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Geraint JewellView profile

Professor

• Electrical Machines
• Electromagnetic Modeling
• Aerospace Applications
• +10 more

Professor Geraint Jewell is affiliated with the University of Sheffield , serving as Director of the Rolls-Royce University Technology Centre in Advanced Electrical Machines (since 2006) and Director of the EPSRC Future Electrical Machines Manufacturing Hub (since 2019). He is a graduate of the university (BEng 1988, PhD 1992) and has held academic roles since 1994. EPSRC Advanced Research Fellowship (2000-2005) Royal Society Industry Fellowship at Rolls-Royce (2006-2008) Former Faculty Director of Research and Innovation (2008-2011) Former Head of Department (2013-2019) His research focuses on power-dense electrical machines for aerospace applications , including permanent magnet machines , switched reluctance machines , and linear actuators . He has supervised ~20 PhD students and led collaborations with Rolls-Royce on high-temperature devices (up to 800°C) and aero-engine starter-generators. Recent publications analyze stator insulation thermal degradation , eddy current control in additively manufactured materials , and magnetic loss prediction in silicon steel. His work spans electromagnetic modeling , core loss calculation , and advanced manufacturing techniques for electrical machines. EPSRC Advanced Research Fellowship (2000-2005) Royal Society Industry Fellowship (2006-2008) He has advised PhD students across topics like consequent-pole PM machines , doubly salient SynRMs , and core loss characterization . His Electrical Machines and Drives Research Group explores modular motor design and magnetic material optimization for aerospace and electric vehicles.

Haipeng LuoView profile

Associate Professor

• Machine Learning
• Online Learning
• Reinforcement Learning
• +3 more

Haipeng Luo is an Associate Professor at the Thomas Lord Department of Computer Science, University of Southern California, holding the IBM Early Career Chair. He previously worked as a Postdoctoral Researcher at Microsoft Research, NYC, and has held visiting roles at Google and Amazon. His research focuses on developing practical machine learning algorithms with strong theoretical guarantees, particularly in online learning, bandit problems, reinforcement learning, and game theory. PhD in Computer Science, Princeton University (2011–2016) BSc in Computer Science, Peking University (2007–2011) His work spans adversarial and stochastic environments, addressing challenges in reinforcement learning, game dynamics, calibration, and omniprediction. Recent publications highlight advancements in regret minimization, game equilibrium computation, and robust optimization frameworks. Key contributions include algorithms for zero-sum games, bandit problems with feedback graphs, and theoretical analyses of convergence properties in multi-agent systems. Scientific accolades include Best Paper Awards at COLT 2021, COLT 2018, NeurIPS 2015, and ICML 2015. He has received prestigious grants such as the NSF CAREER Award (2020), Google Faculty Research Award (2020), and NSF CRII Award (2018). His students have secured academic and industry positions, and he actively teaches graduate courses in machine learning and online optimization.

Teresa FortView profile

Associate Professor

• International Trade
• Industrial Organization
• Political Economy
• +4 more

Teresa Fort is an Associate Professor at the Tuck School of Business at Dartmouth College , focusing on international trade, industrial organization, and political economy. She teaches core microeconomics and seminars on firm globalization, while maintaining affiliations as a Faculty Research Fellow at the National Bureau of Economic Research (NBER) and a Research Affiliate at the Centre for Economic Policy Research (CEPR). Her work bridges economic theory with empirical analysis using U.S. Census Bureau data. PhD , University of Maryland (2012) MA , University of Maryland (2008) BA , University of Virginia (2000) Her research explores global production fragmentation , technology's impact on offshoring , and globalization's heterogeneous effects . Key findings include: technology's disproportionate role in domestic production reorganization, the rise of factoryless goods producers redefining manufacturing statistics, and evidence on how trade policies create interdependent sourcing dynamics . Recent publications in Review of Economic Studies and Journal of Political Economy Macroeconomics examine tariff escalation welfare effects and multinational firm trade patterns . Her work has been featured in major media outlets including Financial Times , The Wall Street Journal , and Marketplace . NBER Faculty Research Fellow CEPR Research Affiliate Coauthor of influential studies on global value chains She actively participates in academic conferences, recently discussing topics like multinational firm strategies and labor mobility during recessions . Her empirical methods often involve novel data infrastructure projects, including redesigning the U.S. Census Bureau's Longitudinal Business Database and County Business Patterns imputation techniques.

Inbar FijalkowView profile

Professor

• Signal Processing
• Wireless Communications
• Optimization
• +3 more

Inbar Fijalkow is a Full Professor at the National School of Electronics and Computer Science (ENSEA) within CY Cergy Paris University. She is a member of the ETIS Research Unit (UMR 8051), focusing on signal processing for wireless communications, optimization, and machine learning applications. Her research bridges theoretical advancements with practical implementation in emerging communication systems. Education & Career: PhD in Signal Processing from TelecomParisTech (1993) Postdoctoral Fellow at Cornell University (1994–1995) Professor at ENSEA since 1999 Former Head of ETIS Research Unit (2004–2013) Research Interests: Signal processing for wireless communications Optimization techniques in massive MIMO and NOMA systems Machine learning applications in communication systems Nonlinear effects mitigation in high-power amplifiers Community & Awards: Member of CoNRS Section 7 (National Committee for Scientific Research) Chevalier de l’Ordre National du Mérite (2015) Founder of the CY Alliance Women in Science Prize (2017) Recent Projects: Active in ANR-funded initiatives (e.g., EcoBioH2, AI4code) and EU projects (e.g., PERSEUS). Her work emphasizes sustainability and AI-driven communication systems. Teaching: Teaches signal processing and wireless communications at ENSEA. Supervises PhD students and master’s theses in communication systems and signal processing.

• Internet of Things (IoT)
• Wireless Communications
• Statistical Signal Processing
• +3 more

Professor Jinho Choi is a Chair and Professor in Radio Frequency at the School of Electrical and Mechanical Engineering, University of Adelaide, Australia. He holds a B.E. (magna cum laude) from Sogang University, and M.S.E. and Ph.D. degrees from KAIST. His research focuses on advancing wireless communication and sensing technologies, particularly in IoT, 5G/6G, non-terrestrial networks, and cognitive satellite systems. He authored three books and has been recognized with the 1999 EURASIP Best Paper Award, IEEE Fellowship, and inclusion in Stanford's Top 2% Scientists list since 2020. He currently serves as a Senior Editor of IEEE Wireless Communications Letters and editorial roles in multiple journals. Education: B.E. (Electronics Engineering) - Sogang University, Seoul (1989) M.S.E. (Electrical Engineering) - KAIST (1991) Ph.D. (Electrical Engineering) - KAIST (1994) Research Interests: Professor Choi's work addresses connectivity challenges in non-terrestrial networks, leveraging statistical signal processing and machine learning. Current projects include UAV-assisted LEO satellite technologies, cognitive satellite radios, and semantic communication protocols. His research aims to enhance global connectivity and efficiency in terrestrial and satellite networks. Publications: His recent work spans semantic communication, satellite quantum key distribution, federated learning optimization, and coverage diversity in mega constellations. These studies reflect trends in 6G-ready technologies, AI-driven communication systems, and hybrid satellite-terrestrial networks. Awards: 1999 Best Paper Award for Signal Processing (EURASIP) IEEE Fellow (Leadership in technical excellence) World’s Top 2% Scientists (Stanford University, 2020–present) Grants & Supervision: As a senior academic, he oversees grants in wireless innovation and has advised numerous students on advanced communication systems. His lab focuses on next-generation networks, integrating theoretical insights with practical implementations. Labs/Teams: Active in interdisciplinary teams at the University of Adelaide, collaborating on projects funded by industry and government to bridge gaps between academic research and real-world applications.

Georgios B. GiannakisView profile

Professor

• Data Science
• Power Systems
• Network Science
• +7 more

Georgios B. Giannakis is a Full Professor, Endowed Chair, and Presidential Chair in the Department of Electrical and Computer Engineering at the University of Minnesota since 1999. He directs the Digital Technology Center and has held academic roles at the University of Virginia (1987-1999) and USC (1982-1986). His research spans Data Science, Wireless Communications, Network Science, and Statistical Signal Processing , with applications to IoT and power systems. Diploma in Electrical Engineering, NTUA (1981) MSc in Electrical Engineering, USC (1983) MSc in Mathematics, USC (1986) PhD in Electrical Engineering, USC (1986) His publications (470+ journals, 770+ conferences, 34 patents) focus on fading channel modeling, UWB localization, blind signal estimation, and cross-layer wireless design . Articles emphasize multicarrier systems, time-varying channels, and ultra-wideband communication , with citations exceeding 76,000 (H-index 145). Scientific Awards : EURASIP 'Athanasios Papoulis' Society Award (2020) IEEE Fourier Technical Field Award (2015) Gugliermo Marconi Prize Paper Award (2003) 9 Best Journal Paper Awards (IEEE/SPS & ComSoc) IEEE SPS Technical Achievement Award (2001) He has mentored over 50 PhD students and 25 postdocs, served IEEE as Distinguished Lecturer, and contributed to Greek university accreditation panels. His work bridges theoretical signal processing and practical communication systems .

Tony StillfjordView profile

Associate Professor

• Numerical Analysis
• Partial Differential Equations
• Stochastic Optimization
• +4 more

Tony Stillfjord is an Associate Professor at the Centre for Mathematical Sciences, Lund University, Sweden. He previously held postdoctoral positions at the Max Planck Institute for Dynamics of Complex Technical Systems and Chalmers/University of Gothenburg. Ph.D. and MSc in Numerical Analysis from Lund University Funded by WASP (Wallenberg AI, Autonomous Systems and Software Program) Research Interests : Numerical methods for partial differential equations, splitting schemes, stochastic optimization, and large-scale differential Riccati equations. His work focuses on developing low-rank approximations and robust optimization algorithms. Recent Publications highlight advancements in Lie/Strang splitting for operator-valued Riccati equations, stochastic descent methods, and GPU-accelerated splitting schemes. Software Contributions : Developed DREsplit (MATLAB package for differential Riccati equations) and BST20_CODE for stochastic optimization experiments. Contact : Office at MH:562E, Lund University. Email: tony.stillfjord@math.lth.se . URL: tonystillfjord.net

• Theoretical Computer Science
• Sublinear Algorithms
• Complexity Theory
• +4 more

Elena Grigorescu is an Adjunct Associate Professor in the Department of Computer Science at Purdue University, where she has been a faculty member since Fall 2012. Her research program spans theoretical computer science with a focus on foundational algorithmic challenges in large-scale data processing and computational limits, maintaining strong connections to cryptography, communications, and optimization applications. Her educational background includes a PhD from the Massachusetts Institute of Technology (MIT), establishing her expertise in rigorous theoretical frameworks. Professor Grigorescu's research emphasizes designing algorithms that operate in sublinear time or space for massive datasets, analyzing complexity of error-correcting codes and lattices, and exploring information-theoretical computation limits. Current investigations integrate differential privacy with learning-augmented techniques to solve online optimization problems, network design challenges, and data stream processing bottlenecks. Her work bridges abstract theory with practical implementations in cryptographic systems and quantum computing paradigms, demonstrating consistent innovation in algorithmic foundations. Analysis of her recent publications (2022-2025) reveals a dominant focus on sublinear-time algorithms, particularly at the intersection with differential privacy and machine learning augmentation. Key contributions include novel spanner constructions for network design, privacy-preserving clustering frameworks, and breakthroughs in trace reconstruction and coding theory. A pronounced trend shows increasing integration of learning-based predictions to enhance classical online algorithms for packing/covering problems while maintaining theoretical guarantees, alongside sustained contributions to error-correcting code analysis and graph-theoretic foundations. No specific scientific awards or major fellowships were documented in the provided materials, though her publication record in premier venues like STOC, FOCS, and APPROX/RANDOM indicates significant peer recognition. Professor Grigorescu actively mentors graduate students in theoretical computer science research, guiding investigations in sublinear algorithms, complexity theory, and coding theory. Her collaborative projects involve interdisciplinary teams across institutions, focusing on cryptographic applications and quantum information theory, though specific grant details were not included in the source texts. Ongoing work suggests expansion into quantum algorithm design and privacy-preserving machine learning frameworks. While dedicated laboratory facilities were not specified, her research operates within Purdue's theoretical computer science group, leveraging university-wide computational resources and fostering collaborations through conference participation and workshop organization.

• Cryptography
• Computer Security
• Information Security
• +4 more

Hongjun Wu is an Associate Professor at the Division of Mathematical Sciences, School of Physical & Mathematical Sciences, Nanyang Technological University, Singapore. His research focuses on Cryptography and Computer Security , with contributions to authenticated encryption, stream ciphers, hash functions, and cryptographic vulnerabilities. Doctor of Engineering (2005-2008), Electrical Engineering, Katholieke Universiteit Leuven Master of Engineering (1998-2000) & Bachelor of Engineering (1994-1998), Electrical Engineering, National University of Singapore Research spans Lightweight Cryptography (TinyJAMBU finalist 2021), Authenticated Encryption (CAESAR winners ACORN/AEGIS), Hash Function Design (JH finalist in SHA-3), and Stream Cipher Development (HC-128 in eSTREAM). He has advised PhD students Huang Tao, Ivan Tjuawinata, Yu Haiwan, and Master students Peng Lunan. Former team members include Tao Biaoshuai, Wei Lei, and Yosua Michael Maranatha. Key publications include work on AES biclique attacks (2015), ICEPOLE differential-linear analysis (2015), Microsoft Office encryption flaws (2005), and CAESAR/AEGIS (2013-2015). Awards: Finalist, NIST Lightweight Competition (2021) CAESAR Competition Winner (2019) SHA-3 Finalist (2011) eSTREAM Selection (2008) He leads the JH Hash Function and TinyJAMBU projects, with hardware/software implementations and active participation in cryptographic standards development. His work involves security analysis of Microsoft Office encryption (2005) and advisories on Adobe Reader vulnerabilities (CVE-2014-0522 to CVE-2014-9165).

• Reinforcement Learning
• Machine Learning Theory
• Sequential Decision Making
• +4 more

Csaba Szepesvári is a Professor and Canada CIFAR AI Chair in the Department of Computing Science at the University of Alberta, affiliated with Amii. Since 2017, he has been on partial leave, leading the Foundations team at DeepMind in Edmonton. His work bridges theoretical machine learning and practical reinforcement learning systems. His research interests lie at the intersection of reinforcement learning, sequential decision making, bandit algorithms, and theoretical machine learning . He develops and analyzes algorithms for efficient learning in complex environments, focusing on sample efficiency, function approximation, and optimization. His recent work explores policy gradient methods, offline and online RL, uncertainty estimation, and foundational limits of learning algorithms. The trends in his recent publications (2023–2024) emphasize theoretical advances in RL and bandits , particularly in q π -realizability, natural policy gradient, ensemble sampling, and lower bounds. The work spans NeurIPS, ICML, COLT, and AISTATS, reflecting deep theoretical engagement with practical implications. He is recognized as a Canada CIFAR AI Chair , a prestigious award supporting leading researchers in artificial intelligence. Csaba Szepesvári mentors students and collaborates extensively, often with researchers like András György, Tor Lattimore, Gellért Weisz, and Dale Schuurmans. He has co-organized RL theory seminars and workshops and co-authored the influential book Bandit Algorithms (2020). His leadership in both academic and industrial research (DeepMind) underscores his impact on the field. He is actively involved in the machine learning community through research, mentorship, and event organization, contributing to both foundational theory and real-world applications.

O.J. LuitenView profile

Professor

• Quantum Physics
• Materials Science
• Nanotechnology
• +4 more

O.J. Luiten is Full Professor in the Coherence and Quantum Technology group at Eindhoven University of Technology. His research focuses on fundamental quantum physics, materials science, nanotechnology, and life sciences, with emphasis on improving temporal resolution in electron microscopy and developing ultracold electron sources. He leads the Coherence and Quantum Technology group and is a core member of ICMS. His research interests center on quantum materials, ultrafast electron microscopy, and coherent light-electron interactions. Key areas include: Ultracold plasma applications for high-coherence electron sources Coherent manipulation of electron beams using laser light X-ray generation via electron beams His publications demonstrate a consistent focus on advancing charged particle beam technologies and light-matter interactions, with recent work emphasizing compact X-ray sources, ultrafast microscopy, and quantum electron manipulation. Scientific Awards: Smart*Light: Een tafelmodel synchrotron (2016) He leads multiple research projects including 'ICS-SAXS: Hard X-ray metrology' and 'Smart*Light 2.0', collaborating with institutions like ASML. Manages labs for ultrafast electron microscopy and quantum beam technology.

• Numerical Modeling
• Geotechnical Engineering
• Renewable Energy Systems
• +3 more

Dr. Liang Cui is an Associate Professor at the University of Surrey , affiliated with the School of Sustainability, Civil and Environmental Engineering and Institute for Sustainability . With a PhD from University College Dublin (2006) and BE (1st honor) from Tsinghua University (2002) , his career spans geotechnical research and education since joining Surrey in 2009. Key roles: Undergraduate Programme Leader (2020-2022, 2023-on), MSc Programme Leader for Advanced Geotechnical/Civil/Structural Engineering (2022-2023) Professional memberships: Chartered Engineer (CEng), Member of Institution of Civil Engineers (MICE), Fellow of Higher Education Academy (FHEA) His primary research focuses on numerical modeling (DEM/FEM) for geotechnical applications including offshore wind foundations , geothermal energy systems , methane hydrate exploitation , and extra-terrestrial soil mechanics . Secondary interests involve material characterization of polymeric foams , porous media , and biological tissues . Recent 15 publications (2023-2025) demonstrate expertise in soil-structure interaction for renewable energy infrastructure, thermal feedback in groundwater heat pumps, and hypothesis-driven DEM simulations for lunar/martian environments. Collaborative projects span institutions including Tsinghua University , University of Bristol , and Indian Institute of Technology Bhubaneswar . Scientific Awards: Sustainability Fellow (University of Surrey, 2023) Chartered Engineer (CEng) and MICE FHEA for educational contributions Dr. Cui supervises 7 postgraduate researchers and contributes to teaching modules in soil mechanics and energy geotechnics. His work addresses challenges in hybrid marine energy systems , needleless drug delivery , and seismic resilience of critical infrastructure.

Peter HaasView profile

Professor

• Data Management
• Machine Learning
• Simulation
• +6 more

Peter Haas is a Professor at the Manning College of Information and Computer Sciences at the University of Massachusetts Amherst, with an adjunct role in Industrial Engineering. Previously, he spent 30 years as a Principal Research Staff Member at IBM Research and held a consulting professorship in Management Science and Engineering at Stanford University. His research focuses on applying probability and statistics to data management, simulation of complex systems, and machine learning scalability. Education : PhD, Operations Research, Stanford University, 1986 MS, Statistics, Stanford University, 1984 MS, Environmental Engineering, Stanford University, 1979 SB, Engineering and Applied Physics, Harvard University, 1978 Research Interests : Haas’s work spans stochastic systems, probabilistic databases (e.g., MCDB and SimSQL), sampling techniques, and simulation optimization. He pioneered methods for managing uncertain data and scalable machine learning, including compressed linear algebra for declarative systems. His recent focus includes in-database decision support and hybrid simulation metamodeling with neural networks. Key Contributions : He developed the Online Aggregation framework (SIGMOD 1997), which earned a Test-of-Time Award in 2007. His work on matrix factorization and distributed stochastic gradient descent (DSGD) revolutionized large-scale machine learning. He also advanced techniques for estimating distinct-values and correlation discovery in databases. Awards : A six-time recipient of IBM’s Pat Goldberg Memorial Award, he is an ACM and INFORMS Fellow. His honors include the VLDB Best Paper Award (2016), EDBT Best Paper (2018), and recognition in Communications of the ACM. Advising & Grants : He advises four current PhD students and has graduated Matteo Brucato. His IBM career included over 30 patents, including foundational work for DB2’s sampling capabilities and IBM Watson analytics. He leads the DREAM Lab, focusing on data systems for exploration and analytics. Labs/Teams : Directs the Data systems Research for Exploration, Analytics, and Modeling (DREAM) Lab, advancing projects like Splash (health system simulation) and SuDocu (document summarization by example).

• Bioinformatics
• Structural Biology
• Computational Biology
• +3 more

Martin Steinegger is a researcher affiliated with Johns Hopkins School of Medicine and previously held roles at institutions such as the Max Planck Institute for Biophysical Chemistry and Technical University of Munich. His research focuses on bioinformatics, protein structure prediction, and computational methods for analyzing large genomic datasets. He has contributed to tools like MMseqs2, ColabFold, and Foldseek, advancing fields like metagenomics and structural biology. His work emphasizes scalable algorithms and open-source software development. Education includes a Master of Computer Science from Ludwig-Maximilians-Universität München (2013-2014) and a Ph.D. from Technical University of Munich (2014-2018). He has also held visiting scholar positions at Seoul National University, Centre for Genomic Regulation, and University of California, San Francisco. Key research interests revolve around protein structure prediction, metagenomic analysis, and developing machine learning frameworks for biological data. His publications highlight innovations in protein language models, structural phylogenetics, and database management systems. Notable contributions include the AlphaFold Protein Structure Database, MMseqs2 sequence search tool, and ColabFold for accessible protein folding predictions. His work bridges computational methods with biological discovery, addressing challenges in structural biology and genomic data interpretation.

• Earthquake Engineering
• Structural Control
• Optimal Control
• +3 more

Prof. Dr. Unal Aldemir is a Professor at the Department of Civil Engineering, Istanbul Technical University (ITU). He has held significant administrative roles including Dean (2015-2016, 2017-2020, 2020-present), Vice Dean (2009-2010), Director of the Research and Application Center (2012-2018), and Institute Director (2012-2015). His academic journey includes postdoctoral research at Duke University and a research scholarship at Oregon State University. Educational Background: PhD in Structural Engineering, Istanbul Technical University (1995-1999) Master's in Structural Engineering, Istanbul Technical University (1992-1994) Bachelor's in Civil Engineering, Bogazici University (1989-1992) His research focuses on earthquake engineering, structural dynamics, and control systems. Primary interests include: Development of active/semi-active control algorithms for seismic response reduction Vibration control in 3D structures Energy distribution analysis in controlled systems Optimization techniques for structural stability Earthquake-excited structure modeling His publications (25 research outputs) demonstrate consistent focus on structural control methodologies, with recent work advancing magnetorheological damper applications and 3D vibration control algorithms. Key trends include optimization of control performance indices and time-delay compensation in active control systems. Professional Memberships: National Earthquake Committee (Member since 2010) TUMTMK (Member since 2005) He has supervised 18 graduate students (PhD/Master's) with thesis topics spanning seismic isolation design, structural-soil interaction, and vibration control algorithms. As principal investigator, he led the research project "Investigation of Energy Entering the Structure Under Structural Control" (2010-2018).