Find Academic Supervisors

• Precision Agriculture
• Remote Sensing
• Soil Science
• +3 more

Dr. Patrick Filippi is a Lecturer in Precision Crop Management at the School of Life and Environmental Sciences, University of Sydney. He is affiliated with the Precision Agriculture Laboratory and the Sydney Institute of Agriculture. His work focuses on integrating remote sensing, machine learning, and geostatistics to address challenges in precision agriculture, particularly in crop yield modeling, soil mapping, and environmental monitoring. Research interests include precision agriculture technologies, soil science applications, data-driven crop management, and the use of satellite and proximal sensing for agricultural decision-making. He has contributed to projects funded by the Grains Research and Development Corporation (GRDC) and the University of Sydney, focusing on spatial variability in crop production, soil constraints, and machine learning interpretability. Key achievements include developing the LimeSoDa dataset for soil mapping and winning the 2016 CSIRO AgData Challenge Hackathon. His grants span topics like nitrogen fixation mapping in legumes and frost/heat management analytics. Filippi collaborates closely with industry to translate research into practical tools for farmers. Awards: 2nd Place CSIRO AgData Challenge Hackathon (2016) Labs: Precision Agriculture Laboratory (https://precision-agriculture.sydney.edu.au/) Grants: Includes Strategic Partnership Seeding Grants (2024), GRDC-funded projects (2022–2024), and Start-Up Research Funding (2024).

Diogo AlmeidaView profile

Associate Professor

• Neurolinguistics
• Language Processing
• Cognitive Science
• +3 more

Diogo Almeida is an Associate Professor of Psychology and Global Network Associate Professor of Psychology at New York University Abu Dhabi. His research focuses on neurolinguistics and language processing, employing methodologies such as MEG/EEG, fMRI, and cross-linguistic studies. He holds an MSc in Cognitive Science from the Ecole des Hautes Etudes en Sciences Sociales and an PhD in Linguistics from the University of Maryland, College Park. Prior to NYU Abu Dhabi, he held positions at the University of California, Irvine, and Michigan State University. Almeida’s research explores perceptual processes underlying language understanding, including syntactic structure processing, agreement phenomena, and the neural bases of lexical access. His work integrates behavioral experiments with neuroimaging techniques to investigate how linguistic structures are represented in the brain. Key areas include sentence processing selectivity in Broca’s area, cross-linguistic differences in agreement encoding, and the role of phonological and morphological features in real-time language comprehension. His publications span topics like masked priming effects, sociolinguistic influences on speech perception, and the reliability of experimental syntax methods. He leads the Language, Mind and Brain research group at NYU Abu Dhabi, focusing on interdisciplinary approaches to language cognition. Almeida has contributed to debates on empirical rigor in syntax, advocating for robust experimental designs and large-scale data collection to validate theoretical claims.

Shahar KovalskyView profile

Assistant Professor

• Optimization
• Geometry
• Computer Graphics
• +4 more

Shahar Kovalsky is an Assistant Professor of Mathematics at the University of North Carolina at Chapel Hill (UNC-CH), with secondary appointments in the School of Data Science and Society and an adjunct role in the Department of Computer Science (both within the College of Arts and Sciences). He holds a Ph.D. in Computer Science and Applied Mathematics from the Weizmann Institute of Science, and B.Sc./M.Sc. degrees in Mathematics and Electrical Engineering from Ben-Gurion University. His research bridges optimization, geometry, computer graphics, machine learning, and their applications in biology and medicine. Kovalsky's work includes advancements in geometric modeling, medical imaging diagnostics, and evolutionary biology analysis. He has been recognized with awards such as the Günter Enderle Best Paper Award (Eurographics 2016) and the SGP 2015 Best Paper Award. Education: Ph.D., Computer Science & Applied Mathematics, Weizmann Institute of Science, Israel B.Sc./M.Sc., Mathematics & Electrical Engineering, Ben-Gurion University, Israel Research Interests: His work focuses on geometric optimization, machine learning for medical diagnostics (e.g., thyroid cytopathology), and computational methods in evolutionary biology. He develops algorithms for injectivity-preserving parameterizations, medical image analysis, and Gaussian process landmarking for morphometric studies. Key Contributions: Deep learning models for thyroid cancer prediction from smartphone images Geometric algorithms for surface parameterization and injectivity Applications of Gaussian processes in evolutionary shape analysis Awards: Günter Enderle Best Paper Award (Eurographics 2016) SGP 2015 Best Paper Award Teaching & Mentoring: Kovalsky advises graduate and undergraduate students (e.g., Fengyu Yang, Maddy Vinal) and teaches courses like Optimization in Machine Learning at UNC-CH. He also co-advises students in interdisciplinary projects with Caroline Moosmüller and Jeremy Marzuola. Labs & Affiliations: Member of the Carolina Center for Interdisciplinary Applied Mathematics (CCIAM) and previously a Phillip Griffiths Assistant Research Professor at Duke University (2017-2020).

• Poultry Science
• Precision Livestock Feeding
• Animal Nutrition
• +5 more

Dr. Martin Zuidhof is a Professor in the Department of Agricultural, Food & Nutritional Science at the University of Alberta. His primary research focus is on Poultry Systems Modeling and Precision Feeding, particularly in optimizing broiler breeder management and energy partitioning. He holds a PhD in Animal Science from the University of Alberta and has pioneered transformative precision feeding systems that achieve unprecedented flock uniformity ( Education: PhD, Animal Science, University of Alberta Research Interests: Dr. Zuidhof’s work centers on advancing precision livestock systems through mathematical modeling (e.g., multiphasic growth models), optimizing pullet body weight for reproductive efficiency, and addressing societal concerns about animal welfare in poultry production. His innovations in precision feeding systems reduce nutrient waste while enabling precise metabolic studies. Publications: Over 50 peer-reviewed articles since 2004, spanning topics from energy partitioning to smart farming technologies. Recent work emphasizes low-cost sensor applications and Big Data integration in poultry systems. Teaching: Instructs courses like Applied Poultry Science (AFNS 571/AN SC 471) and Principles of Animal Agriculture , emphasizing experiential learning and critical scientific thinking. Grants/Advising: No explicit grants listed, but his research has received institutional support. No advisee names found in provided texts.

Francesco FedeleView profile

Associate Professor

• Nonlinear Wave Phenomena
• Coastal and Ocean Engineering
• Fluid Mechanics
• +11 more

Francesco Fedele is an Associate Professor at Georgia Tech, concurrently affiliated with the School of Civil and Environmental Engineering and the School of Electrical and Computer Engineering. He holds a Ph.D. in Civil Engineering from the University of Vermont (2004) and a Laurea (magna cum laude) from the University Mediterranea, Italy (1998). His research spans nonlinear wave phenomena, coastal engineering, fluid mechanics, and sustainable ocean energy, with a focus on rogue waves, stereo imaging, and computational methods. His work has been published in high-impact journals like Physical Review Letters and IEEE Transactions series. Research Interests include wave turbulence, signal processing for biomedical and radar applications, and mathematical modeling of tidal energy systems. Notable contributions involve analyzing extreme waves in the Mediterranean and North Sea, contributing to maritime safety and hurricane impact studies. Publications reflect expertise in fluid dynamics, oceanography, and computational methods. Recent work addresses the geometrical phases of nonlinear systems and interval-based finite element analysis under uncertainty. His research has been featured in Georgia Tech news for applications in rogue wave prediction and structural engineering. Dr. Fedele’s academic positions and collaborations include postdoctoral research at NASA Goddard Space Flight Center (pre-Georgia Tech tenure). No specific awards are explicitly listed in the provided text, though his work has garnered media attention for its societal impact.

Ziquan LiuView profile

Lecturer

• Reliable Machine Learning
• Uncertainty Quantification
• Computer Vision
• +4 more

Dr. Ziquan Liu is a Lecturer (Teaching & Research) at Queen Mary University of London's School of Electronic Engineering and Computer Science, affiliated with the Centre for Multimodal AI. He holds a PhD from City University of Hong Kong (2023) and dual B.Sc./B.Eng. degrees from Beihang University (2017). His research focuses on trustworthy machine learning, adversarial robustness, and uncertainty quantification in foundation models. He has served as a reviewer for top conferences like NeurIPS, ICLR, and CVPR, earning an Outstanding Reviewer Award in 2021. His teaching includes modules on machine learning for visual data analysis and principles of machine learning. He supervises PhD students in AI safety and reliability, with notable work on conformal prediction, adversarial attacks, and multimodal learning. His research outputs span top venues such as ICML, CVPR, and NeurIPS, addressing challenges in algorithmic fairness, model certification, and cross-modal alignment.

Svetlana YanushkevichView profile

Professor

• Biometric Technologies
• Machine Learning
• Signal Processing
• +3 more

Dr. Svetlana Yanushkevich is a Professor in the Department of Electrical and Software Engineering at the Schulich School of Engineering, University of Calgary. She is also a Full Member of the Hotchkiss Brain Institute and the Mathison Centre for Mental Health Research and Education. Her research focuses on biometric technologies, decision support systems, biomedical applications, and computational intelligence. She leads the Biometric Technologies Laboratory, developing strategies for risk assessment in biometric systems and healthcare monitoring through machine reasoning and signal processing. Education : BSc/MSc in Electrical Engineering (1989), State University of Informatics and Radioelectronics, Minsk PhD in Electrical Engineering (1992), same institution Dr. Habilitated in Technical Sciences (1999), Warsaw University of Technology Research Interests : Dr. Yanushkevich’s work spans biometric system design (e.g., gait analysis, facial attributes), decision support via probabilistic models (Bayesian networks, causal inference), biomedical applications (stroke rehabilitation, wearable sensors), and computational intelligence for data science. She emphasizes fairness, bias mitigation, and trustworthiness in AI systems, particularly in healthcare and accessibility contexts. Recent Research Trends : Her recent publications address causal modeling for accessibility barriers, UAV operator cognitive workload, and medical device optimization in radiation therapy. She explores AI ethics, stress contagion in human-robot teams, and cross-spectral biometric systems. Awards & Recognition : 2024 FEIC Fellow (Engineering Institute of Canada) 2019 Research Excellence Award (Schulich School of Engineering) 2001 Senior IEEE Membership Advising & Grants : She coordinates courses like ENCM 509 (Biometric Systems Design) and ENEL 610 (Biometric Technologies). Her research is supported by grants focusing on healthcare AI, accessibility technologies, and computational epidemiology. Labs & Collaborations : Her Biometric Technologies Lab collaborates with institutions like Hokkaido University and the IEEE Computational Intelligence Society. Projects include wearable health monitoring, decision support platforms, and AI-driven epidemiological modeling.

Alan LindsayView profile

Associate Professor

• Applied Partial Differential Equations
• Computational Mathematics
• Mathematical and Computational Biology
• +3 more

Alan Lindsay is an Associate Professor in the Department of Applied and Computational Mathematics and Statistics (ACMS) at the University of Notre Dame, within the College of Science. He holds a Ph.D. from the University of British Columbia (2010) and a B.S. from the University of Edinburgh (2005). His research focuses on computational and analytical methods for partial differential equations (PDEs) modeling physical and biological systems, including Micro-Electromechanical Systems (MEMS), mathematical ecology, imaging, and inverse problems. His email is a.lindsay@nd.edu, and he is based in Crowley Hall. Education: Ph.D., Applied Mathematics, University of British Columbia, 2010 B.S., Mathematics, University of Edinburgh, 2005 Research Interests: Applied Partial Differential Equations Numerical Methods for PDEs Mathematical Biology and Biophysics Scientific Computing and Simulation MEMS and Micro-Electromechanical Systems Mathematical Modeling of Biological Processes Recent Research Trends: Lindsay’s work emphasizes computational techniques like boundary integral methods, kinetic Monte Carlo simulations, and bifurcation analysis to study diffusion processes, first passage times, and pattern formation in biological and physical systems. His studies bridge theoretical analysis and practical applications, such as optimizing T cell antigen recognition and modeling moth mating strategies. Grants & Advising: While no students are listed, his research is supported by grants in computational mathematics and biological modeling. His work often involves interdisciplinary collaborations with biologists and engineers. Labs/Teams: His research is conducted within the ACMS department, leveraging Notre Dame’s computational infrastructure.

Chao TianView profile

Associate Professor

• Information Theory
• Distributed Data Storage
• Coding Theory
• +6 more

Dr. Chao Tian is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, part of the College of Engineering. He holds a Ph.D. from Cornell University (2005) and a B.E. from Tsinghua University (2000). His research focuses on information theory, distributed storage systems, coding theory, and machine learning applications, including large language models (LLMs) and privacy-preserving techniques like steganography and private information retrieval (PIR). He has received notable awards, including the 2017 IEEE Jack Wolf ISIT Best Student Paper Award and the 2014 IEEE ComSoc DSTC Best Paper Award. Dr. Tian’s work bridges theoretical foundations and practical applications, such as optimizing storage codes for distributed systems, developing secure PIR protocols, and exploring LLMs in energy and steganographic contexts. His group has pioneered methods for variable-order Markov chain modeling with transformers and designed quantization techniques for perceptual quality. Recent activities include a faculty development leave at MIT/Harvard and delivering distinguished lectures globally on AI and information theory. He advises students on cutting-edge topics like LLM-based steganography and diffusion models. His publications span journals like IEEE Transactions on Information Theory and conferences such as NeurIPS and ISIT, with a focus on coding theory, privacy, and machine learning. He has also contributed to open-source tools like the CAI toolbox for investigating information-theoretic limits. Current projects include optimizing cryptocurrency mining in energy markets and advancing federated learning with adversarial robustness.

Sharmistha GuhaView profile

Assistant Professor

• Bayesian Methods
• High-Dimensional Regression
• Causal Inference
• +4 more

Sharmistha Guha is an Assistant Professor in the Department of Statistics at Texas A&M University, part of the College of Arts & Sciences. Her research focuses on Bayesian methods for analyzing complex, high-dimensional data, with applications in neuroscience, network security, and social sciences. She develops techniques for supervised network data analysis, causal inference, and data privacy preservation. Education: Ph.D. in Statistical Science (Duke University, implied by dissertation recognition). Research Interests: Bayesian high-dimensional regression, object-oriented regression, causal inference in randomized trials, probabilistic record linkage, Bayesian nonparametric mixture models, and integration of heterogeneous data types like networks and functional data. Her work addresses challenges in neuroimaging (dMRI/fMRI) and big data analytics. Recent Contributions: Her articles span Bayesian methodologies for network analysis, differential privacy in treatment effect estimation, and applications in environmental health (e.g., PM2.5 exposure studies). Notable trends include leveraging Bayesian hierarchical models for complex data structures and advancing causal inference frameworks. Awards: 2022 Blackwell-Rosenbluth Award (ISBA) 2021 Savage Award Honorable Mention 2024 NSF-DMS Grant 2413721 Grants & Advising: Secured NSF funding for heterogeneous data integration research. Mentored students like Jose Rodriguez-Acosta (NSF GRFP Fellow) and Jacob Pagel (NIH-funded CoSIBS participant). Lab/Team: Leads a research group integrating statistical method development with domain collaborations in neuroscience and network security. Active in organizing workshops and serving on panels to foster academic engagement.

Rudy GuerraView profile

Professor

• Biostatistics
• Bioinformatics
• Statistical Genetics
• +4 more

Rudy Guerra is a Professor and Chair of the Department of Statistics at Rice University, where he has been since 2000. His research spans biomedical applications of statistics, including bioinformatics, statistical genetics, and medical imaging, alongside sociological research in education and Mexican migration. He holds academic leadership roles, including Director of the Data Science Minor and member of the BRIDGE and Doerr Institute steering committees. Guerra earned his Ph.D. in Statistics from UC Berkeley, M.A. in Mathematics from UC Berkeley, and B.S. in Applied Mathematics from UT San Antonio. Education: Ph.D., Statistics, UC Berkeley (1992) M.A., Mathematics, UC Berkeley (1987) B.S., Applied Mathematics, UT San Antonio (1984) Key Roles: Department Chair, Statistics (2019–present) Associate Chair, Statistics (2016–2019) Former Jones College Magister (Residential College Leader, 2005–2011) Research Interests: Dr. Guerra’s work integrates statistical methods with biomedical and social science challenges. His biomedical focus includes cancer genomics (e.g., osteosarcoma metastasis, biomarker discovery), medical imaging (e.g., CT ventilation analysis), and bioinformatics. In social sciences, he examines educational inequities and Mexican migration impacts on health. His recent projects include collaborations with Texas Medical Center institutions and sociologists at Rice. Articles Trends: His publications emphasize interdisciplinary applications, combining statistical rigor with domain-specific insights. Recent work spans oncology, public health, and computational biology, reflecting a commitment to bridging theory and practical medical/sociological challenges. Awards & Roles: Panel Member, Ford Foundation Fellowship (2016–present) Associate Editor, BMC Genetics (2014–present) Former Residential College Master of Jones College (2005–2011) Advising & Grants: Guerra advises students on statistical research and curricula. He has led initiatives like the Keck Center for Quantitative Biomedical Sciences Training and co-founded the Empowering Leadership Alliance (ELA) to support underrepresented minorities in STEM. His grants include funding for bioinformatics consortia and educational outreach programs. Labs & Teams: Active in the Gulf Coast Consortia for Bioinformatics and collaborates with multidisciplinary teams at MD Anderson, Baylor College of Medicine, and UT Health Science Center.

Marina VannucciView profile

Professor

• Bayesian statistics
• Variable selection
• Graphical models
• +5 more

Marina Vannucci is the Noah Harding Professor of Statistics at Rice University, with an adjunct appointment at the UT MD Anderson Cancer Center. She holds a Ph.D. and Laurea in Mathematics from the University of Florence, Italy. Her research focuses on Bayesian statistical methods for complex problems in genomics, neuroimaging, and engineering. She has supervised 31 Ph.D. students and 13 postdocs, published over 185 papers, and received prestigious awards including the Mitchell Prize, Zellner Medal, and Don Owen Award. She has served as Editor-in-Chief of Bayesian Analysis and co-Editor of the Journal of the American Statistical Association. Education: Ph.D. in Statistics (University of Florence, 1996), Laurea in Mathematics (University of Florence, 1992). Research Interests: Bayesian statistics, variable selection, graphical models, statistical computing, applications in genomics, neuroscience, and engineering. Awards: Includes Fellowships from ASA, IMS, AAAS, ISBA, and the 2020 Zellner Medal. Recent recognitions include the 2025 Don Owen Award for excellence in research and contributions to the statistical community. Grants/Advising: Over 30 Ph.D. students and 13 postdocs trained. Key roles include Department Chair (2014–2019) and President of the International Society for Bayesian Analysis (2018). Labs/Teams: Affiliated with Rice Neuroengineering, Ken Kennedy Institute, and the W.M. Keck Center for Interdisciplinary Bioscience Research.

Peter ChinView profile

Professor

• Machine Learning
• Artificial Intelligence
• Differential Geometry
• +8 more

Professor Peter Chin is a Professor of Engineering at Dartmouth College and Director of the Learning, Intelligence + Signal Processing (LISP) Lab. He holds affiliations with the Thayer School of Engineering and serves as Associate Editor of IEEE Transactions on Computational Social Systems. His research bridges signal processing, machine learning, game theory, and differential geometry, with applications in cybersecurity, healthcare, and network analysis. Education: Bachelor of Science in Electrical Engineering, Computer Science, and Mathematics from Duke University (1993) Doctor of Philosophy in Mathematics from MIT (1998) Research Interests: Chin’s work focuses on fundamental questions at the intersection of machine learning, game theory, and signal processing. His lab explores topics like adversarial defense mechanisms, topological machine learning, and computational neuroscience. Recent projects include cybersecurity resilience modeling, medical imaging enhancements via GANs, and multi-agent reinforcement learning frameworks. Publications Trends: His most recent articles address cutting-edge challenges in cybersecurity (e.g., autonomous defense systems), medical AI (e.g., Alzheimer’s classification), and adversarial robustness. A notable 2025 focus is on quantitative resilience modeling for cyber defense, reflecting growing demand for AI-driven security solutions. Awards: Faculty Scholar Award, Duke University George Sherred III Award, Duke University Julia Dale Memorial Award, Duke University Grants & Leadership: Recipient of DARPA cybersecurity research grants Co-chair for SPIE/DSS Cyber Sensing Conference (2013–2020) Developed novel compressive sensing microscope for biological imaging LISP Lab: This interdisciplinary lab pioneers projects like nFlip (multiplayer security game models) and topological machine learning frameworks, emphasizing practical applications of theoretical advancements.

Chen XuView profile

Associate Professor

• Sparse Modeling
• Statistical Learning
• Feature Selection
• +5 more

Chen Xu is an Associate Professor in the Department of Mathematics and Statistics at the University of Ottawa. He holds an M.A. from York University and a PhD from the University of British Columbia. His research focuses on sparse modeling, statistical learning, and big data processing, with an emphasis on both theoretical and computational advancements. Dr. Xu is affiliated with the Faculty of Science and contributes to editorial roles for journals such as the Journal of the American Statistical Association and Electronic Journal of Statistics. Education: M.A., York University PhD, University of British Columbia Research interests include feature selection, regularization methods, kernel methods, and high-dimensional regression. His work addresses computational challenges in big data, proposing efficient algorithms for tasks like singular value decomposition, clustering, and distributed feature screening. Recent publications highlight advancements in multiview PCA, low-tubal-rank tensor recovery, and model-free regression techniques. Publications span prestigious journals like the Journal of the American Statistical Association and IEEE Transactions series, focusing on statistical methodology, machine learning applications, and scalable computational solutions for complex data problems. Editorial Service: Associate Editor, Journal of American Statistical Association-T&M (2023–present) Associate Editor, Electronic Journal of Statistics (2023–present) Former Associate Editor, The Canadian Journal of Statistics (2019–2021) His research groups are Statistics and Biostatistics, and Data Science, Machine Learning, and Artificial Intelligence. He has no listed awards but maintains active editorial and academic collaborations in computational statistics and machine learning.

Simon MochrieView profile

Professor

• Biophysics
• Condensed Matter Physics
• Single Molecule Biophysics
• +7 more

Simon Mochrie is a Professor of Physics and Applied Physics at Yale University, affiliated with the Department of Physics within the Faculty of Arts and Sciences. His research focuses on experimental biophysics and condensed matter physics, with emphasis on chromatin dynamics, nuclear mechanics, and super-resolution microscopy. He holds a Ph.D. from MIT (1985) and has pioneered techniques such as optical tweezers and STED microscopy to study biological systems like the ubiquitin-proteasome system in yeast. Current projects include single-molecule measurements on nucleosomes and developing novel imaging methods like LIVE-PAINT for live-cell super-resolution imaging. Educations: Ph.D., Physics, MIT (1985) Research interests center on understanding how chromatin organization influences nuclear mechanics, with studies on heterochromatin condensation, cohesin-driven loop extrusion, and chromatin-envelope interactions. His lab develops advanced microscopy techniques to visualize protein dynamics and subnuclear structures in real time. Recent work explores diffusive states of membrane proteins and the role of phase separation in heterochromatin mechanics. His articles demonstrate a focus on interdisciplinary approaches, combining biophysical experimentation with computational modeling to elucidate fundamental mechanisms in cell biology and soft matter physics. Notable themes include the interplay between chromatin structure and nuclear stiffness, loop extrusion dynamics, and quantitative analysis of intrachromosomal contacts. Teaching contributions include developing introductory physics courses tailored for life sciences students, emphasizing applications in biology and medicine. He actively participates in STEM education initiatives, including collaborative research networks for graduate students in physical biology. The Mochrie Lab also emphasizes instrumentation innovation, such as building fast-scanning STED microscopes and reversible peptide-based imaging systems.