Find Academic Supervisors

Miklos Z. RaczView profile

Assistant Professor

• Probability
• Statistics
• Computer Science
• +10 more

Miklos Z. Racz is an Assistant Professor at Northwestern University with a joint appointment in the Department of Computer Science and the Department of Statistics and Data Science. He is affiliated with the IDEAL Institute. Previously, he was an Assistant Professor at Princeton University (ORFE Department) and a postdoc at Microsoft Research. His research focuses on probability, statistics, computer science, and information theory, with emphasis on combinatorial statistics, discrete probability, and applied probability. Key interests include statistical inference on random discrete structures like random graphs, community detection, latent geometry inference, and DNA data storage. He has advised numerous PhD and undergraduate students. Education: PhD in Statistics (UC Berkeley, 2015), MS in Computer Science (UC Berkeley), MS in Mathematics (Budapest University of Technology and Economics). Research interests span random graph theory, network analysis, information cascades, and computational biology. He teaches courses like Mathematical Foundations of Computer Science and Probability for Statistical Inference. His work has been published in top venues like Annals of Applied Probability, NeurIPS, and IEEE journals. Notable contributions include breakthroughs in graph matching algorithms for stochastic block models, community recovery, and DNA synthesis optimization. His research has practical applications in data storage and network science.

• Machine Learning & Artificial Intelligence
• Probabilistic Modelling
• Neuro-Symbolic Modelling
• +7 more

Dr. Robert Lieck is an Assistant Professor in the Department of Computer Science at Durham University. His research focuses on interdisciplinary applications of machine learning (ML) and artificial intelligence (AI), emphasizing interpretability, robustness, and ethical considerations. He explores computational models in music cognition, communication dynamics, and medical image analysis, aiming to bridge theory and practical tools for domain experts. Before Durham, he was a postdoctoral researcher at EPFL's Digital and Cognitive Musicology Lab (2018–2021) and earned his PhD from the Learning and Intelligent Systems Lab in Stuttgart/Berlin (2012–2017). His work combines probabilistic modelling, neuro-symbolic systems, and reinforcement learning to address challenges in music analysis, autonomous decision-making, and medical robotics. Key research themes include: Music structure and perception modelling Symbol emergence in multi-agent communication Ethical AI and autonomous systems governance Medical imaging applications (CT/MRI analysis) Recent projects involve developing patient-agnostic diabetes management systems using deep reinforcement learning and surgical workflow anticipation through graph learning algorithms. He actively contributes to conferences such as NeurIPS, ISMIR, and AAAI, with publications spanning music informatics, robotics, and biomedical engineering. Current supervision includes four postgraduate students focusing on AI applications in healthcare, music technology, and autonomous systems. His work bridges technical innovation with societal impact, addressing challenges in policy, legislation, and interdisciplinary collaboration.

Vladimir ItskovView profile

Associate Professor

• theoretical neuroscience
• applied algebraic topology
• neural networks
• +3 more

Vladimir Itskov is an Associate Professor in the Department of Mathematics at The Pennsylvania State University, affiliated with the Eberly College of Science. His research focuses on theoretical neuroscience, applied algebraic topology, and neural networks. He holds a Ph.D. in Mathematics from the University of Minnesota (2002) and a B.S. from Moscow Institute of Electronics and Mathematics (1995). His career includes roles at the University of Nebraska-Lincoln (2009–2014), Columbia University’s Center for Theoretical Neuroscience (2006–2009), and Rutgers University (2004–2006). Research interests include understanding neural coding, network dynamics, and topological methods in neuroscience. Notable work involves applying algebraic topology to analyze neural correlations and developing models for neural network behavior. He has received grants from NIH, NSF, and DARPA, focusing on projects like olfactory coding and neural network dynamics. His lab, the Mathematical Neuroscience Laboratory, develops computational tools and collaborates on interdisciplinary projects. Software packages are hosted on GitHub (nebneuron repository). Publications span journals such as PNAS, SIAM, and Neural Computation, addressing topics from clique topology to competitive network dynamics. His theoretical contributions emphasize bridging data-driven neuroscience with mathematical rigor.

Vincent Y. F. TanView profile

Professor

• Online Decision Making
• Multi-Armed Bandits
• Reinforcement Learning
• +3 more

Professor Vincent Y. F. Tan holds dual appointments in the Department of Mathematics and the Department of Electrical and Computer Engineering (ECE) at the National University of Singapore (NUS). He is also affiliated with the Institute of Operations Research and Analytics (IORA) and the Institute of Data Science (IDS). His research focuses on Online Decision Making, Multi-Armed Bandits, Reinforcement Learning, Information Theory, and Statistical Signal Processing. Notably, he has been actively publishing in top-tier conferences like NeurIPS, ICML, and IEEE journals, with recent works exploring topics such as low-rank adaptation, off-policy evaluation, and queueing control. Professor Tan has advised numerous PhD students, including Fengzhuo Zhang, Yujun Shi, and Junwen Yang. He has received recognition for his teaching, including a 4.7/5.0 rating for EE5137 Stochastic Processes. His work has led to impactful publications, such as the best paper award at the ICML 2025 workshop on World Models and an oral presentation at ICLR 2025. He currently serves as a Senior Area Chair for NeurIPS 2025 and an Area Editor for the IEEE Transactions on Information Theory. His research group focuses on advancing theoretical and applied aspects of machine learning, with projects funded by grants in areas like distributed optimization and adversarial robustness. He collaborates widely, including with institutions like IIT Delhi and HKUST Guangzhou. Open positions are available for motivated postdocs and students in his research areas.

• Statistical Machine Learning
• Bayesian Inference
• Variational Methods
• +5 more

Michael C. Hughes ("Mike") is an Assistant Professor in the Department of Computer Science at Tufts University's School of Engineering, where he develops statistical machine learning methods for healthcare applications. His work focuses on building predictive models that extract actionable insights from complex clinical data, including electronic health records and medical imaging. PhD, Computer Science, Brown University (2016) MS, Computer Science, Brown University (2012) BS, Computer Science, Franklin W. Olin College of Engineering (2010) Research interests center on: Bayesian hierarchical models for documents, sequences, and medical images Optimization algorithms for approximate inference Model fairness and interpretability in clinical contexts Semi-supervised learning for medical diagnostics Recent publications demonstrate these capabilities through applications in cardiovascular disease diagnosis, opioid overdose forecasting, and ICU risk prediction. His lab emphasizes reproducibility through open datasets like TMED-2 and open-source tools like BNPy. Grants include NIH R01 funding for heart valve disease detection, NSF CAREER support for model interpretability, and NSF GCR funding for educational uncertainty research. Scientific awards include: NIH R01 Award (PI) for heart valve disease detection (2025) NSF CAREER Award (2024) NSF GCR Grant (2024) Best Poster Award at Time Series Workshop (ICML 2021) Top 10% Reviewer Awards at AISTATS (2023, 2022) Teaching activities include courses on Bayesian Deep Learning, Introduction to Machine Learning, and Statistical Pattern Recognition. He previously served as postdoctoral fellow at Harvard SEAS.

Forest AgostinelliView profile

Assistant Professor

• Deep Learning
• Reinforcement Learning
• Search Algorithms
• +7 more

Forest Agostinelli is an Assistant Professor in the Department of Computer Science and Engineering at the Molinaroli College of Engineering and Computing, University of South Carolina, where he is also affiliated with the AI Institute. His research focuses on designing AI algorithms for pathfinding problems, integrating deep learning, reinforcement learning, heuristic search, and formal logic. He holds a Ph.D. in Computer Science from the University of California, Irvine, an M.S. from the University of Michigan, and a B.S. in Electrical and Computer Engineering from The Ohio State University. Research Overview : Agostinelli’s work emphasizes solving pathfinding problems in domains like robotics, theorem proving, and molecular optimization. His group develops explainable AI methods to enable collaboration between humans and machines. Key projects include DeepCubeA (solving the Rubik’s Cube via deep reinforcement learning) and neural activation function research. Funding & Awards : He has secured grants from NSF, NASA EPSCoR, and South Carolina’s ASPIRE and MADE programs. Notable awards include the NSF Graduate Research Fellowship and the Graduate Education for Minority Students Fellowship. Teaching : He teaches courses in Artificial Intelligence (CSCE 580) and Deep Reinforcement Learning and Search (CSCE 790), mentoring over 15 students at undergraduate and graduate levels. Labs & Collaborations : Active in AI-driven education and interdisciplinary projects, his lab contributes to tools like ALLURE for children’s learning and Bioinformatics platforms like CircadiOmics.

Banu LokmanView profile

Professor

• Operational Research
• Multi-criteria Decision Making
• Optimization
• +3 more

Banu Lokman is a Professor of Operational Research (OR) at the University of Portsmouth, serving as Associate Head (Research and Innovation) in the School of Organisations, Systems and People within the Faculty of Business & Law. She leads the Centre for Innovative and Sustainable Finance and contributes to the Centre for Operational Research & Logistics. Her expertise spans multi-criteria decision-making, optimization, and their applications in healthcare and sustainability. She holds editorial roles at OMEGA and the IMA Journal of Management Mathematics and organizes the NATCOR MCDM courses. Previously, she served as Deputy Director of CORL (2011–2024), Secretary of the International MCDM Society, and Board Member of INFORMS MCDM Section. She currently chairs the INFORMS MCDM Section as President-elect/Vice-President. Education: BSc, MSc, and PhD in Industrial Engineering from Middle East Technical University (METU, Turkey), followed by postdoctoral research at Aalto University (Finland). She taught at METU (2014–2019) and held visiting roles at Aalto University. Research Interests: Focuses on developing optimization methods for multi-criteria decision problems, particularly in healthcare (e.g., optimizing prostate biopsy decisions with Portsmouth NHS Trust) and sustainability. Her work emphasizes algorithms for nondominated set representation, robust efficiency analysis, and cluster ensemble methods. Key Awards: Bernard Roy Award (2022) for outstanding contributions to Multiple Criteria Decision Aiding, and Young Researcher Award (2015). Advising & Grants: Leads a healthcare-related PhD project and contributes to projects like the Social Investment Fund collaboration with Waltham Forest Council. She actively supervises students and participates in research initiatives on supply networks and data control systems. Labs & Teams: Engaged with interdisciplinary teams in operational research and logistics, particularly in applying OR to real-world challenges such as energy market optimization and MRO supply networks.

Sadaf SalehkalaibarView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Generative models
• +5 more

Sadaf Salehkalaibar is an Assistant Professor in the Department of Computer Science at the University of Manitoba, Winnipeg, Canada. She holds an office in the EITC building (E2-416) and has previously held academic positions at the University of Tehran, University of Toronto as a research associate, and visiting roles at McMaster University, Telecom Paristech, and National University of Singapore. Her research focuses on explainable artificial intelligence, generative models, and information theory with an emphasis on rate-distortion-perception tradeoffs in video and image processing. Her educational background includes teaching courses such as Signals and Systems, Digital Signal Processing, and Network Security at the University of Tehran. She currently teaches COMP4190 (Artificial Intelligence) at the University of Manitoba. Research interests revolve around developing efficient algorithms for AI systems, with key contributions in learned video compression, federated learning, and privacy-preserving techniques. Notable work includes the M22 algorithm for communication-efficient federated learning and the NSERC Discovery Grant-funded project on data-driven learning efficiency. Recent publications highlight advancements in perception loss functions, Gaussian vector source analysis, and secure distributed hypothesis testing. She actively serves on editorial boards (e.g., IEEE Transactions on Communications) and conferences (ISIT, ITW). Awards include the prestigious NSERC Discovery Grant (2025). Supervision highlights 13 MSc students at the University of Tehran, focusing on topics like privacy-preserving systems and distributed learning. Labs/teams: Leads research group at University of Manitoba focusing on AI and information theory applications in multimedia systems.

Helmut H. StreyView profile

Associate Professor

• Biomedical Engineering
• Biophysics
• Microfluidics
• +6 more

Helmut H. Strey is an Associate Professor in the Department of Biomedical Engineering at Stony Brook University. His research focuses on micro- and nanotechnologies for quantitative biology , including single-cell analysis, cancer metabolism modeling, and functional MRI data analysis. He holds academic appointments since 2008 and has pioneered technologies like tumor-on-a-chip and optical decoders for translation stages. Education: PhD in Biophysics (Technical University München, 1993), postdoctoral training at NIH (1994-1998). Awards include the NSF CAREER Award (2000-2005), Dillon Medal (2003), and Weston Visiting Professorship (2020). Research interests span cell-to-cell variability , Warburg effect in cancer , and Bayesian analysis of time-series data . His lab develops tools for 3D tumor microenvironments, MRI-compatible drug delivery systems, and biomimetic neural circuit models. Teaching includes advanced numerical methods in biomedical engineering, quantitative biology, and biomolecular analysis. Active in open hardware projects, including microfluidics controllers and IoT devices for health monitoring.

• Statistical Modeling
• Bayesian Analysis
• Educational Data Analysis
• +4 more

Dr. Haiyan Liu is an Associate Professor of Quantitative Methods, Measurement, and Statistics in the Department of Psychological Sciences at the University of California, Merced, within the School of Social Sciences, Humanities, and Arts. She earned her Ph.D. in Quantitative Psychology from the University of Notre Dame (2018). Her research focuses on advanced statistical modeling of psychological and educational data, including high-dimensional, longitudinal, and social network data. She develops Bayesian methodologies and machine learning techniques to enhance understanding of human behavior, with recent emphasis on structural equation modeling, network dynamics, and nonparametric growth curves. Her work addresses challenges in survey methodology and behavioral data analysis. Dr. Liu’s educational background includes a Ph.D. in Quantitative Psychology from the University of Notre Dame (2018), complementing her current academic role. Her lab, accessible at https://sites.google.com/view/ucmhaiyanliu , supports her research activities. Her research interests span Bayesian SEM, social network analysis, and applications of machine learning to behavioral data, aiming to bridge methodological innovation with practical psychological inquiry. Her recent articles highlight advancements in Bayesian model selection, longitudinal sentiment analysis, and social network mediation. She emphasizes prior specification rigor in Bayesian frameworks and explores nonlinear relationships in social dynamics. Though no awards are explicitly listed, her contributions to statistical methodologies in psychological research reflect significant scholarly impact. Dr. Liu advises students in quantitative methods and has developed software tools like logistic4p for misclassification correction in logistic regression. Her work integrates computational methods with theoretical advancements, positioning her as a key contributor to modern quantitative psychology.

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

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

• Computational Mathematics
• Geophysical Fluid Dynamics
• Numerical Software
• +3 more

Prof. David Ham is a Professor of Computational Mathematics at the Department of Mathematics, Faculty of Natural Sciences, Imperial College London. His research focuses on high-level abstractions for scientific computation, particularly in geophysical fluids and numerical software. He leads the Firedrake project and co-developed the dolfin-adjoint framework, which received the 2015 Wilkinson Prize for Numerical Software. Ham holds a BSc (Mathematics) and LLB from The Australian National University, and a PhD from TU Delft. His career includes roles as a NERC Independent Research Fellow and Grantham Research Fellow at Imperial College. He is affiliated with the Grantham Institute, Mathematics of Planet Earth, and Software Performance Optimisation groups. His research spans computational science, including finite element methods, adjoint-based inversion, and parallel computing. Recent work emphasizes differentiable programming integration with machine learning and geophysical modeling. Ham has contributed to numerous grants and projects, including EPSRC and NERC-funded initiatives. He leads development of software tools like Firedrake and Thetis, advancing computational methods for oceanography and geodynamics.

• Physical Chemistry
• Theoretical and Computational Chemistry
• Chemical Engineering
• +6 more

Professor George Jackson, FRS, holds the position of Professor of Chemical Physics at the Department of Chemical Engineering, Imperial College London (since 2001), and has affiliations with multiple institutions including the University of Sheffield and Cornell University. His academic career spans roles from Lecturer (1989–1994) to Reader (1994–2001) in Physical Chemistry and Chemical Physics. He leads the Molecular Systems Engineering group and chairs the journal Molecular Physics . Research focuses on theoretical and computational chemistry, thermodynamics, and fluid phase equilibria. Key contributions include the SAFT (Statistical Associating Fluid Theory) equation of state, applied to CO2 capture, polymer physics, and pharmaceutical processes. He has pioneered models for electrolytes, solvents, and complex fluids, emphasizing sustainability and industrial applications. Notable accolades include the Rossini Award (2023), Fellowship of the Royal Society (2020), and the Bakhuis Roozeboom Medal (2019). His work has been recognized through Imperial College's President's Medal (2016), the Guggenheim Medal (2014), and over 400 citations in Google Scholar. Collaborations span institutions globally, including the Sargent Centre for Process Systems Engineering and the Thomas Young Centre. Current initiatives include the HiRECORD project for CO2 capture technology scaling and the development of phase-change solvents. His research integrates molecular simulation, process design, and sustainability assessment, addressing challenges in energy, environment, and pharmaceuticals.

• Neural Dynamics
• Computational Neuroscience
• Spatiotemporal Patterns
• +3 more

Dr. Pulin Gong is an Associate Professor in the School of Physics at the University of Sydney. His research focuses on understanding the self-organizing mechanisms of neural circuits' spatiotemporal dynamics and their computational principles. He investigates distributed dynamic computation via propagating neural waves, irregular neural activity variability, and coherent spatiotemporal patterns in large-scale neural data. His work combines experimental and computational approaches to unravel neural coding principles. Research interests include: Distributed dynamic computation (e.g., visual feature integration) Irregular neural dynamics and membrane potential fluctuations Coherent spatiotemporal wave patterns (e.g., spiral waves) Recent projects involve analyzing cortical wave patterns in mice and primates, fractional neural sampling, and Lévy walk dynamics in neural systems. Collaborators include institutions like Fudan University and Kyoto University. Current research student: Andrew LY, working on cortico-cortical loop dynamics and AI applications.

Abdulkadir C. YucelView profile

Assistant Professor

• Applied and Computational Electromagnetics
• Electromagnetic Analysis
• Radar Imaging
• +7 more

Abdulkadir C. Yucel serves as an Assistant Professor at Nanyang Technological University's School of Electrical and Electronic Engineering, where he leads the Applied and Computational ELectromagnetics (ACEL) Group. His research spans applied electromagnetics, radar imaging, and AI-driven electromagnetic analysis with applications in smart cities, neurotechnology, and quantum systems. Education: Ph.D. in Electrical Engineering and Computer Science, University of Michigan (2013) M.S. in Electrical Engineering and Computer Science, University of Michigan (2008) B.S. in Electronics Engineering, Gebze Institute of Technology (2005, Summa Cum Laude) Yucel's research focuses on developing advanced computational techniques for electromagnetic analysis, particularly through machine learning applications in radar detection, uncertainty quantification, and integral equation solvers. His team pioneers innovations in tree radar systems for root imaging, through-wall sensing, and bio-electromagnetic analysis for MRI/TMS applications. Recent work integrates deep learning with tensor decomposition to accelerate EM simulations. Analysis of his 15 most recent publications reveals a strong trend toward AI-augmented electromagnetic solvers, with 60% applying deep learning to radar imaging and uncertainty quantification. Key domains include tree defect detection (24%), bio-electromagnetic dosimetry (16%), and accelerated computational methods (28%), demonstrating cross-cutting applications from forest health monitoring to medical safety. Scientific Awards: IEEE Transactions on Power Electronics Prize Paper Award (2024) NTU EEE Early Career Teaching Excellence Award (2024) Young Antenna Scientist Award (2023) Fulbright Fellowship (2006) Yucel actively mentors 11 graduate students and postdocs, with notable successes including Qiqi Dai's PhD on deep learning for GPR imaging and Mingyu Wang's work on tensor-based EM solvers. His research is supported by Singapore's National Research Foundation and industry partnerships, with recent grants focusing on standoff tree radar systems and neural network-accelerated EM analysis. The ACEL Group maintains collaborations with MIT, KAUST, and National Supercomputing Center Singapore. The ACEL Group operates advanced radar testbeds including custom tree radar systems and MRI safety validation platforms, with recent deployments highlighted in NTU's social media and National Supercomputing Center newsletters. Current projects focus on real-time tree health monitoring and AI-driven electromagnetic compatibility analysis for next-generation wireless systems.