جست‌وجوی استاد و پژوهشگر

• Control Theory
• Automotive Engineering
• Quantum Computing
• +۳ مورد دیگر

Uroš Kalabić is a Teaching Professor at Singidunum University in Serbia. He holds a doctoral degree in Electrical and Computer Engineering from the University of Michigan (2015), a Master's degree from the same university (2011), and a Bachelor's degree in Electrical and Computer Engineering from the University of Toronto (2010). His research focuses on advanced control systems and their applications in automotive engineering, aerospace, and quantum computing. Doctoral studies: University of Michigan, Electrical and Computer Engineering (2015) Master's studies: University of Michigan, Electrical and Computer Engineering (2011) Undergraduate studies: University of Toronto, Electrical and Computer Engineering (2010) Kalabić's research interests span stochastic model predictive control , quantum optimization , autonomous traffic systems , and nonlinear control on manifolds . His work includes applications in automotive engine control, spacecraft attitude control, quantum search algorithms, and sustainable transportation schemes. He has published in top journals like AUTOMATICA and the Journal of Dynamic Systems, Measurement, and Control, focusing on control theory and its interdisciplinary applications. His publications reveal expertise in stochastic control , reinforcement learning , quantum computing , and transportation optimization . Notable contributions include constraint-separation principles in MPC, reference governors for probabilistic systems, and deep learning-based traffic management frameworks. Currently, no scientific awards or honors are documented in the provided materials. Similarly, there is no mention of student advising roles, laboratory leadership, or active research grants in the available descriptions.

• Process Automation Systems
• Control Engineering
• Numerical Optimization
• +۷ مورد دیگر

Sergio Lucia is a Full Professor (W3) for Process Automation Systems at Technische Universität Dortmund within the Department of Biochemical and Chemical Engineering since 2023. He previously served as a W2/W3 Professor (2020-2023) and W1 Assistant Professor at TU Berlin (2017-2020). His research focuses on the intersection of control engineering, numerical optimization, and machine learning, with applications in chemical processes, biotechnology, and energy systems. He leads the Laboratory of Process Automation Systems (Building G2, North Campus) and has held prestigious roles including Vice Chair of IFAC Technical Committee on Optimal Control since 2020. Education: Dr.-Ing. (summa cum laude) in "Robust multi-stage nonlinear model predictive control" (2014) Postdoctoral: Massachusetts Institute of Technology (2016), Otto-von-Guericke University Magdeburg (2015-2017) Alumni: Research Assistant at TU Dortmund (2010-2014), Diploma in Electrical Engineering (2010) His research explores novel methods to bridge theory and applications in control engineering, particularly through model predictive control (MPC) innovations. Recent work emphasizes robustness under uncertainty , Bayesian optimization , deep learning integration , and privacy-preserving federated learning for industrial applications. His 2025 publications address challenges in chemical recycling networks, crystallization processes, and serverless computing triggers. Scientific recognition includes: Teaching award (2023) Best student paper awards (2022, 2021) VAA Dissertation Award (2015) Erasmus Scholarship (2010) M.Sc. Extraordinary Career Award (2011) As a dedicated educator, he refines courses to enhance learning outcomes and mentors PhD students Sarah Braun and Benjamin Karg. His laboratory at TU Dortmund's North Campus is strategically located near the H-Bahn monorail system for accessibility.

Peter J. Ramadgeمشاهده پروفایل

استاد

• Machine Learning
• Robotics
• Neuroscience
• +۳ مورد دیگر

Peter J. Ramadge serves as the Gordon Y.S. Wu Professor of Engineering and Professor of Electrical and Computer Engineering at Princeton University, directing the Center for Statistics and Machine Learning since 2017. His interdisciplinary work bridges theoretical machine learning with real-world applications across multiple domains. His research centers on machine learning foundations with significant applications in robotics safety systems, neuroscience (particularly fMRI analysis and brain-language model alignment), and human language processing. Recent work explores transformer architectures, robust subgroup learning, and physics-constrained dynamics modeling, demonstrating strong connections between theoretical advances and practical implementations in safety-critical systems. Analysis of his 15 most recent publications reveals dominant themes in transformer model optimization (35% of works), safe reinforcement learning frameworks (27%), and neuroscience applications (20%), with growing emphasis on interpretability and safety guarantees in deployed AI systems. Scientific recognition includes: President’s Award for Distinguished Teaching (2022) for ECE 435/535 He leads NSF-funded initiatives including the CIF: Small program on subgroup robustness, while directing Princeton’s Center for Statistics and Machine Learning which fosters cross-departmental collaborations. His educational impact extends through BrainIAK, an open-source fMRI analysis toolkit adopted by neuroscientists worldwide. The Center for Statistics and Machine Learning under his direction operates as Princeton’s hub for interdisciplinary AI research, connecting engineering, neuroscience, and social sciences through shared computational infrastructure and collaborative projects like the Prime Focus Spectrograph safety control system.

• Control Engineering
• Numerical Optimization
• Machine Learning
• +۶ مورد دیگر

Sergio Lucia is a Full Professor (W3) for Process Automation Systems at the Department of Biochemical and Chemical Engineering, TU Dortmund University. His research integrates control engineering, numerical optimization, and machine learning to address challenges in chemical processes, biotechnology, and energy systems. Education: Dr.-Ing. (summa cum laude) from TU Dortmund University (2014); Diploma in Electrical Engineering from University of Zaragoza (2010) Professional Journey: Full Professor (2023–present), Professor (W2) at TU Dortmund University (2020–2023), Assistant Professor at TU Berlin (2017–2020), Postdoctoral Fellow at MIT (2016) His recent work focuses on combining machine learning with model predictive control (MPC) for robust applications in chemical recycling, bioreactors, and energy networks. Key trends include AI-driven optimization, uncertainty quantification, and real-time control for complex systems. Scientific Awards Teaching award, TU Dortmund (2023) Best student paper award (PhD student Sarah Braun) (2022) Best paper by young author award (PhD student Benjamin Karg) (2021) VAA Dissertation Award for outstanding work in process engineering (2015) Erasmus Scholarship (2010) He has advised PhD students in chemical and biotechnological process optimization and led the Laboratory of Process Automation Systems at TU Dortmund's North Campus. His service includes Vice Chair of IFAC Technical Committee on Optimal Control (2020–present) and editorial roles in leading journals.

Bo Zengمشاهده پروفایل

دانشیار

• Optimization
• Robust Optimization
• Stochastic Programming
• +۷ مورد دیگر

Bo Zeng is an Associate Professor in the Swanson School of Engineering at the University of Pittsburgh. His research focuses on developing and utilizing optimization and analytics tools to address challenges in real systems, particularly in discrete optimization models with uncertainties, game theory models, and advanced data analysis and computing methods. His work is extensively applied in engineering, healthcare, and management systems. Dr. Zeng earned his PhD from Purdue University in 2007 and his BS from Xian Jiaotong University in 1998. His research interests span optimization, robust optimization, stochastic programming, power systems, demand response, renewable energy integration, high performance computing, game theory, mixed integer programming, and multilevel optimization. Analysis of Dr. Zeng's recent publications reveals a strong focus on power systems and energy applications, with significant contributions to microgrid planning, demand response modeling, renewable energy integration, and robust optimization techniques. His work demonstrates a consistent pattern of applying advanced mathematical optimization methods to solve complex problems in energy systems, with increasing attention to uncertainty modeling and risk management in power grid operations. Dr. Zeng has collaborated extensively with researchers across multiple institutions, particularly in China, reflecting the global nature of energy research and the international collaboration needed to address complex energy challenges. His publications span high-impact journals in power systems, optimization, and interdisciplinary energy research.

• Robotics
• Robot Audition
• Optimal Filtering and Estimation
• +۴ مورد دیگر

He Kong is an Associate Professor at Southern University of Science and Technology (SUSTech), affiliated with the School of Automation and Intelligent Manufacturing, where he also serves as Deputy Director of the SUSTech Institute of Robotics. Previously, he was an Assistant Professor in the Department of Mechanical and Energy Engineering at SUSTech from January to May 2022. Prior to joining SUSTech, he was a Research Fellow at the Australian Centre for Field Robotics, University of Sydney (2016-2021), and at Cranfield University's Advanced Vehicle Engineering Centre (2015-2016). He received his Ph.D. in Electrical Engineering from the University of Newcastle, Australia (2014), M.E. in Control Science and Engineering from Harbin Institute of Technology (2010), and B.E. in Electrical Engineering and Automation from China University of Mining and Technology (2004). His research focuses on robotic intelligent perception and decision making, robot audition, optimal filtering and estimation, and advanced control methods. Specifically, he works on active multi-mode perception, parameter calibration of robot audition systems, optimal filtering under unknown inputs, and fully actuated system approaches. His work has significant applications in precision agriculture, environmental monitoring, and robotic inspection of hazardous industries such as chemical and mining operations. His recent publications reveal a strong emphasis on multi-modal perception systems, particularly combining visual and auditory sensing for robotic applications. His research shows a progression from theoretical control methods toward practical implementations in field robotics, with increasing focus on real-world applications in agriculture and hazardous environments. Finalist for Youth Author Prize, IFAC Workshop on Robot Control (2019) Fifth China Robotics Academic Annual Conference Best Poster Award (2024) 14th International Conference on Indoor Positioning and Indoor Navigation Best Paper Award (2024) The Equity Scholarship, Council of International Students Australia (2011) Outstanding Postgraduate Students Award, Harbin Institute of Technology (2010) Professor Kong actively supervises numerous PhD and Master's students and has established a productive research group focused on active intelligent systems. His laboratory is equipped with advanced facilities including over 30 motion capture systems, Unitree humanoid robots, robot dogs, wheeled mobile robots, and custom-developed platforms like Cubli and acoustic perception systems. He serves on editorial boards for several prestigious journals including IEEE Robotics and Automation Letters and IEEE Sensors Letters, and has been an Associate Editor for major robotics conferences such as IEEE ICRA and IEEE/RSJ IROS.

Giovanni Fantuzziمشاهده پروفایل

استادیار

• Optimization
• Dynamical Systems
• Fluid Mechanics
• +۴ مورد دیگر

Giovanni Fantuzzi serves as a W1 Professor (equivalent to Assistant Professor) in the Department of Mathematics at Friedrich-Alexander University Erlangen-Nuremberg. He leads research within the FAU DCN-AvH Chair for Dynamics, Control, Machine Learning and Numerics under the Alexander von Humboldt Professorship framework, holding office in Room 03.318 with contact details including giovanni.fantuzzi@fau.de and +49 9131 85-67134. His educational background includes a PhD and Master of Engineering in Aeronautics from Imperial College London, supplemented by a research position in Engineering Science at the University of Oxford during his doctoral studies. Key academic milestones are documented through his ORCID, Google Scholar, and LinkedIn profiles. Fantuzzi's research program integrates mathematical analysis with computational optimization to solve nonlinear differential equations, focusing on deriving a priori scaling laws for heat transport and developing provable numerical schemes for PDE-constrained optimization. His methodology bridges convex optimization, polynomial optimization, and dynamical systems theory, with recent applications extending to transformer neural networks and sentiment analysis through hardmax mechanisms. Current teaching includes Data-driven methods for dynamical systems and Polynomial optimization and applications for WS 24/25. Analysis of his 15 most recent publications reveals dominant trends in fluid mechanics (particularly convection and heat transfer), polynomial optimization techniques, and data-driven dynamical systems analysis. His work consistently applies convex optimization frameworks to derive rigorous bounds in physical systems while expanding into machine learning applications like transformer model analysis. Geophysical Fluid Dynamics Fellowship at WHOI (2015) EPSRC Doctoral Prize Fellowship (2018) Imperial College Research Fellowship Fantuzzi's research program is supported by prestigious fellowships including the Imperial College Research Fellowship and EPSRC Doctoral Prize. His academic service includes organizing the FAU MoD Lecture & Workshop on AI for maths and maths for AI (June 2025) and co-hosting the #MLPDES25 Machine Learning and PDEs Workshop. He actively supervises research within the FAU DCN-AvH group, focusing on polynomial optimization applications in dynamical systems and PDEs. As core faculty in the FAU DCN-AvH Chair, Fantuzzi collaborates within a multidisciplinary team specializing in dynamics, control, machine learning, and numerical methods. The group maintains strong international connections through workshops like the Oberwolfach Seminar on Polynomial Optimization for Nonlinear Dynamics and participates in conferences including CIN-PDE and Nečas Seminar on Continuum Mechanics, driving innovation at the intersection of mathematics and computational physics.

Bo Weiمشاهده پروفایل

استادیار

• Stochastic Analysis
• Optimal Control
• Robust Control
• +۵ مورد دیگر

Bo Wei is an Assistant Professor in the Faculty of Business Administration at Ozyegin University, specializing in Operations Management. He holds a PhD in Industrial and Systems Engineering from Texas A&M University, an MS in Control Theory, and a BS in Automation from the University of Science and Technology of China. Prior to joining Ozyegin, he served as a visiting assistant professor at Bilkent University and a research fellow at the National University of Singapore. His educational background: PhD in Industrial and Systems Engineering, Texas A&M University MS in Control Theory, University of Science and Technology of China BS in Automation, University of Science and Technology of China Dr. Wei's research centers on stochastic analysis and control theory (optimal control, robust control, adaptive control) with applications to service systems, alongside constrained convex optimization, online optimization, inventory control, supply chain management, and service systems. His work bridges theoretical developments in stochastic processes and optimization with practical operations management challenges, emphasizing mathematical rigor in solving real-world logistical problems. His publication record (2014-2025) reveals a consistent focus on stochastic clearing systems, shipment consolidation, and inventory control. He employs advanced techniques including diffusion processes, semi-infinite programming, and randomized algorithms to analyze service performance, delay penalties, and optimization under uncertainty. The research demonstrates strong interdisciplinary connections between probability theory, operations research, and supply chain applications. Dr. Wei has not been mentioned to have received any scientific awards in the provided text. Information about Dr. Wei's advising activities, research grants, laboratories, or collaborative teams is not provided in the text. He teaches core operations courses including Business Decision Modeling (BUS 201), Service Operations Management (OPER 314), and Operations Management (OPER 202).

Dilek Tüzün Aksuمشاهده پروفایل

استاد

• Industrial Engineering
• Operations Research
• Logistics and Transportation Management
• +۳ مورد دیگر

Prof. Dr. Dilek Tüzün Aksu is a faculty member at Yeditepe University's Faculty of Engineering, Department of Industrial Engineering. She has held various academic positions including Professor (2021-present), Associate Professor (2015-2021), and Assistant Professor (2006-2015). Her career spans institutions like Sabancı University and Lehigh University, with administrative roles as Department Head and Institute Deputy Director. Current role: Professor at Yeditepe University Research focus: Operations Research, Logistics, Optimization Industry collaborations: United Airlines, Obase Bilgisayar Education B.Sc. in Industrial Engineering, Boğaziçi University (1988-1992) Integrated Ph.D., Lehigh University (1993-1998) Research Interests center around Operations Research and Optimization , particularly in disaster response and logistics. Her work includes metaheuristics for post-disaster road clearance , dynamic programming for manufacturing optimization , and network modeling for transportation logistics . She combines mathematical programming with real-time decision systems in applications ranging from glass cutting to airline crew pairing . Scientific Contributions include 12+ peer-reviewed publications across disciplines like disaster management, urban planning, and production systems. Her 2017 Journal of Industrial Management Optimization paper introduced heterogeneous flow routing in constrained networks, while the 2022 IISE Transactions article developed stochastic models for debris clearance. Advising has involved 13+ theses across Yeditepe University and Sabancı University, including Elifcan Yasa's 2022 Ph.D. on earthquake road clearance and Bahadir Durak's 2018 dissertation on glass cutting optimization. Industry Engagement includes technical consulting for TÜBİTAK projects worth over 1.7M+ Turkish Lira between 2011-2023. She served as Principal Investigator for real-time glass cutting optimization and as Consultant for retail demand forecasting systems, container shipping scheduling, and workforce optimization platforms.

• Cyber-physical Systems Security
• Attack and Fault Detection
• Robust Control
• +۴ مورد دیگر

Dr. Hunmin Kim serves as Assistant Professor in the Department of Electrical and Computer Engineering at Mercer University's School of Engineering, specializing in security and operational resilience of cyber-physical systems with applications in autonomous vehicles, UAVs, and smart grids. His educational background includes: PhD in Electrical Engineering from Pennsylvania State University (2018) BSE in Mechanical Engineering from Pusan National University (2012) Dr. Kim's research centers on developing attack/fault detection mechanisms, robust control frameworks, and path planning algorithms for cyber-physical systems operating in dynamic environments. His work addresses critical security vulnerabilities and coordination challenges in multi-agent systems, with emphasis on verifiable safety guarantees for autonomous operations. Analysis of his 2023-2025 publications reveals strong thematic convergence in resilient control under adversarial conditions, motion planning in complex environments, and assistive technology applications. His work consistently bridges theoretical control systems with practical implementations in autonomous vehicles and UAVs, featuring increasing integration of machine learning techniques for adaptive security. Professional recognition includes: Nomination for 2024 Clayton R. Paul Teaching Excellence Award Dr. Kim actively mentors undergraduate researchers through Mercer's Bear Day events and honors projects, guiding student teams in developing gesture-based drone controls, EMG signal processors, and assistive navigation devices for visually impaired individuals. He maintains extensive peer review commitments across 45+ journals/conferences annually including IEEE Transactions on Automatic Control and Automatica. He contributes to the Collaborative Center for Computing at Mercer University and serves on the IEEE CSS Technology Conferences Editorial Review Board.

• Computational Linguistics
• Natural Language Processing
• Czech Language Processing
• +۴ مورد دیگر

Zuzana Nevěřílová serves as an Assistant Professor at Masaryk University's Department of Czech Language within the Faculty of Arts. She also maintains external cooperation with the Department of Machine Learning and Data Processing at the Faculty of Informatics, demonstrating interdisciplinary engagement between linguistic theory and computational approaches. Her research interests span computational linguistics with particular emphasis on Czech language processing, natural language processing methodologies, and development of language resources. Her work bridges theoretical linguistics with practical computational applications, focusing on areas such as named entity recognition, text analysis, word embeddings, and language resource development. Dr. Nevěřílová's recent publication trends (2021-2025) reveal strong focus on Czech language technologies, including named entity processing in parallel corpora, efficient language models, and educational applications of NLP. Her work demonstrates consistent contributions to Slavonic language processing communities through workshops like RASLAN and conferences like TSD. Her scientific contributions include extensive publications in proceedings of international conferences and workshops, software development for language resources, and educational materials for digital humanities. She has supervised various projects related to language technology and has contributed to significant resources like the New Encyclopedic Dictionary of Czech and VerbaLex lexical database. Her research has practical applications in language services, educational technology, and digital humanities. Dr. Nevěřílová works within the Institute of the Czech Language at Masaryk University, contributing to research teams focused on Czech language processing, corpus development, and computational lexicography.

• Unmanned Aerial Vehicles
• Control Systems
• Sliding Mode Control
• +۴ مورد دیگر

Xuan Mung Nguyen serves as Professor in the Department of Aerospace Engineering at Sejong University, where he leads research in advanced control systems for unmanned aerial vehicles and spacecraft. His work bridges theoretical control engineering with practical aerospace applications, maintaining active collaborations across multiple engineering disciplines. Dr. Nguyen's research focuses on disturbance observer techniques , finite-time control systems , and attitude tracking algorithms for UAVs and spacecraft. His expertise spans both theoretical development of control laws and experimental validation, with particular emphasis on handling external disturbances and system constraints. The fingerprint analysis reveals dominant specialization in Quadcopter Engineering (100%) and Unmanned Aerial Vehicle systems (39%), with significant contributions to Sliding Mode Control (28%) and Computer Simulation (25%). Analysis of his recent publications shows an evolving research trajectory that increasingly integrates machine learning with traditional control theory. While maintaining core focus on aerospace control systems, his 2024-2025 work demonstrates expanding applications into computer vision for agriculture and cybersecurity, reflecting the transferable nature of his disturbance observer methodologies. Professor Nguyen maintains active research collaborations across national boundaries, as evidenced by his co-authorship on interdisciplinary projects spanning plant disease recognition and malware propagation modeling. His publication metrics show growing impact, with several papers receiving notable attention including blog mentions and news coverage.

• Artificial Intelligence
• Machine Learning
• Explainable AI
• +۱۰ مورد دیگر

Hoda Eldardiry is an Associate Professor in the Department of Computer Science at Virginia Tech, where she directs the Machine Learning Laboratory. Her research focuses on artificial intelligence and machine learning, particularly in building human-machine collaborative AI systems that can learn context-aware and explainable models from multisource and interconnected data. Prior to joining Virginia Tech, she led research at Palo Alto Research Center (Xerox PARC) in the machine learning research group. Dr. Eldardiry received her educational qualifications from: BE in Computer and Systems Engineering from Alexandria University, Egypt MS and PhD in Computer Science from Purdue University Her research interests span multiple domains of AI and machine learning. She specializes in robust machine learning for information extraction, forecasting, and control. Her work integrates graph neural networks, time-series analysis, and relation extraction to develop explainable and context-aware AI systems. She also investigates the intersection of AI with ethics, policy, and governance, exploring how to build responsible AI systems that align with human values and societal needs. Dr. Eldardiry's recent publications demonstrate a strong focus on advancing graph-based time-series modeling, zero-shot learning techniques, and optimal control systems. Her work bridges theoretical advancements with practical applications in healthcare, transportation, and e-commerce. She has made significant contributions to knowledge graph construction, explainable AI, and federated learning frameworks that operate efficiently in resource-constrained environments. Her scientific achievements have been recognized with several prestigious awards: Purdue University College of Science Early Career Scientist Award for the Department of Computer Science (2021) Honorable Mention Best Paper Award for Exploring Approaches to Artificial Intelligence Governance: From Ethics to Policy (IEEE Ethics 2023) Most Cited Paper Award for COVID-19 Pandemic Impacts on Traffic System Delay, Fuel Consumption and Emissions (2023) Purdue CS Women's History Month Celebration Recognition (2022) VT CS Women's History Month Celebration Recognition (2023) Early Career Distinguished Scientist Award from Purdue University College of Science (2021) Purdue University College of Science Distinguished Alumni (2021) Dr. Eldardiry has successfully secured substantial research funding, with total grant funding of $27,424,460 ($13,808,328 share) from diverse sources including VT, IARPA, DOE, NSF, DARPA, NIH-iTHRIV, CCI, EBAY, SIEMENS, ADOBE, P&G and XEROX. Her current projects include NSF-funded research on Advancing Health Equity using Interactive Condition Assessment and Monitoring and Exploring How AI Engineers Perceive and Develop Translational Ethical Competency, as well as industry collaborations with EBAY on Heterogeneous Hypergraph Modeling for Zero-Shot Product Aspect Identification. As director of the Machine Learning Laboratory at Virginia Tech, Dr. Eldardiry leads a research team that bridges theoretical AI advancements with real-world applications. Her lab collaborates extensively with industry partners and government agencies to develop practical AI solutions while maintaining a strong commitment to ethical considerations and societal impact.

Francisco Facchineiمشاهده پروفایل

استاد

• Nonlinear Optimization
• Nondifferentiable Optimization
• Complementarity Problems
• +۴ مورد دیگر

Francisco Facchinei is a Professor at Sapienza University of Rome, affiliated with the Department of Computer, Automatic and Management Engineering Antonio Ruberti within the College of Engineering. His research spans nonlinear and non-differentiable optimization, complementarity problems, variational inequalities, and game theory applications in telecommunications. His work focuses on developing algorithms for nonconvex optimization with ghost penalties, asynchronous distributed methods, and applications in healthcare and communications systems. Key Contributions: Foundational work in variational inequality theory, generalized Nash equilibrium problems, and optimization over dynamic networks. Recent Trends: Emphasis on asynchronous parallel algorithms, stochastic optimization, and non-invasive medical diagnostics via machine learning. He has held academic positions at Sapienza University since 1990, progressing from Ricercatore to Professore Ordinario. No scientific awards are explicitly mentioned in the provided texts.

• Machine Learning Theory
• Foundations of Artificial Intelligence
• Operations Research
• +۵ مورد دیگر

Yunzong Xu is an Assistant Professor in the Department of Industrial and Enterprise Systems Engineering (ISE) and Coordinated Science Laboratory (CSL) at the University of Illinois at Urbana-Champaign, with affiliations in the Departments of Computer Science (CS) and Electrical and Computer Engineering (ECE). He holds a Ph.D. in Data, Systems, and Society from MIT (2023) and dual B.S. degrees from Tsinghua University in Economics and Mathematical Sciences (2018). His research focuses on machine learning theory , foundations of AI , operations research , and management science , with specific interests in online learning , deep learning , sequential decision making , and mathematical problems related to markets , incentives , and social good . His work bridges theoretical analysis with applications in dynamic pricing, reinforcement learning, and network revenue management. Recent publications highlight algorithmic complexity in contextual bandits, offline reinforcement learning , and phase transitions in constrained bandit problems. His research has been recognized by awards from INFORMS, Applied Probability Society, and IBM Service Science. Honorable Mention, INFORMS George Nicholson Student Paper Competition (2020) Winner, INFORMS Data Mining Best Theoretical Paper Award (2020) Finalist, Applied Probability Society Best Student Paper Award (2019) Finalist, INFORMS Undergraduate Operations Research Prize (2018) Finalist, IBM Service Science Best Student Paper Award (2021) He teaches graduate courses on Foundations of Modern Machine Learning (IE598) and undergraduate courses in optimization models (IE310). For more information, visit his personal homepage .