Yiannis Koutis

Education

Current Appointments

• 2022–present Associate Chair of Graduate Studies, Department of Computer Science, NJIT
• 2017–present Associate Professor, New Jersey Institute of Technology

Past Appointments

• 2010–2020 Adjunct Faculty, Carnegie Mellon University
• 2014–2018 Associate Professor, University of Puerto Rico–Río Piedras
• 2010–2014 Assistant Professor, University of Puerto Rico–Río Piedras
• Sep–Dec 2014 Visiting Scientist, Simons Institute
• Feb–May 2014 Visiting Professor, ICERM, Brown University
• 2008–2010 Systems Scientist, Special Faculty, Carnegie Mellon University
• 2007–2008 Postdoctoral Researcher, Carnegie Mellon University

Grants

• 2023–2025 co-PI in NSF: Cybertraining: Pilot: Cyberinfrastructure-Enabled Machine Learning for Understanding and Forecasting Space Weather [$190K]
• 2020–2022 NSF: Spectral Network Alignment [$150K]
• 2018–2021 NSF: Practice-Friendly Theory and Algorithms for Linear Regression Problems [$250K]
• 2012–2018 NSF CAREER: Fast algorithms via a spectral theory for graphs with a prescribed cut structure [$500K]
• 2010–2013 co-PI in NSF: Algorithm Design Using Spectral Graph Theory [$500K]
• 2012–2014 UPR – Institute of Functional Nanomaterials (IFN): Computational analysis of neural images via spectral methods [$50K]
• 2008–2009 Co-investigator in the University of Pittsburgh Medical Center (UPMC) grant for the development of new medical imaging methods based on spectral approaches [$700K]

Refereed Conference and Workshop Publications

31. M. Dindoost, O. Alvarado Rodriguez, B. Bryg, I. Koutis, D. A. Bader, HiperMotif: Novel parallel subgraph isomorphism in large-scale property graphs. In IEEE High Performance Extreme Computing, HPEC 2025.
32. A. Moradi Karkaj, M. J. Nelson, I. Koutis, A. Hoover, Prompt Wrangling: On replication and generalization in large language models for PCG levels. In Proceedings of the 15th Workshop on Procedural Content Generation, FDG 2024.
33. I. Bustany, G. Gasparyan, A. Kahng, I. Koutis, B. Pramanik, Z. Wang, TritonPart: An Open-Source Constraints-Driven General Partitioning Multi-Tool for VLSI Physical Design. In 42nd International Conference on Computer-Aided Design, ICCAD 2023.
34. I. Koutis, M. Wlodarczyk, M. Zehavi, Sidestepping Barriers for Dominating Set in Parameterized Complexity. In 18th International Symposium on Parameterized and Exact Computation, IPEC 2023.
35. E. Beikihassan, A. Parviz, N. Aghaieabiane, A. Hoover, I. Koutis, Resource-constrained knowledge diffusion processes inspired by human peer learning. In 26th European Conference on Artificial Intelligence, ECAI 2023 full oral presentation.
36. I. Bustany, A. Kahng, I. Koutis, B. Pramanik, Z. Wang, SpecPart: A supervised spectral framework for hypergraph partitioning solution improvement. In 41st International Conference on Computer-Aided Design, ICCAD 2022 best paper award.
37. E. Beikihassan, A. Hoover, I. Koutis, A. Parviz, Ensemble Learning as a Peer Process. In Agent Learning in Open-Endedness Workshop (ALOE), ICLR 2022.
38. I. Koutis, B. Pramanik, Spectral Hypergraph Partitioning Revisited. In SIAM Conference on Applied and Computational Discrete Algorithms, ACDA 2021.
39. D. Wei, I. Koutis, S. Basu-Roy, Peer Learning Through Targeted Dynamic Groups Formation. In 37th IEEE International Conference on Data Engineering, ICDE 2021.
40. I. Koutis and H. Le, Spectral Graph Modification for Improved Spectral Clustering. In 33rd Conference on Neural Information Processing Systems, NeurIPS 2019.
41. D. Calandriello, I. Koutis, A. Lazaric, M. Valco, Improved Large-Scale Graph Learning through Ridge Spectral Sparsification. In 35th International Conference on Machine Learning, ICML 2018.
42. A. Kolla, I. Koutis, V. Madan, A. K. Sinop, Spectrally Robust Graph Isomorphism. In 45th International Colloquium on Automata, Languages, and Programming, ICALP 2018.
43. A. Björklund, P. Kaski, I. Koutis, Directed Hamiltonicity and Out-Branchings via Generalized Laplacians. In 44th International Colloquium on Automata, Languages, and Programming, ICALP 2017 best paper award.
44. I. Abraham, D. Durfee, I. Koutis, S. Krinninger, R. Peng, On fully dynamic graph sparsifiers. In 57th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2016.
45. M. Cucuringu, I. Koutis, S. Chawla, G. Miller, R. Peng, Simple and Scalable Constrained Clustering: A Generalized Spectral Method. In The 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016.
46. C. Mavroforakis, R. Garcia-Lebron, I. Koutis, E. Terzi, Spanning edge centrality: large-scale computation and applications. In Proceedings of the 24th International World Wide Web Conference, WWW 2015.
47. I. Koutis, Simple parallel and distributed algorithms for spectral graph sparsification. In Proceedings of the 26th Annual Symposium on Parallelism in Algorithms and Architectures, SPAA 2014.
48. L. Brueggeman, M. Fellows, R. Fleischer, M. Lackner, C. Komusiewicz, I. Koutis, A. Pfandler, F. Rosamond, Train Marshalling Is Fixed Parameter Tractable. In Proceedings of Fun with Algorithms, FUN 2012.
49. I. Koutis, A. Levin, R. Peng, Improved spectral sparsification and numerical algorithms for SDD matrices. In Proceedings of the 29th Annual Symposium on Theoretical Aspects of Computer Science, STACS 2012.
50. I. Koutis, G. Miller, R. Peng, A nearly-m log n solver for SDD linear systems. In Proceedings of the 52nd Annual IEEE Symposium on Foundations of Computer Science, FOCS 2011.
51. G. Blelloch, I. Koutis, A. Gupta, G. Miller, R. Peng, K. Tangwongsan, Near linear-work parallel SDD solvers, low-diameter decomposition and low-stretch subgraphs. In Proceedings of the 23rd Annual Symposium on Parallelism in Algorithms and Architectures, SPAA 2011.
52. I. Koutis, G. Miller, R. Peng, Approaching optimality for solving symmetric diagonally dominant systems. In Proceedings of the 51st Annual IEEE Symposium on Foundations of Computer Science, FOCS 2010.
53. G. Blelloch, I. Koutis, G. Miller, K. Tangwongsan, Hierarchical Diagonal Blocking with precision reduction applied to combinatorial multigrid. In Proceedings of the 23rd ACM/IEEE Conference on High Performance Computing, SC 2010.
54. I. Koutis, G. Miller, D. Tolliver, Combinatorial preconditioners and multilevel solvers for problems in computer vision and image processing. In Proceedings of the 5th International Symposium on Visual Computing, ISVC 2009.
55. I. Koutis and R. Williams, Limits and applications of group algebras for parameterized problems. In Proceedings of the 35th International Colloquium on Automata, Languages and Programming, ICALP 2009.
56. C. Tsourakakis, P. Drineas, E. Michelakis, I. Koutis, C. Faloutsos, Spectral counting of triangles in power-law networks via element-wise sparsification. In Proceedings of the 2009 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2009.
57. I. Koutis, Faster algebraic algorithms for path and packing problems. In Proceedings of the 35th International Colloquium on Automata, Languages and Programming, ICALP 2008.
58. I. Koutis, G. Miller, Graph partitioning into isolated, high conductance clusters: theory, computation and applications to preconditioning. In Proceedings of the 20th Symposium on Parallelism in Algorithms and Architectures, SPAA 2008.
59. I. Koutis, G. L. Miller, A linear work, O(n^1/6) time, parallel algorithm for solving planar Laplacians. In Proceedings of the 18th ACM–SIAM Symposium on Discrete Algorithms, SODA 2007.
60. I. Koutis, On the hardness of multivariate integration. In Proceedings of the 6th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2003, pp. 122–128.
61. C. Bekas, E. Kokiopoulou, I. Koutis and E. Gallopoulos, Parallel computation of matrix pseudospectra. In Proceedings of the 15th ACM International Conference on Supercomputing, ICS 2001, pp. 260–269.

Journal Publications

18. S. Rajendra Patil, A. Parmanand Pandey, I. Koutis, M. Xu, Hierarchical Mamba Meets Hyperbolic Geometry: A New Paradigm for Structured Language Embeddings. In Transactions of Machine Learning Research, 2026.
19. N. Aghaieabiane, I. Koutis, SGCP: A spectral self-learning method for clustering genes in co-expression networks. In BMC Bioinformatics, 2024.
20. I. Bustany, A. Kahng, I. Koutis, B. Pramanik, Z. Wang, K-SpecPart: Supervised Embedding Algorithms and Cut Overlay for Improved Hypergraph Partitioning. In IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2023.
21. I. Koutis, G. Miller, R. Peng, A generalized Cheeger inequality. In Linear Algebra and Its Applications, 2023.
22. N. Aghaieabiane, I. Koutis, A novel calibration step in gene co-expression network construction. In Frontiers in Bioinformatics, 2021.
23. I. Koutis and S. C. Xu, Simple parallel and distributed algorithms for spectral graph sparsification. In ACM Transactions on Parallel Computing, 2016.
24. I. Koutis and R. Williams, Limits and applications of group algebras for parameterized problems. In ACM Transactions on Algorithms (TALG), 2016.
25. I. Koutis, Multilinear Monomial Detection. Invited in Encyclopedia of Algorithms, 2016.
26. I. Koutis, R. Williams, Algebraic Fingerprints for faster algorithms. In Communications of the ACM, January 2016.
27. I. Koutis, A. Levin, R. Peng, Faster spectral sparsification and numerical algorithms for SDD matrices. In ACM Transactions on Algorithms (TALG), 2015.
28. I. Koutis, G. Miller, R. Peng, Approaching optimality for solving symmetric diagonally dominant systems. Invited in SIAM Journal on Computing, special issue FOCS 2010, Vol. 43, No. 1, pp. 337–354, 2014.
29. G. Blelloch, I. Koutis, A. Gupta, G. Miller, R. Peng, K. Tangwongsan, Near linear-work parallel SDD solvers, low-diameter decomposition and low-stretch subgraphs. Invited in Theory of Computing Systems, March 2013.
30. I. Koutis, G. Miller, R. Peng, A fast solver for a class of linear systems. Invited in Communications of the ACM, October 2012.
31. I. Koutis, Constrained multilinear detection for faster functional motif discovery. In Information Processing Letters, Vol. 112, No. 22, pp. 889–892, 2012.
32. I. Koutis, G. Miller, D. Tolliver, Combinatorial preconditioners and multilevel solvers for problems in computer vision and image processing. Invited in Computer Vision and Image Understanding, 115(12), pp. 1638–1646, 2011.
33. C. Tsourakakis, P. Drineas, E. Michelakis, I. Koutis, C. Faloutsos, Spectral counting of triangles in power-law networks via element-wise sparsification and triangle-based link recommendation. In Social Network Analysis and Mining, Vol. 1, No. 2, pp. 75–81, 2011.
34. I. Koutis, Parameterized complexity and improved inapproximability for computing the largest j-simplex in a V-polytope. In Information Processing Letters, Vol. 1, No. 1, pp. 8–13, 2006.
35. I. Koutis, A faster parameterized algorithm for set packing. In Information Processing Letters, No. 1, pp. 4–7, 2005.

Other Publications and Preprints

3. I. Koutis, Dimensionality restrictions on sums over Z_p^d. CMU-CS-07-103 Technical Report, 2007.
4. I. Koutis, Spectrum through pseudospectrum. Arxiv Report No. 0701368, 2001.
5. I. Koutis and E. Gallopoulos, Iterations on domains for computing the matrix pseudospectrum. Manuscript, 1999.

Patents

• 2013 I. Koutis and G. L. Miller. Methods for solving graph Laplacians. US 8516029.
• 2014 I. Koutis and G. L. Miller. Method and apparatuses for solving weighted planar graphs. US 8711146.

Awards and Honors

• 2022 William J. McCalla ICCAD Best Paper Award (back end)
• 2020 Ying Wu College of Computing Excellence in Teaching Award
• 2017 ICALP – Track A: Best Paper Award
• 2012 NSF CAREER Award
• 2002 SIAM Student Travel Award
• 1999 Best Engineering Student award from the Technical Chamber of Greece
• 1998 University of Patras Engineering School Valedictorian
• 1993–1998 Yearly awards from the Greek State Scholarships Foundation

Invited Talks

• Spectral Graph Modification for Improved Spectral Clustering.
  The New York Colloquium on Algorithms and Complexity, CUNY University, November 15, 2019
• Pragmatic Ridge Spectral Sparsification for Large-Scale Graph Learning.
  DIMACS Workshop on Randomized Numerical Linear Algebra, Statistics, and Optimization, Rutgers University (New Brunswick), NJ, September 18, 2019
• Spectrally Robust Graph Isomorphism.
  Simons Institute of Technology, Berkeley, CA, September 25, 2018
• Directed Hamiltonicity and Out-Branchings via Generalized Laplacians.
  Rutgers Business School, Newark, NJ, February 22, 2018
• A survey of provably fast linear system solvers.
  Alan Turing Institute, London, UK, November 1, 2017
• Improved algebraic algorithms for out-branchings problems.
  Randomization in Parameterized Complexity, Schloss Dagstuhl, Germany, January 2017
• On fully dynamic graph sparsifiers.
  Algebraic and Spectral Graph Theory, Banff International Research Station, Banff, Canada, August 4, 2016
• On fully dynamic graph sparsifiers.
  Recent Advances on Randomized Numerical Linear Algebra, NII Shonan Meeting, Shonan Village, Japan, July 26, 2016
• Spectral algorithms for graph mining and analysis.
  Workshop on Algorithms for Modern Massive Data Sets, MMDS 2014, Berkeley, California, June 17, 2014
• Spectral algorithms for graph mining and analysis.
  Workshop on Eigenvectors in graph theory and related problems in numerical linear algebra, ICERM, Providence, Rhode Island, May 7, 2014
• Spectral graph sparsification and fast Laplacian solvers.
  Boston University, May 5, 2014
• Segmenting neurons in 3D EM images.
  IBDR PI workshop, Washington DC, May 2, 2014
• Spectral sparsification of graphs: an overview of theory and practical methods.
  Workshop on Large Scale Matrix Analysis and Inference, NIPS 2013, Lake Tahoe, Nevada, December 9, 2013
• Algebraization in parameterized algorithms and complexity.
  AMS Special Session on the Mathematical Underpinnings of Multivariate Complexity and Algorithm Design, San Diego, January 12, 2013
• Laplacian Solvers: Theory and Practice.
  Workshop on Randomized Numerical Linear Algebra: Theory and Practice, FOCS 2012, New Brunswick, October 20, 2012
• SDD Solvers: Bridging the Gap Between Theory and Practice.
  Workshop on Algorithms for Modern Massive Data Sets, MMDS 2012, Stanford, July 13, 2012
• Spectral graph theory, matrix sums and near-optimal SDD solvers.
  SIAM Applied Linear Algebra Conference, Valencia, June 18, 2012
• The power of group algebras in constrained monomial detection problems.
  Seminar on the Exact Complexity of NP-hard problems, Schloss Dagstuhl, November 10, 2010
• Graph Sparsification p.2 — An O(m log² n) algorithm for solving SDD systems.
  Microsoft Research, Redmond, September 2, 2010
• Graph Sparsification p.1 — The Combinatorial Multigrid Solver.
  Microsoft Research, Redmond, September 2, 2010
• How to make a computer see better?
  University of Puerto Rico, Río Piedras, September 15, 2009
• Fast detection of square-free terms in multivariate polynomials: one algo-stone, many algo-birds.
  University of Puerto Rico, Río Piedras, September 14, 2009
• M-matrix systems and solvers in Computer Science.
  University of Wyoming, March 30, 2009
• Spectral Graph Theory meets Practice: The Combinatorial Multigrid Solver.
  Yale University, January 23, 2009
• Advances in the Theory and Computation of Pseudospectra.
  SIAM 50th Anniversary and Annual Meeting, Matrix Spectra and Pseudospectra Minisymposium, July 8–12, 2002, Philadelphia, PA

Conference and Workshop Presentations

• Theoretical Foundations of Data Science: Algorithmic, Mathematical, and Statistical (TFoDS).
  April 28–30, 2016
• Fast SDD solvers via sampling by approximate leverage scores.
  6th International ERCIM Conference on Computational and Methodological Statistics, 2013
• Spectral Algorithms for Segmenting Neurons in their Three-dimensional Space. [with R. Garcia, J. Farrington, J. Serrano-Velez, E. Rosa-Molinar]
  Neuroscience 2011
• The Combinatorial Multigrid Solver. [with Gary L. Miller]
  14th Copper Mountain Conference on Multigrid Methods, 2009
• Unassisted segmentation of multiple retinal layers via spectral rounding. [with D. Tolliver, I. Koutis, H. Ishikawa, J. S. Schuman, G. L. Miller]
  Association of Research in Vision and Ophthalmology, ARVO 2008 Annual Meeting
• A Linear Work, Parallel Algorithm for Solving Planar Laplacians. [with Gary L. Miller]
  Combinatorial Scientific Computing (CSC07), Costa Mesa, California, 2007
• Efficiently Solving Linear Systems using Support Tree Preconditioners. [with Gary L. Miller]
  Parallel Processing for Scientific Computing (PP06), San Francisco, California, February 2006
• Hermitian Methods for Computing Eigenvalues. [with E. Gallopoulos]
  5th IMACS Conference on Iterative Methods in Scientific Computing, May 2001
• Iterations on Domains for the Computation of Matrix (Pseudo)-Spectrum. [with E. Gallopoulos]
  Conference on the Foundations of Computational Mathematics (FOCM), Oxford, July 1999

Professional Service

• Participation in NSF Funding Panels (CCF/AF, IIS/RI)
• Participation in DoE Funding Panel
• European Research Council (ERC)
• Program Committee Member: PKDD-Nectar 2014, 2015; WSDM 2016; WWW 2015–2018; WebConf 2019–2022; KDD 2017; KDD-Applied Track 2018; LATIN 2018; CIKM 2018; AAAI 2019–2020; ICCAD 2024–2026; DAC 2025–2026
• Referee for CS conferences (STOC, FOCS, SODA, SPAA, STACS, ESA, MFCS)
• Referee for journals including JACM, ACM TALG, JCSS, Algorithmica, TOPC, IPL, SIMAX
• Workshop Organizer, "Electrical Flows, Graph Laplacians and Algorithms: Spectral Theory and Beyond", ICERM, 2014

Teaching

• Online Course Design: NJIT Machine Learning (DS 675), Deep Learning (DS 677)
• Machine Learning (CS/DS 675), NJIT — S20, F20, F21, Su21, F22, S22, Su22, S23, S24
• Deep Learning (CS/DS 677), NJIT — F21, S21, F22, S23, Su23, F23, S24, Sum24
• Data Structures and Algorithms (CS 610), NJIT — F19, Su20, Su21, S26
• Computational Complexity (CS 611), NJIT — S18, S19, S20, S21
• Advanced Data Structures and Algorithm Design (CS 435), NJIT — F17, F18
• Theory of Computability, UPRRP — F15, F16
• Fun with programming interview questions, UPRRP — S15
• Theory of Computation, UPRRP — F13
• Design and Analysis of Algorithms, UPRRP — S12, S17
• High Level Programming Languages, UPRRP — S12, S15
• Undergraduate Machine Learning, UPRRP — F11, F12, F13
• Numerical Analysis, UPRRP — S11, S13
• Linear Algebra for Computer Scientists, UPRRP — F10
• Teaching Assistant, Principles of Programming, CMU — S02
• Teaching Assistant, Formal Languages Automata and Computation, CMU — S01
• Teaching Assistant, Advanced Scientific Computing, University of Patras — F99

Advising

• Current PhD students: Soroush Vahidi, Azadeh Naderi, Arash Moradi Karkaj
• Graduated NJIT PhD students:
  • Ali Parviz, 2025 [first position: Visiting Researcher at Google]
  • Ehsan Beikihassan, 2024 [first position: CCC Intelligent Solutions]
  • Niloofar Aghaieabiane, 2023 [first position: JP Morgan]
  • Huong Le, 2021 [first position: Lecturer at NJIT]
• PhD Committee Member (NJIT), current: Mehtab Sidhu, Yupeng Xu, Chunhui Xu, Hongyang Zhang, Haotian Yin, Mohammad Dindoost, Zhibo Ye, Siqi Jiang, Swastik Biswas, Zhenduo Wang
• Awarded PhDs — Committee Member (NJIT): 2019: Abdulrhman Fahad Aljouie; 2020: Xin Yin; 2021: Zhihang Hu, Yunzhe Xue, Dong Wei; 2022: Shibo Yao, Cavidan Yakupoglu, Gerges Firas, Yasser Abduallah; 2023: Mojtaba Zaheri, Mahsa Asadi, Hessam Mohammadi, Sepideh Nikookar, Md Moinul Islam, Xiang Lin; 2024: Wenlu Du, Minjuan Zhang; 2025: Oliver Alvarado Rodriguez, Fuhuan Li, Jingyi Gu, Haoran Liu; 2026: Shen Fan
• Awarded PhDs — Committee Member (external): Shen Chen Xu (CMU, 2017), Yixuan He (University of Oxford, 2024), Tijn de Vos (University of Salzburg, 2024), Jingbang Chen (University of Waterloo, 2025)
• MS Committee Member (NJIT): Rahul Basu (2020), Sanyamee Patel (2020), Sarvesh Shukla (2020), Joseph Patchett (2022), Ritwik Reddy Kolan (2026)
• Graduated MS students: Kadir Altunel (2025), Richard Garcia-Lebron (2014)
• Former Undergraduate: Richard Garcia-Lebron, Jose Farrington, Karlo Martinez, Leonardo Cardona, Carlos Feliciano, Idalyn Mirabal, Alejandro Vientos, Alberto Ruiz, Gustavo Gratacos

Service at NJIT

• 2025 Spearheaded the creation and launch of the Barclays–NJIT MS in Computer Science partnership program
• 2024–2026 Center for Educational Innovation and Excellence, Advisory Board
• 2024–2025 Member of the NJIT group participating in AAC&U's Institute for AI, Pedagogy, and the Curriculum
• 2024–2026 Member of the AI Teaching/Learning Group (charged by the Provost)
• 2023–2025 CS representative on the Faculty Senate Committee on Academic Assessment
• 2021–2025 YWCC representative on the Faculty Senate Committee on Research, Scholarship and Creative Academic Activity
• 2019–2022 Committee on Information Technology, Library, and Academic Resources
• 2018–2019 Innovative Educational Technology Working Group

Service at UPRRP

• 2016–2017 Department Coordinator for Student Learning Assessment
• 2012–2014 Member of the committee for the creation of an MS program in Computer Science
• 2011 Undergraduate Research Coordinator of the Computer Science Department