Find Academic Supervisors

• Reinforcement Learning
• Stochastic Dynamic Optimization
• Operations Research
• +7 more

Somayeh Moazeni is an Associate Professor at the School of Business, Stevens Institute of Technology. She holds a PhD in Computer Science from the University of Waterloo and has held academic appointments including Visiting Associate Professor at Northwestern University and Postdoctoral Research Associate at Princeton University. Her research focuses on Reinforcement Learning, Stochastic Dynamic Optimization, and applications in Energy Markets, Inventory Management, and Algorithmic Trading. She has authored over 30 peer-reviewed articles and serves as an associate editor for INFOR and PLOS One . Education: PhD (Computer Science, 2012), University of Waterloo; Postdoc (Operations Research, 2012-2014), Princeton University Industry Experience: Senior Risk Analyst at RBC (2011-2012), Risk Analyst at BMO (2010) Awards: IEEE Senior Member (2019), Anita Borg Institute GHC Faculty Scholar (2017), MITACS Poster Competition First Place (2009) Her research spans Bayesian Optimization , Resilient Network Design , and Energy Efficiency . Current funded projects include PSEG Foundation grants for energy resilience and NSF funding for distributed energy resource controls. She advises PhD students in Operations Research and Energy Systems and teaches graduate courses in Reinforcement Learning and Financial Engineering. Key Contributions: Developed stochastic optimization frameworks for energy storage, contact center reliability modeling, and risk-aware trading strategies. Her work on sequential learning for consumer-driven demand response programs has advanced smart grid applications.

Daniel McKenzieView profile

Assistant Professor

• Derivative-free optimization
• Implicit neural networks
• Geometric methods in data science
• +3 more

Daniel McKenzie is an Assistant Professor in the Department of Applied Mathematics and Statistics at the Colorado School of Mines. His research focuses on derivative-free optimization, implicit neural networks, and geometric methods in data science. He holds a B.Sc.(hons) and M.Sc. in Mathematics from the University of Cape Town (2010, 2014) and a PhD in Mathematics from the University of Georgia (2019). B.Sc.(hons): Mathematics and Applied Mathematics, University of Cape Town, 2010 M.Sc.: Mathematics, University of Cape Town, 2014 PhD: Mathematics, University of Georgia, 2019 His research explores the intersection of optimization theory and machine learning, with applications in spatial data modeling, geometric data analysis, and high-dimensional clustering. Recent work emphasizes curvature-aware algorithms, comparison-based optimization, and implicit network architectures like LatticeVision. His methods address challenges in non-stationary spatial data and convex game equilibria prediction. Key contributions include Fermat distance metrics for clustering, Jacobian-Free Backpropagation (JFB) for implicit networks, and zeroth-order algorithms for black-box optimization. While no scientific awards are listed, his publications reflect a strong focus on advancing optimization techniques for modern data science problems. No specific grants or advising roles are detailed in the provided text. His work bridges computational mathematics and applied AI, with potential applications in robotics, spatial statistics, and algorithmic game theory.

Burhaneddin İzgiView profile

Associate Professor

• Game Theory
• Stochastic Differential Equations
• Numerical Analysis
• +3 more

Burhaneddin İzgi serves as an Associate Professor in the Department of Mathematics Engineering at Istanbul Technical University, where he maintains an active research profile with projects extending through 2025. His work bridges theoretical mathematics with computational applications, particularly in game-theoretic problem solving. Research interests center on Game Theory and Stochastic Differential Equations, with specialized focus on matrix norm-based solution methods for zero-sum, fuzzy, and stochastic matrix games. Recent work integrates Artificial Intelligence techniques to address large-scale game complexity, reflecting interdisciplinary innovation across Numerical Analysis and Fuzzy Mathematics. Article trends (2022-2025) reveal consistent advancement of matrix norm methodologies across diverse game types, increasingly incorporating machine learning for computational efficiency. Publications demonstrate strong theoretical grounding in Mathematics while addressing practical applications in finance (e.g., IPO modeling) and decision systems. Scientific recognition includes: 2210 - Yurt İçi Yüksek Lisans Burs Programı (2008) 2211 - Yurt İçi Doktora Burs Programı (2010) Matematik Bölüm Birinciliği (2008) Üniversite İkinciliği ödülü (2008) Research funding includes TUBITAK-supported projects such as 'Stokastik Oyunlar İçin Matris Norm Tabanli Yeni Çözüm Yöntemleri Ve Yapay Zeka Uygulamalari' (2021-2023) and current grants developing chaos theory approaches for stochastic matrix games (2024-2025), demonstrating sustained external validation of his research program.

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.

Thalia AnagnosView profile

Professor

• Earthquake Engineering
• Structural Analysis
• Seismic Hazard and Risk Assessment
• +3 more

Dr. Thalia Anagnos is a former Professor of Civil Engineering and Vice Provost for Undergraduate Education at San José State University (retired 2023). She specializes in earthquake engineering, structural analysis, and engineering education. Her research focuses on seismic hazard assessment, infrastructure resilience, and loss estimation methodologies. Education: Ph.D. in Civil Engineering from Stanford University. Key roles include Past-President of EERI, former editor of Earthquake Spectra, and leader of NEES Education/Outreach programs. Awards include SJSU Outstanding Professor (2012) and EERI Honorary Membership (2018). Research highlights include developing substation equipment fragility databases and leading regional building inventory projects for seismic risk analysis. She has contributed to national initiatives like HAZUS loss estimation frameworks and educational programs such as the Engineering Leadership Pathway Scholars (ELPS). Awards: 1991 Distinguished Alumnus (Stanford), 2000 McCoy Family Award, 2013 Applied Materials Teaching Award. Grants and projects include NSF-funded initiatives on earthquake engineering education, leadership development for engineering students, and public outreach through partnerships like the Golden Gate Bridge educational project.

Amirali AbariView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Computational Decision Making
• +7 more

Dr. Amirali Abari is an Associate Professor in the Faculty of Business and Information Technology at Ontario Tech University, specializing in Artificial Intelligence, Machine Learning, and Computational Decision Making. His research focuses on decision-making under uncertainty, including preference learning, group recommendation systems, and probabilistic inference. He holds a PhD in Computer Science from the University of Toronto (2016) and was an NSERC Postdoctoral Fellow at the University of Toronto and University of Waterloo. Dr. Abari's research has been recognized with awards including the NSERC Postdoctoral Fellowship and a U.S. patent. He serves on program committees for top AI conferences (e.g., AAAI, IJCAI) and reviews for journals like IEEE Transactions and ACM Transactions. His work bridges computational methods with real-world applications in supply chain optimization, risk management, and sustainable infrastructure planning. Key contributions include integrating machine learning with queueing theory for EV charging stations, stochastic optimization for resilient supply chains, and decision support systems for industrial processes. His research group collaborates with industry partners to apply AI-driven solutions to business analytics and operations challenges. Education: PhD in Computer Science, University of Toronto (2016) Awards: NSERC Postdoctoral Fellowship, Bell Scholarship, Student Best Paper Award Service: Program committees for AAAI, IJCAI; reviewer for IEEE, ACM, and Algorithmica

• Stochastic Analysis
• Mathematical Finance
• Option Pricing
• +5 more

Blanka Horvath is a Lecturer at King's College London and Honorary Lecturer at Imperial College London's Department of Mathematics (Faculty of Natural Sciences). Her research focuses on stochastic analysis and mathematical finance, particularly in numerical methods, machine learning applications, and volatility modeling (e.g., SABR and rough volatility models). She holds a PhD from ETH Zurich (2015), a Diplom in Mathematics from the University of Bonn, and an MSc in Economics from the University of Hong Kong. She has organized major conferences such as the SIAM MMF 2017 mini-symposium and co-organized the Rough Volatility Meeting at Imperial College. Her honors include the 2019 Risk Rising Star Award and the 2024-25 LMS Emmy Noether Fellowship. She collaborates with institutions like UBS, The Alan Turing Institute, and Quantennium LTD. Her teaching includes courses on numerical methods in finance and Python/R programming. She supervises PhD and MSc students in areas like rough volatility, machine learning, and quantitative finance. Recent work explores quantum GANs for option pricing and regime detection using Wasserstein distances.

Karthik RamachandranView profile

Professor

• Operations Management
• Product Design and Development
• Entrepreneurship
• +3 more

Karthik Ramachandran is the Dunn Family Professor and Area Chair of Operations Management at the Scheller College of Business, Georgia Institute of Technology. His research focuses on operational and organizational aspects of innovation in product design, technology management, and entrepreneurship. He holds editorial roles at Production and Operations Management and Decision Sciences , and teaches courses in New Product Development and Entrepreneurship. Ramachandran earned his Ph.D. from the University of Texas at Austin and a B.Tech. from the Indian Institute of Technology, Madras. Research interests span behavioral operations, sustainability, and strategic decision-making in innovation ecosystems. He investigates how firms balance operational flexibility with market demands, leveraging analytical models and empirical studies. His work has appeared in top journals like Management Science and Manufacturing & Service Operations Management . Notable contributions include studies on nonprofit service optimization, entrepreneurial advising frameworks, and innovation project delegation. Ramachandran actively mentors startups through Georgia Tech’s CREATE-X initiative, emphasizing hands-on learning and venture creation. Education: Ph.D. Operations Management, McCombs School of Business, UT Austin M.S. Operations Research, UT Austin B.Tech. Mechanical Engineering, IIT Madras Awards: 2024 Best Paper Award (MSOM Society) Grants & Advising: Overseeing research on innovation ecosystems and mentoring dozens of startups, with a focus on scaling entrepreneurial ventures.

Jiaming LiangView profile

Assistant Professor

• Mathematical Optimization
• Sampling Algorithms
• Stochastic Programming
• +3 more

Jiaming Liang is an Assistant Professor in the Department of Computer Science and Data Science at the Hajim School of Engineering & Applied Sciences, University of Rochester. His research focuses on designing efficient algorithms for optimization and sampling in data science, with expertise in convex/nonconvex optimization, stochastic programming, and algorithmic game theory. He holds a PhD in Operations Research from Georgia Tech and completed a postdoctoral fellowship at Yale University under Andre Wibisono. Education PhD in Operations Research, Georgia Institute of Technology Postdoctoral Researcher, Yale University (Yale CS Department) Research Interests Liang's work bridges theoretical optimization and practical algorithm design. He develops scalable methods for nonsmooth, stochastic, and high-dimensional problems, with applications in machine learning and data analysis. Key areas include: Proximal bundle methods for hybrid optimization Sampling algorithms under non-smooth conditions Accelerated gradient techniques Algorithmic game theory applications Publications His recent work emphasizes algorithmic unification (e.g., combining accelerated gradients with proximal bundle methods) and variance reduction strategies. He explores stochastic optimization without smoothness assumptions and develops sampling methods for nonconvex potentials. Labs/Teams While specific lab affiliations aren't detailed here, his research aligns with computational optimization and data science groups at the Hajim School.

Alina EneView profile

Associate Professor

• Algorithms
• Combinatorial Optimization
• Submodularity
• +3 more

Alina Ene is an Associate Professor in the Department of Computer Science at Boston University, within the Faculty of Arts & Sciences. She holds a BSE in Computer Science (High Honors) from Princeton University (2008) and a PhD from the University of Illinois at Urbana-Champaign (2013), advised by Chandra Chekuri. Her research focuses on algorithms, combinatorial optimization (submodularity, graph theory), and their applications to machine learning. She previously served as Assistant Professor at the University of Warwick, Faculty Fellow at the Alan Turing Institute, and postdoctoral researcher at Princeton's Center for Computational Intractability. Key research themes include distributed submodular maximization, streaming algorithms, optimization theory, and machine learning. Her work bridges theoretical guarantees with practical applications in data science and distributed systems. Notable contributions include frameworks for submodular maximization under constraints, randomized coordinate descent methods, and routing algorithms with balance considerations. Publications emphasize algorithm design for submodular functions, stochastic optimization, and graph problems. Recent work explores high-probability convergence in stochastic gradient methods, clipped gradient techniques, and online ad allocation strategies. Her 2016 FOCS paper on distributed submodular maximization remains influential in large-scale optimization. Professional service includes editorial work for conferences like FOCS/STOC/SODA and grants from NSF's CAREER program. No explicit scientific awards are listed, though her fellowships and postdoctoral appointments reflect scholarly recognition.

Brandon KarchewskiView profile

Teaching Professor

• Computational Geoscience
• Engineering Geology
• Natural Hazards
• +2 more

Brandon Karchewski is an Associate Head (Undergraduate) and Teaching Professor in the Department of Earth, Energy and Environment at the University of Calgary. He earned his PhD in Civil Engineering from McMaster University and teaches courses including Engineering Geology, Computational Methods, and Natural Disasters. His research program focuses on: Computational methods in geophysics and geomechanics Geoscience education innovation Climate change impacts on frozen soils Inverse modeling applications Karchewski has pioneered virtual field experiences and developed open-source tools for modeling climate impacts on permafrost. His educational research examines metaphor use in geoscience communication and field pedagogy. Recent computational work includes Python-based permafrost modeling and stochastic inversion methods for biogeochemical transport. He leads the geophysics field school program emphasizing team-based learning. Award recognition includes: Geoscience Teaching Award (2019) Best Poster Award for teaching innovation research (2018) Team Teaching Excellence award (2016) Multiple teaching assistant awards

Ahmad HemmatiView profile

Associate Professor

• Maritime Logistics
• Inventory Routing Problems
• Combinatorial Optimization
• +3 more

Dr. Ahmad Hemmati is an Associate Professor at the Department of Informatics, University of Bergen. His research focuses on optimization models and algorithms for logistics, transportation, and maritime systems. He holds a PhD from the Norwegian University of Science and Technology (NTNU) in 2015. His work addresses challenges in inventory routing, drone delivery systems, and combinatorial optimization using methods like reinforcement learning and metaheuristics. Key contributions include models for maritime cargo optimization, synchronized truck-drone delivery, and feeder network design. He has collaborated internationally with institutions like MIT and the University of Antwerp. His findings are published in journals such as European Journal of Operational Research and Computers & Operations Research , with a focus on practical solutions for industrial logistics problems. Education: PhD in Operations Research (NTNU, 2015). Research Interests: Maritime Logistics, Inventory Routing Problems, Combinatorial Optimization, Drone Delivery Systems, and Algorithm Design. His work bridges theoretical models with real-world applications in transportation networks and sustainability. Publications Trends: Recent articles emphasize deep reinforcement learning applications and maritime environmental monitoring. Earlier work includes foundational contributions to tramp shipping and VMI service routing. His methodologies often blend heuristic algorithms with mathematical programming. Grants/Awards: No specific awards mentioned, but his work has been funded by the Research Council of Norway and industry collaborations.

• Control Systems
• Model Predictive Control
• Power Systems Engineering
• +3 more

Morten Hovd is a Professor in the Department of Engineering Cybernetics at the Norwegian University of Science and Technology (NTNU). His research focuses on advanced control systems, model predictive control (MPC), power electronics, and optimization algorithms. He has contributed significantly to the development of robust control strategies for uncertain systems and has published extensively in leading journals and conferences in the field of control engineering. His research interests span several key areas in control systems engineering, including model predictive control, nonlinear control systems, optimization under uncertainty, and applications in power systems and energy efficiency. He is particularly known for his work on discrete-time bilinear systems, modular multilevel converters (MMCs), and the integration of machine learning techniques with control theory. His contributions address both theoretical advancements and practical implementations in industries such as energy and petroleum engineering. Hovd's recent publications highlight advancements in energy-efficient control systems, stochastic surrogate modeling for subsurface flows, and optimization algorithms tailored for complex engineering problems. His work often combines rigorous mathematical frameworks with real-world applications, such as improving the reliability of power systems and enhancing reservoir management through data-driven methods. He is actively involved in teaching courses such as TTK4210 (Advanced Control of Industrial Processes) and TK8118 (Mini-seminar in Cybernetics). His research has led to innovations in fault detection for power systems, energy-efficient building climate control, and robust MPC strategies for uncertain systems.

Margarida CARVALHOView profile

Associate Professor

• Algorithmic Game Theory
• Operations Research
• Combinatorial Optimization
• +4 more

Margarida Carvalho is an Associate Professor in the Department of Computer Science and Operations Research at Université de Montréal. She holds the FRQ–IVADO Research Chair in Data Science for Combinatorial Game Theory and was awarded the 2018 EURO Doctoral Dissertation Award. Her research integrates algorithmic game theory and integer programming to address strategic decision-making challenges in healthcare, urban planning, and sustainability. Education: Bachelor's and Master's in Mathematics PhD in Computer Science (Universidade do Porto, 2016) Postdoctoral Fellow at Polytechnique Montréal (2017) Research Interests: Algorithmic Game Theory and Combinatorial Optimization Applications in Kidney Exchange Programs, EV Infrastructure, and Urban Decision-Making Development of fair and scalable optimization frameworks Awards & Grants: FRQ–IVADO Research Chair (2018–present) Teaching Excellence Award (2024) Lead on projects funded by CRSNG, MITACS, and Fonds de recherche du Québec Lab & Collaborations: Member of CIRRELT, CRM, Mila, and the AI Cybersecurity Lab (talents). Her team focuses on interdisciplinary solutions for complex socio-economic systems.

Roberto BertolussoView profile

Senior Lecturer

• Systems Biology
• Cancer Research
• Computational Biology
• +3 more

Roberto Bertolusso is a Senior Pfeiffer Lecturer in the Department of Statistics at Rice University. He has held this position since 2013, focusing on teaching probability, statistics, and data science courses. His research integrates computational and systems biology approaches to study cancer mechanisms. Bertolusso earned his Ph.D. and M.S. in Statistics from Rice University and holds a graduate degree in Industrial Engineering from Universidad de Buenos Aires. His academic background includes significant contributions to cancer biostatistics and systems biology, highlighted by his participation in the AACR Methods in Cancer Biostatistics Workshop (2015) and a CPRIT-funded training program (2013–14). His publications span epidemiological modeling, orthopedic outcomes, and computational cancer research, reflecting interdisciplinary expertise. Education: Ph.D. & M.A. in Statistics, Rice University Graduate Level in Industrial Engineering, Universidad de Buenos Aires Teaching Focus: Probability theory, regression analysis, SAS programming, and data science methodologies. Research interests emphasize systems biology applications in cancer, stochastic modeling of biological systems, and computational methods for medical data analysis. His work bridges statistical theory with clinical and biomedical challenges, as seen in studies on viral infection dynamics and tumor growth modeling. Recent publications reflect both his cancer research and collaborative work in orthopedic surgery outcomes, showcasing a commitment to interdisciplinary problem-solving. Awards include recognition for methodological contributions to cancer clinical trial design and biostatistical training initiatives.