جست‌وجوی استاد و پژوهشگر

Zohreh Sharafiمشاهده پروفایل

استادیار

• Software Engineering
• Human Aspects of Software Engineering
• Gender Studies in Software Engineering
• +۷ مورد دیگر

Zohreh Sharafi is an Assistant Professor of Software Engineering in the Department of Computer and Software Engineering (GIGL) at Polytechnique Montréal. Previously, she served as a Senior Research Fellow in the Department of Electrical and Computer Engineering at the University of Michigan, Ann Arbor, where she worked with Dr. Westley Weimer and was awarded the prestigious NSERC Postdoctoral Fellowship. Prior to her academic career, she worked as a software engineer at Morgan Stanley, contributing to the firm's electronic trading platform and serving as principal architect of SURF, a market data simulator. Her educational background includes a Ph.D. in Computer Engineering from École polytechnique de Montréal under the supervision of Dr. Giuliano Antoniol and Dr. Yann-Gaël Guéhéneuc, a Master of Applied Science in Software Engineering from Concordia University, and a Bachelor of Computer Engineering from the University of Tehran. Dr. Sharafi leads the SENSE Lab, a multidisciplinary software engineering research laboratory focused on understanding problem-solving strategies developers use during software development, with particular attention to human factors such as gender and native language. Her research combines human-centric design with experimental methodologies, investigating cognitive processes involved in software development using biometric measures including eye tracking and neuroimaging. Current active projects include evaluating trustworthiness perceptions of software artifacts and studying the role of creativity in software engineering tasks. She has made significant contributions to understanding how gender influences program comprehension and code review processes. Her publication record demonstrates a strong focus on empirical methods in software engineering, particularly eye tracking and neuroimaging techniques to study developer cognition. Her work spans program comprehension, code review, requirements engineering, and the impact of human factors on software development processes. She has developed methodological frameworks for conducting eye tracking studies in software engineering and has made notable contributions to understanding how visualization techniques affect software development tasks. NSERC Postdoctoral Fellowship NSERC Discovery Grant Program and Launch Supplements (Sep 2024-Sep 2029) IVADO Startup & Operation Fund (Jan 2022-Jan 2023) Scholarship for Doctoral Studies from Fonds de Recherche du Quebec Distinguished Reviewer Awards from IEEE ICPC 2020 and ACM FSE 2024 Dr. Sharafi actively mentors students including Mahta Amini (PhD Candidate, IVADO Scientifique en résidence 2024 Laureate), Cameron Cherif (PhD Candidate), Sara Yabesi (Master's Student), and Anthonia Njoku (Graduate research intern). She serves on numerous conference organizing committees including as Local Arrangement Chair for SANER 2025, Program Co-chair for SEMLA 2024, and as a reviewer for top-tier journals including IEEE Transactions on Software Engineering and ACM Computing Surveys. Her research is supported by multiple grants focused on understanding human factors in software engineering through empirical methods. At Polytechnique Montréal, Dr. Sharafi directs the SENSE Lab which brings together computer scientists, cognitive scientists, and software engineering researchers to investigate the cognitive aspects of software development. The lab employs advanced methodologies including eye tracking, functional near-infrared spectroscopy (fNIRS), and functional magnetic resonance imaging (fMRI) to study how developers comprehend, navigate, and modify software systems. Current projects examine trustworthiness perceptions in code review, the role of creativity in software engineering tasks, and gender differences in software development processes.

Reinhard Heckelمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Computational Imaging
• +۵ مورد دیگر

Reinhard Heckel is a Tenured Associate Professor (equivalent to Professor) of Machine Learning at the Department of Computer Engineering, Technical University of Munich (TUM), and Adjunct Faculty in Electrical and Computer Engineering at Rice University. He was previously an Assistant Professor at Rice (2017–2019), a postdoc in the Berkeley Artificial Intelligence Research (BAIR) Lab at UC Berkeley, and a researcher at IBM Research Zurich. Education: PhD, 2014 – ETH Zurich Visiting PhD student – Department of Statistics, Stanford University Research Interests: His work centers on machine learning and information processing with three major thrusts: (1) developing algorithms and theoretical foundations for deep learning, especially for accelerated magnetic resonance imaging ; (2) establishing rigorous mathematical and empirical underpinnings for modern machine-learning systems; and (3) leveraging DNA as a digital information-storage medium , including error-correction coding and system design for DNA-based storage. Across more than 100 peer-reviewed papers since 2017, Heckel’s research exhibits a strong interdisciplinary blend of computational imaging , machine-learning theory , and molecular data storage . Recent 2024–2025 publications show intensive focus on robust MRI reconstruction using diffusion priors, evaluation of bias in large web-text corpora, and state-of-the-art error-correcting codes for DNA storage channels. A forthcoming book, Deep Learning for Computational Imaging (Oxford University Press), consolidates his contributions to the field. Outreach & Media: Keynote and panel talks at DLD, TUM, and major ML conferences Op-eds in Frankfurter Allgemeine on ChatGPT and DNA storage Science features on Netflix, BBC, and German television (Galileo, “Gut zu Wissen”) Research Environment: At TUM he leads a group investigating theoretical and applied aspects of deep learning, compressed sensing, and coding for DNA storage. Open-source repositories on GitHub (e.g., dna_data_storage , supplement_deep_decoder ) provide code and data supplements accompanying his publications.

Kevin P. O'Brienمشاهده پروفایل

دانشیار

• Quantum Computing
• Superconducting Circuits
• Nonlinear Optics
• +۳ مورد دیگر

Kevin P. O'Brien is an Associate Professor in the Department of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT), affiliated with the Research Laboratory of Electronics (RLE). He leads the Quantum Coherent Electronics (QCE) group, focusing on advancing superconducting quantum computing, microwave quantum optics, and quantum metamaterials. His research explores nonlinear and quantum-mechanical light-matter interactions using superconducting circuits, aiming to improve quantum technologies like qubits and amplifiers. Education: B.S. in Physics from Purdue University, Ph.D. in Physics from UC Berkeley, and postdoctoral research at UC Berkeley developing superconducting quantum processors. His group collaborates with MIT Lincoln Laboratory and institutions nationwide. Research Interests: Quantum computing hardware, superconducting circuits, parametric amplifiers, qubit measurement systems, and metamaterials for quantum applications. His work emphasizes scalable architecture design, noise reduction, and novel device concepts. Key projects include directional qubit readout resonators, Floquet-mode amplifiers, and quarton couplers for ultrafast readout. The group actively engages in training graduate students and postdocs, emphasizing open collaboration and problem-solving in quantum technologies. Advising & Grants: Supervises a dynamic team of graduate students and postdocs. Students like Bright Ye and Kaidong Peng have contributed to award-winning projects. The group receives support through fellowships (e.g., Jin Au Kong, NSF GRFP) and industry partnerships. Labs/Teams: Quantum Coherent Electronics Group at MIT, collaborating on quantum device fabrication, theoretical modeling, and experimental validation of quantum systems.

Stuart C. Althorpeمشاهده پروفایل

استاد

• Theoretical Chemistry
• Quantum Dynamics
• Quantum Mechanics
• +۴ مورد دیگر

Professor Stuart C. Althorpe is a Professor of Theoretical Chemistry at the Department of Chemistry, University of Cambridge, where he leads the Althorpe Research Group. His work focuses on quantum dynamics and the application of quantum mechanics to chemical reactions, particularly examining how quantum effects influence atomic and molecular motion in systems ranging from isolated reactions to liquid water and ice. Professor Althorpe's research interests center on quantum dynamics , with specific focus on quantum tunneling , conical intersections , and path-integral methods . His group develops computational techniques that bridge quantum statistics and classical dynamics, creating methods like Matsubara dynamics to model quantum effects in complex systems. Key research areas include visualizing complete wave functions of chemical reactions, calculating tunneling splittings in water clusters, and investigating quantum interference effects at electronic degeneracies. The group employs both pen-and-paper theoretical derivations and high-performance computational algorithms for parallel CPUs and GPUs to tackle these challenging problems. The trends in Professor Althorpe's recent publications (2018-2024) reveal an evolution from foundational quantum dynamics methods toward increasingly sophisticated applications in complex, dissipative environments. His work consistently bridges computational chemistry and quantum physics, with growing emphasis on connections between quantum dynamics, chaos theory, and quantum information concepts. The publications demonstrate methodological innovation in path-integral approaches, particularly in handling quantum effects in condensed-phase systems like water and ice, while maintaining strong connections to experimental observations. Professor Althorpe has mentored over two dozen PhD students and postdoctoral researchers who have secured positions at leading institutions including ETH Zürich, UCL, EPFL, and various industry roles. His research group maintains active international collaborations, most notably with Professor D.J. Wales at Cambridge (on water clusters) and Professor Richard N. Zare at Stanford University, where theoretical calculations interpret detailed experimental measurements of reaction dynamics. The Althorpe Group operates within the Department of Chemistry at the University of Cambridge as part of the Theoretical Research Interest Group. The group's work contributes significantly to understanding quantum mechanical phenomena in chemical processes, with implications for fields ranging from atmospheric chemistry to biochemistry. Professor Althorpe organized the 2019 Faraday Discussion on "Quantum effects in complex systems," demonstrating his leadership in advancing theoretical frameworks for quantum phenomena in chemistry.

• Mathematical Logic
• Model Theory
• O-Minimal Structures
• +۵ مورد دیگر

Patrick Speissegger is a Professor in the Department of Mathematics and Statistics at McMaster University. He holds a Ph.D. in Mathematics from the University of Illinois at Urbana-Champaign and a Diploma in Mathematics from the Swiss Federal Institute of Technology Lausanne. His research focuses on O-Minimal Structures, Real Analytic Geometry, and their applications to Differential Equations and Dynamical Systems. Education: Ph.D., University of Illinois at Urbana-Champaign; Diploma, Swiss Federal Institute of Technology Lausanne His recent work explores multisummable series, transseries, and Hilbert's 16th problem. Publications span foundational topics in Model Theory, Pfaffian systems, and geometric definability. He has served as an instructor for various undergraduate and graduate courses, including Advanced Calculus and Quantum Computing. His scholarly activity includes collaborations and presentations on O-Minimal Structures and their implications in Number Theory and Analysis. Despite no explicit mention of scientific awards, his contributions to mathematical logic and analytic geometry remain significant.

Anirban Chakrabortiمشاهده پروفایل

استاد

• Econophysics
• Sociophysics
• Complex Systems
• +۱۴ مورد دیگر

Anirban Chakraborti is a Professor at the School of Computational and Integrative Sciences, Jawaharlal Nehru University (JNU), New Delhi, India, where he has been a faculty member since 2014. He previously held academic positions at École Centrale Paris (France) as Chercheur Senior (Associate Professor) and Chargé de Recherche (Assistant Professor), and earlier roles at Banaras Hindu University, Brookhaven National Laboratory (USA), and Helsinki University of Technology (Finland). He is a leading figure in the interdisciplinary field of econophysics and complex systems. Education: Diplôme d’Habilitation à Diriger des Recherches (2013), Université Pierre et Marie Curie – Paris VI, France (Physics) Ph.D. in Physics (2003), Saha Institute of Nuclear Physics, Jadavpur University, India Post-M.Sc. in Physics (1999), Saha Institute of Nuclear Physics, Jadavpur University, India (Ranked First) M.Sc. in Physics (1998), University of Calcutta, India (Ranked First) B.Sc. in Physics (1996), Scottish Church College, University of Calcutta, India His research interests lie at the intersection of physics, economics, and data science. He is particularly known for pioneering work in econophysics , including the statistical mechanics of money, wealth distribution, agent-based market models, and network-based analysis of financial and social systems. He also works on complex systems , computational finance , statistical physics , and nanosciences , with applications in sensing and imaging. His work often involves modeling socio-economic phenomena using tools from statistical physics. His recent publications span topics such as financial fluctuations, wealth inequality, network analysis of conflicts, order book dynamics, and nanomaterial characterization. These works reflect a strong trend toward interdisciplinary research combining physics, economics, and data analytics, with a focus on real-world applications in finance, inequality, and social systems. Scientific Awards: Indian National Science Academy Young Scientist Medal (2009) He has advised Ph.D. students such as Kiran Sharma and leads the ETC (Experimental-Theoretical-Computational) Lab at JNU, which brings together physicists, computer scientists, and mathematicians. The lab has been involved in international collaborations, including projects funded by the Estonian Ministry of Education and Research and consultancies with TCS Innovation Labs and DONO Consulting. His research has been supported through grants and collaborative projects, reflecting strong industry and global academic engagement.

Julian Tachellaمشاهده پروفایل

پژوهشگر

• Signal Processing
• Machine Learning
• Computational Imaging
• +۵ مورد دیگر

Dr. Julian Tachella is a CNRS Research Scientist at the Sisyph Laboratory of École Normale Supérieure de Lyon, with co-founder/CSO roles at Blur Labs. His career spans signal processing, machine learning, and computational imaging, focusing on inverse problems and self-supervised learning. Affiliation: CNRS (French National Centre for Scientific Research), Sisyph Laboratory, École Normale Supérieure de Lyon Co-founder & CSO: Blur Labs (AI/Imaging startup) Research Interests: At the intersection of signal processing and deep learning , his work addresses imaging inverse problems through self-supervised methodologies (e.g., UNSURE, Generalized R2R) that eliminate ground-truth requirements. Key contributions include equivariant imaging frameworks for stability, spline sketches for photon-counting lidar compression, and uncertainty quantification techniques with equivariant bootstrapping. Recent Trends: 2025 publications emphasize lightweight architectures for multi-domain reconstruction (CT, super-resolution) and noise-agnostic SURE methods. 2024 works focus on audio declipping , compressed lidar , and nonlinear algorithm unrolling with applications in autonomous vehicles and medical imaging. Scientific Awards: Best Student Paper Award at ICASSP’22 Collaborations & Leadership: He leads the DeepInverse open-source project and develops algorithms for real-time 3D lidar reconstruction. His team includes researchers from University of Edinburgh and Grenoble INP, with applications in automotive lidar and underwater imaging.

• Mathematical Physics
• Quantum Chaos
• Spectral Theory
• +۳ مورد دیگر

Stéphane Nonnenmacher is a Professor of Mathematics at Université Paris-Saclay, where he leads the Ecole Doctorale Mathématique Hadamard. His research focuses on quantum chaos, spectral theory, and dynamical systems, with notable contributions to eigenfunction localization and resonance distribution in chaotic systems. He co-organizes events like the Paris-Saclay conference on Analysis and PDE (2024) and the monthly seminar 'Problèmes spectraux en physique mathématique.' He is affiliated with ANR ADYCT and the Groupement de Recherches Dynamique Quantique. Teaching includes advanced courses on semiclassical analysis and partial differential equations at the undergraduate and graduate levels. Research interests span theoretical and applied mathematical physics, emphasizing spectral properties of quantum systems, scattering theory, and the interplay between classical and quantum dynamics. His work often involves collaboration with leading researchers in the field. Teaching activities in 2024-25 include an analysis course for physics students and a master's-level course on semiclassical analysis. He also taught a course on measure theory and probability at the Université des Sciences et Technologies de Chine (USTC).

Jarmo Malinenمشاهده پروفایل

مدرس

• Applied Mathematics
• Numerical Analysis
• Mathematical Systems Theory
• +۳ مورد دیگر

Jarmo Malinen is a Lecturer at the Department of Mathematics and Systems Analysis, Aalto University , School of Science. He leads the speech modeling group and participates in the Comspeech consortium, collaborating with institutions like the University of Helsinki and Turku University Hospital. His research spans mathematical systems theory, numerical analysis, and computational speech science, focusing on MRI-based vocal tract modeling, glottal flow dynamics, and speech acoustics. He develops physical models for speech production and contributes to educational technologies like the STACK system. Key publication trends include applications of finite element methods to vowel formants, inverse filtering for glottal flow estimation, and eigenvalue problem solutions for coupled systems. His work intersects applied mathematics, biomedical imaging, and speech physiology. Malinen advises master's and PhD students in mathematical modeling and speech research, though specific student names are not listed in the provided texts. He has developed tools for MRI data post-processing and vocal tract resonance optimization.

Lothar Nannenمشاهده پروفایل

دانشیار

• Finite Element Methods
• Scattering Problems
• Resonance Problems in Open Systems
• +۴ مورد دیگر

Lothar Nannen is an Associate Professor at Vienna University of Technology's Institute of Analysis and Scientific Computing. His research focuses on finite element methods, scattering and resonance problems in open systems, and transparent boundary conditions for wave propagation modeling. Key research areas: Computational Waves, Hardy space infinite elements, time-harmonic systems Current work: 2024 publications on Krylov eigenvalue solvers for wave problems Nannen contributes to teaching through courses on numerical analysis and differential equations, with oral exams conducted by appointment. He actively publishes in journals like Computers & Mathematics with Applications and Numerische Mathematik , emphasizing numerical methods for wave scattering and resonance analysis.

• Hamiltonian Systems
• Dynamical Systems
• Bifurcation Theory
• +۳ مورد دیگر

Henk Broer is a Professor at the Bernoulli Institute within the Faculty of Science and Engineering at the University of Groningen. His work spans key areas in dynamical systems, Hamiltonian mechanics, and bifurcation theory, with contributions to KAM theory, integrable systems, and singularity theory. Fields of Interest: Hamiltonian systems, bifurcation theory, KAM theory, integrable systems, singularity theory, dynamical systems. Broer’s research focuses on nonlinear dynamics, parametrized KAM theory, and the interplay between phase space structures and parameter spaces. His recent work explores double Hopf bifurcations and monodromy in integrable systems. Recent publications highlight his expertise in Hamiltonian systems, bifurcation analysis, and quasi-periodic invariant tori. Broer has supervised 22 research projects, though specific student names are not listed. He contributes to addressing UN Sustainable Development Goals through mathematical modeling and theoretical advancements.

Wei-Tang Changمشاهده پروفایل

استادیار

• Neuroimaging
• Magnetic Resonance Imaging (MRI)
• Functional MRI (fMRI)
• +۴ مورد دیگر

Dr. Wei-Tang Chang is an Assistant Professor in the Department of Radiology at the University of North Carolina School of Medicine. His research focuses on advancing ultrahigh-resolution functional and diffusion MRI techniques, with emphasis on improving spatial and temporal resolution while reducing scan times. Key projects include submillimeter isotropic-resolution fMRI for hippocampal subfield analysis (funded by NIH R21), novel dMRI approaches to overcome resolution limits, and clinical translation of robust imaging methods resistant to motion/noise artifacts. Dr. Chang holds a PhD in Biomedical Engineering from National Taiwan University and completed postdoctoral training at the Martinos Center for Biomedical Imaging (MGH), Massachusetts General Hospital, and Singapore BioImaging Consortium (SBIC). His research innovations include SORDINO fMRI for awake rodent imaging, pPRISM diffusion MRI for submillimeter resolution, and ZTE pulse sequences for ultra-fast acquisitions. Awards include the 2011 OHBM Trainee Award for work on MEG source localization and fMRI temporal resolution breakthroughs. Current work bridges basic neuroimaging science with clinical applications, particularly in neurodegenerative disease biomarker development and rodent disease model studies. Education: PhD in Biomedical Engineering, National Taiwan University Postdoctoral Fellowships: Martinos Center for Biomedical Imaging (MGH) Singapore BioImaging Consortium (SBIC) Key Technologies Developed: ZTE pulse sequences (25 ms temporal resolution fMRI) pPRISM diffusion MRI (navigator-free submillimeter imaging) Draining-vein suppression layer-dependent fMRI Awards & Funding: NIH R21 Grant (2019) for hippocampal subfield fMRI OHBM Trainee Award (2011) Dr. Chang's translational focus involves adapting laboratory innovations for clinical use, with particular interest in Alzheimer's disease biomarkers through hippocampal network analysis and Huntington's disease models using rodent functional connectivity studies. His lab actively develops open-source MRI reconstruction algorithms and collaborates internationally on multi-center neuroimaging projects.

• VLSI Design
• Computer-Aided Design
• Electronic Design Automation
• +۴ مورد دیگر

Matthew R. Guthaus is a Professor in the Electrical and Computer Engineering Department at the University of California Santa Cruz's Baskin School of Engineering. With an active research career spanning over two decades, his work focuses on VLSI design, Electronic Design Automation, and memory systems. His research interests include VLSI Design, Computer-Aided Design, Electronic Design Automation, Memory Design, Clock Distribution, Resonant Clocking, and Machine Learning for EDA. Guthaus has made significant contributions to open-source EDA tools, most notably OpenRAM, which has become an important resource for memory compiler development in academia and industry. Analysis of his recent publications (2021-2025) reveals a strategic evolution in his research, incorporating machine learning techniques into traditional EDA problems while maintaining strong foundations in circuit design. His work spans from fundamental circuit design to high-level system considerations, with particular emphasis on open-source frameworks that enable broader collaboration in the semiconductor community. Guthaus has been actively involved in national initiatives addressing semiconductor research and workforce development, as evidenced by his contributions to NSF workshops on integrated circuits. His recent publications show continued innovation in memory design, clock distribution, and the application of AI to traditional EDA challenges. His research has practical applications in low-power circuit design, high-performance computing systems, and the growing field of neuromorphic computing. The consistent publication record through 2025 demonstrates ongoing active research and leadership in the EDA community.

Vasanth Sarathyمشاهده پروفایل

عضو هیئت علمی

• Artificial Intelligence
• Natural Language Processing
• Neuro-symbolic Machine Learning
• +۷ مورد دیگر

Vasanth Sarathy is a Research Assistant Professor of Computer Science at Tufts University, specializing in the intersection of Artificial Intelligence and Natural Language Processing. His work combines neuro-symbolic machine learning techniques with social and cognitive sciences to develop socially-competent and safe AI systems. Education: Ph.D. in Computer Science and Cognitive Science, Tufts University (2020) Juris Doctor (J.D.), Boston University School of Law (2010) M.S. in Electrical Engineering and Computer Science, MIT (2005) B.S. in Electrical Engineering, University of Arkansas (2003) His research spans three interconnected themes: Social NLP, Reasoning with Social Norms, and Sense-making and Problem-Solving. Through Social NLP, he develops computational methods to uncover patterns in human social behavior using qualitative texts. His work on Reasoning with Social Norms focuses on building AI architectures that can understand and apply social norms in reasoning and language interpretation. His Sense-making research explores how humans and AI systems simplify complex worlds for efficient problem-solving. His recent publications demonstrate a growing trend toward integrating Large Language Models with symbolic reasoning for more trustworthy AI. His work spans applications including automated writing assistance, intelligent tutoring, fact-checking, social media content moderation, and embodied social robots. His research shows particular strength in developing methods that combine neural and symbolic approaches to address complex social reasoning tasks that neither approach could solve alone. Previously, Dr. Sarathy worked as a Senior Researcher of AI at Smart Information Flow Technologies, collaborating with the U.S. Department of Defense and Intelligence Community. Before his AI career, he practiced intellectual property law at Ropes and Gray for nearly a decade. His unique interdisciplinary background bridges technology, law, and cognitive science. He advises students in AI, NLP, and cognitive systems, with research supported by grants from DARPA and other government agencies. His work has applications in human-robot interaction, social media analysis, and educational technologies. Outside academia, he creates single-panel cartoons, practices martial arts, plays chess, and designs puzzle video games. His interdisciplinary journey from law to AI has been featured in a BU Law article.

Alexander Volyaمشاهده پروفایل

استاد

• Nuclear Theory
• Quantum Chaos
• Many-Body Physics
• +۳ مورد دیگر

Alexander Volya is a Professor of Physics at Florida State University (FSU), specializing in nuclear theory and mesoscopic physics. His research integrates quantum many-body systems, nuclear structure, and interdisciplinary approaches to explore phenomena such as alpha clustering, quantum chaos, and superradiance. He focuses on bridging nuclear physics with astrophysics, fundamental science, and quantum signal transmission. His work emphasizes computational methods and high-performance computing to address complex problems in nuclear dynamics. He collaborates with experimental facilities like FRIB to study exotic nuclei and their decay processes. Key themes include cross-shell excitations, pairing correlations, and the interplay between collective motion and chaotic dynamics in open quantum systems. Recent studies highlight his exploration of superradiance in alpha-clustered nuclei, the quenching of octupole rotational bands, and the development of novel shell model interactions. His research also addresses astrophysical reaction rates and the impact of nuclear structure on stellar nucleosynthesis. Volya’s contributions span theoretical frameworks for mesoscopic systems, eigenstate thermalization in fermionic systems, and the application of random interaction models to bosonic systems. His work bridges foundational physics with technological advancements in nuclear research.