Find Academic Supervisors

Aravind N.R.View profile

Associate Professor

• Algorithms
• Graph Theory
• Combinatorics
• +5 more

Dr. N.R. Aravind is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Hyderabad. Holding a Ph.D. in Theoretical Computer Science from The Institute of Mathematical Sciences, Chennai, he is affiliated with the College of Engineering. His research spans algorithms, graph theory, combinatorics, and determinantal complexity, with a focus on parameterized algorithms and approximation techniques for NP-hard problems. Education: Ph.D., The Institute of Mathematical Sciences, Chennai (Advisor: Prof. C.R. Subramanian) Postdoctoral research under Prof. Sundar Vishwanathan at IIT Bombay Aravind's research explores structural questions in graph theory, social network modeling, and complexity bounds. His work on parameterized algorithms addresses tractable instances via input parameters, while his studies in graph coloring and forbidden subgraphs contribute to theoretical understanding. Recent publications focus on matching cut problems, happy coloring, and determinantal complexity. His 15 most recent publications (2010-2024) span computational complexity, graph algorithms, coloring problems, and determinantal complexity. These works emphasize parameterized approaches, structural graph theory, and algorithmic bounds for intractable problems. Scientific Awards: Aravind has mentored numerous PhD and MTech students, including Roopam Saxena, Anjeneya Swami Kare, and R.B. Sandeep, now holding academic positions at prestigious institutions. He has taught courses like Algorithms, Probability in Computing, and Cryptology, often collaborating with co-instructors such as Dr. Rakesh Venkat. His office is located in Room CS-408, Indian Institute of Technology Hyderabad.

Simon WeberView profile

Researcher

• Unique Sink Orientations
• Computational Complexity
• Combinatorial Algorithms
• +7 more

Simon Weber is a researcher affiliated with the ETH Zurich (Department of Computer Science). His work focuses on Unique Sink Orientations (USOs) , a combinatorial abstraction of optimization problems like Linear and Quadratic Programming. Simon's research spans three areas: (1) Structure of USOs and their links to Oriented Matroids; (2) Constructions of high-dimensional USOs to analyze algorithm complexity; and (3) Algorithmic improvements for sink-finding. He also explores topics in graph compression, neural networks, and ∃R-complete problems. Key Publications: PhD thesis on USO reductions, ∃R-completeness in neural training, and USO phase analysis. Scientific Contributions: Advances in USO complexity, FPT algorithms for MaxCut, and recognition of geometric hypergraphs and nerves of convex sets. He has supervised multiple theses at ETH Zurich, including topics on USO visualization, MaxCut algorithms, and necklace splitting. His teaching experience includes being a Head Assistant for courses like Geometry: Combinatorics & Algorithms and Topological Data Analysis . Simon's work has been recognized with Best Paper and Best Student Paper Finalist awards at SC19.

Kirill SimonovView profile

Associate Professor

• Parameterized Complexity
• Algorithm Design
• Graph Theory
• +3 more

Kirill Simonov is an Associate Professor in the Department of Informatics at the University of Bergen. His research focuses on parameterized complexity, algorithm design, and graph theory, with particular emphasis on clustering algorithms, graph modification problems, and algorithmic graph theory. He has contributed to foundational work in fair clustering, approximate algorithms for graph cycles, and structural analysis of sparse graphs. His notable contributions include studies on coresets for fair clustering, algorithmic extensions of Dirac's theorem, and techniques for building large k-cores from sparse graphs. His work is supported by the Research Council of Norway (Project 314528). He frequently collaborates with leading researchers like Fedor Fomin and Petr Golovach on topics such as parameterized algorithms and combinatorial optimization. Simonov's publications span venues like the Journal of Computer and System Sciences and Leibniz International Proceedings in Informatics. His research bridges theoretical computer science with practical algorithmic solutions for graph problems and data clustering challenges.

• Combinatorics
• Graph Theory
• Optimization on Graphs
• +3 more

Martí Farré is a faculty member in the Department of Mathematics at the Faculty of Mathematics and Statistics, Universitat Politècnica de Catalunya (UPC). He is actively involved in the COMBGRAPH research group (Combinatorics, Graph Theory, and Applications) and the OMGRAPH subgroup focusing on optimization methods in graphs. His research spans theoretical and applied aspects of discrete mathematics. Research Interests: Martí's work centers on Combinatorics and Graph Theory , with particular emphasis on extremal problems, connectivity, matching theory, independence number, and spectral graph properties. His research often investigates structural and optimization properties of graphs under constraints such as fixed diameter, degree sequences, or matching numbers. The analysis of his recent publications reveals a consistent focus on extremal graph theory, where he derives bounds and characterizes graphs maximizing or minimizing certain parameters (e.g., edges, diameter, connectivity) under given constraints. Key themes include vulnerability and robustness of networks, spectral properties, and combinatorial optimization on graphs. Scientific Awards and Recognition: Premi o reconeixement (Recognition or Prize) Advising and Grants: Martí has contributed to competitive R&D+i projects and innovation initiatives in education. He has supervised doctoral theses, indicating an active role in mentoring graduate students, although specific student names are not listed in the provided data. Labs and Research Teams: He is a key member of the COMBGRAPH research group at UPC, a leading team in combinatorics and graph applications, and participates in the OMGRAPH subgroup dedicated to optimization on graphs, contributing to both theoretical advances and real-world applications.

• Power Electronics
• Electrical Machines
• Motor Drives
• +5 more

Paolo Pescetto is a Fixed-term tenure-track Assistant Professor in the Department of Energy (DENERG) at Politecnico di Torino, where he is also a member of the Interdepartmental Center PEIC (Power Electronics Innovation Center). He serves on multiple academic boards including the College of Electrical and Energy Engineering, College of Computer, Film and Mechatronics Engineering, and College of Mechanical, Aerospace, and Automotive Engineering. His research focuses on power electronics, electrical machines, and motor drives with particular emphasis on electric vehicle applications. His work spans motor control strategies, thermal management of high-power density motors, sensorless control techniques, and integrated power systems for e-mobility. He has developed advanced methodologies for flux mapping, torque ripple compensation, and fault protection in permanent magnet and synchronous reluctance machines. Analysis of his recent publications reveals a strong trend toward solving practical challenges in electric vehicle powertrains, with significant contributions in multi-phase motor drives, thermal management, and fault-tolerant control systems. His work bridges theoretical advances with practical automotive applications, particularly in third-generation electric vehicle technologies. Dr. Pescetto holds multiple patents in motor control technologies, including methods for MTPA tracking without HF injection, spatial harmonic flux-map identification, and isolated on-board battery chargers for electric vehicles. His intellectual property demonstrates practical innovation in the field of motor drives and power electronics. He actively supervises PhD students Andrei Bojoi and Chen Chen in the Electrical, Electronics, and Communications Engineering program, focusing on electric motor drives and sustainable traction electrification. His research projects include commercial contracts on sensorless control of synchronous reluctance machines, firmware implementation for motor control, and advanced sensorless control methodologies for brushless motors. As a member of the PEEMD Research Group within DENERG, Dr. Pescetto contributes to cutting-edge research in power electronics and motor drives, with a strong industry collaboration focus that translates academic research into practical automotive solutions.

Yang CaoView profile

Associate Professor

• Computational Biology
• Bioinformatics
• High-Performance Computing (HPC)
• +3 more

Yang Cao is an Associate Professor in the Department of Computer Science at Virginia Tech, affiliated with the College of Engineering. His research focuses on computational biology, bioinformatics, and high-performance computing, particularly in modeling cell cycle dynamics and stochastic processes in biochemical systems. He holds a Ph.D. from the University of California, Santa Barbara, and degrees from Tsinghua University. Education: Ph.D., Computer Science, University of California at Santa Barbara (2003) M.S., Computational Math, Tsinghua University, Beijing, China (1998) B.S., Applied Math, Tsinghua University (1993) Research Interests: Computational modeling of biological systems, including Caulobacter crescentus and budding yeast cell cycles Stochastic simulation algorithms for biochemical reactions HPC applications in systems biology and computational science Recent Work Trends: Recent articles highlight advancements in cell cycle regulation, parameter estimation for stochastic models, and algorithmic improvements for hybrid simulation methods, emphasizing computational efficiency and biological accuracy.

Reyan AhmedView profile

Assistant Professor

• Graph Theory
• Algorithms
• Network Visualization
• +3 more

Reyan Ahmed serves as an Assistant Professor of Practice at The University of Arizona, holding a Ph.D. from the institution with office GS 831. His academic profile bridges theoretical computer science and practical visualization systems. Ph.D., The University of Arizona Dr. Ahmed's research centers on graph-theoretic foundations with emphasis on sparsification techniques, spanner constructions, and geometric optimization. His work advances computational geometry through polygon-based facility assignments and stress-invariant metrics, while pioneering neural network integrations for Steiner tree approximations and vertex-splitting algorithms. Key contributions include multi-level graph sketching frameworks and scalable layout methodologies addressing NP-hard combinatorial problems. Analysis of his 2021-2025 publications reveals three dominant threads: (1) Algorithmic innovations in graph sparsification using machine learning-assisted search, (2) Theoretical advancements in additive spanners with lightness guarantees, and (3) Practical visualization systems for evolving networks. His methodology consistently merges discrete mathematics with gradient-based optimization for real-world network applications. Scientific recognition: No awards documented in source materials Research leadership includes development of Monte Carlo tree search frameworks for graph optimization and semantic zooming architectures for massive network visualization. Current projects focus on scale-invariant stress metrics and multi-priority sparsification, with ongoing exploration of neural network accelerators for classical combinatorial problems. No student advising or grant details were provided. Lab infrastructure supports large-scale graph processing through custom visualization pipelines, though specific team compositions remain undocumented.

Haiyang ChaoView profile

Associate Professor

• Unmanned Aircraft Systems (UAS)
• Control Systems Engineering
• Remote Sensing
• +4 more

Haiyang Chao is an Associate Professor at the Department of Aerospace Engineering, College of Engineering, University of Kansas. His research focuses on Unmanned Aircraft Systems (UAS) with emphasis on control systems, wind estimation, remote sensing, and cooperative UAV operations. Recent work includes FireCrowdSensing for prescribed fire monitoring Wake vortex hazard estimation for airport UAS operations Thermal imaging for fire spread measurement Vertical wind velocity analysis in fire plumes System identification techniques for flying-wing UAVs Honors include the 2016 Summer Faculty Fellowship at Air Force Research Lab. He leads the Cooperative Unmanned Systems Lab (CUSL) and collaborates with NASA on gust sensing projects. Teaching interests include Avionics, Control Systems, and Autonomous Aerospace Vehicles.

V. ArvindView profile

Professor

• Computational Complexity Theory
• Structural Complexity
• Randomized Computation
• +5 more

V. Arvind is a Professor in the Theoretical Computer Science faculty at the Institute of Mathematical Sciences (IMSc) , Chennai. His research is centered on computational complexity theory, with a focus on structural complexity, randomized and algebraic computation, and quantum information and computation. He explores the deep connections between theoretical computer science and mathematics. Institution: Institute of Mathematical Sciences (IMSc), Chennai School: Theoretical Computer Science Academic Rank: Professor Arvind's research interests include computational complexity, structural complexity theory, algebraic computation, derandomization, and quantum computing. He is particularly interested in the interplay between mathematical structures and computation. His work often bridges theoretical computer science with algebra, combinatorics, and logic. His recent publications, primarily expository articles in the EATCS Bulletin’s Computational Complexity Column, cover a wide range of topics such as robust oracle machines, the Alon-Roichman theorem, noncommutative arithmetic circuits, graph isomorphism, and quantum computation. These works reflect trends in foundational complexity theory, algebraic methods in computation, and the exploration of quantum models. The articles emphasize structural insights, lower bounds, and connections to mathematical disciplines. Professional Service and Editorial Roles: Associate Editor, ACM Transactions on Computation Theory Editor, EATCS Computational Complexity Column (since June 2011) Editorial Board Member, International Journal of Computer Mathematics (2009–2013) Co-organizer, ICM Satellite Conference on Algebraic and Probabilistic Aspects of Combinatorics and Computing Program Committee Member for WALCOM 2014, STACS 2012, COCOON 2009, FSTTCS (multiple years, including chair roles), CCC 2006, INDOCRYPT (2002, 2005), and others Teaching: Arvind has taught advanced courses including Computational Complexity, Algorithms, Algebra and Computation, and Discrete Mathematics, often based on foundational texts and notes from leading experts. Lecture notes from his courses have been compiled by students and collaborators. Collaborations: He has an extensive list of co-authors, including prominent researchers such as Manindra Agrawal, Eric Allender, Johannes Köbler, Meena Mahajan, Jacobo Torán, and Ramprasad Saptharishi, indicating strong collaborative research networks in complexity theory and algorithms.

• Optimization
• Control Theory
• Nonlinear Systems
• +4 more

Pontus Giselsson is an Associate Professor at the Department of Automatic Control within the Faculty of Engineering, LTH at Lund University . His research focuses on optimization theory and its applications to control systems, machine learning, and robotics. He teaches advanced courses including: Optimization for Learning (Master’s level, held annually) Large-Scale Convex Optimization (PhD level, last held 2015) His research projects span: Scalable control of interconnected systems Nonlinear control theory Robot learning and control Cloud-native control systems Autonomous real-time systems Optimal transport and numerical optimization His scientific work emphasizes: Operator splitting algorithms Convergence analysis Low-rank optimization Applications to machine learning and control systems Pontus leads research initiatives in the RobotLab LTH and contributes to national projects such as WASP (Wallenberg AI, Autonomous Systems and Software Program) and ELLIIT (Excellence Center at Linköping–Lund in Information Technology).

• Fluid Power Technology
• Hydraulic Systems Design
• Renewable Energy Engineering
• +3 more

Andrea Vacca is a Professor of Agricultural and Biological and Mechanical Engineering at Purdue University, holding the MAHA Fluid Power Faculty Chair. He is affiliated with the Agricultural & Biological Mechanical Engineering department and contributes to interdisciplinary research in fluid power systems, electro-hydraulic actuation, and energy-efficient mobile hydraulics. Vacca leads research initiatives in hydraulic machinery design, renewable energy integration, and tribology, with a focus on off-road vehicles and agricultural applications. His research interests span fluid power technology advancements, including hydraulic pump dynamics, energy efficiency optimization, and the integration of hydrogen-fueled systems. He has pioneered methodologies for compact electro-hydraulic unit design and has developed novel approaches to fault diagnostics in hydraulic systems using particle filter algorithms. Vacca also focuses on curriculum development, having created virtual reality tools for fluid power education and hands-on hydraulic trainer systems. Vacca’s work bridges mechanical engineering with renewable energy, addressing challenges in wind turbine hydrostatic transmissions and low-energy water desalination systems. His recent efforts include energy regeneration systems for construction equipment and multi-pressure rail architectures for agricultural machinery. He actively participates in academic committees, including the Engineering Named Professorships Committee at Purdue. His contributions to hydraulic system design, fault detection, and energy-efficient technologies have been validated through experimental studies and industry partnerships. Vacca’s research emphasizes practical applications of fluid power innovation in agricultural, construction, and renewable energy sectors.

Stéphane BonhommeView profile

Professor

• Microeconometrics
• Econometric Theory
• Labour Economics
• +3 more

Stéphane Bonhomme is the Ann L. and Lawrence B. Buttenwieser Professor of Economics and the College at the University of Chicago's Kenneth C. Griffin Department of Economics. His research focuses on microeconometrics, econometric theory, and labor economics, with emphasis on latent variable modeling and panel data analysis. He holds a PhD from the University of Paris I, Panthéon-Sorbonne (2005). Key contributions include methodologies for handling unobserved heterogeneity in panel data, nonlinear persistence in consumption dynamics, and bias reduction in econometric models. His work has been published in top journals like Econometrica, Journal of Econometrics, and the Journal of Political Economy. Recent research explores grouped patterns of heterogeneity, firm-worker sorting effects, and functional differencing in networks. He is a Fellow of the Econometric Society (2017) and has developed widely used Stata/Python packages for bias correction and discrete heterogeneity estimation.

Petr GolovachView profile

Research Professor

• Discrete Mathematics
• Theoretical Computer Science
• Graph Theory
• +6 more

Petr Golovach is a Research Professor at the Department of Informatics, University of Bergen. His research focuses on Discrete Mathematics and Theoretical Computer Science, particularly graph theory, algorithms, parameterized complexity, and clustering. He has held academic positions at Syktyvkar State University (1991–2007), Durham University (2009–2011), and currently teaches advanced courses like Advanced Algorithms Techniques and Enumeration Algorithms at the University of Bergen. His research interests include graph algorithms, parameterized complexity, matroid theory, and algorithmic enumeration. He has organized notable events like the Dagstuhl Seminar 2018 and serves on program committees for STACS, IPEC, and SWAT. Notable contributions include work on hybrid clustering algorithms, graph cuts under matroid constraints, and parameterized tractability of path and cycle problems. He has supervised PhD students like Nidhi Purohit and master’s students including Øyving Stette Haarberg and Andreas Steinvik. His publications span over 200 peer-reviewed papers in top venues such as Journal of the ACM and conferences like SODA and ICALP. His research bridges extremal combinatorics with algorithm design, emphasizing practical and theoretical advancements in graph-based problems.

• Financial Markets
• Mathematical Finance
• Derivatives
• +6 more

Vedat Akgiray is a Professor at Bogazici University, holding a Ph.D. from Syracuse University. His teaching and research areas focus on Derivatives , Portfolio Management , Probability , and Financial Markets , with a particular emphasis on Mathematical Finance . He has published extensively on topics such as FinTech, corporate governance, pension systems, and risk management. Education : Ph.D., Syracuse University Email: akgirayv@bogazici.edu.tr

Michael WeharView profile

Lecturer

• Computer Science
• Algorithms
• Computational Art
• +7 more

Michael Wehar is a Lecturer in Computer Science at Bryn Mawr College , where he teaches Introductory Programming Courses. He is a multidisciplinary researcher and developer with a focus on algorithms , computational art , and software engineering , creating innovative tools that bridge computer science with creative applications. Co-founder of AlgoArt.org , a platform for algorithmic art creation and exhibition Creator of Word of The Hour , a multilingual vocabulary learning platform Co-developer of Treegle Dictionary , a structured definition platform His research spans computational complexity, human-computer interaction, and educational technology. Key areas include: Algorithmic Art : Generative design systems, interactive visualizations Pattern Matching : Matrix algorithms, Voronoi diagrams, formal verification Language Technology : Multilingual dictionaries, crowdsourced translation systems Educational Tools : Git repository analysis, interactive learning platforms His 15 most recent publications focus on topics ranging from 2D pattern matching to ETH-based complexity lower bounds , with significant contributions to automata theory and algorithm design. He has received academic recognition including the Best Faculty Poster at CCSC:EA 2022 and Honorable Mention at IFoRE 2022 . As a dedicated mentor, he has guided over 100 students across institutions like Swarthmore College, Temple University, and University at Buffalo in projects spanning: Web development frameworks AI applications in art and education Mobile productivity tools Language learning platforms Game development projects