Find Academic Supervisors

Constantinos DaskalakisView profile

Professor

• Theory of Computation
• Game Theory
• Economics
• +3 more

Constantinos Daskalakis is the Avanessians Professor of Computer Science at MIT's Electrical Engineering and Computer Science department within the Schwarzman College of Computing. He is a member of CSAIL (Computer Science and Artificial Intelligence Laboratory) and affiliated with LIDS (Laboratory for Information and Decision Systems) and ORC (Operations Research Center). Additionally, he serves as an investigator in the NSF-funded Foundations of Data Science Institute and is a co-founder and chief scientist of the Archimedes AI research center. Dr. Daskalakis holds a diploma in electrical and computer engineering from the National Technical University of Athens and earned his PhD in computer science from UC Berkeley under advisor Christos H. Papadimitriou. His doctoral thesis focused on "On the Existence of Pure Nash Equilibria in Graphical Games with succinct description." His research spans the intersection of theoretical computer science with economics, game theory, machine learning, statistics, and probability theory. Daskalakis has made fundamental contributions to understanding the computational complexity of Nash equilibrium, the mathematical structure of multi-item auctions, and the behavior of machine learning algorithms like expectation-maximization. His work includes developing computationally efficient methods for statistical hypothesis testing and learning in high-dimensional settings, as well as characterizing the structure of high-dimensional distributions. His extensive publication record demonstrates a consistent focus on the theoretical foundations of computation and its applications to economic and learning systems. His recent work shows increasing integration of game theory with machine learning, particularly in areas like generative models, equilibrium computation, and mechanism design. The breadth of his publications across top venues in computer science, economics, and machine learning reflects the interdisciplinary nature of his research. 2007 Microsoft Graduate Research Fellowship 2008 ACM Doctoral Dissertation Award Game Theory and Computer Science (Kalai) Prize from the Game Theory Society 2010 Sloan Fellowship in Computer Science 2011 SIAM Outstanding Paper Prize 2011 Ruth and Joel Spira Award for Distinguished Teaching 2012 Microsoft Research Faculty Fellowship 2015 Research and Development Award by Giuseppe Sciacca Foundation 2017 Google Faculty Research Award 2018 Simons Investigator Award 2018 Rolf Nevanlinna Prize from the International Mathematical Union Best Paper awards at ACM Conference on Economics and Computation (2006, 2013) FOCS 2022 Test of Time Award Daskalakis has advised numerous PhD students and has been involved in organizing major academic programs including the Causality program at the Simons Institute (Spring 2022). His research group focuses on theoretical aspects of computation with applications to economic systems and machine learning. Current work explores connections between generative models, game theory, and statistical learning, with applications to mechanism design and econometrics.

Yanan FanView profile

Adjunct Professor

• Bayesian analysis
• Markov chain Monte Carlo methods
• Approximate Bayesian computation
• +3 more

Professor Yanan Fan is an adjunct full Professor of Statistics at the School of Mathematics and Statistics , University of New South Wales, and a senior Principal research scientist at CSIRO's Data61 in Australia. Their expertise lies in Bayesian models and computational methodology for real-world problem-solving. Current research includes Bayesian semiparametric models, computational methods for large spatial data from medical images, and approximate Bayesian inference. Supervision areas: Bayesian inference, computational statistics, quantile regression, medical image analysis, and bias detection in language. Based in The Red Centre, Room 2055, Sydney NSW 2052, they bridge statistical theory with applications in ecology, climate models, and medical imaging.

Paul DobsonView profile

Assistant Professor

• Markov Processes
• Algorithms
• Differential Equations
• +7 more

Paul Dobson is an Assistant Professor in the Department of Actuarial Mathematics & Statistics at Heriot-Watt University's School of Mathematical and Computer Sciences, Edinburgh. His research centers on probabilistic methods and computational mathematics with applications in optimization and imaging science. His primary research areas include Markov Processes , Algorithms , Differential Equations , Convergence , Approximations , Monte Carlo Methods , and Discretization . He develops computational frameworks for Bayesian inference and stochastic systems, bridging theoretical mathematics with practical imaging solutions through algorithmic innovation. Analysis of his 2019-2025 publications reveals a cohesive trajectory: advancing optimization theory through differential equation connections (2025), enhancing Bayesian imaging via Langevin sampling (2024), solving stochastic differential equations with Poisson frameworks (2024), and extending Markov processes to infinite dimensions (2023). His work consistently appears in high-impact SIAM journals, demonstrating expertise in mathematical analysis and computational implementation. No scientific awards are documented. Dr. Dobson teaches core undergraduate courses including Probability and Statistics A (F78PA) and Probability and Statistics B (F78PB), though research grants and student advising details remain unspecified.

• High-dimensional statistics
• Statistical learning theory
• Online learning
• +3 more

Jaouad Mourtada is an Assistant Professor in the Department of Statistics at ENSAE Paris, a founding member of the Institut Polytechnique de Paris, and a permanent member of CREST (Center for Research in Economics and Statistics). Since September 2020 he has held this faculty position, after completing a post-doctoral fellowship at the University of Genoa and earning his PhD from École Polytechnique. Education PhD in Statistics, École Polytechnique (2016–2019) MSc in Mathematics, "Probability and Random Models", Université Pierre et Marie Curie (2016) MSc in Mathematics, "Fundamental Mathematics", Université Pierre et Marie Curie (2015) BSc in Mathematics, Université Pierre et Marie Curie & École Normale Supérieure (2013) Student at École Normale Supérieure (2012–2016) Research Interests Mourtada’s work lies at the intersection of statistics and learning theory, with a focus on understanding the fundamental complexity of prediction and estimation tasks. His interests span: High-dimensional statistics and minimax theory Statistical learning theory and generalization bounds Online learning, regret minimization, and expert aggregation Density estimation and robust statistics Random forests, kernel methods, and convex optimization Research Output Trends Across more than fifteen recent publications, Mourtada has systematically advanced the understanding of statistical and computational limits in learning. His contributions range from exact minimax analyses of linear least squares and novel robust regression guarantees to refined PAC-Bayesian bounds for aggregation and sharp asymptotics for ridge regression. A recurrent theme is the development of estimators that achieve optimal or near-optimal rates while remaining computationally tractable and adaptive to unknown parameters. Scientific Awards & Recognition While the provided materials do not list specific awards, his sustained publication record in top venues such as Annals of Statistics , Journal of Machine Learning Research , Journal of the European Mathematical Society , and leading ML conferences (NeurIPS, COLT, AISTATS) attests to significant peer recognition. Teaching & Mentoring Mourtada has extensive teaching experience at both undergraduate and graduate levels, covering probability, statistics, and machine learning. Courses delivered include: Statistical Learning Theory (M2 Data Science, École polytechnique & ENSAE) Probability Theory (ENSAE) Python for Probability, Statistics, and Machine Learning (École polytechnique) Optimization for Data Science (M2 Data Science, École polytechnique) Laboratories & Collaborations He is affiliated with CREST/ENSAE and has previously collaborated with the Laboratory for Computational and Statistical Learning at the University of Genoa, the Center for Applied Mathematics (CMAP) at École Polytechnique, and maintains ongoing research ties with international scholars in statistical learning and optimization.

Alejandra Alvarez MuneraView profile

Researcher

• Quantitative Genetics
• Genomic Prediction
• Animal Science
• +3 more

Alejandra Alvarez Munera is a researcher in the Animal & Dairy Science department at the University of Georgia's College of Agricultural & Environmental Sciences . Her work focuses on statistical genetics and genomic prediction methodologies for livestock improvement. Research interests include: Quantitative genetics modeling Linear mixed models optimization Genomic selection algorithms Biostatistical computation Recent publications highlight trends in: Genomic prediction software development (Blupf90 suite) Random regression modeling for animal growth Double hierarchical generalized linear models Reliability approximation techniques

Alexandre Sébastien Julien LevisseView profile

Lecturer

• Computer Architecture
• VLSI Design
• Low-Power Electronics
• +4 more

Alexandre Sébastien Julien Levisse is a Lecturer at the Institute of Electrical Engineering within EPFL's School of Engineering, with additional roles as System Specialist in PAT/SEL departments and Scientist in IEM. His work centers on energy-efficient hardware architectures for edge computing and AI applications. Research focuses on: Computer Architecture & VLSI Design Low-Power Electronics for edge devices Emerging Memory Technologies (RRAM, OxRAM) Neuromorphic & Approximate Computing In-Memory/ Near-Memory Computing Edge AI Hardware Acceleration His 2022-2025 publications reveal strong emphasis on specialized edge AI hardware, including near-DRAM architectures (SideDRAM), RISC-V microcontrollers for healthcare (HEEPOcrates), and overflow-free memory systems. Key innovations involve hardware-software co-design for energy efficiency, variable precision computation, and novel memory hierarchies targeting wearable sensors and edge inference. Levisse teaches Fundamentals of VLSI Design and Lab in Advanced VLSI Design at EPFL. He actively contributes to digital education as a member of EPFL's Centre for Digital Education (CCE), developing curricula for next-generation hardware engineers.

Jae Kyoung KimView profile

Associate Professor

• Dynamical systems
• Biological oscillators
• Circadian rhythms
• +7 more

Jae Kyoung Kim is an Associate Professor in the Department of Mathematical Sciences at KAIST (Korea Advanced Institute of Science and Technology) and serves as Chief Investigator in the Biomedical Mathematics Group at the Institute for Basic Science (IBS). He is also a Member of the Young Korean Academy of Science and Technology and the KOFST Future Generation Committee. His academic journey began with a B.S. from Seoul National University, followed by a Ph.D. from the University of Michigan, Ann Arbor, under advisors Daniel Forger and Victoria Booth. He completed postdoctoral training at the Mathematical Biosciences Institute at Ohio State University. Kim's research spans mathematical and biological domains, focusing on dynamical systems, biological oscillators (particularly circadian rhythms), systems pharmacology, cancer chronotherapy, stochastic processes, Bayesian inference, and single-cell sequencing data analysis. His work bridges abstract mathematical concepts with practical biomedical applications, developing mathematical frameworks to solve complex biological problems related to sleep patterns, drug development, and cancer treatment timing. His publications demonstrate a strong trend toward integrating machine learning with traditional mathematical modeling, particularly in sleep medicine and circadian biology. Recent work emphasizes wearable technology applications, personalized chronotherapy, and the development of clinical tools with mathematical foundations. The research shows increasing interdisciplinary collaboration across mathematics, biology, medicine, and data science. Choi Seok-jeong Award (Minister of Science and ICT Award) KAIST Global Research Cooperation Awards Citation from the Minister Ministry of Science and ICT Innovation Award from MEDITEK J. Shelton Horsley Research Award Yonam International Collaborative Research Professorship KSIAM Young Researcher Award Kim actively advises numerous graduate students and postdoctoral fellows, with several former students securing prestigious academic positions. His research has been supported by significant grants including Samsung Science & Technology Foundation, LG Yonam Foundation, and Pfizer Inc. He organizes major international conferences and serves on editorial boards for leading journals including SIAM Review, PLOS Computational Biology, and Current Opinion in Systems Biology. His book '수학이 생명의 언어라면' (If Mathematics is the Language of Life) has gained recognition as a significant contribution to mathematical biology education.

Sotirios XydisView profile

Assistant Professor

• Hardware/Software Co-design
• High Level Synthesis
• Hardware Accelerators
• +7 more

Sotirios Xydis is an Assistant Professor in the Division of Computer Science at the School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), with prior faculty appointment at Harokopion University of Athens (2020-2023). He maintains ongoing collaboration with the Institute of Communication and Computer Systems (ICCS) since 2014 and previously served as an engineer at HEDNO (2015-2018) and postdoctoral researcher at Politecnico di Milano (2011-2013). His academic credentials include: BSc in Electrical and Computer Engineering, NTUA (2005) MSc in Techno-Economic Systems, NTUA (2011) PhD in Electrical and Computer Engineering, NTUA (2011) Dr. Xydis specializes in hardware/software co-design , energy-efficient hardware acceleration , and memory management for embedded and cloud-edge systems. His research bridges low-power circuit design , heterogeneous architecture optimization , and resource management frameworks , with particular emphasis on AI workloads and serverless infrastructures. Current projects target Edge AI accelerators (CONVOLVE), disaggregated memory systems, and LLM inference optimization through hardware-aware algorithms. Analysis of his 2023-2025 publications reveals three dominant trends: (1) Energy-efficient hardware accelerators for Edge AI using approximate computing techniques, (2) Memory/resource management innovations for disaggregated serverless environments, and (3) GPU/FPGA optimization for LLM inference through dynamic frequency scaling and predictive throttling. These works consistently address the power-performance tradeoffs in heterogeneous computing systems. His scientific recognition includes: Best Paper Award, IEEE/NASA/ESA AHS (2007) Best Paper Award, ACM PARMA (2013) Best Paper Award, ACM Computing Frontiers (2020) Hipeac Award at DAC (2019, 2020) Dr. Xydis has secured over 15 European/national research grants as Principal Investigator and Technical Coordinator, focusing on hardware acceleration frameworks and energy-efficient computing. His advising encompasses graduate research in hardware design and optimization, though specific student names aren't publicly listed. Current projects include CONVOLVE for Edge AI and CollectiveHLS for collaborative hardware synthesis. He is a core member of NTUA's Microelectronics Laboratory (Microlab) and collaborates with ICCS on hardware acceleration projects. His team develops frameworks like CollectiveHLS and throttLL'eM, with active participation in DATE, DAC, and ISCA conference communities.

Elena A. EroshevaView profile

Professor

• Statistical Methodology
• Discrete Data Analysis
• Latent Variable Models
• +7 more

Elena A. Erosheva is a Professor of Statistics at the University of Washington with a joint appointment in the School of Social Work. She serves as Associate Director of the Center for Statistics and the Social Sciences (CSSS) and is an Associate Editor for the Journal of the American Statistical Association and the Annals of Applied Statistics. Education PhD in Statistics from Carnegie Mellon University (2002) Elena Erosheva is a statistician whose research focuses on the development and application of modern statistical methods for addressing complex substantive questions in the social, medical and health sciences. Her ongoing methodological research focuses on latent variable and mixed membership models, multivariate and longitudinal data analysis, and text and network analysis. Her work has important applications in understanding disability in elderly populations, criminal desistance patterns, and scientific collaboration networks. Dr. Erosheva's research bridges statistical theory with practical applications, making significant contributions to both methodology and substantive fields. Analysis of Dr. Erosheva's recent publications reveals a strong focus on mixed membership models and their applications across diverse domains. Her work spans from theoretical developments in statistical methodology (such as variational inference techniques and bootstrap methods) to applied research in health sciences (particularly aging and disability) and social sciences (including criminology and scientific collaboration patterns). A notable trend in her recent work is the application of advanced statistical techniques to understand gender dynamics in scholarly publishing and to improve peer review processes. Scientific Awards 2013 Mitchell Prize from the International Society of Bayesian Analysis First Prize from the National Institutes of Health's Peer Review Challenge Dr. Erosheva has successfully advised numerous graduate students across multiple departments including Statistics, Sociology, Health Services, and Educational Psychology. Her research has been supported by grants from the National Institute on Aging (funding her work on operational definition of disability in the National Long Term Care Survey) and NIH (funding the COAP - Cognitive Outcomes with Advanced Psychometrics project). She has collaborated extensively with researchers across disciplines, demonstrating the interdisciplinary nature of her work. Dr. Erosheva is actively involved in the Center for Statistics and the Social Sciences (CSSS) at the University of Washington, where she serves as Associate Director. She leads research projects focused on measuring gender-based homophily in scientific collaborations, modeling life course transitions, developing partial mastery cognitive diagnosis models, and identifying commensuration bias in grant review.

• Statistics
• Machine Learning
• Dynamic Networks
• +10 more

Francesco Sanna Passino is an Associate Professor in Statistics at the Department of Mathematics, Imperial College London. His research focuses on Bayesian statistical methods for dynamic networks, latent variable models, and event-time data analysis. PhD in Statistics, Imperial College London (2021) MSc in Statistics, Imperial College London (2017) BSc in Statistics, University of Glasgow (2016) Laurea in Scienze Statistiche, University of Bologna (2016) His work spans dynamic network modeling, latent structure inference, and applications in cybersecurity, social networks, and urban mobility systems. Key methodologies include Bayesian inference , model-based clustering , and topic modeling . Applications extend to cybersecurity , music streaming , and bike-sharing systems . The 15 most recent publications emphasize dynamic network analysis using Bayesian frameworks, time series modeling, and applications to financial trends, cybersecurity, and mobility systems. Topics include changepoint detection, latent factor models, and synthetic data generation. He co-organizes the 2025 NetStat Workshop on 'Statistics and Machine Learning for Networks' and organizes an RSS webinar on cybersecurity methods.

Zhenman FangView profile

Associate Professor

• Hardware Acceleration
• Computer Architecture
• Big Data Computing
• +7 more

Dr. Zhenman Fang is an Associate Professor in the School of Engineering Science (Computer Engineering Option) and Associate Member in the School of Computing Science at Simon Fraser University, Canada. He founded and directs the HiAccel Lab, focusing on accelerator-rich architectures. His PhD (2014) is from Fudan University, China, with 15 months spent at the University of Minnesota. Prior to SFU, he was a Staff Software Engineer at Xilinx (2017-2019) and a postdoc at UCLA (2014-2017). His research spans: Hardware acceleration for ML, big data, genomics, and HPC FPGA-based customizable computing and near-data processing Compiler/runtime systems for heterogeneous platforms Performance/reliability optimization of accelerator-rich systems His recent publications (2024-2025) focus on FPGA acceleration for machine learning (e.g., on-device training, quantization), computational chemistry, image/video compression, database systems, and reconfigurable computing, demonstrating cross-domain applications of specialized hardware. Awards & Honors: Best Paper Awards: FPL 2024, MEMSYS 2017, TCAD 2019 Best Paper Nominations: ICCAD 2025, FCCM 2025, HPCA 2017, ISPASS 2018 SFU Research Excellence Horizon Award (2025) NSERC Alliance, CFI JELF, and Xilinx University Awards He advises 20+ PhD/Master's students in HiAccel Lab, focusing on accelerator design. Major grants include NSERC Alliance (2020) and CFI JELF (2019). The lab operates a 10-node cluster with FPGA/GPU infrastructure.

Rob A. RutenbarView profile

Professor

• Integrated Circuit Design Tools
• Nanoscale Statistics in Chip Design
• Hardware Accelerators for AI
• +3 more

Rob A. Rutenbar is the Senior Vice Chancellor for Research at the University of Pittsburgh and holds Distinguished Professorships in Computer Science (School of Computing and Information) and Electrical and Computer Engineering (Swanson School of Engineering). He also serves as Adjunct Professor at UIUC and CMU. With nearly 40 years in academia and industry, his research focuses on integrated circuit tools, nanoscale chip design statistics, and AI hardware accelerators. He led the University of Illinois' CS department (ranked #5 nationally) and spent 25 years at CMU, where he pioneered analog CAD tools and founded Neolinear (acquired by Cadence) and Voci Technologies (acquired by Medallia). Education: PhD (Computer Engineering, University of Michigan, 1984), MS (Computer Engineering, University of Michigan, 1979), BS (Electrical Engineering, Wayne State University, 1978). Research interests include analog circuit synthesis, statistical methods for nanoscale ICs, and hardware architectures for AI. Notable contributions include the first MOOC on chip design tools (60k+ learners) and the Phil Kaufman Award for lifetime contributions to EDA. His work spans over 200 publications and 14 patents, with funding from DARPA, NSF, and industry partners like Google and IBM. Awards include ACM SIGDA Pioneering Achievement Award (2021), AAAS Fellowship (2019), and NAI Fellow (2019). He has advised over 50 PhD students, many of whom lead industry and academia (e.g., Amith Singhee at IBM, Saurabh Tiwary at Google). Rutenbar's leadership initiatives at Pitt include the Momentum Funds for team science, LifeX Labs for life sciences commercialization, and the Office of Industry Partnerships to align research with industry needs.

Nicole AretzView profile

Research Fellow

• Uncertainty Quantification
• Digital Twins
• Bayesian Inversion
• +3 more

Nicole Aretz is a Research Fellow at the Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, working with Prof. Karen Willcox in the Willcox Research Group. Her educational background includes: Master's degree in Mathematics, RWTH Aachen University Doctoral studies at the Aachen Institute for Advanced Studies in Computational Engineering Science (AICES) Member of the International Research Training Group "Modern Inverse Problems" (IRTG MIP) Her research focuses on uncertainty quantification for digital twins, with specific interests in Bayesian inversion, multi-fidelity approximations, and optimal experimental design. She also has expertise in model order reduction, particularly reduced basis methods and non-intrusive operator inference. During her doctoral training, she served as a student representative and advised master's students. She is actively involved in the Willcox Research Group, collaborating across disciplines on challenges in computational engineering.

Steffen GoebbelsView profile

Professor

• 3D City Modeling
• Computer Graphics
• Numerical Analysis
• +5 more

Prof. Dr. Steffen Goebbels is a Professor of Mathematics and Computer Science at Niederrhein University of Applied Sciences, Faculty of Electrical Engineering and Computer Science in Krefeld, Germany. He maintains an office in room F 202 and is actively involved in teaching and research. His academic work spans multiple disciplines with a strong focus on applied mathematics and computer science. His research interests center around 3D city modeling, mathematical optimization, and computer graphics. He has made significant contributions to the field of CityGML data processing and has developed algorithms for calculating 3D building models from land registry data and laser scan data. His work with the iPattern Institute has led to practical applications in cities like Krefeld, Leverkusen, and Dortmund. He has also contributed to neural network approximation theory and various optimization problems. Prof. Goebbels has published extensively in recent years, with publications spanning computer graphics, mathematical optimization, and machine learning. His research shows a consistent pattern of applying mathematical techniques to solve practical problems in 3D modeling and computer vision. He has also co-authored several influential textbooks on mathematics for computer science students. He has received recognition for his work through publications in reputable journals and conference proceedings, though specific awards are not mentioned in the available information. His research has practical applications in urban planning, architectural visualization, and manufacturing processes. Prof. Goebbels is actively involved in teaching mathematics courses (Mathematics 1-3), Numerical Analysis, Logic Programming, Functional Programming, and Scientific Computing. He has developed teaching materials including online courses and textbooks that are widely used in his institution.

Yingzhen LiView profile

Senior Lecturer

• Machine Learning
• Probabilistic Modelling
• Bayesian Deep Learning
• +7 more

Yingzhen Li is a Senior Lecturer in Machine Learning at the Department of Computing, Imperial College London, within the Faculty of Engineering. She holds a Turing Fellowship at The Alan Turing Institute (2024). Her research focuses on building reliable machine learning systems through probabilistic methods, emphasizing uncertainty quantification, robustness, explainability, and adaptive techniques. Key areas include trustworthy ML models, generative modeling (especially sequential and spatiotemporal data), and approximate inference applied to Bayesian deep learning. Education: PhD in Engineering from the University of Cambridge, supervised by Prof. Richard E. Turner. Previously a senior researcher at Microsoft Research Cambridge and intern at Disney Research. Research interests span uncertainty-aware AI, causal representation learning, and scalable inference techniques. Her work has influenced industrial applications in deep learning frameworks like TensorFlow Probability and Pyro. Notable contributions include tutorials on approximate inference at NeurIPS 2020 and organizing conferences like AABI and AISTATS. Her recent publications address challenges in generative AI, calibration of vision-language models, and energy-based models. Awards include the Turing Fellowship recognizing her leadership in foundational AI research. Collaborations include affiliations with The Alan Turing Institute and prior industry experience. She actively mentors students and advises on adaptive learning systems through her academic and industrial networks.