online algorithms, machine learning and computational economics
אילן כהן
Identity: verifiedThe roster's אילן כהן maps to the Bar-Ilan faculty member whose official pages use Ilan Reuven Cohen; retain the expanded middle name to avoid collision with namesakes. [119][120]
Documented foundation
Bar-Ilan engineering faculty member working on online algorithms, learning, scheduling and economic allocation problems.[119][120]
online optimizationresource-constrained classificationschedulingfair allocation and mechanism design
A four-page first-party CV was inspected, but its publication list ends in 2020; the personal publication page includes newer work.[120][121]
Open CVRepresentative records, not a complete publication list. Metadata confirms attribution; it does not independently replicate a result.
2023 · paper
The researcher's publication page lists an ICDM 2023 paper joining classification costs with constrained resource allocation.[120]
0 catalogued patent records
Coverage: No attributable record found in this search
Full-name and Ilan R. variants plus first-party research/publication page were examined. No attributable inventor-patent entry was located. Generic Ilan Cohen and Reuven Cohen results were not merged into this subject. This is scoped search coverage, not patent absence.
No publication records verified in this search; this does not establish absence of patents.
Original evidence: not verified
No attributable patent record was verified in the bounded search.
Records are counted separately from identified families. Author-reported entries are labelled and may still need publication verification. Inventorship, publication-time applicant and current ownership are different facts. No legal-status, patentability or freedom-to-operate conclusion is made.
Scores prioritize research fit from 1–10; they are not probabilities.
Review: Reviewed with limitations
Proposed capability matches, not confirmed relationships. Scores are analyst judgments with low forecast confidence; researcher interests, capacity and feasibility need confirmation.
12 candidates
Connection 1
Original proposal
Proposal hypothesis: Singer's cost-sensitive prescriptive learning and Ilan Cohen's resource-constrained classification directly meet in allocating scarce decisions online.[79][80][119][120]
Proposed first test: Use a synthetic labelled demand stream and compare cost-sensitive static allocation with an online budgeted policy on cost and fairness.
Rank 1/12; fit 9/10: topic overlap 4/4, complementarity 2/3, first-test feasibility 3/3. Original initiative connection retained. No higher-scoring new candidate displaces this original. Equal scores retain originals first, then stable profile order. The underlying capabilities and proposed first test explain the component judgments. Specific scientific limitation: The methods substantially overlap, so complementarity is lower than for an operations researcher paired with a domain-measurement specialist.
Analyst proposal hypothesis, not an established collaboration, commitment, evidence-confidence rating or assessment of researcher quality; confirm participation and access before work. Original initiative conditions remain: o03 Ilan Reuven Cohen: Conditional later role: add online/fair-allocation rules if the chosen service decision requires them.
Connection 2
Original proposal
Proposal hypothesis: Izack Cohen can define a stochastic service process while Ilan Cohen supplies online scheduling and fair-allocation rules for arrivals not known in advance.[97][98][119][120]
Proposed first test: Replay a synthetic arrival stream under a static schedule and an online rule; report waiting-time tails, regret and group allocation gaps.
Rank 2/12; fit 9/10: topic overlap 3/4, complementarity 3/3, first-test feasibility 3/3. Original initiative connection retained. No higher-scoring new candidate displaces this original. Equal scores retain originals first, then stable profile order. The underlying capabilities and proposed first test explain the component judgments. Specific scientific limitation: The original later-stage allocation role is needed only if the service problem requires online or fairness constraints.
Analyst proposal hypothesis, not an established collaboration, commitment, evidence-confidence rating or assessment of researcher quality; confirm participation and access before work. Original initiative conditions remain: o03 Ilan Reuven Cohen: Conditional later role: add online/fair-allocation rules if the chosen service decision requires them.
Connection 3
Proposal hypothesis: Fetaya's robust learning could provide uncertain predictions for Ilan Cohen's online resource-constrained decisions.[73][119][120]
Proposed first test: Compare forecast-assisted and forecast-free allocation under adversarial prediction errors on regret and constraint violations.
Rank 3/12; fit 9/10: topic overlap 3/4, complementarity 3/3, first-test feasibility 3/3. Added capability match outside the original initiative graph; prior collaboration or novelty was not established. Equal scores retain originals first, then stable profile order. The underlying capabilities and proposed first test explain the component judgments. Specific scientific limitation: A robust predictor is useful only if its downstream online decisions improve.
Analyst proposal hypothesis, not an established collaboration, commitment, evidence-confidence rating or assessment of researcher quality; confirm participation and access before work.
Connection 4
Proposal hypothesis: Goldberger can calibrate prediction uncertainty while Ilan Cohen chooses which classifications receive scarce resources.[75][76][119][120]
Proposed first test: Compare fixed-threshold and budget-aware review on a public classification set with synthetic review costs; report missed errors and resource use.
Rank 4/12; fit 9/10: topic overlap 3/4, complementarity 3/3, first-test feasibility 3/3. Added capability match outside the original initiative graph; prior collaboration or novelty was not established. Equal scores retain originals first, then stable profile order. The underlying capabilities and proposed first test explain the component judgments. Specific scientific limitation: The cost model and fairness constraints are hypothetical and need stakeholder definition.
Analyst proposal hypothesis, not an established collaboration, commitment, evidence-confidence rating or assessment of researcher quality; confirm participation and access before work.
Connection 5
Identity check needed: Conditional proposal: confirm the researcher identity and research interests before assessing this match.
Proposal hypothesis: Conditional on confirmation, Shtern's optimization under uncertainty and Ilan Cohen's online allocation can separate robustness to model error from adaptation to new arrivals.[83][84][85][119][120]
Conditional proposed first test: After identity confirmation, compare a robust offline rule and an online rule under identical synthetic demand shifts; report regret and constraint violations.
Rank 5/12; fit 9/10: topic overlap 4/4, complementarity 3/3, first-test feasibility 2/3. Added capability match outside the original initiative graph; prior collaboration or novelty was not established. Equal scores retain originals first, then stable profile order. The underlying capabilities and proposed first test explain the component judgments. Specific scientific limitation: Present institutional role is unresolved despite a strong optimization topic match.
Analyst proposal hypothesis, not an established collaboration, commitment, evidence-confidence rating or assessment of researcher quality; confirm participation and access before work. Conditional: confirm Shimrit Shtern identity mapping and current institutional affiliation before any internal team assignment. The BIU directory and current Technion appointment leave affiliation unresolved. Documented optimization expertise is conditional on that mapping; excluded from confirmed-team claims.
Connection 6
Proposal hypothesis: Medina and Ilan Cohen both develop online algorithms; a useful division is fault recovery versus resource-constrained scheduling.[100][119][120]
Proposed first test: Compare an online schedule with and without failure-aware reassignment on regret, missed deadlines and reassignment count.
Rank 6/12; fit 9/10: topic overlap 4/4, complementarity 2/3, first-test feasibility 3/3. Added capability match outside the original initiative graph; prior collaboration or novelty was not established. Equal scores retain originals first, then stable profile order. The underlying capabilities and proposed first test explain the component judgments. Specific scientific limitation: High algorithmic overlap reduces complementarity unless responsibilities are defined distinctly.
Analyst proposal hypothesis, not an established collaboration, commitment, evidence-confidence rating or assessment of researcher quality; confirm participation and access before work.
Connection 7
Proposal hypothesis: Leshem's distributed resource allocation and Ilan Cohen's online/fair-allocation algorithms could compare efficiency against repeated-access inequity.[111][112][119][120]
Proposed first test: Simulate changing network demand and compare a throughput-only policy with an online fairness constraint on regret and access gaps.
Rank 7/12; fit 9/10: topic overlap 3/4, complementarity 3/3, first-test feasibility 3/3. Added capability match outside the original initiative graph; prior collaboration or novelty was not established. Equal scores retain originals first, then stable profile order. The underlying capabilities and proposed first test explain the component judgments. Specific scientific limitation: Fairness criteria are design choices; no deployment or participant agreement is implied.
Analyst proposal hypothesis, not an established collaboration, commitment, evidence-confidence rating or assessment of researcher quality; confirm participation and access before work.
Connection 8
Original proposal
Proposal hypothesis: Somin's temporal models can forecast short-lived load changes while Ilan Cohen's online algorithms decide allocations before future arrivals are known.[64][65][119][120]
Proposed first test: Compare a forecast-assisted online scheduler with a forecast-free rule under synthetic coordination shocks; report regret when forecasts are wrong.
Rank 8/12; fit 8/10: topic overlap 2/4, complementarity 3/3, first-test feasibility 3/3. Original initiative connection retained. New candidate Ethan Fetaya ranks higher at 9/10 (overlap 3, complementarity 3, feasibility 3). Its proposed capability split: Proposal hypothesis: Fetaya's robust learning could provide uncertain predictions for Ilan Cohen's online resource-constrained decisions. Compare the cited first experiments; these are analyst priorities, not measured success rates. Equal scores retain originals first, then stable profile order. The underlying capabilities and proposed first test explain the component judgments. Specific scientific limitation: Only add temporal forecasting if it improves the chosen allocation task under misspecification.
Analyst proposal hypothesis, not an established collaboration, commitment, evidence-confidence rating or assessment of researcher quality; confirm participation and access before work. Original initiative conditions remain: o03 Ilan Reuven Cohen: Conditional later role: add online/fair-allocation rules if the chosen service decision requires them. o03 Shahar Somin: Conditional later role: test temporal network signals only if approved event-network data adds value beyond the queueing baseline.
Connection 9
Proposal hypothesis: Ilan Cohen's online allocation decisions provide explicit constraints and state changes for Ernst's proposition-aligned explanations.[93][94][95][119][120]
Proposed first test: Compare summaries of synthetic scheduling traces on constraint attribution, missed exceptions and unsupported fairness statements.
Rank 9/12; fit 8/10: topic overlap 2/4, complementarity 3/3, first-test feasibility 3/3. Added capability match outside the original initiative graph; prior collaboration or novelty was not established. Equal scores retain originals first, then stable profile order. The underlying capabilities and proposed first test explain the component judgments. Specific scientific limitation: An explanation must distinguish the rule followed from the desirability of the resulting allocation.
Analyst proposal hypothesis, not an established collaboration, commitment, evidence-confidence rating or assessment of researcher quality; confirm participation and access before work.
Connection 10
Proposal hypothesis: Ilan Cohen can specify a fair allocation rule and Mor Weiss can examine whether its execution can be verified on private bids or constraints.[119][120][123]
Proposed first test: Encode a small allocation rule on synthetic inputs and compare proof cost with direct recomputation and disclosed-input volume.
Rank 10/12; fit 8/10: topic overlap 3/4, complementarity 3/3, first-test feasibility 2/3. Added capability match outside the original initiative graph; prior collaboration or novelty was not established. Independent review lowered feasibility by one point: The allocation proof proposal leaves the rule, protected inputs and public statement unspecified. Other private-execution pairs use feasibility 2/3. Name a fixed small rule and leakage/security assumptions before maximum feasibility; otherwise use 2/3. Equal scores retain originals first, then stable profile order. The underlying capabilities and proposed first test explain the component judgments. Specific scientific limitation: Proofs establish rule execution, not that the chosen fairness rule is normatively appropriate.
Analyst proposal hypothesis, not an established collaboration, commitment, evidence-confidence rating or assessment of researcher quality; confirm participation and access before work.
Connection 11
Original proposal
Proposal hypothesis: Chalutz-Ben Gal's person-skill fit work can define meaningful skill constraints for Ilan Cohen's fair online allocation.[22][23][119][120]
Proposed first test: Create synthetic tasks and skill profiles, then compare efficiency and repeated assignment burden under two allocation rules.
Rank 11/12; fit 7/10: topic overlap 2/4, complementarity 3/3, first-test feasibility 2/3. Original initiative connection retained. New candidate Ethan Fetaya ranks higher at 9/10 (overlap 3, complementarity 3, feasibility 3). Its proposed capability split: Proposal hypothesis: Fetaya's robust learning could provide uncertain predictions for Ilan Cohen's online resource-constrained decisions. Compare the cited first experiments; these are analyst priorities, not measured success rates. Equal scores retain originals first, then stable profile order. The underlying capabilities and proposed first test explain the component judgments. Specific scientific limitation: Fairness weights are a research design choice requiring worker input; the original optional role is not automatic team readiness.
Analyst proposal hypothesis, not an established collaboration, commitment, evidence-confidence rating or assessment of researcher quality; confirm participation and access before work. Original initiative conditions remain: o03 Ilan Reuven Cohen: Conditional later role: add online/fair-allocation rules if the chosen service decision requires them.
Connection 12
Identity check needed: Conditional proposal: confirm the researcher identity and research interests before assessing this match.
Proposal hypothesis: Conditional on confirmation, Simonovsky could coordinate prototype teams around an online allocation problem designed by Ilan Cohen.[16][17][119][120]
Conditional proposed first test: After identity confirmation, draft a fixed arrival-stream exercise and checklist for reporting regret and constraint violations before recruiting teams.
Rank 12/12; fit 4/10: topic overlap 1/4, complementarity 2/3, first-test feasibility 1/3. Added capability match outside the original initiative graph; prior collaboration or novelty was not established. Equal scores retain originals first, then stable profile order. The underlying capabilities and proposed first test explain the component judgments. Specific scientific limitation: This is conditional event participation, not evidence of algorithmic or economics expertise for Simonovsky.
Analyst proposal hypothesis, not an established collaboration, commitment, evidence-confidence rating or assessment of researcher quality; confirm participation and access before work. Conditional: confirm Alexandra Simonovsky identity and current role before any team assignment. Evidence supports AI-agent event coordination only; research appointment, teaching role and technical research expertise are not verified. Excluded from confirmed-team claims.
10 candidates
Connection 1
Massachusetts Institute of Technology
Proposed capability match: Ilan Reuven Cohen's online optimization, resource-constrained classification can be paired with Dimitris Bertsimas's documented optimization, machine learning for auditable dynamic allocation for scarce public services. The specific contribution is optimization-based prescriptive decisions; this transfer is an analyst hypothesis.[86][119][120][437]
Compare a constrained optimization policy with a fixed rule using the same forecasts and capacity limits using a synthetic public-service allocation simulator with scarce capacity and changing demand. Compare objective value, feasibility and sensitivity to misspecified inputs with a fixed priority rule with the same capacity and eligibility constraints.
Rank 1/10 after semantic revision; analyst score 10 = max(1, 4+3+3): topic overlap 4/4, complementarity 3/3, feasible first test 3/3. A bounded offline comparison is specified; required datasets and domain assumptions must still be checked.
Proposed fit, not an assertion of a new or active relationship. Independent review pending; forecast confidence low. Partner interest, capacity, data access and any required experimental approvals/resources are unverified.
Connection 2
Princeton University
Proposed capability match: Ilan Reuven Cohen's online optimization, resource-constrained classification can be paired with Warren Buckler Powell's documented sequential decision analytics, optimization under uncertainty for auditable dynamic allocation for scarce public services. The specific contribution is sequential decisions under uncertainty; this transfer is an analyst hypothesis.[81][119][120][486]
Replay two sequential policies on identical stochastic event streams with delayed observations using a synthetic public-service allocation simulator with scarce capacity and changing demand. Compare cumulative cost, constraint violations and worst-case regret proxies with a fixed priority rule with the same capacity and eligibility constraints.
Rank 2/10 after semantic revision; analyst score 10 = max(1, 4+3+3): topic overlap 4/4, complementarity 3/3, feasible first test 3/3. A bounded offline comparison is specified; required datasets and domain assumptions must still be checked.
Proposed fit, not an assertion of a new or active relationship. Independent review pending; forecast confidence low. Partner interest, capacity, data access and any required experimental approvals/resources are unverified.
Connection 3
RWTH Aachen University
Proposed capability match: Ilan Reuven Cohen's online optimization, resource-constrained classification can be paired with Wil van der Aalst's documented process mining, workflow conformance for auditable dynamic allocation for scarce public services. The specific contribution is workflow discovery and conformance; this transfer is an analyst hypothesis.[99][119][120][429]
Reconstruct an event-log process and compare observed paths with the proposed process model using a synthetic public-service allocation simulator with scarce capacity and changing demand. Compare conformance violations, bottleneck localization and replay error with a fixed priority rule with the same capacity and eligibility constraints.
Rank 3/10 after semantic revision; analyst score 9 = max(1, 3+3+3): topic overlap 3/4, complementarity 3/3, feasible first test 3/3. A bounded offline comparison is specified; required datasets and domain assumptions must still be checked.
Proposed fit, not an assertion of a new or active relationship. Independent review pending; forecast confidence low. Partner interest, capacity, data access and any required experimental approvals/resources are unverified.
Connection 4
Stanford University
Proposed capability match: Ilan Reuven Cohen's online optimization, resource-constrained classification can be paired with Matthew O. Jackson's documented network science and economics, economic modelling for auditable dynamic allocation for scarce public services. The specific contribution is network structure and economic incentives; this transfer is an analyst hypothesis.[119][120][463]
Compare allocation or diffusion on the measured graph with degree-preserving shuffled graphs using a synthetic public-service allocation simulator with scarce capacity and changing demand. Compare distributional effects and sensitivity to network-position assumptions with a fixed priority rule with the same capacity and eligibility constraints.
Rank 4/10 after semantic revision; analyst score 9 = max(1, 3+3+3): topic overlap 3/4, complementarity 3/3, feasible first test 3/3. A bounded offline comparison is specified; required datasets and domain assumptions must still be checked.
Proposed fit, not an assertion of a new or active relationship. Independent review pending; forecast confidence low. Partner interest, capacity, data access and any required experimental approvals/resources are unverified.
Connection 5
Harvard University
Proposed capability match: Ilan Reuven Cohen's online optimization, resource-constrained classification can be paired with Michael Mitzenmacher's documented algorithms and theory, systems and networks for auditable dynamic allocation for scarce public services. The specific contribution is algorithmic and systems baselines; this transfer is an analyst hypothesis.[101][119][120][478]
Compare two explicit sampling, load-balancing or scheduling algorithms under the same adversarial event trace using a synthetic public-service allocation simulator with scarce capacity and changing demand. Compare tail latency, failure rate and sensitivity to the event distribution with a fixed priority rule with the same capacity and eligibility constraints.
Rank 5/10 after semantic revision; analyst score 9 = max(1, 3+3+3): topic overlap 3/4, complementarity 3/3, feasible first test 3/3. A bounded offline comparison is specified; required datasets and domain assumptions must still be checked.
Proposed fit, not an assertion of a new or active relationship. Independent review pending; forecast confidence low. Partner interest, capacity, data access and any required experimental approvals/resources are unverified.
Connection 6
Stanford University
Original proposal
The institutional biography identifies Roth's market-design and experimental-economics expertise, complementing Cohen's algorithmic scheduling and allocation work; the match is a proposal only.[119][120][122][490]
Run preregistered simulations on synthetic and public historical allocation scenarios, comparing efficiency, regret, access equity and strategic manipulation.
Rank 6/10 after semantic revision; analyst score 8 = max(1, 3+2+3): topic overlap 3/4, complementarity 2/3, feasible first test 3/3. A bounded offline comparison is specified; required datasets and domain assumptions must still be checked. Roth contributes incentives and matching; Powell/Bertsimas more directly address sequential capacity decisions. This is a task-priority difference, not a researcher-quality judgment. Original retained exactly at rank 6; preceding alternatives are Dimitris Bertsimas (10; 4+3+3), Warren Buckler Powell (10; 4+3+3), Wil van der Aalst (9; 3+3+3), Matthew O. Jackson (9; 3+3+3), Michael Mitzenmacher (9; 3+3+3). The preserved scientific explanation above describes the narrower contribution; current ordering follows these displayed component totals, not comparative researcher quality.
Proposed fit, not an assertion of a new or active relationship. Independent review pending; forecast confidence low. Partner interest, capacity, data access and any required experimental approvals/resources are unverified.
Connection 7
Stanford University
Proposed capability match: Ilan Reuven Cohen's online optimization, resource-constrained classification can be paired with Andrea Montanari's documented high-dimensional statistics, posterior sampling for auditable dynamic allocation for scarce public services. The specific contribution is high-dimensional statistical baselines; this transfer is an analyst hypothesis.[119][120][479]
Compare a regularized low-complexity estimator with a flexible model while varying sample size and dimensionality using a synthetic public-service allocation simulator with scarce capacity and changing demand. Compare generalization error, calibration and the sample-size threshold with a fixed priority rule with the same capacity and eligibility constraints.
Rank 7/10 after semantic revision; analyst score 8 = max(1, 2+3+3): topic overlap 2/4, complementarity 3/3, feasible first test 3/3. A bounded offline comparison is specified; required datasets and domain assumptions must still be checked.
Proposed fit, not an assertion of a new or active relationship. Independent review pending; forecast confidence low. Partner interest, capacity, data access and any required experimental approvals/resources are unverified.
Connection 8
Massachusetts Institute of Technology
Proposed capability match: Ilan Reuven Cohen's online optimization, resource-constrained classification can be paired with Gregory Wornell's documented signal processing, statistical inference for auditable dynamic allocation for scarce public services. The specific contribution is joint statistical inference and information constraints; this transfer is an analyst hypothesis.[110][119][120][503]
Compare full-data inference with task-specific compressed statistics at fixed communication or storage budget using a synthetic public-service allocation simulator with scarce capacity and changing demand. Compare estimation error, calibration and bits per valid decision with a fixed priority rule with the same capacity and eligibility constraints.
Rank 8/10 after semantic revision; analyst score 8 = max(1, 2+3+3): topic overlap 2/4, complementarity 3/3, feasible first test 3/3. A bounded offline comparison is specified; required datasets and domain assumptions must still be checked.
Proposed fit, not an assertion of a new or active relationship. Independent review pending; forecast confidence low. Partner interest, capacity, data access and any required experimental approvals/resources are unverified.
Connection 9
Stanford University
Proposed capability match for Ilan Reuven Cohen with Susan Athey: Causal evaluation can assess an allocation intervention after treatment, outcome and assignment are explicit; its empirical relevance depends on the assumed service model.[119][120][433]
Define a synthetic service-allocation study with treatment as randomized use of an adaptive priority rule and outcome as waiting-time cost at identical capacity and eligibility. Compare stratified and pooled treatment-effect estimates against planted simulator effects under shifting demand. Keep policy-performance comparison and effect-estimator validation separate.
Rank 9/10 after semantic revision; analyst score 7 = max(1, 3+2+2): topic overlap 3/4, complementarity 2/3, feasible first test 2/3. Causal evaluation can assess an allocation intervention after treatment, outcome and assignment are explicit; its empirical relevance depends on the assumed service model. A bounded offline comparison is specified; required datasets and domain assumptions must still be checked.
Proposed fit, not an assertion of a new or active relationship. Independent review pending; forecast confidence low. Partner interest, capacity, data access and any required experimental approvals/resources are unverified. Post-review scope: Causal evaluation can assess an allocation intervention after treatment, outcome and assignment are explicit; its empirical relevance depends on the assumed service model. This revised proposal awaits independent targeted re-review; simulated outcomes would establish model behavior only, not biological, clinical or deployed benefit.
Connection 10
Carnegie Mellon University
Proposed capability match for Ilan Reuven Cohen with Kathleen M. Carley: Network diagnostics are an indirect branch of allocation research and require a graph plus a meaningful coordination-failure label, not merely a capacity simulator.[66][119][120][445]
Construct a timestamped service-request graph whose edges denote shared resource use and whose planted coordination failures are missed handoffs causing delays. Compare a temporal failure detector with an activity-preserving shuffled-time control using the same graph events; measure event precision/recall and warning lead time. Keep the allocation policy fixed.
Rank 10/10 after semantic revision; analyst score 5 = max(1, 1+2+2): topic overlap 1/4, complementarity 2/3, feasible first test 2/3. Network diagnostics are an indirect branch of allocation research and require a graph plus a meaningful coordination-failure label, not merely a capacity simulator. A bounded offline comparison is specified; required datasets and domain assumptions must still be checked.
Proposed fit, not an assertion of a new or active relationship. Independent review pending; forecast confidence low. Partner interest, capacity, data access and any required experimental approvals/resources are unverified. Post-review scope: Network diagnostics are an indirect branch of allocation research and require a graph plus a meaningful coordination-failure label, not merely a capacity simulator. This revised proposal awaits independent targeted re-review; simulated outcomes would establish model behavior only, not biological, clinical or deployed benefit.
Ilan Reuven Cohen works on online algorithms, machine learning, scheduling and fair resource allocation. [119][120][121]
Moderate confidenceReview: reviewedThe CV is dated; newer output relies on the first-party publication page.
Auditable dynamic allocation for scarce public services is a future research hypothesis linking Cohen's algorithms with Roth's market-design expertise. [120][122]
Low confidenceReview: reviewedObjectives, data validity and stakeholder acceptance are unknown.
