جست‌وجوی استاد و پژوهشگر

Piotr Zwiernikمشاهده پروفایل

دانشیار

• Graphical Models
• Covariance Matrix Estimation
• Convex Analysis
• +۷ مورد دیگر

Piotr Zwiernik is a Professor Agregat in the Department of Economics and Business at Universitat Pompeu Fabra (UPF), Barcelona, and a member of the BSE Data Science Center. He is currently on leave from the Department of Statistical Sciences and the Department of Mathematics at the University of Toronto. His research bridges statistics, algebraic geometry, and machine learning, focusing on graphical models, covariance estimation, tensors, and algebraic methods in statistics. Research Interests: Graphical Models Covariance Matrix Estimation Convex Analysis Tensors Algebraic and Combinatorial Methods in Statistics Statistical Learning Theory His recent publications span high-impact journals such as the Annals of Statistics , Biometrika , and Journal of the Royal Statistical Society . The work demonstrates a consistent trend in developing mathematically rigorous frameworks for understanding dependence structures, latent variable models, and high-dimensional inference, often leveraging tools from algebraic geometry and convex optimization. Key themes include total positivity, tensor methods, and the geometry of statistical models. Scientific Recognition: Editorial Board Member, Journal of the Royal Statistical Society Series B (JRSS-B) Editorial Board Member, Biometrika Editorial Board Member, Scandinavian Journal of Statistics Editorial Board Member, Algebraic Statistics Zwiernik actively mentors students and is seeking PhD candidates with strong mathematical backgrounds at UPF and the Institute of Mathematics of UPC. He has led the development of several open-source R packages, including golazo , MTP2binary , and StructuralEM , facilitating research in graphical models and latent structures. His work is supported by a network of collaborations with leading statisticians and has significant theoretical and applied implications in data science. Laboratories and Research Groups: Statistics@UPF BSE Data Science Center

• Positron Physics
• Materials Science
• Solid-State Physics
• +۳ مورد دیگر

Prof. Dr. Christoph Hugenschmidt is an Associate Professor of Experimental Physics at the Technical University of Munich (TUM), affiliated with the Heinz Maier-Leibnitz Research Neutron Source (FRM II). His work focuses on positron physics, including the development of advanced positron beam techniques and novel measurement methods for analyzing surfaces, crystal defects, and fundamental leptonic systems. Education: Studied physics at the Universities of Freiburg and Bonn, completed his doctorate in 1997. Since joining TUM, he has led the Positron Physics Research Group and pioneered the NEPOMUC intense positron source. Research stays included the Institut Laue-Langevin (Grenoble) and institutions in Japan. Research Interests: Positron-based defect analysis, 3D defect imaging, spin-resolved electronic structure studies, and antimatter experiments. His group develops state-of-the-art tools like scanning positron microbeams and buffer-gas traps for precision measurements. Key Achievements: EU Erasmus+ Fellowship (2020), JSPS Research Award (2014), TUM Silver Badge (2013). Recent publications emphasize positron annihilation spectroscopy, plasma confinement, and defect characterization in materials like tungsten, lithium titanate, and diamond. Grants & Advising: Supervised numerous TUM scholarship holders. Active in international collaborations such as the APEX experiment for electron-positron pair plasmas. Involved in facility upgrades at FRM II and NEPOMUC. Labs/Teams: Leads the Positron Physics Research Group at TUM, collaborating with global institutions on projects like the NEPOMUC beamline and magnetic trapping systems.

• Artificial Intelligence
• Machine Learning
• Multimodal Models
• +۴ مورد دیگر

Dr. Silvio Savarese is an Adjunct Professor in the Department of Computer Science at Stanford University. His research focuses on advancing artificial intelligence, particularly in multimodal models, autonomous agents, and large language models (LLMs). He has contributed to foundational work in vision-language models like BLIP3 and XGen, as well as frameworks for training action models and optimizing AI agents. His projects emphasize practical applications such as CRM systems, code generation, and ethical AI. Current interests include scalable reasoning in LLMs, efficient model architectures, and multimodal pretraining for 3D understanding. Key research directions include developing unified multimodal models capable of handling vision, language, and point cloud data, as seen in projects like ULIP and Merlion. He explores agent systems through benchmarks like Behavior-1K and BOLAA, aiming to improve real-world interaction capabilities. Recent work addresses challenges in data generation (Text2Data), agent optimization (PRACT), and safety through methods like LZ Penalty for text repetition control. His contributions span open-source libraries (LAVIS, OMNIXAI) and datasets (DialogStudio, Eye-BEHAVIOR), emphasizing reproducibility and community impact. While no formal awards are listed, his extensive publication record and industry collaborations highlight academic and industrial relevance. Current projects include high-fidelity video synthesis (xGen-videosyn-1), time series forecasting (Moirai-MoE), and lightweight agent frameworks (AgentLite).

Michael Gide JABBOURمشاهده پروفایل

دانشیار

• Quantum Information Theory
• Quantum Optics
• Mathematical Physics
• +۶ مورد دیگر

Michael Gide JABBOUR is an Associate Professor in Quantum Communication at Télécom SudParis, part of the Institut Polytechnique de Paris . He is affiliated with the ISTeC institute and the Department of Communications, Images, and Information Processing (CITI). His research focuses on quantum information theory, quantum optics, and mathematical physics, particularly in infinite-dimensional systems. Key areas include entropy continuity bounds, bosonic quantum channels, and quantum interference phenomena. Education and Career: PhD from École polytechnique de Bruxelles (2015), thesis: Bosonic systems in quantum information theory Postdoctoral fellowships at University of Cambridge (2016-2018), Technical University of Denmark (2018-2019), and École polytechnique de Bruxelles (F.R.S.-FNRS Senior Fellow) Joined Télécom SudParis as Associate Professor in 2020 Research Interests: Explores fundamental aspects of quantum information through entropy analysis, Gaussian channels, and bosonic systems. Recent work addresses entanglement engines, timelike quantum interference, and majorization principles in quantum phase space. His studies bridge quantum thermodynamics with mathematical rigor, emphasizing non-Gaussian operations and infinite-dimensional systems. Publications: 15+ peer-reviewed articles and preprints since 2015, including high-impact journals like Physical Review Letters , IEEE Transactions on Information Theory , and Quantum . Key themes include entropy continuity, bosonic Gaussian states, and quantum interference in time. Future Work: Current projects involve boson-fermion complementarity, complexity of Gaussian optics, and central limit theorems for distinguishable bosons. Ongoing collaborations span quantum thermodynamics and foundational entropy principles.

Frazier Nyasuluمشاهده پروفایل

عضو هیئت علمی

• Chemical Education
• Laboratory Curriculum Development
• Inquiry-Based Learning
• +۷ مورد دیگر

Frazier Nyasulu is a Professor of Instruction in the Department of Chemistry and Biochemistry at Ohio University's College of Arts and Sciences, where he contributes significantly to undergraduate chemistry education. He teaches foundational courses such as CHEM 151: Fundamentals of Chemistry I and CHEM 241: Quantitative Analysis, and is based in Chemistry 303 on the Athens Campus. His research is centered on chemical education, with a focus on developing innovative and engaging laboratory curricula that foster critical thinking, teamwork, and hands-on learning. His work emphasizes student-driven experimentation, the use of modern instrumentation, and data analysis in entry-level chemistry labs. Nyasulu's scholarly output over the past decade reflects a consistent commitment to pedagogical excellence, with publications primarily in journals like Journal of Chemical Education and The Chemical Educator . His recent articles explore diverse topics such as acid-base titrations, calorimetry, conductivity, and electrochemical methods, revealing a strong thematic focus on integrating fundamental chemical concepts through inquiry-based laboratory experiences. He has collaborated extensively with colleagues including R. Barlag, L. McMills, and P. Arthasery on developing cost-effective, safe, and educationally robust laboratory exercises. His work often incorporates green chemistry principles, such as reducing heavy metal waste, and emphasizes accessibility through student-built equipment and simplified procedures. Nyasulu earned his Ph.D. from Salford University in the United Kingdom, and his active publications and current faculty listing confirm his ongoing engagement in academic life. He advises on laboratory curriculum development and has contributed to improving lab efficiencies and equipment. While no formal advisees are listed, his mentorship is evident through collaborative publications with peers and staff. He is involved in multiple research initiatives related to laboratory instruction, including the development of nano-band electrodes for trace metal detection and electrodeposition techniques for teaching materials chemistry. These projects bridge analytical methods with educational applications, demonstrating a unique niche in applied chemical education.

• Thermal Engineering
• Heat Transfer
• Energy Systems
• +۵ مورد دیگر

Carlo Cima is a Tenured Assistant Professor in the Department of Energy (DENERG) at the Polytechnic University of Turin, Italy. He is actively engaged in teaching and research within the fields of thermal engineering and energy systems. He serves as a member of the College of Biomedical Engineering and the College of Electrical and Energy Engineering, contributing to academic governance and curriculum development. His teaching responsibilities include Experimentation and Design of Energy Technologies and Applied Thermodynamics and Heat Transfer across multiple degree programs, particularly in Energy Engineering and Biomedical Engineering, where he has consistently held roles as course lecturer and collaborator over several academic years. Carlo Cima's research focuses on thermal performance in energy systems, heat transfer phenomena, fluid mechanics, and fire safety engineering. His work spans applications in building energy efficiency, cryogenic systems, and tunnel safety. The publications indicate a strong emphasis on experimental and numerical analysis of thermal systems, with recurring themes in entropy optimization, heat transfer in extreme conditions, and innovative energy technologies. The most recent research outputs, published between 2006 and 2012, highlight his expertise in thermal evaluation of double glazing, entropy-based control of numerical solutions, heat transfer in liquid nitrogen flows, and full-scale testing of tunnel fire mitigation systems. These works reflect a multidisciplinary approach bridging mechanical, energy, and safety engineering. While no scientific awards are listed in the provided text, his sustained academic activity and contributions to high-level conferences such as the International Heat Transfer Conference and specialized journals demonstrate recognition in his field. Carlo Cima has advised or collaborated on research involving topics such as energy-efficient building components and fire safety systems, though specific names of students are not mentioned. There is no indication of external grants or funded projects in the available data. His research appears to be conducted within institutional frameworks at Politecnico di Torino, potentially in collaboration with other researchers in the department, but no specific lab or research group is named.

• Network Science
• Data Science
• Machine Learning
• +۳ مورد دیگر

Luis Ramada Pereira is an Associate Researcher at ISTAR-Iscte Research Center and Visiting Professor at ISCTE-IUL. He holds a PhD in Complexity Sciences from ISCTE-IUL/University of Lisbon and has extensive industry experience at IBM managing multinational tech projects. Research Focus: His work centers on Network Science applications, including: Temporal network modeling Machine learning for complex systems Information-theoretic analysis of sports dynamics Computational frameworks for network generation Publication Trends: Recent articles demonstrate a consistent focus on sports analytics (particularly football) through network science lenses, with publications in Q1 journals like Chaos, Solitons and Fractals . Research methodologies emphasize information theory, temporal clustering, and multilayer network modeling. Teaching: Data Structures and Algorithms (2019/2020) Programming Fundamentals (2019/2020) Data in Science, Management and Society (2019/2020)

• Physical Unclonable Functions
• Hardware Security
• Side-Channel Analysis
• +۵ مورد دیگر

Michael Pehl is an Associate Professor at the Chair of Security in Information Technology at Technische Universität München (TUM), where he leads the Hardware-Intrinsic Security research group. His work focuses on Physical Unclonable Functions (PUFs), side-channel analysis, and secure hardware design. His primary research interests include Physical Unclonable Functions, Hardware Security, Side-Channel Analysis, Fault Injection Attacks, Post-Quantum Cryptography, Secure Hardware Design, and Trusted Electronics. Pehl has developed significant expertise in PUF security mechanisms, with particular emphasis on countermeasures against side-channel attacks and entropy optimization in security primitives. His recent publications demonstrate strong trends in hardware security, particularly in PUF architectures (oscillator-based, resistor-based, memristor-based), side-channel countermeasures, and entropy analysis. The research spans both theoretical foundations and practical implementations for secure embedded systems. Kurt-Fischer Prize (2013) Outstanding Reviewer Award at ICASSP 2022 Outstanding Reviewer Award at DATE 2024 Pehl has secured substantial research funding through multiple high-profile projects including STAMPS-PLUS (DFG), Edu4Chip (EU), APRIORI (BMBF), and VE-FIDES (BMBF). He serves as an Associated Editor for IEEE Transactions on Information Forensics & Security and has held leadership roles in major conferences including Program Co-Chair of COSADE 2023. His research group actively collaborates with industry partners and contributes to TUM's Center of Competence for Design of Electronic Circuits and Systems (DECS). Pehl's laboratory focuses on Hardware-Intrinsic Security research, with specialized facilities for PUF evaluation, side-channel analysis, and fault injection testing. The team develops innovative security primitives for embedded systems and IoT devices, with particular emphasis on post-quantum security solutions.

• Computational Complexity
• Cryptography
• Zero-Knowledge Proofs
• +۵ مورد دیگر

Boaz Barak is a Professor at the Weizmann Institute of Science in the Department of Computer Science, Faculty of Mathematics and Computer Science. With an h-index of 64 and over 17,301 citations, he is a leading researcher in theoretical computer science with significant contributions spanning computational complexity, cryptography, and machine learning theory. His research interests include: Computational Complexity Cryptography Zero-Knowledge Proofs Program Obfuscation Interactive Proofs Privacy-Preserving Computation Machine Learning Theory Barak's publication record reveals a trajectory from foundational work in theoretical computer science to contemporary research at the intersection of theory and practice. His early work established impossibility results for program obfuscation and advanced techniques for zero-knowledge proofs beyond black-box simulation. His influential textbook "Computational Complexity: A Modern Approach" has become a standard reference in the field. More recently, his research has expanded into machine learning phenomena like double descent and scaling laws for language models, demonstrating the evolving nature of his theoretical contributions. His work consistently bridges deep theoretical insights with practical implications for computing. His notable collaborations include extensive work with Sanjeev Arora (77 publications, 6,879 citations), David Steurer (95 publications, 4,945 citations), and Oded Goldreich, among others. Professor Barak leads a research group at the Weizmann Institute focused on theoretical aspects of computer security and complexity theory. His work has been consistently supported by major research funding, enabling significant contributions to the theoretical foundations of computer science. He maintains an active research program with publications spanning over two decades, demonstrating sustained impact in multiple subfields of theoretical computer science.

Jon Leeمشاهده پروفایل

استاد

• Mathematical Optimization
• Combinatorial Optimization
• Integer Programming
• +۳ مورد دیگر

Jon Lee is the G. Lawton and Louise G. Johnson Professor of Engineering at the University of Michigan's College of Engineering. He previously held faculty positions at Yale University and the University of Kentucky, and served as an adjunct professor at New York University. Before his academic career, he was a Research Staff member at IBM T.J. Watson Research Center where he managed the mathematical programming group. Lee's research focuses on mathematical optimization, particularly combinatorial optimization, integer programming, and maximum-entropy sampling. His work bridges theoretical foundations with practical applications in experimental design, statistical modeling, and computational mathematics. He has made significant contributions to D-optimal design theory, perspective relaxations for nonconvex optimization, and generalized inverse computations. His recent publication trends (2022-2025) show consistent work in maximum-entropy sampling problems, D-optimal design algorithms, convex relaxations for nonconvex optimization, and generalized inverse computations. The articles demonstrate increasing sophistication in handling large-scale optimization problems while maintaining theoretical rigor, with particular emphasis on algorithmic efficiency for real-world applications. Lee has received notable recognition including: INFORMS Computing Society Prize (2010) Fellow of INFORMS (since 2013) As an academic leader, Lee has served as founding Managing Editor of Discrete Optimization (2004-06), currently serves as Co-Editor of Mathematical Programming, and is on editorial boards for Optimization and Engineering and Discrete Applied Mathematics. He chaired the Mathematical Optimization Society (2008-10) and the INFORMS Optimization Society (2010-12). His textbook A First Course in Combinatorial Optimization (Cambridge University Press) and open-source book A First Course in Linear Optimization have become standard references in the field. Lee maintains active research collaborations through his work with the Mathematical Optimization Society and INFORMS, and has participated in significant research programs including the Fall 2017 program on Bridging Continuous and Discrete Optimization at the Simons Institute.

Yuxin Chenمشاهده پروفایل

استاد

• Machine Learning Theory
• Diffusion Models
• Large Language Models (LLM)
• +۷ مورد دیگر

Yuxin Chen is a Professor of Statistics and Data Science and Electrical and Systems Engineering at the University of Pennsylvania (UPenn), where he has been since 2022. Prior to UPenn, he served as an Assistant Professor at Princeton University (2017–2021) and a postdoc at Stanford University (2015–2017). His research spans machine learning theory , diffusion models , reinforcement learning , nonconvex optimization , and high-dimensional statistics . Education : Ph.D. in Electrical Engineering (Stanford, 2015) Awards : SIAM Activity Group on Imaging Science Best Paper Prize (2024), IEEE Transactions on Power Electronics Prize Paper Award (2024), ICCM Best Paper Award (Gold Medal, 2017), Alfred P. Sloan Fellowship His recent work focuses on theoretical foundations of diffusion models , including convergence analysis, generalization behavior, and sampling efficiency. He has developed probability flow distance (PFD) to quantify distributional generalization and explored low-dimensional adaptation in diffusion processes. His research also addresses implicit regularization in nonconvex statistical estimation, with applications to phase retrieval and matrix completion. Key scientific applications of his work include controllable image editing, inverse problems in scientific imaging, and robustness in generative models. Collaborative projects with Prof. Qing Qu and Liyue Shen at ICML 2025 highlight his leadership in bridging theory and practice of generative AI. He has mentored several Ph.D. students who now hold academic positions, including Zihan Zhang (HKUST), Yuchen Zhou (UIUC), Hong Hu (WUSTL), Joshua Agterberg (UIUC), and Yuling Yan (UW-Madison). His grants include Google Research Scholar and Amazon Research Awards, supporting advancements in theoretical foundations of AI.

Liviu Chioncelمشاهده پروفایل

استاد

• Condensed Matter Physics
• Spintronics
• Electronic Structure Theory
• +۳ مورد دیگر

Professor Liviu Chioncel leads the Theoretical Physics III group at the Institute of Physics, University of Augsburg, affiliated with the Faculty of Mathematics, Natural Sciences, and Materials Engineering. His research focuses on strongly correlated electronic systems, combining theoretical frameworks like Dynamical Mean-Field Theory (DMFT) with computational methods to study quantum materials, spintronics, and transport phenomena. Research Interests: Key areas include electronic structure calculations of correlated materials, momentum density analysis via Compton scattering and positron annihilation, non-equilibrium transport in nanodevices, half-metallic magnetism, and cluster-based quantum many-body approaches. His work bridges fundamental theory with applications in next-generation electronic and spintronic devices. Publication Trends: Recent articles (2021–2025) emphasize kagome magnets, high-entropy alloys, and correlated electron transport. Predominant themes include topological magnetism, Fermi surface engineering, and advanced computational techniques (e.g., DFT+DMFT). This reflects a focus on quantum materials design and electron correlation effects in solids. Team & Advising: Dr. Chioncel supervises PhD candidates (Dylan Jones, Shreyas Nadiger) and collaborates with postdoctoral researchers. His group investigates condensed matter systems using analytical and numerical tools, with projects spanning from ab initio methods to experimental spectroscopy validation.

Negin Forouzeshمشاهده پروفایل

دانشیار

• Computer Science
• Life Sciences
• Drug Discovery
• +۶ مورد دیگر

Negin Forouzesh is a tenure-track Associate Professor in the Department of Computer Science at California State University, Los Angeles, with a joint appointment in the Biomedical Engineering (BME) Minor Program. She co-directs the NSF-PREC program and contributes to AMBER, an open-source molecular dynamics software. Ph.D. in Computer Science from Virginia Tech (2020) M.Sc. and B.Sc. from Amirkabir University of Technology (Tehran Polytechnic) Her research focuses on computational sciences for drug discovery, combining machine learning , scalable optimization algorithms , and high-performance computing to model biomolecular systems. She pioneered physics-informed neural networks for binding free energy calculations and developed novel geometric metrics for protein pocket analysis. Recent work trends show expertise in deep generative models for ligand discovery, virtual reality applications in molecular docking, and hybrid physics-ML approaches for predictive modeling in biomolecular systems. As a principal investigator for NIH-R16 and co-director of NSF-PREC programs, she leads interdisciplinary projects spanning computational biology, medical imaging, and software development for molecular simulations. Her lab, COMB Lab, maintains active GitHub repositories and collaborates with institutions like Stanford Center for Genomics and MolSSI.

• Quantum Chromodynamics
• Nuclear Physics
• Particle Physics
• +۳ مورد دیگر

Zhoudunming (Kong) Tu is an Associate Adjunct Professor in the Department of Physics and Astronomy at Stony Brook University and Associate Physicist at Brookhaven National Laboratory's Physics Department, where he leads the EIC Group. As an experimental nuclear and particle physicist, he investigates fundamental Quantum Chromodynamics (QCD) questions, particularly color confinement—the mechanism binding quarks and gluons into hadrons. His educational background includes: M.S & Ph.D in high energy nuclear physics, Rice University, 2018 B.S. in Physics & Mathematics, University of Kansas, 2013 Dr. Tu's research spans both 'hot' QCD (studying quark-gluon plasma via heavy-ion collisions at RHIC) and 'cold' QCD (probing proton structure through deep inelastic scattering at the EIC). His work addresses the color confinement problem through quantum entanglement studies, gluonic structure measurements in nuclei, and investigations of the Chiral Magnetic Effect. He actively contributes to the EPIC experiment at the EIC, STAR experiment at RHIC, and previously participated in CMS at the LHC (2012-2018) and H1 at HERA. Analysis of his 11 publications (2017-2024) reveals a distinct trend toward applying quantum information science to particle physics, particularly through entanglement entropy in QCD. His experimental focus on J/ψ photoproduction has yielded breakthroughs in mapping gluonic nuclear structure, while his precision studies of azimuthal correlations have reshaped understanding of topological effects in heavy-ion collisions. Dr. Tu's scientific contributions have been recognized with: ElCUG award for early career scientists (2024) Inter-American Network of QCD Challenges exchange award (2022) Brookhaven LDRD Award with $400,000 funding (2022-2024) Goldhaber Distinguished Fellowship (2018) Quark Matter Conference Young Scientist Award (2017) Committed to scientific development, Dr. Tu mentors students through the DOE SULI program and actively recruits diverse talent into his research group. He serves as a peer reviewer for Physical Review Letters and grant reviewer for the US Department of Energy and National Science Center of Poland, emphasizing his dedication to advancing both research and scientific community standards. As a core member of Brookhaven's EIC Group, Dr. Tu collaborates across international teams including STAR and EPIC, leveraging major facilities like RHIC and the future EIC. His work integrates theoretical frameworks with cutting-edge experimental techniques to address foundational questions about matter's structure in the universe.

Miriam Botrosمشاهده پروفایل

پژوهشگر

• Materials Science
• Electrochemistry
• Solid-State Batteries
• +۳ مورد دیگر

Dr. Miriam Botros serves as Group Leader for Energy Materials and Process Design within the Materials for Electrochemistry Research Unit (RU Janek) at the Institute of Nanotechnology, Karlsruhe Institute of Technology (KIT), Germany. Her laboratory operates across Buildings 640/0-354 and 717/204 at the Eggenstein-Leopoldshafen campus, with office hours conducted remotely on Tuesdays and Fridays. Her research centers on high-entropy materials and solid-state electrochemistry , specializing in compositionally complex systems for energy storage. Key focus areas include garnet-type solid electrolytes , high-entropy oxides/fluorides , and advanced cathode architectures , with particular emphasis on doping effects, grain boundary engineering, and structural transformations in battery components. Recent work demonstrates expertise in photonic synthesis techniques and mechanochemical processing for novel material discovery. Analysis of her 15 most recent publications reveals a dominant research trajectory in solid-state battery materials (73% of works), with significant contributions to high-entropy systems (60%) and electrocatalysis (27%). Her collaborative network spans 12+ international institutions, consistently publishing in high-impact journals including Nature Reviews Materials , Science , and Energy & Environmental Science . Dr. Botros maintains active laboratory operations within KIT's Institute of Nanotechnology infrastructure, directing research on energy materials synthesis and characterization. Her team employs advanced techniques including nebulized spray pyrolysis, field-assisted sintering, and in-situ electrochemical analysis to develop next-generation battery components.