جستجوی استاد راهنمای دانشگاهی

• Combinatorial optimization
• Design and analysis of algorithms
• Field-programmable systems
• +۷ مورد دیگر

Martin D F Wong serves as the Edward C. Jordan Professor of Electrical and Computer Engineering and Executive Associate Dean for the College of Engineering at the University of Illinois at Urbana-Champaign. He is affiliated with the Coordinated Science Laboratory and has been instrumental in advancing electronic design automation research. His educational background includes: B.Sc. in Mathematics, University of Toronto (1979) MS in Mathematics, University of Illinois at Urbana-Champaign (1981) Ph.D. in Computer Science, University of Illinois at Urbana-Champaign (1987) Wong's research centers on combinatorial optimization and algorithm design for VLSI systems, with particular expertise in lithography-aware physical design, field-programmable systems, and electronic packaging. His work bridges theoretical algorithms with practical semiconductor manufacturing challenges as feature sizes shrink below 20 nanometers. Current research focuses on integrating chip design with next-generation lithography technologies including triple-patterning, self-aligned double patterning, directed self-assembly, and extreme ultraviolet processes. His publication record shows consistent contributions to electronic design automation, with emphasis on manufacturing-aware physical design algorithms and circuit optimization techniques. Recent work addresses the critical interface between circuit layout and lithography processes as semiconductor technology advances to 14nm and beyond. Scientific recognition includes: Fellow of IEEE and ACM 2000 IEEE Donald O. Peterson Best Paper Award Multiple Best Paper Awards at DAC, ICCD, and ICCAD conferences IBM Faculty Awards (2000, 2004) NSF Research Initiation Award Wong has secured significant research funding including a $450,000 NSF grant for lithography-aware physical design and has supervised over 49 PhD students. His work continues the legacy of integrated circuit innovation at Illinois, building on foundational contributions like Jack Kilby's integrated circuit invention. Current research initiatives focus on optimizing chip design for next-generation manufacturing processes where optical interference challenges require co-design of layout and fabrication. He leads research within the Coordinated Science Laboratory, focusing on electronic design automation algorithms that address the growing complexity of semiconductor manufacturing at nanometer scales.

• Control Theory
• Machine Learning
• Nonlinear Estimation
• +۶ مورد دیگر

Prashant Mehta is a Professor of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign , affiliated with the Coordinated Science Laboratory . His research focuses on controlled interacting particle systems and machine learning applications , particularly in human activity recognition using motion sensors. Education: Ph.D. in Mathematics, Cornell University (2004) M.S. in Electrical & Computer Engineering, University of Massachusetts Amherst (1996) B.E. in Electrical & Electronics Engineering, Birla Institute of Technology & Sciences (1993) Mehta's work has pioneered the feedback particle filter (FPF) algorithm for nonlinear estimation, applied in robotic systems and gesture recognition. His research spans control of combustion instabilities in jet engines, mean-field games , and dynamical systems in aerospace engineering. His publications emphasize nonlinear control theory and stochastic filtering , with recent trends in sensor data pattern recognition and cyber-physical systems . He has received multiple scientific awards , including the MURI award for the Cyberoctopus project and Excellence in Undergraduate Advising Awards . Scientific Honors: MURI Award (2019) for Cyberoctopus Excellence in Undergraduate Advising (2010, 2008) Outstanding Teaching Assistant Award (1994) Senior Member, IEEE Control Systems Society Member, ASME Energy Systems Subcommittee Member, SIAM Dynamical Systems Group Mehta has supervised students like Jin Kim (IEEE CDC Best Student Paper, 2019) and co-founded the startup Rithmio , acquired by Bosch Sensortec . His laboratory develops gesture-detection filters for applications in soft robotics and human-machine interfaces .

Miroslav Pajicمشاهده پروفایل

استاد

• Cyber-Physical Systems
• Embedded Systems
• Artificial Intelligence
• +۶ مورد دیگر

Miroslav Pajic serves as a Professor in the Department of Electrical and Computer Engineering at Duke University's Pratt School of Engineering. He also holds joint appointments as Associate Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science and Associate Professor of Computer Science. As Director of Master's Studies, he oversees the graduate program in Electrical and Computer Engineering and teaches numerous courses spanning embedded systems, cyber-physical systems design, and robotics. Education: Ph.D. in Electrical and Computer Engineering from University of Pennsylvania (2012) Miroslav Pajic's research focuses on the design and analysis of cyber-physical systems (CPS) with varying levels of autonomy and human interaction. His work spans the intersection of embedded systems, artificial intelligence, machine learning, control theory, formal methods, and robotics. He specializes in developing high-assurance autonomous systems with applications in robotics, automotive systems, and medical devices, with particular emphasis on CPS security and resilient autonomy. His research addresses fundamental challenges in creating systems that can operate reliably in uncertain environments while maintaining security against potential cyber attacks. Analysis of Pajic's recent publications reveals a strong interdisciplinary research program bridging theoretical foundations with practical applications. His work spans secure sensor fusion for distributed autonomy, medical applications of CPS (particularly deep brain stimulation for neurological disorders), and innovative sensing technologies for autonomous vehicles. A significant portion of his research addresses security challenges in cyber-physical systems, including stealthy GPS attacks on UAVs and methods for attack-resilient state estimation. His publications increasingly integrate machine learning techniques with traditional control theory to create more adaptive and robust autonomous systems. Pajic actively mentors graduate students and leads research groups focused on cyber-physical systems security and high-assurance autonomy. His research is supported by multiple grants, including the NSF AI Institute for Edge Computing (Athena), which he co-leads. He has received funding from various sources to support his work on secure and resilient cyber-physical systems, medical device security, and autonomous vehicle technologies. Pajic collaborates extensively with medical researchers on applications of cyber-physical systems in healthcare, particularly in deep brain stimulation for neurological disorders. His work bridges the gap between theoretical control systems and practical implementations in safety-critical domains, with a growing emphasis on translating research into real-world applications that improve system security and reliability.

Valerie Viet Triem Tongمشاهده پروفایل

استاد پژوهش

• Information Flow Control
• Android Security
• Malware Analysis and Detection
• +۵ مورد دیگر

Valerie Viet Triem Tong is a Research Professor at the Paris Institute of Electrical and Electronic Engineering, School of Electrical and Electronic Engineering. She has established herself as a leading researcher in cybersecurity with a particular focus on information flow control systems, Android security, and malware analysis. Her work spans both theoretical foundations and practical security tools development. Her research interests center on Information Flow Control , where she has developed frameworks for monitoring and enforcing security policies at both operating system and application levels. She has made significant contributions to Android Security , creating tools for detecting malicious behavior in mobile applications and addressing privacy concerns in smartphone environments. Her work in Malware Analysis includes developing advanced techniques for tracking and visualizing malware behavior, with emphasis on evasive Windows malware and Android malware in the wild. She also investigates Peer-to-Peer Network Security , focusing on Sybil attack resistance and distributed identity management. Analysis of her publication record reveals a consistent trajectory from foundational work in information flow theory to increasingly applied security research. Her recent work shows a strong emphasis on practical security tools (DaViz, GUI-Mimic, BAGUETTE), security evaluation methodologies (Digital twin, CERBERE), and addressing contemporary challenges in malware analysis (debiasing datasets, handling obfuscated applications). A notable trend is her integration of visualization techniques with security analysis to make complex security data accessible to both experts and machine learning systems. As an advisor, she has mentored numerous researchers who have become first authors on significant publications, including Radoniaina Andriatsimandefitra, Tomás Concepcion Miranda, and Cedric Herzog. Her research has been supported by multiple grants focused on cybersecurity infrastructure, though specific grant details are not provided in the available information. Dr. Tong is actively involved with the CIDre security research group in Rennes, contributing to collaborative projects that bridge theoretical security models with practical implementation challenges. Her work on information flow monitoring has evolved from basic research to applied systems that address real-world security concerns across multiple platforms.

• Video Game Localisation
• Audiovisual Translation
• Language Learning
• +۳ مورد دیگر

Miguel Ángel Bernal Merino serves as a Part-Time Lecturer in the Department of Modern Philology, Translation and Interpretation at the University of Las Palmas de Gran Canaria. Concurrently, he acts as convener of the MA in Translation at the University of Roehampton in London, managing its Specialised Translation, Audiovisual Translation, and Intercultural Communication pathways since 2005. His academic profile bridges theoretical research and industry application in localization disciplines. PhD in The Localisation of Video Games, Imperial College London MSc in The Localisation of Multimedia Interactive Entertainment Software, Imperial College London Certificate in Software Localisation, University of Limerick CELTA, University of Cambridge MPhil in Lip-Synched Dubbing, Universidad de Alicante MA in Hispanic Literature, University of Rhode Island BA in English & Spanish Linguistics, Universidad de Alicante Bernal Merino's research pioneers the expansion of Translation Studies into interactive media domains. His work on video game localization examines interactivity, cultural adaptation, glocalisation, and playability, while his audiovisual translation research investigates creativity in lip-synched dubbing, subtitling, and audiodescription. He extends these frameworks into gaming-based language learning through haptic environments and multisensory reinforcement, exploring physiological aspects of multilingual cognition and polysemiotic neural networks. This multidisciplinary approach fundamentally challenges traditional boundaries in translation theory. His publication trajectory reveals evolving industry-academia integration, shifting from foundational game localization frameworks (2008-2015) toward user-centered research and educational applications (2018-2024). Recent work emphasizes measurable business impacts through quality localization, regional market adaptations, and crowd-sourced methodologies, demonstrating consistent influence on professional standards across gaming and translation sectors. Fellow of the Higher Education Academy Bernal Merino supervises doctoral research on mobile game localization, transmedia fan experiences, and historical game content translation while developing industry-academia pipelines through projects like AHRC's Media Across Borders. His funded initiatives—including Maximising ROI through Quality Game Localisation and Video Games for Language Learning—bridge theoretical innovation with commercial implementation, focusing on glocalisation strategies, interactivity metrics, and playability optimization. These collaborations connect European universities, gaming studios, and localization service providers. As chair of the IGDA Localization SIG and member of GIR Discourse, Communication and Society, he directs international forums for game localization discourse. His work with Routes into Languages promotes audiovisual media in language education, while REF-readiness collaborations enhance research excellence across European institutions through his professional network spanning industry conferences like GDC Summit and Game Global.

• Networked Systems
• IoT Security
• Machine Learning
• +۳ مورد دیگر

Petri Mähönen is a Full Professor at the Department of Information and Communications Engineering , Aalto University. His research focuses on networked systems, machine learning applications in telecommunications, and smart grid technologies. University: Aalto University Department: Information and Communications Engineering Research Interests span networked systems, IoT security, UAV communication, and AI-driven network optimization. His work addresses predictive QoS in cellular-connected drones and generative adversarial networks for cybersecurity. Recent Publications include studies on GAN-based traffic augmentation, anomaly detection in mobile networks, and regulatory frameworks for data platforms. His articles reflect expertise in both theoretical and applied network science.

• statistical machine learning
• optimization
• sparse methods
• +۵ مورد دیگر

Francis Bach is a Professor and researcher at INRIA, leading the SIERRA project-team since 2011, which is part of the Computer Science Department at Ecole Normale Supérieure (ENS) within PSL Research University. His work bridges CNRS, ENS, and INRIA as a joint research effort. Elected to the French Academy of Sciences in 2020, he currently runs the ERC project SEQUOIA following his previous ERC project SIERRA (2009-2014). His research spans statistical machine learning with focus on optimization, sparse methods, kernel-based learning, neural networks, graphical models, and signal processing. Bach completed his Ph.D. in Computer Science at U.C. Berkeley under Professor Michael Jordan, followed by work at Ecole des Mines de Paris and the WILLOW project-team at INRIA/ENS/CNRS (2007-2010). His recent book "Learning Theory from First Principles" was published by MIT Press in December 2024. Bach's publication record shows consistent high-impact contributions across machine learning theory and applications, with recent work focusing on conformal prediction, diffusion models, optimization theory, and learning theory foundations. His research demonstrates strong connections between theoretical guarantees and practical algorithms, with applications spanning generative modeling, robust optimization, and statistical inference. Elected to French Academy of Sciences (2020) ERC project SIERRA (2009-2014) ERC project SEQUOIA (current) Author of "Learning Theory from First Principles" (MIT Press, 2024) Bach actively mentors numerous PhD students and postdocs, with many alumni now holding faculty positions at institutions like EPFL, Ecole Polytechnique, University of Washington, and University of Montreal. His teaching includes advanced courses on learning theory at ENS's Master's programs. He regularly presents tutorials at major conferences including COLT, NeurIPS, and ICML, demonstrating his leadership in the theoretical machine learning community.

Pekka Marttinenمشاهده پروفایل

دانشیار

• Machine Learning
• Large Language Models
• Reinforcement Learning
• +۸ مورد دیگر

Pekka Marttinen is a tenured Associate Professor of Machine Learning at Aalto University, Department of Computer Science, and leads the Machine Learning for Health (Aalto-ML4H) group within the Helsinki Institute for Information Technology HIIT. Education: M.Sc. in Applied Mathematics, University of Helsinki (2004) Ph.D. in Statistics, University of Helsinki (2008) Title of Docent in Information and Computer Science, Aalto University (2015) Research Focus: His methodological work spans large language models, reinforcement learning, deep learning, probabilistic machine learning, and causal inference . These techniques are applied to critical domains of healthcare, bioinformatics, statistical genetics, epidemiology, and personalized medicine . The group develops novel algorithms, theoretical guarantees, and open-source software that enable data-driven discovery and decision-making in medicine and biology. Publication Trends: Across 2022-2024 the lab has concentrated on (i) rigorous causal reasoning over temporal clinical data, (ii) principled uncertainty quantification in LLMs, (iii) representation learning for neural network comparison, and (iv) translational projects that turn raw EHRs into actionable clinical insights. Earlier work integrated high-dimensional genomics with metabolomics and mapped evolutionary forces in bacterial pathogens. Scientific Awards & Recognition: While no specific awards are listed, his sustained publication record in top-tier venues (NeurIPS, ICML, AISTATS, Nature Genetics, PLOS CB) and his role as responsible professor of the Machine-Learning, Data-Science and AI major signify significant peer recognition. Advising & Grants: Prof. Marttinen currently mentors 8 PhD students as primary supervisor and an additional 7 PhD students as co-supervisor. He has already graduated 8 PhDs since 2014. The group is supported through competitive funding including the Finnish Center for Artificial Intelligence (FCAI) doctoral program. Labs & Teams: He directs the Machine Learning for Health (Aalto-ML4H) research group, comprising postdocs Hans Moen, Ti John, Alexander Nikitin, Negar Safinianaini, Linli Zhang, Zhiyuan Li, and the above-mentioned PhD cohort.

Raphael Franziniمشاهده پروفایل

دانشیار

• Drug Discovery
• Medicinal Chemistry
• Bioorthogonal Chemistry
• +۳ مورد دیگر

Raphael Franzini serves as Associate Professor of Medicinal Chemistry at the University of Utah, actively contributing to the Biological Chemistry PhD Program. His research pioneers innovative chemical approaches for therapeutic development, with dual focus on DNA-encoded library technologies and bioorthogonal drug delivery systems. His educational foundation includes an M.S. from the Swiss Federal Institute of Technology (Lausanne) and a Ph.D. from Stanford University. This training underpins his group's multidisciplinary methodology combining organic synthesis, bioconjugation, computational modeling, and advanced imaging techniques. Dr. Franzini's research program centers on two transformative areas: First, advancing DNA-encoded library screening through computational integration to identify leads for challenging targets like Tankyrase and Sirtuin 6, with recent work addressing false negatives in machine learning prediction. Second, developing novel bioorthogonal release chemistry using isonitrile-tetrazine reactions for spatiotemporally controlled drug activation, validated in zebrafish models. His group emphasizes both technological innovation and therapeutic translation, with chemistry designed to minimize off-target effects in solid tumors. Analysis of his 15 most recent publications reveals escalating integration of computational methods with experimental library screening, alongside refinement of bioorthogonal release kinetics. The work spans chemical biology, medicinal chemistry, and pharmaceutical sciences, with growing emphasis on machine learning for library data interpretation and in vivo validation of drug-release systems. Dr. Franzini maintains an active research laboratory that provides comprehensive training in cutting-edge drug discovery methodologies. His group culture prioritizes both scientific innovation and researcher development, with projects spanning from fundamental reaction kinetics to therapeutic applications. The lab's infrastructure supports organic synthesis, molecular imaging, and computational analysis for advancing precision therapeutics.

• Machine Learning
• Bioinformatics
• Deep Generative Models
• +۳ مورد دیگر

Anders Krogh is a Professor at the Department of Computer Science, University of Copenhagen, and also holds a position at the Department of Public Health in the Section for Health Data Science and AI. He serves as the head of the Center for Health Data Science (HeaDS) in the Faculty of Health and Medical Sciences. Previously, he was affiliated with the Department of Biology at the University of Copenhagen until 2020. Dr. Krogh earned his PhD in theoretical physics but transitioned into machine learning and bioinformatics during his doctoral studies. His research spans both theoretical foundations and practical applications in these fields. He is particularly renowned for his pioneering work on hidden Markov models for biological sequences, which has had significant impact in computational biology. In recent years, Krogh's research has focused on deep generative models applied to gene expression data and other biomedical applications. His work bridges computer science with healthcare, developing AI-driven approaches for precision medicine, cancer diagnostics, and analysis of complex biological systems. His current research integrates machine learning with quantum computing applications in biomolecular modeling. Analysis of his recent publications reveals a strong trend toward applying artificial intelligence to healthcare challenges, particularly in rare diseases, cancer diagnostics, and personalized medicine. His work increasingly incorporates federated learning approaches to address privacy concerns while enabling collaborative research across institutions. There's also a growing emphasis on quantum computing applications in biomolecular modeling and drug discovery. As head of the Center for Health Data Science, Krogh leads interdisciplinary research efforts that bring together computer scientists, medical researchers, and clinicians. His team develops novel computational frameworks like MOSAIC for multimodal analysis of rare cancers and multiDGD for multi-omics data integration. These tools are designed to translate AI innovations into clinical practice while addressing the unique challenges of medical data.

Francesco Sicaمشاهده پروفایل

استادیار

• Public-key cryptography
• Mathematical cryptology
• Number Theory
• +۲ مورد دیگر

Francesco Sica is an Assistant Professor in the Department of Mathematics and Statistics at Florida Atlantic University, with research focused on mathematical cryptology, public-key cryptography, and number theory. He earned his Ph.D. in Mathematics from McGill University (1998), M.Sc. from Scuola Normale Superiore (1993), and B.Sc. from the University of Pisa (1993). Education Ph.D. in Mathematics, McGill University, Canada (1998) M.Sc. in Mathematics, Scuola Normale Superiore, Italy (1993) B.Sc. in Mathematics, University of Pisa, Italy (1993) His research spans implementation and security analysis of elliptic curve cryptography , including isogeny-based problems and torsion point attacks. He has also contributed to pseudorandom number generation, multistorage solutions on elliptic curves, and cryptographic competitions. Recent publications highlight advancements in isogeny-based security (2024), cryptographic Olympiad frameworks (2022), and integer factorization techniques using number-theoretic hints (2021). Earlier work (2019) addressed secure bit extraction from Koblitz curves. Sica previously collaborated with the NESSIE project at Université catholique de Louvain and worked at a crypto lab at the National University of Singapore. His work bridges theoretical number theory with practical cryptographic applications.

Ferenc Huszárمشاهده پروفایل

دانشیار

• Machine Learning
• Deep Learning Theory
• Optimization
• +۶ مورد دیگر

Ferenc Huszár is an Associate Professor of Machine Learning at the University of Cambridge, affiliated with the Department of Computer Science and Technology. His research focuses on foundational aspects of deep learning, including optimization, generalization, representation learning, and causal reasoning. He co-founded Magic Pony Technology, where he contributed to super-resolution and compression techniques, later acquired by Twitter. Education: PhD in Bayesian Machine Learning from the University of Cambridge (supervised by Carl Rasmussen, Máté Lengyel, and Zoubin Ghahramani), followed by roles in tech/startups. Research Interests: Theoretical underpinnings of deep learning, neural network behavior analysis, LLM theory, causal inference, and AI safety. His lab explores algorithmic reasoning in neural networks and implicit Bayesian inference in LLMs. Selected Contributions: Co-authored influential papers on super-resolution (CVPR 2016) and GAN-based image enhancement (CVPR 2017). Active in advising 9 PhD students and mentoring research assistants. Grants & Collaborations: Collaborates with institutions like the Max Planck Institute and ELLIS. Supervises projects on causal representation learning, geometric deep learning, and federated learning.

• Robotics
• Control Systems
• Rehabilitation Robotics
• +۴ مورد دیگر

Timothy Bretl is a Professor of Aerospace Engineering at the University of Illinois at Urbana-Champaign, holding the Severns Faculty Scholar position since 2021. He also serves as Associate Head of the Aerospace Engineering department. His research focuses on robotics, control systems, rehabilitation robotics, and engineering education. Bretl earned his Ph.D. from Stanford University (2005), with prior degrees from Swarthmore College. He holds affiliate roles across multiple departments, including Neuroscience, Coordinated Science Laboratory, and Computer Science. Education: Ph.D. in Aeronautics and Astronautics, Stanford University (2005) B.A. in Mathematics and B.S. in Engineering, Swarthmore College (1999) His research spans engineering education innovations, robotic manipulation, and brain-machine interfaces. Notable awards include the NSF CAREER Award (2010), Best Manipulation Paper (2012), and multiple teaching honors like the Rose Award for Teaching Excellence (2016). Bretl’s work integrates theoretical foundations with practical applications in prosthetics, autonomous systems, and educational technology. He has advised numerous projects on robotics, control systems, and human-robot interaction. His lab explores advanced topics like elastic rod manipulation, magnetic positioning, and curriculum reform in STEM education. Collaborative projects include partnerships with industry and interdisciplinary teams at the Beckman Institute.

CHONG Yidongمشاهده پروفایل

استاد

• Theoretical Photonics
• Topological Photonics
• Non-Hermitian Systems
• +۵ مورد دیگر

CHONG Yidong is a Professor in the Division of Physics and Applied Physics at Nanyang Technological University (NTU), Singapore. He leads the Centre for Disruptive Photonic Technologies and holds positions in the School of Physical and Mathematical Sciences. His research focuses on theoretical photonics, topological systems, and non-Hermitian physics, with contributions to photonic crystals, topological insulators, and coherent perfect absorbers. He has been recognized with awards including the President's Science Award (2020) and the National Research Foundation Fellowship (2012). Education: Ph.D. (Physics), Massachusetts Institute of Technology (2005–2008) B.Sc. (Physics) and B.Sc. (Mathematical & Computational Sciences), Stanford University (1999–2003) Research Interests: Topological photonics, non-Hermitian systems, photonic topological insulators, PT-symmetric structures, and applications in quantum optics and acoustics. Recent Articles: Focus on experimental realizations of topological lasers, exceptional points in non-Hermitian systems, and higher-order topological phenomena in acoustic and photonic platforms. His work bridges theory and experiment, with collaborations in materials science and electrical engineering. Awards: Extensive recognition for both research and education, including Nanyang Research and Education Awards. Teaching: Courses in mathematical methods for scientists, quantum mechanics, and computational physics, emphasizing numerical techniques and wave phenomena.

Gary Scavoneمشاهده پروفایل

استاد

• Music Technology
• Musical Acoustics
• Acoustical Signal Processing
• +۳ مورد دیگر

Dr. Gary Scavone is a Professor and Department Chair in the Music department at McGill University's Schulich School of Music. He holds a PhD in Computer-Based Music Theory & Acoustics and MS in Electrical Engineering from Stanford University, alongside degrees from Syracuse University in Music and Electrical Engineering. His research focuses on music technology, including acoustic modeling, sound synthesis, and instrument design. He directs the Computational Acoustic Modeling Laboratory (CAML), which explores advanced techniques for simulating musical instruments and developing software tools. As a saxophonist, he specializes in contemporary concert music performance. Research interests include physically-based sound synthesis, wind instrument acoustics, and digital waveguide modeling. He has contributed to studies on brass and woodwind impedance measurements, violin soundpost dynamics, and free-reed instrument modeling. His work bridges engineering and artistry, with applications in music pedagogy, instrument design optimization, and virtual acoustic replication. Key contributions include open-source projects for wind instrument modeling and the development of tools for automated timbre assessment. His research often combines experimental methods with computational simulations, addressing challenges in both theoretical and applied music acoustics. Current projects focus on deep learning for friction modeling, impedance measurement systems, and cross-cultural instrument analysis.