Sarath Chandar is an Associate Professor at Polytechnique Montréal and Core Faculty Member at Mila, the Quebec AI Institute. He holds a Canada CIFAR AI Chair and Canada Research Chair in Lifelong Machine Learning. His research focuses on developing interactive learning algorithms for continual and lifelong learning, with expertise in deep learning, reinforcement learning, and natural language processing. Education: Ph.D. in Computer Science, University of Montreal (advisor: Yoshua Bengio) M.S. in Computer Science, Indian Institute of Technology Madras (advisor: Balaraman Ravindran) Research Themes: Continual Learning and Lifelong Learning Deep Reinforcement Learning Optimization for Deep Networks Natural Language Processing AI for Scientific Discovery Notable Contributions: Founder of the Conference on Lifelong Learning Agents (CoLLAs) Developed Chandar Research Lab (CRL), focusing on adaptive learning algorithms Contributions to model-based reinforcement learning and bias mitigation in AI systems Awards & Grants: Canada CIFAR AI Chair Canada Research Chair Tier 2 MITACS-funded projects on reinforcement learning applications Lab & Collaboration: CRL collaborates with academic/industrial partners (e.g., IBM, Samsung) Hosts annual symposium showcasing research in AI, optimization, and multi-agent systems
Arya Mazumdar is a tenured Professor of Data Science and Computer Science at the Halıcıoğlu Data Science Institute (HDSI) , part of the School of Computing, Information and Data Sciences at the University of California San Diego (UCSD). He also holds affiliations with the Computer Science and Engineering and Electrical and Computer Engineering departments at UCSD. Previously, he was an Assistant and Associate Professor at the University of Massachusetts Amherst (2015–2021), and held a postdoctoral position at MIT (2011–2012). He is an IEEE Distinguished Lecturer (2023–2024) and recipient of the NSF CAREER Award (2015–2020). Education : PhD in 2011 from the University of Maryland, College Park (advisor: Alexander Barg) Postdoctoral scholar at MIT (2011–2012, advisor: Greg Wornell) Internships at IBM Almaden (2010) and HP Labs (2008) Research Interests : Focus on algorithmic and statistical aspects of machine learning, error-correcting codes, optimization, signal processing, and distributed systems. Key areas include clustering algorithms, compressed sensing, federated learning, and information-theoretic foundations of data science. He has contributed to theoretical guarantees for learning mixtures, distributed optimization, and robust coding schemes. Recent Articles Trends : Recent work emphasizes distributed optimization (e.g., vqSGD, Byzantine-resilient algorithms), sparse recovery in high-dimensional models, and theoretical foundations of learning (e.g., support recovery, parameter estimation). His research bridges information theory and machine learning, with applications in storage systems and large-scale data processing. Awards & Roles : 2020 EURASIP JASP Best Paper Award Co-PI and co-leader of the NSF AI Institute for Learning-Enabled Optimization at Scale Editorships: IEEE Transactions on Information Theory and Foundations and Trends in Communications Grants & Labs : Leads the EnCORE Institute as UCSD Site Lead, focusing on theoretical perspectives of large language models and computational-statistical gaps. Active in organizing workshops on topics like LLMs, clustering, and distributed optimization. His research is funded by NSF and industry collaborations. Teaching : Courses include Algorithms for Data Science , Probability and Statistics for Data Science , and Coding Theory , emphasizing foundational theory and scalable methods.
John Dalsgaard Sørensen is a Professor and Head of Research Group at the Department of the Built Environment, Aalborg University, within the Faculty of Engineering and Science. He leads the Risk, Resilience, Safety, and Sustainability of Systems Research Group and is affiliated with the Danish Centre for Risk and Safety Management. His research focuses on structural safety, wind turbine reliability, probabilistic design, and risk assessment of infrastructure systems. He has supervised 13 PhD students and contributed to over 600 publications. Key research areas include wind turbine structural integrity, fatigue analysis of offshore and onshore structures, probabilistic design standards (e.g., Eurocodes), and risk-based decision-making for infrastructure. He leads projects like Windscanner (remote sensing for wind measurements) and MANTIS (cyber-physical maintenance systems). Collaborations span academia and industry, addressing challenges in energy systems, civil infrastructure, and safety engineering. His work emphasizes practical applications of advanced modeling techniques, such as Bayesian networks and stochastic simulations, to enhance reliability and reduce operational costs. He is actively involved in standardization efforts for structural design and serves on boards like Energi- og MiljøData Fonden. Recent activities include presenting at international conferences and advising on media debates related to structural safety.
Nicole Megow is a Professor holding the chair for Combinatorial Optimization in the Faculty of Mathematics and Computer Science at the University of Bremen since 2016. She is affiliated with several research clusters including Humans on Mars Initiative, Minds, Media, Machines, and Dynamics in Logistics. Her academic journey includes positions at TU Berlin, Max Planck Institute for Informatics, TU Darmstadt, and TU Munich. Professor Megow's research focuses on mathematical optimization, algorithm design and analysis, and operations research. Her specific interests span combinatorial and discrete optimization, efficient algorithms, scheduling theory, resource allocation, packing problems, network design, routing, and uncertainty models including online, stochastic, robust, and explorable approaches. Her work bridges theoretical foundations with practical applications in logistics and decision-making systems. Her recent publications demonstrate a strong trend toward integrating prediction models with traditional optimization frameworks, particularly in scheduling and matching problems. She has made significant contributions to understanding the role of uncertainty in optimization problems, developing algorithms that work effectively with incomplete or uncertain information. Her work spans multiple prestigious venues including Mathematical Programming, Algorithmica, SODA, STACS, and NeurIPS. Dissertation Award by the German Operations Research Society (2007) Berlin Science Award for Young Researchers (2013) Heinz Maier-Leibnitz Prize (2013) Listed among Germany's top 40 researchers below 40 (Capital, 2014, 2015) Professor Megow actively supervises PhD students and postdocs, including Max Stahlberg, Joes Biburger, Sarah Morell, Bart Zondervan, Zhenwei Liu, and Alexander Lindermayr. She serves on numerous program committees for major conferences including SODA, IPCO, and STOC, and holds editorial positions for several prestigious journals. Her current research projects include Optimization under Explorable Uncertainty (DFG funded), How robots learn how to use structure (seed grant from MMM research cluster), and Scheduling Invasive Multicore Programs Under Uncertainty (within TCRC 89).
Wolf Ketter is a Full Professor of Next Generation Information Systems at the Department of Technology and Operations Management, Rotterdam School of Management, Erasmus University, and Chaired Professor of Information Systems at the University of Cologne. He serves as Director of the Institute of Energy Economics (EWI) in Cologne and leads the Erasmus Centre for Future Energy Business in Rotterdam. He is a leading figure in designing sustainable smart markets using advanced computing and simulation techniques. His research focuses on Information Systems , Machine Learning , Energy Economics , and Sustainable Smart Markets . He pioneered the use of Competitive Benchmarking through simulation platforms like Power TAC to tackle complex sustainability challenges. His work bridges computer science, economics, and business to design intelligent systems for energy, transportation, and resource allocation. The recent articles highlight a strong trend toward real-time decision-making in sustainable systems—such as electric bus operations, shared electric vehicles, traffic signal control via reinforcement learning, and local energy markets. These reflect his focus on AI-driven optimization , smart market design , and urban sustainability . Scientific Awards: INFORMS ISS Design Science Award (2012) Runner-up for Best European IS Research Paper (2013) ERIM Top Article Award (2013) ERIM Impact Award (2014) He has supervised over 10 PhD students and secured significant research impact through grants and collaborative projects. His editorial roles include serving on the boards of Information Systems Research and MIS Quarterly , the top journals in the IS field. He has chaired over 20 international conferences and workshops, advancing global discourse in trading agents and sustainable systems. Wolf Ketter founded and leads the Learning Agents Group at Erasmus University and chairs the annual Erasmus Energy Forum . His labs and teams focus on building simulation environments and AI agents to model and improve real-world sustainable markets, particularly in energy and mobility.
Andreas Grothey is a Senior Lecturer in the School of Mathematics at The University of Edinburgh, a position he has held since 2011. He completed his MSc in Numerical Algebra and Mathematical Computing at the University of Dundee (1995) and his PhD in Optimization at the University of Edinburgh (2001), supervised by Ken McKinnon. His research focuses on stochastic programming, interior point methods, decomposition approaches, high-performance computing, and energy systems optimization. He has contributed to energy planning, power grid reliability, and emergency response strategies for power networks. Grothey has advised seven PhD students, including work on unit commitment, top-percentile traffic routing, and power flow optimization. His projects include the OOPS solver, CESI energy integration center, and the Structured Modelling Language (SML). Recent work addresses pandemic policy optimization and exascale computational challenges. Education: MSc in Numerical Algebra and Mathematical Computing (University of Dundee, 1995) PhD in Optimization (University of Edinburgh, 2001) Research Interests: Stochastic Programming Interior Point Methods Decomposition Methods High-Performance Computing Energy Systems Optimization Advising & Projects: PhD Supervision (7 students, 2007–2022) OOPS Parallel Solver Development CESI Energy Systems Integration SML Structured Modelling Language Labs/Teams: Member of the Edinburgh Research Group on Optimization, leading projects in power grid stability and energy planning.
Glaucio H. Paulino holds the Margareta Engman Augustine Professorship in Civil and Environmental Engineering at Princeton University, where he also serves as a Professor at the Princeton Institute for the Science and Technology of Materials (PRISM). His work bridges computational mechanics, topology optimization, and materials science. Paulino leads a research group focused on advancing structural design methodologies, fracture mechanics, and functionally graded materials. His team has pioneered polygonal finite elements and multiresolution topology optimization techniques, addressing challenges in mesh bias and computational efficiency. He has published over 240 peer-reviewed articles and mentored 19 PhD and 11 MS students. Notable contributions include the PPR cohesive model for fracture analysis and adaptive mesh refinement for dynamic simulations. Paulino's research extends to practical applications such as high-rise building design and sustainable construction materials. Awards include election to the European Academy of Sciences and Arts and ASME’s Melville Medal. Current projects involve functionally graded cement-based materials, extrusion processing, and digital image correlation for material characterization. His lab collaborates with industry partners like Skidmore, Owings & Merrill LLP to translate topology optimization into real-world engineering solutions. Paulino’s interdisciplinary approach integrates computational modeling with experimental validation, fostering innovations in civil infrastructure resilience.
Niels Richard Hansen is a Professor at the Department of Mathematical Sciences , University of Copenhagen, leading research at the intersection of Artificial Intelligence and Statistics . He co-founded the Copenhagen Causality Lab and focuses on automating causal explanation discovery from data using Bayesian networks, stochastic processes, predictive models, and machine learning. His work emphasizes creating interpretable and robust AI systems capable of generalizing across domains. His research has produced over 56 publications spanning causal inference , graphical modeling , stochastic processes , and machine learning . Recent work includes: Predictive and causal learning (2018 keynote) High-dimensional regression solutions (2016 lecture) Interdisciplinary applications in actuarial science , environmental statistics , and neuroscience He actively contributes to scientific communication through media appearances and public explanations of statistical concepts, including analyses of: Gaussian correlation inequality proofs Daylight saving time and blood clots Mathematical approaches to lotteries Climate change vs lunar effects
Zhijian Huang is an Associate Professor in the Department of Finance and Accounting at Saunders College of Business, Rochester Institute of Technology, with expertise in corporate finance, behavioral finance, and risk management. Education: B.Eng., Shanghai Jiaotong University (China) M.S., Michigan State University M.Eng., Cornell University Ph.D., Pennsylvania State University His research focuses on financial markets, cognitive dissonance in investor behavior, cryptocurrency volatility, and climate policy impacts on stock prices. Recent publications explore asymmetric responses to earnings news, social media sentiment effects, and credit risk modeling. Huang teaches courses in equity analysis, options/futures, and risk management, with a strong emphasis on derivative instruments and portfolio optimization strategies.
Dr. Yongchao Huang is a Lecturer (Assistant Professor) in the School of Natural and Computing Sciences at the University of Aberdeen, where he has been employed since August 2023. He also holds affiliations with the University of Oxford and the University of Cambridge through past postdoctoral and collaborative roles. He is actively involved in research, teaching, and academic service, and is currently accepting PhD students. His educational background includes: DPhil in Engineering Science, University of Oxford (2013–2017) Additional training in Machine Learning at Oxford (2015–2019) Dr. Huang's research focuses on fundamental and physics-informed machine learning, with core interests in Bayesian inference, variational methods, generative modeling (especially score-based), reinforcement learning, and interdisciplinary AI applications in mechanics, biology, energy, climate, and finance. A central theme of his work is the inference and sampling of probability densities, particularly through innovative particle-based and physics-inspired computational frameworks. He founded the Computational and Physical Learning (CPL) lab at Aberdeen in 2023. His recent publications (2020–2025) reflect a strong trend in probabilistic machine learning, with increasing focus on physics-based inference methods such as electrostatics, fluid dynamics, and material point methods. These works bridge machine learning with applied mathematics and physical simulation, demonstrating a unique interdisciplinary approach. Topics span Bayesian neural networks, acoustic wave propagation, mortality modeling, and adversarial cybersecurity. Dr. Huang has received academic recognition through invitations to serve on program committees and editorial roles: Program Committee Member, ECAI 2024 Organizing Committee, Bioinference 2024 Guest Editor, Journal of Theoretical Biology Senior Scientific Advisor to a UK firm He has supervised 57 MSc theses independently and currently supervises one PhD student. He has secured research engagement through collaborations with institutions including Oxford, Cambridge, and industry partners. His teaching includes courses such as Introduction to Software Engineering , Software Process and Management , and Computational Intelligence at Aberdeen, as well as practicals in inference at Cambridge. Dr. Huang leads the Computational and Physical Learning (CPL) lab at the University of Aberdeen, a curiosity-driven research group focused on foundational advances in machine intelligence. Though currently a solo researcher due to limited resources, the lab emphasizes end-to-end research and open collaboration. He encourages student mobility and interdisciplinary exploration.
Isaac Gross is a Senior Lecturer in the Department of Economics at Monash University, Faculty of Business and Economics. He holds a PhD and is actively involved in research, teaching, and policy advisory roles. His work bridges academic theory and real-world economic policy, particularly in macroeconomic and monetary domains. His research interests center on macroeconomics , monetary policy , DSGE modeling , and commodity price dynamics . He employs advanced quantitative methods to analyze policy effectiveness and economic stability, with a regional focus on Australia and global commodity markets. The recent articles highlight a consistent focus on nonlinear modeling of macroeconomic systems, optimal policy design , and structural analysis of monetary and resource sectors . His work combines theoretical rigor with empirical validation, often using large-scale models like MARTIN for policy simulation. Scientific Awards: Best Paper at the Melbourne Institute Macroeconomic Policy Meeting (2018) Dean's Citations for Outstanding Contribution to Student Learning (2021) Advising and Grants: Isaac Gross served as the Primary Chief Investigator on the 2022 research project Estimating Optimal Policy Rules for Australian Monetary Policy with MARTIN . While formal student advising is not listed, his Dean’s Citation underscores significant contributions to student learning. He has also contributed to educational initiatives such as continuing education in macroeconometrics. Labs, Teams, and Collaborations: He collaborates with prominent economists including Andrew Leigh and J. Hansen. His work involves external engagement with key institutions such as the Reserve Bank of Australia and the Standing Committee on Economics, indicating integration into national policy networks.
Jens Krause is a Professor and Head of Department at the Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB) in Berlin, leading the Research Group on Mechanisms and Functions of Group-Living. He holds a full professorship in Fish Ecology at Humboldt-Universität zu Berlin, Faculty of Life Sciences, Thaer-Institute, and since 2018 has been an Adjunct Professor at Technical University Berlin within the Excellence Cluster 'Science of Intelligence'. His research is centered on collective intelligence, social networks, decision-making, and behavioural ecology in fish and other animals. Full Professor in Fish Ecology, Humboldt-Universität zu Berlin Adjunct Professor at Technical University Berlin (since 2018) Head of Department, IGB Berlin PhD, University of Cambridge Diploma, Free University Berlin His work integrates experimental biology, network analysis, and biomimetic robotics to understand how animals make collective decisions. His expertise spans animal behaviour, evolution, and ecological physiology, with a strong focus on group-living dynamics. Recent research explores group hunting, predator evasion, social foraging, and the impact of environmental stressors on collective behaviour. The analysis of his recent publications reveals a strong trend in understanding collective behaviour in fish, including escape waves, social foraging, group hunting in marlins and sailfish, and the use of robotic agents to study social integration. His interdisciplinary approach combines marine biology, physics, robotics, and data science to uncover the mechanisms behind collective intelligence in both animal and human systems. Editorial Board, Behavioral Ecology Editorial Board, Fish and Fisheries Executive Board, Excellence Cluster 'Science of Intelligence' Advisory Board, Bimini Biological Field Station Foundation He advises numerous PhD students and postdoctoral researchers, and leads major research projects, including 'Developing exploration behaviour' funded by the Excellence Cluster. His work has been supported by extensive collaborations across Europe and North America, and he frequently publishes in top-tier journals such as Nature , Science Advances , Proceedings of the Royal Society , and Current Biology . His lab employs cutting-edge methods including automated tracking, social network analysis, and interactive robotics to study animal groups. His research group, 'Mechanisms and Functions of Group-Living', is embedded within the Excellence Cluster 'Science of Intelligence', where they investigate collective cognition, social information use, and the role of individual differences in group performance. The team combines field studies with laboratory experiments and computational modelling to understand the evolution and function of collective behaviour across species.
Brian Ingalls is a Professor in the Department of Applied Mathematics and cross-appointed to Biology at the University of Waterloo. His research applies mathematical and control-theoretic approaches to biological systems, including genetic regulatory networks, microbial communities, and cellular metabolism. Institutional Affiliation: Faculty of Mathematics, University of Waterloo Contact: bingalls@uwaterloo.ca His work focuses on systems biology and synthetic biology , particularly sensitivity analysis of biochemical networks, optimal experimental design, and mathematical modeling of cellular processes. Research funding comes from NSERC and CIHR . Notable contributions include the textbook Mathematical Modeling in Systems Biology (MIT Press, 2013) and the Ingalls Quantitative Cell Biology Lab , which investigates intracellular and intercellular network dynamics through computational and experimental methods. Key Collaborations: iGEM Waterloo, Chemical Engineering, and international synthetic biology networks Advising: Mentored 15+ graduate students and postdocs across applied math, biology, and engineering fields
Kash Barker serves as the John A. Myers Professor and David L. Boren Professor at the University of Oklahoma in the Department of Industrial & Systems Engineering within the College of Engineering. As Graduate Liaison, he leads research on network resilience, supply chains, and systems engineering for societal good, with applications spanning infrastructure, supply chains, and community systems. His lab has produced 11 Ph.D. graduates (10 in academia) and 31 M.S. graduates. Research Domains: Resilient networks and interdependent systems Risk and decision analytics Supply chain survivability Pandemic economic impact modeling Climate migration optimization Cyber-Physical-Social Systems Article Trends emphasize disinformation defense , network restoration optimization , and multi-layer resilience modeling across infrastructure, supply chains, and community systems. His work combines game theory , machine learning , and decision analysis frameworks. Scientific Awards & Roles: Fellow, Institute of Industrial and Systems Engineers Senior Member, IEEE Fellow, Fulbright Finland Foundation (2023) Associate Editor roles in IISE Transactions and Naval Research Logistics Editorial Board Member for Risk Analysis and Scientific Reports Faculty Advisor, OU INFORMS student chapter Educational Background: Ph.D., Systems Engineering, University of Virginia M.S., Industrial Engineering, University of Oklahoma B.S., Industrial Engineering, University of Oklahoma
Lukas Seitner is a researcher at the Technical University of Munich (TUM), affiliated with the School of Computation, Information and Technology and the Department of Electrical Engineering. He operates within the Associate Professorship of Computational Photonics led by Prof. Christian Jirauschek, focusing on advanced modeling of quantum cascade devices and terahertz photonics systems. His research spans quantum cascade lasers (QCLs), terahertz frequency combs, optical solitons, and computational photonics. Seitner has developed sophisticated simulation frameworks including Maxwell-Bloch and density matrix approaches to study nonlinear dynamics in optoelectronic devices. Key contributions involve passive mode-locking mechanisms in THz QCLs, graphene-integrated saturable absorbers for pulse generation, and backscattering effects in ring-cavity soliton formation. His work bridges theoretical modeling with practical device engineering for next-generation terahertz sources. As an educator, Seitner serves as assistant lecturer for multiple courses including Computational Photonics Laboratory (5 PR), Partial Differential Equations for Electrical Engineering (4 VI), and Simulation of Quantum Devices (4 VI). He actively participates in doctoral candidate seminars and specialized courses on quantum engineering, demonstrating strong commitment to academic training in photonics and quantum device physics. His teaching integrates cutting-edge research concepts into practical computational exercises. Seitner maintains active collaboration within the EU Project QOMBS and contributes to TUM's Computational Photonics group research infrastructure. His technical expertise encompasses numerical methods for partial differential equations, semiconductor device simulation, and nonlinear optical modeling. Current projects focus on optimizing THz comb sources for spectroscopic applications and extending quantum walk models for novel frequency comb generation mechanisms.
