Find Academic Supervisors

Ameet SoniView profile

Associate Professor

• Machine Learning
• Computational Biology
• Computational Medicine
• +2 more

Ameet Soni is an Associate Professor in the Computer Science Department at Swarthmore College, currently on leave (2021–2024) serving as Associate Dean of the Faculty for Diversity, Recruitment, and Retention. He holds a Ph.D. in Computer Science from the University of Wisconsin (2011), where he was advised by Professor Jude Shavlik. His research integrates machine learning with computational biology and medicine, focusing on probabilistic graphical models, deep learning, and applications in genomics, medical diagnosis, and biomedical data analysis. His core research interests span: Machine Learning : Probabilistic inference, statistical relational learning, and deep neural networks. Biomedical Applications : Protein-structure prediction, gene modeling, Alzheimer's/Parkinson's diagnosis, and biomedical text/image analysis. His publications emphasize machine learning applications in bioinformatics and medicine, with trends including deep learning for neurodegenerative disease diagnosis (e.g., Alzheimer’s, Parkinson’s), ethical AI education, protein-structure determination, and biomedical data mining. Recent work (2020) also explores AI ethics pedagogy. Awards : Best Paper Award, ACM International Conference on Bioinformatics and Computational Biology (2010) Advising & Labs : He has supervised 25+ students (22 alumni, 3 current) in machine learning and computational biology projects. Alumni pursue careers in academia (e.g., Michigan State University), industry (Google, Amazon), and medicine. His lab investigates transcription factor binding, brain MRI analysis for Alzheimer’s, and statistical relational learning.

Ryan IpView profile

Researcher

• Risk Analysis
• Spatial Statistics
• Agricultural Science
• +3 more

Ryan Ip is a researcher affiliated with Charles Sturt University's Computing, Mathematics and Engineering School, contributing to the Data Science and Engineering Research Unit, DaMRG (Data Mining Research Group), and the Imaging and Sensing Research Group. He holds a PhD in Statistics (2015) and a Bachelor of Science in Risk Management Science from The University of Hong Kong (2010). His research focuses on spatial statistics, risk analysis, agricultural sustainability, and machine learning applications. Key areas include geostatistical modeling of environmental data, resilience in agricultural systems, and decision-making frameworks under uncertainty. Collaborative projects span climate-smart agriculture, pandemic modeling, and cybersecurity in agrotech. Notable achievements include grants for modeling pandemic interventions and agricultural data governance frameworks. He has published widely in environmental science, statistics, and data mining, with recent work addressing regenerative agriculture and resilient community planning. Education : PhD in Statistics, The University of Hong Kong (2011–2015) Bachelor of Science (Risk Management Science & Finance), The University of Hong Kong (2007–2010) Grants/Awards : DSRU Summer Scholarship (2021) Covid-19 firewall strategy modeling grants (2020) Labs/Teams : Data Mining Research Group (DaMRG) Imaging and Sensing Research Group His interdisciplinary work bridges statistical methodologies with real-world challenges in agriculture, health, and infrastructure, emphasizing data-driven decision frameworks.

Dimitri VolchenkovView profile

Professor

• Nonlinear Dynamics
• Complex Systems
• Stochastic Processes
• +3 more

Dr. Dimitri Volchenkov is a Professor in the Department of Mathematics and Statistics at Texas Tech University, specializing in nonlinear dynamics, complex systems, and stochastic processes. His research integrates mathematical physics, statistical mechanics, and network theory to study phenomena ranging from social dynamics to urban mobility. His core research areas include developing mathematical frameworks for complex networks, analyzing predictability in non-ergodic systems, and applying statistical mechanics to biological and social systems. Recent work focuses on information propagation in social networks, detection of disinformation campaigns, and modeling human navigation patterns in urban environments. Volchenkov has published extensively on network-based analysis of social phenomena, including studies on troll networks, social conformity, and opinion dynamics. His applied research includes pedestrian movement modeling on university campuses, tornado severity prediction, and polymer physics. He received the International George M. Zaslavsky Award in 2020 and maintains active research projects funded by organizations including the Department of Defense. Volchenkov teaches courses in linear algebra, mathematical statistics, and specialized topics in complex systems.

Michael HouleView profile

Senior Lecturer

• Data Mining
• Machine Learning
• Dimensionality Reduction
• +5 more

Michael Houle is a Senior University Lecturer in the Department of Computer Science at New Jersey Institute of Technology (NJIT). He holds a PhD from McGill University (1989) in computational geometry and has held academic and research positions at institutions including the University of Melbourne, National Institute of Informatics (Tokyo), IBM Tokyo Research Laboratory, and universities in Australia and Japan. His research focuses on dimensionality reduction, scalability in AI, machine learning, data mining, and outlier detection. He has made significant contributions to local intrinsic dimensionality estimation and its applications in clustering, classification, and adversarial vulnerability analysis. Education: PhD, McGill University, Computer Science, 1989 BSc, McGill University, Mathematics and Computer Science, 1984 Research interests include algorithmics, relational visualization, and theoretical foundations of data analysis. He has been recognized with multiple awards for best research papers at conferences such as SIAM ICDM, SISAP, and WIMS. His work bridges theoretical insights and practical applications in high-dimensional data processing and AI systems. Key areas of contribution include the development of shared-neighbor clustering methods, analysis of adversarial attack vulnerability linked to high dimensionality, and methodologies for estimating intrinsic dimensionality in local data regions. He also explores applications of these concepts in anomaly detection and machine learning model interpretability.

Lise GetoorView profile

Academic

• Machine Learning
• Reasoning under Uncertainty
• Databases
• +7 more

Lise Getoor is a Distinguished Professor in the Computer Science Department at the University of California, Santa Cruz (UCSC), part of the Baskin School of Engineering. She leads the LINQS group, focusing on statistical relational learning, probabilistic reasoning, and responsible data science. Her research integrates machine learning, databases, and AI to address complex structured data challenges. She directs the UCSC D3 Data Science Research Center, collaborating with industry to develop open-source tools for data-driven decision-making. Her research interests include machine learning, reasoning under uncertainty, entity resolution, data integration, and ethical AI. She has pioneered methods like Probabilistic Soft Logic (PSL) and contributed to foundational work in statistical relational learning. Getoor has been recognized with prestigious awards, including ACM Fellow (2019), IEEE Fellow (2021), and election to the American Academy of Arts and Sciences (2024). Awards: ACM Fellow, IEEE Fellow, WiSE Award (UCSC), Distinguished Alumna Award (UCSB) Advising & Grants: Advised over 15 PhD/Master’s students, including notable alumni like Varun Embar and Sriram Srinivasan. Leads NSF-funded TRIPODS projects and industry partnerships through D3. Labs/Teams: LINQS Research Group (statistical relational learning) and D3 Center (data science tools).

Chris AntonopoulosView profile

Lecturer

• Dynamical Systems
• Chaotic Systems
• Computational Neuroscience
• +7 more

Dr. Chris Antonopoulos is a Lecturer in Applied Mathematics at the University of Essex, affiliated with the School of Mathematics, Statistics and Actuarial Science (SMSAS) and the Department of Mathematical Sciences. His research focuses on dynamical systems, chaotic systems, computational neuroscience, and complex networks, with particular emphasis on neural synchronization, network inference, and mathematical tools for analyzing brain dynamics. He holds a PhD from the University of Patras (2007), specialized in Hamiltonian systems and statistical mechanics. Key research areas include: Computational neuroscience: Development of mathematical tools to study neural synchrony, brain plasticity, and disorders like epilepsy. Network inference: Using mutual information and statistical tests to infer connectivity in systems like brain networks and financial markets. Nonlinear dynamics: Analysis of chaos, energy localization in Hamiltonian systems, and q-statistics in non-extensive systems. Applications: Modeling pandemic spread (e.g., SIR models for COVID-19), time-series forecasting, and machine learning integration. Professional affiliations include the Institute of Mathematics and its Applications (MIMA), London Mathematical Society (LMS), and status as a Fellow of the Higher Education Academy (FHEA). His work combines analytical and numerical methods, including Lyapunov exponents, bifurcation analysis, and SALI/GALI chaos indicators. Collaborations span interdisciplinary fields, from neuroscience to statistical physics.

Jinbo BiView profile

Professor

• Artificial Intelligence
• Machine learning
• Data mining
• +7 more

Jinbo Bi is the Frederick H. Leonhardt Professor of Computer Science and Associate Head in the Department of Computer Science & Engineering at the University of Connecticut's School of Engineering. He holds additional affiliations with the Department of Community Medicine and Health Care at UConn Health Center, Department of Mathematics, Department of Statistics in the College of Liberal Arts and Sciences, and the Department of Biomedical Engineering. Bi earned his Ph.D. from Rensselaer Polytechnic Institute and M.Sc. from Beijing Institute of Technology. His research spans artificial intelligence, machine learning, data mining, pattern recognition, computer vision, bioinformatics, medical informatics, and drug discovery. He directs two laboratories: the Laboratory of Machine Learning & Health Informatics and the Laboratory of Artificial Intelligence & Drug Discovery. His recent publications demonstrate a strong focus on applying machine learning to healthcare challenges, particularly in mental health monitoring through smartphone data, depression prediction, substance use disorder analysis, and drug discovery. His work combines theoretical advances in AI with practical healthcare applications, showing particular strength in multi-view learning, graph neural networks, and optimization techniques. Bi has received recognition including a best paper award at BIBM 2014 workshop and two highly accessed articles in BMC Genomics and BMC Genetics. His research is supported by numerous grants from NSF, NIH, VA, and industry partners including Travelers Insurance and Pfizer. As an educator, Bi teaches advanced courses in machine learning and reinforcement learning. His laboratory currently has multiple postdoctoral fellowship openings, reflecting his active research program and mentorship of numerous students who appear as co-authors on his publications.

Shakeel Gavioli-AkilagunView profile

Research Fellow

• Changepoints
• Feature Detection
• Multiscale Statistics
• +4 more

Shakeel Gavioli-Akilagun is a Research Fellow in the Department of Statistics at the London School of Economics and Political Science (LSE), part of the Time Series and Statistical Learning research group. He holds a BSc in Economics and Econometrics from the University of York and an MSc in Statistics (Research) from LSE, where he completed an ESRC studentship. His primary research focuses on changepoints, feature detection, multiscale statistics, and causal inference, with applications in time series analysis and machine learning. His academic contributions include developing algorithms for non-standard change point problems and has published in journals like Electronic Journal of Statistics and Journal of the Royal Statistical Society Series B . He teaches courses such as Distributed Computing for Big Data and Graph Data Analytics at LSE. Recent invited talks include presentations at the Institute of Mathematical Statistics Asia Pacific Rim Meeting (2026) and the EcoSta conference on change point detection (2025).

Amir Reza AsadiView profile

Research Fellow

• Machine Learning
• Differential Privacy
• Information Theory
• +3 more

Dr. Amir Reza Asadi is a Leverhulme Early Career Fellow at the Statistical Laboratory, part of the Department of Pure Mathematics and Mathematical Statistics at the University of Cambridge (since September 2023). He is also a Fellow of the Isaac Newton Trust and a Postdoctoral Affiliate of Trinity College, Cambridge. Previously, he held a Postdoctoral Research Associate position at the same department (2021–2023) and completed his Ph.D. and M.A. in Electrical and Computer Engineering at Princeton University (2017–2021), following dual B.Sc. degrees in Mathematics and Electrical Engineering from Sharif University of Technology (2010–2015). His research focuses on machine learning, differential privacy, and information theory, with contributions to entropic regularization, generalization bounds, and privacy-preserving algorithms. His work bridges theoretical foundations and practical applications, such as neural network training and synthetic data generation. Education: B.Sc. in Mathematics & Electrical Engineering, Sharif University of Technology (2010–2015) M.A. in Electrical and Computer Engineering, Princeton University (2015–2017) Ph.D. in Electrical and Computer Engineering, Princeton University (2017–2021) His recent articles emphasize differential privacy in machine learning, entropy-based regularization techniques, and hierarchical learning models. He has organized workshops, including the 1st and 2nd Cambridge Information Theory Colloquia and the 8th London Symposium on Information Theory. His awards include the Leverhulme Early Career Fellowship, the Teaching Assistant Award from Princeton, and the Bronze Medal in the Iranian Mathematical Olympiad. Amir actively collaborates with institutions worldwide, including MIT, NYU, ETH Zürich, and the Institute for Advanced Study. His work reflects a commitment to advancing theoretical frameworks while addressing real-world challenges in data privacy and algorithmic fairness.

• Psychological Methods
• Causal Inference
• Developmental Psychology
• +3 more

Felix Thoemmes is an Assistant Professor in the Department of Human Development at Cornell University, within the College of Human Ecology. His work focuses on advancing statistical methods for causal inference, mediation analysis, and longitudinal research design. He specializes in addressing methodological challenges in psychological and educational research, particularly regarding nonexperimental data and missing data problems. His research integrates advanced statistical techniques with substantive questions in developmental psychology, mental health, and education policy. Thoemmes' research interests include causal modeling, sensitivity analysis for unobserved confounding, and the application of structural equation modeling. He has contributed to understanding mechanisms linking socioeconomic status to child development, mental health trajectories in college students, and the effects of educational policies. His recent work emphasizes methodological rigor in addressing biases and improving reproducibility in psychological science. He is particularly known for advancing methodologies in mediation analysis, regression discontinuity designs, and inverse probability weighting. His work bridges theoretical causal frameworks with practical applications, providing researchers with tools to better understand complex human developmental processes. Thoemmes has collaborated on studies involving chronic pain management, fire department wellness programs, and the impact of military training on personality development. Thoemmes teaches courses on advanced statistical methods and has contributed to open-source software implementations of causal inference techniques. His research has been published in top journals across psychology, statistics, and education, demonstrating both methodological innovation and substantive scientific impact.

• Renewable Energy
• Energy Storage
• GIS Analysis
• +3 more

Dr. Cheng Cheng is a Senior Research Officer at the Australian National University's School of Engineering. He holds a Bachelor of Engineering (Honours) and PhD from ANU. His research focuses on renewable energy integration, decarbonization pathways, energy system modeling, and GIS analysis. He specializes in optimizing off-river pumped hydro energy storage systems. Cheng leads multiple renewable energy projects funded by government and industry grants, and has developed methodologies for energy demand forecasting and least-cost optimization. His work bridges theoretical energy modeling with practical implementation through collaborative outreach activities. Education: B.Eng(Hons) & PhD (Australian National University) Research interests emphasize sustainable energy transitions, with particular attention to geospatial analysis for energy infrastructure planning. His recent projects include developing frameworks for integrating renewable generation with storage solutions. Over 150 publications span machine learning applications in energy systems, astrophysics, and bioinformatics. Awards include academic fellowships supporting his interdisciplinary work. Cheng collaborates with industry partners to translate research into policy and technology. He directs the ANU's Renewable Energy Integration Lab (RE100), advancing grid stability solutions through innovative storage technologies. His work has been recognized in special issues of journals focusing on robust machine learning applications.

Rachel WangView profile

Associate Professor

• Statistical Network Modeling
• Statistical Machine Learning
• Genomic Data Analysis
• +3 more

Dr. Rachel Wang is an Associate Professor in the School of Mathematics and Statistics at the University of Sydney. Her research focuses on statistical network modeling, machine learning, and applications to genomics. She holds a PhD from UC Berkeley and completed postdoctoral work at Stanford University. Previously, she was a Harrington Faculty Fellow at the University of Texas, Austin. Education: PhD in Statistics, UC Berkeley Postdoctoral Fellowship, Department of Statistics, Stanford University Research Interests: Statistical methods for biological networks High-dimensional data analysis Single-cell genomics and multimodal data integration Conformal prediction and robust classification Grants and Awards: Harrington Faculty Fellow (2021-2022) Australian Research Council Discovery Early Career Researcher Award (2017) Teaching: STAT3023/3923 Statistical Inference (University of Sydney) STAT2011 Probability and Estimation Theory (course coordinator) Past teaching roles at Stanford University Research Tools: Developed scTIE and scJoint methods for single-cell data integration, and contributed to TAD boundary detection algorithms in genome analysis.

Morgane AusternView profile

Assistant Professor

• Applied Probability
• Machine Learning Theory
• High Dimensional Statistics
• +6 more

Morgane Austern is an Assistant Professor of Statistics at Harvard University and an affiliate of the Center of Mathematical Sciences and Applications (CMSA). She holds a PhD in Statistics from Columbia University (2019), where her research focused on limit theorems for dependent/structured data under advisors Peter Orbanz and Arian Maleki. From 2019-2021, she conducted postdoctoral research at Microsoft Research New England. She has been a long-term visitor at the Simons Institute for Theoretical Computing (2022) and was invited to speak at the National Academies of Science, Engineering, and Medicine on AI for mathematical reasoning (2023). Her research spans Probability theory (Stein method, Gaussian universality, concentration inequalities, ergodic theory, optimal transport) Machine learning theory (graph neural networks, data augmentation, deep learning theory) Statistical inference (resampling methods, causal inference, high-dimensional statistics for dependent data) Awards Kavli Fellow (National Academy of Sciences, 2022) NSF CAREER Award (2025) Her work bridges theoretical foundations across probability, statistics, and machine learning, with a focus on structured data analysis and algorithmic robustness.

Gonzalo MateosView profile

Associate Professor

• Statistical Signal Processing
• Machine Learning
• Network Science
• +4 more

Gonzalo Mateos is an Associate Professor in the Department of Electrical and Computer Engineering and holds a secondary appointment in the Department of Computer Science at the University of Rochester. He is also the Asaro Biggar Family Fellow in Data Science and a member of the Goergen Institute for Data Science and Artificial Intelligence. Additionally, he serves as an Associate Editor for IEEE Transactions on Signal Processing and IEEE Transactions on Signal and Information Processing over Networks , and is part of the IEEE SigPort Editorial Board. Dr. Mateos earned his B.Sc. in Electrical Engineering from Universidad de la Republica, Uruguay in 2005. He then pursued graduate studies at the University of Minnesota, Twin Cities, where he received his M.Sc. in 2009 and Ph.D. in 2011. Prior to joining the University of Rochester in 2014, he worked as a Systems Engineer at Asea Brown Boveri (ABB) in Uruguay from 2004 to 2006 and served as a visiting scholar at Carnegie Mellon University's Computer Science Department during the 2013 academic year. His research interests focus on statistical learning from Big Data , network science , decentralized optimization , and graph signal processing , with applications in dynamic network health monitoring , social networks , power grid analysis , and large-scale data analytics . His work emphasizes blind deconvolution, graph topology inference, fairness-aware methodologies, and explainable AI for medical and engineering systems. Dr. Mateos has been honored with the NSF CAREER Award (2018), the IEEE Young Author Best Paper Award (2017), and multiple conference best paper awards including ICASSP 2018, SSP Workshop 2016, and SPAWC 2012. His doctoral research was recognized with the University of Minnesota's Best Dissertation Award (2013, Honorable Mention) in Physical Sciences and Engineering. In advising, he mentors graduate students in his research areas, though specific names are not listed here. His grants include the NSF CAREER Award, and he collaborates on projects like fairness-aware graph filter design and dynamic network monitoring. His research teams and labs operate within the Goergen Institute, focusing on interdisciplinary data science and AI applications. He actively contributes to the Hajim School of Engineering and the broader University of Rochester community, fostering innovation at the intersection of electrical engineering and computer science. His industry experience and academic roles reflect a commitment to both theoretical and practical advancements in networked systems and machine learning.

Carly KnightView profile

Assistant Professor

• Economic Sociology
• Cultural Sociology
• Social Theory
• +5 more

Carly Knight is an Assistant Professor of Sociology at New York University's College of Arts & Science. Her research focuses on economic sociology, cultural sociology, and social theory, with particular emphasis on language, moral discourse, and computational text analysis. Ph.D., Sociology, Harvard University (2018) M.A., Sociology, Harvard University (2013) B.A., Economics & Political Science, Duke University (2007) Dr. Knight investigates the historical development of corporate personhood and metaphorical frameworks in legitimating corporate capitalism. Her ongoing projects use computational text analysis to examine cultural schemas in corporate responses to market transitions, alongside historical research on financial speculation cycles and asset bubbles. Her recent publications span topics including the sociological implications of "mechanism" in theory, discrimination lawsuits in organizational contexts, and gender bias in image recognition systems. She co-organizes NYU’s Sociology of Culture Workshop. Contact: carly.knight@nyu.edu | Office: 79 Washington Square E, Room 322, New York, NY 10003