Find Academic Supervisors

Gabriela V. Cohen FreueView profile

Associate Professor

• Statistical Methodology
• High-Dimensional Data Analysis
• Robust Statistics
• +6 more

Gabriela V. Cohen Freue is an Associate Professor in the Department of Statistics at the University of British Columbia (UBC), Vancouver Campus, and holds a Canada Research Chair (CRC Tier 2). She leads an interdisciplinary research program focusing on developing robust statistical methodologies for analyzing high-dimensional data in genomics and proteomics, with applications in medical sciences. Her work addresses challenges such as outliers, collinearity, and measurement errors, with applications in biomarker discovery for diseases like multiple sclerosis, cardiovascular disorders, and asthma. Her academic journey includes collaborations across disciplines, including with the BC Cancer Agency, PROOF Centre of Excellence, and iCAPTURE. She has pioneered methods like the Penalized Elastic Net S-Estimator (PENSE) and contributed to proteomic data analysis tools such as the Protein Group Code Algorithm (PGCA). She also co-developed the MDQC quality control method for microarrays. Research interests include robust regression, biomarker development, and statistical methods for big data. Her team includes postdocs, PhD, and MSc students, with a focus on training in both statistical rigor and interdisciplinary collaboration. Notable grants include a CANSSI Collaborative Research Team Project (CRT) award for robust causal inference and prediction modeling. Teaching responsibilities span statistical consulting, high-dimensional biological data analysis, and generalized linear models. She emphasizes active learning and real-world problem-solving in her courses. Her lab’s work is supported by grants from the Data Science Institute (DSI) and collaborations with institutions like the PROOF Centre. Alumni of her group hold positions in academia (e.g., George Mason University) and industry (e.g., Merck, BC Cancer Research Centre).

• Atomic, Molecular, Nuclear, Particle and Plasma Physics
• Mathematical Physics
• Quantum Physics
• +3 more

Professor Kellogg Stelle is a distinguished academic in the Department of Physics at Imperial College London, affiliated with the Faculty of Natural Sciences. He holds the title of Professor of Physics and is part of research groups including the Physics of Universe and Theoretical Physics. His academic career includes a PhD from Brandeis University (1972–1977) and an AB in History and Science from Harvard University (1966–1970). His research focuses on Atomic, Molecular, Nuclear, Particle and Plasma Physics; Mathematical Physics; Quantum Physics; Astronomical and Space Sciences; Pure and Applied Mathematics. He has made significant contributions to supergravity, string theory, and cosmology, exploring topics like higher-order gravity, braneworld models, and quantum gravity phenomena. His work often bridges theoretical frameworks and cosmological implications, emphasizing unification theories and symmetry principles. Professor Stelle’s publications reflect a deep engagement with advanced topics such as compactification on Calabi-Yau manifolds, localized gravity in braneworld scenarios, and the ultraviolet problem in supergravity. His research often intersects with cutting-edge areas like quantum geometry and holography, contributing to foundational debates in theoretical physics. Affiliations include the Physics of Universe and Theoretical Physics groups at Imperial College, reflecting his interdisciplinary approach to fundamental physics. He is fluent in French, Russian, Italian, and German, enhancing his international collaborations.

• Planetary Health
• Urban Planning
• Public Health
• +5 more

Professor Thomas Astell-Burt is a Professor of Cities and Planetary Health at the University of Sydney's School of Architecture, Design and Planning. His work focuses on the intersection of urban environments, green spaces, and public health. He is a member of the Sydney Environment Institute and the Charles Perkins Centre. His research examines how urban planning and green infrastructure can mitigate health inequities, combat climate change, and improve mental/cardiometabolic health. Key areas include nature prescriptions, heat-related mortality, and the role of green spaces in reducing loneliness. Recent grants include projects on aged care workforce sustainability, nature-based strategies for loneliness reduction, and the PANDA Trial for cardiometabolic disease prevention. Over 180+ publications span peer-reviewed journals like Scientific Reports , Climate , and PloS One , with a focus on global health trends and urban environmental policies. His work bridges epidemiology, urban design, and interdisciplinary collaboration to inform evidence-based health policies. Key affiliations include leadership roles in global health initiatives and collaborations with institutions like the University of Glasgow. His research has been cited in policy frameworks addressing urban sustainability and planetary health.

Pragya SurView profile

Assistant Professor

• High-dimensional Statistics
• Machine Learning
• Artificial Intelligence
• +3 more

Pragya Sur is an Assistant Professor of Statistics at Harvard University and currently on leave as a Visiting Professor at MIT’s Laboratory for Information and Decision Systems (LIDS). Her research focuses on high-dimensional statistics, machine learning, and artificial intelligence, particularly in overparametrized models, causal inference, and learning under distribution shifts. She has held postdoctoral positions at Harvard’s Center for Research on Computation and Society (hosted by Cynthia Dwork) and was a Simons Institute Long-Term Participant at UC Berkeley. She earned her Ph.D. in Statistics from Stanford University under Emmanuel Candès, and completed her B.Stat and M.Stat at the Indian Statistical Institute, Kolkata. Sur’s work has been supported by NSF awards, the Eric and Wendy Schmidt Fund, and the William F. Milton Fund. She was named an International Strategy Forum (ISF) Fellow (2023) and led the Institute of Mathematical Statistics (IMS) New Researchers Group (2022–2024). She serves as an Associate Editor for Statistical Science and Guest Co-Editor for a special issue on AI and statistics. Her research contributions span theoretical guarantees for machine learning, transfer learning, and debiasing techniques in high-dimensional inference. Awards: NSF CAREER Award, Theodore W. Anderson Dissertation Award, Ric Weiland Fellowship Education: Ph.D. in Statistics (Stanford, 2019); M.Stat (ISI Kolkata, 2014); B.Stat (ISI Kolkata, 2012) Professional Roles: Associate Editor, Statistical Science ; ISF Fellow (2023); former IMS New Researchers Group Lead Her current research emphasizes statistical theory for modern machine learning, including foundational work on overparametrized models and robust inference across heterogeneous environments.

Wojciech JaroszView profile

Associate Professor

• Computer Graphics
• Rendering
• Light Transport Simulation
• +3 more

Wojciech Jarosz is an Associate Professor of Computer Science at Dartmouth College, affiliated with the College of Engineering and Computer Science. His research focuses on computer graphics, particularly light transport simulation, rendering algorithms, and digital fabrication. He co-founded the Visual Computing Lab and previously led the rendering group at Disney Research Zürich. Jarosz holds a Ph.D. and M.S. from UC San Diego and a B.S. from the University of Illinois Urbana-Champaign. His educational background includes studies in computer science and engineering, with a strong emphasis on graphics and rendering. Research interests span light transport simulation, Monte Carlo methods, appearance capture, and fabrication. Notable achievements include the Eurographics Young Researcher Award (2013) and the NSF CAREER Award (2019). Jarosz's work integrates theoretical rigor with practical applications, such as real-time rendering techniques and volumetric light transport. His lab develops tools for artistic authoring, including intuitive metaphors for volumetric lighting in animated films. Recent projects explore wave-optics BSDF models, optical heterodyne rendering, and unifying radiative transfer models. Key awards include the SIGGRAPH 2024 Best Paper Award and Neukom Institute prizes. His teaching includes courses on rendering algorithms, computer graphics, and computational photography. Jarosz collaborates with industry (e.g., Disney, NVIDIA) and advocates for diversity in computer graphics research.

Xiaolei FangView profile

Associate Professor

• Statistical Learning
• Deep Learning
• Federated Learning
• +3 more

Xiaolei Fang is Associate Professor in the Edward P. Fitts Department of Industrial and Systems Engineering at North Carolina State University. His research develops advanced statistical learning, deep learning, and optimization methods for industrial applications involving high-dimensional data, with particular focus on condition monitoring, failure prognostics, and system performance optimization. He holds a PhD in Industrial Engineering and MS in Statistics from Georgia Tech. Professor Fang's research integrates machine learning with industrial engineering to solve complex problems in predictive maintenance, quality control, and energy systems. His methodological innovations include federated learning approaches for privacy-preserving prognostics, distributionally robust machine learning models, and tensor-based statistical methods for manufacturing quality diagnostics. He has received multiple prestigious awards including the ISE Outstanding Research Award (2024), Sigma Xi Best PhD Thesis Award (2019), and SAS Data Mining Best Paper Award (2016). His research has been funded by NSF, Cisco Systems, and the US Department of Energy. Professor Fang teaches courses in Quality Design & Control, Statistical Models for Systems Analytics, High-Dimensional Data Analytics, and Optimization Models. He has supervised 9 PhD students to completion and currently advises 7 graduate students working on projects spanning federated learning for prognostics, tensor-based quality control, and machine learning applications in manufacturing and energy systems.

• Combustion Engineering
• Alternative Fuels
• Hydrogen Energy
• +5 more

Terese Løvås serves as Vice Dean of Research and Innovation at the Faculty of Engineering, Norwegian University of Science and Technology (NTNU), where she leads strategic development of research and innovation activities. She concurrently holds the position of Professor of Combustion and Thermodynamics within the Department of Energy and Process Engineering. Her leadership responsibilities include oversight of Centers of Excellence, Horizon Europe projects, and PhD researcher training. Her research focuses on combustion engineering and alternative fuel technologies , particularly investigating ammonia and hydrogen combustion for zero-emission engines, biomass gasification processes, and reactive multiphase flow modeling. She heads the Engine Lab at NTNU and teaches Thermodynamics, Heat, and Combustion courses. Her work bridges theoretical modeling with experimental validation in sustainable energy systems. Løvås actively contributes to major research initiatives including LowEmission (SFI center), ACTIVATE (ammonia-powered agricultural vehicles), AMAZE (ammonia zero-emission), and CAHEMA (marine ammonia/hydrogen engines). Her publications reveal strong trends in ammonia combustion chemistry , emissions reduction , and advanced computational modeling for sustainable fuel systems, with increasing focus on nitrogen oxide formation mechanisms and dual-fuel strategies. Member of the Board of Directors, Combustion Institute (2022–present) Joint Editor, Proceedings of the Combustion Institute (2019–present) Alumni Fellow in Engineering, Churchill College, Cambridge University As Vice Dean, she manages NTNU's Research and Innovation Committee and represents the faculty in NTNU's Research and Innovation Committee. She supervises multiple PhD candidates and leads international collaborations through projects funded by the Norwegian Research Council, Nordic Energy Research, and EU programs. Her laboratory work focuses on optical engine diagnostics and advanced combustion testing. Løvås maintains active industry engagement through her leadership in the ComKin Research Group and membership in the Institute of Physics and Scandinavian-Nordic Section of the Combustion Institute. Her current work emphasizes practical implementation of ammonia-fueled engine technologies for marine and agricultural applications.

David BurtonView profile

Professor

• Soil Science
• Nitrogen Cycling
• Greenhouse Gas Emissions
• +5 more

Dr. David Burton is a Professor in the Department of Plant, Food, and Environmental Sciences at Dalhousie University's Faculty of Agriculture. He serves as Director of the Centre for Sustainable Soil Management and leads initiatives in soil health, greenhouse gas emissions, and sustainable agricultural practices. His teaching spans undergraduate and graduate courses in soil science, nutrient management, and climate change. Research focuses on microbial metabolism in soil, nitrogen cycling, and the environmental impacts of agricultural practices. He co-founded the Atlantic Soil Health Lab and manages the Greenhouse Gas Analysis Lab. Key affiliations include the Canadian Society of Soil Science (Fellow), Soil Conservation Council of Canada, and Fertilizer Canada's 4R Research Network. Recent work emphasizes soil's role in climate resilience, including presentations on regenerative farming and soil carbon sequestration. He collaborates with government and industry to develop climate-smart soil management policies and tools for nitrogen management optimization. Awards: Fellow of the Canadian Society of Soil Science Labs: Centre for Sustainable Soil Management, Greenhouse Gas Analysis Lab, Atlantic Soil Health Lab Grants: NSERC CREATE Climate Smart Soils

Shirin Saeedi BidokhtiView profile

Assistant Professor

• Information Theory
• Networking
• Data Compression
• +2 more

Shirin Saeedi Bidokhti is an Assistant Professor at the University of Pennsylvania's School of Engineering and Applied Science with primary appointment in Electrical and Systems Engineering and secondary appointment in Computer and Information Science. She is affiliated with the Warren Center for Network and Data Sciences. She holds M.Sc. and Ph.D. degrees from EPFL and completed postdoctoral work at Stanford and Technical University of Munich. Her research focuses on information theory, networking, data compression, and machine learning. Her recent publications demonstrate strong emphasis on neural compression algorithms, network optimization during the COVID-19 pandemic, and age-of-information theory. Awards include: 2023 IEEE Communications Society & Information Theory Society Joint Paper Award 2021 NSF CAREER Award 2019 NSF-CRII Award Swiss National Science Foundation Fellowships She advises PhD students including Xingran Chen. Current research involves developing data compression algorithms for IoT applications and network strategies for pandemic response.

Kamal SenView profile

Associate Professor

• Neural Coding of Natural Sounds
• Hierarchical Auditory Processing
• Neural Discrimination
• +3 more

Kamal Sen is an Associate Professor in the Department of Biomedical Engineering at Boston University, serving as Director of the Natural Sounds and Neural Coding Laboratory and Director of Admissions and Recruitment for Master’s Programs. He holds a PhD and MA in Physics from Brandeis University and a BA in Physics from Bates College. His research focuses on understanding how neurons encode natural sounds, particularly in the auditory cortex. Key areas include neural coding efficiency, hierarchical auditory processing, and the role of learning in shaping receptive fields. He developed the BOSSA algorithm to address sound segregation challenges in noisy environments, with applications for hearing aid technology. Sen’s work integrates electrophysiological techniques with theoretical approaches from signal processing, information theory, and systems theory. His lab explores neural discrimination of behaviorally relevant sounds and models cortical processing dynamics using computational frameworks. Recent studies investigate parvalbumin neuron contributions to temporal coding and cortical noise reduction in complex auditory scenes. His publications span neural circuit modeling, fNIRS applications in BCI, and biomimetic algorithms for auditory scene analysis. Research highlights include exploring schizophrenia-related gene effects on neural circuits and developing 3D neurosphere models for Parkinson’s disease.

Cheng, Reynold C.K.View profile

Professor

• Data Science
• Big Graph Analytics
• Uncertain Data Management
• +3 more

Professor Reynold Cheng is a faculty member at the University of Hong Kong (HKU), specifically within the Department of Computer Science in the School of Computing and Data Science (CDS). He currently serves as the Division Head of the AI & Data Science Division at CDS and is part of the Steering Committee of the Musketers Foundation Institute of Data Science. His academic journey includes a BEng and MPhil from HKU (1998–2000) and an MSc and PhD from Purdue University (2003–2005). Prior to HKU, he was an Assistant Professor at the Hong Kong Polytechnic University (HKPU) from 2005 to 2008. Cheng’s research focuses on data science, big graph analytics, and uncertain data management. He has received numerous awards, including the SIGMOD Research Highlights Reward 2020, HKICT Awards 2021, and HKU Knowledge Exchange Award (Engineering) 2021. His work has been recognized through grants such as the HKU-TCL Joint Research Centre for AI-funded project (HKD 1M, 2020–2022) and a CRF-funded project for real-time monitoring of infectious diseases (HKD 6.5M, 2021–2022). Cheng actively contributes to academic service, including serving as PC co-chair for IEEE ICDE 2021 and editorial roles in journals like IS and DAPD. His publications span top venues like SIGMOD, VLDB, and KDD, emphasizing algorithm design for large graphs and probabilistic data systems.

• Theoretical Computer Science
• Error-Correcting Codes
• Approximation Algorithms
• +5 more

Venkatesan Guruswami is a Chancellor's Professor in the Department of EECS and a Senior Scientist at the Simons Institute for the Theory of Computing at UC Berkeley . He also holds a Professor position in the Department of Mathematics . His academic journey began with a B.Tech in Computer Science from the Indian Institute of Technology, Madras (1997) , followed by a Ph.D. in Computer Science from the Massachusetts Institute of Technology (2001) . After a Miller Research Fellowship at UC Berkeley (2001–02), he held faculty roles at the University of Washington and Carnegie Mellon University before returning to UC Berkeley in January 2022. Education : B.Tech, IIT Madras (1997) Ph.D., MIT (2001) Professional Affiliations : Chancellor's Professor, UC Berkeley (EECS) Senior Scientist & Interim Director, Simons Institute Professor, UC Berkeley (Mathematics) Guruswami's research spans multiple domains within Theoretical Computer Science , focusing on Error-Correcting Codes , Approximation Algorithms , Randomness in Computing , Probabilistically Checkable Proofs , and Computational Complexity . His groundbreaking work in List Decoding has enabled codes with minimal redundancy for correcting worst-case errors, while recent advancements include Polar Codes , Deletion-Correcting Codes , and Constraint Satisfaction Problems . He has also contributed to Quantum Coding Theory , Locally Recoverable Codes , and Approximation Hardness in various computational contexts. His publications reflect a deep engagement with interdisciplinary topics. Key trends include: Quantum Information Theory : Quantum LDPC codes, transversal gates, and quantum storage. Algebraic Coding : Reed-Solomon codes, AG codes, and polynomial-based constructions. Computational Complexity : Hardness of approximation, CSPs, and parameterized intractability. Data Transmission : Polar codes, deletion channels, and feedback mechanisms. Algorithmic Techniques : Spectral methods, semirandom models, and Lasserre hierarchy applications. Guruswami has received numerous accolades, including the Simons Investigator Award , Presburger Award , Packard Fellowship , Sloan Research Fellowship , ACM Doctoral Dissertation Award , and the IEEE Information Theory Society Paper Award . He is an ACM Fellow (2017) and IEEE Fellow (2019) , with recent honors like the Guggenheim Fellowship (2023) and AMS Fellow (2023) . As an advisor, he has mentored over 25 PhD and postdoctoral researchers , including Atri Rudra , Prasad Raghavendra , and Peter Manohar , whose work has won awards like the Edmund M. Clarke Doctoral Dissertation Award and CRA Outstanding Undergraduate Researcher Award . His research is supported by grants from the National Science Foundation , Packard Foundation , and Sloan Foundation . He also serves as Editor-in-Chief of the Journal of the ACM and holds leadership roles in IEEE and arXiv moderation. Guruswami is actively involved in Simons Institute programs and co-organized workshops on Coded Computation and Information Theory . His work bridges theoretical advancements with practical applications in Cloud Storage , Quantum Computing , and Group Testing , including pandemic-era contributions like AC-DC: Amplification Curve Diagnostics for SARS-CoV-2 .

Edouard BoujoView profile

Lecturer

• Fluid Dynamics
• Flow Stability
• Flow Control
• +7 more

Dr. Edouard Boujo is a Scientist and Lecturer at the Swiss Federal Institute of Technology Lausanne (EPFL) , affiliated with the School of Engineering (STI) and working in the Institute of Mechanical Engineering (IGM) and Laboratory of Fluid Mechanics and Instabilities (LFMI) . He also teaches in the SGM-ENS department of the School of Engineering. Scientist at EPFL STI IGM LFMI Lecturer at EPFL STI-SGM SGM-ENS His research focuses on Fluid Dynamics with expertise in Flow Stability , Flow Control , Aeroacoustics , Thermoacoustics , Fluid-Structure Interaction , and Coating Flow Dynamics . He employs advanced mathematical modeling and computational methods to study complex fluid behaviors. Recent publications highlight his work on stochastic modeling of fluid instabilities, adjoint-based optimization of flow systems, and nonlinear dynamics of coating flows. His 15 most recent papers cover topics ranging from symmetry-breaking bifurcations to spin coating optimization and noise-induced transitions in fluid systems. Dr. Boujo actively collaborates with institutions across Europe and New Zealand, mentoring PhD student Atharva Lagwankar . He has received research funding from the Swiss National Science Foundation for two PhD theses and contributes to major fluid dynamics conferences like the European Fluid Dynamics Conference and APS Division of Fluid Dynamics meetings. His laboratory work at LFMI involves experimental and computational studies of fluid instabilities, with applications in aerospace, mechanical engineering, and industrial coating processes. He develops adjoint-based control methods for optimizing flow systems and reducing drag in various fluid configurations.

Aswin SankaranarayananView profile

Professor

• Computational Photography
• 3D Shape Estimation
• Imaging Systems
• +4 more

Aswin Sankaranarayanan is a Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU) , where he leads the Image Science Lab . His research focuses on computational photography , 3D shape estimation , and novel imaging system design . He earned his Ph.D. in Electrical and Computer Engineering (2009) from the University of Maryland and completed a postdoctoral fellowship at Rice University (2012) . Research Themes: Developing imaging systems that exploit low-dimensional signal models to overcome traditional sensing limitations Co-design of optics and processing algorithms for efficient sensing Application of non-linear signal models to high-dimensional data Advancing compressed sensing and big data processing techniques Scientific Recognition: SIGGRAPH 2023 Best Paper Award (Split-Lohmann Multifocal Displays) CVPR 2019 Best Paper Award (Fermat Paths for NLOS Reconstruction) NSF CAREER Award (2017) Dean’s Early Career Fellowship (2018-2021) Herschel Rich Invention Award (2016) Technical Contributions: His recent publications reveal expertise in non-line-of-sight shape reconstruction , VR/AR display systems , and biomedical imaging . Collaborations span institutions like University College London and University of Toronto.

• Sustainable Machining Processes
• Tool Wear Monitoring
• Surface Roughness Optimization
• +3 more

Doç. Dr. Muhammed Aras is an Associate Professor in the Department of Mechanical Engineering at Baskent University (Başkent Üniversitesi), with a research focus on sustainable machining processes, tool wear monitoring, and surface roughness optimization. His work spans advanced manufacturing technologies, energy storage systems, and biomedical device design. PhD in Manufacturing Engineering (2018), Gazi Üniversitesi MSc in Mechanical Education (2013), Gazi Üniversitesi BSc in Mechanical Engineering (2010), Tabriz Islamic Azad University Research interests include sustainable machining (dry/hard turning, cooling-lubrication strategies), tool wear analysis (CBN, ceramic and coated inserts), and surface integrity optimization using AI-based methods (firefly algorithm, TOPSIS, Grey Relational Analysis). His 15 most recent articles (2017-2024) examine topics like: Surface roughness prediction in dry hard turning Energy storage technology viability assessments Cutting parameter optimization for various steels Acoustic/vibration monitoring in machining He has received scientific awards including the Teşvik Ödülü (Encouragement Award, 2015). His patent on an automatic orthognathic surgery articulator and book chapters on tool monitoring systems demonstrate his multidisciplinary impact.