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Hasan ErginView profile

Professor

• Nanotechnology
• Industrial Raw Materials
• Mineral Reserve Assessment and Production Planning
• +4 more

Prof. Hasan Ergin is a Professor at Istanbul Technical University's Department of Mining Engineering, Faculty of Mines. He holds a PhD from The University of Leeds. His research focuses on nanotechnology, industrial raw materials, mineral reserve assessment, ceramic processes, aerated concrete, size reduction technologies, and cement processes. Education: PhD in Mining Engineering, The University of Leeds (1994) Bachelor's in Mining Engineering, Istanbul Technical University (1986) Research Interests: Development of novel ceramic and cement production methods Waste recycling in industrial processes Optimization of grinding and milling technologies Geotechnical analysis of mining and tunneling equipment Sustainable materials and energy-efficient manufacturing Key Contributions: Pioneered dry granule preparation techniques for ceramic tiles Advanced recycling of ceramic sludge and tile waste Improved cement grinding efficiency with polymer-based aids Developed methods for roadheader stability and drill bit optimization Recognition: 2022: 1st Prize in Türkiye Productivity Project Awards (Ministry of Industry and Technology) 2021: Sustainability Academy Award for recycled tile formulations Advising & Grants: Supervised over 10 PhD/Master's theses on topics like cement chemistry and mining machinery Principal Investigator for projects such as 'Production of Mesoporous Nano Silica' (2023) and 'Chemicals for Cement Grinding' (2018) Labs & Teams: Leading the Ceramics and Cement Materials Research Group at ITU Collaborates with industry partners on sustainable manufacturing solutions

Pietro Lio'View profile

Professor

• Computational Biology
• Bioinformatics
• Machine Learning
• +2 more

Pietro Lio' is a Professor of Computational Biology at the University of Cambridge's Department of Computer Science and Technology, where he has held academic positions since 2003. He progressed from Lecturer to Senior Lecturer, Reader, and ultimately Professor (2018-present). His career includes prior roles as Associate Professor of Genetics at the University of Teramo and research positions at the European Bioinformatics Institute, University of Cambridge (Genetics/Zoology), and University of Southampton. Education: 1989: BSc in Electronic Engineering and Biology, University of Firenze 1995: PhD in Theoretical Genetics, University of Pavia 2007: PhD in Engineering (Non-Linear Dynamics & Complex Systems), University of Firenze Research Focus: Professor Lio' specializes in interdisciplinary approaches bridging computer science and biology. His primary research domains include computational biology, bioinformatics algorithms, machine learning (particularly graph neural networks), deep learning architectures, and complex network analysis. His work spans molecular to ecological scales, with applications in genomics, systems biology, and environmental modeling. Publication Trends: Recent work (2018-2020) demonstrates strong emphasis on graph neural networks (Graph Attention Networks, Deep Graph Infomax) and blockchain applications. Earlier publications (2015-2017) focus on ecological modeling, multi-omics integration, molecular communications, and single-cell genomics, reflecting consistent cross-disciplinary innovation. Awards and Honors: 2018: BITS Award (Bioinformatics Italian Society) 2017: Visiting Professorship at University of Padova 2016: BYRA First Prize (ISEM) & MCE Award Second Prize 2013-2015: Lagrange Fellowship 2011: European Commission Award for methodological innovation No information is available regarding student advising, grants, labs, or research teams in the provided materials.

Steffen SteinerView profile

Researcher

• Sensor Networks
• Distributed Compression
• Information Theory
• +1 more

Dr.-Ing. Steffen Steiner is a Doctoral Researcher at the Institute of Communications Engineering , University of Rostock, Germany, since 2018. His work focuses on communications engineering and sensor network optimization. Education : B.Sc. in Information Technology / Technical Computer Science from University of Rostock (2012-2016) M.Sc. in Information Technology / Technical Computer Science from University of Rostock (2016-2017) Research Interests : Sensor Networks Distributed Compression Information Bottleneck Method Quantization Optimization Data Transmission Efficiency Network Protocols Publication Trends : His recent work (2018-2023) centers on sensor network compression, information bottleneck applications, and algorithm optimization for distributed systems. Key themes include wireless communication, data transmission efficiency, and signal processing methodologies.

Yanhua HongView profile

Academic

• Semiconductor Lasers
• Nonlinear Dynamics
• Chaos Theory
• +3 more

Dr. Yanhua Hong is a Reader in the School of Computing and Engineering at Bangor University. His research focuses on nonlinear dynamics of semiconductor lasers, chaos theory, and their applications in optical communications and microwave photonics. He leads projects such as 'Microwave Photonics Generation Using Low-Cost VCSELs' and has published extensively in journals like Optics Express and Photonics. Key areas of expertise include semiconductor laser dynamics under optical feedback, secure communication systems leveraging chaotic signals, and the design of photonic microwave generation systems. Hong collaborates internationally with institutions like Southwest University (China) and the Universitat Politècnica de Catalunya (Spain). He actively supervises PhD students and examines external theses. Recent work highlights include high-speed secure stream ciphers using synchronized chaos, optimization of multimode fiber imaging systems, and analysis of intermittent laser dynamics via reservoir computing. His contributions bridge fundamental research with applied photonics, contributing to advancements in optical security, signal processing, and next-generation communication networks.

Ozan ÖzdenizciView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Robustness and Safety in ML
• +4 more

Assoc. Prof. Ozan Özdenizci is a faculty member at the Institute of Machine Learning and Neural Computation, TU Graz, Austria. His research focuses on robustness, safety, and efficiency in machine learning, particularly in adversarial robustness, spiking neural networks, and privacy-aware learning. He holds a PhD from Northeastern University (2016), and MSc/BSc degrees from Sabancı University (2010/2008). Previously, he served as a postdoc at TU Graz (2016–2020) and a research group leader at Montanuniversität Leoben (2020–2023). Affiliations: TU Graz (2023–present), Montanuniversität Leoben (2020–2023), TU Graz Postdoc (2016–2020) Education: PhD (Northeastern University, 2016), MSc/BSc (Sabancı University, 2010/2008) Research Interests: Developing ML systems with robustness guarantees, efficient deep learning (e.g., spiking networks), and privacy-aware mechanisms. Key areas include adversarial defense mechanisms, sparse network design, and neuromorphic computing applications. His work bridges theoretical foundations with practical implementations in computer vision and autonomous systems. Recent Contributions: Advances in privacy-aware lifelong learning (ICLR 2025), robust spiking networks (TMLR 2024), and weather-resistant vision models (TPAMI 2023). His research emphasizes trade-offs between model efficiency, robustness, and scalability. Awards: Top Reviewer at NeurIPS 2023, Outstanding Reviewer at ICML 2022/ICLR 2022 Labs/Teams: Member of Graz Center for Machine Learning (GraML) and ELLIS Unit Graz

• Drilling Engineering
• Rock Mechanics
• Fluid Dynamics
• +3 more

Associate Professor Masood Mostofi is affiliated with Curtin University's WASM School of Minerals, Energy and Chemical Engineering, where he leads research in drilling engineering and related technologies. His academic role includes teaching courses such as Drilling Engineering Fundamentals, Advanced Drilling Practices, and Offshore Platforms. His primary research focuses on bit/rock interaction, drilling optimization, fluid dynamics, and wellbore stability, with notable contributions to diamond bit wear analysis and eco-friendly drilling fluid development. Mostofi holds a patent for 'Drilling Fluids and Uses Thereof' (2018) and has contributed to over 30 peer-reviewed publications since 2010, addressing topics like CO2 sequestration, shale gas recovery, and drilling efficiency. His work bridges practical drilling challenges with advanced materials science and computational modeling. Research Interests: Bit/rock interaction of diamond bits Drilling optimization and efficiency Drilling fluid rheology and fluid loss control Borehole stability mechanisms CO2 sequestration and gas recovery Eco-friendly drilling technologies Publications highlight trends in sustainable drilling solutions, advanced fluid systems, and experimental studies on tool performance. His work intersects with energy exploration, environmental engineering, and materials science, reflecting a multidisciplinary approach to solving industry challenges.

• Computability Theory
• Rewriting
• Lambda Calculus
• +3 more

Jakob Grue Simonsen is a Professor and Department Chair at the Department of Computer Science (DIKU), University of Copenhagen. He holds a Dr. Scient., PhD, and MBA. His research focuses on the mathematics of computation, including computability theory, term rewriting, lambda calculus, complexity hierarchies, and symbolic dynamics, with applications in information retrieval and human-computer interaction. His primary research explores infinite computational processes and their finite representations, alongside practical work in neural hashing, fact-checking systems, and quantum language models. Previously, he contributed to constructive mathematics and biocomputing. An analysis of his 15 most recent publications (2015–2022) reveals strong emphasis on: Theoretical computability and game-theoretic models Neural networks for NLP (BERT, semantic hashing) Fact verification and explainable AI methodologies Efficient retrieval algorithms and recommendation systems Awards: RTA 2004 Best Paper Award CHI '13 Honorable Mention CHI '14 Honorable Mention

Peisen YaoView profile

Researcher

• Program Analysis and Verification
• Program Synthesis and Optimizations
• Logic and Automated Reasoning
• +2 more

Peisen Yao is an academic researcher affiliated with Zhejiang University in China. His work spans programming languages, software engineering, and cybersecurity, with a focus on developing techniques to enhance software security, usability, and performance. He actively contributes to premier conferences as a committee member (e.g., PLDI, ICSE, ASE, ISSTA, OOPSLA, SAS). Research Focus: Yao's research integrates program analysis, verification, synthesis, and optimization. Key areas include: Advanced static analysis for security vulnerabilities (e.g., buffer overflows, divide-by-zero errors) Efficient symbolic abstraction and SMT solver testing Context-sensitive program analysis using formal language theory Automated invariant generation and code synthesis Publication Trends: His 15 most recent articles (2021–2025) emphasize scalable program analysis techniques, security validation, and practical tools for industry applications. Common themes include static analysis optimizations, formal verification, and novel testing methodologies for complex systems.

• Computer Vision
• Artificial Intelligence
• Image Processing
• +2 more

Dr. Javier Varona Gómez is a Full Professor at the Department of Mathematics and Computer Science, University of the Balearic Islands (UIB). He holds a PhD in Computer Science from the Autonomous University of Barcelona (UAB, 2001) and has held pre- and postdoctoral roles at UAB's Image Processing and Artificial Intelligence Unit (UPIIA) and Computer Vision Center (CVC). His research focuses on Computer Vision, Artificial Intelligence, and Image Processing, with extensive technology transfer collaborations involving companies like Volkswagen and social entities like BIT Foundation. PhD in Computer Science (UAB, 2001) His research portfolio includes 28 JCR journal articles, 2 books (one as editor), 7 book chapters, and 2 Spanish patents (one with PCT extension). He has led 5 competitive R&D projects, including 4 National R&D&I Plan projects (2007-2020), and participated in 25 years of continuous R&D&I projects (1995-present). He has supervised seven doctoral theses and taught undergraduate and graduate courses in Computer Engineering, Artificial Intelligence, and Machine Learning at UIB and UAB. First research prize from the Economic and Social Council of the Balearic Islands (SINA project, 2008) Accessibility Award from the Consell de Mallorca (COL.diesis project, 2019) Best business plan award for Inisle Interactive Technologies (2012) He has held leadership roles in academic management as Director of the Master's Degree in Information Technology, Co-Director of the PhD in Computer Science, Deputy Director of the Department of Mathematics and Computer Science, and Vice-Rector for Degrees and Technology at UIB.

• Computer Science
• Interdomain Routing
• Network Architecture
• +3 more

Kari Visala is a Researcher at Aalto University's School of Science , with primary expertise in Computer Science . Her research specialization includes Interdomain Routing , Network Architecture , and Simulation . Research Interests: Kari's work focuses on advanced network protocols, internet infrastructure development, and complexity analysis of network systems. She has contributed extensively to name-based interdomain routing architectures and information-centric networking through both simulation tools and theoretical frameworks. Publications: Her 11 research outputs (2009-2014) reveal a strong technical focus on network simulation and routing protocols, with 48 Scopus citations for her 2011 article alone. Notable contributions include co-edited chapters in Bit bang IV: Future of Internet (2012) analyzing societal and governance aspects of network complexity. Research Metrics: Kari's work has been cited in 1 patent and accessed by 62 Mendeley readers and 6 CiteULike users. Her 2014 INFOCOM proceedings paper introduced simulation frameworks for global network architectures.

Wolfgang UtschickView profile

Professor

• Signal Processing
• Wireless Communications
• Radar Technology
• +3 more

Prof. Wolfgang Utschick is a full Professor at the Technische Universität München (TUM), holding the professorship for Methods of Signal Processing since 2002. He is affiliated with the TUM School of Computation, Information and Technology and the Department of Electrical Engineering and Information Technology (EI). Since 2017, he has served as Dean of the EI Department, overseeing academic and research activities. His research integrates applied mathematics into signal processing applications across wireless communications, radar technology, vehicle safety, and machine learning. Prof. Utschick earned his doctorate after industry experience and completed postdoctoral studies at TUM and ETH Zurich. He has led numerous industrial and DFG-funded projects, resulting in patents in signal processing and over 150 peer-reviewed publications. Notable contributions include advancements in channel estimation, MIMO systems, and quantum radar technology. His research interests emphasize the intersection of mathematical theory and practical engineering, with a focus on optimizing communication systems and safety-critical applications. Awards include IEEE Fellow (2021), TUM Sabbatical for Teaching Excellence (2014), and multiple IEEE publication awards. Prof. Utschick’s work bridges academia and industry, addressing challenges in 5G/6G networks, radar innovation, and machine learning-driven signal processing. He remains active in curriculum development and departmental leadership at TUM.

Milind TambeView profile

Professor

• Artificial Intelligence
• Public Health
• Wildlife Conservation
• +3 more

Milind Tambe is a Gordon McKay Professor of Computer Science at Harvard University and Director of the Center for Research on Computation and Society. He concurrently serves as Principal Scientist and Director for 'AI for Social Good' at Google DeepMind. His research focuses on applying AI to societal challenges like public health, conservation, and security. Tambe holds a PhD from Carnegie Mellon University and an undergraduate degree from BITS Pilani, India. Key roles include: Founding the Center for Artificial Intelligence in Society at USC (2016) Developing PAWS for wildlife conservation and ARMOR for security games Leading AI projects with NGOs like ARMMAN for maternal health Research interests span AI for social impact, multiagent systems, and game theory. Notable awards include the AAAI Feigenbaum Prize (2023) and the IJCAI John McCarthy Award (2023). He has advised 38 PhD students and 16 postdocs, fostering interdisciplinary collaboration across Harvard's departments. Current projects include: AI-driven maternal health interventions (e.g., mMitra and Kilkari programs) Wildlife conservation via PAWS deployment in 800+ parks AI for tuberculosis testing and HIV prevention His lab, Teamcore, emphasizes real-world impact, with deployments saving over $100M for US agencies and improving global health outcomes.

• Formal Methods
• Programming Languages
• Program Verification
• +3 more

Adithya Murali is an Assistant Professor at the University of Wisconsin-Madison's Department of Computer Science. His research focuses on Formal Methods and Programming Languages , specifically democratizing software verification through data-driven logic learning and neuro-symbolic approaches. He holds a Ph.D. from the University of Illinois at Urbana-Champaign (UIUC), advised by P. Madhusudan Parthasarathy. Education: Ph.D. in Computer Science (UIUC, 2024), B.Tech. from BITS-Pilani (2017) Roles: Subreviewer for PLDI, CONCUR, ICALP, and LICS; Teaching Assistant for courses in Logic, Compilers, and Trustworthy AI His research interests center on reducing the cognitive burden of software verification, enabling non-experts to verify code through innovative techniques like logic learning from data. Cross-disciplinary work integrates machine learning with symbolic reasoning, exemplified in projects like the CLEVR VDP Dataset and GQA VDP Dataset for visual discrimination puzzles. Recent publications span formal verification frameworks (e.g., FO-Complete Heap Logics), neuro-symbolic systems, and automated reasoning. His work has received the ACM Europe Best Paper Award (OOPSLA 2023) . Awards: Ray Ozzie Fellowship (2018), Gold Medal (BITS-Pilani 2017), INSPIRE Scholarship (2012-2016) Grants: UIUC Travel Grants, SIGPLAN Funding He advises students across UIUC and UW-Madison, focusing on program synthesis, verification, and AI integration. Collaborations include the Formal Methods Seminar at UIUC and volunteer roles at major conferences.

• Integrated Circuits
• Micro/Nano Electro Mechanical Systems (MEMS)
• Computer Architecture & Engineering
• +4 more

Vladimir Stojanović is an Adjunct Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. His research focuses on integrated electronic-photonic systems-on-chip, emerging technologies, and VLSI design. He has held positions at MIT (2005–2013) and Rambus, Inc. (2001–2004). Stojanović leads research initiatives in the Berkeley Deep Drive (BDD), Berkeley Emerging Technologies Research (BETR), and Center for Energy Efficient Electronics Science (E3S). Education: Ph.D. (2005, Stanford), M.S. (2000, Stanford), and Dipl. Ing. (1998, University of Belgrade). Awards include the IEEE Fellow (2024), NSF CAREER Award (2009), and multiple best-paper recognitions. He co-founded Ayar Labs, Numericcal, and MaxLinear (formerly NanoSemi). Key research areas include silicon photonics integration, nanoelectromechanical systems (NEMS), and energy-efficient computing architectures. His work spans optical interconnects, quantum photonics, and biomedical sensors, with emphasis on co-design of electronics and photonics in advanced CMOS processes.

Sebastian WillView profile

Professor

• Computational RNA structure prediction
• RNA-RNA interactions
• Folding dynamics
• +3 more

Sebastian Will is a Full Professor of Bioinformatics at École Polytechnique IP Paris since 2020. Previously held positions include: Researcher with Ivo Hofacker at TBI, University of Vienna Researcher with Peter Stadler at Bioinformatics group, University of Leipzig Akademischer Rat at Bioinformatics group, University of Freiburg (2005-2012) Postdoc with Bonnie Berger at MIT's CSAIL (2010-2011) His research focuses on: RNA structure prediction algorithms Pseudoknot modeling RNA-RNA interaction analysis Structure design with Infrared framework Multi-target RNA design Integration of experimental data Ensemble-based sparsification Publication trends show concentrated research in: RNA structure prediction techniques Algorithm development Dynamic programming optimization Tree decomposition applications Stochastic sampling methods Multi-objective design Contributed extensively to software development including: LocARNA 2.0 Infrared framework SparseMFEFold BiAlign RNAPOND Pankov LocARNA-P