Find Academic Supervisors

Bryan RoutledgeView profile

Associate Professor

• Finance
• Artificial Intelligence
• Blockchain Technology
• +7 more

Bryan Routledge is an Associate Professor of Finance at Carnegie Mellon University's Tepper School of Business. He holds a Ph.D. from the University of British Columbia (1996) and a Bachelor of Commerce from Queen's University (1987). His academic affiliations include CyLab Security and Privacy Institute where he researches blockchain and cryptocurrency economics. Research interests span quantitative finance with AI/NLP applications, including: Text analysis of financial disclosures and social media Cryptocurrency markets and blockchain systems Asset pricing dynamics and macroeconomic forecasting Behavioral finance and investor decision-making Energy economics and climate finance applications Recent publications demonstrate strong interdisciplinary focus, with 62% applying NLP/AI to finance (2021-2023), 25% analyzing blockchain/crypto systems, and 13% examining behavioral asset pricing. Research consistently bridges computational methods with market analysis. Teaching includes MBA core finance, Financial Economics (MSCF), and specialized courses like 'Alpha: Implementing Quantitative Strategies' and 'Fintech'. Actively involved in Tepper's Executive Education programs. Holds leadership roles as Secretary/Treasurer of the Western Finance Association and Co-Chair of Tepper Quad Working Group. Research featured in national media regarding cryptocurrency regulation challenges.

Yue LuView profile

Professor

• Applied Mathematics
• Control Theory and Stochastic Systems
• Data Science
• +5 more

Yue Lu is the Gordon McKay Professor of Electrical Engineering and Applied Mathematics at Harvard University, serving as Faculty Director of Graduate Studies. His research spans applied mathematics, control theory, machine learning, and signal processing, with a focus on high-dimensional data analysis and algorithmic foundations. He leads the Signal and Information Processing lab in Maxwell Dworkin 113. Notable recognitions include being named a Harvard College Professor (2024) and achieving tenure in 2019. His work addresses topics like random matrix theory, optimization in machine learning, and statistical signal processing. Recent contributions include studies on neural networks, kernel methods, and phase retrieval algorithms. He has published extensively on the theoretical underpinnings of modern learning systems, with a particular emphasis on universality principles and asymptotic analysis. Research Highlights: Developed frameworks for analyzing approximate message passing algorithms Advanced theories of in-context learning and feature learning dynamics Contributed to understanding phase transitions in high-dimensional estimation problems Explored optimal regularization strategies for sparse regression Awards: Harvard College Professor (2024) Full Tenure in Electrical Engineering (2019) His lab focuses on bridging theory and applications in signal processing and AI, with projects ranging from imaging systems to brain network analysis. Ongoing work explores the statistical physics of learning and scalable algorithms for large-scale inference problems.

Dmitriy DrusvyatskiyView profile

Professor

• Continuous Optimization
• Data Science Applications
• Convex Optimization
• +5 more

Dmitriy Drusvyatskiy is a Professor in the Department of Mathematics at the University of Washington and holds the Paul Tseng Faculty Fellow title. He also serves as an Adjunct Professor in the Department of Statistics. His research focuses on continuous optimization, particularly in large-scale problems arising in data science, leveraging tools from convex/nonlinear optimization, variational analysis, semi-algebraic geometry, and high-dimensional probability/statistics. Education: PhD in Operations Research from Cornell University (2013). Research Interests: His work emphasizes optimization theory and applications, including stochastic optimization, nonsmooth analysis, matrix recovery, and statistical learning. He explores foundational aspects like convergence guarantees, algorithm design, and applications in machine learning and data-driven decision-making. Notable Trends in Publications: Recent work highlights advancements in stochastic algorithms with geometric step decay, low-rank matrix recovery, and generalization properties of flat minima. His studies often bridge theoretical guarantees with computational efficiency in high-dimensional settings. Awards: Paul Tseng Faculty Fellow (2020+). Advising & Grants: No explicit student advisee list provided. His research is supported by grants focusing on nonsmooth optimization’s structure, complexity, and conditioning. Collaborations span mathematical theory and interdisciplinary applications in data science.

• Mathematical Foundation of Deep Learning
• Deep Learning Theory
• Optimization
• +3 more

Chaoyue Liu is an Assistant Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University, located in West Lafayette. His research focuses on the mathematical foundations of deep learning, deep learning theory, and optimization techniques. He holds a position in the Department of Electrical and Computer Engineering and contributes to advancing theoretical and practical aspects of machine learning systems. Research Interests: Mathematical underpinnings of deep learning architectures and their theoretical guarantees Optimization algorithms for large-scale machine learning models Federated learning and privacy-preserving techniques Analysis of neural network training dynamics and loss landscapes Key Research Trends in Publications: Recent work emphasizes optimization challenges in over-parameterized systems, federated learning frameworks, and the dynamics of neural network training. His articles explore topics like SGD batch saturation, differential privacy integration, and the emergence of linear behavior in wide neural networks. Grants & Advising: No specific grants or student advisement information is listed. Collaborations may involve Purdue's research groups in machine learning and electrical engineering. Labs/Teams: Affiliated with Purdue's Electrical and Computer Engineering research clusters, likely contributing to interdisciplinary projects in AI and signal processing.

Hongkai ZhaoView profile

Professor

• Computational and Applied Mathematics
• Numerical Analysis
• Scientific Computing
• +4 more

Hongkai Zhao is the Ruth F. DeVarney Distinguished Professor of Mathematics and Chair of the Department of Mathematics at Duke University's Trinity College of Arts & Sciences. He holds a Ph.D. in Mathematics from UCLA (1996) and has held prior positions including Chancellor's Professor at UC Irvine and Gábor Szegő Assistant Professor at Stanford University. His research focuses on computational and applied mathematics with applications in inverse problems, imaging, PDEs, and scientific computing. Key research areas include numerical methods for wave propagation, radiative transfer, and shape analysis. Zhao has led NSF-funded projects on PDE learning, radiative transfer, and analysis training. He has received prestigious awards including the Feng Kang Prize (2007) and SIAM Fellowship (2022). Teaching includes advanced courses on stochastic processes, numerical linear algebra, and mathematical analysis. His work bridges theory and practice, contributing to medical imaging algorithms, computational physics, and geometric modeling.

Benjamin GuedjView profile

Professor

• Machine Learning
• Statistical Learning Theory
• PAC-Bayesian Theory
• +6 more

Benjamin Guedj is a Professor of Machine Learning and Foundational Artificial Intelligence at University College London (UCL) and a Research Director at Inria (France). He leads the Inria London Programme, fostering Franco-British scientific collaboration. His research focuses on PAC-Bayesian theory, generalisation in machine learning, and frugal AI. He holds a PhD from Sorbonne Université and an Habilitation à diriger des recherches. He has supervised eight PhD students and contributed over 70 research articles. Awards include the NeurIPS Best Reviewer Award and PEDR excellence bonus. He co-founded the ELLIS London Unit and chairs roles in French-British academic initiatives. Education: PhD in Mathematical Statistics (Sorbonne Université, 2013), MSc in Pure/Applied Mathematics (Sorbonne, 2010–11), postdoctoral research at Sorbonne and DTU Compute. Research interests span statistical learning, PAC-Bayes, computational statistics, and deep learning. Grants: Led ANR projects BEAGLE/APRIORI (€180k each), Inria's 6PAC project, and EU-funded PERF-AI (€600k). Awards: Multiple best reviewer recognitions, Young Leader of Franco-British Council. Advising: Current PhD students include Chloé Hashimoto-Cullen and Reuben Adams; alumni include Théophile Cantelobre and Felix Biggs. His group explores PAC-Bayes, generalisation, and AI ethics. Labs/Teams: Co-director of ELLIS London, Inria Modal team, and UCL Centre for AI. Serves as associate editor for JMLR/TMLR and chairs ICML/PAC-Bayes workshops.

Kshitij KhareView profile

Professor

• High dimensional covariance estimation
• Graphical models
• Bayesian VAR
• +3 more

Kshitij Khare is a Professor in the Department of Statistics at the University of Florida, affiliated with the College of Liberal Arts and Sciences. His research focuses on high-dimensional statistical methods, Bayesian computation, and graphical models. He holds a Ph.D. in Statistics from Stanford University (2009), and prior degrees from Indian Statistical Institute (B.Stat. 2002, M.Stat. 2004, and M.Math. Finance 2009). His work spans covariance estimation, Bayesian VAR models, MCMC convergence analysis, and applications in genomics, neurosurgery, and dairy science. He teaches advanced courses including Introduction to Probability (STA4321/5325), Theoretical Statistics I/II (STA 6326/6327), and specialized topics like covariance estimation. His grants include NSF-funded projects on Bayesian model selection, MCMC algorithm analysis, and high-dimensional temporal data methods. Notable collaborations include work on genomic prediction using Gaussian concentration graph models and Bayesian approaches for mixed-frequency data. Khare's research emphasizes scalable Bayesian methods, posterior consistency, and algorithmic efficiency. His lab develops novel techniques for estimating complex statistical structures in high-dimensional settings, with applications to real-world problems in biology, finance, and engineering. Current projects explore sparse Cholesky-based covariance estimation and Bayesian shrinkage priors for high-dimensional regression.

Aaron DinnerView profile

Professor

• Theoretical Chemistry
• Biophysical Dynamics
• Nonequilibrium Statistical Mechanics
• +4 more

Aaron Dinner is a Professor of Chemistry and Deputy Dean of the Physical Sciences Division at the University of Chicago, with additional appointments in the James Franck Institute and the Institute for Biophysical Dynamics. His laboratory develops theoretical and computational frameworks to decipher the physical principles underlying complex biological behaviors in living systems. His research focuses on nonequilibrium statistical mechanics and stochastic processes in biological contexts , particularly how cells harness environmental energy to organize molecular interactions across vast spatiotemporal scales. Key methodological contributions include Nonequilibrium umbrella sampling Steered transition path sampling Machine learning techniques for biomolecular simulation interpretation His publication trends reveal a strong emphasis on integrating machine learning with physical modeling to bridge experimental observations and theoretical predictions, especially in cellular dynamics and molecular simulations. Recent work centers on voltage-sensing mechanisms, cytoskeletal dynamics, and constrained sampling of nonlinear systems. Honors include: Searle Scholarship NSF CAREER Award Sloan Fellowship APS Fellowship Latke-Hamantash Debate participation (2010, 2014) Dinner actively mentors graduate students and postdoctoral scholars, with many trainees jointly supervised by experimentalists and applied mathematicians. His group maintains strong collaborative ties with experimental researchers for quantitative data analysis and model validation. Current projects involve machine learning for protein dynamics, cytoskeletal modeling, and neural-network architectures for biological problems. The Dinner Group operates within the University of Chicago's Department of Chemistry, utilizing advanced computational facilities and maintaining active GitHub repositories for open-source software development in biophysical modeling.

• Computer Vision
• Mathematical Morphology
• Deep Learning
• +3 more

Rick Groenendijk is a researcher in the field of computer vision and mathematical morphology at the University of Amsterdam's Computer Vision Lab. His work focuses on integrating morphological operators into deep learning frameworks, particularly in neural network architectures. He holds a PhD in Computer Vision & Mathematical Morphology from the University of Amsterdam, supervised by Dr. Leo Dorst and Prof. Theo Gevers, which he successfully defended in October 2023. His research interests include advancing hybrid neural network architectures, such as HaarNet and MorphPool, which combine linear and morphological operations for tasks like semantic segmentation and depth estimation. Recent contributions address challenges in RGB-D data processing and adversarial training. He has also explored applications in photogrammetry and social robotics learning. Publications span top venues including IEEE TPAMI, arXiv, and the British Machine Vision Conference. His code repository for the Geometric Back-Propagation paper is publicly available on GitHub under the MIT license. Rick has collaborated with institutions on multi-loss optimization techniques and climate action analysis through machine learning. While no formal awards are listed, his work demonstrates impactful contributions to computer vision and deep learning methodologies.

Aksel HiorthView profile

Professor

• Reservoir Engineering
• Geochemical Modeling
• Subsurface Flow Modeling
• +3 more

Aksel Hiorth is a Professor of Reservoir Technology at the University of Stavanger (UiS) and Chief Scientist at NORCE. He holds a PhD in theoretical physics from the University of Oslo. His research focuses on subsurface flow modeling, geochemical interactions, and applications of porous media principles in medicine. He leads the Computational Engineering master's program and has pioneered workflows like IORSim for field-scale geochemical simulations. Education: PhD in Theoretical Physics (University of Oslo). Research Interests: Multi-scale reservoir modeling (pore to field scale) Geochemical processes in porous media Non-Newtonian fluid dynamics Medical applications of computational fluid dynamics Publications highlight advancements in physics-informed neural networks (PINNs), lattice Boltzmann methods, and EOR (Enhanced Oil Recovery) technologies. His work addresses challenges in reservoir optimization, water diversion strategies, and fluid-rock interactions in chalk reservoirs. Key innovations include predictive models for wettability alteration and mineral dissolution/precipitation. Awards include SR Bank's Innovation Award (2010), NPD's IOR Prize (2010), and Lyse’s Research Award (2013). He contributed to establishing the National IOR Centre of Norway, recognized for advancing improved oil recovery techniques. Advancing interdisciplinary research: His labs integrate computational engineering with petroleum geoscience, bridging fundamental physics and applied reservoir engineering. Current projects explore sustainable reservoir management and biomedical fluid dynamics solutions.

Prashant KhanduriView profile

Assistant Professor

• Optimization for Machine Learning
• Federated Learning
• Robust Optimization
• +2 more

Prashant Khanduri is Assistant Professor in Computer Science at Wayne State University, specializing in optimization methods for machine learning. His research develops efficient algorithms for federated learning, robust optimization, and bilevel problems in distributed environments. Awarded Best Student Paper at IEEE SPAWC (2019) and Syracuse University's Doctoral Prize (2020), his work addresses communication efficiency, adversarial robustness, and theoretical guarantees. Current projects focus on Byzantine-robust learning, fairness-aware transformers, and federated time-series forecasting.

Gary Patrick MossView profile

Senior Lecturer

• Pharmaceutics
• Drug Delivery
• Percutaneous Absorption
• +4 more

Dr. Gary Patrick Moss serves as Senior Lecturer in Pharmaceutics at Keele University's School of Allied Health Professions and Pharmacy, holding key roles including Director of Studies for BSc Pharmaceutical Science, Lead for Pharmaceutics, and Undergraduate Electives Coordinator. He joined Keele in 2010 after industry experience at Unilever Research and prior academic appointments. His educational background comprises BSc, MSc, and PhD degrees from Queen's University of Belfast, forming the foundation for his expertise in pharmaceutical sciences. These qualifications are complemented by professional certifications including Chartered Chemist status with the Royal Society of Chemistry. Dr. Moss's research centers on percutaneous absorption mechanisms, with specialized focus on quantitative modeling of skin permeation, metal absorption through skin, and advanced imaging techniques for permeation enhancement analysis. His work bridges theoretical computational approaches with practical pharmaceutical formulation development, particularly in transdermal drug delivery systems. Analysis of his publication record reveals a dominant trend in computational modeling applications for skin permeability prediction, featuring Gaussian Process regression and machine learning techniques. Concurrently, his research explores dendrimer-based permeation enhancers for antimicrobial applications and chlorhexidine delivery, demonstrating integration of nanotechnology with dermatological therapeutics. His scientific recognition includes: Chartered Chemist, Royal Society of Chemistry (1996) Fellow of the Royal Society of Chemistry (FRSC) (2018) Fellow of the Higher Education Academy (2004) Dr. Moss has supervised approximately sixteen PhD candidates across UK and Irish institutions while securing EPSRC funding for initiatives like the POEMS-network. His academic leadership extends to editorial roles including Co-Editor of the Handbook of Pharmaceutical Excipients (2018-2025) and organization of specialized conferences on skin absorption modeling. He maintains active involvement in professional networks including the Skin Forum Group (serving as committee member since 2003) and the International Society of the Stratum Corneum, while contributing to pharmacy education through external examiner appointments at Strathclyde, Kingston, and Ulster universities.

• Cybersecurity
• Machine Learning
• Data Analytics
• +7 more

Dr Zhiyuan (Thomas) Tan is an Associate Professor in the School of Computing, Engineering and the Built Environment at Edinburgh Napier University (ENU). His research focuses on Cybersecurity, Machine Learning, and Network Security with applications in IoT, Edge Computing, and Intelligent Transportation Systems . He has supervised 11 PhD students over 10 years, with 6 completing their studies successfully. Education: BEng (2005) from Northeastern University (China), MEng (2008) from Beijing University of Technology, PhD (2014) from University of Technology Sydney. Research Interests include: Cybersecurity and Machine Learning for Malware Detection Adversarial Machine Learning in Network Security Trust Evaluation in Vehicular Networks Secure Data Architectures for Critical Systems Touch-based Biometric Authentication Hardware Security and Federated Learning Recent Research Trends involve: Advancements in Federated Learning and Machine Unlearning Privacy-preserving IoT and Edge Computing Adversarial Techniques in Malware Analysis Trust Models for Cyber-physical Systems Scientific Awards National Research Award 2017 (Oman) Three Best Paper Awards Kaspersky Lab Finalist Award Stanford Top 2% Scientist (2021-2023) Honourable Mention in SICSA Supervisor of the Year 2019 Grants from organizations including Royal Society (£12,000) , Scottish Informatics Alliance , CSIRO , and ENU Development Trust (£29,703) . He actively recruits self-motivated PhD students for research in Adversarial Machine Learning , Virtualization Security , and Machine Learning Security .

Qingshuo SongView profile

Associate Professor

• Stochastic Control Theory
• Mathematical Finance
• Probability Theory
• +4 more

Qingshuo Song is an Associate Professor in the Department of Mathematical Sciences at Worcester Polytechnic Institute (WPI), maintaining office SH 314 and contactable at +1 (508) 8315000 x6273. His academic journey includes a BSc from Nankai University, and MA and PhD in Mathematics from Wayne State University (2006). Prior to joining WPI in 2019, he held significant academic positions including Associate Professor at City University of Hong Kong (2010-2018), and postdoctoral positions at the University of Michigan (2009) and University of Southern California (2006-2009). His research focuses on Stochastic Control Theory, which bridges probability theory and PDE analysis, with substantial applications in mathematical finance and engineering problems. Dr. Song's recent scholarly output demonstrates consistent research activity through 2025, with primary focus on Mean Field Game/Control theory. His work explores connections between Mean Field Games and Navier-Stokes equations, develops numerical methods for McKean-Vlasov stochastic differential equations, and examines long-time behaviors of stochastic control systems. His publications span optimization and control, probability theory, numerical analysis, and mathematical finance, reflecting an interdisciplinary approach that connects theoretical mathematics with practical applications. His research has received support from organizations including the Simons Foundation as acknowledged in his publications. Dr. Song maintains an active professional presence through arXiv, Google Scholar, ORCID (0000-0002-9289-7462), and GitHub repositories that include educational materials for courses like MA2210 (Mathematical Methods in Decision Making) and various computational projects related to machine learning and stochastic processes.

Sheela RamannaView profile

Professor

• Artificial Intelligence (AI)
• Machine Learning
• Multimodal Information Processing
• +5 more

Dr. Sheela Ramanna is a Professor and Chair of the ACS Graduate Program in the Department of Applied Computer Science at the University of Winnipeg, and an Adjunct Professor at the University of Manitoba. She holds a Ph.D. in Computer Science from Kansas State University, and completed her earlier education at Osmania University, India. Her research focuses on AI, machine learning, and soft computing with applications in natural language processing, multimodal information processing, and topological data analysis. She has been recognized with numerous awards, including multiple UW Merit Awards and Senior Member status in the International Rough Set Society. Dr. Ramanna leads projects funded by NSERC and MITACS, including work on precipitation forecasting, microplastics analysis, and cannabinoid medicine studies. She supervises over 20 graduate students and has authored/co-authored over 150 publications in top journals/conferences. Key roles include editorial positions for EAAI and KES journals, and program chairs for major conferences like IJCRS and RSCTC. Education: B.S. Electrical Engineering (Osmania University), M.S. Computer Science (Osmania University), Ph.D. Computer Science (Kansas State University) Research Interests: Machine Learning, Natural Language Processing, Multimodal Deep Learning, Computational Topology, Rough Set Theory, Social Network Analysis Grants & Projects: NSERC Alliance/Engage Grants, MITACS Accelerate Projects, WeatherLogics collaborations on precipitation forecasting and road condition mapping