جستجوی استاد راهنمای دانشگاهی

David I. Augustمشاهده پروفایل

استاد

• Compilers
• Computer Architecture
• Parallel Processing
• +۴ مورد دیگر

David I. August is a Professor of Computer Science at Princeton University, affiliated with the Department of Electrical Engineering. He earned his Ph.D. from the University of Illinois at Urbana-Champaign in 2000. His research focuses on compilers and computer architecture, emphasizing synergistic design between compilers and microarchitecture. He leads the Liberty Research Group, which explores topics such as automatic parallelization, memory profiling, and speculative execution. August joined Princeton in 1999 as a lecturer, advancing to full professor in 2012. He has served as program chair for MICRO 2009 and on committees for ISCA, PLDI, and ASPLOS. His notable accolades include the IEEE Fellow designation, Best Paper Awards at PLDI and CGO, and teaching awards from Princeton's School of Engineering. His work spans compiler optimizations, hardware-software co-design, and security architectures like TrustGuard. Recent research includes GPU scheduling (GhOST), memory profiling frameworks (PROMPT), and instruction prefetching (PDIP). He advises over 20 graduate students, many now leading roles at tech companies and academia. August teaches courses such as COS-126 (Intro to CS), COS-375 (Computer Architecture), and graduate seminars. His projects often bridge theory and practice, with tools like NOELLE and Liberty Research Group initiatives advancing compiler infrastructure and parallelism extraction.

• Machine Learning
• Reinforcement Learning
• Online Learning
• +۴ مورد دیگر

Karan Singh serves as an Assistant Professor of Operations Research at Carnegie Mellon University's Tepper School of Business, where he develops theoretically rigorous algorithms for machine learning systems with emphasis on reinforcement learning and control theory. His work synthesizes techniques from online learning, optimization, and statistics to address complex interactive learning challenges. His academic journey includes: PhD in Computer Science from Princeton University under Elad Hazan Postdoctoral research at Microsoft Research (Redmond) Bachelor's degree in Computer Science from Indian Institute of Technology (IIT) Kanpur Singh's research program centers on three interconnected pillars: Algorithmic Reductions : Creating efficient methods to solve complex learning problems (e.g., reinforcement learning) using solvers for simpler tasks, yielding breakthroughs in online boosting and RL with concave rewards Nonstochastic Control : Establishing an algorithmic foundation for control theory through provably efficient instance-optimal algorithms that extend online learning to stateful systems Privacy-Preserving Online Learning : Investigating fundamental limits of regret minimization under differential privacy constraints while maintaining performance His approach consistently bridges theoretical computer science and practical control applications. Analysis of his 15 most recent publications reveals a clear evolution toward integrating algorithmic reductions with nonstochastic control frameworks. Recent work (2023-2025) demonstrates increasing focus on sample efficiency in agnostic boosting, privacy-aware optimization without smoothness assumptions, and competitive ratio analysis in online control. A unifying thread is the development of regret-optimal algorithms for linear dynamical systems under adversarial disturbances. His contributions have earned significant recognition: Best Paper Award at OptRL workshop (NeurIPS 2019) Spotlight Prize from New York Academy of Sciences' ML Symposium (2018) Multiple oral presentations at NeurIPS/ICML (acceptance rate Though specific student advisees aren't listed, Singh's extensive publication record with junior co-authors indicates active mentorship. His research has secured substantial support including a US patent (11,138,513 B2) for dynamic learning systems and collaborations through CMU's Machine Learning and Optimization group. Current projects involve interdisciplinary work on differentiable control libraries (Deluca) and medical applications like mechanical ventilation control. Singh leads research within CMU's Machine Learning and Optimization ecosystem, collaborating across computer science and engineering departments. His team develops foundational tools like the Deluca differentiable control library while pursuing real-world applications in healthcare systems, demonstrating strong cross-disciplinary integration.

• Mathematical Optimization
• Algorithmic Intelligence
• Energy Systems Modeling
• +۵ مورد دیگر

Thorsten Koch serves as Head of the Department of Applied Algorithmic Intelligence Methods within the Division of Mathematical Algorithmic Intelligence at Zuse Institute Berlin (ZIB). His research spans mathematical optimization, energy systems modeling, quantum computing applications, and scientometrics. Koch leads significant research projects including FAN (focusing on AI in scholarly communication), UNSEEN (energy scenarios), HPO-NAVI (research software visibility), and Multi-Energy Models for European Energy System Planning. Koch's research interests center on developing advanced optimization algorithms for complex systems, particularly in energy networks and scientific data analysis. His work bridges theoretical mathematics with practical applications in gas network optimization, wind farm design, portfolio management, and quantum computing. He has pioneered methods for large-scale mixed-integer programming, scenario generation, and the integration of machine learning with traditional optimization techniques. His recent publications demonstrate growing emphasis on quantum optimization, scientometrics, and the application of AI to scientific communication infrastructure. His publication trends reveal a strategic expansion from traditional mathematical optimization into quantum computing applications and scientific data infrastructure. Recent work shows increasing collaboration across disciplines - connecting energy systems analysis with financial modeling, integrating machine learning with optimization solvers, and applying computational methods to scientometrics. The 15 most recent articles highlight three major thrusts: quantum optimization (33%), energy systems modeling (27%), and scientific data infrastructure (40%), reflecting his leadership in both theoretical algorithm development and practical implementation for societal challenges. Koch actively contributes to research infrastructure through leadership roles in projects like KOBV (Berlin-Brandenburg Cooperative Library Network), HDC (Humanities Data Centre), and CIB (future library networks). His work on the DeepGreen initiative focuses on establishing legally secure workflows for implementing open-access components in scientific publication licensing agreements, demonstrating his commitment to open science principles and research data management.

Pierre Marquisمشاهده پروفایل

استاد

• Artificial Intelligence
• Knowledge Representation
• Automated Reasoning
• +۶ مورد دیگر

Pierre Marquis is a distinguished Professor of Computer Science at Université d'Artois , affiliated with the Centre de Recherche en Informatique de Lens (CRIL-CNRS, UMR 8188) . Since December 2024, he has served as the vice-president for research and doctoral studies at Université d'Artois. His research focuses on Artificial Intelligence , particularly knowledge representation , automated reasoning , inconsistency handling , and knowledge compilation , with recent emphasis on Explainable AI (XAI) . Research Interests: Marquis's work spans foundational AI topics including abduction , induction , belief revision , and preference modeling . He has pioneered knowledge compilation techniques to optimize AI tasks and developed frameworks for reasoning under inconsistency through paraconsistent logics and argumentation. His EXPEKTATION chair (2020-2026) under France's national AI program drives his current focus on interpretable machine learning models. Scientific Awards: 2025: CNRS Silver Medal 2022: AAIA Fellow 2017: Senior Member of Institut Universitaire de France (IUF) 2009: EurAI (ECCAI) Fellow Doctoral Students: Mentoring Clément Lens (critical patient monitoring systems) and Mehdi Sabiri (data-knowledge integration for AI explanations). Collaborating with students like Louenas Bounia (formal XAI models) and Romain Wallon (pseudo-Boolean constraints). Grants & Projects: Leads the EXPEKTATION research chair (2020-2026) and participates in ANR PING/ACK (2019-2023), ANR THEMIS (2021-2025), CNRS IRP MAKC (2020-2024), and H2020 TAILOR (2020-2024). Previously led PIA4 MAIA (2023-2032) and Pint (2022-2023). Labs & Teams: Active in CRIL-CNRS, contributing to PyXAI (Python XAI library) and d4 (model counting), while mentoring teams on consensus belief merging and dynamic constraint processing .

Job Boekhovenمشاهده پروفایل

دانشیار

• Synthetic Chemistry
• Biophysics
• Biochemistry
• +۷ مورد دیگر

Prof. Dr. Job Boekhoven is an Associate Professor at the Department of Bioscience , TUM School of Natural Sciences , Technical University of Munich . His research focuses on synthetic life , chemically fueled self-assembly , and supramolecular materials , aiming to synthesize life from scratch. Research Interests include creating synthetic cells that compete for resources, replicate, and undergo Darwinian evolution . His lab designs molecules like lipids , peptides , and nucleic acids that self-assemble into active compartments regulated by chemical energy. These systems exhibit life-like hallmarks such as emergence , self-division , and controllable lifetimes . Scientific Awards include: ERC Consolidator Grant (2024) Lecturer Award by Association of the Chemical Industry (2024) ERC Starting Grant (2019) Volkswagen Foundation 'Life?' Grant (2019) Max Planck Fellow (2019) VCI Dozentenpreis (2021) Thieme Chemistry Journal Award (2017) Rubicon Postdoctoral Fellowship (2013) Publications highlight trends in nonequilibrium materials , dynamic combinatorial libraries , and protocell engineering . His work bridges synthetic chemistry with biophysics to explore life's origins and applications in materials science .

Morteza Haghir Chehreghaniمشاهده پروفایل

استاد

• Machine Learning
• Decision Making
• Reinforcement Learning
• +۱۷ مورد دیگر

Morteza Haghir Chehreghani is a Professor of Artificial Intelligence and Machine Learning at the Data Science and AI Division of Chalmers University of Technology , Sweden. He leads the Machine Learning and Decision Making Lab and is affiliated with WASP , CHAIR , and ELLIS . Education : PhD in Computer Science (2014) from ETH Zurich under Prof. Dr. Joachim M. Buhmann Prior Roles : Staff Research Scientist at Naver Labs Europe (2014-2018) Research spans Interactive Machine Learning , Sequential Decision Making , Federated Learning , Efficient Deep Learning , and Graph-Based Learning . Key application areas include Transport , Autonomous Systems , Energy , Drug Discovery , and Computational Biology . Selected Publications (2020-2025) demonstrate expertise in Reinforcement Learning for drug design, Minimax Distance Measures for clustering, and Graph Neural Networks for trajectory analysis. Current work focuses on Combinatorial Bandits and Human-in-the-loop AI . Teaching includes graduate courses like Advanced Topics in Machine Learning (DAT441/DIT41), Algorithms for Machine Learning (TDA233/DIT382), and PhD-level Advanced Reinforcement Learning . He has also taught Statistical Methods for Data Science and Theoretical Foundations of ML . Patents include systems for Autonomous Vehicle Motion Control , K-NN Search via Minimax Distances , and Trip Prediction Algorithms . Collaborative projects involve Nature Communications (2022) and multiple ICML / CVPR publications.

Stefan Klusمشاهده پروفایل

مدرس

• Data-driven model reduction
• Transfer operator approximation
• Machine learning
• +۸ مورد دیگر

Dr. Stefan Klus is a Lecturer at the School of Mathematics and Physics, University of Surrey. His research focuses on data-driven model reduction, transfer operator approximation, and kernel-based machine learning applied to dynamical systems. He specializes in interdisciplinary applications across quantum physics, fluid dynamics, and computational biology. Education: PhD in Industrial Mathematics (2011, Paderborn University) and Habilitation (2020, Freie Universität Berlin). Research Interests : Data-driven modeling and reduced-order methods Koopman operator theory and transfer operators Machine learning for dynamical systems (e.g., Deeptime library) Tensor decompositions and quantum systems analysis Graph-based analysis (e.g., microbiome dynamics) Publications : Klus has contributed to over 50 peer-reviewed articles, with recent work emphasizing: Kernel methods for quantum chemistry and physics Tensor-based approaches for high-dimensional systems Applications in climate science (e.g., Pacific SST modeling) Agent-based modeling and social systems Technical Contributions : Co-developer of the Deeptime Python library for dynamical modeling Pioneer in Koopman operator-based model reduction Advanced graph kernel methods for microbiome analysis

Joseph P.A. Harrityمشاهده پروفایل

استاد

• Synthetic Organic Chemistry
• Heterocyclic Chemistry
• Organic Synthesis
• +۳ مورد دیگر

Professor Joseph P.A. Harrity is a Professor of Synthetic Organic Chemistry at the University of Sheffield, affiliated with the School of Mathematical and Physical Sciences. He leads Chemistry recruitment and specializes in developing novel synthetic methodologies for heterocyclic compounds and natural products. His research focuses on carbon-carbon bond formation strategies, particularly involving boron-based intermediates and transition metal catalysis. Education: BSc (Hons.) in Chemistry (University of Strathclyde, 1991), PhD in Chemistry (University of Strathclyde, 1994). Postdoctoral research at Boston College, USA (1994–1997). Academic career progression includes roles as Lecturer (University of Sheffield, 1997), Senior Lecturer, Reader, and Professor (2009). Research interests include cycloaddition reactions, fluorinated heterocycles, and medicinal chemistry applications. He pioneered a Royal Society Industry Fellowship (2012–2015) collaborating with Peakdale Molecular to develop functionalized intermediates for pharmaceutical libraries. Awards: Pfizer Discovery Award (2004), AstraZeneca Research Award (2006), RSC Bader Award (2018). Teaching: Undergraduate/postgraduate modules on organic synthesis, stereoselective methods, and laboratory supervision. Active in mentoring research projects and organic chemistry education. Key contributions include the development of Pd-catalyzed annulation strategies, boronic acid-based synthesis platforms, and stereocontrolled natural product syntheses. His work bridges academic research and industrial applications in drug discovery and materials science.

Sylvain Lombardyمشاهده پروفایل

استاد

• Automata Theory
• Formal Languages
• Theoretical Computer Science
• +۳ مورد دیگر

Sylvain Lombardy is a Professor at the University of Bordeaux, affiliated with the Laboratoire Bordelais de Recherche en Informatique (LaBRI) and the Enseirb-Matmeca engineering school within the Institut Polytechnique de Bordeaux. His research focuses on automata theory, formal languages, and theoretical computer science, particularly in weighted automata, formal methods, and algebraic properties of automata. He leads the Formal Methods research team at LaBRI and contributes to projects like the Awali and Vaucanson software platforms for automata manipulation. Education: PhD in Computer Science (2001, ENST Paris), Habilitation à Diriger des Recherches (2005, University of Paris Diderot). His work bridges theoretical foundations with practical tools, emphasizing algorithmic and algebraic aspects of automata. Notable contributions include studies on automata minimization, determinization, and the interplay between rational expressions and automata constructions. Research Interests: Automata Theory, Formal Power Series, Weighted Automata, Tropical Semirings, Algebraic Automata Theory, and Computational Models for Discrete Systems. His recent work explores two-way automata, Hadamard series, and applications in formal verification. Publications highlight contributions to the structure and properties of automata, with a focus on formal methods and algorithmic decidability. Key works address unambiguity, determinism, and the minimization of weighted automata across various semirings. Collaborations include projects on automata-based kernels for machine learning and XML formats for automata descriptions. He has developed influential software tools such as Awali (finite-state machine platform) and Vaucanson (automata manipulation framework), demonstrating practical applications of theoretical research. His work is supported by grants exploring automata in computational linguistics and discrete mathematics.

E. Thomas (Tommy) Pashuckمشاهده پروفایل

استادیار

• Biomaterials
• Tissue Engineering
• Regenerative Medicine
• +۶ مورد دیگر

E. Thomas (Tommy) Pashuck is an Assistant Professor in the Department of Bioengineering at Lehigh University , where he joined after postdoctoral training at Rutgers University and Imperial College London . His research focuses on designing enzyme-responsive biomaterials using peptide self-assembly to control hydrogel chemistry and nanoscale organization , aiming to improve regenerative medicine applications. Education: Ph.D. in Materials Science from Northwestern University (2009) Pashuck's work explores biomaterials that direct cell behavior through precise spatial and mechanical cues . Recent publications highlight advancements in high-throughput peptide screening , 4D bioprinting , and protease-activated hydrogels , demonstrating his expertise in dynamic biomaterial design and cell-matrix interactions . His research group investigates cell adhesion optimization via ligand mobility control , enzyme-mimicking nanomaterials , and sequence-controlled polymerization while maintaining clinical translation goals. Publications from 2021-2025 emphasize hydrogel mechanics , biodegradable scaffolds , and supramolecular catalytic systems , spanning biomaterial engineering , peptide nanotechnology , and regenerative medicine .

• Materials Science and Engineering
• Mechanical Deformation Behavior of Materials
• Nanomechanics
• +۴ مورد دیگر

Gaurav Mohanty is an Assistant Professor at Tampere University's Materials Science and Environmental Engineering department within the Faculty of Engineering and Natural Sciences. His research focuses on deformation mechanisms of engineering materials at micro/nano scales using in-situ micromechanical testing in SEM environments. Key techniques include FIB-milled micropillars and cantilevers for studying mechanical properties under extreme conditions (cryogenic to ultra-high temperatures, strain rates from 0.0001 to 10,000/s). He explores transient plasticity, high-cycle fatigue, and combinatorial materials science across metals, ceramics, semiconductors, and thin films. Funding sources include the Academy of Finland, MATINE, and SAFIR. His work contributes to novel testing methodologies for small-scale materials and understanding deformation mechanisms. Research highlights include high-temperature nanoindentation systems, fracture studies in tungsten, and interfacial fracture toughness in dental prostheses. His research group emphasizes interdisciplinary collaborations, leveraging synchrotron X-ray diffraction and advanced EBSD analysis. Current projects address challenges in microscale fracture, high-rate deformation, and material design through combinatorial libraries.

John J. Lavigneمشاهده پروفایل

دانشیار

• Molecular recognition
• Supramolecular chemistry
• Sensors
• +۷ مورد دیگر

John J. Lavigne is an Associate Professor in the Department of Chemistry and Biochemistry at the University of South Carolina, affiliated with the McCausland College of Arts and Sciences. He also serves as the Building Facilities Coordinator. His research focuses on molecular recognition, supramolecular chemistry, and the development of sensors and materials for applications in cancer diagnostics, food spoilage detection, and energy storage. Lavigne holds a B.S. (1993), M.Ed. (1997) from St. Lawrence University, and a Ph.D. (2000) from the University of Texas at Austin. His research interests include boronate-linked materials, conjugated polymers, and synthetic lectins. He has mentored numerous graduate and undergraduate students, leading to patents and impactful publications. Lavigne has received awards such as the Mortar Board Excellence in Teaching Award (2012) and the Michael J. Mungo Undergraduate Teaching Award (2009). His grants include support from the NIH, NSF, and the Research Corporation. Education: B.S., Chemistry, St. Lawrence University, 1993 M.Ed., General Education, St. Lawrence University, 1997 Ph.D., Chemistry, University of Texas at Austin, 2000 His work spans the design of self-assembling materials, sensors for biomedical and environmental applications, and collaborations with engineering and medical fields. Notable projects include synthetic lectins for cancer diagnostics and boronate-based polymers with self-repairing capabilities. Grants & Awards: NIH COBRE Center (Colon Cancer Diagnostics), NSF (Boronate-Linked Materials), and multiple teaching awards. His research has been supported by the American Chemical Society, South Carolina EPSCoR, and others. Labs & Teams: Lavigne’s lab focuses on interdisciplinary projects, combining organic synthesis, materials science, and analytical methods. Collaborations include work on covalent organic frameworks (COFs) and peptide-based sensors.

Cameron Neylonمشاهده پروفایل

استاد

• Open Access
• Scholarly Communication
• Research Policy
• +۵ مورد دیگر

Professor Cameron Neylon is a leading academic in the field of Research Communication at Curtin University’s Centre for Culture and Technology . With an international reputation as an Open Access activist and scholarly communication strategist , he combines empirical science expertise with systemic change advocacy. His work spans open data , research policy , and digital scholarship , advising global institutions and governments. 2012–2015 : Advocacy Director, Public Library of Science (PLOS) 2005–2012 : Senior Scientist, ISIS Neutron Scattering Facility, UK 2001–2009 : Lecturer in Combinatorial Chemistry, University of Southampton Neylon’s research focuses on the intersection of systems theory , design methodologies , and open science . He explores how digital networks and open practices can reshape knowledge creation , research metrics , and institutional policy . His recent projects include Curtin’s New Models of Publishing and global open access performance analysis . His publications since 2012 emphasize open science infrastructure , data integrity , and equitable research systems . Articles like Open Access Research Outputs Receive More Diverse Citations (2024) and A User-Friendly Dashboard for Tracking Global Open Access Performance (2023) reflect his commitment to measurable, inclusive scholarly systems. 2012 : Fellow of the Open Forum Academy 2010 : SPARC Innovator Award for Panton Principles 2010 : Blue Obelisk for Open Data Contributions Neylon has held leadership roles in organizations such as Crossref and ImpactStory , and currently serves on the Future of Research Communication and E-scholarship board. His work integrates neutron scattering , bioinformatics , and policy design to address systemic challenges in academic practice.

Michael Baakeمشاهده پروفایل

استاد

• Aperiodic Order
• Dynamical Systems
• Combinatorics
• +۴ مورد دیگر

Michael Baake is a Professor of Mathematics at Bielefeld University, affiliated with the Faculty of Mathematics. His research focuses on Aperiodic Order, Dynamical Systems, Combinatorics, and Mathematical Physics. He leads multiple research projects, including the SFB/TR 358 'Integral Structures in Geometry and Representation Theory' and SFB 1283 'Taming Uncertainty'. His work bridges pure mathematics with applications in crystallography and stochastic systems. Affiliations: Representative for the Faculty Library, Co-Director of the Research Focus on Mathematical Modeling. Cooperations: Collaborates with international experts like Uwe Grimm, Daniel Lenz, and Nicolae Strungaru on topics such as aperiodic tilings and spectral theory. Research Groups: Leads a vibrant research group with members including Anna Klick, Daniel Luz, and Timo Spindeler, focusing on quasicrystals, combinatorial structures, and number theory applications. His contributions include co-editing the book 'Aperiodic Order: Crystallography and Almost Periodicity' and organizing workshops on spectral theory and dynamical systems. He actively engages in academic service, including editorial roles and conference organization.

• Theoretical Computer Science
• Algorithm Engineering
• Parallel Computing
• +۳ مورد دیگر

Prof. Dr. Peter Sanders is a full professor in Theoretical Computer Science at the Karlsruhe Institute of Technology (KIT), leading the Algorithm Engineering group. His academic career includes a doctoral degree from Karlsruhe University and research stints at institutions like the Max Planck Institute for Informatics. He specializes in algorithm theory and engineering, focusing on parallel computing, large-scale data processing, and graph partitioning. His research bridges theoretical foundations with practical implementations, emphasizing real-world applications in optimization, route planning, and distributed systems. Education: Ph.D. in Computer Science, Karlsruhe University (1996) Bachelor/Master studies at Karlsruhe University (1988-1996) Research Interests: Algorithm design and analysis Parallel and distributed algorithms Graph algorithms and partitioning Algorithm engineering for big data High-performance computing Publications: Over 250 papers, emphasizing parallel algorithms, distributed systems, and graph theory. Recent work includes scalable SAT solving, hypergraph partitioning, and distributed string sorting. His contributions have advanced practical applications in route planning, load balancing, and large dataset processing. Awards: Recipient of the prestigious Leibniz Prize (DFG) and Baden-Württemberg State Research Prize. He coordinated the DFG Priority Program on Algorithm Engineering and is an active reviewer for major funding bodies. Consulting: Engages with companies like SAP and Google, focusing on optimization, route planning, and database algorithms. Leads projects on algorithm scalability and real-world problem-solving. Labs/Teams: Heads the Algorithm Engineering group at KIT, fostering collaborations in distributed computing and algorithmic research.