جست‌وجوی استاد و پژوهشگر

Krishnamurthy Dvijothamمشاهده پروفایل

پژوهشگر

• AI Safety
• Robustness in AI
• Differential Privacy
• +۵ مورد دیگر

Krishnamurthy Dvijotham (Dj) is a research scientist with a focus on developing safe, reliable, and secure AI systems. His current role as a Research Lead at ServiceNow Research (2024-2025) builds on his extensive experience at Google DeepMind (Researcher, 2017-2024), Pacific Northwest National Laboratory (Researcher, 2016-2017), and a postdoctoral fellowship at Caltech's Center for Mathematics of Information. Research Interests: Mathematical optimization, control theory, AI robustness, differential privacy, neural network verification, power systems optimization. Scientific Awards: Best Paper at ICML 2024, Best Paper at UAI 2018, Best Paper at Constraints 2016, Best Student Paper at UAI 2014. Dvijotham's work emphasizes the application of rigorous mathematical frameworks to AI systems. His publications span certified robustness , adversarial defense mechanisms , privacy-preserving learning , and power grid optimization , reflecting interdisciplinary contributions to both theoretical and applied domains. He has mentored numerous PhD students and postdoctoral fellows, many of whom now hold academic or research positions at institutions like UC Berkeley, Google DeepMind, and Microsoft Research. His recent articles highlight advancements in diffusion models , selective deferral systems , and formal verification techniques , with a strong focus on security and reliability in AI deployment.

Yuqing Wangمشاهده پروفایل

پژوهشگر ارشد

• Deep Learning
• Machine Learning
• Applied Mathematics
• +۹ مورد دیگر

Yuqing Wang is a postdoctoral researcher in the Department of Applied Mathematics and Statistics (AMS) at Johns Hopkins University (JHU). Prior to this, they were a Research Fellow at the Simons Institute at UC Berkeley during the fall 2024 MPG program. Yuqing earned their PhD in Mathematics from Georgia Institute of Technology under the supervision of Prof. Molei Tao. Yuqing’s research focuses on the mathematical foundations of deep learning theory, particularly from a dynamical systems perspective. Their work bridges machine learning and applied mathematics, employing tools from optimization, stochastic dynamics, computational math, analysis, topology, and sampling. Current interests include large language models and diffusion models. Yuqing’s publications highlight themes such as training dynamics, large learning rate effects, implicit biases in neural networks, diffusion model design, and optimization techniques. Key contributions include analyses of gradient flow in neural networks, balancing effects in training, and control-theoretic approaches to sampling and attention mechanisms. As an instructor at JHU, Yuqing teaches Bayesian Statistics and Optimization courses. They have also served as a teaching assistant for differential equations and multivariable calculus at Georgia Tech. Yuqing actively participates in academic talks, with recent presentations at institutions like MPI MiS, UCLA, and conferences such as ICLR, NeurIPS, and SIAM DS25.

Rose Yuمشاهده پروفایل

دانشیار

• Machine Learning
• Spatiotemporal Data Analysis
• AI for Scientific Discovery
• +۵ مورد دیگر

Rose Yu is an Associate Professor in the Department of Computer Science and Engineering at the University of California, San Diego. She serves as a primary faculty member with the AI Group and is affiliated with the Halıcıoğlu Data Science Institute. Previously, she was a faculty member at Northeastern University where she taught advanced machine learning courses. Her research focuses on machine learning for large-scale spatiotemporal data, with particular emphasis on AI for scientific discovery. She develops physics-guided deep learning models that integrate physical principles with neural networks, with applications spanning climate science, healthcare, and dynamical systems. Her work on symmetry in neural networks has led to significant advances in model generalization and optimization. Dr. Yu's recent publications demonstrate a strong trend toward grounding large language models with physical laws, developing symmetry-aware learning frameworks, and applying AI to climate science. Her work bridges theoretical advances in machine learning with practical applications in scientific domains, particularly in spatiotemporal forecasting and scientific discovery. Major Awards and Honors: Presidential Early Career Award for Scientists and Engineers (PECASE) DARPA Young Faculty Award NSF CAREER Award MIT Technology Review Innovators Under 35 Hellman Fellowship Multiple faculty awards from major tech companies (Google, Amazon, Meta, etc.) Dr. Yu actively mentors PhD students, postdocs, and undergraduate researchers, with many of her former students now holding positions at leading institutions and companies. She serves as program chair for major conferences including ICLR 2025 and has received substantial research funding from agencies including DARPA, NSF, DOE, and CDC. Her research group develops tools for scientific machine learning, with particular focus on climate informatics, symmetry-aware learning, and physics-integrated AI systems.

Assaf Talمشاهده پروفایل

استاد

• Neuroimaging
• Magnetic Resonance Spectroscopy
• Brain Disease Monitoring
• +۳ مورد دیگر

Prof. Assaf Tal is a faculty member in the Department of Bio-Medical Engineering at The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University. His research focuses on developing advanced neuroimaging methodologies using magnetic resonance spectroscopy (MRS) and imaging (MRI) to investigate brain function and disease mechanisms. His primary research interests center on neuroimaging physics and brain disease monitoring , with specific emphasis on: Developing novel MRS/MRI techniques combining spin physics and signal processing Tracking neurochemical changes during cognitive processes Detecting and monitoring neurodegenerative diseases including multiple sclerosis, traumatic brain injury, and Alzheimer's Disease Understanding brain encoding mechanisms across neurochemical, electrophysiological, and structural levels His work bridges biomedical engineering with clinical neuroscience to create improved diagnostic and monitoring tools. Prof. Tal's recent publications (2022-2025) demonstrate strong focus on functional MRS and advanced spectral-temporal analysis , with significant contributions to motion correction, uncertainty estimation, and microstructural modeling in neuroimaging. His research shows increasing integration of computational methods like Bayesian inference and machine learning for precision neuroimaging. His laboratory develops specialized software tools including the Visual Display Interface (VDI) for MRS data processing and simulation, supporting both preclinical and clinical neuroimaging research.

Nikolas Kantasمشاهده پروفایل

دانشیار

• Computational Statistics
• Stochastic Processes
• Numerical Methods
• +۷ مورد دیگر

Dr. Nikolas Kantas is a Reader in Statistics at Imperial College London's Department of Mathematics. He completed his undergraduate studies and PhD at the University of Cambridge's Signal Processing Group. His research focuses on developing numerical methods for complex problems in inference, optimisation, filtering, and control. Research Interests: Kantas specializes in computational statistics and stochastic processes, with expertise in particle filtering, Sequential Monte Carlo, and Markov Chain Monte Carlo methods. His work bridges theoretical foundations with applications in data assimilation, optimization under uncertainty, and high-dimensional statistical modeling. Publication Trends: Recent work (2022-2025) demonstrates strong focus on optimization algorithms, stochastic differential equations, and Monte Carlo methods. Key themes include multi-objective optimization, privacy-preserving algorithms, Langevin dynamics, and distributed computing. Methodological innovations frequently address high-dimensional and real-time computational challenges. Student Advising & Grants: Currently supervises 4 PhD students and has graduated 9 doctoral candidates. Research funding includes JP Morgan AI Faculty Research Awards and support from the National Physical Laboratory (NPL). Academic Leadership: Co-organizes the annual Greek Stochastics workshop on Statistics and Applied Probability. Coordinates PhD programs through the Mathematics Research program, MFC CDT, and Statistics and Machine Learning CDT.

Maria Vlasiouمشاهده پروفایل

استاد

• Stochastic processing networks
• Queueing theory
• Energy networks and EV charging
• +۶ مورد دیگر

Maria Vlasiou is a Full Professor of Mathematics of Operations Research at the University of Twente, an Associate Professor at Eindhoven University of Technology, and a Research Fellow of EURANDOM. Her work focuses on stochastic processing networks, particularly layered architectures arising in energy systems, high-tech manufacturing supply chains, and bandwidth-sharing networks. Education & Qualifications UTQ (University Teaching Qualification) diploma obtained in 2010; continuous professional-education training thereafter. Research Interests Vlasiou’s research spans the performance analysis of stochastic networks, with special emphasis on: Electric-vehicle charging networks and congestion control. Extreme-value theory for fork-join queues inspired by ASML/Philips/Boeing supply chains. Lévy-driven queues, heavy-tailed risk models, and large-deviations asymptotics. Resource-allocation problems involving non-convex optimisation and heavy-traffic limits. She combines rigorous probabilistic methods (fluid & diffusion approximations, perturbation analysis) with practical applications in energy, logistics and manufacturing. Scientific Awards & Recognition AMSI-ANZIAM Lecturer 2017 Marcel Neuts Student Paper Award (MAM8) 3rd Prize, 8th Conference in Actuarial Science & Finance 2014 Best Paper Award, ICORES 2013 Excellent Course Prize, University of Twente 2021 Grants & Projects She has attracted over €4.5 million in external funding from NWO, Australian Research Council, 4TU.CEE, and industry, including current NWO LTP ROBUST programme on “AI for Energy Grids” (2023-2032). Teaching & Supervision Vlasiou teaches Probability, Stochastic Processes, Performance Modelling and Infectious-Disease Epidemiology. She has supervised nine PhD dissertations and numerous BSc/MSc theses, internships and PDEng projects, and leads educational-innovation grants on mini-lectures and student engagement. Editorial & Leadership Roles Associate editor for OR Spectrum, IISE Transactions, Math. Methods of OR, and INFOR. She represents the Netherlands at the IMU Committee for Women in Mathematics and serves on advisory boards for national women-in-math organisations in Cyprus and Greece.

• Positron Emission Tomography (PET)
• Magnetic Resonance Imaging (MRI)
• Biomedical Informatics
• +۴ مورد دیگر

Thibault Marin, PhD, is an Assistant Professor at the Yale School of Medicine in both the Department of Radiology & Biomedical Imaging and the Biomedical Informatics & Data Science program. He is affiliated with the Bioimaging Sciences Division , Center for Brain & Mind Health , and Center for Molecular Imaging Technology and Translation (CMITT) . Education: PhD in Electrical Engineering from Illinois Institute of Technology (2010) Key Collaborations: Georges El Fakhri, Chao Ma, Yue Zhuo, Jinsong Ouyang, Paul Han His research focuses on PET/MRI instrumentation , dynamic imaging reconstruction , and motion correction algorithms . He develops diffusion models for tumor segmentation and Bayesian methods for kinetic parameter estimation, contributing to advancements in radiomics-driven radiotherapy and ultra-high resolution PET systems . Recent publications highlight innovations in TOF-DOI panel detectors , linear tangent space alignment (LTSA) for cardiac imaging, and adversarial domain generalization for PET denoising. His work bridges biomedical engineering , machine learning , and clinical imaging applications .

Noemi Anau Montelمشاهده پروفایل

پژوهشگر ارشد

• Astrophysics
• Cosmology
• Particle Physics
• +۴ مورد دیگر

Noemi Anau Montel is a postdoctoral research fellow at the Max Planck Institute for Astrophysics in Garching, Germany. Her work spans astrophysics, cosmology, and particle physics, focusing on developing probabilistic machine learning techniques for analyzing astrophysical datasets. Research interests include: Simulation-based inference for dark matter studies Generative modeling in cosmological simulations Statistical analysis of high-energy astrophysical data Neural ratio estimation techniques Applications to gravitational lensing and dark Universe problems She has published extensively on machine learning applications in astrophysics, with recent works focusing on dark matter mass constraints, cosmological initial condition reconstruction, and model validation techniques.

Dean Ecklesمشاهده پروفایل

استاد

• Social Influence
• Communication Technologies
• Causal Inference
• +۷ مورد دیگر

Dean Eckles is the William F. Pounds Professor of Management and a Professor of Marketing at the MIT Sloan School of Management. He also serves as Associate Director of the Institute for Data, Systems & Society (IDSS) within the Schwarzman College of Computing, reflecting his interdisciplinary engagement with data science and societal systems. His educational background includes a BA in philosophy, BS and MS in cognitive science, MS in statistics, and a PhD in communication—all from Stanford University. These diverse foundations support his research at the intersection of human behavior, technology, and statistical methodology. Eckles' research focuses on how communication technologies shape social influence, particularly through digital platforms. He investigates peer effects, misinformation dynamics, network structures (such as triadic closure and long ties), and behavioral contagions. His methodological expertise lies in causal inference, field experimentation, and high-dimensional statistical adjustment, often applied to large-scale social media data. The trends in his recent publications reveal a consistent focus on understanding social network dynamics using experimental and observational data from online environments. His work spans disciplines including network science, behavioral economics, political science, and human-computer interaction, with increasing emphasis on real-world implications of algorithmic and social interventions. Amazon Research Award (2018) Working Paper Award with Sinan Aral (2020) Eckles has led significant research initiatives involving field experiments on platforms like Facebook and Twitter, often in collaboration with industry scientists. His advisory or mentoring roles are not explicitly detailed, but his leadership as Associate Director of IDSS and publication record suggest active involvement in guiding research teams and securing external funding, particularly in areas related to data, society, and ethical AI. His work has been supported by organizations such as Amazon and likely federal agencies given the scope and nature of his projects. Dean Eckles is affiliated with the Institute for Data, Systems & Society (IDSS), where he contributes to interdisciplinary research that integrates data science with social and engineering systems. His work likely involves collaboration across labs focused on computational social science, network analysis, and digital media impact assessment.

Xiaolin Xuمشاهده پروفایل

دانشیار

• Security
• Machine Learning
• AI Security/Privacy
• +۵ مورد دیگر

Xiaolin Xu is an Associate Professor in the Department of Electrical and Computer Engineering at Northeastern University. His research focuses on hardware and AI security, machine learning privacy, and energy-efficient deep learning. He earned a PhD from the University of Massachusetts Amherst and completed postdoctoral work at the Florida Institute for Cybersecurity Research. Education PhD, Electrical and Computer Engineering, University of Massachusetts Amherst, 2016 M.S., B.E., University of Electronic Science and Technology of China Research Interests : His work spans secure hardware design, AI model protection, and embedded systems security. Notable projects include securing FPGA-based ML accelerators and developing defenses against adversarial attacks. Recent Article Trends : Recent publications emphasize robustness in neural architectures, secure multi-party computation, and lightweight graph-based models for edge devices. Grants & Awards 2025 DAC Under-40 Innovators Award NSF CAREER Award (2023) Multiple NSF grants for hardware security research Advising : Supervises PhD students including Shijin Duan and Jiaxing, focusing on ML security and hardware-software co-design.

Jeremy Magruderمشاهده پروفایل

استاد

• Development Economics
• Agricultural Economics
• Labor Economics
• +۳ مورد دیگر

Jeremy Magruder is the George W. and Elsie M. Robinson Professor of Food and Agricultural Resource Economics at the University of California, Berkeley. He holds a Ph.D. in Economics from Yale University and has taught courses in applied econometrics, development economics, and probability/statistics at both undergraduate and graduate levels. His research focuses on development economics, agricultural economics, and labor markets, with notable work on technology adoption, social networks, and policy evaluation in developing countries. Magruder’s research emphasizes rigorous empirical methods, including randomized controlled trials and network analysis. He has collaborated on studies in Liberia, India, Kenya, and Rwanda, addressing topics like digital platforms’ impact on employment, irrigation adoption constraints, and youth development programs. He has advised numerous Ph.D. students, many of whom have taken academic roles at institutions like the University of Chicago, Stanford, and the World Bank. Magruder is affiliated with UC Berkeley’s Agricultural and Resource Economics (ARE) Department and has contributed to policy-oriented research through initiatives like the Development Impact Lab. His work often intersects econometrics and real-world policy challenges, advocating for transparent research designs to avoid false discoveries.

• Statistical Physics
• Non-equilibrium Thermodynamics
• Theoretical Physics
• +۳ مورد دیگر

Prashant Singh is a Postdoctoral Researcher (Researcher) in the Biocomplexity group at the Niels Bohr Institute, University of Copenhagen. His work focuses on theoretical and statistical physics with applications in biological systems, leveraging analytical methods to solve complex non-equilibrium problems. His research spans statistical physics, non-equilibrium thermodynamics, and theoretical biophysics, investigating phenomena like active matter phase transitions, positional information in boundary-driven systems, and stochastic thermodynamics. Key interests include diffusion with resetting, entropy production inference, and reaction-diffusion modeling, often revealing fundamental trade-offs in biological information processing and thermodynamic efficiency. Analysis of his 2024-2025 publications shows a cohesive focus on exact solutions for non-equilibrium systems, particularly single-file diffusion under resetting and active matter with long-range interactions. Recurring themes include cost-time optimization, boundary effects on information transmission, and the thermodynamic constraints of biological computation. No information is available regarding student advising or research grants. He is embedded within the Biocomplexity research group at the Niels Bohr Institute, which employs theoretical frameworks to study collective dynamics in biological systems, including pattern formation, cellular information processing, and emergent behavior in active matter.

Rainer J. Friesمشاهده پروفایل

دانشیار

• Theoretical Nuclear Physics
• Quantum Chromodynamics (QCD)
• Quark-Gluon Plasma (QGP)
• +۳ مورد دیگر

Rainer J. Fries is an Associate Professor at the Texas A&M University , affiliated with both the Cyclotron Institute and the Department of Physics and Astronomy . He holds a doctorate in theoretical physics from the University of Regensburg (2001) and has held prestigious fellowships including the Feodor Lynen Fellowship (2002-2003) and RIKEN/BNL Research Center RHIC Fellow (2006-2011). His research focuses on the Strong Nuclear Force , Quark-Gluon Plasma , and Quantum Chromodynamics (QCD), particularly at extreme temperatures and high-energy nuclear collisions. Education PhD in Theoretical Physics, University of Regensburg (2001) Dual BSc in Physics and Mathematics, University of Regensburg (1998) Prof. Fries explores the behavior of quarks and gluons in relativistic heavy-ion collisions, such as those at the Relativistic Heavy Ion Collider (RHIC) and the Large Hadron Collider (LHC) . His work spans from initial state dynamics to final state observables like shear viscosity , angular momentum transfer , and jet quenching in QGP. He leads the JETSCAPE collaboration , integrating multi-stage jet evolution frameworks with Bayesian inference for precision QCD studies. Recent publications highlight his 15 most recent articles (2023-2025), covering topics like hybrid hadronization , photon-triggered jet tomography , and multi-scale jet-medium interactions . These works emphasize QGP transport properties , viscous hydrodynamics , and Bayesian calibration of nuclear collision models. Scientific Awards IUPAP Young Scientist Prize in Nuclear Physics (2007) Montague Scholar for Teaching Excellence (2008) NSF CAREER Award (2009) Invited JSPS Fellowship (2009-2010) He has advised the research team including Stephen Robicheaux , Arjun Sengupta , and Cameron Parker . Active in workshop organization , he co-hosted events like Hard Probes 2020 and previous Hot Quarks conferences (2016, 2014, 2012). His work bridges theoretical nuclear physics with experimental programs at international facilities including CERN and Brookhaven National Laboratory .

• Causal Machine Learning
• Healthcare Informatics
• Medical Data Analysis
• +۳ مورد دیگر

Dr. Vinod Kumar Chauhan is a Research Fellow at the Institute of Biomedical Engineering, University of Oxford. His work focuses on causal machine learning and healthcare applications, including individualized treatment effects estimation and addressing sample selection bias in medical data. Previously, he held a postdoctoral position at the University of Cambridge and earned his PhD from Panjab University, Chandigarh, India. Education: BSc, MCA, PhD (Panjab University, India). Research interests include causal inference, healthcare informatics, and graph neural networks for EHR analysis. Recent projects involve postoperative atrial fibrillation prediction and optimizing treatment effects for composite outcomes. Key Awards: 2021 Institute for Manufacturing Postdoctoral Award (Cambridge), Travel Grant for Asian Conference on Machine Learning 2017. Selected Talks: Keynote on HyperNetworks (2023 Panjab University), invited talks on treatment effects estimation (IISER Pune, 2023). Labs/Teams: Computational Health Informatics group under Prof. David Clifton. Active in departmental EDI committee roles.

Vikas Gargمشاهده پروفایل

استادیار

• Quantum Computing
• Artificial Intelligence
• Machine Learning
• +۱۱ مورد دیگر

Vikas Garg is an Assistant Professor in the Department of Computer Science, specializing in theoretical and applied aspects of Quantum Computing and Artificial Intelligence. His research focuses on applications of quantum methods and AI/ML in Computational Biology, Healthcare, Material Synthesis, Cybersecurity, Energy, and more. He is also the Co-Founder and Chief Scientist at YaiYai Oy, a company providing quantum and AI solutions to industries like Biopharma and FinTech. Education: PhD in Computer Science (EECS) from MIT, supervised by Tommi Jaakkola, and held roles at IBM Research, Amazon A9, and Microsoft Research. His work contributes to UN Sustainable Development Goals related to innovation and infrastructure. Research Interests: Quantum Computing, Deep Learning, Generative Models, Drug Design, and applications in Healthcare and Energy. His work bridges machine learning with optimization and game theory, emphasizing practical solutions for real-world challenges. Projects: Principal Investigator for initiatives like HEALED/Garg (drug design) and the Finnish Center for Artificial Intelligence. Collaborates widely with academia and industry leaders, including Regina Barzilay and Stefanie Jegelka. Awards: Energy Fellow at MIT. His research has led to patents and publications in top venues like NeurIPS, ICML, and CVPR. Advising & Grants: Leads projects funded by the Academy of Finland, focusing on drug discovery and AI advancements. Active in the Machine Learning for Pharmaceutical Discovery and Synthesis Symposium.