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Shahin ShahrampourView profile

Assistant Professor

• Machine Learning
• Optimization and Control
• Distributed Learning
• +5 more

Shahin Shahrampour is an Assistant Professor in the Department of Mechanical & Industrial Engineering at Northeastern University, with a courtesy appointment in Electrical & Computer Engineering. He holds a Ph.D. in Electrical & Systems Engineering from the University of Pennsylvania, an M.A. in Statistics from The Wharton School, and a B.S. in Electrical Engineering from Sharif University of Technology. Prior to Northeastern, he was at Texas A&M University and held a postdoctoral fellowship at Harvard University. Research Interests: Machine learning, optimization and control, distributed and sequential learning, with a focus on computationally efficient methods for data analytics. Specific areas include manifold optimization, online and reinforcement learning, non-convex optimization, and statistical signal processing. His work bridges theory and applications in networked systems and dynamic environments. Awards & Grants: Received a $500,000 NSF grant (2023) for distributed optimization in non-convex environments. Best Paper Award at IEEE ICASSP 2022 for contributions to signal processing. TEES Engineering Genesis Award (2020) for multidisciplinary research at Texas A&M. Principal Investigator on NSF-funded projects in collaborative online optimization and decentralized learning. Labs & Teams: Leads research in machine learning and control systems at Northeastern University, focusing on interdisciplinary challenges in distributed optimization and real-time learning. His lab emphasizes theoretical foundations and practical implementations in networked and dynamic systems.

Gareth PetersView profile

Professor

• Actuarial Science
• Statistical Risk Modeling
• Financial Engineering
• +5 more

Professor Gareth Peters is the Janet & Ian Duncan Endowed Chair of Actuarial Science and holds the Chair in Statistics for Risk and Insurance at the Department of Statistics & Applied Probability, University of California Santa Barbara (UCSB). His research focuses on statistical solutions for risk and insurance, with emphasis on actuarial science, financial engineering, environmental data analysis, and cybersecurity risk. He has received notable recognition including the 2024 IFoA Peter-Clark Prize, the oldest actuarial award established in 1891. His academic contributions span advanced statistical modeling techniques for financial markets, climate-related risk analysis, and healthcare analytics. Notable projects include developing methodologies for green bond valuation, cyber insurance frameworks, and machine learning applications in audiology and speech disorders. He has pioneered software tools such as CovRegpy for covariance regression and DivFolio for portfolio divestment analysis. Educations: Advanced degrees in statistics and actuarial science (specific details not provided) Professional Memberships: Fellow of multiple academic societies including the Royal Statistical Society and Institute of Mathematics and its Applications Research interests include: Statistical causality in financial and environmental time series Mortality modeling incorporating long-memory processes Quantifying climate change impacts on energy consumption Machine learning for cybersecurity risk mitigation His recent work emphasizes interdisciplinary applications of statistics in climate finance, sustainable investing, and precision agriculture. Over 2020-2025, his publications explore topics ranging from municipal green bonds to vocal analysis for Parkinson’s diagnostics. Awards: 2024 IFoA Peter-Clark Prize Elected Member of ISI (International Statistical Institute) IEEE Senior Member Labs/Initiatives: Develops open-source software packages for statistical analysis (e.g., AdvEMDpy, PyKronecker) and contributes to regulatory frameworks via tools like AuditChain for blockchain-based trading audits.

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.

Yan LiView profile

Assistant Professor

• Dynamic Optimization
• Robust Optimization
• Stochastic Optimization
• +5 more

Dr. Yan Li is an Assistant Professor in the Department of Industrial and Systems Engineering at Texas A&M University. He earned his Ph.D. in Operations Research from Georgia Institute of Technology (2024), advised by George Lan and co-advised by Tuo Zhao, with collaborations involving Anton Kleywegt and Alexander Shapiro. His research focuses on the computational perspective of learning optimal decisions from data, emphasizing first-order methods and their applications in data science. B.S. in Mathematics and Applied Mathematics & B.B.A. in Accounting, Nankai University (2016) M.S. in Statistics, Georgia Institute of Technology (2018) Ph.D. in Operations Research, Georgia Institute of Technology (2024) Dr. Li’s research interests span dynamic optimization (Markov decision processes, reinforcement learning), robust/risk-averse optimization, minimax optimization, and optimization for machine learning. His work addresses computation- and sample-efficient algorithms, distributional ambiguity in dynamic optimization, and gradient-based methods for ML applications. Industry collaborations include Meta (ads ranking optimization) and Bytedance (deep retrieval models), with ongoing projects in urban transportation and recommendation systems. His publications include 15 recent articles on policy optimization, stochastic minimax methods, and implicit regularization in reinforcement learning. Awards include the Alice and John Jarvis Ph.D. Student Research Award, The Margaret and Stephen Kendrick Research Excellence Award, and the ISyE Outstanding Graduate Student Instructor Award. Teaching accolades highlight his clarity, passion, and accessibility in courses like ISEN 310 (Uncertainty Modeling) and ISEN 320 (Operations Research I). Peer reviewer for journals like Mathematical Programming and conferences including ICML and NeurIPS Industry partnerships with Ford (multi-agent RL) and Meta (embedding learning) Active in optimization communities (INFORMS, MOPTA)

Seo Eun YangView profile

Assistant Professor

• Political Communication
• Political Psychology
• Computational Social Science
• +8 more

Seo Eun "Sunny" Yang is an Assistant Professor of Political Science and Communication Studies at Northeastern University, affiliated with the College of Social Sciences and Humanities and the College of Arts, Media and Design. Her research integrates graph/network theory, machine learning, NLP, and computer vision to study visual politics, political communication, and neuroscience. She holds a PhD in Political Science from The Ohio State University (2022), and degrees in Statistics from Korea University (BE 2013, MS 2015) and a Master's in Political Science from the University of Rochester (2017). Education: PhD in Political Science, Ohio State University, 2022 MS in Political Science, University of Rochester, 2017 MS in Statistics, Korea University, 2015 BE in Statistics, Korea University, 2013 Research Interests: Explores how media, politicians, and institutions use visual/verbal strategies to shape political meaning. Central questions include neural mechanisms of visual interpretation, multimodal framing analysis, and biased media representation of minorities. Uses computational methods like neural networks and computer vision to analyze photojournalism and legislative politics. Awards & Grants: Network Science Institute Seed Grant ($10,000) 2023 Transforming Interdisciplinary Experiential Research Grant ($50,000) 2023 NULab Seedling Grant ($2,500) 2022 Advising & Labs: Leads projects on neuro-racial bias in political evaluations and AI-driven analysis of media narratives. Office hours: Fridays 9-10am.

Sven ScheweView profile

Professor

• Automata Theory
• Game Theory
• Formal Verification
• +12 more

Sven Schewe is a Professor in the Department of Computer Science at the University of Liverpool, affiliated with the School of Electrical Engineering, Electronics and Computer Science. He leads the AI Section and is a founding member and former leader of the Verification Group. He also has secondary affiliations with the Algorithms, Complexity Theory and Optimisation Group and the Institute for Risk and Uncertainty. Research Interests: His research centers on automata theory and game theory, particularly their applications in the verification and synthesis of reactive and safety-critical systems. He investigates infinite-duration games, automata over infinite words and trees, and develops algorithms and tools for automated verification, synthesis, and learning of optimal control strategies. His work extends to reinforcement learning with formal guarantees, cyber-physical systems, and AI safety. Recent Research Trends: His recent publications demonstrate a strong integration of formal methods with machine learning, particularly in adversarial training, neural network robustness, and model-free reinforcement learning under omega-regular objectives. He also applies formal reasoning to interdisciplinary domains such as chemical space exploration and materials science. Scientific Awards: Finalist for the ERCIM Cor Baayen Award 2010 Dr. Eduard Martin Preis 2009 GI Dissertation Award 2008 Advising and Grants: He actively supervises numerous PhD students and postdoctoral researchers. He is Principal Investigator (PI) or Co-Investigator (CI) on multiple major grants, including EPSRC Programme Grants, Royal Society Fellowships, and Horizon Europe projects. His funded research spans topics such as game theory, verification, synthesis, reinforcement learning, and risk analysis. He has hosted visiting researchers and collaborated internationally with institutions in Germany, France, India, Taiwan, and the US. Labs and Teams: He co-founded and led the Verification Group and previously led the AI Section at the University of Liverpool. These groups focus on formal methods, automata, games, and their applications in AI and safety-critical systems.

• Stochastic Simulation
• Optimization
• Machine Learning
• +5 more

Giulia Pedrielli is an Associate Professor at Arizona State University's School of Computing and Augmented Intelligence, with affiliations as a Senior Global Futures Scientist. She holds a Ph.D., M.Sc., and B.S. in Mechanical and Industrial Engineering from Politecnico di Milano, Italy. Her research focuses on stochastic simulation, optimization, and machine learning applications in biomanufacturing, digital twins, and cyber-physical systems. She has contributed to frameworks for pandemic modeling (PySIRTEM), RNA design, and high-dimensional optimization. Pedrielli has collaborated with institutions like NUS and UC Berkeley, and her work spans biomedical engineering, supply chain resilience, and security of critical infrastructure. Recent articles emphasize co-simulation tools, AI-driven logic translation, and falsification methods for CPS security. Her teaching includes advanced stochastic simulation courses and thesis supervision across multiple disciplines. Education: Ph.D. Mechanical Engineering, Politecnico di Milano, Italy M.Sc. Industrial Engineering, Politecnico di Milano, Italy B.S. Industrial Engineering, Politecnico di Milano, Italy Research interests integrate simulation-based optimization with machine learning, particularly in safety-critical systems like healthcare and smart infrastructure. She develops digital twin methodologies for real-time control under uncertainty, leveraging Gaussian processes and Bayesian approaches. Pedrielli's work bridges theoretical advancements (e.g., BASSO optimization algorithm) with practical applications in biomanufacturing and pandemic response.

Irena VodenskaView profile

Professor

• Network Theory in Finance
• Systemic Risk Modeling
• Complexity Science in Macroeconomics
• +12 more

Irena Vodenska is Professor of Finance and Director of Finance Programs at Boston University’s Metropolitan College, Department of Administrative Sciences. She holds a PhD in statistical finance and an MA in economics from Boston University, an MBA from Vanderbilt University, and a BS in computer information systems from the University of Belgrade. She is also a Chartered Financial Analyst (CFA) charter holder. Her research is at the intersection of finance, complexity science, and artificial intelligence, focusing on systemic risk modeling, ESG investments, and financial network dynamics. She has led major interdisciplinary research projects funded by the National Science Foundation, the European Commission, and the U.S. Army Research Office. PhD, Statistical Finance – Boston University MA, Economics – Boston University MBA – Owen Graduate School of Management, Vanderbilt University BS, Computer Information Systems – University of Belgrade Dr. Vodenska’s research interests include network theory in finance, systemic risk propagation, AI-powered ESG analysis, cryptocurrency price forecasting, and financial regulation. She employs big data, machine learning, and natural language processing to analyze financial news, market dynamics, and corporate sustainability. Her work investigates how climate disinformation spreads via social networks and influences public policy and governance. The recent articles highlight a consistent focus on modeling financial and economic systems using network science and AI. Trends include systemic stress testing, sentiment analysis in financial markets, cascading failures, and the interplay between macroeconomic indicators and financial networks. Her work spans econophysics, behavioral finance, public health economics, and ethical AI in fintech. National Science Foundation (NSF) research grant (2023) NSF EAGER Award (2014–2015) European Commission FET Open Grant (2012–2014) U.S. Army Research Office (ARO) Grant (2020–2021) MEXT Post-K Computer Grant, Japan (2016–2019) Alexander Hamilton Fulbright Fellowship (1994) Owen Graduate School Fellowship (1995–1996) Dr. Vodenska teaches core finance courses such as Investment Analysis and Portfolio Management, Derivatives Securities, and Financial Regulation and Ethics. She co-developed the MET AD 678 course with Professor Tamar Frankel from BU Law, emphasizing real-world case studies and ethical decision-making. Her research grants have supported innovative work in systemic risk modeling, AI for ESG, and financial network stability. She is actively involved in mentoring, conference organization, and editorial roles in leading journals. She is a key organizer of the International School and Conference on Network Science (NetSci) and the Big Data in Economics, Science, and Technology (BEST) Conference. Her lab and research team focus on complexity in financial systems, bringing together economists, physicists, computer scientists, and data analysts to study global financial stability and sustainability.

• Computer Vision
• Pattern Recognition
• Facial Analysis
• +5 more

Jorge Manuel M. C. Pereira Batista is an Associate Professor at the Department of Electrical and Computer Engineering , University of Coimbra, Portugal. He serves as a senior researcher at the Institute for Systems and Robotics (ISR-UC) and leads the Computer Vision Group. His career spans academic roles, research coordination, and industry collaboration. Academic Affiliation: University of Coimbra (Electrical & Computer Engineering Department) Research Institute: Institute for Systems and Robotics (ISR-UC) His research focuses on Computer Vision , Pattern Recognition , and applications of Differential Geometry in these fields. Key subdomains include facial analysis, visual surveillance, real-time vision systems, and machine learning integration. From 2010–2023, his recent publications highlight advancements in probabilistic models , transformer architectures , multi-branch learning , and biomimetic robotics . He has coordinated multiple funded projects such as: STORK : Avian protection systems NeuroCity : Intelligent street lighting 4D Facial Dynamics : Identity recognition iTRAFFIC : BRISA highway traffic monitoring His work bridges theoretical innovation (e.g., Riemannian manifold applications) with practical deployments in transportation, energy, and healthcare sectors.

Shunqiao SunView profile

Assistant Professor

• Statistical Signal Processing
• Sparse Signal Processing
• Mathematical Optimization
• +7 more

Dr. Shunqiao Sun is an Assistant Professor in the Department of Electrical and Computer Engineering at The University of Alabama, College of Engineering. He joined the faculty in August 2019 as a tenure-track professor after working at Aptiv’s radar core team in Malibu, California. His research focuses on advanced signal processing, machine learning, and optimization for automotive and MIMO radar systems in autonomous vehicles. Ph.D. : Electrical and Computer Engineering, Rutgers University, 2016 M.S. : Electrical Engineering, Fudan University, 2011 B.S. : Electrical Engineering, Southern Yangtze University, 2004 Dr. Sun's research lies at the intersection of statistical and sparse signal processing , mathematical optimization , and machine learning , with applications in automotive radar , MIMO radar , and autonomous driving . His work emphasizes sparsity-oriented frameworks, AI-powered radar perception, and high-resolution 4D sensing. He leads a dynamic research group focused on next-generation radar technologies for intelligent transportation systems. His recent publications demonstrate a strong trend in deep learning for radar signal recovery , collaborative radar imaging , direction-of-arrival estimation with sparse arrays , and integrated sensing and communication . Several of his papers are among the most downloaded and cited in IEEE journals, including top articles in IEEE Signal Processing Magazine and IEEE Journal of Selected Topics in Signal Processing. Scientific Awards and Honors: NSF CAREER Award (2024) NSF CRII Award (2022) IEEE AESS Robert T. Hill Best Dissertation Award (2016) Best Student Paper Award at IEEE SAM Workshop (2020) Rutgers ECE Academic Achievement Award (2015–2016) University of Alabama Hewson Engineering Faculty Fellow (2025) Dr. Sun is actively involved in academic service and leadership. He is an Associate Editor for IEEE Signal Processing Letters and IEEE Open Journal of Signal Processing . He serves as Vice Chair of the IEEE Signal Processing Society’s Autonomous Systems Initiative and is an elected member of the IEEE Sensor Array and Multichannel (SAM) Technical Committee and the Integrated Sensing and Communication (ISAC) Technical Working Group. He has co-organized numerous workshops and special sessions at ICASSP, EUSIPCO, and IEEE Radar Conference. His lab has secured significant research funding from the National Science Foundation , NXP Semiconductors , MathWorks , and NOAA . He mentors multiple Ph.D. students, several of whom have interned at leading industry labs such as NXP and GM Cruise. He has co-organized the Workshop on Signal Processing for Autonomous Systems (SPAS) at ICASSP and EUSIPCO and delivered invited seminars at institutions including TU Delft, UC Davis, and Lehigh University.

Rolf FindeisenView profile

Professor

• Model Predictive Control (MPC)
• Cyber-Physical Systems
• Battery Systems Optimization
• +5 more

Prof. Rolf Findeisen is a Professor in the Department of Control and Cyber-Physical Systems (CCPS) at Technische Universität Darmstadt. His work focuses on advancing control theory and its applications in cyber-physical systems, autonomous systems, and energy storage systems. Key areas include model predictive control (MPC), battery management systems, machine learning integration into control frameworks, and optimization of crystallization processes. He leads research on safety-critical systems, data-driven control methods, and interdisciplinary applications in robotics and biotechnology. His research spans theoretical advancements in MPC stability, stochastic control, and Gaussian process modeling, alongside practical implementations in autonomous vehicles, lithium-ion battery systems, and bioprocess optimization. Notable contributions include frameworks like HILO-MPC for integrating machine learning with control systems, and methodologies for safe exploration in autonomous navigation. Prof. Findeisen's publications emphasize energy-efficient trajectory planning, fault detection in battery systems, and real-time optimization of manufacturing processes. His work bridges academic theory with industrial applications, addressing challenges in scalability, safety, and computational efficiency. His lab collaborates on national projects like IN-Fly-Tec and INFLIGHT, focusing on innovative flight control systems and sensor technologies. Current research trends include hybrid intelligent optimization, cybergenetic control of microbial systems, and safe reinforcement learning for control systems.

David TodemView profile

Professor

• Biostatistics
• Epidemiology
• Longitudinal Data Analysis
• +6 more

David Todem is a Professor in the Department of Epidemiology and Biostatistics at Michigan State University. His work focuses on statistical methodology for longitudinal and clustered data, particularly in applications to cancer research and public health. He holds a Ph.D. in Statistics from the University of Wisconsin-Madison, an M.S. in Biostatistics from Hasselt University (Belgium), and an M.S. in Population Health from the University of Wisconsin-Madison. Dr. Todem's research interests include joint models for time-to-event and longitudinal outcomes, non-identifiable models with informative nonresponse, and testing heterogeneity in mixture models. His applied work addresses critical questions in cancer treatment efficacy, maternal-child health, and chronic disease surveillance. He has developed innovative methods for analyzing quality-of-life outcomes in cancer patients and spatial epidemiology of co-infections. Key contributions include the Validation of geospatial health indices Advancements in zero-inflated model testing Development of nonparametric scanning techniques Statistical frameworks for pediatric intervention evaluation He received a prestigious National Cancer Institute K01 award and has collaborated on federally-funded programs improving cardiovascular care in underserved populations. His work spans multiple domains including maternal-fetal medicine, environmental health, and global health equity. Methodological strengths include: Handling non-random missing data High-dimensional feature selection Weakly identifiable model sensitivity analysis Current research emphasizes translating statistical innovations into clinical practice through partnerships with healthcare systems and public health agencies.

Qiyang HanView profile

Associate Professor

• Mathematical Statistics
• High Dimensional Probability
• Empirical Process Theory
• +7 more

Qiyang Han is an Associate Professor in the Department of Statistics at Rutgers, The State University of New Jersey, within the School of Arts and Sciences. His research lies at the theoretical interface of statistics, probability, and algorithms, with a strong focus on foundational and high-dimensional problems. His research interests include mathematical statistics , high-dimensional probability , empirical process theory , nonparametric and shape-restricted inference , and Bayesian nonparametrics . He also investigates high-dimensional statistics , convex optimization , and large-scale iterative algorithms , with increasing attention to gradient descent dynamics and message passing methods. His recent publications reveal a consistent focus on precise asymptotic analysis, universality phenomena, and inference under geometric constraints. Themes across his work include robustness, adaptivity, and the theoretical underpinnings of modern statistical learning in overparameterized regimes. Ph.D. in Statistics, University of Washington, 2018 Supervised by Professor Jon A. Wellner He has collaborated with prominent researchers such as C.-H. Zhang, Bodhisattva Sen, Kengo Kato, and Richard J. Samworth. His work is published in top journals including The Annals of Statistics , Journal of the Royal Statistical Society Series B , and IEEE Transactions on Information Theory . Although no formal advising list is provided, his collaborative output suggests active mentorship and research leadership. He is involved in theoretical research with implications for machine learning, signal processing, and statistical inference. His lab or research group focuses on developing rigorous mathematical frameworks for understanding complex statistical algorithms and models, particularly in high-dimensional settings.

Martin Wulf GerdesView profile

Associate Professor

• eHealth
• Artificial Intelligence
• Machine Learning
• +9 more

Martin Wulf Gerdes is an Associate Professor at the Department of Information and Communication Technology, University of Agder, Norway. With a Ph.D. in eHealth and over two decades of experience in telecommunications and healthcare technology research, he focuses on developing user-friendly, interoperable eHealth solutions using ICT and IoT. His career spans roles at Ericsson Eurolab and academic leadership in Norway. Education: M.Sc. equivalent in General Electrical Engineering (1998) from RWTH Aachen, emphasizing ICT. Ph.D.: Information and Communication Technologies with eHealth specialization (2019) from University of Agder. Research Interests: Gerdes explores AI-driven decision support systems, distributed eHealth infrastructure, telemedicine, and assistive technologies for chronic disease management. His work bridges IoT, data privacy, and user-centered design in healthcare robotics. Teaching: Since 2014, he has taught at bachelor's and master's levels, currently leading courses in Communication & Cooperation, Technology Understanding, and seminar projects in eHealth. Scientific Publications: His 15 most recent articles (2020-2024) span AI in dental radiology, blood glucose prediction, digital twins, and ethical considerations in healthcare algorithms. Topics include IoT integration, data privacy, and pandemic analytics.

• Signal Processing
• Machine Learning
• Neural Networks
• +3 more

Professor Danilo P. Mandic, affiliated with Imperial College London, UK, is a leading researcher in signal processing, machine learning, and biomedical signal analysis. His work spans quaternion algebra, tensor networks, and neural networks for real-world applications. 2025: Published 11+ works on EEG/PPG analysis, quantum learning, and tensor-based LLM compression 2024: Active in interpretable transformers, graph learning for financial data, and hearable devices Research focuses on hypercomplex signal processing, graph neural networks, and medical AI applications. Recent work explores quaternion calculus for signal processing, tensor network structures for LLMs, and hearable device optimization. Key publication trends include: 2025 emphasis on quantum-aware learning, 2024 graph-based time series clustering, and 2023 foundational work on graph CNNs and matched filtering approaches. Collaborates extensively with Dongpo Xu, Sayed Pouria Talebi, Clive Cheong Took, and Tobias Reichenbach on projects involving ear-EEG, ECG enhancement, and financial sentiment analysis.