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Erik SchloglView profile

Professor

• Quantitative Finance
• Financial Mathematics
• Computational Finance
• +5 more

Erik Schlogl is a Professor in the School of Mathematical and Physical Sciences at the University of Technology Sydney (UTS), Australia, with a focus on Financial Mathematics and Quantitative Finance. He previously held academic appointments at UTS Business School and the School of Finance and Economics at UTS, as well as the School of Mathematics at UNSW Australia and the University of Bonn, Germany. He received a Doctorate in Economics from the University of Bonn, Germany (1992–1997), specializing in term structure models and the pricing of fixed income derivatives. His research spans quantitative finance, including model calibration, interest rate term structure modeling, credit risk, and the integration of multiple sources of risk. He has published in journals such as Finance and Stochastics , Quantitative Finance , Risk , and Journal of Economic Dynamics and Control . Erik's recent work includes a 2021 publication in Risks on parameter learning and change detection using a particle filter incorporating genetic algorithms. This aligns with his expertise in computational financial engineering and practical implementation of quantitative finance models. Scientific Awards & Recognition : Highly Commended in the Gerald Durrell Award for Endangered Wildlife (2002) by the Natural History Museum/BBC Wildlife Photographer of the Year competition. He is available for media inquiries and supervises Masters/PhD students in quantitative finance research. Erik also co-organizes the Quantitative Methods in Finance (QMF) conference and chairs the Sydney Financial Mathematics Workshop (SFMW).

Chihwa KaoView profile

Professor

• Econometrics
• Panel Data Analysis
• Time Series Econometrics
• +6 more

Chihwa Kao is a Professor in the Department of Economics at the University of Connecticut, affiliated with the College of Liberal Arts and Sciences. His research focuses on panel data econometrics, structural change analysis, cointegration, high-dimensional models, and time series econometrics. He has extensively contributed to methodologies addressing cross-sectional dependence, factor models, and non-stationary panel data. Key research interests include developing robust statistical techniques for panel data, testing for structural breaks in economic relationships, and analyzing economic growth factors. His work bridges theoretical econometrics with practical applications in finance and policy analysis. His articles emphasize advancements in panel data methodologies, such as bias-correction in dynamic models, clustering techniques for fixed effects, and handling serial correlation. Recent trends show a focus on high-dimensional data challenges, factor models, and structural stability assessments in large datasets. Awards and honors are not explicitly listed in the provided materials. He advises no students listed here, but his courses include Econ5315/3315 and Econ5323/4323. His CV and additional materials are available via provided links.

Christian IkenmeyerView profile

Professor

• Computational Complexity
• Algebraic Combinatorics
• Geometric Complexity Theory
• +2 more

Christian Ikenmeyer is Professor in Computer Science and Mathematics at the University of Warwick. His research focuses on algebraic complexity theory, geometric complexity theory (GCT), and representation theory, with emphasis on tensor rank, Kronecker coefficients, and polynomial identity testing. Research Focus: Dr. Ikenmeyer develops mathematical frameworks to solve fundamental problems in computational complexity, including P vs NP. His work connects representation theory with algebraic geometry to establish complexity lower bounds and classify computational hardness. Leadership: He organizes workshops on algebraic complexity and GCT, including the 2023 Algebraic Complexity Theory Workshop at ICALP. His research is funded by EPSRC and DFG grants, supporting investigations into homogeneous complexity and branching programs. Teaching: Courses include 'Groups and Representations' and programming contest coaching. He has previously taught at MIT, Texas A&M, and Saarland University, developing lecture notes on GCT.

• Machine Learning
• Deep Learning
• Video Classification
• +3 more

Reza Bosagh Zadeh is an Adjunct Professor at the Institute for Computational and Mathematical Engineering (ICME) at Stanford University. His research focuses on machine learning, deep learning, and their applications in video classification, healthcare analytics, and distributed algorithms. He specializes in developing scalable computational methods for real-time data processing and has contributed to advancements in neural networks and optimization techniques. Reza's work spans theoretical and applied domains, with notable contributions to TensorFlow frameworks, video summarization systems, and medical imaging analysis. His research often integrates interdisciplinary approaches, leveraging both academic and industrial collaborations. Notable projects include developing machine learning models for glaucoma detection and creating efficient algorithms for large-scale data processing in environments like Apache Spark. His publications emphasize real-time video stream analysis, distributed computing architectures, and practical implementations of deep learning. Reza holds a strong presence in both academic and tech sectors, with contributions to platforms like Twitter's Who-to-Follow system and innovations in edge computing for video surveillance.

Burak SarsilmazView profile

Assistant Professor

• Distributed Control
• Adaptive Control
• Trajectory Optimization
• +2 more

Dr. Burak Sarsilmaz is an Assistant Professor in the College of Engineering at Utah State University, specializing in Electrical and Computer Engineering. His research focuses on distributed control of multi-agent systems, adaptive control, and trajectory optimization for aerospace applications. Education: PhD in Mechanical Engineering, University of South Florida (2020) MA in Mathematics, University of South Florida (2020) MS in Aerospace Engineering, Middle East Technical University (2016) BS in Aerospace Engineering, Middle East Technical University (2013) Research: His work bridges control theory and practical applications in autonomous systems, with emphasis on robustness under uncertainty. Recent publications explore novel approaches to cooperative control and optimization in constrained environments. Students: Actively mentors graduate students including Akalu Desta Teklu and Kursad Metehan Gul. Leads the Autonomy, Robotics, Control, and Optimization (ARCO) research group. Awards: Holds a Canada Research Chair in Translational Cancer Research.

• Algorithms and Complexity
• Parameterized Algorithms
• Approximation Algorithms
• +7 more

Matthias Mnich is a Professor and Head of the Institute for Algorithms and Complexity at Hamburg University of Technology (TUHH), within the School of Electrical Engineering, Computer Science and Mathematics. He also serves as Deputy Dean International, reflecting his leadership in academic administration and international collaboration. He is a principal investigator at the Helmholtz Graduate School for the Structure of Matter, further emphasizing his interdisciplinary impact. His research lies at the intersection of theoretical computer science and practical algorithm design, focusing on parameterized algorithms , approximation algorithms , combinatorial optimization , scheduling , and algorithmic game theory . His work often bridges theoretical guarantees with real-world applications in energy systems, quantum computing, and logistics. The recent publications (2023–2025) highlight his sustained excellence in top-tier venues such as FOCS, ICALP, ESA, STACS, and journals like Mathematical Programming and ACM Transactions on Algorithms . These works explore foundational problems in vector bin packing , integer programming , graph algorithms , and kernelization , while also applying algorithmic techniques to microgrid energy optimization and quantum algorithm engineering . He is deeply embedded in the theoretical computer science community, having served on program committees of major conferences including: STACS 2023 ESA 2024 FOCS 2023 ICALP 2024 IJCAI 2019–2025 AAAI 2018 SWAT 2018 He has successfully supervised several PhD students to completion, including Matthias Kaul , Roland Vincze , and Alexander Göke , many of whom have taken postdoctoral positions at institutions like the University of Bonn and University of Augsburg. His current research projects include PATTERN (2025–2031) , Hamburg Quantum Computing (2024–2029) , and Kernelization for Big Data , indicating long-term funding and strategic research directions. He leads the Institute for Algorithms and Complexity (E-11) , fostering a research environment focused on high-impact algorithmic research.

Yann PontyView profile

Researcher

• RNA Folding
• RNA Design
• RNA Evolution
• +9 more

Yann Ponty is a tenured CNRS Researcher at the Computer Science Department (LIX) of École Polytechnique (Institut Polytechnique de Paris, France). He leads the AMIBio team and serves as Deputy Director of LIX. His work focuses on developing bioinformatics methods at the intersection of computer science, mathematics, and molecular biology, particularly for RNA structure prediction, design, and evolution. He holds leadership roles in the ISCB Board of Directors (2025-2027) and the HDR referent for the IDIA department (CS&Interactions) at IP Paris. Research Interests: RNA folding/design/evolution, RNA-RNA/RNA-protein interactions, random generation, enumerative combinatorics, discrete algorithms, parameterized complexity, RNA visualization Key Contributions: Developed algorithms for RNA inverse folding, pseudoknot modeling, and dynamic programming optimization Collaborations: Partnerships with institutions like Simon Fraser University, Boston College, and Université Paris-Saclay His recent publications (15 most recent) span RNA structure prediction, pseudoknot partition functions, linear-time inverse folding algorithms, and parameterized sampling techniques. The work emphasizes dynamic programming, combinatorial approaches, and integration of experimental data for improving RNA modeling. Scientific awards include election to the ISCB Board of Directors (2025-2027) and leadership roles in academic networks like GdR BIM. He actively contributes to software development (VARNA, RNANR, SPARCS, IncaRNAtion, RNARedPrint) and serves as Associate Editor for Bioinformatics (OUP). Teaching engagements include graduate-level courses in combinatorial optimization, RNA bioinformatics, and algorithms at Université Paris-Saclay and École Polytechnique.

Chris WhiddenView profile

Assistant Professor

• Algorithms and Bioinformatics
• Computational Biology
• Phylogenetics
• +10 more

Chris Whidden is an Assistant Professor in the Faculty of Computer Science at Dalhousie University, where he leads research in algorithms and bioinformatics. His work bridges theoretical computer science with practical applications in computational biology and ocean data analytics. Whidden's research interests include approximation and fixed-parameter algorithms, computational biology, evolutionary trees and networks, graph theory, hybridization and lateral gene transfer, NP-hardness, and ocean data analytics. He develops efficient algorithms and software to solve NP-hard problems, particularly in the context of phylogenetics and large-scale biological data. His work applies both theoretical algorithm design and practical software engineering to create novel solutions for understanding biodiversity, bacterial and viral evolution, and oceanographic systems. His recent publications reflect a strong trend toward interdisciplinary research, combining deep learning and machine learning with oceanographic data analysis, fish detection and classification, echosounder data processing, and environmental monitoring. Many of his algorithmic contributions focus on phylogenetic tree comparison, including SPR distances, maximum agreement forests, and supertree construction. He has developed several widely used software tools such as rspr, SPR Supertrees, uspr, and phylogenetic topographer. NSERC Killam Trusts Tula Foundation NSF Simons Foundation (via Life Sciences Research Foundation) DeepSense (industry-academic collaboration) He is actively involved in mentoring and has funding available for PhD and MCS students in computer science, particularly in algorithms, bioinformatics, and data analytics. He teaches courses such as Algorithm Engineering (CSCI 4118/6105), Software Development (CSCI 2134), and Design and Analysis of Algorithms (CSCI 3110). Whidden has collaborated extensively with industry through DeepSense, working on projects that apply data analytics and machine learning to the ocean sector, including predictive modeling for ocean buoys, automated fish detection, and tidal energy monitoring.

Archontia C. GiannopoulouView profile

Associate Professor

• Graph Algorithms
• Structural Graph Theory
• Parameterized Algorithms and Complexity
• +3 more

Archontia C. Giannopoulou is an Associate Professor at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Greece. Her research focuses on Graph Algorithms, Structural Graph Theory, and Parameterized Algorithms and Complexity. She explores advanced topics such as matching minors, tree-cut width, immersion minors, and kernelization. Her work emphasizes algorithmic design and structural graph theory with applications in combinatorial optimization. Giannopoulou’s academic contributions include seminal papers on edge deletion problems, cutwidth obstructions, and polynomial kernelization. Notable publications include studies on bipartite graphs and their structural properties, as well as algorithmic advancements in parameterized complexity. Her research bridges theoretical foundations with practical algorithmic solutions. Her contact information includes an email at archontia.giannopoulou@gmail.com and a postal address at the University of Athens. Her work spans international collaborations, reflected in co-authored articles with leading researchers in the field.

Fedor FominView profile

Professor

• Theoretical Computer Science
• Graph Algorithms
• Parameterized Complexity
• +3 more

Fedor Fomin is a Professor in the Department of Informatics at the University of Bergen. His research focuses on Theoretical Computer Science, including Graph Algorithms, Parameterized Complexity, Combinatorics, and Combinatorial Games. He is affiliated with the Norwegian Academy of Science and Letters, the Norwegian Academy of Technological Sciences, and the Academia Europaea, and holds fellowships from ACM and EATCS. His work has been recognized with the EATCS Nerode Prize in 2015 and 2017. Dr. Fomin has authored influential books such as Kernelization: Theory of Parameterized Preprocessing and Parameterized Algorithms , which are foundational in the field of algorithm design. His recent publications explore cutting-edge topics in parameterized complexity, graph theory, and distributed computing, with contributions to approximation algorithms, kernelization, and combinatorial optimization. His awards and honors reflect his significant impact on theoretical computer science. Fomin has been awarded an ERC Advanced Grant and has mentored numerous researchers, contributing to the advancement of algorithmic techniques and their applications.

Pinar HeggernesView profile

Professor

• Algorithms
• Graph Theory
• Combinatorics
• +3 more

Pinar Heggernes is a Professor at the Department of Informatics at the University of Bergen , specializing in algorithms and graph theory . Her research focuses on solving NP-hard graph problems through polynomial-time and fixed parameter tractable algorithms , particularly on chordal , split , and outerplanar graphs . She served as the elected deputy rector of the University of Bergen (2021–2025) with responsibility for education and digital knowledge, and previously as head of the Department of Informatics until 2021. Her career spans leadership in externally funded projects, editorial roles in international journals, and active participation in national research policy, including co-founding the Norwegian Artificial Intelligence Research Consortium (NORA). Education: PhD in Informatics from the University of Bergen. Research Interests: Algorithms for graph classes, computational complexity, parameterized algorithms, and combinatorial optimization. Collaborations: Extensive partnerships with academia, businesses, and public administration, including advisory roles in the Bergen Chamber of Commerce and the Norwegian Cognitive Center . Grants: Project leader for four Research Council of Norway FRIPRO grants (CLASSIS, SCOPE, MIST, and earlier projects) and participant in EU, Trond Mohn Foundation, and National Security Authority grants. Students: Supervised Paloma T. Lima (PhD, 2019) and co-supervised numerous advisees through collaborative projects. Labs/Teams: Coordinated interdisciplinary ICT research and education initiatives at UiB, with international collaborations in the USA, France, and Turkey.

• Algorithmic Game Theory
• Market Equilibrium Computation
• Fair Division
• +5 more

Ruta Mehta is an Associate Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign . Since 2016 she has led a vibrant research program in algorithmic game theory, market design, and fair division, while actively shaping the community through service roles such as Program Co-Chair of WINE 2020 and Area Chair of EC 2021. Education Ph.D. in Computer Science & Engineering, Indian Institute of Technology Bombay, 2012. (Advisors: Prof. Milind Sohoni & Prof. Bharat Adsul; ACM India Doctoral Dissertation Award 2012) M.Tech. in Computer Science & Engineering, Indian Institute of Technology Bombay, 2005 B.E. in Computer Engineering, Maharaja Sayajirao University (MSU) Baroda, 2003 Research Interests Mehta’s work lies at the intersection of theoretical computer science , mathematical economics , and social choice theory . She investigates the computability and complexity of equilibria—both market and Nash—under a variety of utility models, and designs provably efficient algorithms that are practical for real-world resource-allocation tasks. Current themes include: Algorithmic Game Theory: equilibrium computation, smoothed analysis, learning in games Fair Division: envy-freeness up to any good (EFX), mixed manna, competitive equilibrium with chores Interdisciplinary Applications: genetic evolution, machine-learning markets, climate-aware allocation Publication Trends Her 15 most recent works (2017–2025) span Operations Research , Mathematics of Operations Research , STOC, SODA, EC, ITCS, AAMAS, and NeurIPS. The articles cluster around three thrusts: (i) rigorous hardness and approximation results for market equilibrium in Leontief and PLC exchange economies, (ii) algorithmic advances toward guaranteed EFX allocations and competitive equilibrium with mixed manna, and (iii) novel game-theoretic analyses of genetic diversity and strategic resource allocation under budget constraints. Scientific Awards & Honors NSF CAREER Award (2018) Outstanding Post-Doctoral Researcher Award, Georgia Tech (2014) Rising Stars in EECS (2013) ACM India Doctoral Dissertation Award (2012) Google India Anita Borg Memorial Scholarship (2012) IBM PhD Award (2010) IBM PhD Fellowship (2009–2010) Grants, Advising, and Community Leadership Mehta currently mentors a growing group of graduate students and post-docs. She is PI on an NSF CAREER grant and has served on federal panels reviewing NSF CISE proposals. Beyond research, she founded the EC (AGT) Mentoring Workshop , co-located with the ACM Economics & Computation conference, to broaden participation of women and under-represented minorities in algorithmic game theory. Labs & Teams Her research group operates within the Theory & Algorithms cluster at UIUC, leveraging ties with the Decision & Control group and the Social & Algorithmic Thinking initiative. She is an active member of ACM SIGecom and regularly organizes reading groups on algorithmic game theory and fair division.

Pontus EkbergView profile

Associate Professor

• Algorithms
• Real-Time Scheduling Theory
• Computational Complexity
• +3 more

Pontus Ekberg is an Associate Professor at Uppsala University's Department of Information Technology. His email address is pontus.ekberg@it.uu.se , and he can be reached at +46 18 471 73 41. He is affiliated with the Division of Computer Systems and holds the academic merit 'Docent' in real-time scheduling theory. Ekberg's research focuses on algorithms and computational problems in real-time scheduling theory. His work addresses NP-hardness in scheduling, fixed-priority algorithms, and formal verification of task feasibility. Current projects include applying pseudo-polynomial time analysis, combating butterfly attacks, and integrating deep learning for schedulability verification in safety-critical systems. His recent publications span topics like pseudo-polynomial time analysis (2025), optimistic period predictions (2024), and explainability in real-time schedulability (2023). He collaborates frequently with Sanjoy Baruah and others on uniprocessor and multiprocessor scheduling models. Although no formal awards are listed, his contributions to real-time scheduling theory are evident through 15+ publications in top conferences like RTSS and ECRTS.

Davide GuccioneView profile

Researcher

• Rock Mechanics
• Geomechanics
• Photogrammetry
• +2 more

Davide Guccione is an ARC Industry Fellow at the University of Newcastle within the College of Engineering, Science and Environment . His research focuses on rock mechanics , rockfall analysis , and photogrammetry , with significant contributions to understanding rock fragmentation dynamics and improving monitoring systems. Education : PhD in Civil Engineering (University of Newcastle, 2021), Master of Civil Engineering (University of Parma), Bachelor of Civil Engineering (University of Parma), and Diploma in Mining and Geoenvironmental Qualified Industrial Technician. Research Interests span experimental rock mechanics, rockfall hazard assessment, and photogrammetry. His work includes developing a novel VoxFall algorithm for volumetric rockfall detection and a stochastic fragmentation model for rockfall simulations. Current projects involve coastal cliff monitoring and deep learning-based photogrammetric calibration. Scientific Awards include: D.H. Trollope Medal (2023) Early Career Researcher Excellence Award - Highly Commended (2023) NSW Young Geomechanical Professionals Award 1st Runner-Up (2024) Australian Geomechanics Society Postgraduate Research Prize (2019) Grants and Funding highlight a $496,614 ARC Early Career Industry Fellowship and a $300,000 Newcastle City Council grant for coastal cliff monitoring. He supervises three PhD students and collaborates with institutions like Rocscience and the European Commission.

• Machine Learning
• Online Learning
• Algorithmic Game Theory
• +4 more

Andrea Celli is an Assistant Professor in the Department of Computing Sciences at Bocconi University. He is affiliated with the ELLIS Society and the Bocconi Institute for Data Science and Analytics. Previously, he was a postdoctoral researcher at Meta (formerly Facebook Research) in London. He holds a Ph.D. in Computer Science from Politecnico di Milano and conducted research at Carnegie Mellon University's Electronic Marketplaces Lab. His research focuses on intersections of computer science, machine learning, and economics, particularly in strategic interactions and online learning environments. Notable achievements include a NeurIPS 2020 Best Paper Award and the 2017 Lesmo Prize for his MSc thesis. His research is supported by grants including an ERC Starting Grant (2024) for the PLA-STEER project and an MUR-PRIN grant (2022). He teaches courses such as Deep Learning and Reinforcement Learning, Machine Learning, and Optimization at the Ph.D. level. His work spans topics like equilibrium finding, online learning under constraints, and mechanism design. Key publications include contributions to ICLR, NeurIPS, EC, and other top venues. Celli advises Master’s students including Annalisa Barbara, Emanuele Coccia, Davide Drago, and Antonio Preiti. He collaborates with postdocs like Riccardo Poiani and Martino Bernasconi. He actively organizes academic events, such as the EC 2025 workshop on Online Learning and Economics. His research emphasizes theoretical foundations while addressing practical challenges in algorithmic game theory and data-driven decision-making.