Find Academic Supervisors

Joel SokolView profile

Professor

• Sports Analytics
• Applied Operations Research
• Health Informatics
• +7 more

Joel Sokol is the Harold E. Smalley Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. He serves as Director of the interdisciplinary Master of Science in Analytics (MSA) degree, offered both on-campus and online. His academic journey began with a Ph.D. in Operations Research from MIT (1999), followed by bachelor's degrees in Mathematics, Computer Science, and Applied Sciences in Engineering from Rutgers University (1994). Education Ph.D. in Operations Research (MIT, 1999) B.S./B.A. in Mathematics, Computer Science, Applied Sciences in Engineering (Rutgers, 1994) Dr. Sokol's research focuses on Sports Analytics , Health Informatics , and Supply Chain Optimization . He pioneered the LRMC (Logistic Regression/Markov Chain) method for NCAA basketball tournament predictions, which has become an industry standard. His work extends to organ transplantation logistics, maritime shipping networks, and semiconductor manufacturing optimization, blending machine learning with traditional operations research techniques. The articles reflect his interdisciplinary expertise: 2025 introduced a Smart Stadium Testbed for real-time sports analytics, while 2024 addressed Language Model Safety . Earlier publications (2023–2018) focused on transplant survival modeling, vaccine scheduling, and maritime logistics, showcasing his ability to apply analytics to diverse domains. Scientific Awards EURO Management Science Strategic Innovation Prize (2008) Cozzarelli Prize finalist (non-sports research) Georgia Tech's highest teaching awards (multiple years) INFORMS and IISE recognitions for curriculum development As a leader in analytics education, Sokol co-founded the INFORMS Sports Operations Research section and served as INFORMS Vice President of Education. His work has practical applications in professional sports, healthcare, and industry, with methodologies adopted by teams, medical institutions, and global logistics networks.

Abolfazl SimorghView profile

Researcher

• Aircraft Trajectory Optimization
• Climate Change Mitigation in Aviation
• Robust Control
• +3 more

Abolfazl Simorgh is a researcher at Charles III University of Madrid's Department of Aerospace Engineering, specializing in climate-optimized aviation systems. His work bridges mathematical control theory with practical climate impact mitigation, focusing on robust trajectory optimization under environmental and operational uncertainties. He leads development of open-source tools for sustainable flight planning while contributing to major European aviation initiatives. Education: B.Sc. in Control Engineering (2017) M.Sc. in Control Engineering (2020) Ph.D. in Aerospace Engineering from Charles III University of Madrid Dr. Simorgh's research centers on developing mathematical frameworks that reconcile aircraft trajectory optimization with climate impact reduction. His expertise spans robust control systems, optimization under uncertainty, and climate modeling integration, with particular emphasis on non-CO₂ emissions. His methodology addresses both CO₂ and non-CO₂ climate forcing mechanisms through computationally efficient algorithms that account for weather variability and climate metric uncertainties. This work directly supports aviation's decarbonization by providing operational strategies that reduce environmental footprint without prohibitive cost increases. Analysis of his 15 most recent publications reveals a cohesive research trajectory focused on operationalizing climate-optimal flight planning. His work consistently integrates climate science with aerospace engineering through robust optimization frameworks, demonstrating particular innovation in handling multiple uncertainty sources (weather, climate models, emissions). The publications cluster around three interconnected themes: 1) Development of open-source computational tools (ROOST, CLIMaCCF), 2) Network-scale implementation of climate-aware air traffic management, and 3) Risk analysis of climate mitigation strategies. This body of work establishes new methodological standards for quantifying and minimizing aviation's total climate impact. Scientific Awards: Luis Azcárraga Aeronautical Innovation Award for collaborative research impact Best Paper Award (2022) from a high-impact aerospace journal Dr. Simorgh secures significant research funding through European Commission projects including FlyATM4E (climate-optimized flight planning), ALARM (aviation emissions reduction), and RefMAP (sustainable aviation pathways). His grant portfolio emphasizes practical implementation of climate mitigation strategies, with strong industry-academia collaboration. He mentors junior researchers through project teams and has developed three major open-source Python libraries (CLIMaCCF, ROOST, ROC) that have become community standards for climate impact assessment in aviation research. His current work focuses on scaling climate-optimized trajectories to continental airspace while addressing operational constraints and economic viability. He leads a research group focused on climate-aware air traffic management, developing the ROOST simulation framework for GPU-accelerated trajectory optimization and the CLIMaCCF library for standardized climate metric calculations. His team collaborates with European air navigation service providers and aircraft manufacturers to transition research into operational practice, with current projects emphasizing real-time implementation and regulatory compliance frameworks.

Arish SateesanView profile

Professor

• Computer Engineering
• Network Security
• Hardware Acceleration
• +3 more

Dr. Arish Sateesan serves as Professor and Chair of the Institute for Networked Systems at RWTH Aachen University's Faculty of Electrical Engineering and Information Technology, located at Kackertstrasse 9 in Aachen, Germany. His research group operates from House C (Room C046) with direct contact via asa@inets.rwth-aachen.de. His primary research domains center on hardware-accelerated network security solutions, specializing in FPGA implementations for high-speed networking. Key focus areas include: Real-time network monitoring and intrusion detection systems Hardware-optimized cryptographic and non-cryptographic algorithms Machine learning integration for wireless beamforming and LiDAR processing Ultra-high-speed flow measurement architectures His work bridges theoretical computer science with practical hardware constraints, emphasizing throughput optimization for security-critical applications. Analysis of his 15 most recent publications (2021-2025) reveals a pronounced shift toward hardware-software co-design for next-generation networks. The research trajectory shows increasing integration of quantized neural networks with traditional security primitives, particularly for mm-Wave and 5G/6G applications. A consistent theme across all publications is the prioritization of hardware friendliness through algorithmic simplification and architectural innovation. As Institute Chair, he leads a research ecosystem focused on developing deployable security solutions for modern network infrastructures, with current projects targeting autonomous vehicle communication systems and infrastructure protection against distributed denial-of-service attacks.

Yeo Howe LimView profile

Professor

• Water Resources Engineering
• Hydrologic Modeling
• Open Channel Hydraulics
• +5 more

Dr. Yeo Howe Lim is a Professor and Department Chair of Civil Engineering at the University of North Dakota, with a focus on water resources engineering. He serves as Graduate Program Director for Civil and Environmental Engineering, teaching courses in fluid mechanics, hydrology, and applied hydraulics. Education: BEng & MEng from University of Canterbury, PhD from Memorial University of Newfoundland Research Areas: Open channel hydraulics, flood frequency analysis, streambank stabilization, urban stream revitalization, cold region hydrodynamics His research explores climate change impacts on flood patterns, hydrodynamic modeling of wetlands, and innovative use of Unmanned Surface Vehicles for aquatic studies. Recent publications emphasize lithium extraction technologies in oilfields, distributed Muskingum flood routing models, and optimization algorithms for cold climate water systems. Scientific awards include the Dean’s Outstanding Faculty Award (2013), ASCE Outstanding Reviewer recognition (2009), and the Institution of Civil Engineers (UK) Overseas Prize (2001). He has supervised numerous graduate students in hydrological modeling, including Mohammed Almousa and Vahid Atashi. Current projects involve HYCAT technology for bridge scour assessment, LiDAR bathymetry modeling, and climate change adaptation tools for cold region water management. His work bridges computational hydrology, hydraulic structure design, and sustainable water resource solutions.

Ryan K. WilliamsView profile

Assistant Professor

• Real-Time Systems
• Robotics
• Multi-Agent Systems
• +5 more

Ryan K. Williams is an Assistant Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. His research focuses on real-time systems optimization, multi-agent robotics, and computational frameworks for autonomous systems. He holds an NSF Career Award (2021) and has contributed to advancing algorithms for resilient and efficient multi-robot coordination. His work addresses challenges in distributed systems, including scheduling, fault tolerance, and resource allocation. Key areas include probabilistic security in multi-robot teams, topology control for stable coordination, and anticipatory planning for search and rescue operations. He also explores intersections with education, such as analyzing teacher professional learning impacts on student outcomes in STEM. Awards: NSF Career Award 2021 Grants: AF: Small grants (2024, 2021), CPS: Medium grant (2019) Labs/Teams: Collaborates on multi-robot systems and computational autonomy projects

Venkat N. KroviView profile

Professor

• Robotics
• Autonomous Vehicles
• Human-Robot Interaction
• +5 more

Prof. Venkat N. Krovi serves as the Michelin Endowed Chair Professor of Vehicle Automation in the Departments of Automotive Engineering and Mechanical Engineering at Clemson University's College of Engineering, Computing and Applied Sciences (CECAS). He directs the Automation, Robotics and Mechatronics Laboratory (ARMLab) at the International Center for Automotive Research (CU-ICAR), focusing on smart embedded systems for autonomy in challenging environments. He earned his Ph.D. in Mechanical Engineering and Applied Mechanics from the University of Pennsylvania in 1998. His research leverages distributed autonomy and human-robot synergy to extend human capabilities, with applications spanning plant automation, consumer electronics, automobile, defense, and healthcare. The work emphasizes lifecycle treatment (design through verification) of robotic systems under uncertainty. Recent publications (2024-2025) demonstrate strong trends in digital twin frameworks for autonomous vehicle validation, sim2real transfer via reinforcement learning, and integration of large language models for editable simulations. Key themes include scalable cloud-based architectures, Koopman operator theory for robustness, and containerization for reproducible robotics development. His accolades include: National Science Foundation (NSF) CAREER Award Petro-Canada Young Innovator Award Multiple best paper awards at conferences and journals ASME Dedicated Service Award (2024) Prof. Krovi has advised doctoral students including Dr. Srivatsan Srinivasan (2024). His research receives substantial funding from NSF, DARPA, ARO, and industrial partners like Michelin. He leads the NSF I/UCRC RoSeHuB center and the AutoDRIVE ecosystem for autonomous driving education. As ARMLab director, he oversees projects including OpenCAV, the Robotics for AV Systems Bootcamp, and containerized terramechanics simulations. The lab specializes in mechatronic design, verification/validation frameworks, and human-autonomy coexistence studies for next-generation mobility solutions.

• Computational Neuroscience
• Neural Networks
• Deep Learning
• +4 more

Prof. Sander M. Bohte holds a part-time appointment as a Professor of Computational Neuroscience at the Swammerdam Institute for Life Sciences (SILS), University of Amsterdam, and is a researcher at the CWI Machine Learning group. His research focuses on computational models of neural information processing, emphasizing spiking neural networks, predictive coding, and reinforcement learning. He bridges computational neuroscience and machine learning, exploring how biological insights can improve neural network designs and vice versa. Key collaborations include work with Cyriel Pennartz (UvA), Pieter Roelfsema (NIN), and Steven Scholte (B&C). His applied research spans scientific machine learning applications in finance and genomics. He actively supervises MSc thesis students, prioritizing those from UvA, with projects ranging from biologically inspired neural architectures to efficient spiking network simulations. Research highlights include developing biologically plausible learning rules for deep networks, predictive coding models for sensory data, and spiking network models for working memory tasks. His work also addresses challenges in temporal dynamics and scalable neural computation, leveraging both theoretical and applied perspectives.

Kuldeep S. MeelView profile

Associate Professor

• Automated Reasoning
• Formal Methods
• Artificial Intelligence
• +3 more

Kuldeep S. Meel is the Stephen Fleming Early-Career Associate Professor at Georgia Institute of Technology's School of Computer Science and an Associate Professor at the University of Toronto (currently on leave). His research focuses on the intersection of Formal Methods and Artificial Intelligence, emphasizing scalable automated reasoning techniques. He holds prestigious awards including the 2022 ACP Early Career Researcher Award and the 2019 NRF Fellowship for AI. His work has been recognized with multiple best paper awards at conferences like ICLP, CAV, and IJCAI. Meel's research spans automated reasoning, formal methods, and their applications in AI. He has developed influential tools like ApproxMC and UniGen, advancing model counting and uniform sampling. His academic journey includes roles at NUS and collaborations with institutions globally. Teaching excellence is highlighted by NUS Annual Teaching Awards (2022, 2023). Awards include Distinguished Paper Awards at CAV-23 and CAV-24, and 1st place in Model Counting Competitions. His lab has produced notable advisees securing tenure-track positions worldwide. Current projects explore distribution testing, probabilistic reasoning, and AI verification.

Adel AbdelnabyView profile

Associate Professor

• Structural dynamics
• Earthquake engineering
• Structural health monitoring
• +6 more

Dr. Adel Abdelnaby is an Associate Professor in the Department of Civil, Construction, and Environmental Engineering at The University of Memphis College of Engineering. He holds a Ph.D. from the University of Illinois at Urbana-Champaign (2012) and has been on the faculty since fall 2012. Dr. Abdelnaby is a licensed Professional Engineer (P.E.) in multiple states and a licensed Structural Engineer (S.E.) in Illinois. His research interests span structural dynamics, earthquake engineering, structural health monitoring, life-cycle analysis of structures, application of innovative materials, and nonlinear finite element methods. He specializes in analyzing structures subjected to multiple hazards and developing methods for structural assessment and improvement. Dr. Abdelnaby's publications reveal a strong focus on earthquake engineering, particularly the effects of multiple earthquakes on reinforced concrete structures. His work includes fragility analysis, hybrid simulation techniques, and vulnerability assessment of bridges and buildings. He has established the Multi-Axial Testing and Simulation (MAT-SIM) Facility at the University of Memphis for advanced structural testing. Engaged Learning Fellowship for redesigning undergraduate steel design courses American Institute of Steel Construction Educator Workshop participant Dr. Abdelnaby has advised numerous graduate students on topics including semi-rigid steel connections, multiple earthquake effects, fragility analysis, and structural health monitoring. He directs the MAT-SIM facility, which includes sophisticated equipment for structural testing under complex loading conditions. His professional affiliations include ASCE, AISC, ACI, EERI, and ASEE, and he serves as a reviewer for several structural engineering journals.

Katie SteeleView profile

Professor

• Rational Choice and Inference
• Ethics and Risk
• Climate-Change Policy
• +4 more

Katie Steele is a Professor in the School of Philosophy at the Australian National University (ANU), where she holds an ANU Futures Scheme grant. She previously served at the London School of Economics (2008–2016) and conducted postdoctoral research at the University of Sydney's Australian Centre of Excellence for Risk Analysis. Her roles include co-editing Economics & Philosophy and co-investigator on major grants like the 'Ethics and Risk' ARC Discovery Project and the 'Climate Change and Future Generations' grant. Education: BSc in Mathematics, University of Queensland (1999) MA in Philosophy, University of Queensland (2003) PhD in Philosophy, University of Sydney (2007) Research Interests: Her work focuses on rational choice theory, ethics of climate policy, intergenerational fairness, and moral uncertainty. She explores decision-making under risk, the implications of climate change for future generations, and the philosophical foundations of belief revision and Bayesian reasoning. Grants & Advising: She leads the ANU Futures Scheme grant (2018–2025) and advises on projects addressing ethics in risk analysis. Her advisory work includes mentoring students in decision theory and climate ethics. Labs/Teams: Active in interdisciplinary collaborations at ANU and international institutions like the Institute for Futures Studies in Stockholm.

Josie ClowneyView profile

Associate Professor

• Neural Circuits
• Developmental Neuroscience
• Chemosensory Systems
• +3 more

Josie Clowney is an Associate Professor in the Department of Molecular, Cellular, and Developmental Biology at the University of Michigan, where she has held faculty position since 2017. Her research investigates the genomic algorithms that construct neural circuits during development, using Drosophila as a model to study how chemosensory systems drive both instinctual behaviors and learning. She teaches Bio 172 and an upper-level seminar on cellular diversity and scientific writing, and mentors graduate students through MCDB, CMB, NGP, and BIOINF PhD programs. Her educational background includes: Ph.D. in Biomedical Sciences (2012) from the University of California, San Francisco B.S. in Cellular and Molecular Biology (2005) from the University of Michigan, where she conducted research with Cunming Duan Clowney's research centers on understanding how definitive neuronal parameters are encoded in genomic information and translated into cellular architectures. Her lab hypothesizes that developmental algorithms for learning circuits versus instinctual circuits differ fundamentally in their genomic requirements, with chemosensory circuits serving as key models. Using fruit flies for their tractable brain organization, her work bridges computational principles and biological implementation to uncover universal brain organization rules. Analysis of her 15 most recent publications (2016-2025) reveals consistent focus on Drosophila mushroom body development, neural sexual differentiation, and spatial constraints in circuit formation. Key themes include non-deterministic mechanisms diversifying cell surface expression, chromatin dynamics in circadian regulation, and how input density tunes sensory responses. Her work integrates genomics, neuroanatomy, and behavior to model how compact genomic information generates complex neural architectures. No scientific awards were mentioned in the provided text. Dr. Clowney advises graduate students through multiple PhD programs at the University of Michigan, though specific student names and grant details are not provided in the source material. Her teaching includes foundational undergraduate coursework and advanced seminars emphasizing scientific writing. The active publication record spanning 2016-2025 indicates sustained research funding supporting her lab's investigations into neural circuit development. The Clowney Lab, housed in the Biological Sciences Building (4218 BSB), employs Drosophila genetics and neuroanatomical techniques to dissect developmental algorithms of brain wiring. Current projects explore how spatial constraints structure learning circuits, mechanisms of neural sexual differentiation, and the genomic encoding of circuit diversity. The lab collaborates within Michigan's neuroscience community through the Program in Biology and participates in interdisciplinary initiatives studying brain evolution and function.

• Algorithm Design
• Discrete Optimization
• Approximation Algorithms
• +2 more

Nikhil Bansal is Patrick C. Fischer Professor of Theoretical Computer Science at the University of Michigan. His research focuses on algorithms for discrete optimization problems, discrepancy theory, and scheduling. Research Contributions: Developed foundational techniques in randomized rounding and algorithmic discrepancy minimization. Current investigations include online algorithms, approximation methods for scheduling, and quantum-classical complexity separations. Teaching: Offers courses on randomized algorithms, linear algebra, and approximation algorithms. Created instructional materials on discrepancy theory and semidefinite programming. Professional Service: Serves on editorial boards for Journal of the ACM and Theory of Computing. Organized international workshops on discrepancy theory and optimization.

Cláudia ReisView profile

Assistant Professor

• Coastal Engineering
• Structural Engineering
• Natural Hazards Engineering
• +5 more

Cláudia Reis is an Assistant Professor at Lehigh University with a joint appointment in the Department of Civil & Environmental Engineering and the Institute for Cyber-Physical Infrastructure and Energy (I-CPIE) within the P.C. Rossin College of Engineering and Applied Science. She teaches courses in Coastal and Offshore Infrastructure Engineering, Structural Analysis I, and Finite Element Method in Structural Engineering. Her research focuses on fluids-solids interaction in coastal and offshore engineering, multi-risk management, and multi-scale/multi-physics modeling of critical infrastructure in multi-hazard regions. Dr. Reis leads the NHERI Lehigh Experimental Facility as a Co-Principal Investigator and chairs the Advanced Technology for Large Structural Systems (ATLSS) Engineering Research Center. She has secured grants including a PITA Grant ('Resiliency of Coastal Infrastructure') and a PEER Transportation Systems Research Program project ('Cascading Seismic and Tsunami Loads for the Design of Open Wharves'). Her work integrates computational methods like smoothed particle hydrodynamics (SPH) and finite volume techniques to address tsunami and earthquake impacts. She contributes to national/international committees such as NHERI and FIB, and serves on editorial boards and grant review panels. Dr. Reis' research emphasizes infrastructure resilience, cascading hazards, and exascale computing applications for natural hazard mitigation.

Alexander BrownView profile

Lecturer

• Robotics
• Vehicle Dynamics
• Human-Robot Interaction
• +5 more

Alexander Brown is a Lecturer in the Department of Mechanical Engineering at Lafayette College, specializing in dynamic systems and controls. His research bridges robotics, vehicle dynamics, and bio-inspired modeling, with a focus on human-robot and animal-robot interactions. B.S., Pennsylvania State University (2008) M.S., Pennsylvania State University (2012) Ph.D., Pennsylvania State University (2013) Research interests include robotics, vehicle dynamics, and mathematical modeling of physical systems. His work explores interactions between robots, vehicles, humans, and animals, with applications in autonomous driving and ethorobotics. Alexander's recent publications highlight trends in fish-robot interaction studies, autonomous vehicle safety, and simulation platforms like Webots. He emphasizes interdisciplinary collaboration and hands-on student engagement in his research and teaching. Outside academia, he builds and restores motorcycles, surfs in Monmouth County, N.J., and plays music with his band 'Imposters In Rome'.

Robert PeharzView profile

Assistant Professor

• Probabilistic Machine Learning
• Tractable Probabilistic Models
• Artificial Intelligence
• +6 more

Robert Peharz is an Assistant Professor at Graz University of Technology, where he leads research at the Institute of Machine Learning and Neural Computation. His work focuses on probabilistic machine learning, with particular emphasis on tractable probabilistic models, causality, and neurosymbolic AI. Education and Career PhD from TU Graz (Austria) in 2015 Postdoc at Medical University of Graz Postdoc and Marie-Curie Individual Fellow at University of Cambridge (2017-2019) Assistant Professor at Eindhoven University of Technology (2019-2021) Current: Assistant Professor at Graz University of Technology Research Interests Peharz's research spans multiple areas of artificial intelligence with a focus on making probabilistic reasoning both theoretically sound and practically efficient. His work addresses fundamental challenges in tractable probabilistic inference and learning, probabilistic circuits as a unified framework for deep generative models, Bayesian causal inference, and neurosymbolic AI combining sub-symbolic and symbolic approaches. His research has applications in cybersecurity, healthcare, and energy systems. Research Projects VENTUS (2024-present): Physics-informed, probabilistic and causal machine learning for wind energy systems NEO DNA (2023-present): DNA-based data storage systems using computer vision and probabilistic ML VanillaFlow (2023-present): AI-guided development of novel vanillin-based molecules for redox flow batteries Bilateral AI : Cluster of Excellence focused on Broad AI combining sub-symbolic and symbolic AI approaches Awards and Recognition Finalist for TUG's Excellent Teaching Award (2023) for all 3 of his courses Marie-Curie Individual Fellow at University of Cambridge Academic Service Peharz is actively involved in the academic community through conference organization and reviewing: Area Chair: UAI (2022), ECML/PKDD (2022) Senior Committee Member: UAI (2021), IJCAI (2019, 2020) Reviewer for major conferences including ICML, NeurIPS, AAAI, IJCAI-ECAI Teaching and Mentorship Peharz supervises multiple PhD students working on diverse projects at the intersection of machine learning, causality, and neurosymbolic AI. His current advisees include Sepideh Adamiat, Irina Dobrianski, Johannes Exenberger, Giacomo Di Gobbi, Tim d'Hondt, Christian Toth, and Thomas Wedenig. Previous students include Alvaro Correia, Martin Trapp, and David Montalvan.