Find Academic Supervisors

Shiyu ChangView profile

Associate Professor

• Machine Learning
• Natural Language Processing
• Computer Vision
• +4 more

Shiyu Chang is an Associate Professor in the Department of Computer Science at the University of California, Santa Barbara , focusing on machine learning with applications in natural language processing and computer vision . He previously worked as a research scientist at the MIT-IBM Watson AI Lab alongside Prof. Regina Barzilay and Prof. Tommi Jaakkola, and earned both his B.S. and Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign , advised by Prof. Thomas S. Huang. Education : PhD, University of Illinois at Urbana-Champaign BS, University of Illinois at Urbana-Champaign His research centers on enhancing AI systems through human-AI interaction , aiming to improve interpretability , transferability , and adversarial robustness in LLMs. Recent work includes LLM watermarking defense , uncertainty decomposition , and self-denoised smoothing for model robustness. His publications span premier venues like ICML , NeurIPS , CVPR , and ACL , with recurring themes in diffusion models , LLM optimization , and ethical AI (e.g., hallucination detection, unlearning frameworks). He actively mentors students, several of whom are marked as advisees (☆) in his publications.

Anna ChoromanskaView profile

Associate Professor

• Machine Learning
• Deep Learning
• Optimization
• +6 more

Anna Choromanska is an Associate Professor in the Department of Electrical and Computer Engineering at NYU Tandon School of Engineering, with affiliations to NYU Center for Data Science (CDS), NYU Center for Urban Science and Progress (CUSP), NYU Center for Advanced Technology in Communications (CATT), and the C2SMART Center. Her research focuses on deep learning optimization, generalization, and applications in autonomous driving and large-scale data analysis. She holds an Alfred P. Sloan Fellowship and NSF CAREER Award, and her work impacts industries like NVIDIA and Facebook. She directs the Learning Systems Laboratory (LSL), emphasizing interdisciplinary experimental/theoretical work. Research Interests: Machine Learning fundamentals, DL optimization, continual learning, autonomous vehicle systems, large data analysis. Her lab explores DNN learning dynamics, training architecture design, and scalable algorithms. Professional Impact: Over 50 invited talks, workshop organization for top ML conferences, and contributions to open-source projects like Vowpal Wabbit. Her algorithms are deployed in production systems at Facebook and Baidu. Awards: NSF CAREER Award Alfred P. Sloan Fellowship IBM Global University Program Academic Award (2x) Columbia University Presidential Fellowship Advising & Labs: Leads LSL, supervising interdisciplinary projects in optimization and autonomy. Actively involved in NYU's Modern AI seminar series and industry partnerships through CATT. Personal Interests: Accomplished pianist, salsa dancer, and fashion design enthusiast with notable performances and certifications in dance and music.

Kwang-Sung JunView profile

Assistant Professor

• Interactive Machine Learning
• Reinforcement Learning
• Multi-Armed Bandits
• +7 more

Kwang-Sung Jun is an Assistant Professor at the University of Arizona, Department of Computer Science. His research spans interactive machine learning, reinforcement learning, and learning theory, with a focus on multi-armed bandits, Bayesian optimization, and generalized linear models. Education : Ph.D. in Computer Science from the University of Wisconsin-Madison (2015). Research Trends : Kwang-Sung's recent work (2023-2025) emphasizes bandit algorithms with second-order bounds, adaptive experimentation, and PAC-Bayes frameworks. He explores low-rank structures in regression, explainable reward shaping, and environmental risk modeling via probabilistic assessments of postfire debris-flows. His publications often bridge theoretical guarantees (e.g., regret bounds) with practical applications in machine learning and environmental hazards. Expertise : Interactive machine learning Multi-armed bandits Confidence sequences Reinforcement learning Human-machine hybrid systems

Michael U. GutmannView profile

Senior Lecturer

• Machine Learning
• Bayesian Inference
• Likelihood-Free Inference
• +9 more

Michael U. Gutmann is a Senior Lecturer in Machine Learning at the School of Informatics, University of Edinburgh, and a member of the Institute for Adaptive and Neural Computation. His research lies at the intersection of machine learning, statistics, and scientific applications, with a focus on developing inference methods for complex and implicit models. Education: PhD in Computational Neuroscience, University of Tokyo MSc in Engineering and Applied Mathematics, Swiss Federal Institute of Technology (ETH) Zurich MSc, Ecole Centrale Paris His primary research interests include Bayesian inference, likelihood-free inference, optimal experimental design, unsupervised learning, and applications in computational biology and neuroscience. He is best known for introducing Noise-Contrastive Estimation (NCE), a foundational technique for training unnormalized statistical models. His recent work spans variational inference, density ratio estimation, flow models for missing data, and AI-driven experimental design in behavioral and biological sciences. His publications, including in NeurIPS , ICML , JMLR , and eLife , demonstrate a strong emphasis on methodological innovation for scientific discovery. He has contributed to open-source tools such as ELFI (Engine for Likelihood-Free Inference) and developed practical implementations of robust inference algorithms. Scientific Awards: No specific awards listed in the provided texts. Michael Gutmann actively supervises students and collaborates with leading researchers in machine learning and computational biology. He has secured research funding from EPSRC and BBSRC for projects in generative modeling and infectious disease epidemiology. He teaches advanced courses such as Probabilistic Modelling and Reasoning and Data Mining, reflecting his deep engagement with both theoretical and applied aspects of machine learning. Labs and Research Groups: Institute for Adaptive and Neural Computation (ANC), University of Edinburgh Former affiliations with Department of Mathematics and Statistics and Department of Computer Science at the University of Helsinki and Aalto University

Manish KumarView profile

Professor

• Unmanned Aerial Vehicles
• Robotics
• Swarm Systems
• +4 more

Dr. Manish Kumar is a Professor in the Department of Mechanical Engineering at the University of Cincinnati, leading the Cooperative Distributed Systems (CDS) Laboratory and the UAV MASTER Lab. He specializes in robotics, unmanned aerial systems (UAVs), and swarm systems, with a focus on decision-making, control in complex systems, and multi-sensor data fusion. His research has been supported by grants from the National Science Foundation, Department of Defense, and industry partners. Education: Ph.D. in Mechanical Engineering, Duke University (2004) M.S. in Mechanical Engineering, Duke University (2002) Research Interests: Dr. Kumar's work addresses challenges in autonomous systems, including UAV coordination, swarm robotics, and emergency response technologies. He integrates machine learning, control theory, and optimization to develop robust systems for applications such as wildfire management, telehealth, and industrial automation. Grants & Labs: Directed projects funded by federal agencies (NSF, DoD) and industry, totaling over $5M. Co-directs the Collaboratory for Medical Innovation and Implementation and the UAV MASTER Lab. Key Contributions: Pioneered algorithms for UAV path planning and swarm coordination. Developed PDE-based models for epidemic spread prediction and control.

• Bayesian Statistics
• Computational Statistics
• Change Point Detection
• +5 more

Professor Paul Fearnhead is a leading academic in Statistics at Lancaster University 's School of Mathematical Sciences . His research focuses on Bayesian and Computational Statistics , with applications in Anomaly Detection , Continuous-time Markov Processes , and Changepoint Analysis . Department: Mathematics and Statistics Academic Rank: Professor Email: p.fearnhead@lancaster.ac.uk His work bridges theoretical statistics and computational efficiency, notably through pruning techniques for change detection and novel Monte Carlo methods. Current projects include AI Hub initiatives, probabilistic AI foundations, and real-time anomaly detection in streaming data. Research outputs span Bayesian Analysis , Time Series Modeling , and Scalable Statistical Algorithms , with applications in fields like astronomy and epidemiology. Recent publications emphasize simulation-based composite likelihoods and efficient distributed changepoint detection. Scientific contributions include leadership roles in the STOR-i Centre for Doctoral Training and Data Science Institute (DSI) projects such as CoSInES and Statscale. He supervises PhD students including Dylan Bahia, Yuntang Fan, and Ziyang Yang.

Huiyan SangView profile

Professor

• Spatial Statistics
• Bayesian Nonparametrics
• Machine Learning
• +9 more

Huiyan Sang is a Professor and Director of the Undergraduate Program in the Department of Statistics at Texas A&M University (College of Arts & Sciences). She earned her Ph.D. in Statistics from Duke University and a B.Sc. in Mathematics and Applied Mathematics from Peking University. Her research focuses on spatial statistics, Bayesian nonparametric methods, machine learning, computational statistics, and applications in environmental sciences, geosciences, urban planning, and biomedical research. Her interdisciplinary work integrates statistical methodologies with real-world challenges, such as analyzing extreme environmental events, optimizing urban infrastructure, and modeling complex systems like human mobility during pandemics. She has contributed to advancing spatio-temporal modeling, Gaussian processes, and Bayesian hierarchical frameworks for large datasets. Recent publications highlight innovations in nonparametric regression, spatial functional data analysis, and stochastic frontier analysis, often leveraging computational efficiency and scalability. Her work addresses critical societal issues, including the impact of community design on public health and environmental monitoring through remote-sensing data. No scientific awards are explicitly listed in the provided texts. She advises no students or grants in the current dataset but collaborates widely on interdisciplinary projects. Her research lab focuses on developing cutting-edge statistical tools with applications in engineering, public health, and environmental science.

Surya GanguliView profile

Associate Professor

• Theoretical Neuroscience
• Computational Neuroscience
• Machine Learning
• +4 more

Surya Ganguli is an Associate Professor in the Department of Applied Physics at Stanford University, with courtesy appointments in Neurobiology and Electrical Engineering. He serves as Senior Fellow at the Stanford Institute for Human-Centered AI and is affiliated with the Stanford Neuroscience Institute , Bio-X , and Wu Tsai Neurosciences Institute . His research spans theoretical neuroscience, machine learning, and statistical mechanics. Ph.D. , UC Berkeley, Theoretical Physics (2004) M.A. , UC Berkeley, Physics (2000) M.A. , UC Berkeley, Mathematics (2004) M.Eng. , MIT, Electrical Engineering and Computer Science (1998) B.S. , MIT, Physics (1998) B.S. , MIT, Mathematics (1998) B.S. , MIT, Electrical Engineering and Computer Science (1998) His lab explores how higher-level cognitive phenomena emerge from neural network dynamics, focusing on perception, memory, attention, and decision-making . Research themes include statistical mechanics of learning , neural representational geometry , and biologically plausible learning rules . Current work examines nonlinear interactions in neural networks through the Schmidt Science Polymath Award (2023). Key article trends reveal expertise in neural coding limits (2022 Nature), synaptic plasticity models (2022 Neural Computation), and deep learning theory (2022 NeurIPS publications). His 2019 Annual Review chapter on statistical mechanics of deep learning established foundational insights into network criticality. Scientific Awards : NSF Career Award (2019) Simons Foundation Investigator (2016) McKnight Scholar Award (2015) James S. McDonnell Foundation Scholar (2014) Sloan Research Fellow (2013) As advisor, he mentors 10+ doctoral students across Applied Physics, Neurosciences, and Computer Science, including Vamshi Balanaga and Mason Kamb. His lab collaborates with experimental teams at Stanford and beyond, supported by grants from NSF , Simons Foundation , and Swartz Foundation . The Neural Dynamics & Computation Lab unites physicists, mathematicians, and neuroscientists to decode cognition through interdisciplinary methods.

• Machine Learning
• Dynamical Systems
• Optimization
• +3 more

Michael Muehlebach leads the independent Learning and Dynamical Systems research group at the Max Planck Institute for Intelligent Systems in Tuebingen, Germany. His interdisciplinary work bridges machine learning, dynamical systems theory, and control engineering to develop algorithms for cyber-physical systems with theoretical guarantees and practical implementations. Dr. Muehlebach received his B.Sc. and M.Sc. in Mechanical Engineering from ETH Zurich in 2010 and 2013, specializing in robotics and control systems. He completed his Ph.D. at ETH's Institute for Dynamic Systems and Control under Prof. R. D'Andrea in 2018, followed by postdoctoral research with Prof. Michael I. Jordan at UC Berkeley. His research focuses on constrained optimization, reinforcement learning, and control theory with applications in robotics. He pioneered approaches that express constraints in terms of velocities rather than positions, enabling more efficient optimization algorithms. His work spans theoretical foundations to physical implementations, including the One-Wheel Cubli balancing robot and electromagnetic navigation systems. Recent publications reveal a strong trend toward physics-informed machine learning, particularly for robotics applications requiring real-time performance and safety guarantees. Dr. Muehlebach has received numerous prestigious awards: Outstanding D-MAVT Bachelor Award Willi-Studer prize for best Master's degree ETH Medal and HILTI prize for doctoral thesis Branco Weiss Fellowship (2018) Emmy Noether Fellowship (2020) Amazon Fellowship (2024) He actively mentors doctoral researchers including Hao Ma, Melis Ilayda Bal, and Onno Eberhard, with research supported by multiple grants. His group maintains strong collaborations with Bernhard Schölkopf's Empirical Inference group at the Max Planck Institute. The Learning and Dynamical Systems group develops innovative hardware and software platforms, including Floaty (a wind-harnessing flying robot), advanced electromagnetic navigation systems, and data-efficient learning methods for robotic table tennis. Their approach combines rigorous theoretical analysis with practical validation on physical systems, emphasizing the integration of known physical structure into machine learning algorithms to improve sample efficiency and ensure generalization.

Markus HeinonenView profile

Research Fellow

• Machine Learning
• Probabilistic Modeling
• Bayesian Inference
• +5 more

Markus Heinonen is an Academy Research Fellow at Aalto University's Department of Computer Science within the School of Science. His academic position is tied to Harri Lähdesmäki's Professorship, focusing on probabilistic machine learning. He holds a Doctoral degree in Engineering and Technology from the University of Helsinki (2013). His research integrates probabilistic modeling , deep learning , and differential equations , with applications in computational biology, drug discovery, and biophysics. Key themes include Gaussian processes, Bayesian inference, generative models, and their use in understanding complex biological systems like immune cell behavior (e.g., T cell receptor analysis in aplastic anemia) and molecular design. He leads major projects such as the Deep Learning with Differential Equations initiative (2020–2025), exploring continuous-time models and physics-informed neural networks. His work bridges theory and application, evidenced by collaborations in diffusion models , optimal transport , and single-cell analysis . Publications span over 65 peer-reviewed outputs, with recent emphases on robust neural network training, multi-target molecular prediction, and interpretable drug design frameworks. His research contributes to UN Sustainable Development Goal 3 (Good Health) through advancements in disease modeling and therapeutic development. He has undertaken visiting research roles at the University of California, San Francisco (2017) and Telecom ParisTech (2013–2014). Media highlights include recognition for work on TCR-epitope prediction and AI-driven enzyme engineering.

Masoud AsgharianView profile

Professor

• Survival Analysis
• Changepoint Problem
• Nonparametric Bayesian Analysis
• +5 more

Masoud Asgharian is a Professor in the Department of Mathematics and Statistics at McGill University. His research focuses on survival analysis, changepoint problems, nonparametric Bayesian methods, and data envelopment analysis. He has contributed to influential studies on dementia survival rates, censored data methodologies, and statistical efficiency measures. His work bridges biostatistics and operations research, with applications in public health and medical sciences. Key contributions include methodologies for prevalent cohort survival analysis, input relaxation efficiency measures in stochastic DEA, and causal inference techniques. Asgharian has collaborated extensively with researchers in epidemiology and biomedical engineering, as evidenced by his co-authored publications on topics ranging from tooth enamel properties to low-precision neural network quantization. His research has been published in high-impact journals such as New England Journal of Medicine , Journal of the American Statistical Association , and Biometrics . Current affiliations include leadership roles in statistical research at McGill, with ongoing projects in computational statistics and healthcare analytics.

Amit SingerView profile

Professor

• Computational Mathematics
• Structural Biology
• Cryo-Electron Microscopy
• +3 more

Amit Singer is a Professor of Mathematics at Princeton University, specializing in computational methods for structural biology and cryo-electron microscopy (cryo-EM). His work focuses on developing mathematical frameworks and algorithms for analyzing large-scale microscopy datasets, particularly in 3D reconstruction and heterogeneity analysis of molecular structures. He leads research in manifold learning, optimal transport, and harmonic analysis, with applications to cryo-EM, signal processing, and inverse problems. Research interests include: (1) Mathematical methods for cryo-EM, including particle alignment, density map analysis, and subspace-based reconstruction techniques; (2) Development of rotation-invariant representations for imaging problems; (3) Application of machine learning and optimization to biomedical imaging challenges. His contributions bridge pure mathematics (e.g., harmonic analysis, manifold theory) with applied computational techniques for real-world microscopy data. Key trends in his recent articles (2023–2025) include advancements in multi-reference alignment methods, Wasserstein distance-based image registration, and overcoming particle detection limitations in cryo-EM. He also explores sparsity constraints, autocorrelation analysis, and novel algorithms for handling heterogeneous datasets. These methods improve resolution and reduce computational costs in analyzing molecular structures at atomic scales. Notable contributions include the ASPiRE software package for steerable PCA, and foundational work on synchronization problems in cryo-EM orientation estimation. His research often addresses algorithmic scalability and robustness to noise in experimental setups.

Sai RavelaView profile

Researcher

• Nonlinear Stochastic Dynamics
• Coherent Fluids
• Uncertainty Quantification
• +6 more

Sai Ravela is a Principal Research Scientist in the Department of Earth, Atmospheric and Planetary Sciences (EAPS) at the Massachusetts Institute of Technology (MIT). His research focuses on nonlinear stochastic dynamics, coherent fluid systems, uncertainty quantification, and autonomous observing technologies. He specializes in developing data-driven methodologies for natural hazard detection, climate change impacts, and environmental risk assessment. Ravela’s work integrates computational science with geophysical applications, including storm surge modeling, extreme rainfall analysis, and geothermal exploration. He pioneers techniques like neural dynamical systems and adversarial learning to improve predictive accuracy in nonstationary climate regimes. His contributions span environmental monitoring systems, autonomous aircraft resilience frameworks, and policy-informed climate vulnerability assessments. Key research areas include: Coastal flood risk in Bangladesh and Vietnam Dynamic data-driven applications systems (DDDAS) Machine learning for geosciences and environmental systems Uncertainty quantification in complex fluid dynamics He leads interdisciplinary projects at MIT’s Computational Science and Engineering (CSE) program, advancing methods for data assimilation, surrogate modeling, and real-time environmental observatories. His innovations bridge theoretical frameworks with practical solutions for climate adaptation and disaster resilience.

Andrea Lecchini VisintiniView profile

Associate Professor

• Systems Modelling and Control
• Aerospace Engineering
• Biomedical Applications
• +1 more

Dr. Andrea Lecchini Visintini is an Associate Professor at the School of Electronics and Computer Science , University of Southampton. He specializes in systems modelling and control with applications in aerospace engineering and biomedical domains, utilizing Monte Carlo methods for stochastic optimization. Cyber-Physical Systems Research Group Institute for Life Sciences Research Focus: His work bridges computational methods with practical applications in: Neurovascular coupling and brain tissue pulsation analysis Advanced control strategies for aerospace systems Stochastic optimization in machine learning and fault detection Medical imaging and diagnostic protocol development Publication Trends: Recent work emphasizes interdisciplinary approaches combining computational neuroscience with engineering, focusing on brain hemodynamics, MIMO system control, and data augmentation techniques for imbalanced datasets. Supervision: Currently supervising PhD student Xuankun Cai in Computer Science.

• Self-organized criticality
• Neuronal network dynamics
• Excitation/Inhibition balance
• +5 more

Anna Levina is an Assistant Professor for Computational Neuroscience at the University of Tübingen , affiliated with the Department of Computer Science under the Faculty of Science. Her research focuses on the self-organization of neuronal activity, critical dynamics in neural networks, and the excitation/inhibition balance in cortical circuits. Current positions: Assistant Professor (since 2018), Group Leader (2017-2018), Equality Officer (Computer Science) Previous roles: IST Fellow (2015-2017), Associated Researcher (2011-2015), Postdoc/PI (2011-2015), Postdoc (2008-2011) Her research integrates mathematical modeling , statistical physics , and computational neuroscience to study criticality phenomena, neural avalanches, and adaptive network dynamics. Key interests include: Self-organized criticality in neural systems Excitation/Inhibition balance mechanisms Network topology and dynamics Timescale analysis in neural processing Stochastic modeling of neural activity Recent publications reveal trends in understanding critical dynamics across biological and artificial networks, with applications to memory systems, sensorimotor integration, and disease modeling. She has received recognition as an IST Fellow .