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Glen ChouView profile

Assistant Professor

• Robotics
• Trustworthy Autonomy
• Machine Learning
• +4 more

Glen Chou is an Assistant Professor in the Department of Electrical and Computer Engineering at Georgia Tech. He holds dual B.S. degrees from UC Berkeley (Electrical Engineering & Computer Science and Mechanical Engineering) and M.S./Ph.D. degrees from the University of Michigan (Electrical & Computer Engineering). Prior to Georgia Tech, he was a postdoctoral researcher at MIT CSAIL. Chou directs the Trustworthy Robotics Lab, focusing on algorithms for general-purpose robots and autonomous systems to ensure capability, safety, and resilience in real-world conditions. His research integrates control theory, machine learning, optimization, and formal methods. He emphasizes both theoretical foundations (e.g., proving algorithmic properties) and practical deployment on real robots. Key research areas include trustworthy autonomy, human-robot interaction, and constraint learning from demonstrations. His lab addresses challenges in perception, motion planning, and system reliability through interdisciplinary approaches. Chou has received notable awards including the Robotics: Science & Systems Pioneer (2022), NDSEG Fellowship (2019), and NSF Graduate Fellowship (2019). His work bridges foundational theory with real-world applications, with a focus on safety-critical systems.

Santiago BadiaView profile

Professor

• Computational Science and Engineering
• Numerical Analysis
• Finite Element Methods
• +3 more

Santiago Badia is a Full Professor of Computational Science and Engineering at Universitat Politècnica de Catalunya (UPC), holding an adjoint researcher position at the International Center for Numerical Methods in Engineering (CIMNE). He leads the Large Scale Scientific Computing (LSSC) group at CIMNE, focusing on finite element methods, numerical analysis, and high-performance computing. His research emphasizes fluid dynamics, multiphysics problems, and scalable solvers for large-scale systems. Previously, he worked at Politecnico di Milano and Sandia National Labs. He developed the FEMPAR software framework, a parallel finite element tool for PDE simulations, achieving landmark scalability (e.g., 60 billion unknowns on 458,672 cores). FEMPAR is recognized in the High-Q Club of European codes. His expertise includes discontinuous Galerkin methods, XFEM, and domain decomposition preconditioners. Research interests span metal additive manufacturing, superconductor devices, and nuclear engineering applications. Awards include FEMPAR's High-Q Club inclusion. He advises PhD and MSc students (e.g., Jesus Bonilla, Eric Neiva, Marc Olm) and has open positions in postdoc/PhD levels. His team includes researchers like Javier Principe and Alberto Martín. Ongoing projects involve advancing parallel algorithms, multiphysics simulations, and software scalability for exascale computing.

Jesús A. De LoeraView profile

Professor

• Combinatorics
• Convex Geometry
• Discrete Optimization
• +6 more

Jesús A. De Loera serves as a Distinguished Professor in the Department of Mathematics at the University of California, Davis. He is also affiliated with the Applied Mathematics and Computer Science graduate groups, reflecting his interdisciplinary research approach. His primary research interests include Combinatorics, Convex Geometry, Discrete Optimization, and Algebraic Combinatorics . Professor De Loera has developed expertise in computational aspects of polyhedral combinatorics and mathematical optimization, often using specialized software like polymake for polyhedral computations. Throughout his career, he has supervised 15 PhD students who have gone on to prestigious positions at institutions including Google, Facebook, U.S. Naval Academy, Harvard University, and various research universities. His teaching portfolio includes advanced courses such as Convex Geometry (MATH 114), Algebraic Combinatorics (MATH 146), and Applied Linear Algebra (MATH 167). Professor De Loera is recognized with the title of Distinguished Professor, indicating significant scholarly achievement. His work bridges theoretical mathematics with practical computational applications, particularly in data science and optimization problems. He maintains an active research seminar for both graduate and undergraduate students, with a strong commitment to mentoring and increasing diversity in mathematics through programs supporting underrepresented groups.

Sam LindleyView profile

Associate Professor

• Programming Languages
• Formal Methods
• Type Systems
• +3 more

Sam Lindley is a Reader in Programming Language Design and Implementation at the School of Informatics, University of Edinburgh. He holds a UKRI Future Leaders Fellowship in Effect Handler Oriented Programming. His research focuses on foundational aspects of programming languages, including effect systems, session types, type theory, and functional programming. Key research interests span algebraic effects, concurrency models, type systems for resource management, and language implementation techniques. Lindley's work bridges theory and practice, addressing challenges in systems programming, web technologies (e.g., WebAssembly), and compiler design. Recent publications (2023–2025) emphasize effect handlers for low-level languages (e.g., C, WebAssembly), modal type systems for memory management, and formal semantics for concurrent systems. His work often explores how advanced type systems can improve expressivity and efficiency in programming models. Scientific Awards: UKRI Future Leaders Fellowship Labs/Teams: Laboratory for Foundations of Computer Science (LFCS) Grants: Active funding via UKRI Fellowship

• Polyhedral combinatorics
• Extended formulations
• Combinatorial optimization
• +2 more

Samuel Fiorini is Associate Professor in the Department of Mathematics at the Université libre de Bruxelles (ULB) , member of the Algebra and Combinatorics group (CP 216). His research centres on polyhedral combinatorics, extended formulations, combinatorial optimisation and approximation algorithms , with frequent overlap into structural graph theory. Research in depth: Fiorini’s work explores how high-dimensional polytopes can sometimes be expressed compactly through extended formulations, proving exponential lower bounds when they cannot. He has contributed new approximation algorithms for classical problems such as vertex cover, clique transversal and odd-cycle packing, and has advanced the understanding of sorting and entropy in partially ordered sets. His papers often combine tools from graph minors, communication complexity and polyhedral theory. Scientific recognition: Best Paper Award, 44th ACM Symposium on Theory of Computing (STOC 2012) Programme committees: FOCS, IPCO, APPROX, STACS, WAOA Organiser, Sixth Cargese Workshop on Combinatorial Optimization Advising & grants: He currently supervises PhD students Carole Muller and Matthew Drescher and has mentored six completed PhDs as well as more than a dozen post-doctoral researchers. His group has been supported by an ERC starting grant and other national and international projects focusing on polyhedral approaches to hard optimisation problems. Lab & team: Fiorini leads a vibrant team within the Algebra and Combinatorics cluster at ULB, maintaining active collaborations with researchers worldwide and hosting frequent visitors working on discrete optimisation and polyhedral combinatorics.

• Electronics
• Optics
• Physics
• +8 more

Dr. Cristina de Dios Fernández is an Associate Professor at Carlos III University of Madrid, affiliated with the School of Engineering and the Department of Electronic Technology . She leads the research group Sensors and Instrumentation Techniques (SIT) and holds an ORCID iD 0000-0001-5474-7407 . Research Interests span Photonics , Terahertz Technologies , Optical Frequency Combs , Quantum Sensing , Biomedical Optics , and Integrated Photonics . Her work focuses on Developing compact confocal microscopes for nitrogen-vacancy centers Advancing dual-comb spectroscopy for biomedical and industrial applications Optimizing VCSEL-based optical frequency combs Designing photonic chips for THz dual-comb spectrometers Projects & Grants include principal investigator roles in initiatives like Cátedra Universidad-Empresa EPIQ (2024-2027) and HYPERTERA (2019-2020), alongside researcher roles in EU-funded programs such as OILTEBIA (2013-2017) and CELTA (2016-2020). Patents include Hyperspectral Imaging based on Dual Frequency Comb (2022), and she supervised the thesis VCSEL-based Optical Frequency Comb Generation, Expansion and Optimization (2016).

• Algebraic Topology
• Geometric Group Theory
• Cohomology
• +3 more

Dr. Robin J. Sroka is an Investigator in Mathematics Münster and a member of the Collaborative Research Center (CRC) 1442 Geometry: Deformations and Rigidity at the University of Münster. Affiliated with the Mathematisches Institut within the Faculty of Mathematics and Computer Science, his research focuses on Algebraic Topology and Geometric Group Theory, with particular emphasis on cohomology, homological algebra, and symplectic geometry. He contributes to projects such as the CRC's T1 program on K-Groups and Cohomology. His recent publications explore topics like Temperley-Lieb algebras, cohomology of arithmetic groups, scissors automorphism groups, and symplectic Steinberg modules. Sroka’s work bridges pure mathematics disciplines, combining algebraic structures with geometric and topological insights. His research often involves collaborations with leading mathematicians in the field, addressing foundational questions in algebraic topology and group theory. While no specific awards or grants are listed, his active participation in high-profile research initiatives underscores his contribution to advancing geometric and algebraic methodologies. Sroka’s academic webpage provides further details on his research and collaborations.

• Bioinformatics
• Algorithms
• Computational Biology
• +5 more

Gabriel Alejandro Valiente Feruglio is a Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Barcelona School of Informatics (FIB) and the Department of Computer Science. He is a member of the ALBCOM research group, focusing on Algorithms, Bioinformatics, Complexity, and Formal Methods. His work integrates theoretical computer science with applications in computational biology, including phylogenetic analysis, graph algorithms, and metagenomics. Valiente’s research emphasizes the development of algorithms for biological networks, phylogenetic tree and network comparison, and efficient graph representation. He has contributed to tools like TANGO for taxonomic assignment in metagenomics and AligNet for protein-protein interaction network alignment. His publications span over 140 works in journals like BMC Bioinformatics, IEEE-ACM Transactions on Computational Biology, and Bioinformatics. He leads and collaborates in competitive research projects funded by institutions like the Catalan government, focusing on bioinformatics, computational biology, and algorithmic methods. His research also extends to LaTeX typesetting for scientific documents and the structural analysis of scientific collaborations in graph transformations.

Mark GatesView profile

Academic

• High Performance Computing
• Scientific Computing
• Linear Algebra
• +3 more

Mark Gates is a Research Assistant Professor at the Innovative Computing Laboratory (ICL) at the University of Tennessee, Knoxville. His work focuses on high performance computing (HPC), scientific computing, and linear algebra, particularly leveraging multi-core and GPU architectures. He contributes to projects like SLATE, PLASMA, and MAGMA, advancing dense and sparse linear algebra libraries for modern parallel systems. His research emphasizes scalable algorithms, communication-avoiding techniques, and exascale computing. Education: Ph.D., M.S., and B.S. in Computer Science from the University of Illinois Urbana-Champaign (2011, 2007, 1998). Research interests include parallel algorithms, numerical stability, and GPU acceleration. Recent work highlights include task-based polar decomposition, mixed-precision algorithms, and efficient sparse linear algebra operations. His articles address challenges in distributed computing, library development, and performance optimization. Labs/Teams: Active in ICL, collaborating on HPC software for exascale systems. No scientific awards explicitly listed, though contributions to foundational HPC libraries are notable.

• Power Electronics
• Control Theory
• Mathematical Optimization
• +5 more

Petros Karamanakos is an Assistant Professor in the Department of Electrical Engineering at Tampere University's Faculty of Information Technology and Communication Sciences. His research focuses on advanced control methodologies for power electronic systems, emphasizing optimization, mathematical programming, and model predictive control. He holds a Ph.D. and Diploma in Electrical and Computer Engineering from the National Technical University of Athens (2007–2013). Prior to his academic role, he worked at ABB Corporate Research Center (2010–2011) and was a PostDoc Research Associate at Technische Universität München (2013–2016). His mission involves developing control methods that maximize hardware capabilities while minimizing hardware requirements. Key research topics include variable speed drives, grid-connected converters, multilevel power converters, and modulation strategies. He has received prestigious awards, including the Third Best Paper Award in IEEE Transactions on Industry Applications and four IEEE conference prize paper awards. Teaching responsibilities include courses on power electronics and control systems. He actively supervises BSc, MSc, and PhD students. His work bridges theoretical advancements with practical implementations in power electronics, with a focus on real-time control and system optimization.

Gruia CalinescuView profile

Associate Professor

• Algorithms
• Approximation Algorithms
• Theory of Computation
• +3 more

Gruia Calinescu is an Associate Professor of Computer Science at Illinois Institute of Technology (IIT), affiliated with the College of Computing's Computer Science Department. He joined IIT in 2000 and has held visiting positions at the University of Bonn and the University of Wisconsin-Milwaukee. His research focuses on approximation algorithms, combinatorial optimization, and theoretical computer science, with contributions to graph theory, network design, and algorithmic problems in wireless networks. Education includes a PhD from Georgia Tech's Algorithms, Combinatorics, and Optimization program (1998) under Howard Karloff. He also holds a diploma from the University of Bucharest in scheduling theory. Key research interests include algorithms for Steiner trees, network connectivity, scheduling, and power optimization. He has published extensively on topics like minimum power covering, relay placement, and LP rounding techniques. His work often bridges theoretical foundations with practical applications in wireless networks and distributed systems. Recent work includes advancements in combination algorithms for Steiner tree variants (2022), energy-aware scheduling (2016), and improved approximation algorithms for relay placement (2014). He is also involved in teaching, such as CS 530 - Theory of Computation.

Bang Nam JeonView profile

Professor

• International Economics
• Financial Economics
• Global Banking
• +9 more

Bang Nam Jeon is a Professor in the Department of Economics at Drexel University's LeBow College of Business. His research focuses on international economics, financial economics, global banking, and Asian economies, with a strong emphasis on macroprudential policy, bank risk, and financial stability in emerging markets. He has published extensively in top-tier journals and presented at numerous international conferences. His research interests include: International and comparative banking Financial regulation and macroprudential policy Monetary policy transmission Financial contagion and systemic risk Economic uncertainty and financial stability Foreign direct investment and trade linkages in Asia His recent publications show a consistent focus on the interplay between macroeconomic conditions, regulatory frameworks, and bank risk behavior, particularly in China and other emerging Asian economies. He frequently employs empirical and panel data methods to analyze bank-level data. His work contributes significantly to understanding financial stability in developing economies amid global shocks such as the 2008 crisis and the COVID-19 pandemic. Scientific awards and honors include: Fulbright U.S. Scholar Award (2020) Service and Appreciation Awards from the Korea-America Economic Association Peter G. Stercho Memorial Research and Service Award (2005) Excellence in Teaching Award (2003) University Research Scholar Award (1990–1991) The Henry M. Oliver Memorial Award (1987) Jeon has advised numerous researchers and co-authored extensively with scholars like Ji Wu, Minghua Chen, and Qiaoling Kang. He has received research funding and institutional support, evidenced by his visiting positions and co-directorship of executive training programs. He has held leadership roles including President of the Korea-America Economic Association (2013) and has served on advisory boards for journals such as Journal of Economic Integration and Panoeconomicus . He has also been a visiting scholar at institutions in Hong Kong, Korea, China, and Romania, indicating strong international collaboration. He is actively involved in research dissemination through working papers, conference presentations, and editorial roles, including Guest Editor for the International Finance Review . His research program is well-established, continuously funded, and highly productive, with publications extending into 2025.

Linghao SongView profile

Assistant Professor

• Accelerator Architecture
• FPGAs
• Hardware Acceleration Systems
• +4 more

Linghao Song is an Assistant Professor in the Department of Electrical & Computer Engineering at Yale University. His research focuses on accelerator architecture design, FPGA-based acceleration systems, and ReRAM-based computing. He is affiliated with the School of Engineering & Applied Science and holds expertise in sparse matrix processing, high-level synthesis, and task-parallel programming frameworks. Education: Ph.D. and M.S. from Duke University and University of Pittsburgh, respectively, with a B.S.E. from Shanghai Jiao Tong University. Research Interests: Song's work spans FPGA-accelerated computing, resistive memory (ReRAM) architectures for deep learning and graph processing, and high-performance sparse matrix operations. His recent projects include the TAPA framework for FPGA programming, ReFloat for iterative linear solvers, and the Sextans/Serpens accelerators for sparse matrix computations. Key Contributions: Over 20 peer-reviewed publications on topics like ReRAM-based accelerators (e.g., GraphR, PipeLayer), FPGA optimization frameworks (TAPA, RapidStream), and novel hardware designs for neural networks and graph analytics. His work emphasizes energy efficiency, scalability, and cross-domain applicability of hardware accelerators. Awards: Recipient of the Duke ECE Outstanding Dissertation Award (2021), EDAA Outstanding Dissertation Award (2020), and National Scholarship of China (2012). Labs/Teams: Leads research in FPGA acceleration and memory-based computing, collaborating on projects involving HBM integration, resistive memory architectures, and task-parallel dataflow frameworks.

Rakesh NagiView profile

Professor

• Data Science & Analytics
• Machine Learning & AI
• Operations Management
• +7 more

Rakesh Nagi is a Professor and Head of the Engineering Systems and Design Pillar at Singapore University of Technology and Design (SUTD), where he joined in August 2023. He concurrently holds the Donald Biggar Willett Professorship at the University of Illinois, Urbana-Champaign (UIUC), on leave. His academic leadership includes serving as Department Head of Industrial and Enterprise Systems Engineering at UIUC (2013–2019) and as Interim Director of the Illinois Applied Research Institute (2016–2018). Previously, he was Chair of Industrial and Systems Engineering at the University at Buffalo (SUNY) from 2006 to 2012. Education: PhD (1991) and MS (1989) in Mechanical Engineering from the University of Maryland, College Park, with work at the Institute for Systems Research and INRIA, France. BE (1987) in Mechanical Engineering from the University of Roorkee (now IIT Roorkee), India. Research focuses on Data Science, Machine Learning, Operations Research, GPU-accelerated computing, and military applications. Key areas include Big Graphs, High-level Information Fusion, Production Systems, and Multi-Agent Systems. His work often leverages parallel computing and optimization techniques. Recipient of prestigious awards: IISE David F. Baker Award (2022), INFORMS Koopman Award (2021, 2018), and multiple DARPA Graph Challenge recognitions. Contributions span over 100 peer-reviewed articles in top journals (e.g., Operations Research, IEEE Transactions) and conferences. Research projects include Hybrid AI/ML-Optimization for cloud workflows, GPU-accelerated algorithms for multi-target tracking, and interventions against illicit supply chains. Active collaborations with IBM-Illinois and NSF-funded initiatives address strategic resource allocation and network analysis. Labs/Teams: Leads the Engineering Systems and Design Pillar at SUTD and coordinates interdisciplinary projects at UIUC’s Coordinated Science Laboratory.

Partha LahiriView profile

Professor

• Survey Sampling
• Small-Area Estimation
• Bayesian Inference
• +3 more

Partha Lahiri is a Professor at the University of Maryland, College Park, holding dual appointments in the Joint Program in Survey Methodology (JPSM) and the Department of Mathematics . He also serves as an Adjunct Research Professor at the Institute of Social Research, University of Michigan, Ann Arbor. Previously, he was the Milton Mohr Distinguished Professor of Statistics at the University of Nebraska-Lincoln. His expertise spans Survey Sampling , Small-Area Estimation , Bayesian and Empirical Bayes Inference , and Resampling Methods . He has advised organizations such as the United Nations Development Program, World Bank, and Gallup, and contributed to panels like the U.S. Census Advisory Committee. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics, and an elected member of the International Statistical Institute. Research interests include methodological advancements in small area estimation, survey data integration, and statistical linkage. His work emphasizes practical applications in policy and public health, including poverty measurement and pandemic-related studies (e.g., mask effectiveness perception during the pandemic). He has authored influential papers on topics like multi-goal prior selection, probabilistic record linkage, and resampling techniques for uncertainty quantification. His leadership roles, including directing JPSM, highlight his commitment to training future survey methodologists. Awards: ASA and IMS Fellowships, ISI Elected Membership Advisory Roles: U.S. Census Advisory Committee, National Academy of Sciences panels Publications Trends: Focus on small area estimation, Bayesian methods, and policy-relevant applications Grants/Projects: Collaborations with international organizations and academic institutions on statistical methodologies Lahiri’s research bridges theoretical statistics and practical survey challenges, with a strong emphasis on data integration and uncertainty estimation in complex systems.