Find Academic Supervisors

James E. AguirreView profile

Associate Professor

• Astronomy
• Cosmology
• Galaxy Formation
• +3 more

James E. Aguirre is an Associate Professor in the Department of Physics and Astronomy at the University of Pennsylvania. His research focuses on understanding galaxy formation, cosmology, and large-scale structure through advanced instrumentation and observational techniques. He leads projects such as HERA (Hydrogen Epoch of Reionization Array) and TIM (Terahertz Intensity Mapper), dedicated to studying the early universe and distant star-forming galaxies. Aguirre’s work involves cutting-edge millimeter-wave and radio instrumentation design, including Z-Spec, PAPER, and MUSTANG. He has contributed to significant discoveries, such as detecting massive water reservoirs around quasars and determining distances to gravitationally lensed galaxies. Supported by NSF grants, his research bridges observational astronomy with cosmological theory. Education: Ph.D. in Astrophysics (thesis work on TopHat balloon-borne telescope). Teaching: ASTR011 Introduction to Astrophysics I. Current Projects: HERA, TIM, Simons Observatory, and PAPER. Grants: NSF Grant No. 0807990 and others. His research group collaborates on instrumentation like the Bolocam Galactic Plane Survey and explores techniques for mitigating calibration errors and improving signal analysis in radio interferometry. Aguirre’s efforts advance both observational methods and our understanding of cosmic evolution from the epoch of reionization to present-day galaxy formation.

Jonathan NovakView profile

Associate Professor

• Algebraic Combinatorics
• Special Functions
• Random Matrices
• +3 more

Jonathan Novak is an Associate Professor of Mathematics at the University of California San Diego (UCSD). His research focuses on the combinatorial structure and high-dimensional behavior of multivariate special functions in random matrix theory, representation theory, and mathematical physics. He serves on the editorial board of the open-access journal Algebraic Combinatorics . Key research interests include algebraic combinatorics, special functions, and their intersections with random matrices and mathematical physics. His work explores combinatorial structures in high-dimensional spaces, asymptotic analysis of integrals, and connections between probability theory and algebraic structures. Recent publications emphasize topics like Berezin-Karpelevich integrals, quasimodular asymptotics, and topological expansions in matrix models. These studies highlight advancements in understanding complex systems through combinatorial and probabilistic lenses. No scientific awards are explicitly mentioned in the provided information. His contributions extend to editorial work and advancing interdisciplinary research in algebraic combinatorics and mathematical physics.

Conrad BessantView profile

Professor

• Bioinformatics
• Computational Biology
• Artificial Intelligence in Biomedicine
• +5 more

Prof Conrad Bessant is a Professor of Bioinformatics at Queen Mary University of London (QMUL), affiliated with the School of Biological and Behavioural Sciences. He leads the MSc Bioinformatics program and is academic lead of the UKRI AI for Drug Discovery Doctoral Training Programme. His research focuses on automating scientific discovery in biomedicine using AI, machine learning, and network science. Key areas include drug response prediction, kinase networks, and health data analytics from online forums. Research interests span data science, computational biology, and AI applications in healthcare. He has contributed to projects on tumor microenvironment analysis, proteomics, and biomarker discovery in rheumatoid arthritis. His work integrates multi-omics data with machine learning to identify therapeutic targets and predict drug responses. Recent grants include AI-driven omics data integration (BBSRC), biomarker studies in ALS (Barts Charity), and collaborations with pharmaceutical companies like Exscientia and Merck. Publications highlight contributions to automated cell identification, kinase network modeling, and health informatics. He has pioneered tools like MRMaid for proteomics and Galaxy workflows for transcriptomics-informed analyses.

• Materials Science
• Metallurgy
• Additive Manufacturing
• +3 more

Greta Lindwall is a Lecturer at the Royal Institute of Technology (KTH), specializing in materials science and additive manufacturing. She teaches courses such as Metallic Materials, Powder Metallurgy, and Materials in Design and Product Development. Her roles include examiner, course coordinator, and teacher for several advanced programs. Her research focuses on solidification processes in additive manufacturing, particularly using high-energy X-ray techniques and synchrotron imaging to study material behavior during electron beam and laser powder bed fusion. She has contributed to understanding microstructural evolution in steels, grain refinement mechanisms, and process optimization for materials like tool steels and stainless steel. Her work integrates experimental methods with computational modeling, including CALPHAD-based approaches for phase equilibria and microstructural prediction. Key research areas include real-time tracking of solidification, smoke mechanisms in electron beam processes, and the development of novel alloys for AM applications. Lindwall collaborates with initiatives like the LIGHTer Academy and has published extensively on topics such as phase transformations, material properties under AM conditions, and process monitoring technologies. Her contributions bridge fundamental materials science with advanced manufacturing techniques, aiming to improve material performance and process reliability.

• Mathematical Statistics
• Non-Euclidean Data Analysis
• Algebraic Statistics
• +3 more

Andrew McCormack is an Assistant Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. His research focuses on mathematical statistics, with a particular emphasis on non-Euclidean data analysis, algebraic statistics, and information geometry. His research interests include: Non-Euclidean Data Analysis: Developing methodologies for data on manifolds and metric spaces. Algebraic Statistics: Applying nonlinear algebra to understand statistical models, especially graphical models. Tensor Decompositions: Exploring low-dimensional representations of high-dimensional data using matrices and tensors. Information Geometry: Studying statistical models through geometric lenses, incorporating ideas from optimal transport. Statistical Decision Theory: Investigating foundational approaches to evaluating statistical methods from both classical and Bayesian perspectives. Mccormack's recent work spans robust statistical methods, information geometry in covariance modeling, and the application of algebraic techniques to statistical problems. His contributions often bridge theory and computation, addressing challenges in high-dimensional and geometric data analysis. No scientific awards listed. No advisees or grants mentioned. Labs or teams are not specified in the provided information.

Milica OrlandicView profile

Associate Professor

• Hyperspectral Imaging
• Remote Sensing
• Digital Design
• +7 more

Milica Orlandic is an Associate Professor in the Department of Electronic Systems at NTNU. She holds an MSc from the University of Montenegro (2009) and a PhD from NTNU (2015). Her research focuses on hyperspectral imaging, remote sensing, FPGA-based systems, and embedded computing for aerospace applications. She is actively involved in the HYPSO CubeSat mission, developing onboard processing systems for Earth observation. Education: MSc in Electrical Engineering, University of Montenegro (2009) PhD in Electronics, NTNU (2015) Research Interests: Her work spans hyperspectral data processing , including compression, anomaly detection, and onboard computing for satellites. She also explores reconfigurable hardware (FPGAs) for real-time signal processing, cyber-physical systems, and spaceborne sensor systems. Publications Trends: Recent work emphasizes lightweight machine learning for anomaly detection, FPGA acceleration of hyperspectral compression (CCSDS 123), and algorithm co-design for CubeSat missions. Key contributions include robust onboard processing frameworks for HYPSO-1 and adaptive hardware-software systems. Advising & Teams: She supervises a dynamic team of over 40 PhD and MSc students working on FPGA implementations, satellite systems, and hyperspectral algorithms. Notable collaborations include the HYPSO CubeSat project, which aims to deliver high-resolution Earth observation data with low latency. Labs & Infrastructure: Her research leverages NTNU’s facilities for embedded systems prototyping, FPGA development, and CubeSat payload testing. The HYPSO mission integrates her team’s hardware-software co-design innovations for space applications.

• Urban Computing
• Spatiotemporal Data Management
• Artificial Intelligence
• +3 more

Associate Professor Ke Deng is affiliated with the School of Computing Technologies at RMIT University. His research focuses on urban computing, spatiotemporal data analysis, and social networks, with expertise in data mining and artificial intelligence. He holds a PhD in Computer Science from The University of Queensland (2007), a Master's in Information and Communication Technology (2001), and a Bachelor's in Electrical Engineering (1994). Previously, he was a postdoctoral researcher at CSIRO ICT Centre and a researcher at Huawei Noah's Ark Lab. His work emphasizes practical applications of AI, such as fairness-aware recommendation systems, traffic scenario modeling, and energy-efficient protocols for IoT. Dr. Deng's career includes roles as an acting lecturer at The University of Queensland and co-supervisor of a PhD student. He has supervised numerous research projects, including advancements in quantum annealing for recommenders, fake news mitigation via reinforcement learning, and smart human sensing using millimeter-wave radar. His research outputs span journals like ACM Transactions on Information Systems and IEEE Transactions on Knowledge and Data Engineering. Education: PhD in Computer Science, The University of Queensland (2007) MSc in Information and Communication Technology (2001) BEng in Electrical Engineering (1994) Key Research Themes: Urban computing and smart cities Mechanisms for fair AI systems Energy-efficient IoT protocols Spatiotemporal data mining Grants and Collaborations: ARC Australian Postdoctoral Fellowship (2010–2012) Research at Huawei Noah's Ark Lab (2013) His recent projects highlight interdisciplinary innovation, such as quantum computing for feature selection and self-supervised networks for traffic scenario clustering. Dr. Deng is actively involved in mentoring PhD/Masters students and contributes to RMIT’s research initiatives in AI and urban informatics.

Jake LevinsonView profile

Assistant Professor

• Algebraic Geometry
• Algebraic Combinatorics
• Moduli Spaces
• +5 more

Jake Levinson is an Assistant Professor in the Department of Mathematics and Statistics at Université de Montréal. His research focuses on algebraic geometry and algebraic combinatorics, with particular interests in moduli spaces, Schubert calculus, toric varieties, and equivariant free resolutions. He previously held positions at Simon Fraser University (2020–2023), the University of Washington (2017–2020), and completed a postdoctoral fellowship at LaCIM (UQAM). His Ph.D. from the University of Michigan (2017) was advised by David Speyer. Research Interests: - Algebraic Geometry: Moduli spaces, Schubert calculus, toric varieties, homological algebra. - Algebraic Combinatorics: Crystal graphs, Young tableaux, representation theory, combinatorial aspects of algebraic geometry. - Recent work includes studies on Schubert curves, Springer fibers, and applications of algebraic methods to problems in combinatorics and theoretical computer science. Teaching: - Taught MAT 6620 (Algebraic Geometry: Schemes) at Université de Montréal (Winter 2024). - Previously taught courses on intersection theory, representation theory, and Lean formal proof systems. Key Contributions: - Developed combinatorial models for Schubert curves using crystals and tableaux. - Contributed to Boij-Söderberg theory for Grassmannians and studies of class groups in algebraic geometry. - Collaborated on projects linking algebraic geometry to neural networks and random matrix theory.

Yubai YuanView profile

Assistant Professor

• Complex Network Analysis
• Causal Inference
• Optimal Transport
• +7 more

Yubai Yuan is an Assistant Professor of Statistics at the Pennsylvania State University, affiliated with the Department of Statistics within the Eberly College of Science. He holds a PhD from the University of Illinois Urbana-Champaign (2020) and completed postdoctoral research at UC Irvine. His research focuses on network science, causal inference, and statistical machine learning, with applications to neuroscience and social systems. Notable awards include the 2022 NSF-Simons Center Fellow Award and the 2019 ASA Student Paper Award. Education: PhD in Statistics (UIUC, 2020), MS in Statistics (Sun Yat-sen University, 2016), BS in Mathematics (Shandong University, 2012). Research interests span complex network analysis, optimal transport, active learning, and mediation analysis. Current projects include de-confounding causal inference and hypergraph modeling. Teaching includes courses on probability theory and statistical modeling at Penn State. Advises PhD students Yuanchen Wu (active learning on graphs) and Siyu Huang (latent network structures). Collaborates with the Center for Social Data Analytics and organizes workshops in statistical network science. Publications emphasize methodological advancements in network analysis, causal pathways, and data integration. Recent work addresses disaster response via social media data and neuronal activity analysis using optimal transport frameworks.

Yunxiao ChenView profile

Associate Professor

• Multivariate Statistics
• Social Statistics
• Latent Variable Models
• +5 more

Dr. Yunxiao Chen is an Associate Professor in the Department of Statistics at the London School of Economics and Political Science (LSE), where he co-leads a psychometric lab with Professor Irini Moustaki. Previously, he was an Assistant Professor at Emory University (2016–2018) and earned his PhD in Statistics from Columbia University (2016). His research focuses on developing statistical and computational methods for social data science, addressing challenges in high-dimensional data analysis, latent variable models, and educational assessment. Education: PhD in Statistics, Columbia University, 2016 Research Interests: High-dimensional factor models (matrices, tensors, counting processes) Dynamic behavioral data analysis Sequential decision theory in personalized learning Statistical inference for large-scale item response data Applications in education, psychology, and marketing Publications: Recent work includes advancements in factor analysis, change-point detection, and DIF statistical inference Key journals: Journal of the American Statistical Association , Psychometrika , Journal of Machine Learning Research Awards: 2024 Psychometrics Society Best Reviewer Award 2022 Early Career Award 2018 NCME Loyd Dissertation Award Advising & Grants: Accepts PhD students in statistical methodology Funded by National Academy of Education/Spencer Fellowship (2018–2020) and IEA R&D grants (2022–2023) Labs & Teams: Runs LSE’s psychometric lab focused on educational measurement Collaborates with interdisciplinary teams on machine learning applications

Ajay Achath MohananView profile

Lecturer

• Flexible Electronics
• Nanotechnology
• Sensor Technology
• +3 more

Dr. Ajay Achath Mohanan is a Lecturer at the Malaysia School of Engineering, Monash University. He holds a PhD in Engineering from Monash University Malaysia (2016) and a Bachelor of Engineering in Electrical and Computer Systems Engineering from the same institution (2009). His research focuses on flexible surface acoustic wave (SAW) sensors integrated with ZnO nanostructures, transitioning from rigid to flexible substrates. Key areas include piezoelectric ZnO thin films on polymers, low-temperature hydrothermal synthesis of nanowires, and UV-LED photolithography for sensor fabrication. He teaches ECE2071 - Computer Organisation and Programming and ECE3091 - Engineering Design . Mohanan leads or collaborates on projects such as flexible Lamb wave resonators for wastewater monitoring and graphene-integrated acoustic sensors. He has contributed to over 15 peer-reviewed publications since 2009, addressing topics like SAW resonator design, nanowire growth, and semiconductor defect analysis. His research aligns with UN SDG 4 (Quality Education) and SDG 9 (Industry, Innovation, and Infrastructure). Mohanan’s work emphasizes sustainable sensor technologies for environmental and industrial applications. He operates in the Micro and Nano Devices Lab, advancing flexible electronics and wearable sensor platforms.

Christian IkenmeyerView profile

Professor

• Computational Complexity
• Algebraic Combinatorics
• Geometric Complexity Theory
• +2 more

Christian Ikenmeyer is Professor in Computer Science and Mathematics at the University of Warwick. His research focuses on algebraic complexity theory, geometric complexity theory (GCT), and representation theory, with emphasis on tensor rank, Kronecker coefficients, and polynomial identity testing. Research Focus: Dr. Ikenmeyer develops mathematical frameworks to solve fundamental problems in computational complexity, including P vs NP. His work connects representation theory with algebraic geometry to establish complexity lower bounds and classify computational hardness. Leadership: He organizes workshops on algebraic complexity and GCT, including the 2023 Algebraic Complexity Theory Workshop at ICALP. His research is funded by EPSRC and DFG grants, supporting investigations into homogeneous complexity and branching programs. Teaching: Courses include 'Groups and Representations' and programming contest coaching. He has previously taught at MIT, Texas A&M, and Saarland University, developing lecture notes on GCT.

• Scientific Workflow Management
• Distributed Computing
• Numerical Linear Algebra
• +3 more

Wilfried Gansterer is a Professor at the Faculty of Computer Science, University of Vienna, leading the Theory and Applications of Algorithms research group. His work focuses on numerical algorithms, distributed computing, and machine learning, with notable contributions to graph neural networks and fault-tolerant systems. Active in projects such as Algorithmic Data Science for Computational Drug Discovery (2020–2028) and REPEAL (Resilience vs. Performance in Numerical Linear Algebra, 2016–2020). Research Interests: Dr. Gansterer’s expertise spans graph neural networks, matrix compression, adversarial defense mechanisms, and high-performance computing. His work addresses challenges in efficient computation, resilience against node failures, and optimizing distributed systems. Projects : Algorithmic Data Science for Computational Drug Discovery (2020–2028) REPEAL: Resilience vs. Performance in Numerical Linear Algebra (2016–2020) Verteiltes Rechnen (Distributed Computing, 2007–2014) Awards : 2023 Best Paper Award for work on Crossfire: An Elastic Defense Framework for Graph Neural Networks. Labs/Teams : Directs the Theory and Applications of Algorithms group, focusing on algorithmic innovation in distributed and high-performance computing environments.

Giulia GuidiView profile

Assistant Professor

• High-Performance Computing
• Parallel Computing
• Sparse Linear Algebra
• +4 more

Giulia Guidi is an Assistant Professor of Computer Science at Cornell University, affiliated with the Cornell Ann S. Bowers College of Computing and Information Science. She leads the Cornell High-Performance Computing (HPC) Group and is an Affiliate Faculty at Lawrence Berkeley National Laboratory’s Performance and Algorithms Research Group. Her research focuses on high-performance computing for computational sciences, sparse linear algebra, and scalable software infrastructure for parallel systems. She holds a PhD in Computer Science from UC Berkeley (2022) and has been recognized with awards including the 2024 SIAG/Supercomputing Early Career Prize and the 2023 ISSNAF Young Investigator Award. Her work addresses challenges in genomics, population genetics, and scalable computational methods through collaborations like the NSF-funded 'ACED' project with April Wei’s Lab. Guidi mentors a diverse group of PhD, MEng, and undergraduate students, emphasizing parallel programming and HPC applications. Her lab’s research spans GPU-accelerated algorithms, sparse matrix computations, and bioinformatics tools like the Popcorn and BELLA aligners. She is also a Graduate Field Faculty in Computational Biology and Applied Mathematics at Cornell.

Soheil MohajerView profile

Associate Professor

• Information Theory
• Wireless Networks
• Distributed Storage Systems
• +5 more

Soheil Mohajer is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Minnesota. His research focuses on Information Theory, Wireless Networks, Distributed Storage Systems, Bioinformatics, and Dynamic Systems. Accepting new graduate research students His research includes Distributed Optimization with applications in wireless networks, Group Testing for secure distributed computation, and Coded Caching techniques. Recent work explores Fact-Checking Algorithms and Matrix-Vector Computation Security . Key trends in his publications include: Optimization algorithms for distributed systems Error correction and security in networked environments Information theory applications in storage and communication Machine learning for reliability assessment Projects and grants: Fake News Detection (USDOD Air Force, 2023-2025) CAREER: Coded Caching for Wireless Networks (NSF, 2018-2025) Repair-Efficient Cloud Storage (NSF, 2016-2021) He leads the Information Processing Group , focusing on collaborative research in networked systems and information theory.