Find Academic Supervisors

• Constraint Programming
• Numerical Optimization
• Artificial Intelligence
• +3 more

Jamila Sam is a Senior Lecturer and Researcher at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Computer and Communication Sciences (IC). She holds positions in the Systems and Infrastructure (SIN) and Communication Systems (SSC) departments, focusing on teaching and research. Her roles include membership in the Conference of the Teaching Staff (CCE) and administrative responsibilities within EPFL’s academic structure. Her research interests revolve around constraint programming, numerical optimization, and distributed systems, with a particular emphasis on interval analysis, constraint satisfaction problems (CSPs), and algorithm design. She has contributed to advancements in numerical methods, global optimization, and distributed constraint satisfaction protocols such as backjumping and dynamic ordering techniques. Jamila Sam has authored and co-authored over 30 publications, including works on interval propagation, branch-and-prune strategies, and clustering techniques for numerical problems. Her articles span topics like directed acyclic graphs for constraint solving, approximation methods for non-linear systems, and distributed algorithms for collaborative design. In teaching, she instructs courses such as Introduction to Programming , Object-Oriented Programming , and Information, Calculation, Communication , though these course sheets are pending validation. Her academic contributions also extend to administrative roles, including membership in EPFL’s CCE, which oversees teaching policies and staff coordination.

Gregory ProvanView profile

Professor

• Complex Systems
• Artificial Intelligence
• Diagnostics
• +3 more

Professor Gregory Provan is a Full Professor and Programme Coordinator for BSc Data Science and Analytics at University College Cork's School of Computer Science and Information Technology. He directs the Complex Systems Laboratory and holds a PhD from the University of Oxford, with prior academic appointments at the University of Pennsylvania. His educational journey includes: BSE from Princeton University MSc from Stanford University DPhil from University of Oxford This foundation supports his interdisciplinary research approach. Professor Provan's research spans: complex systems design and analysis with applications in: Embedded diagnostics algorithms for large-scale systems Stochastic model-based diagnosis frameworks Automated benchmark model generation Sustainable energy system optimization Co-design of embedded systems His work bridges artificial intelligence, data analytics, and network architectures to solve real-world engineering challenges. Analysis of his extensive publication record reveals core themes: Advancements in model-based diagnosis and control Computational methods for complex systems Applications in sustainable energy and building automation Innovations in stochastic optimization and machine learning His recent work increasingly focuses on cybersecurity for cyber-physical systems and explainable AI. Professor Provan has secured significant research funding including: Science Foundation Ireland grants totaling over €1.2M Industry partnerships with Tyndall National Institute Enterprise Ireland support for competence centers His research group addresses fundamental challenges in complex systems engineering. He has supervised numerous doctoral candidates to completion, including: Jun Wang (Modeling real-world complex systems) Alie El-Din Mady (Networked control systems) Margarita Razgon (Constraint satisfaction problems) Ma Ji (Uncertainty visualization) Current doctoral researchers continue work on wireless sensor networks and analog system diagnosis. Honors include the 2008 Best Paper Award from the IEEE Prognostics and Health Management Conference for his work on approximation techniques in model-based diagnostics. Professor Provan leads the Complex Systems Laboratory which focuses on: Modeling and analysis of sustainable energy systems Diagnostics and control of large-scale networks Embedded system co-design The lab maintains collaborations with international research institutions and industry partners.

• Finite Geometry
• Extremal Combinatorics
• Coding Theory
• +7 more

Anurag Bishnoi is a tenured Assistant Professor in the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology (TU Delft). His research lies at the intersection of discrete mathematics, finite geometry, extremal combinatorics, coding theory, and the polynomial method. He has held prior research positions at The University of Western Australia as a DECRA Research Fellow (2019–2020) and at Freie Universität Berlin as a postdoctoral researcher in the Combinatorics and Graph Theory group (2017–2019). His research interests include: Finite Geometry Extremal Combinatorics Coding Theory Spectral Graph Theory Polynomial Method Ramsey Theory Incidence Geometry Anurag’s recent publications focus on deep connections between combinatorial structures, coding theory, and finite geometry. His work includes explicit constructions of optimal blocking sets and minimal codes using expander graphs, improvements on bounds for the trifference problem, and advances in Ramsey theory via geometric constructions. He frequently employs algebraic and combinatorial techniques, including the Chevalley-Warning theorem and expander mixing lemma, to solve extremal problems. He has received recognition as a DECRA Research Fellow and has published in leading journals such as Transactions of the AMS , Journal of the London Mathematical Society , and Combinatorica . His research is highly collaborative, involving mathematicians such as Noga Alon, John Bamberg, and Bart De Bruyn. Notable scientific contributions and awards include: DECRA Research Fellowship Significant results on Ryser’s conjecture and minimal Ramsey graphs Novel constructions of clique-free pseudorandom graphs Generalizations of classical theorems in finite field theory Anurag advises no formal students listed in the text but collaborates extensively. He also serves as Chief Scientific Advisor for the edtech startup Not A Bot , where he designs mathematical games like 'It’s a trap'. He maintains a research blog that functions as his official website and has been instrumental in disseminating his work and mentoring through expository writing.

René ThiemannView profile

Associate Professor

• Program Verification
• Interactive Theorem Proving
• Termination Analysis
• +7 more

René Thiemann is an Associate Professor in the Department of Computer Science at the University of Innsbruck, Austria, where he is a member of the Computational Logic group. His research is centered on formal methods, with a focus on program verification, termination analysis, and interactive theorem proving using Isabelle/HOL. He has led and contributed to several FWF-funded research projects, including Automation of Rewriting Infrastructure and Certifying Termination and Complexity Proofs of Programs . His research interests include: Program Verification via Interactive Theorem Proving (Isabelle, CeTA) Termination and Complexity Analysis of Programs and Rewriting Systems Term Rewriting and Dependency Pairs SAT and SMT Solving Formalization of Algebraic Structures (e.g., algebraic numbers, Smith normal form) Automated Reasoning and Certified Software The most recent articles highlight his continued work in formal verification, particularly in certifying termination, confluence, and complexity proofs. His publications show a strong trend toward mechanizing mathematical and logical foundations in Isabelle/HOL, with applications in rewriting, arithmetic, and program analysis. He frequently publishes in top venues such as CPP, ITP, LICS, and FSCD. He has served on program committees for conferences including CPP and RTA, and he regularly advises and collaborates on formalization projects. While no explicit grants or advising roles are listed, his leadership in multiple FWF projects indicates significant grant involvement. He teaches courses such as Program Verification, Functional Programming, Constraint Solving, and Interactive Theorem Proving in Isabelle/HOL, reflecting his expertise in formal methods and programming languages.

Thorsten RaaschView profile

Professor

• Numerical Mathematics
• Adaptive Discretization Methods
• Partial Differential Equations
• +7 more

Thorsten Raasch is a Professor of Numerical Mathematics at the Institute of Mathematics, Johannes Gutenberg University Mainz, Germany. He is affiliated with the Department of Physics, Mathematics, and Computer Science, and leads research in numerical analysis, particularly adaptive methods for PDEs and inverse problems. Research Interests: His work focuses on adaptive discretization methods for partial differential equations and inverse problems, non-smooth numerical optimization , wavelet systems , multilevel frames , and GPGPU computing . These interests are deeply rooted in applied and computational mathematics, with applications in scientific computing and engineering. The most recent articles show a strong trend in developing adaptive wavelet-based numerical schemes for solving inverse and stochastic PDEs, with a particular emphasis on sparsity, convergence analysis, and preconditioning. His publications span high-impact journals in numerical analysis and inverse problems, indicating sustained scholarly activity. Teaching: He has taught a variety of courses including Fundamentals of Numerics, Convex Optimization, Numerical PDEs, Spectral Methods, and advanced seminars on topics like semi-smooth Newton methods and reduced basis methods. Thorsten Raasch earned his doctorate in 2007 from Philipps University of Marburg and has held academic positions at JGU Mainz, the University of Siegen, and the University of Rostock. He was appointed W2 Professor at JGU Mainz in 2015 and previously held a junior professorship there. No scientific awards are mentioned in the provided texts. Advising and Grants: While no students or grants are explicitly listed, his role as a professor and active researcher suggests involvement in supervising graduate students and participating in research projects, possibly within the 'Mathematics of Computation' research network. Labs and Teams: He is part of the Numerical Mathematics Working Group at JGU Mainz and contributes to the 'Mathematics of Computation' research network, indicating collaboration within a structured research environment.

René ThiemannView profile

Associate Professor

• Program Verification
• Interactive Theorem Proving
• Termination Analysis
• +7 more

René Thiemann is an Associate Professor in the Department of Computer Science at the University of Innsbruck, Austria, where he is a key member of the Computational Logic Group. His work bridges theoretical computer science and practical formal verification, with a strong emphasis on automated reasoning and program correctness. Research Interests: Program Verification using interactive theorem proving (Isabelle/HOL) Termination and complexity analysis of programs Term rewriting systems and dependency pairs SAT/SMT solving and decision procedures Formalization of algebraic algorithms (LLL, Smith normal form, algebraic numbers) Development of the Certification Problem Format (CPF) and the CeTA tool His recent publications (2017–2025) reflect a consistent focus on formalizing advanced algorithms in Isabelle/HOL, especially those related to termination, complexity, and algebraic computation. These works are published in top venues like CPP, FSCD, LICS, and ITP, demonstrating rigorous, machine-checked proofs. His research often centers on verifying tools like AProVE and developing foundational libraries for number theory and rewriting. Scientific Projects: ARI : Automation of Rewriting Infrastructure (Task Leader, since 2022) Certifying Termination and Complexity Proofs : Project Leader (2014–2021) Constrained Rewriting and SMT : Task Leader (2012–2015) Improving Certifiers for Termination Proofs : Project Leader (2010–2014) Teaching Activities: Lecture and Proseminar: Program Verification (SS 2023–2025) Lecture: Constraint Solving (SS 2024–2025) Lecture and Proseminar: Functional Programming (WS 2021–2025) Lecture: Advanced Functional Programming (WS 2024/25) Lecture: Interactive Theorem Proving in Isabelle/HOL (SS 2022–2024) Lecture: Decision Procedures (SS 2021) He has supervised no listed students in the provided data but actively contributes to collaborative research. His email is rene.thiemann@uibk.ac.at.

Jan-Joachim RückmannView profile

Professor

• Non-linear Programming
• Mathematical Optimization
• Semi-infinite Programming
• +12 more

Jan-Joachim Rückmann is a Professor in the Department of Informatics at the University of Bergen, Norway. His research lies at the intersection of mathematical optimization, nonlinear programming, and their applications in finance and engineering. He is actively engaged in theoretical and applied research, with a focus on generalized semi-infinite programming and complementarity constraints. His research interests include non-linear programming, parametric optimization, robust optimization, data envelopment analysis, and the application of topological and differential geometric methods to optimization problems. He has made significant contributions to the theory of stability and stationarity in mathematical programs with complementarity constraints (MPCC) and semi-infinite programming. The recent publications highlight a strong trend in theoretical optimization, particularly in MPCC, stability of stationary points, and generalized semi-infinite programming. Several works explore duality, constraint qualifications, and convexification techniques. There is also interdisciplinary work applying optimization to mathematical finance, gas explosion modeling, and computational social choice using machine learning. While no scientific awards are listed, his extensive publication record in top journals such as Mathematical Programming , SIAM Journal on Optimization , and Optimization underscores his scholarly impact. He has served as guest editor for multiple special issues on parametric optimization, reflecting his leadership in the field. Though no students or grants are explicitly mentioned, his long-standing collaboration with researchers such as H. Günzel, D. Hernandez Escobar, F. Guerra Vazquez, and O. Stein indicates active research advising and team involvement. His work with the European Space Agency on trajectory analysis and space applications also points to applied research collaborations. Dr. Rückmann is affiliated with the Department of Informatics, contributing to both theoretical advances and practical applications of optimization methods. His work integrates deep mathematical analysis with real-world problem solving in engineering and economics.

Leonid GurvitsView profile

Professor

• Mathematics
• Computer Science
• Quantum Computing
• +5 more

Leonid Gurvits is a Professor of Computer Science at The City College of New York, part of the City University of New York (CUNY) system. His career spans institutions like Los Alamos National Laboratory (lanl.gov) and collaborations with leading researchers in mathematics and computer science, including Ankit Garg and Rafael Oliveira. He has held visiting scientist roles at programs such as the Simons Institute's Geometry of Polynomials (Spring 2019) and Bridging Continuous and Discrete Optimization (Fall 2017). Fields of Interest: Quantum computing, hyperbolic polynomials, operator scaling, and computational complexity. Key Contributions: Pioneering deterministic polynomial-time algorithms, solving problems in mixed discriminants, and advancing understanding of quantum entanglement and matrix stability. His work is deeply rooted in the interplay between mathematics and theoretical computer science, with applications to optimization, control theory, and quantum information. Notably, he resolved conjectures related to the permanent of matrices and developed capacity-preserving operators for combinatorial applications. Publications include groundbreaking articles on the complexity of quantum entanglement (2003), stability of switched linear systems (2007), and operator scaling for Brascamp-Lieb inequalities (2017). These works have been cited extensively, reflecting their impact on theoretical computer science and mathematical optimization.

Saras D. SarasvathyView profile

Professor

• Entrepreneurship
• Effectuation Theory
• Cognitive Foundations of Entrepreneurship
• +5 more

Professor Saras D. Sarasvathy, a leading scholar at the Darden School of Business (University of Virginia), specializes in entrepreneurship , effectuation theory , and business ethics . With a Ph.D. in information systems from Carnegie Mellon University, she has shaped global entrepreneurship education through her research, teaching, and advisory roles. Her work spans multiple continents, including collaborations with academic institutions in Europe, Asia, Latin America, and Africa. Education: B.Com., University of Bombay, India MSIA, Ph.D., Carnegie Mellon University Sarasvathy’s research focuses on the cognitive basis for high-performance entrepreneurship , emphasizing effectuation —a decision-making framework that prioritizes action over prediction. She explores applications in sustainability , social entrepreneurship , and education innovation . Her publications highlight non-predictive strategies , stakeholder-driven ventures , and serial entrepreneurship . The 15 most recent articles (2006–2022) reveal trends in effectuation theory, uncertainty management, and collective action. Key themes include venture creation , resource allocation , and ethical decision-making . Her 2022 paper on stakeholder engagement and 2019 work on sustainability exemplify her interdisciplinary approach. Scientific awards include 2022 Global Award for Entrepreneurship Research Fortune Small Business magazine’s top 18 entrepreneurship professors Mead-Colley Honored Faculty from UVA Legacy Impact Award from the Global Consortium of Entrepreneurship Centers Three professorships and four honorary doctorates from international universities

Samuel A. BurerView profile

Professor

• Optimization
• Operations Research
• Analytics
• +4 more

Samuel A. Burer is the Tippie-Rollins Professor and Departmental Executive Officer in Business Analytics at the University of Iowa's Tippie College of Business. He earned his Ph.D. in Algorithms, Combinatorics, and Optimization from Georgia Institute of Technology and a B.S. in Mathematics from the University of Georgia. Research Interests: Optimization, operations research, management sciences, discrete and continuous optimization, decision making under uncertainty. Editorial Roles: Area Editor for Operations Research (2020-2026), Associate Editor for SIAM Journal on Optimization, Mathematical Programming, and others. Teaching: Teaches across all business education levels and received multiple teaching awards, including the University of Iowa President & Provost Award for Teaching Excellence. Scientific Awards: INFORMS Computing Paper Prize (2020) SIAM Optimization Test of Time Award (2023) President & Provost Award for Teaching Excellence (2022) Collegiate Teaching Award (2020) Optimization Prize for Young Researchers (2002) Grants: Principal Investigator for NSF CAREER grant (2006-2012) and collaborative NSF grants (2002-2005) focused on nonconvex quadratic and conic optimization theory. Projects: Developed optimization algorithms for Trader Joe's warehouse location analysis, college football rankings, and created software tools like QuadProgBB and OPTDNN for solving semidefinite programs.

CLEMENTE COBOS SANCHEZView profile

Professor

• Transcranial Magnetic Stimulation (TMS)
• Biomedical Imaging
• Biosignal Processing
• +3 more

Clemente Cobos Sánchez is a Professor at the University of Cádiz, Spain, affiliated with the Department of Automation, Electronics, Architecture and Computer Networks Engineering. His research focuses on Transcranial Magnetic Stimulation (TMS) coil design , Biomedical Imaging , and Biosignal Processing . He leads projects related to electromagnetic design at the TIC138 research group. Primary Affiliation : University of Cádiz Department : Automation, Electronics, Architecture and Computer Networks Engineering Research Group : TIC138 Electronic and Electromagnetic Design Research Highlights: His work spans from theoretical mathematical optimization (e.g., nonconvex set minimization, operator norm analysis) to applied biomedical engineering (TMS depth optimization, concurrent TMS-fMRI systems). Recent publications also touch on space engineering applications, including magnetoresistive sensors for CubeSats and low-noise magnetic measurement systems. Scientific Trends: Combines computational electromagnetics with medical device innovation, applying advanced mathematical frameworks to both neurostimulation and space instrumentation challenges. His articles show interdisciplinary expertise in boundary element methods , multi-objective optimization , and inverse problem solving .

Ryan LaRoseView profile

Assistant Professor

• Computational Physics
• Quantum Information Science
• Quantum Computing
• +4 more

Ryan LaRose is an Assistant Professor at Michigan State University (MSU) with appointments in the Department of Computational Mathematics, Science and Engineering and the Department of Physics & Astronomy within the College of Natural Science. He also holds affiliations with the Department of Electrical and Computer Engineering and MSU's quantum initiative (MSU-Q), focusing on quantum computing research and development. His educational background includes a PhD in Computational Mathematics, Science and Engineering from Michigan State University (2022) and a BS with Distinction in Mathematics and Physics from the University of Michigan, Ann Arbor (2017). LaRose's research bridges computational physics and quantum information science through two interconnected themes: (1) the physics of computation, examining how quantum principles inform information theory and computer science, and (2) the computation of physics, leveraging quantum computers to solve complex problems in physical sciences. His work emphasizes practical quantum algorithm development, error mitigation techniques for noisy hardware, and open-source software tools for quantum computing. Analysis of his recent publications (2022-2024) reveals strong emphasis on quantum error mitigation strategies—particularly zero-noise extrapolation—and variational quantum algorithms for optimization and linear algebra problems. His contributions span hardware solutions, software frameworks like Mitiq and BGLS, and theoretical advances in quantum volume measurement. His notable scientific awards include: Engineering Distinguished Fellowship Fitch H. Beach Award for Outstanding Graduate Research NASA Space Technology Graduate Research Fellowship As an active contributor to the quantum community, LaRose co-organizes the QuIC Seminar series at MSU and develops open-source quantum software. He leads research efforts within MSU-Q, collaborating on quantum algorithm implementation and error mitigation techniques for near-term quantum devices.

David MonniauxView profile

Researcher

• program verification
• abstract interpretation
• static analysis
• +8 more

David Monniaux is a senior researcher (directeur de recherche) at CNRS and an adjunct professor at École polytechnique. He works at VERIMAG, a computer science laboratory jointly operated by CNRS and the University of Grenoble. Dr. Monniaux obtained his PhD in 2001 from Université Paris Dauphine under Professor Patrick Cousot, with a dissertation on the static analysis of probabilistic programs by abstract interpretation. He later earned his habilitation in computer science in 2009 from Université Joseph Fourier, Grenoble, and also holds an agrégation in mathematics. Monniaux's research focuses on program verification, with particular emphasis on proving software correctness. His work spans theoretical foundations in computability theory and practical applications in safety-critical systems. He has made significant contributions to abstract interpretation, static analysis, and the verification of numerical properties in programs. His research bridges computer science theory with practical engineering challenges, particularly in the context of critical embedded systems where software failures can have severe consequences. His work connects to diverse fields including game theory, algebra, and convex optimization. His recent publications demonstrate a strong focus on improving the precision and efficiency of static analysis techniques. Key themes include polyhedral approximation, program analysis with local policy iteration, abstraction of arrays and maps, synthesis of ranking functions, and computing worst-case execution times. These works collectively advance the field of program verification by addressing challenges in handling nonlinear constraints, branching, and complex data structures while maintaining computational feasibility. Monniaux has supervised several students including Julien Henry, Alexis Fouilhé, George (Egor) Karpenkov, and Alexandre Maréchal (now at LIP6), with current students Hang Yu and Valentin Touzeau. He has led significant research projects including VERASCO (2012-2015), which aimed at integrating a static analyzer into the CompCert certified compiler, and STATOR (2012-2017), an ERC starting investigator grant exploring advanced techniques for automatic inference of program invariants. At VERIMAG, Monniaux is part of a vibrant research community focused on critical systems. His work connects with broader efforts in formal methods, with applications in aviation, automotive systems, and other safety-critical domains where software reliability is paramount.

Kai ZhouView profile

Research Fellow

• Machine Learning
• Deep Learning
• Heavy Ion Physics
• +4 more

Dr. Kai Zhou is a Research Fellow at the Frankfurt Institute for Advanced Studies (FIAS) in the Theoretical Sciences division, where he leads the 'Deepthinkers' research group. Born in China in 1987, he completed his B.Sc. in Physics from Xi'an Jiaotong University in 2009 and earned his PhD with 'Wu You Xun' Honors from Tsinghua University in 2014. After postdoctoral research at Goethe University Frankfurt's Institute for Theoretical Physics, he joined FIAS in August 2017 as a Research Fellow focusing on Deep Learning applications in physics. Frankfurt Institute for Advanced Studies (FIAS), Theoretical Sciences (2017-present) Goethe University Frankfurt, Institute for Theoretical Physics (Postdoc) Tsinghua University, Physics (PhD, 2014) Xi'an Jiaotong University, Physics (B.Sc., 2009) Dr. Zhou's research bridges artificial intelligence and theoretical physics, with a focus on applying machine learning techniques to complex physical systems. His work spans heavy-ion collisions, lattice quantum field theory, seismology, and renewable energy systems. He has developed innovative deep learning approaches to extract physical insights from complex data, including constructing an Equation-Of-State meter for heavy ion collisions. His research demonstrates how physics can inform AI development while AI enhances our understanding of physical phenomena. Analysis of Dr. Zhou's recent publications reveals a strong trend toward integrating physics principles with machine learning architectures. His work increasingly focuses on Bayesian inference methods applied to QCD phase transitions, physics-informed neural networks for solving inverse problems in nuclear physics, and developing specialized architectures that preserve physical symmetries. The interdisciplinary nature of his research is evident in applications spanning from heavy-ion collisions to neutron star physics and industrial process optimization. Wu You Xun Honors (PhD) Third party funding through Samson AG: AI for science BMBF funding within ErUM data program: Deep Learning for CBM computing DAAD exchange program Xidian-FIAS International Joint Research Center: AI for science BMWI: AI for energy Nvidia: GPU Grant Dr. Zhou actively mentors doctoral and master's students, currently advising seven graduate students across multiple institutions. His research group 'Deepthinkers' has secured significant third-party funding from diverse sources including industrial partners (Samson AG), government agencies (BMBF, BMWI), and international collaborations (DAAD, Xidian University). His approach combines theoretical physics with cutting-edge AI techniques to address complex problems in both fundamental science and industrial applications. The 'Deepthinkers' research group operates at the intersection of AI and physics, developing novel methodologies that leverage physical principles to enhance machine learning and vice versa. Their work on applying deep learning to heavy-ion collisions represents a significant advancement in extracting meaningful physical insights from complex collision data. The group maintains strong international collaborations, particularly with Chinese institutions through the Xidian-FIAS International Joint Research Center.

Aleksandar KarteljView profile

Associate Professor

• Optimization
• Data mining
• Computational biology
• +4 more

Aleksandar Kartelj is an Associate professor at the Department for Computer Science, Faculty of Mathematics, University of Belgrade. He has been working at the Faculty of Mathematics since 2008, starting as a Teaching assistant, then becoming Assistant professor (2015-2023), and currently Associate professor (since October 2023). He also serves as a Guest professor at the Faculty of Natural Science and Mathematics, University of Banjaluka since 2018. Education: B.Sc. in Computer Science from Faculty of Mathematics, University of Belgrade (2005-2008, GPA 9.94/10.00) M.Sc. in Computer Science from Faculty of Mathematics, University of Belgrade (2008-2010, GPA 9.92/10.00) PhD in Computer Science from Faculty of Mathematics, University of Belgrade (2010-2014, GPA 10.00/10.00) M.Sc. in Quantitative Finance from Faculty of Economics, University of Belgrade (2010-present) Aleksandar Kartelj's research primarily focuses on optimization and data mining. He is a member of the "Modelling and optimization group" at the Faculty of Mathematics. His work involves designing exact and non-exact optimization techniques such as integer (linear) programming models, branch & bound, beam search, variable neighborhood search, and genetic algorithms. These techniques are applied to solve NP-hard problems in computational biology, transportation, logistics, social networks, and for improving machine learning algorithms. His recent publications (2021-2025) span diverse areas including symbolic regression, constrained longest common subsequence problems, graph protection algorithms, Roman domination in graphs, and applications in computational biology and epidemiology. His work frequently appears in high-impact journals such as Journal of Big Data, Expert Systems with Applications, and Knowledge-Based Systems, with many publications classified as M21a (Q1). Scientific Awards: Best paper award at the 2023 Second Serbian International Conference on Applied Artificial Intelligence (SICAAI) for "Integrating Top-level Constraints into a Symbolic Regression Search Algorithm" Aleksandar Kartelj has been involved in several research projects, including being a Research member on the project "Mathematical Models and Optimization Methods for Large-Scale Systems" (2011-2020) and a Research leader on the project "Combinatorial optimization for cancer progression inference and comparison" (2019-present). He has also developed numerous software applications for various organizations including the UN FAO, International Monetary Fund, and several private companies. He has been actively involved in teaching, serving as a Computer science teacher at Computer Gymnasium and Mathematical Grammar School in Belgrade, in addition to his university teaching responsibilities.