. Previously, he was an Associate Professor and University Lecturer in Machine Learning at the same institution. Research focuses on probabilistic learning techniques, including Bayesian optimization, Gaussian processes, and scalable inference methods Collaborated with Microsoft Research and Infosys Technologies Active in both academic leadership and industrial AI applications His work has been recognized through a Rafael del Pino Foundation fellowship and academic excellence awards. Publications appear in top venues like Journal of Machine Learning Research , NIPS, and ICML. Scientific Awards Rafael del Pino Foundation postdoctoral fellowship Special prize for best academic record at Universidad Autónoma de Madrid

Euijin ChooView profile

Assistant Professor

• Data-driven Cybersecurity
• Big Data Analytics
• Graph Mining
• +6 more

Dr. Euijin (Alley) Choo is an Assistant Professor in the Department of Computer Science at the University of Alberta, specializing in data-driven cybersecurity and big data analytics. Her research focuses on AI-based cybersecurity solutions, anomaly detection in network traffic, and adversarial attacks on federated learning systems. She holds a Ph.D. from North Carolina State University and has held roles at Qatar Computing Research Institute, Korea University, and the University of Missouri-Rolla. Education: Ph.D., Computer Science, North Carolina State University (2015) M.S., Computer Science & Engineering, Korea University (2008) B.S. Dual Degree in Computer Science & Mathematics, Korea University (2006) Research Interests: Security and big data analysis intersections Federated learning security and privacy Anomaly detection in network logs and enterprise systems Malware/phishing detection using graph inference AI-driven threat intelligence aggregation Recent Grants: Mitacs Accelerate Program Grant: Fraud Detection in Financial Graphs ($60,000, 2025) National CyberSecurity Consortium Grant: IntruderInsight ($2M, 2025-2027) NSERC Discovery Grant: Threat Detection Framework ($180,000, 2025-2031) Awards: Best Paper Award at DBSEC 2015 NSERC Early Career Researcher Award (2025) Provost Fellowship (NC State, 2009-2010) Labs/Teams: Leads the Data-driven Network and Cyber Security (DANS) Lab, focusing on federated learning defenses, compromised entity detection, and malicious domain analysis.

Daniel M. KaneView profile

Professor

• Number Theory
• Combinatorics
• Complexity Theory
• +3 more

Daniel M. Kane is a Professor at the University of California, San Diego (UCSD), holding a joint appointment in the Department of Mathematics and the Department of Computer Science and Engineering (CSE). His research spans mathematics and theoretical computer science, with a focus on number theory, combinatorics, complexity theory, and computational statistics. He earned a Ph.D. in Mathematics from Harvard University (2011) and dual BS degrees in Mathematics with Computer Science and Physics from MIT (2007). Prior to UCSD, he was a postdoctoral researcher at Stanford University (2011–2014) on an NSF fellowship. His research interests include robust statistics, machine learning, polynomial threshold functions, and algorithmic methods for high-dimensional data. Notable achievements include co-authoring the book Algorithmic High-Dimensional Robust Statistics (Cambridge University Press, 2023) and receiving the Best Paper Award at the Conference on Computational Complexity (2013), as well as gold medals at the International Mathematical Olympiad (2002 and 2003). Current teaching includes courses such as Math 96 (Putnam Seminar), Math 154 (Graph Theory), CSE 101 (Algorithms), and CSE 203A (Randomized Algorithms). He has consulted for companies like CASPER Labs and AIble, and his work extends to cryptographic protocols, including quantum money schemes based on quaternion algebras. Key contributions include breakthroughs in robust mean estimation, list-decodable learning, and the development of efficient algorithms for statistical problems. His research often bridges foundational theory with practical applications in machine learning and data analysis.

Antti HonkelaView profile

Professor

• Privacy-preserving machine learning
• Differential Privacy
• Bayesian Machine Learning
• +3 more

Antti Honkela is a Professor of Data Science at the University of Helsinki's Department of Computer Science, within the Faculty of Science. He also serves as the Coordinating Professor for the Privacy-preserving and Secure AI Research Programme at the Finnish Center for Artificial Intelligence (FCAI), and as Deputy Director of the Master's Programme in Data Science. His roles include membership in the Health and Social Data Permit Authority (Findata) and as an Action Editor for Transactions on Machine Learning Research. Honkela's research focuses on privacy-preserving machine learning, differential privacy, Bayesian methods, and their applications in computational biology and healthcare. He leads projects such as the European Lighthouse in Secure and Safe AI (ELSA) and the Data Literacy for Responsible Decision-making initiative. His work emphasizes developing robust frameworks for privacy-aware AI, including differentially private synthetic data and federated learning. Honkela has advised numerous PhD and Master's students, and his contributions span theoretical advancements and practical implementations, such as the D3p Python package for differentially private probabilistic programming. Key contributions include advancements in Bayesian inference from synthetic data, privacy accounting mechanisms, and computational methods for genomic epidemiology. His interdisciplinary approach bridges machine learning, statistics, and healthcare, addressing challenges in data privacy and secure AI deployment.

Shirshendu GangulyView profile

Associate Professor

• Probability Theory
• Statistical Mechanics
• Markov Chains
• +3 more

Shirshendu Ganguly is an Associate Professor in the Department of Statistics at the University of California, Berkeley. His research focuses on probability theory, statistical physics, and their applications, including percolation models, phase transitions, Markov chains, and random graphs. He holds a PhD in Mathematics from the University of Washington and has held postdoctoral positions at UC Berkeley. Ganguly has been recognized with the 2019 Sloan Research Fellowship. Education: PhD in Mathematics, University of Washington, 2011–2016 Miller Postdoctoral Fellow, UC Berkeley, 2016–2018 Research Interests: Probability Theory, Statistical Mechanics, Markov Chains, Random Graphs, Percolation Theory, Sparse Combinatorial Structures His work explores geometric and probabilistic phenomena in disordered systems, including polymer models, self-organized criticality, and random matrix theory. He has advised multiple PhD students and contributes to teaching advanced probability courses at Berkeley. Awards: 2019 Sloan Research Fellowship

Mathieu JoergerView profile

Associate Professor

• Navigation Systems
• Autonomous Systems
• Dynamics and Control
• +6 more

Dr. Mathieu Joerger is an Associate Professor in the Aerospace & Ocean Engineering Department at Virginia Tech, leading the Assured Vehicle Autonomy (AVA) Lab. He holds a Ph.D. (2009), M.S. (2002), and Diplôme d’Ingénieur (2002) from Illinois Institute of Technology and INSA Strasbourg. His research focuses on navigation safety, multi-constellation GNSS, and autonomous system integrity. He serves as Technical Editor for IEEE Transactions on Aerospace and Electronic Systems and co-leads the CARNATIONS initiative for resilient PNT systems. Notable awards include the ION Early Achievement Award (2015) and Bradford W. Parkinson Award (2009). Research interests include GNSS augmentation, LiDAR/IMU integration, and safety quantification for autonomous vehicles. His lab collaborates with industry/government on projects like CAAMS and develops methods to detect GNSS interference using UAS. Key publications address integrity monitoring in SLAM, particle filtering, and Kalman filter applications. Education: Ph.D. Mechanical & Aerospace Engineering, Illinois Tech (2009); M.S. Mechanical Engineering, Illinois Tech (2002); Diplôme d’Ingénieur, INSA Strasbourg (2002). Awards: ION Early Achievement Award, Outstanding NAVIGATION Reviewer, Bradford W. Parkinson Award. Professional Roles: Senior Editor for IEEE Transactions, ARAIM Standards Contributor, CARNATIONS Co-Director. Advises multiple PhD/Master’s students and oversees lab activities involving 20+ researchers. Projects include R-PNT systems, UAS-based RFI localization, and automotive GNSS safety. Active in international conferences like ION GNSS+ and AIAA forums.

Ann B LeeView profile

Professor

• Scientific Machine Learning
• Trust-Worthy UQ
• Likelihood-Free Inference
• +1 more

Ann B. Lee is a Professor and Co-Director of the PhD Program in Statistics at Carnegie Mellon University , with a joint appointment in the Department of Statistics & Data Science and the Machine Learning Department. Prior to joining CMU, she held positions as a J.W. Gibbs Assistant Professor at Yale University and a visiting research associate at Brown University. PhD in Physics, Brown University MSc/BSc in Engineering Physics, Chalmers University of Technology, Sweden Her research focuses on statistical methodology for complex data in the physical sciences , emphasizing trustworthy inference, uncertainty quantification, and integration of classical statistics with machine learning. Recent work includes likelihood-free inference, calibrated forecasting, and diagnostics for generative models. The STAMPS research group , which she co-founded in 2018, hosts weekly meetings and public webinars. In Fall 2024, STAMPS will transition into a CMU Research Center. Recent publications span likelihood-free inference , climate modeling , and astronomy . Notable collaborations include applications to hurricane intensity guidance , galaxy redshift estimation , and cosmological parameter biases . She mentors PhD students and has advised multiple award-winning researchers, including ASA Best Student Paper Award winners. Her teaching includes advanced courses on probability, regression, and AI for climate sciences.

Tamara BroderickView profile

Associate Professor

• Machine Learning
• Statistics
• Bayesian Inference
• +1 more

Tamara Broderick is an Associate Professor in the Department of Electrical Engineering and Computer Science at MIT, specializing in machine learning and statistics. Her research focuses on developing methods for uncertainty quantification in data analysis, Bayesian nonparametrics, and scalable inference algorithms. She leads a research group advising PhD students and postdocs in statistical machine learning. Her work spans Bayesian modeling, variational inference, spatial statistics, and applications in epidemiology and environmental science. Recent projects involve uncertainty-aware forecasting, robustness analysis of statistical methods, and efficient algorithms for high-dimensional inference. Broderick teaches Bayesian Modeling and Inference and contributes to MIT's statistics and data science initiatives.

James BremerView profile

Professor

• Numerical Analysis
• Acoustic and Electromagnetic Scattering Problems
• Integral Equation Methods
• +5 more

James Bremer is a Professor in the Department of Mathematics and holds a cross-appointment in the Department of Computer and Mathematical Sciences at the University of Toronto's Scarborough campus. His research focuses on developing efficient numerical algorithms for solving elliptic boundary value problems, integral equations, and special function transforms.

• Signal Processing
• Applied Mathematics
• Radar Systems
• +3 more

Eric Grivel is a Professor at the University of Bordeaux affiliated with the IMS Bordeaux (Integration Laboratory from Materials to Systems). His research focuses on Signal and Image Processing Spectral Analysis Stochastic Process Modeling His work spans theoretical contributions to signal processing and practical applications in radar systems and biomedical signal analysis. Key trends in his recent publications include Optimization of Detrended Fluctuation Analysis (DFA) for Hurst exponent estimation Development of divergence metrics for comparing ARMA and Gaussian processes Waveform design in MIMO OFDM DFRC (Dual Function Radar-Communication) systems Integration of AI tools like ChatGPT in educational signal processing projects Collaborations and industrial partnerships evident in his publications involve institutions such as Indian Institute of Science Thales Airborne Systems STMicroelectronics CEA Leti Slb (Schlumberger)

Barbara KrausView profile

Professor

• Quantum Information Theory
• Quantum Computing
• Entanglement and Quantum Correlations
• +4 more

Prof. Dr. Barbara Kraus is the Chair of Quantum Algorithms and Applications at the Technical University of Munich (TUM), affiliated with the TUM School of Natural Sciences. She previously held academic positions at the University of Innsbruck, where she founded her research group in 2010. Education : Physics and Mathematics at the University of Innsbruck; Post-doctoral work at MPI for Quantum Optics and University of Geneva. Her research focuses on foundational problems in quantum information theory, particularly entanglement in multipartite systems, quantum simulation, and verification of quantum processors. She develops theoretical tools for quantum many-body systems and explores applications in quantum computing, emphasizing error characterization and experimental validation. Recent publications highlight advancements in Hamiltonian learning, symmetry-resolved entanglement detection, and multipartite state transformations. Her work bridges theoretical quantum physics with practical implementations, including Rydberg platforms and quantum metrology. Key Awards : START Prize (2010), Ignaz L. Lieben Award (2013), Boltzmann Prize (2011), Südtiroler Sparkasse Research Prize (2019). She supervises doctoral students and postdocs in quantum information theory, with a focus on stabilizer states, quantum networks, and entanglement measures. Her courses at TUM include Quantum Information , Quantum Algorithms , and workshops on entanglement manipulation.

Frede BlaabjergView profile

Professor

• Power Electronics
• Wind Turbines
• Photovoltaic (PV) Systems
• +6 more

Frede Blaabjerg is a Professor at Aalborg University (AAU Energy) , affiliated with the Faculty of Engineering and Science . Since 1998, he has pioneered power electronics research in applications such as wind turbines , photovoltaic (PV) systems , reliability engineering , and Power-2-X technologies. Education : PhD in Electrical Engineering (1995, Aalborg University) Honorary Degrees : Honoris Causa at University Politehnica Timisoara (2017) and Tallinn Technical University (2018) His research focuses on power electronics control , system optimization , and reliability for renewable energy and electric mobility . Recent work includes grid-forming converters , virtual synchronous generators , and smart EV charging systems. Key publication trends span 15+ years , with over 3,733 peer-reviewed articles and 900+ journal papers in power electronics , renewables , and energy storage . Notable book series: Control of Power Electronic Converters and Systems (4 volumes, Elsevier). Scientific Awards : 46 IEEE Prize Paper Awards 2020 IEEE Edison Medal 2019 Global Energy Prize 2014 IEEE William E. Newell Power Electronics Award Leadership Roles : Editor-in-Chief, IEEE Transactions on Power Electronics (2006–2012) Chairman, Danish Council for Research and Innovation Policy (2020–) President, IEEE Power Electronics Society (2019–2020)

• Machine Learning
• Computational Neuroscience
• Artificial Intelligence
• +7 more

Aapo Hyvärinen is a Professor of Computer Science at the University of Helsinki , affiliated with the Helsinki Institute for Information Technology and the Helsinki Probabilistic Machine Learning Lab . He previously held the position of Professor of Machine Learning at the Gatsby Computational Neuroscience Unit, University College London (2016-2019). Education : Undergraduate Mathematics at University of Helsinki, Vienna, and Paris; Ph.D. in Information Science from Helsinki University of Technology (1997) His research focuses on machine learning and computational neuroscience , particularly: Independent Component Analysis (ICA) Natural Image Statistics Causal Representation Learning Neural Signal Processing Applications to brain imaging (MEG, CryoEM) Recent publications emphasize causal discovery , identifiable machine learning , and nonlinear ICA . Key projects include: VETURI (AI for health) DIGIMIND (AI in mental health) CIFAR grants (2022-2025) Scientific awards : Highly Cited Researcher (2010) He serves as Action Editor for the Journal of Machine Learning Research and Neural Computation , and has held Area Chair roles at NeurIPS, ICML, ICLR, AISTATS, and UAI conferences. His work bridges theoretical machine learning with neuroscience and philosophical implications of artificial intelligence .